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How many syllables are in the word cat?

The word cat has only one syllable.

What foods does a tortoise eat?

Tortoises eat a range of fruit and vegetables although the mainstay of a tortoise’s diet should be green leafy vegetables such as dandelions, clover, lettuce, vetch and kale. Your tortoise will enjoy sweet fruits such as bananas, strawberries, peaches and apples but these should be given no more than twice a week as the sugar can interfere with their digestive system. They can also become addicted to sweet fruits and you will find they might refuse to eat their healthy greens in favour of something sweet. Tortoises should eat a daily portion of food that is equivalent to their shell size. Ideally, they should also have 1 - 2 days a week when they don’t eat. This is because they have slow digestive systems and need a clear day or two for their systems to catch up. You should give your tortoise a weekly supplement of calcium and D3. Use one specifically formulated for reptiles and simply sprinkle it onto a favourite food. Finally, always ensure your tortoise has access to fresh water by providing them with a bowl that they can climb into. They will drink by dipping their head under the water.

List all the planets in our solar system with more than one moon.

Jupiter, Saturn, Uranus, and Neptune all have more than one moon.

What is 24 Hours of Lemons

24 Hours of Lemons is a series of endurance races for amateur car racing enthusiasts. The name is derived from a light hearted play on the famous professional race "24 hours of Le Mans" twisting the last word into 'Lemons'; indicating that the car used in the race should be of very low value. The Lemons races are legitimate paved-road racing experiences with a grass-roots feel and a lot of humor mixed in as racing teams often adopt amusing themes.

Tell me whether these are countries or cities: Sweden, Bangalore, Panama, Pakistan, Lebanon, Los Angeles

The following are countries: Sweden is a country in Europe, Pakistan is a country in South Asia and Panama is a country in Central America. The following are cities: Bangalore is a city in the southern region of India Los Angeles is a city in the south wested part of the United States of America. Lebanon can refer to both a country and a city. The country of Lebanon is situated in the Middle East, while the city of Lebanon is located in the state of New Hampshire in the United States of America.

How do you install tubeless tires on a bicycle?

Tubeless tire installs require some setup. It's best to have experience with bicycle tires, and in particular, the use of tire levers to remove and install tires. Make sure your tires and rims are tubeless ready. If the manufacturer also provides a tubeless tape, use that, or use a generic brand. You'll also need sealant and a tubeless stem. The stem might require a tool to unscrew the nozzle for adding sealant. You'll also need a large syringe if you plan on using the stem to add sealant. You'll want a electronic pump, or a manual pump with a reservoir. I have found that larger tires are easier to install as tubeless but road tires can also be tubeless. The first step is to clean the rim and install tubeless tape around the rim. This should seal the rim component. You'll need to punch a hole for the stem and insert the stem. Next you'll need to lever the tire onto the rim, first one side, then both. If you don't have a syringe, you can add some sealant at this step before fully installing the tire. For road tires, I've found this method prone to spilling. Next, remove the core / nozzle from the tire stem and hook the stem up to your pump. You want to inject air into the tire quickly and at high pressure to "set" the beads of the tire against the rim. This should result in a popping sound. When you remove the pump, the tire will deflate since you removed the core. If you haven't already, add sealant at this step using a syringe, then reinstall the core and pump up the tire. I usually rotate the tire a bit, then flip it periodically while testing if it holds air. If it does you are done. Happy riding!

Which of the following words starts with the letter S: Snake, Towel, Table, Shower, Yam, Butterfly, Apple

Snake, Shower

3 Best TV series in the World

1. Dexter 2. Breaking Bad 3. Prison Break

Offline evaluation of interactive dialogs on real data is difficult because different policies can lead to different variants of the dialog. Our solution to this issue is to collect data in a way that allows us to simulate all dialog variants possible according to any policy. The dialog variants we are considering for interactive learning differ only in presence of several parts of the dialog. Therefore, we can collect dialogs containing all information used for interactive learning and omit those parts that were not requested by the policy. We collected the dataset (see Section "Dataset Collection Process" ) that enables simulation where the policy can decide how much extra information to the question it requests. If the question is clear to the system it can attempt to answer the question without any other information. It can also ask for a broader explanation with a possibility to answer the question afterwards. If the system decides not to answer the question, we can simulate rerouting the question to another user, to try to obtain the answer from them. The principle of simulated user's answer is shown in the Figure 1 . Note that the simulated user’s answer can be incorrect because human users naturally made mistakes. We intentionally keep these mistakes in the dataset because real systems must address them as well. Our solution to this issue is to collect data in a way that allows us to simulate all dialog variants possible according to any policy.

How does the author address the problem that different policies can lead to different variations of dialog boxes?

Their solution to this issue is to collect data in a way that allows them to simulate all dialog variants possible according to any policy.

Who was the best player in Denver Broncos franchise history?

John Elway is considered the best player in Denver Broncos history. He led the team to five Super Bowls, winning two of them. Elway had great talent as a passer, but would also use his legs to help the team win. In fact, Elway is the only player to compile 50,000 yards passing and 3,000 rushing. His most memorable play was the "helicopter' first down in Super Bowl XXXII.

I would like to build an investment portfolio with a balance of short term and longer term investments. For short term I would like a return on investment within two years. Please tell me which of these investments fall into the short-term bucket and which are longer-term: 10 year Treasury Bonds, Growth Stocks, Bitcoin, Index Funds, Precious Metals, High-yield Savings Accounts, 1-2 year Treasury Bonds, Real Estate.

Short term investment bucket: High-yield savings accounts, 1-2 year Treasury Bonds. Longer term investment bucket: Longer-term investment: Growth stocks, Index Funds, Real Estate, Precious Metals, Bitcoin, 10 year Treasury bonds. Note: This classification is for informational purposes only and is not financial advice. Please consult a financial advisor.

With the advent of social media platforms, increasing user base address their grievances over these platforms, in the form of complaints. According to BIBREF0, complaint is considered to be a basic speech act used to express negative mismatch between the expectation and reality. Transportation and its related logistics industries are the backbones of every economy. Many transport organizations rely on complaints gathered via these platforms to improve their services, hence understanding these are important for: (1) linguists to identify human expressions of criticism and (2) organizations to improve their query response time and address concerns effectively. Presence of inevitable noise, sparse content along with rephrased and structurally morphed instances of posts, make the task at hand difficult BIBREF1. Previous works BIBREF2 in the domain of complaint extraction have focused on static datasets only. These are not robust to changes in the trends reflected, information flow and linguistic variations. We propose an iterative, semi-supervised approach for identification of complaint based tweets, having the ability to be replicated for stream of information flow. The preference of a semi-supervised approach over supervised ones is due to the stated reasons: (a) the task of isolating the training set, make supervised tasks less attractive and impractical and (b) imbalance between the subjective and objective classes lead to poor performance. We propose an iterative, semi-supervised approach for identification of complaint based tweets, having the ability to be replicated for “stream of information flow”.

What ability does the iterative, semi-supervised approach proposed by the authors have?

The approach has the ability to be replicated for “stream of information flow”.

Jing is a 46-year-old male. He is an insurance executive who is married with two children. Jing is active in his church and plays golf on the weekends. He went to see his cardiologist because he experienced recent fatigue with chest pain on exertion. He has never smoked but he consumes 1 to 2 alcoholic drinks per day. His medical history reveals a blood cholesterol level of 260 mg%, a triglyceride level of 198 mg% and an HDL-C value of 43 mg%. After studying a 12-lead ECG tracing of Jing's heart, it is clear that his heart is ischemic. Suggest some follow-up screening tests and recommended interventions.

There are lots of options for follow-up screening tests. For example, we can perform an echocardiogram with a color Doppler, where we can evaluate blood flow through the heart and create a picture of the heart and its moving valves. We can do a myocardial perfusion scan, which also evaluates blood flow in the heart. There’s also a procedure called electron-beam computed tomography (EBCT) which can detect calcium deposits in arteries. There’s also computed tomography angiography which reveals plaque or calcium deposits. We can perform a coronary MRI or MRA, which uses magnetic fields to detect valve problems, heart enlargement, vessel disease, damaged heart tissue, etc. Finally, we can also perform a coronary angiogram, which is invasive, but is the “gold standard” for diagnosing CAD. There are interventions available to Jing, which range from less invasive/intense to most intense. He can do daily things like reduce his high alcohol consumption, eat a heart-healthier and less inflammatory diet to help his cholesterol and triglyceride levels. There are also some medications to treat myocardial ischemia such as a daily aspirin, nitrates, beta blockers, cholesterol-lowering medications, etc. If his situation is very serious/deteriorates, he can consider angioplasty/stenting, or even coronary artery bypass surgery. These interventions are listed in order of least to most aggressive.

1907.08937

In this section, we will show that our defined similarity quantification could help Open IE by identifying redundant relations. To be specific, we set a toy experiment to remove redundant relations in KBs for a preliminary comparison (sec:toy-experiment). Then, we evaluate our model and baselines on the real-world dataset extracted by Open IE methods (sec:real-experiment). Considering the existing evaluation metric for Open IE and Open RE rely on either labor-intensive annotations or distantly supervised annotations, we propose a metric approximating recall and precision evaluation based on operable human annotations for balancing both efficiency and accuracy. FLOAT SELECTED: Figure 3: Precision-recall curve on Open IE task comparing our similarity function with vector-based and angle-based similarity. Error bar represents 95% confidential interval. Bootstraping is used to calculate the confidential interval. In this section, we consider two kinds of relational classification tasks: (1) relation prediction and (2) relation extraction. Relation prediction aims at predicting the relationship between entities with a given set of triples as training data; while relation extraction aims at extracting the relationship between two entities in a sentence. In this section, we will show that our defined similarity quantification could help Open IE by identifying redundant relations. FLOAT SELECTED: Figure 3: Precision-recall curve on Open IE task comparing our similarity function with vector-based and angle-based similarity. Error bar represents 95% confidential interval. Bootstraping is used to calculate the confidential interval. In this section, we consider two kinds of relational classification tasks: (1) relation prediction and (2) relation extraction.

