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Captain Oliver Cromwell Applegate (June 11, 1845 – October 11, 1938) was an American politician, newspaper editor, and Indian agent in the U.S. state of Oregon. A member of the Applegate family that helped open the Applegate Trail, he was raised in Southern Oregon where he later was in charge of the Klamath Indian Reservation. He worked as a scout during the Modoc War, was an Indian agent for all of Oregon, and was editor of the Ashland Tidings and the Klamath Republican. Early years Oliver Applegate was born in a log cabin in Yamhill District, in what is now Polk County, Oregon, on June 11, 1845. At the time the area was part of the Oregon Country, but in 1848 became part of the Oregon Territory. He was the sixth son and seventh child of the well-known pioneer, Lindsay Applegate, a native of Kentucky, and his wife, Elizabeth (Miller) Applegate, who was born in Tennessee in 1816. Lindsay Applegate was one of the leaders of the Great Migration of 1843 which Americanized Oregon and was prominent in the early Indian wars, and as an explorer. When Oliver Applegate was five years old, the family moved to the Yoncalla Valley in middle western Oregon; there were only three or four other families in that region at that time besides the Applegate contingent, which consisted of the brothers, Charles, Lindsay and Jesse, and their families. The system of common schools was rudimentary then, and their continuity could not be depended upon for more than a few weeks or months in each year. The Applegate families were fairly well supplied with books, however, to supplement the otherwise meager opportunities for education, and as a rule the scions of these strong frontiersmen availed themselves of every opportunity offered to inform their minds, as well as to become accomplished horsemen, efficient in the use of the rifle and otherwise prepared for the border wars which were liable to occur at any time with the aboriginal inhabitants of the country. In 1860 the family removed to the Siskiyou Mountains near the California boundary, Lindsay Applegate having become owner of the toll road over the mountains, and in 1862, removed to Ashland, Oregon, which continued to be the family home for many years. Career During the winter of 1862, Oliver attended the district school in Ashland, and the next spring received a certificate and in the ensuing fall became the teacher, and for four successive winters, conducted the Ashland school. In the spring of 1863, he became a member of an independent military company, the only one in Southern Oregon, a cavalry company known as the "Mountain Rangers," to which many of the leading citizens of the country belonged. He served as a private in this company the first year, the second year as a sergeant and in the third year was chosen captain, receiving his commissions before he had reached his twentieth year from Addison C. Gibbs, the old war governor of Oregon. In 1865, his father was appointed United States Indian Agent over the Klamaths and Modocs at Fort Klamath. According to the treaty of 1864, the Indians were to be gathered on the Klamath Reservation. The fort was the only place east of the Cascades in that immediate region where there were any white people . The younger Applegate was appointed assistant to the agent, and that was the beginning of a service that lasted for several years, under various agency administrations, during which time he gained influence over the tribes of southeastern Oregon, which he used to good advantage later when the Modoc outbreak of 1872 occurred. This influence probably more than any other agency resulted finally in the conversion of the most resistant of the Indian tribes into farmers and stockmen. When 21 years of age, Applegate had charge of a unique company of scouts, called the "Ax and Rifle Company," because every man carried an ax as well as a rifle. This company consisted of fifty men, the captain the only white man, while different chiefs of the various tribes ranked as lieutenants and sergeants. They cleared the way through the pine forests for a great wagon train of provisions and beef cattle that came down to the Klamath agency from The Dalles, marking the first step in the commencement of operations under the treaty of 1864 for the benefit of the southeastern tribes of Oregon. This was during the war with the Snake or Paiute Indians. For some time before the Modoc outbreak of 1872, Applegate had charge of Yainax sub-agency, forty miles west of the headquarters' agency, then under supervision of Agent Laroy S. Dyar. Near Yainax was located the main band of the Modocs. under the famous old Chief Schonchin, and with him were to be domiciled the turbulent bands under the Modoc chieftain, Captain Jack. The story of how Captain Jack and his band refused to come onto the reservation, and the subsequent events, make up the history of the Modoc War. Applegate played a prominent part in the bloody drama. In 1873, he became a U.S. Commissioner with jurisdiction committed against the federal law locally. In 1876, some of Applegate's friends asked to have him appointed general Indian agent for Oregon, assuming that in such a way his unusual experience in the management of Indian affairs could be used to good purpose in promoting progressive conditions to the several agencies in the state. Ex-Senator Nesmith, who was himself a Democrat, was an ardent advocate of the plan and wrote as follows, to Hon. Zach Chandler, Grant's Secretary of the Interior, with whom he had served in the U.S. Senate: "Mr. Applegate is a gentleman of culture and ability, and, unlike myself, he is a prominent Republican and is as honest as is possible for a man to be possessing his perverted political notions. You will pardon me, I know, for proposing appointments to an administration which I do not indorse, but I do so in order to promote the reforms which you have so happily inaugurated." In 1898, Applegate took charge of the Klamath Reservation as United States Indian agent, and served as such for five years. Congress then discontinued the position of agent and he was appointed bonded superintendent of the agency and training school, a position which he resigned after serving two years. During this period of seven years he helped establish Indian claims to over half a million dollars for lands excluded from the reservation by erroneous boundary surveys, and developed comprehensive plans for irrigation and drainage, which added to the wealth of the reservation, an area approximating in size the state of Delaware. He was identified with various enterprises looking to the development of southern Oregon, and had a reputation as a writer of both prose and verse. Later years and family Applegate served as editor of the Ashland Tidings starting in 1878, and later edited the Klamath Republican. In 1878, Applegate was married to Miss Ella Anderson, a daughter of Rev. Jesse Marion Anderson, a pioneer Methodist minister, who was widely known in southern Oregon as a man of ability and worth. The bride, like the groom, was a native of Oregon, having been born in the territory in 1855. They had three sons and three daughters. Frank Lindsay, the oldest, was born October 10, 1879; Annie Elizabeth, September 13, 1881; Laroy Gilbert, August 19, 1885; Rachel Emma, November 23, 1887; Jennie Margaret, April 5, 1894; and Oliver C., Jr., July 5, 1896. As of 1912, Applegate resided at Klamath Falls, Klamath County, Oregon. In politics, he was a Republican and a delegate to the Republican National Convention at Chicago in 1892. Oliver C. Applegate died on October 11, 1938, at the age of 93. He is the namesake of Applegate Peak in Crater Lake National Park.

Provide a summary of Oliver Cromwell Applegate's career and later years.

Oliver spent his early career as a teacher at the Ashland school as well as a member of the Mountain Rangers. He quickly rose through the ranks starting as a private and moved to captain in 3 years. In 1865, Oliver was appointed assistant to the United States Indian Agent, who also happened to be his father. From there, Oliver spent several years working under multiple agency administrations which allowed him to gain influence over the tribes of southeastern Oregon. The influence he gained would proved to be beneficial when the Modoc outbreak in 1872 happened. This led to the conversion of the most resistant Indian Tribes to farmers and stockmen. In 1876, friends of Oliver Applegate wanted him to use his experience, from working with Indian affairs, to be appointed general Indian Agent for Oregon. In 1989, Applegate became the United States Indian Agent of the Klamath Reservation until Congress discontinued the position 5 years later. In 1878, Applegate started a position as Editor of the Ashland Tidings and married Miss Ella Anderson. Oliver Applegate had 6 kids between 1879 and 1896.

1904.07342

Our first goal is to train a sentiment analysis model (on training and validation datasets) in order to perform classification inference on event-based tweets. We experimented with different feature extraction methods and classification models. Feature extractions examined include Tokenizer, Unigram, Bigram, 5-char-gram, and td-idf methods. Models include both neural nets (e.g. RNNs, CNNs) and standard machine learning tools (e.g. Naive Bayes with Laplace Smoothing, k-clustering, SVM with linear kernel). Model accuracies are reported in Table FIGREF3 . Models include both neural nets (e.g. RNNs, CNNs) and standard machine learning tools (e.g. Naive Bayes with Laplace Smoothing, k-clustering, SVM with linear kernel).

Which machine learning models are used?

The answers are shown as follows: * RNNs * CNNs * Naive Bayes with Laplace Smoothing * k-clustering * SVM with linear kernel

State-of-the-art automatic speech recognition (ASR) systems BIBREF0 have large model capacities and require significant quantities of training data to generalize. Labeling thousands of hours of audio, however, is expensive and time-consuming. A natural question to ask is how to achieve better generalization with fewer training examples. Active learning studies this problem by identifying and labeling only the most informative data, potentially reducing sample complexity. How much active learning can help in large-scale, end-to-end ASR systems, however, is still an open question. The speech recognition community has generally identified the informativeness of samples by calculating confidence scores. In particular, an utterance is considered informative if the most likely prediction has small probability BIBREF1 , or if the predictions are distributed very uniformly over the labels BIBREF2 . Though confidence-based measures work well in practice, less attention has been focused on gradient-based methods like Expected Gradient Length (EGL) BIBREF3 , where the informativeness is measured by the norm of the gradient incurred by the instance. EGL has previously been justified as intuitively measuring the expected change in a model's parameters BIBREF3 .We formalize this intuition from the perspective of asymptotic variance reduction, and experimentally, we show EGL to be superior to confidence-based methods on speech recognition tasks. Additionally, we observe that the ranking of samples scored by EGL is not correlated with that of confidence scoring, suggesting EGL identifies aspects of an instance that confidence scores cannot capture. In BIBREF3 , EGL was applied to active learning on sequence labeling tasks, but our work is the first we know of to apply EGL to speech recognition in particular. Gradient-based methods have also found applications outside active learning. For example, BIBREF4 suggests that in stochastic gradient descent, sampling training instances with probabilities proportional to their gradient lengths can speed up convergence. From the perspective of variance reduction, this importance sampling problem shares many similarities to problems found in active learning. where the informativeness is measured by the norm of the gradient incurred by the instance.

When formalize the intuition for Expected Gradient Length (EGL), how to measure the informativeness?

The informativeness is measured by the norm of the gradient incurred by the instance.

The effects of climate change are impacting humans everywhere in the world. Impacts can now be observed on all continents and ocean regions, with low-latitude, less developed areas facing the greatest risk. Continued warming has potentially “severe, pervasive and irreversible impacts” for people and ecosystems. The risks are unevenly distributed, but are generally greater for disadvantaged people in developing and developed countries. The WHO has classified climate change as the greatest threat to global health in the 21st century. Extreme weather leads to injury and loss of life, and crop failures to undernutrition. Various infectious diseases are more easily transmitted in a warmer climate, such as dengue fever and malaria. Young children are the most vulnerable to food shortages. Both children and older people are vulnerable to extreme heat. The World Health Organization (WHO) has estimated that between 2030 and 2050, climate change would cause around 250,000 additional deaths per year. They assessed deaths from heat exposure in elderly people, increases in diarrhea, malaria, dengue, coastal flooding, and childhood undernutrition. Over 500,000 more adult deaths are projected yearly by 2050 due to reductions in food availability and quality. By 2100, 50% to 75% of the global population may face climate conditions that are life-threatening due to combined effects of extreme heat and humidity. Climate change is affecting food security. It has caused reduction in global yields of maize, wheat, and soybeans between 1981 and 2010. Future warming could further reduce global yields of major crops. Crop production will probably be negatively affected in low-latitude countries, while effects at northern latitudes may be positive or negative. Up to an additional 183 million people worldwide, particularly those with lower incomes, are at risk of hunger as a consequence of these impacts. Climate change also impacts fish populations. Globally, less will be available to be fished. Regions dependent on glacier water, regions that are already dry, and small islands have a higher risk of water stress due to climate change. Economic damages due to climate change may be severe and there is a chance of disastrous consequences. Climate change has likely already increased global economic inequality, and this trend is projected to continue. Most of the severe impacts are expected in sub-Saharan Africa, where most of the local inhabitants are dependent upon natural and agricultural resources and South-East Asia. The World Bank estimates that climate change could drive over 120 million people into poverty by 2030. Current inequalities based on wealth and social status have worsened due to climate change. Major difficulties in mitigating, adapting, and recovering to climate shocks are faced by marginalized people who have less control over resources. Indigenous people, who are subsistent on their land and ecosystems, will face endangerment to their wellness and lifestyles due to climate change. An expert elicitation concluded that the role of climate change in armed conflict has been small compared to factors such as socio-economic inequality and state capabilities. Low-lying islands and coastal communities are threatened by sea level rise, which makes flooding more common. Sometimes, land is permanently lost to the sea. This could lead to statelessness for people in island nations, such as the Maldives and Tuvalu. In some regions, the rise in temperature and humidity may be too severe for humans to adapt to. With worst-case climate change, models project that almost one-third of humanity might live in extremely hot and uninhabitable climates, similar to the current climate found in the Sahara. These factors can drive environmental migration, both within and between countries. More people are expected to be displaced because of sea level rise, extreme weather and conflict from increased competition over natural resources. Climate change may also increase vulnerability, leading to "trapped populations" who are not able to move due to a lack of resources.

Using examples taken from the paragraph, provide the major risks to humans with climate change in a short bulleted list

- Major threat to global health due to extreme weather, crop shortages, and increases in infectious diseases (more easily transmitted in warmer weather) - Effects to food security due to crop yield reduction and impacts to fish population - Economic damages from adverse weather events and dislocations on natural and agricultural resources - Increases in wealth and socioeconomic inequalities as climate change impacts marginalized populations who depend on land and associated ecosystems

What are the biggest cities in Germany?

The biggest cities in Germany are Berlin, Hamburg, Munich and Cologne. Berlin is the biggest city with almost 3.7 million inhabitants. The second biggest city is Hamburg with 1.9 million inhabitants.

McCaw was born in Daly City, California, in 1971. He has a BFA from the Academy of Art College in San Francisco. McCaw is known for his large-format homemade cameras in which he uses expired gelatin silver photo paper and long exposures to make solarized paper negatives which often include the burned path of the sun within the frame, in a series named Sunburn. McCaw travels to remote places to capture different apparent movements of the sun, including the Arctic Circle in Alaska, the Galápagos Islands and the Mojave Desert. McCaw's earlier work used a 7×17 inch view camera to create large-format negatives from which he made platinum prints. Projects following Sunburn include work with a modified Cirkut camera, resulting in exposures that can take more than 24 hours. A series titled Poli-optic employs a homemade camera with a grid of lenses. Finally, the series Heliograph includes work in which there are multiple exposures of the sun on the same paper negative.

What are the places McCaw travelled to capture movements of the Sun? List them in a comma separated format.

Arctic Circle in Alaska, the Galápagos Islands, the Mojave Desert.

We present an attention-based approach for the detection of harassment language in tweets and the detection of different types of harassment as well. Our approach is based on the Recurrent Neural Networks and particularly we are using a deep, classification specific attention mechanism. Moreover, we present a comparison between different variations of this attention-based approach and a few baseline methods. According to the results of our experiments and considering the F1 Score, the multi-attention method having a projected layer, achieved the highest performance. Also, we tackled the problem of the imbalance between the training, validation and test sets performing the technique of back-translation. In the future, we would like to perform more experiments with this dataset applying different models using BERT BIBREF21. Also, we would like to apply the models presented in this work, in other datasets about hate speech in social media. In the future, we would like to perform more experiments with this dataset applying different models using BERT [22]. Also, we would like to apply the models presented in this work, in other datasets about hate speech in social media.

What does the author hope to do about his future work?

They would like to perform more experiments with this dataset applying different models using BERT and to apply the models presented in this work, in other datasets about hate speech in social media.

Sufficient Explanations generalize the previous point by offering settings X = x that causally suffice for Y = y. Existing approaches come in two flavors. Some approaches define sufficient explanations using Pearl's notion of "probability of sufficiency", the deterministic version of which can be informally stated as: if we set the variables in X to x and do not intervene on any of the other variables, then Y takes on the values y. 3 Elsewhere I have called this interpretation of sufficiency weak sufficiency. Other recent approaches take inspiration from logic and define sufficient explanations of an output as its prime implicants, giving so-called PI-explanations. Informally: if we set the variables in X to x, then Y takes on the values y, regardless of the values of all other variables. In the context of causal models I have coined this direct sufficiency. Obviously the second form of sufficient explanations is stronger. Coming back to our example, X 2 = 45, 001 is a sufficient explanation for Y = 1 according to both readings. But (X 1 = 50, 000, X 3 = 25, 000) is a sufficient explanation of Y = 1 only on the first reading, for although it is weakly sufficient for Y = 1 it is not directly sufficient. Which one of these notions is correct here? Imagine that a prospective applicant with these values is told that their income and initial deposit suffice for getting a loan. As a result the applicant concludes that there is no need to have such high savings and decides to spend 20, 000, so that their loan application is denied. The applicant would be quite right to be upset about this! Such misunderstandings cannot occur when using direct sufficiency, as that gives us settings whose explanatory value is immune to the influence of interventions on other variables. Instead of concluding from this that one should always rely on direct sufficiency, I propose a generalization of sufficient explanations that adds an element to inform us explicitly as to which variables are assumed to be safeguarded from interventions. Concretely, in addition to specifying which variables need to be set to particular values, a sufficient explanation should also specify a set of variables N that are not to be manipulated for the explanation to be action-guiding. Informally, if we set the variables in X to x and the variables in N are safeguarded from interventions, then Y takes on the value y, regardless of the values of all remaining variables. I call the relevant notion of causal sufficiency at work strong sufficiency, which can be formally defined as follows: Note that in this definition N cannot just be any set, but rather we require that it is itself entirely determined by X = x. This is because the variables in N can be thought of as a network that transmits the causal influence of X to Y , and the idea of safeguarding this network is that it can continue fulfilling this role even when intervening on X. (I refer the reader to for an elaborate discussion of this definition as well as an equivalent alternative formulation.) The following straightforward result shows the relative strengths of the above three notions of sufficiency. Proposition 8 If X = x is directly sufficient for Y = y then X = x is strongly sufficient for Y = y along some N , and if X = x is strongly sufficient for Y = y along some N then X = x is weakly sufficient for Y = y. Using strong sufficiency we can define a sufficient explanation so that it specifies all the required elements for it to be action-guiding. If N = Y we speak of a direct sufficient explanation. Of course we do not want to add redundant parts to a sufficient set, as a good explanation should be as concise as possible. Therefore a good sufficient explanation ought to be minimal with respect both to X and to N . This allows us to define what makes for a good sufficient explanation of an observed output. Definition 11 An actual sufficient explanation (X 1 = x 1 , N 1 ) of Y = y in (M, u) is good if it is not dominated by any other actual sufficient explanation in (M, u). To conclude the analysis of sufficient explanations, I offer a first result that shows the extreme limitation Independence poses. We have seen three very different notions of sufficiency, and yet under Independence they all collapse into one. Theorem 12 If a causal model M that agrees with h satisfies Independence then the following statements are all equivalent: (Proofs of all Theorems are to be found in the Supplementary Material.) Definition 6 X = x is directly sufficient for Y = y in M if for all c ∈ R(C), where C = V − (X ∪ Y ), and all u ∈ R(U) we have that (M,u) |= [X ← x, C ← c]Y = y.

What is the set C in definition 6? Is it C = V \ (X \cup Y)? Or is the statement supposed to hold for all or for one C \subset V \ (X \cup Y)?

That was a bit sloppy… The set C is implicitly defined when mentioning c as belonging to the range of V - (X u Y). I will make this explicit.

What is the mission of the Federal Bureau of Investigation?

The Federal Bureau of Investigation (FBI) is the domestic intelligence and security service of the United States and its principal federal law enforcement agency. Operating under the jurisdiction of the United States Department of Justice, the FBI is also a member of the U.S. Intelligence Community and reports to both the Attorney General and the Director of National Intelligence. A leading U.S. counterterrorism, counterintelligence, and criminal investigative organization, the FBI has jurisdiction over violations of more than 200 categories of federal crimes.

John Adams (October 30, 1735 – July 4, 1826) was an American statesman, attorney, diplomat, writer, and Founding Father who served as the second president of the United States from 1797 to 1801. Before his presidency, he was a leader of the American Revolution that achieved independence from Great Britain. During the latter part of the war and in the early years of the nation, he served as a diplomat in Europe. He was the first person to hold the office of vice president of the United States, serving from 1789 to 1797. Adams was a dedicated diarist and regularly corresponded with many important contemporaries, including his wife and adviser Abigail Adams as well as his friend and rival Thomas Jefferson.

Who is John Adams?

John Adams (October 30, 1735 – July 4, 1826) was an American statesman, attorney, diplomat, writer, and Founding Father who served as the second president of the United States from 1797 to 1801. Before his presidency, he was a leader of the American Revolution that achieved independence from Great Britain. During the latter part of the war and in the early years of the nation, he served as a diplomat in Europe. He was the first person to hold the office of vice president of the United States, serving from 1789 to 1797. Adams was a dedicated diarist and regularly corresponded with many important contemporaries, including his wife and adviser Abigail Adams as well as his friend and rival Thomas Jefferson. A lawyer and political activist prior to the Revolution, Adams was devoted to the right to counsel and presumption of innocence. He defied anti-British sentiment and successfully defended British soldiers against murder charges arising from the Boston Massacre. Adams was a Massachusetts delegate to the Continental Congress and became a leader of the revolution. He assisted Jefferson in drafting the Declaration of Independence in 1776. As a diplomat in Europe, he helped negotiate a peace treaty with Great Britain and secured vital governmental loans. Adams was the primary author of the Massachusetts Constitution in 1780, which influenced the United States constitution, as did his essay Thoughts on Government. Adams was elected to two terms as vice president under President George Washington and was elected as the United States' second president in 1796. He was the only president elected under the banner of the Federalist Party. During his single term, Adams encountered fierce criticism from the Jeffersonian Republicans and from some in his own Federalist Party, led by his rival Alexander Hamilton. Adams signed the controversial Alien and Sedition Acts, and built up the Army and Navy in the undeclared naval war (called the "Quasi-War") with France. During his term, he became the first president to reside in the executive mansion now known as the White House. In his bid in 1800 for reelection to the presidency, opposition from Federalists and accusations of despotism from Jeffersonians led to Adams losing to his vice president and former friend Jefferson, and he retired to Massachusetts. He eventually resumed his friendship with Jefferson by initiating a correspondence that lasted fourteen years. He and his wife generated the Adams political family, a line of politicians, diplomats, and historians. It includes their son John Quincy Adams, the sixth president. John Adams died on July 4, 1826 – the fiftieth anniversary of the adoption of the Declaration of Independence – hours after Jefferson's death. Adams and his son are the only presidents of the first twelve who never owned slaves. Surveys of historians and scholars have favorably ranked his administration.

Whats the difference between a bass guitar and a regular guitar?

There are a few significant differences between a bass guitar and a regular guitar. A bass guitar typically has 4 strings (though it can have more), is larger in body & neck, and has higher string gauges (string thickness). A bass guitar is used for keeping the band on track by keeping the rhythm and hitting the root notes of the chords. A regular guitar typically has 6 strings, and can be used for a wider variety of purposes. These include keeping the rhythm with chords, playing foundational riffs, or improvisational solos for instrumental layering. The regular guitarists are almost always the more well-known of the two.

In 1910, Imperial Japan annexed Korea, where it ruled for 35 years until its surrender at the end of World War II on 15 August 1945. The United States and the Soviet Union divided Korea along the 38th parallel into two zones of occupation. The Soviets administered the northern zone and the Americans administered the southern zone. In 1948, as a result of Cold War tensions, the occupation zones became two sovereign states. A socialist state, the Democratic People's Republic of Korea, was established in the north under the totalitarian communist leadership of Kim Il-sung, while a capitalist state, the Republic of Korea, was established in the south under the autocratic leadership of Syngman Rhee. Both governments of the two new Korean states claimed to be the sole legitimate government of all of Korea, and neither accepted the border as permanent.

Based on the paragraph about the Korean War, what is the name of the new sovereign state created in the north?

Democratic People's Republic of Korea

We present the first dataset for QA on social media data by leveraging news media and crowdsourcing. The proposed dataset informs us of the distinctiveness of social media from formal domains in the context of QA. Specifically, we find that QA on social media requires systems to comprehend social media specific linguistic patterns like informality, hashtags, usernames, and authorship. These distinguishing linguistic factors bring up important problems for the research of QA that currently focuses on formal text. We see our dataset as a first step towards enabling not only a deeper understanding of natural language in social media but also rich applications that can extract essential real-time knowledge from social media. We see our dataset as a first step towards enabling not only a deeper understanding of natural language in social media but also rich applications that can extract essential real-time knowledge from social media.

What are the benefits of this dataset?

Not only a deeper understanding of natural language in social media but also rich applications that can extract essential real-time knowledge from social media

Next, we apply a grayscale data augmentation technique/transformation f to these images so that these images become different from the images that the original model was earlier trained on (assuming that the original model has not been trained on grayscale images). We can also use other data augmentation techniques that are not seen during the training process of the original model and that do not change the class of the image (refer to Sec. 11.7 in the appendix).

The process of identifying parameters related to restricted classes seems quite empirically, as a transformation component is needed from some prior knowledge. The authors have mentioned it for images. However, many data privacy related data are also tabular. In this case, how to apply a proper transformation? If this component is quite related to data format, any workaround for this issue?

Our proposed approach only requires the use of data augmentation/transformation techniques that are not used during training. Therefore, for any domain, we can use data augmentation methods compatible with the domain as long as they have not been used during training and do not change the label of the datapoint.