Which tasks do they apply their method to?

The answers are shown as follows: * relation prediction * relation extraction * Open IE

What is a kidney transplant?

A kidney transplant is a medical procedure during which a healthy kidney is removed from either a living or deceased donor and given to a patient without a any healthy kidneys.

Identify which instrument is string or percussion: Teponaztli, Tricordia

Tricordia is string, Teponaztli is percussion.

What are the 5 best ways to control cortisol

Listening to binaural sound with headphones, meditating for at least 5 minutes a day, reading a book that has relaxing content for at least 30 minutes a day, taking in sunlight at dawn or dusk for at least 10 minutes a day, walking daily for at least 30 minutes a day

What Is a Request for Quote (RFQ)?

A request for quote (RFQ) is a structured acquisition process in which a company or Govt. Agency solicits select suppliers and contractors to submit price quotes and bids for the chance to fulfill specific tasks or projects. The RFQ process is especially important to businesses or government agencies that need a consistent supply of a specific number of standard products. Companies may send RFQs alone or before a request for proposal (RFP).

As established by law and government policy, the two official forms of written Norwegian are Bokmål (literally 'book tongue') and Nynorsk ('new Norwegian'). The official Norwegian Language Council (Språkrådet) is responsible for regulating the two forms, and recommends the terms Norwegian Bokmål and Norwegian Nynorsk in English.[citation needed] Two other written forms without official status also exist. One, called Riksmål ('national language'), is today to a large extent the same language as Bokmål though somewhat closer to the Danish language. It is regulated by the unofficial Norwegian Academy, which translates the name as 'Standard Norwegian'. The other is Høgnorsk ('High Norwegian'), a more purist form of Nynorsk, which maintains the language in an original form as given by Ivar Aasen and rejects most of the reforms from the 20th century; this form has limited use.

What are the official official forms of written Norwegian?

- Bokmål - Nynorsk

What is a beard?

A beard is a facial hair style that grows under the nose and on the cheeks as well as on and under the chin.

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

Whether all training data match the target domain?

No.

We experimented with a dataset of 16K annotated tweets made available by the authors of BIBREF0 . Of the 16K tweets, 3383 are labeled as sexist, 1972 as racist, and the remaining are marked as neither sexist nor racist. For the embedding based methods, we used the GloVe BIBREF5 pre-trained word embeddings. GloVe embeddings have been trained on a large tweet corpus (2B tweets, 27B tokens, 1.2M vocab, uncased). We experimented with multiple word embedding sizes for our task. We observed similar results with different sizes, and hence due to lack of space we report results using embedding size=200. We performed 10-Fold Cross Validation and calculated weighted macro precision, recall and F1-scores. We use `adam' for CNN and LSTM, and `RMS-Prop' for FastText as our optimizer. We perform training in batches of size 128 for CNN & LSTM and 64 for FastText. More details on the experimental setup can be found from our publicly available source code. We performed 10-Fold Cross Validation and calculated weighted macro precision, recall and F1-scores.

What metric do they use in experiments?

weighted macro precision, recall and F1.

Sunset, also known as sundown, is the daily disappearance of the Sun below the horizon due to Earth's rotation. As viewed from everywhere on Earth (except the North and South poles), the equinox Sun sets due west at the moment of both the spring and autumn equinoxes. As viewed from the Northern Hemisphere, the Sun sets to the northwest (or not at all) in the spring and summer, and to the southwest in the autumn and winter; these seasons are reversed for the Southern Hemisphere. The time of sunset is defined in astronomy as the moment when the upper limb of the Sun disappears below the horizon. Near the horizon, atmospheric refraction causes sunlight rays to be distorted to such an extent that geometrically the solar disk is already about one diameter below the horizon when a sunset is observed. Sunset is distinct from twilight, which is divided into three stages. The first one is civil twilight, which begins once the Sun has disappeared below the horizon, and continues until it descends to 6 degrees below the horizon. The second phase is nautical twilight, between 6 and 12 degrees below the horizon. The third phase is astronomical twilight, which is the period when the Sun is between 12 and 18 degrees below the horizon. Dusk is at the very end of astronomical twilight, and is the darkest moment of twilight just before night. Finally, night occurs when the Sun reaches 18 degrees below the horizon and no longer illuminates the sky. Locations further north than the Arctic Circle and further south than the Antarctic Circle experience no full sunset or sunrise on at least one day of the year, when the polar day or the polar night persists continuously for 24 hours.

What is Sunset?

Sunset is the daily disappearance of the Sun below the horizon due to Earth's rotation.

What is alkaline ionized water is it good for me?

Alkaline ionized water can be created in a number of ways. Alkaline water ionizers make water alkaline through a process called electrolysis. Electrolysis separates water into two streams of water - alkaline and acidic water using electricity flowing through an electrode plate in the machine. Some of the properties of alkaline ionized water is that it had molecular hydrogen, and it is also alkaline. Molecular hydrogen is a good anti-inflammatory for the body, and it should be consumed daily if possible. Alkaline water ionizers also make the water alkaline without the use of chemicals in the water like baking soda.

1907.00758

For our experiments, we select a dataset whose utterances have been correctly synchronised at recording time. This allows us to control how the model is trained and verify its performance using ground truth synchronisation offsets. We use UltraSuite: a repository of ultrasound and acoustic data gathered from child speech therapy sessions BIBREF15 . We used all three datasets from the repository: UXTD (recorded with typically developing children), and UXSSD and UPX (recorded with children with speech sound disorders). In total, the dataset contains 13,815 spoken utterances from 86 speakers, corresponding to 35.9 hours of recordings. The utterances have been categorised by the type of task the child was given, and are labelled as: Words (A), Non-words (B), Sentence (C), Articulatory (D), Non-speech (E), or Conversations (F). See BIBREF15 for details. We use UltraSuite: a repository of ultrasound and acoustic data gathered from child speech therapy sessions BIBREF15 . We used all three datasets from the repository: UXTD (recorded with typically developing children), and UXSSD and UPX (recorded with children with speech sound disorders). In total, the dataset contains 13,815 spoken utterances from 86 speakers, corresponding to 35.9 hours of recordings. The utterances have been categorised by the type of task the child was given, and are labelled as: Words (A), Non-words (B), Sentence (C), Articulatory (D), Non-speech (E), or Conversations (F).

Do they annotate their own dataset or use an existing one?

Use an existing one

What is COBOL used for?

COBOL was designed for business computer programs in industries such as finance and human resources. Unlike some high-level computer programming languages, COBOL uses English words and phrases to make it easier for ordinary business users to understand.

1708.00111

For supertagging, we observe that the baseline cross entropy trained model improves its predictions with beam search decoding compared to greedy decoding by 2 accuracy points, which suggests that beam search is already helpful for this task, even without search-aware training. Both the optimization schemes proposed in this paper improve upon the baseline with soft direct loss optimization ( INLINEFORM0 ), performing better than the approximate max-margin approach. For supertagging, we observe that the baseline cross entropy trained model improves its predictions with beam search decoding compared to greedy decoding by 2 accuracy points, which suggests that beam search is already helpful for this task, even without search-aware training.

By how much do they outperform basic greedy and cross-entropy beam decoding?

The answers are shown as follows: * 2 accuracy points

Would you rather be stuck on a broken ski lift or a broken elevator?

While both are not ideal situations, at least with the ski lift, you get to have a view and fresh air. It’s also important in terms of communication, if you are stuck in an elevator during a black-out , it might be challenging to communicate before help can arrive. This is important if you are claustrophobic.

A triathlon is an endurance multisport race consisting of swimming, cycling, and running over various distances. Triathletes compete for fastest overall completion time, racing each segment sequentially with the time transitioning between the disciplines included

Given this paragraph about the sport of triathlon, what are the individual sports make up a triathlon

Triathlon is a multi-sport discipline consisting of three sports performed back to back without rest. An event is made up of swimming, followed by cycling and then running. Athletes transition between the individual sports as quickly as possible, with the winner completing the course in the fastest time.

The Human Development Index (HDI) is a statistic composite index of life expectancy, education (mean years of schooling completed and expected years of schooling upon entering the education system), and per capita income indicators, which is used to rank countries into four tiers of human development. A country scores a higher level of HDI when the lifespan is higher, the education level is higher, and the gross national income GNI (PPP) per capita is higher. It was developed by Pakistani economist Mahbub ul Haq and was further used to measure a country's development by the United Nations Development Programme (UNDP)'s Human Development Report Office. The 2010 Human Development Report introduced an Inequality-adjusted Human Development Index (IHDI). While the simple HDI remains useful, it stated that "the IHDI is the actual level of human development (accounting for inequality), while the HDI can be viewed as an index of 'potential' human development (or the maximum level of HDI) that could be achieved if there were no inequality." The index is based on the human development approach, developed by Mahbub ul Haq, anchored in Amartya Sen's work on human capabilities, and often framed in terms of whether people are able to "be" and "do" desirable things in life. Examples include – being: well fed, sheltered, and healthy; doing: work, education, voting, participating in community life. The freedom of choice is central – someone choosing to be hungry (e.g. when fasting for religious reasons) is quite different from someone who is hungry because they cannot afford to buy food, or because the country is in a famine. The index does not take into account several factors, such as the net wealth per capita or the relative quality of goods in a country. This situation tends to lower the ranking of some of the most developed countries, such as the G7 members and others.