Our system comprises of following three steps: Cloze generation: Most of the documents typically follow a template, they begin with an introduction that provides an overview and a brief summary for what is to follow. We assume such a structure while constructing our cloze style questions. When there is no clear demarcation, we treat the first $K\%$ (hyperparameter, in our case 20%) of the document as the introduction. While noisy, this heuristic generates a large number of clozes given any corpus, which we found to be beneficial for semi-supervised learning despite the noise. We use a standard NLP pipeline based on Stanford CoreNLP (for SQuAD, TrivaQA and PubMed) and the BANNER Named Entity Recognizer (only for PubMed articles) to identify entities and phrases. Assume that a document comprises of introduction sentences $\lbrace q_1, q_2, ... q_n\rbrace $ , and the remaining passages $\lbrace p_1, p_2, .. p_m\rbrace $ . Additionally, let's say that each sentence $q_i$ in introduction is composed of words $\lbrace w_1, w_2, ... w_{l_{q_i}}\rbrace $ , where $l_{q_i}$ is the length of $q_i$ . We consider a $\text{match} (q_i, p_j)$ , if there is an exact string match of a sequence of words $\lbrace w_k, w_{k+1}, .. w_{l_{q_i}}\rbrace $ between the sentence $q_i$ and passage $p_j$ . If this sequence is either a noun phrase, verb phrase, adjective phrase or a named entity in $\lbrace p_1, p_2, .. p_m\rbrace $0 , as recognized by CoreNLP or BANNER, we select it as an answer span $\lbrace p_1, p_2, .. p_m\rbrace $1 . Additionally, we use $\lbrace p_1, p_2, .. p_m\rbrace $2 as the passage $\lbrace p_1, p_2, .. p_m\rbrace $3 and form a cloze question $\lbrace p_1, p_2, .. p_m\rbrace $4 from the answer bearing sentence $\lbrace p_1, p_2, .. p_m\rbrace $5 by replacing $\lbrace p_1, p_2, .. p_m\rbrace $6 with a placeholder. As a result, we obtain passage-question-answer ( $\lbrace p_1, p_2, .. p_m\rbrace $7 ) triples (Table 1 shows an example). As a post-processing step, we prune out $\lbrace p_1, p_2, .. p_m\rbrace $8 triples where the word overlap between the question (Q) and passage (P) is less than 2 words (after excluding the stop words). The process relies on the fact that answer candidates from the introduction are likely to be discussed in detail in the remainder of the article. In effect, the cloze question from the introduction and the matching paragraph in the body forms a question and context passage pair. We create two cloze datasets, one each from Wikipedia corpus (for SQuAD and TriviaQA) and PUBMed academic papers (for the BioASQ challenge), consisting of 2.2M and 1M clozes respectively. From analyzing the cloze data manually, we were able to answer 76% times for the Wikipedia set and 80% times for the PUBMed set using the information in the passage. In most cases the cloze paraphrased the information in the passage, which we hypothesized to be a useful signal for the downstream QA task. We also investigate the utility of forming subsets of the large cloze corpus, where we select the top passage-question-answer triples, based on the different criteria, like i) jaccard similarity of answer bearing sentence in introduction and the passage ii) the tf-idf scores of answer candidates and iii) the length of answer candidates. However, we empirically find that we were better off using the entire set rather than these subsets. Pre-training: We make use of the generated cloze dataset to pre-train an expressive neural network designed for the task of reading comprehension. We work with two publicly available neural network models – the GA Reader BIBREF2 (to enable comparison with prior work) and BiDAF + Self-Attention (SA) model from BIBREF1 (which is among the best performing models on SQuAD and TriviaQA). After pretraining, the performance of BiDAF+SA on a dev set of the (Wikipedia) cloze questions is 0.58 F1 score and 0.55 Exact Match (EM) score. This implies that the cloze corpus is neither too easy, nor too difficult to answer. Fine Tuning: We fine tune the pre-trained model, from the previous step, over a small set of labelled question-answer pairs. As we shall later see, this step is crucial, and it only requires a handful of labelled questions to achieve a significant proportion of the performance typically attained by training on tens of thousands of questions. Pre-training: We make use of the generated cloze dataset to pre-train an expressive neural network designed for the task of reading comprehension. We work with two publicly available neural network models — the GA Reader (Dhingra et al., 2017) (to enable comparison with prior work) and BiDAF + Self-Attention (SA) model from Clark and Gardner (2017) (which is among the best performing models on SQuAD and TriviaQA).

Why is BiDAF + Self-Attention (SA) model from Clark and Gardner chosen for pre-training

It is among the best performing models on SQuAD and TriviaQA

What field of science (biology, physics or chemistry) was Marie Curie a researcher in?

Marie Curie was a chemist

Bird migration is the regular seasonal movement, often north and south, along a flyway, between breeding and wintering grounds. Many species of bird migrate. Migration carries high costs in predation and mortality, including from hunting by humans, and is driven primarily by the availability of food. It occurs mainly in the northern hemisphere, where birds are funneled onto specific routes by natural barriers such as the Mediterranean Sea or the Caribbean Sea.

Why do birds migrate north to south for the colder months in the north?

Birds primarily migrate due to the availability of food in warmer climates. The north is a breeding ground, while the south is their wintering ground.

1906.00378

We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction: CNN-mean: taking the similarity score of the averaged feature of the two image sets. CNN-avgmax: taking the average of the maximum similarity scores of two image sets. We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction: CNN-mean: taking the similarity score of the averaged feature of the two image sets. CNN-avgmax: taking the average of the maximum similarity scores of two image sets.

Which vision-based approaches does this approach outperform?

The answers are shown as follows: * CNN-mean * CNN-avgmax

2001.06286

We evaluated RobBERT in several different settings on multiple downstream tasks. First, we compare its performance with other BERT-models and state-of-the-art systems in sentiment analysis, to show its performance for classification tasks. Second, we compare its performance in a recent Dutch language task, namely the disambiguation of demonstrative pronouns, which allows us to additionally compare the zero-shot performance of our and other BERT models, i.e. using only the pre-trained model without any fine-tuning. First, we compare its performance with other BERT-models and state-of-the-art systems in sentiment analysis, to show its performance for classification tasks. Second, we compare its performance in a recent Dutch language task, namely the disambiguation of demonstrative pronouns, which allows us to additionally compare the zero-shot performance of our and other BERT models, i.e. using only the pre-trained model without any fine-tuning.

What language tasks did they experiment on?

The answers are shown as follows: * sentiment analysis * the disambiguation of demonstrative pronouns,

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

Given these paragraphs about Large language models, when was the first model in OpenAI's GPT series trained?

2018.

1707.00995

It is also worth to mention that we use a ResNet trained on 1.28 million images for a classification tasks. The features used by the attention mechanism are strongly object-oriented and the machine could miss important information for a multimodal translation task. We believe that the robust architecture of both encoders INLINEFORM0 combined with a GRU layer and word-embeddings took care of the right translation for relationships between objects and time-dependencies. Yet, we noticed a common misbehavior for all our multimodal models: if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations. The translation is then totally mislead. We illustrate with an example: Yet, we noticed a common misbehavior for all our multimodal models: if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations.

What misbehavior is identified?

The answers are shown as follows: * if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations

Who are the last 10 Masters Champions?

2022 — Scottie Scheffler 2021 — Hideki Matsuyama 2020 — Dustin Johnson 2019 — Tiger Woods 2018 — Patrick Reed 2017 — Sergio Garcia 2016 — Danny Willett 2015 — Jordan Spieth 2014 — Bubba Watson 2013 — Adam Scott

LaVelle Smith Jr is an American choreographer and dancer. Known for his work in the music videos of singers such as En Vogue, Michael Jackson, Janet Jackson and Beyoncé Knowles, Smith has won the MTV Video Music Award for Best Choreography five times.

Who is LaVelle Smith Jr.?

LaVelle Smith Jr is an American choreographer and dancer. Known for his work in the music videos of singers such as En Vogue, Michael Jackson, Janet Jackson and Beyoncé Knowles, Smith has won the MTV Video Music Award for Best Choreography five times. Smith had originally wanted to pursue a career in drama, auditioning at the Youth Performing Arts School in Louisville, Kentucky. Upon failing the audition, Smith was given the option to study dance. Graduating from YPAS in 1983, Smith moved to Chicago and was employed by Gus Giordano in the dance troupe Giordano Jazz Dance Chicago. Unhappy with the level of pay, Smith auditioned for numerous dancing roles, while facing racial prejudice. "When that happens, you realize that you have to be even better or move to L.A. where it doesn't matter," he said later. Noticed by singer Michael Jackson, Smith landed a job as a dancer before becoming his choreographer. Smith worked on three of Jackson's world tours; Bad, Dangerous and HIStory. He also worked as a choreographer in Michael Jackson's Ghosts, and for the musical Thriller - Live. Smith appeared on the concert tours of The Rolling Stones, Diana Ross and Janet Jackson's Rhythm Nation 1814 Tour. Smith also worked for Victoria Beckham after the Spice Girl made a return as a solo artist. Smith was awarded four MTV Video awards for co-choreographing En Vogue's music videos "My Lovin'" (1992), "Free Your Mind" (1993), "Whatta Man" (1994) and Michael Jackson's duet with sister Janet, "Scream" (1995). The music video for Beyoncé's "Crazy in Love" won Smith his fifth MTV Video Music Award for Best Choreography in 2003. His other awards include a SSDC Bob Fosse award and an Emmy award. Smith was also the youngest inductee into the Millers Gallery of Greats. Smith has choreographed Invincible: A Glorious Tribute To Michael Jackson featuring Jeffrey Perez and Pete Carter. Smith's work with Michael Jackson is the subject of the 2019 documentary film The Man Behind The Dance.

The Quiet Family (Korean: 조용한 가족; RR: Joyonghan Gajok) is a 1998 South Korean black comedy horror film directed by Kim Jee-woon. The story centers on a family who owns a hunting lodge in a remote area, whose customers always happen to end up dying. Among the film's main cast are pre-stardom Choi Min-sik and Song Kang-ho. The film was loosely remade in Japanese as The Happiness of the Katakuris by Takashi Miike, in Indian Tamil as Yaamirukka Bayamey, in Kannada as Namo Bhootatma and in Telugu as Next Nuvve.

What is the plot of the movie The Quiet Family

The Quiet Family is a 1998 South Korean black comedy horror film directed by Kim Jee-woon. The story centers on a family who owns a hunting lodge in a remote area, whose customers always happen to end up dying. Among the film's main cast are pre-stardom Choi Min-sik and Song Kang-ho. The film was loosely remade in Japanese as The Happiness of the Katakuris by Takashi Miike, in Indian Tamil as Yaamirukka Bayamey, in Kannada as Namo Bhootatma and in Telugu as Next Nuvve.

1909.00279

Training and Validation Sets We collected a corpus of poems and a corpus of vernacular literature from online resources. The poem corpus contains 163K quatrain poems from Tang Poems and Song Poems, the vernacular literature corpus contains 337K short paragraphs from 281 famous books, the corpus covers various literary forms including prose, fiction and essay. Note that our poem corpus and a vernacular corpus are not aligned. We further split the two corpora into a training set and a validation set. We collected a corpus of poems and a corpus of vernacular literature from online resources. The poem corpus contains 163K quatrain poems from Tang Poems and Song Poems, the vernacular literature corpus contains 337K short paragraphs from 281 famous books, the corpus covers various literary forms including prose, fiction and essay. Note that our poem corpus and a vernacular corpus are not aligned. We further split the two corpora into a training set and a validation set.

What dataset is used for training?

The answers are shown as follows: * We collected a corpus of poems and a corpus of vernacular literature from online resources

We train NMT with RAT to achieve better query translations. We improve a recently proposed NMT baseline, Transformer, that achieves state-of-the-art results for sentence pairs in some languages BIBREF8 . We discuss Transformer, RAT, and our multi-task learning architecture that achieves balanced translation. A basic form of NMT comprises two components: (a) an encoder that computes the representations or meaning of si and (b) a decoder that generates one target word at a time.

What does the basic form of NMT comprise?

A basic form of NMT comprises two components: (a) an encoder that computes the representations or meaning of si and (b) a decoder that generates one target word at a time.

Question classification (QC) deals with question analysis and question labeling based on the expected answer type. The goal of QC is to assign classes accurately to the questions based on expected answer. In modern system, there are two types of questions BIBREF0. One is Factoid question which is about providing concise facts and another one is Complex question that has a presupposition which is complex. Question Answering (QA) System is an integral part of our daily life because of the high amount of usage of Internet for information acquisition. In recent years, most of the research works related to QA are based on English language such as IBM Watson, Wolfram Alpha. Bengali speakers often fall in difficulty while communicating in English BIBREF1. In this research, we briefly discuss the steps of QA system and compare the performance of seven machine learning based classifiers (Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)) in classifying Bengali questions to classes based on their anticipated answers. Bengali questions have flexible inquiring ways, so there are many difficulties associated with Bengali QC BIBREF0. As there is no rich corpus of questions in Bengali Language available, collecting questions is an additional challenge. Different difficulties in building a QA System are mentioned in the literature BIBREF2 BIBREF3. The first work on a machine learning based approach towards Bengali question classification is presented in BIBREF0 that employ the Stochastic Gradient Descent (SGD). In this research, we briefly discuss the steps of QA system and compare the performance of seven machine learning based classifiers (Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)) in classifying Bengali questions to classes based on their anticipated answers.

What specific machine learning-based classifiers did the author compare?

They compare the performance of seven machine learning based classifiers (Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Stochastic Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)) in classifying Bengali questions to classes based on their anticipated answers.

My computer won't start. How do I fix it?

First check that the computer is plugged in. If there's a switch on the power supply make sure the switch is in the on position. Check to see if the fans turn on when you hit the power button. If so, then the computer is likely getting power. Do you hear a beeping sound? That means the computer is successfully able to load the BIOS and you issue might be related to the installation of the OS. If you can't get to the BIOS, you may have a hardware issue with computer. Open the case and check for loose connections. Try reseating the ram. If that doesn't work you may have a short on the motherboard, which might need replacement.

Which of the following are considered hobbies: painting, hiking, rock climbing, peapod, bears, horse riding, chess, emails, clogs, board games, button, sewing, water bottle, keyboard, reading, soccer

painting, hiking, rock climbing, horse riding, chess, board games, reading, soccer

We analyzed the behavior of the negative GNP on three graph-level tasks that we performed in the paper. In all experiments, we found that either GNP+ or GNP− tends to dominate the other side. To validate the observation, we masked the output of GNP+ and GNP− for readout to 0 on the graph-level tasks.

Is it possible to take a peek at the weights to see if a layer clearly chooses p+ over p- or vice versa?

We also analyzed the behavior of the negative GNP on three graph-level tasks and found that either GNP+ or GNP− tends to dominate the other side. For details, please refer to Appendix C.6 (in the revised paper).

To solve a given problem of supervised learning, one has to perform the following steps: Determine the type of training examples. Before doing anything else, the user should decide what kind of data is to be used as a training set. In the case of handwriting analysis, for example, this might be a single handwritten character, an entire handwritten word, an entire sentence of handwriting or perhaps a full paragraph of handwriting. Gather a training set. The training set needs to be representative of the real-world use of the function. Thus, a set of input objects is gathered and corresponding outputs are also gathered, either from human experts or from measurements. Determine the input feature representation of the learned function. The accuracy of the learned function depends strongly on how the input object is represented. Typically, the input object is transformed into a feature vector, which contains a number of features that are descriptive of the object. The number of features should not be too large, because of the curse of dimensionality; but should contain enough information to accurately predict the output. Determine the structure of the learned function and corresponding learning algorithm. For example, the engineer may choose to use support-vector machines or decision trees. Complete the design. Run the learning algorithm on the gathered training set. Some supervised learning algorithms require the user to determine certain control parameters. These parameters may be adjusted by optimizing performance on a subset (called a validation set) of the training set, or via cross-validation. Evaluate the accuracy of the learned function. After parameter adjustment and learning, the performance of the resulting function should be measured on a test set that is separate from the training set.

Extract the steps to resolve a given supervised machine learning problem from the following text

Use the following steps to solve a given problem of supervised learning: 1. Determine the type of training examples, 2. Gather a training set. 3. Determine the input feature representation of the learned function. 4. Determine the structure of the learned function and corresponding learning algorithm. 5. Run the learning algorithm on the gathered training set. 6. Evaluate the accuracy of the learned function.

2001.08051

Speakers were assigned either to training or evaluation sets, with proportions of $\frac{2}{3}$ and $\frac{1}{3}$, respectively; then training and evaluation lists were built, accordingly. Table reports statistics from the spoken data set. The id All identifies the whole data set, while Clean defines the subset in which sentences containing background voices, incomprehensible speech and word fragments were excluded. FLOAT SELECTED: Table 7: Statistics from the spoken data sets (2017) used for ASR. Speakers were assigned either to training or evaluation sets, with proportions of $\frac{2}{3}$ and $\frac{1}{3}$, respectively; then training and evaluation lists were built, accordingly. Table reports statistics from the spoken data set. The id All identifies the whole data set, while Clean defines the subset in which sentences containing background voices, incomprehensible speech and word fragments were excluded. FLOAT SELECTED: Table 7: Statistics from the spoken data sets (2017) used for ASR.

How many of the utterances are transcribed?

Total number of transcribed utterances including Train and Test for both Eng and Ger language is 5562 (2188 cleaned)

In English do adjectives go before or after nouns they describe?

In English adjectives precede the item they are describing

Listeriosis is an infectious but not contagious disease caused by the bacterium Listeria monocytogenes, far more common in domestics animals (domestic mammals and poultry), especially ruminants, than in human beings. It can also occur in feral animals—among others, game animals—as well as in poultry and other birds. The causative bacterium lives in the soil and in poorly made silage, and is acquired by ingestion. It is not contagious; over the course of a 30-year observation period of sheep disease in Morocco, the disease only appeared in the late 2000s (decade) when feeding bag-ensiled corn became common.[better source needed] In Iceland, the disease is called "silage sickness".

Given a reference text about Listeriosis, tell me how how the bacterium is transfered into animals, and people.

Listeriosis is most often associated with animals but can also infect people and is primarily transferred by ingestion.

Kevin Stanley Rohleder (7 April 1920 – 14 August 1983) was an Australian rules footballer who had played with St Kilda in the Victorian Football League (VFL). His brother, Noel Rohleder, played one game for South Melbourne. The son of Veronica Harriet Stanley (1899–1970), Kevin Stanley was born at Carlton, Victoria on 7 April 1920. He later took the surname Rohleder after his mother married Walter John Rohleder (1897–1982) in 1923.

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

• Kevin Stanley Rohleder • Noel Rohleder • Veronica Harriet Stanley • Walter John Rohleder

Julius Steele Barnes (23 February 1792 – 12 November 1870) was an American physician. Besides being a skillful practitioner, and devoted to his calling, he also labored heartily for the social good of the community. He served one term as Connecticut State Senator, and held for a time the office of Judge of Probate.

Identify the political office or offices Julius Steele Barnes held.

Julius Steele Barnes was a Connecticut State senator and a Judge of Probate.

2002.00317

To fine-tune GPT2 for text generation, it is typical to concatenate the conditioning context $X = x_1 \ldots x_n$ and citing sentence $Y = y_1 \ldots y_m$ with a special separator token $\mho $. The model learns to approximate next token probabilities for each index after $\mho $: We measure the closeness of two pairs of documents by measuring cosine distances between vector representations of their content. The abstract of each document is embedded into a single dense vector by averaging the contextualized embeddings provided by the SciBERT model of BIBREF11 and normalizing. The distance between $(S,C)$ and candidate $(N_S,N_C)$ is computed as: To fine-tune GPT2 for text generation, it is typical to concatenate the conditioning context $X = x_1 \ldots x_n$ and citing sentence $Y = y_1 \ldots y_m$ with a special separator token $\mho $. The abstract of each document is embedded into a single dense vector by averaging the contextualized embeddings provided by the SciBERT model of BIBREF11 and normalizing.

Which baselines are explored?

The answers are shown as follows: * GPT2 * SciBERT model of BIBREF11

1909.00694

FLOAT SELECTED: Table 3: Performance of various models on the ACP test set. FLOAT SELECTED: Table 4: Results for small labeled training data. Given the performance with the full dataset, we show BERT trained only with the AL data. As for ${\rm Encoder}$, we compared two types of neural networks: BiGRU and BERT. GRU BIBREF16 is a recurrent neural network sequence encoder. BiGRU reads an input sequence forward and backward and the output is the concatenation of the final forward and backward hidden states. We trained the model with the following four combinations of the datasets: AL, AL+CA+CO (two proposed models), ACP (supervised), and ACP+AL+CA+CO (semi-supervised). The corresponding objective functions were: $\mathcal {L}_{\rm AL}$, $\mathcal {L}_{\rm AL} + \mathcal {L}_{\rm CA} + \mathcal {L}_{\rm CO}$, $\mathcal {L}_{\rm ACP}$, and $\mathcal {L}_{\rm ACP} + \mathcal {L}_{\rm AL} + \mathcal {L}_{\rm CA} + \mathcal {L}_{\rm CO}$. FLOAT SELECTED: Table 3: Performance of various models on the ACP test set. FLOAT SELECTED: Table 4: Results for small labeled training data. Given the performance with the full dataset, we show BERT trained only with the AL data. As for ${\rm Encoder}$, we compared two types of neural networks: BiGRU and BERT. We trained the model with the following four combinations of the datasets: AL, AL+CA+CO (two proposed models), ACP (supervised), and ACP+AL+CA+CO (semi-supervised). The corresponding objective functions were: $\mathcal {L}_{\rm AL}$, $\mathcal {L}_{\rm AL} + \mathcal {L}_{\rm CA} + \mathcal {L}_{\rm CO}$, $\mathcal {L}_{\rm ACP}$, and $\mathcal {L}_{\rm ACP} + \mathcal {L}_{\rm AL} + \mathcal {L}_{\rm CA} + \mathcal {L}_{\rm CO}$.

What are the results?

Using all data to train: AL -- BiGRU achieved 0.843 accuracy, AL -- BERT achieved 0.863 accuracy, AL+CA+CO -- BiGRU achieved 0.866 accuracy, AL+CA+CO -- BERT achieved 0.835, accuracy, ACP -- BiGRU achieved 0.919 accuracy, ACP -- BERT achived 0.933, accuracy, ACP+AL+CA+CO -- BiGRU achieved 0.917 accuracy, ACP+AL+CA+CO -- BERT achieved 0.913 accuracy. Using a subset to train: BERT achieved 0.876 accuracy using ACP (6K), BERT achieved 0.886 accuracy using ACP (6K) + AL, BiGRU achieved 0.830 accuracy using ACP (6K), BiGRU achieved 0.879 accuracy using ACP (6K) + AL + CA + CO.

In which years did the Women's Australian cricket team win the world cup

1978 1982 1988 1997 2005 2013 2022

What is a dig in baseball?

A "dig" or "digging one out" is an expression to describe a defensive player accepting a baseball with a motion that captures the ball very near the ground. This kind of expression is often, though not always, used to describe a first baseman catching a ball thrown by another player that bounces before it reaches them as they hold one foot on first base. This expression can also be used to describe another fielder, typically an infielder, to get a ball into their glove or hand that bounced near them or immediately before reaching them.

Describe some different ways you can make a phone call

There are many ways you can make a phone call. If you have a mobile phone or landline then the easiest way to make a phone call would be to use one of those devices. If you do not then you could look for a telephone box in your local area. Alternatively you can use a computer to access software phone services such as Twilio that provide a bridge between the internet and international telephony networks.

What is an Angul Thermal Power Station

Angul Thermal Power Station is a coal based thermal power project located at Derang village in Angul district in Indian state of Odisha. The power plant is one of the coal based power plants of Jindal India Thermal Power Limited. Coal for the power plant is sourced from Mahanadi Coalfields Limited and water is sourced from Samal Barrage on Brahmani River which is 14 km away. Bharat Heavy Electricals is the EPC contractor for this project.

Maria Magdelana Von Losch Beyyer know as who

Marlene Dietrich

In this section we present the results from our development stage (Table 2), the evaluation stage (Table 3), and two post-evaluation results (Table 3). Since we implemented both bigram and trigam language models during the development stage but only results from trigram language models were submitted to the task, we evaluated bigram language models in the post-evaluation stage. Note that the accuracy and distance measurements listed in Table 2 and Table 3 are defined by the task organizers BIBREF6 . Table 2 shows results from the development stage. These results show that for the tweet data the best setting is to keep the # and @, omit sentence boundaries, be case sensitive, and ignore tokenization. While using these settings the trigram language model performed better on Subtask B (.887) and the bigram language model performed better on Subtask A (.548). We decided to rely on trigram language models for the task evaluation since the advantage of bigrams on Subtask A was very slight (.548 versus .543). For the news data, we found that the best setting was to perform tokenization, omit sentence boundaries, and to be case sensitive. Given that trigrams performed most effectively in the development stage, we decided to use those during the evaluation. Table 3 shows the results of our system during the task evaluation. We submitted two runs, one with a trigram language model trained on the tweet data, and another with a trigram language model trained on the news data. In addition, after the evaluation was concluded we also decided to run the bigram language models as well. Contrary to what we observed in the development data, the bigram language model actually performed somewhat better than the trigram language model. In addition, and also contrary to what we observed with the development data, the news data proved generally more effective in the post–evaluation runs than the tweet data. In addition, after the evaluation was concluded we also decided to run the bigram language models as well. Contrary to what we observed in the development data, the bigram language model actually performed somewhat better than the trigram language model.

Did the bigram language model perform worse than the trigram language model?

No, it performed somewhat better than the trigram language model.

The Porsche 911 (pronounced Nine Eleven or in German: Neunelfer) is a two-door 2+2 high performance rear-engined sports car introduced in September 1964 by Porsche AG of Stuttgart, Germany. It has a rear-mounted flat-six engine and originally a torsion bar suspension. The car has been continuously enhanced through the years but the basic concept has remained unchanged. The engines were air-cooled until the introduction of the 996 series in 1998.

When did Porsche stop producing air-cooled engines in the 911?

Porsche stopped producing air-cooled engines in the 911 in 1998 with the 996 series.

1910.02334

The text and image encodings were combined by concatenation, which resulted in a feature vector of 4,864 dimensions. This multimodal representation was afterward fed as input into a multi-layer perceptron (MLP) with two hidden layer of 100 neurons with a ReLU activation function. The last single neuron with no activation function was added at the end to predict the hate speech detection score. The text and image encodings were combined by concatenation, which resulted in a feature vector of 4,864 dimensions. This multimodal representation was afterward fed as input into a multi-layer perceptron (MLP) with two hidden layer of 100 neurons with a ReLU activation function.

Is the dataset multimodal?

Yes.

Why should people drive cars with manual transmissions?

People should drive cars with manual transmissions because it forces drivers to be more engaged while driving and lowers the incidence of distracted driving, making it the safest choice overall. Environmental concerns about fossil fuels aside, one of the biggest issues with driving today is distracted driving. With improvements in cruise control technology and other driver assistance functions like lane keep assist, cars are taking over more of the driving process than ever before. While the intent is to make it safer for drivers, it also has the unintended consequence of making easier for drivers to be distracted. Cars with manual transmissions not only force greater engagement and focus, but they are also either incompatible with, or only compatible with lesser versions of, certain driver assistance features like adaptive cruise control because lowering the speed while not being able to change the gear (which is manual) could cause the car to stall. Paradoxically, by increasing the required inputs and decreasing the number of driver assistance features, driving a manual transmission car is safest.

Pre-trained language models such as BERT BIBREF0 have significantly improved the accuracy of various language processing tasks. However, we cannot apply BERT to language generation tasks as is because its model structure is not suitable for language generation. Several pre-trained seq-to-seq models for language generation BIBREF1, BIBREF2 based on an encoder-decoder Transformer model, which is a standard model for language generation, have recently been proposed. These models have achieved blackstate-of-the-art results in various language generation tasks, including abstractive summarization. However, when generating a summary, it is essential to correctly predict which part of the source text should be included in the summary. Some previous studies without pre-training have examined combining extractive summarization with abstractive summarization BIBREF3, BIBREF4. Although pre-trained seq-to-seq models have achieved higher accuracy compared to previous models, it is not clear whether modeling “Which part of the source text is important?” can be learned through pre-training. blackThe purpose of this study is to clarify the blackeffectiveness of combining saliency models that identify the important part of the source text with a pre-trained seq-to-seq model in the abstractive summarization task. Our main contributions are as follows: We investigated nine combinations of pre-trained seq-to-seq and token-level saliency models, where the saliency models share the parameters with the encoder of the seq-to-seq model or extract important tokens independently of the encoder. We proposed a new combination model, the conditional summarization model with important tokens (CIT), in which a token sequence extracted by a saliency model is explicitly given to a seq-to-seq model as an additional input text. We evaluated the combination models on the CNN/DM BIBREF5 and XSum BIBREF6 datasets. Our CIT model outperformed a simple fine-tuned model in terms of ROUGE scores on both datasets. The purpose of this study is to clarify the blackeffectiveness of combining saliency models that identify the important part of the source text with a pre-trained seq-to-seq model in the abstractive summarization task.

What black effectiveness does this paper clarify?

It clarifies the blackeffectiveness of combining saliency models that identify the important part of the source text with a pre-trained seq-to-seq model in the abstractive summarization task.