Tell me a few flaws of the Human Development Index from this passage

Human Development Index has been revised in 2010 owing to a few limitations. HDI is believed to estimate the 'potential' human development that could be achieved if there were no inequality. The revised Inequality-adjusted Human Development Index (IHDI) is the actual level of human development (accounting for inequality). Secondly, HDI does not take into account several factors, such as the net wealth per capita or the relative quality of goods in a country. This situation tends to lower the ranking of some of the most developed countries, such as the G7 members and others.

1808.03986

The proposed Multimodal Differential Network (MDN) consists of a representation module and a joint mixture module. We use a triplet network BIBREF41 , BIBREF42 in our representation module. We refereed a similar kind of work done in BIBREF34 for building our triplet network. The triplet network consists of three sub-parts: target, supporting, and contrasting networks. All three networks share the same parameters. Given an image INLINEFORM0 we obtain an embedding INLINEFORM1 using a CNN parameterized by a function INLINEFORM2 where INLINEFORM3 are the weights for the CNN. The caption INLINEFORM4 results in a caption embedding INLINEFORM5 through an LSTM parameterized by a function INLINEFORM6 where INLINEFORM7 are the weights for the LSTM. This is shown in part 1 of Figure FIGREF4 . Similarly we obtain image embeddings INLINEFORM8 & INLINEFORM9 and caption embeddings INLINEFORM10 & INLINEFORM11 . DISPLAYFORM0 The Mixture module brings the image and caption embeddings to a joint feature embedding space. The input to the module is the embeddings obtained from the representation module. We have evaluated four different approaches for fusion viz., joint, element-wise addition, hadamard and attention method. Each of these variants receives image features INLINEFORM0 & the caption embedding INLINEFORM1 , and outputs a fixed dimensional feature vector INLINEFORM2 . The Joint method concatenates INLINEFORM3 & INLINEFORM4 and maps them to a fixed length feature vector INLINEFORM5 as follows: DISPLAYFORM0 The proposed Multimodal Differential Network (MDN) consists of a representation module and a joint mixture module. We use a triplet network BIBREF41 , BIBREF42 in our representation module. The triplet network consists of three sub-parts: target, supporting, and contrasting networks. All three networks share the same parameters. The Mixture module brings the image and caption embeddings to a joint feature embedding space.

How do the authors define a differential network?

The answers are shown as follows: * The proposed Multimodal Differential Network (MDN) consists of a representation module and a joint mixture module.

In 2016, HTC shipped its first units of the HTC Vive SteamVR headset. This marked the first major commercial release of sensor-based tracking, allowing for free movement of users within a defined space. A patent filed by Sony in 2017 showed they were developing a similar location tracking technology to the Vive for PlayStation VR, with the potential for the development of a wireless headset. In 2019, Oculus released the Oculus Rift S and a standalone headset, the Oculus Quest. These headsets utilized inside-out tracking compared to external outside-in tracking seen in previous generations of headsets. Later in 2019, Valve released the Valve Index. Notable features include a 130° field of view, off-ear headphones for immersion and comfort, open-handed controllers which allow for individual finger tracking, front facing cameras, and a front expansion slot meant for extensibility. In 2020, Oculus released the Oculus Quest 2. Some new features include a sharper screen, reduced price, and increased performance. Facebook (which became Meta a year later) initially required user to log in with a Facebook account in order to use the new headset. In 2021 the Oculus Quest 2 accounted for 80% of all VR headsets sold. Robinson R22 Virtual Reality Training Device developed by VRM Switzerland In 2021, EASA approved the first Virtual Reality based Flight Simulation Training Device. The device, for rotorcraft pilots, enhances safety by opening up the possibility of practicing risky maneuvers in a virtual environment. This addresses a key risk area in rotorcraft operations, where statistics show that around 20% of accidents occur during training flights. In 2023, Sony released the Playstation VR2, a follow-up to their 2016 headset. Playstation VR2 comes with inside-out tracking, higher-resolution displays, controllers with adaptive triggers and haptic feedback, and a wider field-of-view.

From the text below, extract the year and the VR headset and the company that released it in the format {Year}-{Headset Name}-{Company} and place them in a bullet list

2016 - HTC Vive SteamVR - HTC 2019 - Oculus Rift S - Oculus 2019 - Oculus Quest - Oculus 2019 - Valve Index - Valve 2020 - Oculus Quest 2 - Oculus 2021 - Robinson R22 Virtual Reality Training Device - VRM Switzerland 2023 - Playstation VR2 - Sony

The shared task features two subtasks: The shared task features two subtasks:

Does the shared task feature two subtasks?

Yes.

2002.03438

Manning and Schütze argue that, even though not quite correct, language text can be modeled as stationary, ergodic random processes BIBREF29, an assumption that we follow. Moreover, given the diversity of language production, we assume this stationary ergodic random process with finite alphabet $\mathcal {A}$ denoted $X = \lbrace X_i, -\infty < i < \infty \rbrace $ is non-null in the sense that always $P(x_{-m}^{-1}) > 0$ and This is sometimes called the smoothing requirement. Manning and Schütze argue that, even though not quite correct, language text can be modeled as stationary, ergodic random processes BIBREF29, an assumption that we follow. Moreover, given the diversity of language production, we assume this stationary ergodic random process with finite alphabet $\mathcal {A}$ denoted $X = \lbrace X_i, -\infty < i < \infty \rbrace $ is non-null in the sense that always $P(x_{-m}^{-1}) > 0$ and This is sometimes called the smoothing requirement.

Is the assumption that natural language is stationary and ergodic valid?

It is not completely valid for natural languages because of diversity of language - this is called smoothing requirement.

Is the following state true or false: Peanuts are vegetables?

This statement is true. Peanuts are part of the legume family and are therefore vegetables.

By 1927, Fleming had been investigating the properties of staphylococci. He was already well known from his earlier work, and had developed a reputation as a brilliant researcher. In 1928, he studied the variation of Staphylococcus aureus grown under natural condition, after the work of Joseph Warwick Bigger, who discovered that the bacterium could grow into a variety of types (strains). On 3 September 1928, Fleming returned to his laboratory having spent a holiday with his family at Suffolk. Before leaving for his holiday, he inoculated staphylococci on culture plates and left them on a bench in a corner of his laboratory. On his return, Fleming noticed that one culture was contaminated with a fungus, and that the colonies of staphylococci immediately surrounding the fungus had been destroyed, whereas other staphylococci colonies farther away were normal, famously remarking "That's funny". Fleming showed the contaminated culture to his former assistant Merlin Pryce, who reminded him, "That's how you discovered lysozyme." He identified the mould as being from the genus Penicillium. He suspected it to be P. chrysogenum, but a colleague Charles J. La Touche identified it as P. rubrum. (It was later corrected as P. notatum and then officially accepted as P. chrysogenum; in 2011, it was resolved as P. rubens.) The laboratory in which Fleming discovered and tested penicillin is preserved as the Alexander Fleming Laboratory Museum in St. Mary's Hospital, Paddington. The source of the fungal contaminant was established in 1966 as coming from La Touche's room, which was directly below Fleming's. Fleming grew the mould in a pure culture and found that the culture broth contained an antibacterial substance. He investigated its anti-bacterial effect on many organisms, and noticed that it affected bacteria such as staphylococci and many other Gram-positive pathogens that cause scarlet fever, pneumonia, meningitis and diphtheria, but not typhoid fever or paratyphoid fever, which are caused by Gram-negative bacteria, for which he was seeking a cure at the time. It also affected Neisseria gonorrhoeae, which causes gonorrhoea, although this bacterium is Gram-negative. After some months of calling it "mould juice" or "the inhibitor", he gave the name penicillin on 7 March 1929 for the antibacterial substance present in the mould.

Given this paragraph about the experiment that led to the discovery of penicillin by Sir Alexander Fleming, tell me upon which types of bacteria penicillin has an anti-bacterial effect, and list the conditions that were necessary for the discovery of penicillin

Penicillin has an anti-bacterial effect of a number of Gram-positive bacteria including but not limited to the bacteria that cause meningitis, diptheria, pneumonia, scarlet fever. It also acts on (Gram-positive) staphylococci bacteria which cause skin and soft tissue infections such as boils. Penicillin was also found to have an anti-bacterial action on the Gram-negative bacteria which causes gonorrhoea. There were a series of fortuitous circumstances which combined to allow the discovery of penicillin, one of which was Sir Alexander's choice to take a holiday with his family after leaving staphylococci bacteria in a culture in his lab before departure. Anecdotally Sir Alexander was a little sloppy with lab hygiene which contributed to his previous discovery of lysozyme and it seems the same lack of care was partly responsible for the discovery of penicillin. Lastly the specific location of his lab and cultures played a role - the fungal contaminant that had an anti-bacterial effect on the staphylococci culture came from Sir Alexander's colleagues lab one floor below