1901.00570

FLOAT SELECTED: TABLE II: A comparison of classification AVCs using word-pairs extracted by different feature selection methods FLOAT SELECTED: TABLE II: A comparison of classification AVCs using word-pairs extracted by different feature selection methods

Were any other word similar metrics, besides Jaccard metric, tested?

Yes.

How do you jump start a car?

1. Make sure both batteries have the same voltage (they probably do unless one car is old). Don’t jump start a frozen battery (it may explode), or a battery with cracks in the casing Keep metal objects away from the battery. If they cause a spark it could be dangerous. 2. Park the two cars facing each other, close but with enough space to walk in between. 3. Remove the keys from the ignitions, turn off lights/blinkers. 4. Disconnect all accessories (voltage spikes can damage them). 5. Clamp one of the red (positive) clamp to the positive terminal of the dead battery. If there is corrosion on the terminal, clean off or scrape the residue, and/or twist the jaws of the clamp until it cuts through the corrosion and into the metal. Make sure the black clamp of the same end isn’t touching any metal. 6. Connect the other red clamp to the positive terminal of the good battery. 7. Connect the black (negative) clamp to the negative terminal of the good battery. 8. Attach the other black clamp to an unpainted metal surface (e.g. bolt, bracket) on the engine of the car with the dead battery, at least a few inches away from the battery. If a labeled remote negative terminal is available, use it. 9. Confirm that the jumper cables are not near any moving engine parts. 10. Start the booster car, and let it idle for 5 minutes to build up a charge in the dead battery. This will take longer if the dead battery is really empty. 11. Start the car with the dead battery and let the two vehicles idle for a few minutes. If the car doesn’t start, wait a few more minutes and try again. In some instances, slightly revving the engine of the running car while charging the dead battery may help. If it still doesn’t start, it probably won’t. 12. Disconnect the jumper cables reversing the connection order, making sure no disconnected clamps touch any metal or each other 13. Drive the jump-started car for at least 20-30 minutes so it can recharge the battery Try to keep the jumper cables with you; you may need to jump again the next time you start the car.

What is APR?

The term annual percentage rate of charge (APR), corresponding sometimes to a nominal APR and sometimes to an effective APR (EAPR), is the interest rate for a whole year (annualized), rather than just a monthly fee/rate, as applied on a loan, mortgage loan, credit card, etc. It is a finance charge expressed as an annual rate. Those terms have formal, legal definitions in some countries or legal jurisdictions, but in the United States: (1) The nominal APR is the simple-interest rate (for a year). (2) The effective APR is the fee+compound interest rate (calculated across a year).

What languages are generally spoken in Macau?

The official languages are Chinese Mandarin and Portuguese. Other spoken languages include Chinese Cantonese.

1905.10810

The methods that we evaluated are baselines are the ones we consider to be basic and with moderate potential of yielding particularly good results. Probably the most straightforward approach to error correction is selecting known words from a dictionary that are within the smallest edit distance from the error. We used the Levenshtein distance metric BIBREF8 implemented in Apache Lucene library BIBREF9 . It is a version of edit distance that treats deletions, insertions and replacements as adding one unit distance, without giving a special treatment to character swaps. The SGJP – Grammatical Dictionary of Polish BIBREF10 was used as the reference vocabulary. Another simple approach is the aforementioned diacritical swapping, which is a term that we introduce here for referring to a solution inspired by the work of BIBREF4 . Namely, from the incorrect form we try to produce all strings obtainable by either adding or removing diacritical marks from characters. We then exclude options that are not present in SGJP, and select as the correction the one within the smallest edit distance from the error. It is possible for the number of such diacritically-swapped options to become very big. For example, the token Modlin-Zegrze-Pultusk-Różan-Ostrołęka-Łomża-Osowiec (taken from PlEWi corpus of spelling errors, see below) can yield over INLINEFORM0 states with this method, such as Módłiń-Żęgrzę-Pułtuśk-Roźąń-Óśtróleką-Lómzą-Óśówięć. The actual correction here is just fixing the ł in Pułtusk. Hence we only try to correct in this way tokens that are shorter than 17 characters. A promising method, adapted from work on correcting texts by English language learners BIBREF11 , expands on the concept of selecting a correction nearest to the spelling error according to some notion of distance. Here, the Levenshtein distance is used in a weighted sum to cosine distance between word vectors. This is based on the observation that trained vectors models of distributional semantics contain also representations of spelling errors, if they were not pruned. Their representations tend to be similar to those of their correct counterparts. For example, the token enginir will appear in similar contexts as engineer, and therefore will be assigned a similar vector embedding. (applied cellwise) in order to obtain the initial setting of parameters for the main LSTM. Our ELMo-augmented LSTM is bidirectional. We used the Levenshtein distance metric BIBREF8 implemented in Apache Lucene library BIBREF9 . Another simple approach is the aforementioned diacritical swapping, which is a term that we introduce here for referring to a solution inspired by the work of BIBREF4 . A promising method, adapted from work on correcting texts by English language learners BIBREF11 , expands on the concept of selecting a correction nearest to the spelling error according to some notion of distance. Here, the Levenshtein distance is used in a weighted sum to cosine distance between word vectors. Our ELMo-augmented LSTM is bidirectional.

What methods are tested in PIEWi?

The answers are shown as follows: * Levenshtein distance metric BIBREF8 * diacritical swapping * Levenshtein distance is used in a weighted sum to cosine distance between word vectors * ELMo-augmented LSTM

Which episode of The X-Files did Dana Scully get diagnosed with cancer?

Memento Mori, episode 14 of season 4.

Give me a bulleted list of who received the last best player awards at the World Cup.

The Best Player award at the World Cup, also known as the Golden Ball for best player, was first awarded in 1982. Here are the last seven recipients of the Golden Ball award: - Brazil Ronaldo (1998 France), - Oliver Kahn (2002 South Korea/Japan), - Zinedine Zidane (2006 Germany), - Diego Forlán (2010 South Africa), - Lionel Messi (2014 Brazil), - Luka Modrić (2018 Russia), - Lionel Messi (2022 Qatar).

Today there are 28 public libraries in Edinburgh but, as the first to open in the Scottish capital, the creation of Central Library was funded with £50,000 by philanthropist Andrew Carnegie. At the opening ceremony a telegram from Carnegie was read out stating: "We trust that this Library is to grow in usefulness year after year, and prove one of the most potent agencies for the good of the people for all time to come." The site selected for the library was the former home of Sir Thomas Hope, 1st Baronet Hope of Craighall, advocate for King Charles I. The structure, built in 1616, was demolished in March 1887 to make way for the library. The lintel from Hope's home, bearing the carved inscription TECUM HABITA 1616 from the fourth satire of Persius, is preserved above an inner doorway of the library. Carnegie's funding was initially an offer of £25,000 in 1886 which was doubled, overcoming prior opposition to the establishment of a public library, the city—last of those to do so in Scotland—adopted the Public Libraries Act and on 9 July 1887, Carnegie laid the foundation stone of architect George Washington Browne's French Renaissance-styled building. Washington Browne's design was the winning entry in the architectural competition for the new library and was selected from 37 submissions. His grand building stands three levels tall above George IV Bridge and reaches down to the Cowgate below, spanning the disjointed streets of Edinburgh's Old Town. Above the main door is the motto, "Let there be Light" which Carnegie insisted was placed above the entrance to every library he funded. The facade of Central Library is also decorated with stone carvings depicting the coat of arms of the City of Edinburgh, Coat of Arms of Scotland and the Royal Arms. There are nine small square reliefs relating to printers and a large sculpture of Caledonia by Alexander Handyside Ritchie. Records for 1890, the first full year the library was open, show that over 440,000 book loans were issued. Central library has been adapted and expanded many times over the years. Only a year after opening the library was already running out of space and a book store was added in 1903. By 1928, the library was short of space again. Proposals were made for a better use of the space and a public lift was installed. In 1930, the adjacent building at no.3 George IV Bridge was acquired allowing the library to expand again. Further nearby premises were bought in the 1940s and in 1961, a mezzanine level was created above the former Newspaper Room. In May 2014, the new children's and music libraries were opened within the main library building. These had previously been housed in a separate building on George IV Bridge. The children's library features a wall graphic by award-winning children's book illustrator Catherine Rayner. In November 2017, on the 100th anniversary of her death, a memorial to Dr Elsie Inglis, the founder of the Scottish Women's Hospitals, was unveiled at Central Library.

Please give me a brief history of the Edinburgh Central Library?

The Edinburgh Central Library was opened in 1890 on the site of the former home of Sir Thomas Hope, 1st Baronet Hope of Craighal with funding from Andrew Carnegie as part of the the Public Libraries Act. The building was designed by architect Washington Browne and stands three levels tall above George IV Bridge in the disjointed streets of Edinburgh's Old Town. Above the main door is the motto, "Let there be Light" which Carnegie insisted was placed above the entrance to every library he funded. The Central library has expanded many times over the years with the addition of a book store in 1903. In 1930, the adjacent building at no.3 George IV Bridge was acquired allowing the library to expand again. Additional nearby premises were bought in 1940 and 1961 to create mezzanine level above the former Newspaper Room.

How did the Australian Shepard get its name?

Despite its name, the Australian Shepard is not actually from Australia but its origins trace back to the United States (California to be specific), where the breed was first developed. Australian Shepards were bred from a variety herding breeds, including shepherds from the Asturias region of Spain, and at one point were known as Spanish Shepherds.

Distinguish between manmade and natural structures from the following: Statue of Liberty Suez canal Niagara Falls Vatical City Valley of Death Himalayas Angorwat Temple Taj Mahal River Rhine The strait of Malacca Brooklyn bridge

Manmade: Brooklyn bridge Taj Mahal Vatical City Angorwat Temple Statue of Liberty Suez canal Natural structures: Niagara Falls Valley of Death The strait of Malacca River Rhine Himalayas

Please classify from which sci-fi universe are following movies, or tv series: The return of the Jedi, Deep Space 9, Avengers

Deep Space 9 is a tv series from Star Trek universe, The return of a jedi is a movie from Star Wars saga, and Avengers is a tv series from Marvel Universe

Is the sun a star or a planet?

The sun is not a planet but a star. It is not only the nearest and sole star in our solar system, it also happens to be its center.

The goal of the summarization task is condensing a piece of text into a shorter version that covers the main points succinctly. In the abstractive approach important pieces of information are presented using words and phrases not necessarily appearing in the source text. This requires natural language generation techniques with high level of semantic understanding BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6. Major research efforts have focused so far on summarization of single-speaker documents like news (e.g., BIBREF7) or scientific publications (e.g., BIBREF8). One of the reasons is the availability of large, high-quality news datasets with annotated summaries, e.g., CNN/Daily Mail BIBREF9, BIBREF7. Such a comprehensive dataset for dialogues is lacking. The challenges posed by the abstractive dialogue summarization task have been discussed in the literature with regard to AMI meeting corpus BIBREF10, e.g. BIBREF11, BIBREF12, BIBREF13. Since the corpus has a low number of summaries (for 141 dialogues), BIBREF13 proposed to use assigned topic descriptions as gold references. These are short, label-like goals of the meeting, e.g., costing evaluation of project process; components, materials and energy sources; chitchat. Such descriptions, however, are very general, lacking the messenger-like structure and any information about the speakers. To benefit from large news corpora, BIBREF14 built a dialogue summarization model that first converts a conversation into a structured text document and later applies an attention-based pointer network to create an abstractive summary. Their model, trained on structured text documents of CNN/Daily Mail dataset, was evaluated on the Argumentative Dialogue Summary Corpus BIBREF15, which, however, contains only 45 dialogues. In the present paper, we further investigate the problem of abstractive dialogue summarization. With the growing popularity of online conversations via applications like Messenger, WhatsApp and WeChat, summarization of chats between a few participants is a new interesting direction of summarization research. For this purpose we have created the SAMSum Corpus which contains over 16k chat dialogues with manually annotated summaries. The dataset is freely available for the research community. The paper is structured as follows: in Section SECREF2 we present details about the new corpus and describe how it was created, validated and cleaned. Brief description of baselines used in the summarization task can be found in Section SECREF3. In Section SECREF4, we describe our experimental setup and parameters of models. Both evaluations of summarization models, the automatic with ROUGE metric and the linguistic one, are reported in Section SECREF5 and Section SECREF6, respectively. Examples of models' outputs and some errors they make are described in Section SECREF7. Finally, discussion, conclusions and ideas for further research are presented in sections SECREF8 and SECREF9. To benefit from large news corpora, built a dialogue summarization model that first converts a conversation into a structured text document and later applies an attention-based pointer network to create an abstractive summary.

How do the dialogue summarization model work?

The authors have built a dialogue summarization model that first converts a conversation into a structured text document and later applies an attention-based pointer network to create an abstractive summary.

What are the words of House Swygert?

"Truth Conquers"

Which episodes of season four of Game of Thrones did Michelle MacLaren direct?

She directed "Oathkeeper" and "First of His Name" the fourth and fifth episodes of season four, respectively.

Dominik Volek (born January 12, 1994) is a Czech professional ice hockey player. He is currently playing for HC Sparta Praha of the Czech Extraliga. Volek made his Czech Extraliga debut playing with HC Sparta Praha during the 2014–15 Czech Extraliga season. Volek is the son of former New York Islanders forward David Volek.

How old was Dominik Volek when he made his Czech Extraliga debut?

Dominik Volek was 20 years old. He debuted in 2014 and was born in 1994, so 2014-1994 = 20.

1910.14497

Geometric bias mitigation uses the cosine distances between words to both measure and remove gender bias BIBREF0. This method implicitly defines bias as a geometric asymmetry between words when projected onto a subspace, such as the gender subspace constructed from a set of gender pairs such as $\mathcal {P} = \lbrace (he,she),(man,woman),(king,queen)...\rbrace $. The projection of a vector $v$ onto $B$ (the subspace) is defined by $v_B = \sum _{j=1}^{k} (v \cdot b_j) b_j$ where a subspace $B$ is defined by k orthogonal unit vectors $B = {b_1,...,b_k}$. The WEAT statistic BIBREF1 demonstrates the presence of biases in word embeddings with an effect size defined as the mean test statistic across the two word sets: Where $s$, the test statistic, is defined as: $s(w,A,B) = mean_{a \in A} cos(w,a) - mean_{b \in B} cos(w,a)$, and $X$,$Y$,$A$, and $B$ are groups of words for which the association is measured. Possible values range from $-2$ to 2 depending on the association of the words groups, and a value of zero indicates $X$ and $Y$ are equally associated with $A$ and $B$. See BIBREF4 for further details on WEAT. The RIPA (relational inner product association) metric was developed as an alternative to WEAT, with the critique that WEAT is likely to overestimate the bias of a target attribute BIBREF4. The RIPA metric formalizes the measure of bias used in geometric bias mitigation as the inner product association of a word vector $v$ with respect to a relation vector $b$. The relation vector is constructed from the first principal component of the differences between gender word pairs. We report the absolute value of the RIPA metric as the value can be positive or negative according to the direction of the bias. A value of zero indicates a lack of bias, and the value is bound by $[-||w||,||w||]$. The neighborhood bias metric proposed by BIBREF5 quantifies bias as the proportion of male socially-biased words among the $k$ nearest socially-biased male and female neighboring words, whereby biased words are obtained by projecting neutral words onto a gender relation vector. As we only examine the target word among the 1000 most socially-biased words in the vocabulary (500 male and 500 female), a word’s bias is measured as the ratio of its neighborhood of socially-biased male and socially-biased female words, so that a value of 0.5 in this metric would indicate a perfectly unbiased word, and values closer to 0 and 1 indicate stronger bias. FLOAT SELECTED: Table 1: Remaining Bias (as measured by RIPA and Neighborhood metrics) in fastText embeddings for baseline (top two rows) and our (bottom three) methods. Figure 2: Remaining Bias (WEAT score) Geometric bias mitigation uses the cosine distances between words to both measure and remove gender bias BIBREF0. The WEAT statistic BIBREF1 demonstrates the presence of biases in word embeddings with an effect size defined as the mean test statistic across the two word sets: Where $s$, the test statistic, is defined as: $s(w,A,B) = mean_{a \in A} cos(w,a) - mean_{b \in B} cos(w,a)$, and $X$,$Y$,$A$, and $B$ are groups of words for which the association is measured. The RIPA (relational inner product association) metric was developed as an alternative to WEAT, with the critique that WEAT is likely to overestimate the bias of a target attribute BIBREF4. The neighborhood bias metric proposed by BIBREF5 quantifies bias as the proportion of male socially-biased words among the $k$ nearest socially-biased male and female neighboring words, whereby biased words are obtained by projecting neutral words onto a gender relation vector. FLOAT SELECTED: Table 1: Remaining Bias (as measured by RIPA and Neighborhood metrics) in fastText embeddings for baseline (top two rows) and our (bottom three) methods. Figure 2: Remaining Bias (WEAT score)

What are the three measures of bias which are reduced in experiments?

RIPA, Neighborhood Metric, WEAT

It has been shown that extractive QA tasks like SQuAD may be tackled by some language independent strategies, for example, matching words in questions and context BIBREF20. Is zero-shot learning feasible because the model simply learns this kind of language independent strategies on one language and apply to the other? To verify whether multi-BERT largely counts on a language independent strategy, we test the model on the languages unseen during pre-training. To make sure the languages have never been seen before, we artificially make unseen languages by permuting the whole vocabulary of existing languages. That is, all the words in the sentences of a specific language are replaced by other words in the same language to form the sentences in the created unseen language. It is assumed that if multi-BERT used to find answers by language independent strategy, then multi-BERT should also do well on unseen languages. Table TABREF14 shows that the performance of multi-BERT drops drastically on the dataset. It implies that multi-BERT might not totally rely on pattern matching when finding answers. To verify whether multi-BERT largely counts on a language independent strategy, we test the model on the languages unseen during pretraining. To make sure the languages have never been seen before, we artificially make unseen languages by permuting the whole vocabulary of existing languages. That is, all the words in the sentences of a specific language are replaced by other words in the same language to form the sentences in the created unseen language.

How do they make sure the languages have never been seen before when verifying whether multi-BERT largely counts on a language-independent strategy?

Invisible sentences are artificially created by replacing all words in a sentence in a particular language with other words in the same language.

Which NFL teams have gone to the Super Bowl at least once but have never won a Super Bowl title?

Buffalo Bills, Minnesota Vikings, Cincinnati Bengals, Atlanta Falcons, Carolina Panthers, Houston/Tennessee/Oilers/Titans, San Diego/Los Angeles Chargers, St. Louis/Phoenix/Arizona Cardinals

LeBron Raymone James Sr. (/ləˈbrɒn/; born December 30, 1984) is an American professional basketball player for the Los Angeles Lakers in the National Basketball Association (NBA). Nicknamed "King James", he is considered to be one of the greatest basketball players in history and is often compared to Michael Jordan in debates over the greatest basketball player of all time. James is the all-time leading scorer in NBA history and ranks fourth in career assists. He has won four NBA championships (two with the Miami Heat, one each with the Lakers and Cleveland Cavaliers), and has competed in 10 NBA Finals. He has four MVP awards, four Finals MVP awards, and two Olympic gold medals. He has been named an All-Star 19 times, selected to the All-NBA Team 18 times (including 13 First Team selections) and the All-Defensive Team six times, and was a runner-up for the NBA Defensive Player of the Year Award twice in his career. James grew up playing basketball for St. Vincent–St. Mary High School in his hometown of Akron, Ohio. He was heavily touted by the national media as a future NBA superstar. A prep-to-pro, he was selected by the Cleveland Cavaliers with the first overall pick of the 2003 NBA draft. Named the 2004 NBA Rookie of the Year, he soon established himself as one of the league's premier players, leading the Cavaliers to their first NBA Finals appearance in 2007 and winning the NBA MVP award in 2009 and 2010. After failing to win a championship with Cleveland, James left in 2010 as a free agent to join the Miami Heat; this was announced in a nationally televised special titled The Decision and is among the most controversial free agency moves in sports history. James won his first two NBA championships while playing for the Heat in 2012 and 2013; in both of these years, he also earned the league's MVP and Finals MVP awards. After his fourth season with the Heat in 2014, James opted out of his contract and re-signed with the Cavaliers. In 2016, he led the Cavaliers to victory over the Golden State Warriors in the Finals by coming back from a 3–1 deficit, delivering the team's first championship and ending the Cleveland sports curse. In 2018, James exercised his contract option to leave the Cavaliers and signed with the Lakers, where he won the 2020 NBA championship and his fourth Finals MVP. James is the first player in NBA history to accumulate $1 billion in earnings as an active player. On February 7, 2023, James surpassed Kareem Abdul-Jabbar to become the all-time leading scorer in NBA history. Off the court, James has accumulated more wealth and fame from numerous endorsement contracts. He has been featured in books, documentaries (including winning two Sports Emmy Awards as an executive producer), and television commercials. He has won 19 ESPY Awards, hosted Saturday Night Live, and starred in the sports film Space Jam: A New Legacy (2021). James has been a part-owner of Liverpool F.C. since 2011 and leads the LeBron James Family Foundation, which has opened an elementary school, housing complex, retail plaza, and medical center in Akron.

Based on this summary of Lebron James career, what are the key reasons he is considered the greatest basketball player of all time?

"King" James is considered one of basketball's all-time greats because of the dominance and longevity displayed over his career. Lebron has personal accolades for days, including: 4 MVPs, 4 Finals MVPs, 19 All-Star seasons, 1st Team All-NBA 13 times, All-Defensives Team 6 times, and he's still going. When you sum up Lebron's career totals, they are equaling mind-blowing, as the NBA's all-time leading scorer, he is also 4th in career assists. Lebron's greatest achievement on the court is commonly viewed as the time he led the Cleveland Cavaliers back to championship glory, coming back from a 3-1 deficit to the Golden State Warriors in 2016.

How do you wheelie a motorcycle?

While moving at constant speed, have your right foot covering the brake, compress the motorcycles suspension by pushing down equally on the handle bars and foot pegs, as the suspension starts to rebound upward, blip the throttle. This will lift the front wheel off the ground. If the wheel goes too high use the rear brake to bring it back down.

Question answering (QA) has been a blooming research field for the last decade. Selection-based QA implies a family of tasks that find answer contexts from large data given questions in natural language. Three tasks have been proposed for selection-based QA. Given a document, answer extraction BIBREF0 , BIBREF1 finds answer phrases whereas answer selection BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 and answer triggering BIBREF6 , BIBREF7 find answer sentences instead, although the presence of the answer context is not assumed within the provided document for answer triggering but it is for the other two tasks. Recently, various QA tasks that are not selection-based have been proposed BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 ; however, selection-based QA remains still important because of its practical value to real applications (e.g., IBM Watson, MIT Start). Several datasets have been released for selection-based QA. wang:07a created the QASent dataset consisting of 277 questions, which has been widely used for benchmarking the answer selection task. feng:15a presented InsuranceQA comprising 16K+ questions on insurance contexts. yang:15a introduced WikiQA for answer selection and triggering. jurczyk:16 created SelQA for large real-scale answer triggering. rajpurkar2016squad presented SQuAD for answer extraction and selection as well as for reading comprehension. Finally, morales-EtAl:2016:EMNLP2016 provided InfoboxQA for answer selection. These corpora make it possible to evaluate the robustness of statistical question answering learning. Although all of these corpora target on selection-based QA, they are designed for different purposes such that it is important to understand the nature of these corpora so a better use of them can be made. In this paper, we make both intrinsic and extrinsic analyses of four latest corpora based on Wikipedia, WikiQA, SelQA, SQuAD, and InfoboxQA. We first give a thorough intrinsic analysis regarding contextual similarities, question types, and answer categories (Section SECREF2 ). We then map questions in all corpora to the current version of English Wikipedia and benchmark another selection-based QA task, answer retrieval (Section SECREF3 ). Finally, we present an extrinsic analysis through a set of experiments cross-testing these corpora using a convolutional neural network architecture (Section SECREF4 ). Recently, various QA tasks that are not selection-based have been proposed (Reddy and Bandyopadhyay, 2006; Hosseini et al., 2014; Jauhar et al., 2016; Sachan et al., 2016); however, selection-based QA remains still important because of its practical value to real applications (e.g., IBM Watson, MIT START).

Why the selection-based QA remains important?

selection-based QA remains still important because of its practical value to real applications (e.g., IBM Watson, MIT START).

Why is NFL football called football when players use their hands mainly?

Sports historians have no clear answer. The current form of football evolved from the 1800s based on rugby and the rules were heavy on a lot of running. My opinion is that influenced the sport and the football word came into origin.

Give me examples of German auto manufacturers still operating.

BMW, Volkswagen, Mercedes-Benz, Audi, Porsche, Opel.

Write a professional email to my landlord asking whether I can make my rent payment 15 days late next month

Mr./Mrs. Landlord, I would like to ask that you allow Brian to turn his rent payment in 15 days late next month. Please note that he has never had a late payment before and actually has a history of turning his payment in early. As a matter of fact, if you review all of the previous months and added up all of the days he turned his rent in early, you would find it's a total of 30 days early. So with that in mind, and considering everything going on in his life right now, he is simply asking to delay his payment for 15 days this upcoming month and then 15 days later you will be receiving the following months payment. Thanks in advance and appreciate your flexibility and patience in this matter.

When was the internet created?

The birthday of the internet is January 1, 1983. This is an important day because it gave computers and computer networks for the first time in history a way to communicate with each other. Prior to this networks were on closed loops. On this this day a new internet protocol was also created called TCP/IP. This stands for Transmission Control Protocol/Internet Protocol.

What is Tianwen 1?

China's 1st Mars Exploration Mission

Switzerland, officially the Swiss Confederation, is a landlocked country located at the confluence of Western, Central and Southern Europe. It is bordered by Italy to the south, France to the west, Germany to the north and Austria and Liechtenstein to the east. Switzerland is geographically divided among the Swiss Plateau, the Alps and the Jura; the Alps occupy the greater part of the territory, whereas most of the country's population of 8.7 million are concentrated on the plateau, which hosts the largest cities and economic centres, including Zürich, Geneva and Basel.

Given the paragraph below, what is the population of Switzerland?

The population of Switzerland is 8.7 million. The majority of the population reside in the plateau region which is home to cities such as Zurich, Geneva, and Basel.

Fairmont has a humid continental climate (Köppen Dfa) with very warm summers and freezing winters. However, it is not uncommon during winter for warm air from the Gulf of Mexico to raise temperatures above 50 °F or 10 °C, which occurs on average six times each January and over eight in December and February. In contrast, when very cold air from Canada moves into West Virginia temperatures can go below 0 °F or −17.8 °C, which can be expected during 3.2 mornings each winter, but which occurred on twelve mornings during the extremely cold January 1977, whose average temperature of 16.0 °F or −8.9 °C was the coldest month on record by 4.0 °F or 2.2 °C. Despite the abundant precipitation throughout the year, the relative dryness of cold air means that most precipitation is rain even during the winter: the most snowfall in a month being 46.5 inches (1.18 m) is November 1950, and the most in a season 77.4 inches (1.97 m) between July 1950 and June 1951. The least snow in a season has been 12.0 inches (0.30 m) between July 1918 and June 1919, whilst the wettest calendar year has been 1956 with 58.12 inches (1,476.2 mm) and the driest – as with all of West Virginia – 1930 with 26.25 inches (666.8 mm). The hottest temperature has been 108 °F (42.2 °C) on August 8, 1918, and the coldest −21 °F (−29.4 °C) on January 21, 1994.