Give me a list of Shakespeare's plays

- Hamlet - Romeo and Juliet - King Lear - Othello - Titus Andronicus - Love's Labour's Lost - Twelfth Night - A Midsummer Night's Dream - A Winter's Tale - The Tempest - Troilus and Cressida - Pericles - Antony and Cleopatra - All's Well That End's Well - The Merchant of Venice - Henry IV Part 1 - Henry IV Part 2 - Henry V - Henry VI Part 1 - Henry VI Part 2 - Henry VI Part 3 - Henry VIII - Richard II - Richard III - King John - Julius Caesar - As You Like It - Macbeth - The Merry Wives of Windsor - Two Gentlemen of Verona - The Taming of the Shrew - Coriolanus - Timon of Athens - Much Ado About Nothing - Cymbeline - The Comedy of Errors - Two Noble Kinsmen - Measure for Measure

categorize each of the following into these groups: solid, liquid, and gas. ice, neon (at room temperature), water, whiskey, a chair, carbon dioxide, and glass

solid: ice, chair, glass liquid: water, whiskey gas: neon, carbon dioxide

It is common knowledge in modern NLP that using large amounts of high-quality training data is a key aspect in building successful machine-learning based systems. For this reason, a major challenge when building such systems is obtaining data in the domain of interest. But what defines a domain? Natural language varies greatly across topics, styles, levels of formality, genres and many other linguistic nuances BIBREF0, BIBREF1, BIBREF2. This overwhelming diversity of language makes it hard to find the right data for the task, as it is nearly impossible to well-define the exact requirements from such data with respect to all the aforementioned aspects. On top of that, domain labels are usually unavailable – e.g. in large-scale web-crawled data like Common Crawl which was recently used to train state-of-the-art pretrained language models for various tasks BIBREF3. Domain data selection is the task of selecting the most appropriate data for a domain from a large corpus given a smaller set of in-domain data BIBREF4, BIBREF5, BIBREF6, BIBREF7. In this work, we propose to use the recent, highly successful self-supervised pre-trained language models, e.g. devlin-etal-2019-bert,liu2019roberta for domain data selection. As pretrained LMs demonstrate state-of-the-art performance across many NLP tasks after being trained on massive amounts of data, we hypothesize that the robust representations they learn can be useful for mapping sentences to domains in an unsupervised, data-driven approach. We show that these models indeed learn to cluster sentence representations to domains without further supervision (e.g. Figure FIGREF2), and quantify this phenomenon by fitting Gaussian Mixture Models (GMMs) to the learned representations and measuring the purity of the resulting unsupervised clustering. We then propose methods to leverage these emergent domain clusters for domain data selection in two ways: Via distance-based retrieval in the sentence embedding space induced by the pretrained language model. By fine-tuning the pretrained language model for binary classification, where positive examples are from the domain of interest. Our methods enable to select relevant data for the task while requiring only a small set of monolingual in-domain data. As they are based solely on the representations learned by self-supervised LMs, they do not require additional domain labels which are usually vague and over-simplify the notion of domain in textual data. We evaluate our method on data selection for neural machine translation (NMT) using the multi-domain German-English parallel corpus composed by BIBREF8. Our data selection methods enable to train NMT models that outperform those trained using the well-established cross-entropy difference method of BIBREF4 across five diverse domains, achieving a recall of more than 95% in all cases with respect to an oracle that selects the “true” in-domain data. Our contributions in this work are as follows. First, we show that pre-trained language models are highly capable of clustering textual data to domains with high accuracy in a purely unsupervised manner. Second, we propose methods to select in-domain data based on this property using vector-space retrieval and positive-unlabeled fine-tuning of pretrained language models for binary classification. Third, we show the applicability of our proposed data selection methods on a popular benchmark for domain adaptation in machine translation. An additional contribution is a new, improved data split we create for this benchmark, as we point on issues with previous splits used in the literature. The code and data for this work is publicly available. We hope this work will encourage more research on understanding the data landscape in NLP, enabling to “find the right data for the task” in the age of massive models and diverse data sources. We evaluate our method on data selection for neural machine translation (NMT) using the multi-domain German-English parallel corpus composed by BIBREF8.

What corpus is their data selected from?

The multi-domain German-English parallel corpus.

When was the World Investment Report 2021 released by UNCTAD?

June 21, 2021

Name some famous songs by the Beatles

Some songs by the Beatles are Penny Lane, Yellow Submarine, While My Guitar Gently Weeps, Let It Be and Here Comes the Sun

We analyse the cost from two perspectives: i) the complexity to the features; and ii) the number of features needed to represent a document. Defining $l$ as the number of different language varieties, and $n$ the number of terms of the document to be classified, the cost of obtaining the features of Table 2 (average, minimum, maximum, probability and proportionality) is $O(l\cdot {n})$ . Defining $m$ as the number of terms in the document that coincides with some term in the vocabulary, the cost of obtaining the standard deviation is $O(l\cdot {m})$ . As the average is needed previously to the standard deviation calculation, the total cost is $O(l\cdot {n}) + O(l\cdot {m})$ that is equal to $O(max(l\cdot {n}, l\cdot {m})) = O(l\cdot {n})$ . Since the number of terms in the vocabulary will always be equal or greater than the number of coincident terms ( $n \ge m$ ), and as the number of terms in the document will always be much higher than the number of language varieties ( $l<<n$ ), we can determine the cost as lineal with respect to the number of terms in the document $O(n)$ . With respect to the number of features needed to represent a document, we showed in Table 6 the considerable reduction of the proposed low dimensionality representation. We analyse the cost from two perspectives: i) the complexity to the features; and ii) the number of features needed to represent a document.

What are the two perspectives of cost analysis?

i) the complexity to the features; and ii) the number of features needed to represent a document.

Which phone OS is better, IOS or Android?

Both IOS & Android are among the most popular phone operating systems available today. IOS is known to be very user friendly and has a limited set of features that work extremely well. To achieve this simplicity, IOS sacrifices the level of customizability that it provides it’s users. Android, on the other hand is known to provide a high level of customization to it’s features and is extremely popular among power users. However, due to the myriad ways that it can be customized, it can be difficult to use and often has a steep learning curve when compared to IOS. Both IOS and Android each have their specific merits that caters to different user communities among which they are wildly popular.

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Additionally, important patterns can emerge from analysis of the fine-grained acts in a dialogue in a post-prediction setting. For example, if an agent does not follow-up with certain actions in response to a customer's question dialogue act, this could be found to be a violation of a best practice pattern. By analyzing large numbers of dialogue act sequences correlated with specific outcomes, various rules can be derived, i.e. "Continuing to request information late in a conversation often leads to customer dissatisfaction." This can then be codified into a best practice pattern rules for automated systems, such as "A request for information act should be issued early in a conversation, followed by an answer, informative statement, or apology towards the end of the conversation." Our analysis helps zone in on how the use of certain dialogue acts may be likely to result in different outcomes. The weights we observe vary in the amount of insight provided: for example, offering extra help at the end of a conversation, or thanking the customer yields more satisfied customers, and more resolved problems (with ratios of above 6:1). However, some outcomes are much more subtle: for example, asking yes-no questions early-on in a conversation is highly associated with problem resolution (ratio 3:1), but asking them at the end of a conversation has as similarly strong association with unsatisfied customers. Giving elaborate answers that are not a simple affirmative, negative, or response acknowledgement (i.e. Answer (Other)) towards the middle of a conversation leads to satisfied customers that are not frustrated. Likewise, requesting information towards the end of a conversation (implying that more information is still necessary at the termination of the dialogue) leads to unsatisfied and unresolved customers, with ratios of at least 4:1. By analyzing large numbers of dialogue act sequences correlated with specific outcomes, various rules can be derived, i.e. "Continuing to request information late in a conversation often leads to customer dissatisfaction." This can then be codified into a best practice pattern rules for automated systems, such as "A request for information act should be issued early in a conversation, followed by an answer, informative statement, or apology towards the end of the conversation." Our analysis helps zone in on how the use of certain dialogue acts may be likely to result in different outcomes. The weights we observe vary in the amount of insight provided: for example, offering extra help at the end of a conversation, or thanking the customer yields more satisfied customers, and more resolved problems (with ratios of above 6:1). However, some outcomes are much more subtle: for example, asking yes-no questions early-on in a conversation is highly associated with problem resolution (ratio 3:1), but asking them at the end of a conversation has as similarly strong association with unsatisfied customers. Giving elaborate answers that are not a simple affirmative, negative, or response acknowledgement (i.e. Answer (Other)) towards the middle of a conversation leads to satisfied customers that are not frustrated. Likewise, requesting information towards the end of a conversation (implying that more information is still necessary at the termination of the dialogue) leads to unsatisfied and unresolved customers, with ratios of at least 4:1.

Which patterns and rules are derived?

The answers are shown as follows: * A request for information act should be issued early in a conversation, followed by an answer, informative statement, or apology towards the end of the conversation * offering extra help at the end of a conversation, or thanking the customer yields more satisfied customers, and more resolved problems * asking yes-no questions early-on in a conversation is highly associated with problem resolution (ratio 3:1), but asking them at the end of a conversation has as similarly strong association with unsatisfied customers * Giving elaborate answers that are not a simple affirmative, negative, or response acknowledgement (i.e. Answer (Other)) towards the middle of a conversation leads to satisfied customers that are not frustrated * requesting information towards the end of a conversation (implying that more information is still necessary at the termination of the dialogue) leads to unsatisfied and unresolved customers

Type Public Traded as ASX: WBC NZX: WBC S&P/ASX 200 component Industry Banking Financial services Predecessor Bank of New South Wales Commercial Bank of Australia Founded 8 April 1817 (as the Bank of New South Wales) 4 May 1982 (as Westpac Banking Corporation) Headquarters Westpac Place Sydney, New South Wales, Australia Area served Worldwide Key people John McFarlane (Chairman) Peter King (CEO) Products Finance and insurance, consumer banking, corporate banking, investment banking, investment management, global wealth management, private equity, mortgages, credit cards Operating income Increase A$21.22 billion (2021) Net income Increase A$5.46 billion (2021) Total assets Increase A$935.88 billion (2021) Total equity Increase A$72.09 billion (2021) Number of employees 40,143 (2021) Subsidiaries St George Bank BankSA Bank of Melbourne Westpac New Zealand Website westpac.com.au westpac.co.nz

Extract the total number of employees in 2021 at Westpac Bank in Australia?

40,143 people

What is the movie "Everything Everywhere All at Once" about?

The movie is about an immigrant Chinese family who runs a laundromat and gets involved into a multi-verse quest to prevent a creature from destroying the universe. The family consists of Evelyn Wang, Waymond Wang and their daughter Joy. In the beginning of the movie their laundromat business is getting audited by the IRS, Waymond is trying to serve Evelyn divorce papers, and Joy is trying to get accepted for her lesbian relationship. In that difficult time for the family, Evelyn Wang discovers that she was chosen to fight against that great evil destroying the universe, and she should do that by connecting with versions of herself from parallel universes. It is an action packed comedy-drama adventure of Emily fulfilling the mission of saving the universe, and in conclusion deciding not to leave her current life for another version of it.

Please explain why adding more indexes to a relational database is not always beneficial for speeding up workloads.

At a high level, an index over a specific table can speed up the execution of a query that accesses the table provided that the query references a compatible subset of columns that the index covers and that the cost of using the index is lower than alternatives (e.g., simply scanning the whole table or using another index). At the same time, the index needs to be kept up-to-date with the table, which means that each table update (insertion or deletion of new rows, or updates to existing rows for the columns that the index covers) will now cost more as the relational database system has to also propagate the update to the index. Thus, the usefulness of adding an index to a workload will depend on the fraction of queries that can benefit from the index, and on the mix of queries and updates.

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Its worth mentioning that TupleInf only combines parallel evidence i.e. each tuple must connect words in the question to the answer choice. For reliable multi-hop reasoning using OpenIE tuples, we can add inter-tuple connections to the support graph search, controlled by a small number of rules over the OpenIE predicates. Learning such rules for the Science domain is an open problem and potential avenue of future work. For reliable multi-hop reasoning using OpenIE tuples, we can add inter-tuple connections to the support graph search, controlled by a small number of rules over the OpenIE predicates. Learning such rules for the Science domain is an open problem and potential avenue of future work.

Is their method capable of multi-hop reasoning?

Yes.

Which electric vehicle company does Elon Musk own?

Elon Musk owns the EV company called Tesla which is named after the famous scientist

List a bunch of international marathons

Boston, LA, New York City, Cleveland, Chicago, Big Sur, Rhode Island

What is India famous for?

India is famous for some of the Spectacular architectural monuments like Taj Mahal, Qutab Minar, Ajanta Ellora caves,Gol gumbaj and many more,India is also famous for its Spices and also known as land of spices as it produces 70 percent of world spices like turmeric, masala, coriander, saffron, to many more. India is also the country from where the knowledge of yoga has been originated. India is known for its rich cultural diversity, rich history, and heritage. There are many more things about India that you can experience once you travel there like spirituality, nature etc..

Waterside Karori were relegated at the end of the 1990 season, to be replaced by the winner of a play-off series between teams from the northern, central, and southern leagues (Mount Albert-Ponsonby, Nelson United, and Burndale United respectively). Nelson United won the series to gain promotion, but there is some controversy about their participation in the play-offs as they had only finished second in the central regional league, which was won by Petone. Gisborne City were relegated at the end of the 1991 season

Which team was relegated from the league at the conclusion of the 1991 Season?

Gisborne City

what do you think makes Japan Unique

- Japanese food (Sushi, Ramen etc.) - Culture of minimalism and respect - Japanese work ethic - Japan consists of over 6000 islands - Japan has a 'suicide forest' - Japan has over 70,000 people who are over 100 years old - Japan has millions of vending machines - Japan's capital Tokyo is one of the largest metropolitan areas in the world

Who is the author of books "It Starts With Us" and "It Ends With Us"?

Colleen Hoover is the author of bestselling books "It Starts With Us" and "It Ends With Us".

What are the best exercises for lower back pain?

The McGill Big 3 are generally the best exercises to help alleviate and improve lower back pain symptoms. The 3 exercises focus on core bracing and core stability which help the back pained individual keep a neutral spine posture, even while bearing load. The three exercises are 1) the modified curl-up, 2) the bird dog, and 3) side planks.

Classify each of these mammals as either marsupial or placental

Marsupial: kangaroo, koala, wallaby, wombat, opossum, bandicoot, possum, glider Placental: cow, dog, sloth, rhinoceros, camel, horse, sheep, humans, elephant, manatee, anteater, cat, bat, pig, deer, giraffe, goat, alpaca, llama Marsupials give birth to undeveloped young that are carried in a pouch located on their mothers' abdomen until they're fully developed. Placental mammals carry a young the fetus in the uterus of its mother to a relatively late stage of development.

Identify which car manufacturer is Chinese or American: Arcfox, Buick

Buick is American, Arcfox is Chinese

Who invented Boltzmann Machines?

Boltzmann machines were invented in 1985 by Geoffrey Hinton and Terrence Sejnowski.

What is The Netherlands?

The Netherlands is a country in Europe, it's capital is Amsterdam and around 18 million people live in the Netherlands.

A rainbow is an optical phenomenon that can occur under certain meteorological conditions. It is caused by reflection, refraction and dispersion of light in water droplets resulting in an effect similar to a spectrum of light appearing in the sky. It takes the form of a multicolored circular arc. Rainbows caused by sunlight always appear in the section of sky directly opposite the Sun.

Explain to me what a rainbow actually is?

A rainbow is an optical phenomenon that occurs by sunlight appearing in the section of the sky directly opposite of the sun.

Identify which instrument is string or percussion: Repique, Neola

Neola is string, Repique is percussion.

In order to analyse the robustness of the low dimensionality representation to different languages, we experimented with the development set of the DSLCC corpus from the Discriminating between Similar Languages task BIBREF1 . The corpus consists of 2,000 sentences per language or variety, with between 20 and 100 tokens per sentence, obtained from news headers. In Table 9 we show the results obtained with the proposed representation and the two distributed representations, Skip-gram and SenVec. It is important to notice that, in general, when a particular representation improves for one language is at cost of the other one. We can conclude that the three representations obtained comparative results and support the robustness of the low dimensionality representation. In order to analyse the robustness of the low dimensionality representation to different languages, we experimented with the development set of the DSLCC corpus11 from the Discriminating between Similar Languages task [12]. The corpus consists of 2,000 sentences per language or variety, with between 20 and 100 tokens per sentence, obtained from news headers.

What does the DSLCC corpus consist of?

The corpus consists of 2,000 sentences per language or variety, with between 20 and 100 tokens per sentence, obtained from news headers.

Water resources are natural resources of water that are potentially useful for humans, for example as a source of drinking water supply or irrigation water. Water occurs as both "stocks" and "flows". Water can be stored as lakes, water vapor, groundwater or aquifers, and ice and snow. Of the total volume of global freshwater, an estimated 69 percent is stored in glaciers and permanent snow cover; 30 percent is in groundwater; and the remaining 1 percent in lakes, rivers, the atmosphere, and biota. The length of time water remains in storage is highly variable: some aquifers consist of water stored over thousands of years but lake volumes may fluctuate on a seasonal basis, decreasing during dry periods and increasing during wet ones. A substantial fraction of the water supply for some regions consists of water extracted from water stored in stocks, and when withdrawals exceed recharge, stocks decrease. By some estimates, as much as 30 percent of total water used for irrigation comes from unsustainable withdrawals of groundwater, causing groundwater depletion.

Given the following paragraph on water resources, in what percentage is freshwater stored in the different storage forms?

It is estimated that 69 percent of freshwater is stored in glaciers and permanent snow cover, 30 percent in groundwater, and the remaining 1 percent is stored in lakes, rivers, the atmosphere, and biota.

Give me a list of the past five Malaysian Prime Ministers

The five most recent Prime Ministers of Malaysia (including the current) are: 1. Anwar Ibrahim (since 2022) 2. Ismail Sabri Yaakob (2021-2022) 3. Muhyiddin Yassin (2020-2021) 4. Mahathir Mohamad (2018-2020) 5. Najib Razak (2009-2018)

Shallow syntax provides an approximation of phrase-syntactic structure of sentences; it can be produced with high accuracy, and is computationally cheap to obtain. We investigate the role of shallow syntax-aware representations for NLP tasks using two techniques. First, we enhance the ELMo architecture to allow pretraining on predicted shallow syntactic parses, instead of just raw text, so that contextual embeddings make use of shallow syntactic context. Our second method involves shallow syntactic features obtained automatically on downstream task data. Neither approach leads to a significant gain on any of the four downstream tasks we considered relative to ELMo-only baselines. Further analysis using black-box probes confirms that our shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo's embeddings. We take these findings as evidence that ELMo-style pretraining discovers representations which make additional awareness of shallow syntax redundant. First, we enhance the ELMo architecture (Peters et al.,2018b) to allow pretraining on predicted shallow syntactic parses, instead of just raw text, so that contextual embeddings make use of shallow syntactic context. Our second method involves shallow syntactic features obtained automatically on downstream task data.

How many methods are used for the investigation?

Two methods. They enhance the ELMo architecture to allow pretraining on predicted shallow syntactic parses and their second method involves shallow syntactic features obtained automatically on downstream task data.