Given this paragraph about the climate in Fairmont, West Virginia, what was the coldest month in the city's history, and what was the average temperature during that month?

January of 1977 was the coldest month in the history of Fairmont, West Virginia with an average of 16.0 degrees Farenheit.

What is the Charles 'Chick' Evans Memorial Scholarship?

The Charles "Chick" Evans Memorial Scholarship is given to deserving young caddies that have demonstrated excellence in the classroom, in their community and on the golf course. It is a full tuition paid scholarship and includes paid housing. The scholarship was referenced in the famous movie 'Caddyshack'.

How does screen printing work?

Screen printing is also referred to as silk screen printing. It is essentially the process of printing an image or artwork onto a surface by pushing ink through a screen. The screens are pulled tight and held in a metal or wood frame. Much like a picture frame. Depending on the detail of the artwork, the screens can have different size weave. The weave is essentially how tight the screen is woven and how large the holes in the screen are. For more detailed designs, the screen used will be woven very close and tight so the holes are very small and let less ink through. This allows for very thin lines and detail. Each screen can print 1 color at a time. So, to print a design with multiple colors, there needs to be multiple screens. 1 screen per color. Many screen printing presses have large rotating arms that hold the screens. The arms rotate for each color to be printed individually.

The generators were implemented using the TensorFlow library BIBREF31 and trained with training, validation and testing ratio as 3:1:1. The hidden layer size, beam size were set to be 80 and 10, respectively, and the generators were trained with a $70\%$ of dropout rate. We performed 5 runs with different random initialization of the network and the training is terminated by using early stopping. We then chose a model that yields the highest BLEU score on the validation set as shown in Table 2 . Since the trained models can differ depending on the initialization, we also report the results which were averaged over 5 randomly initialized networks. Note that, except the results reported in Table 2 , all the results shown were averaged over 5 randomly initialized networks. We set $\lambda $ to 1000 to severely discourage the reranker from selecting utterances which contain either redundant or missing slots. For each DA, we over-generated 20 candidate sentences and selected the top 5 realizations after reranking. Moreover, in order to better understand the effectiveness of our proposed methods, we: (i) performed an ablation experiments to demonstrate the contribution of each proposed cells (Tables 2 , 3 ), (ii) trained the models on the Laptop domain with varied proportion of training data, starting from $10\%$ to $100\%$ (Figure 3 ), (iii) trained general models by merging all the data from four domains together and tested them in each individual domain (Figure 4 ), and (iv) trained adaptation models on merging data from restaurant and hotel domains, then fine tuned the model on laptop domain with varied amount of adaptation data (Figure 5 ). The generators were implemented using the TensorFlow library (Abadi et al., 2016) and trained with training, validation and testing ratio as 3:1:1.

What dataset is used for the generators?

The generators were implemented using the TensorFlow library (Abadi et al., 2016) and trained with training, validation and testing ratio as 3:1:1.

What is a t-style guitar?

A T-style guitar is a guitar based on the Fender Telecaster, but possibly made by a different manufacturer. Such guitars are always similar in shape to the Fender Telecaster, but may deviate in hardware, electronics, or other components. A typical T-style guitar has two single-coil pickups, a bolt-on neck with 21 or 22 frets, and an ash-tray bridge.

Traditional satellite technology utilizes a broad single beam to cover entire continents and regions. With the introduction of multiple narrowly focused spot beams and frequency reuse, IPSTAR is capable of maximizing the available frequency for transmissions. Increasing bandwidth by a factor of twenty compared to traditional Ku-band satellites translates into better efficiencies. Despite the higher costs associated with spot beam technology, the overall cost per circuit is considerably lower as compared to shaped beam technology.

Extract all the metrics used to measure the performance of IPSTAR from the following paragraph. Present the results in a list separated by commas.

The passage mentions available frequency width, bandwidth and cost per circuit.

1807.09671

In this work, we propose to augment the integer linear programming (ILP)-based summarization framework with a low-rank approximation of the co-occurrence matrix, and further evaluate the approach on a broad range of datasets exhibiting high lexical diversity. The ILP framework, being extractive in nature, has demonstrated considerable success on a number of summarization tasks BIBREF20 , BIBREF21 . It generates a summary by selecting a set of sentences from the source documents. The sentences shall maximize the coverage of important source content, while minimizing the redundancy among themselves. At the heart of the algorithm is a sentence-concept co-occurrence matrix, used to determine if a sentence contains important concepts and whether two sentences share the same concepts. We introduce a low-rank approximation to the co-occurrence matrix and optimize it using the proximal gradient method. The resulting system thus allows different sentences to share co-occurrence statistics. For example, “The activity with the bicycle parts" will be allowed to partially contain “bike elements" although the latter phrase does not appear in the sentence. The low-rank matrix approximation provides an effective way to implicitly group lexically-diverse but semantically-similar expressions. It can handle out-of-vocabulary expressions and domain-specific terminologies well, hence being a more principled approach than heuristically calculating similarities of word embeddings. In this work, we propose to augment the integer linear programming (ILP)-based summarization framework with a low-rank approximation of the co-occurrence matrix, and further evaluate the approach on a broad range of datasets exhibiting high lexical diversity.

What do they constrain using integer linear programming?

The answers are shown as follows: * low-rank approximation of the co-occurrence matrix

Which characters belong to DC or Marvel Universe? Black Widow, Black Canary

Black Widow is Marvel, Black Canary is DC

This research addresses the problem of representing the semantics of text documents in multi-lingual comparable corpora. We present a new approach to this problem, based on neural embeddings, and test it on the task of clustering texts into meaningful classes depending on their topics. The setting is unsupervised, meaning that one either does not have enough annotated data to train a supervised classifier or does not want to be limited with a pre-defined set of classes. There is a lot of sufficiently good approaches to this problem in the case of mono-lingual text collections, but the presence of multiple languages introduces complications. When a text collection contains documents in several languages, it becomes impractical to simply represent the documents as vectors of words occurring in them ("bag-of-words"), as the words surface forms are different, even in closely-related languages. Thus, one has to invent means to cross the inter-lingual gap and bring all documents to some sort of shared representation, without losing information about their topics or categories. Of course, one obvious way to solve this problem is to translate all documents into one language, and then apply any clustering algorithm. However, this requires either buying human/machine translation services (which can be expensive if you deal with large text collection) or training own statistical machine translation model (which as a rule requires big parallel corpus). This is the reason to search for other solutions. In this paper, a novel way of reducing the problem of cross-lingual document representation to a monolingual setting is proposed. Essentially, we train Continuous Bag-of-Words models BIBREF0 on large comparable monolingual corpora for two languages our dataset consists of. This provides us with vector representations of words, allowing to measure their semantic similarity. Then, a linear transformation matrix from vectors of language A to vectors of language B is learned, using a small bilingual dictionary as training data. This matrix is then employed to `project' word and document representations from semantic space of language A to semantic space of language B. It allows not only quite accurate `translation' of words, but also of document `semantic fingerprints' (dense representations of document semantics, calculated as an average of the trained distributional vectors for all the words in document). This approach is evaluated in a setting, where the input is a collection of documents in several languages and some number of topics to which these documents belong (we also have large monolingual corpora to train distributional models on). For each document, we are given its language, but not its topic. The task is to cluster this collection so that documents belonging to one topic were clustered together, independent of their language. Note that we are interested in clustering the collection as a whole, not each language separately (which is trivial). Our evaluation data consists of comparable corpora of Russian and Ukrainian academic texts. On this material, we show that the `translated semantic fingerprints' method represents documents in different languages precisely enough to allow almost exact clustering according to document topics, with only 5% of incorrect assignments. It significantly outperforms both naive bag-of-words baseline and the not-so-naive method of `orthographic translation' based on Damerau-Levenshtein distance, even enriched with dictionary mappings. At the same time, it does not require large parallel corpora or a ready-made statistical machine translation model. The rest of the paper is structured as follows. In Section "Related Work" we describe the foundations of our approach and the related work. Section "Academic texts as Comparable Corpora" introduces the employed corpora and the story behind them. Section "Learning to Translate: Ukrainian-to-Russian transformations" is dedicated to learning the transformation matrix, and Section "Experiment Design and Evaluation" describes our experimental setting and evaluation results. We discuss the findings in Section "Discussion" and conclude in Section "Conclusion and Future Work" , also suggesting directions for future work. This research addresses the problem of representing the semantics of text documents in multi-lingual comparable corpora.

What problem does the research address?

Representing the semantics of text documents in multi-lingual comparable corpora.

What is a good anime to watch for someone who has never watched anime before?

This very much depends on on your personal preferences, but I’ll give you a couple of options that I think would be good starter animes. One of the most famous and highly acclaimed anime is Cowboy Bepop, which is a genre-crossing single-season show about a crew of bounty hunters aboard a spaceship called the Bepop. Another popular entry into anime is One Punch Man, which is an action/comedy about a superhero who can defeat anyone with one punch, leading him to grow bored and search for meaning and challenge.

1912.01673

The corpus is freely available at the following link: http://hdl.handle.net/11234/1-3123 The corpus is freely available at the following link: http://hdl.handle.net/11234/1-3123

Is this dataset publicly available?

Yes.

Uncas was born near the Thames River in present-day Connecticut, the son of the Mohegan sachem Owaneco. Uncas is a variant of the Mohegan term Wonkus, meaning "Fox". He was a descendant of the principal sachems of the Mohegans, Pequots, and Narragansetts. Owaneco presided over the village known as Montonesuck. Uncas was bilingual, learning Mohegan and some English, and possibly some Dutch. In 1626, Owaneco arranged for Uncas to marry the daughter of the principal Pequot sachem Tatobem to secure an alliance with them. Owaneco died shortly after this marriage, and Uncas had to submit to Tatobem's authority. Tatobem was captured and killed by the Dutch in 1633; Sassacus became his successor, but Uncas felt that he deserved to be sachem. Owaneco's alliance with Tatobem was based upon a balance of power between the Mohegans and Pequots. After the death of Owaneco, the balance changed in favour of the Pequots. Uncas was unwilling to challenge the power of Tatobem; however, Uncas did begin contesting Pequot authority over the Mohegans. In 1634 with Narragansett support, Uncas rebelled against Sassacus and Pequot authority. Uncas was defeated and became an exile among the Narragansetts. He soon returned from exile after ritually humiliating himself before Sassacus. His failed challenges resulted in Uncas having little land and few followers, but Uncas saw that the newly arriving Puritan colonists, though few in number, had better weapons and much courage, so he started to develop a new strategy and alliance to work towards his ultimate goal of Grand Sachem.

Given this paragraph about Chief Uncas, who was he?

Uncas (c. 1588 – c. 1683) was a sachem of the Mohegans who made the Mohegans the leading regional Indian tribe in lower Connecticut, through his alliance with the New England colonists against other Indian tribes.

In this paper, we are exploring the historical significance of Croatian machine translation research group. The group was active in 1950s, and it was conducted by Bulcsu Laszlo, Croatian linguist, who was a pioneer in machine translation during the 1950s in Yugoslavia. To put the research of the Croatian group in the right context, we have to explore the origin of the idea of machine translation. The idea of machine translation is an old one, and its origin is commonly connected with the work of Rene Descartes, i.e. to his idea of universal language, as described in his letter to Mersenne from 20.xi.1629 BIBREF0. Descartes describes universal language as a simplified version of the language which will serve as an “interlanguage” for translation. That is, if we want to translate from English to Croatian, we will firstly translate from English to an “interlanguage”, and then from the “interlanguage” to Croatian. As described later in this paper, this idea had been implemented in the machine translation process, firstly in the Indonesian-to-Russian machine translation system created by Andreev, Kulagina and Melchuk from the early 1960s. In modern times, the idea of machine translation was put forth by the philosopher and logician Yehoshua Bar-Hillel (most notably in BIBREF1 and BIBREF2), whose papers were studied by the Croatian group. Perhaps the most important unrealized point of contact between machine translation and cybernetics happened in the winter of 1950/51. In that period, Bar-Hillel met Rudolf Carnap in Chicago, who introduced to him the (new) idea of cybernetics. Also, Carnap gave him the contact details of his former teaching assistant, Walter Pitts, who was at that moment with Norbert Wiener at MIT and who was supposed to introduce him to Wiener, but the meeting never took place BIBREF3. Nevertheless, Bar-Hillel was to stay at MIT where he, inspired by cybernetics, would go to organize the first machine translation conference in the world in 1952 BIBREF3. The idea of machine translation was a tempting idea in the 1950s. The main military interest in machine translation as an intelligence gathering tool (translation of scientific papers, daily press, technical reports, and everything the intelligence services could get their hands on) was sparked by the Soviet advance in nuclear technology, and would later be compounded by the success of Vostok 1 (termed by the USA as a “strategic surprise”). In the nuclear age, being able to read and understand what the other side was working on was of crucial importance BIBREF4. Machine translation was quickly absorbed in the program of the Dartmouth Summer Research Project on Artificial Intelligence in 1956 (where Artificial Intelligence as a field was born), as one of the five core fields of artificial intelligence (later to be known as natural language processing). One other field was included here, the “nerve nets” as they were known back then, today commonly known as artificial neural networks. What is also essential for our discussion is that the earliest programming language for artificial intelligence, Lisp, was invented in 1958 by John McCarthy BIBREF5. But let us take a closer look at the history of machine translation. In the USA, the first major wave of government and military funding for machine translation came in 1954, and the period of abundancy lasted until 1964, when the National Research Council established the Automatic Language Processing Advisory Committee (ALPAC), which was to assess the results of the ten years of intense funding. The findings were very negative, and funding was almost gone BIBREF4, hence the ALPAC report became the catalyst for the first “AI Winter”. One of the first recorded attempts of producing a machine translation system in the USSR was in 1954 BIBREF6, and the attempt was applauded by the Communist party of the Soviet Union, by the USSR Committee for Science and Technology and the USSR Academy of Sciences. The source does not specify how this first system worked, but it does delineate that the major figures of machine translation of the time were N. Andreev of the Leningrad State University, O. Kulagina and I. Melchuk of the Steklov Mathematical Institute. There is information on an Indonesian-to-Russian machine translation system by Andreev, Kulagina and Melchuk from the early 1960s, but it is reported that the system was ultimately a failure, in the same way early USA systems were. The system had statistical elements set forth by Andreev, but the bulk was logical and knowledge-heavy processing put forth by Kulagina and Melchuk. The idea was to have a logical intermediate language, under the working name “Interlingua”, which was the connector of both natural languages, and was used to model common-sense human knowledge. For more details, see BIBREF6. In the USSR, there were four major approaches to machine translation in the late 1950s BIBREF7. The first one was the research at the Institute for Precise Mechanics and Computational Technology of the USSR Academy of Sciences. Their approach was mostly experimental and not much different from today's empirical methods. They evaluated the majority of algorithms known at the time algorithms over meticulously prepared datasets, whose main strength was data cleaning, and by 1959 they have built a German-Russian machine translation prototype. The second approach, as noted by Mulić BIBREF7, was championed by the team at the Steklov Mathematical Institute of the USSR Academy of Sciences led by A. A. Reformatsky. Their approach was mainly logical, and they extended the theoretical ideas of Bar-Hillel BIBREF2 to build three algorithms: French-Russian, English-Russian and Hungarian-Russian. The third and perhaps the most successful approach was the one by A. A. Lyapunov, O. S. Kulagina and R. L. Dobrushin. Their efforts resulted in the formation of the Mathematical Linguistics Seminar at the Faculty of Philology in Moscow in 1956 and in Leningrad in 1957. Their approach was mainly information-theoretic (but they also tried logic-based approaches BIBREF7), which was considered cybernetic at that time. This was the main role model for the Croatian efforts from 1957 onwards. The fourth, and perhaps most influential, was the approach at the Experimental Laboratory of the Leningrad University championed by N. D. Andreev BIBREF7. Here, the algorithms for Indonesian-Russian, Arabic-Russian, Hindu-Russian, Japanese-Russian, Burmese-Russian, Norwegian-Russian, English-Russian, Spanish-Russian and Turkish-Russian were being built. The main approach of Andreev's group was to use an intermediary language, which would capture the meanings BIBREF7. It was an approach similar to KL-ONE, which would be introduced in the West much later (in 1985) by Brachman and Schmolze BIBREF8. It is also interesting to note that the Andreev group had a profound influence on the Czechoslovakian machine translation program BIBREF9, which unfortunately suffered a similar fate as the Yugoslav one due to the lack of funding. Andreev's approach was in a sense "external". The modelling would be statistical, but its purpose would not be to mimic the stochasticity of the human thought process, but rather to produce a working machine translation system. Kulagina and Melchuk disagreed with this approach as they thought that more of what is presently called "philosophical logic" was needed to model the human thought process at the symbolic level, and according to them, the formalization of the human thought process was a prerequisite for developing a machine translation system (cf. BIBREF6). We could speculate that sub-symbolic processing would have been acceptable too, since that approach is also rooted in philosophical logic as a way of formalizing human cognitive functions and is also "internal" in the same sense symbolic approaches are. There were many other centers for research in machine translation: Gorkovsky University (Omsk), 1st Moscow Institute for Foreign Languages, Computing Centre of the Armenian SSR and at the Institute for Automatics and Telemechanics of the Georgian SSR BIBREF7. It is worthwhile to note that both the USA and the USSR had access to state-of-the-art computers, and the political support for the production of such systems meant that computers were made available to researchers in machine translation. However, the results were poor in the late 1950s, and a working system was yet to be shown. All work was therefore theoretical work implemented on a computer, which proved to be sub-optimal. The idea was to have a logical intermediate language, under the working name “Interlingua”, which was the connector of both natural languages, and was used to model common-sense human knowledge.

What is the "Interlingua" ?

It was the connector of both natural languages, and was used to model common-sense human knowledge.

What is the difference between whole genome sequencing (WGS) and whole exome sequencing (WES)?

Whole genome sequencing or WGS, is a technic to sequence the entire genome of an organism. In contrast, in whole exome sequencing (WES) we only sequence the region of the genome that codes for proteins.

Why is Pickleball getting so popular?

Pickleball has been getting popular for a number of reasons. First, it is a relatively easy game to get into; you don't need that much equipment or skill to be able to start to play. Second, you can play with people of all ages. Because the games are short, you mix with a lot of people and it's a great way to meet others, making it a great social activity as well. Lastly, covid-19 limited many activities and sports that could be played. But Pickleball can be played in a small space (mainly outdoors) and thus many people picked up this sport during that time. Popularity has continued to grow since now athletes are purchasing professional teams and Pickleball is being televised more often!

Danny Kaye Humanitarian Award 2019, was given to?

Priyanka Chopra, an Indian Actress

How do I make a traditional Bolognese sauce?

Bolognese sauce, sometimes called Bolognese Ragu has many variations, so I will provide the most common method. Dice one brown onion and then sauté in 25g of butter and 4 tablespoons of olive oil. Once the onions start to colour add 35 grams of diced pancetta and let the fat render. Add a mix of 1 chopped carrot, 1 chopped celery stick and 1 diced clove of garlic, stirring for 3-4 minutes. Add 500g of ground beef, seasoning the beef with salt and pepper. Next evaporate 1 cup of red wine, making sure the mixture reduces. Then evaporate 3/4 of a cup of fresh milk, also reducing the mixture. Add 4 chopped and peeled fresh tomatoes, 1 bay leaf and 1/4 tsp of chopped fresh thyme leaves and then simmer the sauce on low to medium heat for 1 and a half to two hours. Season again before serving with pasta.

how does an electric car work

Electric cars work by receiving energy from the battery, which is converted into power by an electric motor. The power is converted from the DC battery to AC for the electric motor. When an accelerator is pressed, it sends signal to the controller which adjusts the vehicle's speed by changing the frequency of the AC power from the inverter to the motor. Since the electric car gets is power from the battery, it usually consists of a rechargeable battery and doesn't contain liquid fuel components like fuel pump or fuel tank etc. This also makes the electric cars emit no exhaust.

How do you shift a manual transmission in vehicle?

First depress the clutch, then put the car into the desired gear, then slowly release the clutch while applying the throttle.

A group of parents is upset about the school lunch menu. Some are concerned that there are too many processed foods, not enough whole grains, and too much-canned fruit instead of fresh fruit. How would you approach this dilemma?

I will review the labels for all the items that parents complained. Highly processed foods are often high in sodium content. The Dietary Guidelines for Americans recommends Americans, including children, consume 2300 mg of sodium or less per day. Based on the budget and shopping situation, I will try to use as much fresh food as we can. If we can’t, I will carefully check the labels to choose frozen vegetables and low-sodium canned vegetables. Using more whole-grain bread, English muffins, bagels, and whole-grain corn tortillas.

A lunar eclipse is an astronomical event that occurs when the Moon moves into the Earth's shadow, causing the moon to be darkened. Such alignment occurs during an eclipse season, approximately every six months, during the full moon phase, when the Moon's orbital plane is closest to the plane of the Earth's orbit. This can occur only when the Sun, Earth, and Moon are exactly or very closely aligned (in syzygy) with Earth between the other two, which can happen only on the night of a full moon when the Moon is near either lunar node. The type and length of a lunar eclipse depend on the Moon's proximity to the lunar node

What are the important points about Lunar eclipse in the given text?

1. When the Moon moves into the Earth's shadow, causing the moon to be darkened an astronomical event occurs which is known as Lunar Eclipse. 2. When moon’s orbit is closet to the earth , during the full moon phase, lunar eclipse occurs. 3. Lunar eclipse can occur only when the Sun, Earth, and Moon are exactly aligned with Earth. 4. The type and length of a lunar eclipse depend on how close the moon is to the lunar node.

1812.06705

Table 2 lists the accuracies of the all methods on two classifier architectures. The results show that, for various datasets on different classifier architectures, our conditional BERT contextual augmentation improves the model performances most. BERT can also augments sentences to some extent, but not as much as conditional BERT does. For we masked words randomly, the masked words may be label-sensitive or label-insensitive. If label-insensitive words are masked, words predicted through BERT may not be compatible with original labels. The improvement over all benchmark datasets also shows that conditional BERT is a general augmentation method for multi-labels sentence classification tasks. The improvement over all benchmark datasets also shows that conditional BERT is a general augmentation method for multi-labels sentence classification tasks.

Does the new objective perform better than the original objective bert is trained on?

Yes.

Modeling temporal and sequential data, which is crucial in machine learning, can be applied in many areas, such as speech and natural language processing. Deep neural networks (DNNs) have garnered interest from many researchers after being successfully applied in image classification BIBREF0 and speech recognition BIBREF1 . Another type of neural network, called a recurrent neural network (RNN) is also widely used for speech recognition BIBREF2 , machine translation BIBREF3 , BIBREF4 and language modeling BIBREF5 , BIBREF6 . RNNs have achieved many state-of-the-art results. Compared to DNNs, they have extra parameters for modeling the relationships of previous or future hidden states with current input where the RNN parameters are shared for each input time-step. Generally, RNNs can be separated by a simple RNN without gating units, such as the Elman RNN BIBREF7 , the Jordan RNN BIBREF8 , and such advanced RNNs with gating units as the Long-Short Term Memory (LSTM) RNN BIBREF9 and the Gated Recurrent Unit (GRU) RNN BIBREF4 . A simple RNN usually adequate to model some dataset and a task with short-term dependencies like slot filling for spoken language understanding BIBREF10 . However, for more difficult tasks like language modeling and machine translation where most predictions need longer information and a historical context from each sentence, gating units are needed to achieve good performance. With gating units for blocking and passing information from previous or future hidden layer, we can learn long-term information and recursively backpropagate the error from our prediction without suffering from vanishing or exploding gradient problems BIBREF9 . In spite of this situation, the concept of gating mechanism does not provide an RNN with a more powerful way to model the relation between the current input and previous hidden layer representations. Most interactions inside RNNs between current input and previous (or future) hidden states are represented using linear projection and addition and are transformed by the nonlinear activation function. The transition is shallow because no intermediate hidden layers exist for projecting the hidden states BIBREF11 . To get a more powerful representation on the hidden layer, Pascanu et al. BIBREF11 modified RNNs with an additional nonlinear layer from input to the hidden layer transition, hidden to hidden layer transition and also hidden to output layer transition. Socher et al. BIBREF12 , BIBREF13 proposed another approach using a tensor product for calculating output vectors given two input vectors. They modified a Recursive Neural Network (RecNN) to overcome those limitations using more direct interaction between two input layers. This architecture is called a Recursive Neural Tensor Network (RecNTN), which uses a tensor product between child input vectors to represent the parent vector representation. By adding the tensor product operation to calculate their parent vector, RecNTN significantly improves the performance of sentiment analysis and reasoning on entity relations tasks compared to standard RecNN architecture. However, those models struggle to learn long-term dependencies because the do not utilize the concept of gating mechanism. In this paper, we proposed a new RNN architecture that combine the gating mechanism and tensor product concepts to incorporate both advantages in a single architecture. Using the concept of such gating mechanisms as LSTMRNN and GRURNN, our proposed architecture can learn temporal and sequential data with longer dependencies between each input time-step than simple RNNs without gating units and combine the gating units with tensor products to represent the hidden layer with more powerful operation and direct interaction. Hidden states are generated by the interaction between current input and previous (or future) hidden states using a tensor product and a non-linear activation function allows more expressive model representation. We describe two different models based on LSTMRNN and GRURNN. LSTMRNTN is our proposed model for the combination between a LSTM unit with a tensor product inside its cell equation and GRURNTN is our name for a GRU unit with a tensor product inside its candidate hidden layer equation. In Section "Background" , we provide some background information related to our research. In Section "Proposed Architecture" , we describe our proposed RNN architecture in detail. We evaluate our proposed RNN architecture on word-level and character-level language modeling tasks and reported the result in Section "Experiment Settings" . We present related works in Section "Related Work" . Section "Conclusion" summarizes our paper and provides some possible future improvements. Using the concept of such gating mechanisms as LSTMRNN and GRURNN, our proposed architecture can learn temporal and sequential data with longer dependencies between each input time-step than simple RNNs without gating units and combine the gating units with tensor products to represent the hidden layer with more powerful operation and direct interaction.

What's the advantage of the proposed architecture?

Their proposed architecture can learn temporal and sequential data with longer dependencies between each input time-step than simple RNNs without gating units and combine the gating units with tensor products to represent the hidden layer with more powerful operation and direct interaction.

Formula One (more commonly known as Formula 1 or F1) is the highest class of international racing for open-wheel single-seater formula racing cars sanctioned by the Fédération Internationale de l'Automobile (FIA). The FIA Formula One World Championship has been one of the premier forms of racing around the world since its inaugural season in 1950. The word formula in the name refers to the set of rules to which all participants' cars must conform. A Formula One season consists of a series of races, known as Grands Prix. Grands Prix take place in multiple countries and continents around the world on either purpose-built circuits or closed public roads. A points system is used at Grands Prix to determine two annual World Championships: one for the drivers, and one for the constructors (the teams). Each driver must hold a valid Super Licence, the highest class of racing licence issued by the FIA, and the races must be held on tracks graded "1", the highest grade-rating issued by the FIA for tracks.

What are the two World Championships in one season of Formula One?

A points system is used at Grands Prix to determine two annual World Championships: one for the drivers, and one for the constructors (the teams).