The goal of multi-document summarization (MDS) is to automatically generate a brief, well-organized summary for a topic which describes an event with a set of documents from different sources. BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 . In the typical setting of MDS, the input is a set of news documents about the same topic. The output summary is a piece of short text document containing several sentences, generated only based on the input original documents. With the development of social media and mobile equipments, more and more user generated content is available. Figure FIGREF2 is a snapshot of reader comments under the news report “The most important announcements from Google's big developers' conference”. The content of the original news report talks about some new products based on AI techniques. The news report generally conveys an enthusiastic tone. However, while some readers share similar enthusiasms, some others express their worries about new products and technologies and these comments can also reflect their interests which may not be very salient in the original news reports. Unfortunately, existing MDS approaches cannot handle this issue. We investigate this problem known as reader-aware multi-document summarization (RA-MDS). Under the RA-MDS setting, one should jointly consider news documents and reader comments when generating the summaries. One challenge of the RA-MDS problem is how to conduct salience estimation by jointly considering the focus of news reports and the reader interests revealed by comments. Meanwhile, the model should be insensitive to the availability of diverse aspects of reader comments. Another challenge is that reader comments are very noisy, not fully grammatical and often expressed in informal expressions. Some previous works explore the effect of comments or social contexts in single document summarization such as blog summarization BIBREF7 , BIBREF8 . However, the problem setting of RA-MDS is more challenging because the considered comments are about an event which is described by multiple documents spanning a time period. Another challenge is that reader comments are very diverse and noisy. Recently, BIBREF9 employed a sparse coding based framework for RA-MDS jointly considering news documents and reader comments via an unsupervised data reconstruction strategy. However, they only used the bag-of-words method to represent texts, which cannot capture the complex relationship between documents and comments. Recently, BIBREF6 proposed a sentence salience estimation framework known as VAESum based on a neural generative model called Variational Auto-Encoders (VAEs) BIBREF10 , BIBREF11 . During our investigation, we find that the Gaussian based VAEs have a strong ability to capture the salience information and filter the noise from texts. Intuitively, if we feed both the news sentences and the comment sentences into the VAEs, commonly existed latent aspect information from both of them will be enhanced and become salient. Inspired by this consideration, to address the sentence salience estimation problem for RA-MDS by jointly considering news documents and reader comments, we extend the VAESum framework by training the news sentence latent model and the comment sentence latent model simultaneously by sharing the neural parameters. After estimating the sentence salience, we employ a phrase based compressive unified optimization framework to generate a final summary. There is a lack of high-quality dataset suitable for RA-MDS. Existing datasets from DUC and TAC are not appropriate. Therefore, we introduce a new dataset for RA-MDS. We employed some experts to conduct the tasks of data collection, aspect annotation, and summary writing as well as scrutinizing. To our best knowledge, this is the first dataset for RA-MDS. Our contributions are as follows: (1) We investigate the RA-MDS problem and introduce a new dataset for the problem of RA-MDS. To our best knowledge, it is the first dataset for RA-MDS. (2) To tackle the RA-MDS, we extend a VAEs-based MDS framework by jointly considering news documents and reader comments. (3) Experimental results show that reader comments can improve the summarization performance, which also demonstrates the usefulness of the dataset. After estimating the sentence salience, we employ a phrase based compressive unified optimization framework to generate a final summary.

How to generate a final summary?

After estimating the sentence salience, the authors employ a phrase based compressive unified optimization framework to generate a final summary.

What is the source or cause of the Sun’s light?

Much like other stars the Sun is powered by nuclear reactions. The process of nuclear fusion takes place in the Sun’s core. The intense pressure and temperature at the core causes nuclear fusion - in this case hydrogen atoms fusing to create helium this process releases energy. The energy is emitted as gamma rays which travel slowly from the Sun’s core to the outer region. During the rise to the surface gamma rays collide with electrons degrading in temperature and energy to finally emerge as visible radiation - this is the light Sun produces.

Who is the lead singer of the band elbow?

Guy Garvey is the lead singer of elbow.

What is the video game Diablo

Diablo is a action time playing dungeon crawler video game series developed by Blizzard entertainment. The series is made up of multiple games, Diablo, Diablo II, Diablo III, Diablo immortal and soon Diablo IV

Describe ZMOT (Zero moment of truth) and how it differs from the 3-step model of marketing that was prevalent at the time of its introduction.

The 3-step model (ZMOT, FMOT, SMOT) of marketing from P&G begins with the consumer discovering a product via an Ad or hearing it from other sources like friends or colleagues. This trigger moment is followed by (FMOT) the first moment of truth where consumers are at the shelf, looking to buy, touch and feel the product and evaluate competitive offerings on the adjacent frames. The last step is the (SMOT) second moment of truth, where consumers purchase and start using the products.  ZMOT (Zero moment of truth) is the time between the trigger and FMOT. It is when the consumers learn more about the products before they appear on the shelf. ZMOT is the pre-shopping experience where consumers can watch YouTube videos of the product, read Amazon reviews, and have a complete product experience before buying.

Are the following items candy bars or gum: trident, Twix, hubba bubba, snickers, three musketeers, and wrigleys.

Three musketeers, Twix, and snickers are candy bars, and trident, hubba bubba, and wrigleys are gum.

Disclaimer: Due to the nature of the paper, some examples contain highly offensive language and hate speech. They don't reflect the views of the authors in any way, and the point of the paper is to help fight such speech. Much recent interest has focused on the detection of offensive language and hate speech in online social media. Such language is often associated with undesirable online behaviors such as trolling, cyberbullying, online extremism, political polarization, and propaganda. Thus, offensive language detection is instrumental for a variety of application such as: quantifying polarization BIBREF0, BIBREF1, trolls and propaganda account detection BIBREF2, detecting the likelihood of hate crimes BIBREF3; and predicting conflict BIBREF4. In this paper, we describe our methodology for building a large dataset of Arabic offensive tweets. Given that roughly 1-2% of all Arabic tweets are offensive BIBREF5, targeted annotation is essential for efficiently building a large dataset. Since our methodology does not use a seed list of offensive words, it is not biased by topic, target, or dialect. Using our methodology, we tagged 10,000 Arabic tweet dataset for offensiveness, where offensive tweets account for roughly 19% of the tweets. Further, we labeled tweets as vulgar or hate speech. To date, this is the largest available dataset, which we plan to make publicly available along with annotation guidelines. We use this dataset to characterize Arabic offensive language to ascertain the topics, dialects, and users' gender that are most associated with the use of offensive language. Though we suspect that there are common features that span different languages and cultures, some characteristics of Arabic offensive language is language and culture specific. Thus, we conduct a thorough analysis of how Arabic users use offensive language. Next, we use the dataset to train strong Arabic offensive language classifiers using state-of-the-art representations and classification techniques. Specifically, we experiment with static and contextualized embeddings for representation along with a variety of classifiers such as a deep neural network classifier and Support Vector Machine (SVM). The contributions of this paper are as follows: We built the largest Arabic offensive language dataset to date that includes special tags for vulgar language and hate speech. We describe the methodology for building it along with annotation guidelines. We performed thorough analysis of the dataset and described the peculiarities of Arabic offensive language. We experimented with Support Vector Machine classifiers on character and word ngrams classification techniques to provide strong results on Arabic offensive language classification. We built the largest Arabic offensive language dataset to date that includes special tags for vulgar language and hate speech.

What does the largest Arabic offensive language dataset include?

Special tags for vulgar language and hate speech.

What are some ways I could commute in to my office in downtown?

If you are located in a city you may have many commute options. You could use public transportation like a light rail or bus, ride your bike, or drive a car. If you have coworkers who live near you, you could consider carpooling.

Golden Gryphon, Silver Gryphon, Bronze Gryphon, The Nikolay Ovsyannikov Prize, Prize of the City of St. Petersburg, Prize of the Board, Prize for Talent and National Recognition

What are the prizes of the Festival of Festival in Saint Petersburg, Russia?

Grand Prix – Gold or Golden Gryphon (Griffon) - Awarded to the film rated highest by guests and participants Silver Gryphon - Awarded to the film rated highest by audiences Bronze Gryphon - Awarded to the best experimental film The Nikolay Ovsyannikov Prize - Awarded for the best debut Prize of the City of St. Petersburg - Awarded for contributions to world cinema made by a director whose film is featured in this year’s festival Prize of the Board - Awarded to the screening section deemed to be this year’s best Prize for Talent and National Recognition - Awarded to exceptional actors, actresses, and directors

Write the first paragraph of an advertising brochure describing an hotel nearby the Annecy Lake in France

This fantastic hotel is ideally located, minutes away from the Annecy Lake, one of the purest in Europe. The water temperature reaches 27 degrees celcius at the peak season, allowing the guests to swim and enjoy several water activities including wake surf and pedalo. Finally, this region is known for being the home of several cheese specialties like "Raclette" and "Tartiflette" to name a few.

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We ran three groups of experiments, to assess both the effectiveness of our approach when compared with the approaches we found in literature and its capability of extracting features that are relevant for sarcasm in a cross-domain scenario. In both cases, we denote with the word model one of the possible combinations of classic/statistical LSA and a classifier. The used classifiers are Support Vector Machine (SVM), Logistic regression (Log.Reg), Random Forest (RF) and gradient boosting (XGB). The used classifiers are Support Vector Machine (SVM), Logistic regression (Log.Reg), Random Forest (RF) and gradient boosting (XGB).

What classical machine learning algorithms are used?

The answers are shown as follows: * Support Vector Machine (SVM) * Logistic regression (Log.Reg) * Random Forest (RF) * gradient boosting (XGB)

What is an actuary?

An actuary is a specialist in the valuation of the financial impact of risk and uncertainty. They apply statistical techniques and expert industry knowledge. Actuaries usually work in the insurance and banking industries.

Classify the movies based on their genre. The Pink Panther, Die Hard, No time to Die, Annabelle

Action - Die Hard, No time to Die Horror - Annabelle Comedy - The Pink Panther

Which of the following are appropriate for a Black Tie dress code? A tuxedo, a blue suit, a loose sundress, a formal floor length gown?

A tuxedo and a formal floor length gown

Give me a bullet point list of reasons to wear sunscreen

- To prevent sunburns - To reduce the risk of skin cancer - To lessen premature aging - To protect your skin

What are some common software pricing models?

There are many types of software pricing models, but broadly they can be divided into three categories: perpetual, subscription, and consumption. Perpetual software pricing models, also known as perpetual licensing models, allow the software to be purchased for a one-time fee. Typically the buyer has nearly unlimited use of the software in perpetuity. If updates to the software are required, a separate software maintenance package may be purchased. Subscription software allows unlimited use of the software for a predetermined period, often 1 year or 3 years, for a price. Updates to the software are often included in the price of the subscription. At the end of the subscription term, the buyer usually has the option to renew their subscription. Consumption-based software allows the buyer to pay for their actual usage of the software during a period of time, for example monthly. In this type of arrangement, the actual usage can vary from period to period, resulting in customer charges that vary as well. Some customers like knowing that they will only pay for what they use, while other customers dislike the unpredictability of their bills.

How do you prepare for case study interview for digital transformation case?

Here is one suggested approach. 1. Gather all requirements related to business problem 2. Understand data sources the business is using. 3. Understand if business would like to build or buy solutions 4. Check for industry specific accelerators 5. Start with one business unit and show the impact of digital transformation and then expand to other business units. 6. Do business value assessment like ROI 7. Focus on tangible and intangible benefits of digital transformation 8. Cost benefit analysis 9. Implementation plan and timelines 10. Production plan 11. Maintenance and support

Identify which instrument is string or percussion: Davul, Begena

Begena is string, Davul is percission.

What's the preferred mode of transportation in Vietnam?

You will often find the quickest, most enjoyable way to explore the cities of Vietnam is by motorbike. Whether it's a mo-ped, vespa, motorcycle, or dirt bike, these two wheeled options are the preferred method my many to get around.

2004.02393

In HotpotQA, on average we can find 6 candidate chains (2-hop) in a instance, and the human labeled true reasoning chain is unique. A predicted chain is correct if the chain only contains all supporting passages (exact match of passages). In MedHop, on average we can find 30 candidate chains (3-hop). For each candidate chain our human annotators labeled whether it is correct or not, and the correct reasoning chain is not unique. A predicted chain is correct if it is one of the chains that human labeled as correct. The accuracy is defined as the ratio: In HotpotQA, on average we can find 6 candidate chains (2-hop) in a instance, and the human labeled true reasoning chain is unique. A predicted chain is correct if the chain only contains all supporting passages (exact match of passages). In MedHop, on average we can find 30 candidate chains (3-hop). For each candidate chain our human annotators labeled whether it is correct or not, and the correct reasoning chain is not unique. A predicted chain is correct if it is one of the chains that human labeled as correct. The accuracy is defined as the ratio: The accuracy is defined as the ratio:

What benchmarks are created?

Answer with content missing: (formula) The accuracy is defined as the ratio # of correct chains predicted to # of evaluation samples

1910.02334

We built a dataset for the task of hate speech detection in memes with 5,020 images that were weakly labeled into hate or non-hate memes, depending on their source. Hate memes were retrieved from Google Images with a downloading tool. We used the following queries to collect a total of 1,695 hate memes: racist meme (643 memes), jew meme (551 memes), and muslim meme (501 Memes). Non-hate memes were obtained from the Reddit Memes Dataset . We assumed that all memes in the dataset do not contain any hate message, as we considered that average Reddit memes do not belong to this class. A total of 3,325 non-hate memes were collected. We split the dataset into train (4266 memes) and validation (754 memes) subsets. The splits were random and the distribution of classes in the two subsets is the same. We didn't split the dataset into three subsets because of the small amount of data we had and decided to rely on the validation set metrics. Hate memes were retrieved from Google Images with a downloading tool. We used the following queries to collect a total of 1,695 hate memes: racist meme (643 memes), jew meme (551 memes), and muslim meme (501 Memes). Non-hate memes were obtained from the Reddit Memes Dataset .

What is the source of memes?

The answers are shown as follows: * Google Images * Reddit Memes Dataset

As this corpus of annotated patient notes comprises original healthcare data which contains protected health information (PHI) per The Health Information Portability and Accountability Act of 1996 (HIPAA) BIBREF16 and can be joined to the MIMIC-III database, individuals who wish to access to the data must satisfy all requirements to access the data contained within MIMIC-III. To satisfy these conditions, an individual who wishes to access the database must take a “Data or Specimens Management” course, as well as sign a user agreement, as outlined on the MIMIC-III database webpage, where the latest version of this database will be hosted as “Annotated Clinical Texts from MIMIC” BIBREF17. This corpus can also be accessed on GitHub after completing all of the above requirements. This corpus can also be accessed on GitHub2 after completing all of the above requirements.

Could any people access the corpus on GitHub2?

No, they couldn't.

Long short-term memory (LSTM) is an artificial neural network used in the fields of artificial intelligence and deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. Such a recurrent neural network (RNN) can process not only single data points (such as images), but also entire sequences of data (such as speech or video). This characteristic makes LSTM networks ideal for processing and predicting data. For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition, machine translation, speech activity detection, robot control, video games, and healthcare.

Given this paragraph, what is the definition of Long short-term memory ?

Long-short term memory is an artificial neural network used in deep learning that can process not only single data points (such as images), but also entire sequences of data (such as speech or video).

Alice's parents have three daughters: Amy, Jessy, and what’s the name of the third daughter?

The name of the third daughter is Alice

Why does Daenerys Targaryen want to rule Westeros?

According to the exiled Targaryen, she is steadfast in her belief that she is the rightful heir to the Iron Throne as she is the only living descendent of the last Targaryen monarch, her father, King Aerys II. Her father was murdered by his Kingsguard, Jaime Lannister, and his throne usurped by Robert Baratheon. Daenerys believes it is her birthright to reclaim the throne.

OWL BIBREF15 is the de-facto standard for machine processable and interoperable ontologies on the SW. In its second version, OWL is equivalent to the description logic $\mathcal {SROIQ}(D)$. Such expressiveness has a higher computational cost but allows the development of interesting applications such as automated reasoning BIBREF16. OWL 2 ontologies consist of the following three different syntactic categories: Entities, such as classes, properties, and individuals, are identified by IRIs. They form the primitive terms and constitute the basic elements of an ontology. Classes denote sets of individuals and properties link two individuals or an individual and a data value along a property. For example, a class :Animal can be used to represent the set of all animals. Similarly, the object property :childOf can be used to represent the parent-child relationship and the data property :birthDate assigns a particular birth date to an individual. Finally, the individual :Alice can be used to represent a particular person called "Alice". Expressions represent complex notions in the domain being described. For example, a class expression describes a set of individuals in terms of the restrictions on the individuals' characteristics. OWL offers existential (SOME) or universal (ONLY) qualifiers and a variety of typical logical constructs, such as negation (NOT), other Boolean operators (OR, AND), and more constructs such as cardinality restriction (MIN, MAX, EXACTLY) and value restriction (VALUE), to create class expressions. Such constructs can be combined in arbitrarily complex class expressions CE according to the following grammar where A is an atomic class, C and D are class expressions, R is an object property, a as well as a$_1$ to a$_m$ with $\texttt {m} \ge 1$ are individuals, and $\texttt {n} \ge 0$ is an integer. Axioms are statements that are asserted to be true in the domain being described. Usually, one distinguish between (1) terminological and (2) assertional axioms. (1) terminological axioms are used to describe the structure of the domain, i.e., the relationships between classes resp. class expressions. For example, using a subclass axiom (SubClassOf:), one can state that the class :Koala is a subclass of the class :Animal. Classes can be subclasses of other classes, thus creating a taxonomy. In addition, axioms can arrange properties in hierarchies (SubPropertyOf:) and can assign various characteristics (Characteristics:) such as transitivity or reflexivity to them. (2) Assertional axioms formulate facts about individuals, especially the classes they belong to and their mutual relationships. OWL can be expressed in various syntaxes with the most common computer readable syntax being RDF/XMLA more human-readable format is the MOS BIBREF17. For example, the class expression that models people who work at a university that is located in Spain could be as follows in MOS: Likewise, expressing that every professor works at a university would read as OWL 2 ontologies consist of Entities, Expressions and Axioms as introduced in subsec:owl. While both expressions and axioms can be mapped to RDF, i.e. into a set of RDF triples, using this mapping and applying the triple-based verbalization on it would lead to a non-human understandable text in many cases. For example, the intersection of two classes :A and :B can be represented in RDF by the six triples The verbalization of these triples would result in Something that is a class and the intersection of something whose first is A and whose rest is something whose first is B and whose rest ist nil., which is obviously far away from how a human would express it in NL. Therefore, generating NL from OWL requires a different procedure based on its syntactic categories, OWL expressions and OWL axioms. We show the general rules for each of them in the following. In theory, class expressions can be arbitrarily complex, but as it turned out in some previous analysis BIBREF22, in practice they seldom arise and can be seen as some corner cases. For example, an ontology could contain the following class expression about people and their birth place: Class expressions do have a tree-like structure and can simply be parsed into a tree by means of the binary OWL class expressions constructors contained in it. For our example, this would result in the following tree: every tree node/.style=align=center,anchor=base,font=, edge from parent/.style= thick, draw, edge from parent path=(.south) – +(0,-8pt) -| () frontier/.style=distance from root=9 [.AND Person [.SOME birthPlace [.AND City [.VALUE locatedIn France ] ] ] ] Such a tree can be traversed in post-order, i.e. sub-trees are processed before their parent nodes recursively. For the sake of simplicity, we only process sub-trees that represent proper class expression in our example, i.e. we omit birthPlace, locatedIn, and France. Moreover and again for simplicity, we'll explain the transformation process by starting from the right-hand side of the tree. Thus, in our example we begin with the class expression City which is transformed to everything that is a city and locatedIn VALUE France resulting in everything that is located in France by application of a rule. Both class expressions are used in the conjunction City AND locatedIn VALUE France. Thus, the next step would be to merge both phrases. An easy way is to use the coordinating conjunction and, i.e. everything that is a city and everything that is located in France. Although the output of this transformation is correct, it still contains unnecessarily redundant information. Therefore, we apply the aggregation procedure described in subsec:grouping, i.e. we get everything that is a city and located in France. Yet, the aggregation can still be improved: if there is any atomic class in the conjunction, we know that this is more specific than the placeholder everything. Thus, we can replace it by the plural form of the class, finally resulting in cities that are located in France. The same procedure is applied for its parent class expression being the existential restriction This will be transformed to everything whose birth place is a city that is located in France. Note, that we used the singular form here, assuming that the property birthPlace is supposed to be functional in the ontology. In the last step, we process the class expression Person, which gives us everything that is a person. Again, due to the conjunction we merge this result with with the previous one, such that in the end we get people whose birth place is a city that is located in France. As we described in sec:owl, OWL axioms can roughly be categorized into terminological and assertional axioms. Therefore, we have different procedures for processing each category: Assertional Axioms (ABox Axioms) - Most assertional axioms assert individuals to atomic classes or relate individuals to another individual resp. literal value. For example, axioms about the type as well as birth place and birth date of Albert Einstein can be expressed by Those axioms can simply be rewritten as triples, thus, we can use the same procedure as we do for triples (sec:singletriple). Converting them into NL gives us Albert Einstein is a person whose birth place is Ulm and whose birth date is 14 March 1879. OWL also allows for assigning an individual to a complex class expression. In that case we'll use our conversion of OWL class expressions as described in subsec:owlce. Terminological Axioms (TBox Axioms) - According to power2010, most of the terminological axioms used in ontologies are subclass axioms. By definition, subclass and superclass can be arbitrarily complex class expressions $\texttt {CE}_1$ and $\texttt {CE}_2$, i.e. [language=manchester,mathescape=true]CE$_1$ SubClassOf CE$_2$, but in praxis it is quite often only used with atomic classes as subclass or even more simple with the superclass also beeing an atomic class. Nevertheless, we support any kind of subclass axiom and all other logical OWL axioms in LD2NL. For simplicity, we outline here how we verbalize subclass axioms in LD2NL. The semantics of a subclass axiom denotes that every individual of the subclass also belongs to the superclass. Thus, the verbalization seems to be relatively straightforward, i.e. we verbalize both class expressions and follow the template : every $\rho (\texttt {\texttt {CE}$1$})$ is a $\rho (\texttt {\texttt {CE}$2$})$. Obviously, this works pretty well for subclass axioms with atomic classes only. For example, the axiom is verbalized as every scientist is a person. This work was supported by the German Federal Ministry of Transport and Digital Infrastructure (BMVI) through the projects LIMBO (no. 19F2029I) and OPAL (no. 19F2028A). This work was supported by the German Federal Ministry of Economics and Technology (BMWI) in the projects RAKI (no. 01MD19012D) as well as by the BMBF project SOLIDE (no. 13N14456). Axioms are statements that are asserted to be true in the domain being described.

What are the axioms in the OWL?

Axioms are statements that are asserted to be true in the domain being described.

A Half-pistole was a Scottish gold coin minted in 1701 under the reign of King William III. Along with the Pistole it was one of last gold coin minted for the Kingdom of Scotland before the Acts of Union 1707. The Half-pistole was equal to 6 Scottish pounds with an approximate value equal to the British Half guinea.

According to this article, how many Scottish pounds would four Half-pistole be worth?

Four Half-pistole would be worth twenty-four (24) Scottish pounds. Each Half-pistole is 6 Scottish pounds and 6 x 4 = 24.

Estimates for the Ethiopian forces under Menelik range from a low of 73,000 to a high of over 100,000 outnumbering the Italians by an estimated five times. The forces were divided among Emperor Menelik, Empress Taytu Betul, Ras Wale Betul, Ras Mengesha Atikem, Ras Mengesha Yohannes, Ras Alula Engida (Abba Nega), Ras Mikael of Wollo, Ras Makonnen Wolde Mikael,Fitawrari Habte Giyorgis, Fitawrari[nb 3] Gebeyyehu, and Negus[nb 4] Tekle Haymanot Tessemma. In addition, the armies were followed by a similar number of camp followers who supplied the army, as had been done for centuries. Most of the army consisted of riflemen, a significant percentage of whom were in Menelik's reserve; however, there were also a significant number of cavalry and infantry only armed with lances (those with lances were referred to as "lancer servants"). The Kuban Cossack army officer N. S. Leontiev who visited Ethiopia in 1895, according to some sources, led a small team of Russian advisers and volunteers. Other sources assert that Leontiev did not in fact participate in the battle, rather he visited Ethiopia first unofficially in January 1895, and then officially as a representative of Russia in August 1895, but then left later that year, returning only after the Battle of Adwa

Give this paragraph abount Battle of Adwa between Ethiopia and Italy, who were the commanders on Ethiopian side.

Emperor Menelik, Empress Taytu, Ras Makonnen, Ras Wale Betul, Tekle Haymanot Tessemma, Ras Mengesha Yohannes, Ras Mikael were the top commanders and leaders.

Why Interlaken is famous

Because of it's beautiful village. There are many wonderful scenic village adjacent to mountains and fountains.

An isobar is a line of equal or constant pressure on a graph, plot, or map; an isopleth or contour line of pressure. More accurately, isobars are lines drawn on a map joining places of equal average atmospheric pressure reduced to sea level for a specified period of time. In meteorology, the barometric pressures shown are reduced to sea level, not the surface pressures at the map locations. The distribution of isobars is closely related to the magnitude and direction of the wind field, and can be used to predict future weather patterns. Isobars are commonly used in television weather reporting.

What are isobars and what are they commonly used for?

An isobar is a line of equal or constant pressure on a graph, plot, or map. Isobars are commonly used in television weather reporting.

Several studies have addressed the NMT-specific problem of missing or repeating words. Niehues2016 optimized NMT by adding the outputs of PBSMT to the input of NMT. Mi2016a and Feng2016 introduced a distributed version of coverage vector taken from PBSMT to consider which words have been already translated. All these methods, including ours, employ information of the source sentence to improve the quality of translation, but our method uses back-translation to ensure that there is no inconsistency. Unlike other methods, once learned, our method is identical to the conventional NMT model, so it does not need any additional parameters such as coverage vector or a PBSMT system for testing. The attention mechanism proposed by Meng2016 considers not only the hidden states of the encoder but also the hidden states of the decoder so that over-translation can be relaxed. In addition, the attention mechanism proposed by Feng2016 computes a context vector by considering the previous context vector to prevent over-translation. These works indirectly reduce repeating and missing words, while we directly penalize translation mismatch by considering back-translation. The encoder-decoder-reconstructor framework for NMT proposed by tu2016neural optimizes NMT by reconstructor using back-translation. They consider likelihood of both of forward translation and back-translation, and then this framework offers significant improvement in BLEU scores and alleviates the problem of repeating and missing words in the translation on a Chinese-English translation task. All these methods, including ours, employ information of the source sentence to improve the quality of translation, but our method uses back-translation to ensure that there is no inconsistency. Unlike other methods, once learned, our method is identical to the conventional NMT model, so it does not need any additional parameters such as coverage vector or a PBSMT system for testing.

What are the benefits of the author's method compared to other methods?

Unlike other methods, once learned, their method is identical to the conventional NMT model, so it does not need any additional parameters such as coverage vector or a PBSMT system for testing.

Football or soccer?

Most of the world prefers to use the term "football". Exceptions include the United States of America, and to some extent, Australia, New Zealand, and even Ireland. These countries all play other sports called football, such as Gaelic football and American football, and so they use the term "soccer" to distinguish it from their other sport.

Hygienic macros are macros whose expansion is guaranteed not to cause the accidental capture of identifiers. They are a feature of programming languages such as Scheme, Dylan, Rust, Nim, and Julia. The general problem of accidental capture was well known within the Lisp community prior to the introduction of hygienic macros. Macro writers would use language features that would generate unique identifiers (e.g., gensym) or use obfuscated identifiers in order to avoid the problem. Hygienic macros are a programmatic solution to the capture problem that is integrated into the macro expander itself. The term "hygiene" was coined in Kohlbecker et al.'s 1986 paper that introduced hygienic macro expansion, inspired by the terminology used in mathematics.

What are some languages that feature hygienic macros?

Some languages that feature hygienic macros include Scheme, Dylan, Rust, Nim, and Julia.

The siege of Béxar (or Béjar) was an early campaign of the Texas Revolution in which a volunteer Texian army defeated Mexican forces at San Antonio de Béxar (now San Antonio, Texas). Texians had become disillusioned with the Mexican government as President and General Antonio López de Santa Anna's tenure became increasingly dictatorial. In early October 1835, Texas settlers gathered in Gonzales to stop Mexican troops from reclaiming a small cannon. The resulting skirmish, known as the Battle of Gonzales, launched the Texas Revolution. Men continued to assemble in Gonzales and soon established the Texian Army. Despite a lack of military training, well-respected local leader General Stephen F. Austin was elected commander.

Given this paragraph, who was the President of Mexico during the seige of Bexar?

Based on this text, during the seige of Bexar Antonio Lopez de Santa Anna was the President of Mexico.

Write a short story (under 250 words) about meditation. The narrator should be uncomfortable.

Our guide asks us to close our eyes and softly leads us through a meditation: we’re in a meadow, leaning against an old tree. The trunk supports us as we become one with the earth, anchoring ourselves in place, feeling at peace. She gives us a mantra: “I am…” and we fill in the blank and silently repeat the statement in our minds. “I am human,” I think to myself as I visualize myself becoming one with the tree, growing roots and hardening, knowing I will eventually be chopped down along with the other trees that must have once been in this lonely meadow. When our guide softly brings us back to reality she encourages us to share our “I am” mantras and I panic as the other participants declare: “I am brave.” “I am enough.” “I am still.” I don’t repeat my mantra. I don’t want to explain why I needed to feel human, how I saw myself transforming into not only the tree, but then a sprite, a creature of the forest. “I am okay,” I whisper. My declaration is met with universal head nodding. I feel guilty about changing my mantra for a moment, but reassure myself of my statement’s authenticity: I’m no longer a tree, I’m in a studio with several other people, attempting to let go of my social anxieties, and I am okay.