Datasets:

DataHammer

/

scimrc

like 8

What is the difference between a fried egg prepared "sunny-side-up" and an egg prepared "over-easy"?

"Sunny-side-up" refers to an egg that is fried only on one side and then removed from the pan. "Over-easy" refers to a fried egg that is briefly flipped and cooked on the other side, but not so long that the yolk becomes hardened.

What is kindergarten?

Kindergarten is the first year of public school education, for children who are 5 or 6 years old. The curriculum helps children learn their colors, shapes, numbers, letters, and letter sounds with the goal of helping children to read independently by the end of the school year.

Heidelberg (German: [ˈhaɪdl̩bɛʁk] (listen); Palatine German: Heidlberg) is a city in the German state of Baden-Württemberg, situated on the river Neckar in south-west Germany. As of the 2016 census, its population was 159,914, of which roughly a quarter consisted of students. Located about 78 km (48 mi) south of Frankfurt, Heidelberg is the fifth-largest city in Baden-Württemberg. Heidelberg is part of the densely populated Rhine-Neckar Metropolitan Region. Heidelberg University, founded in 1386, is Germany's oldest and one of Europe's most reputable universities. Heidelberg is a scientific hub in Germany and home to several internationally renowned research facilities adjacent to its university, including the European Molecular Biology Laboratory and four Max Planck Institutes. The city has also been a hub for the arts, especially literature, throughout the centuries, and it was designated a "City of Literature" by the UNESCO Creative Cities Network. Heidelberg was a seat of government of the former Electorate of the Palatinate and is a popular tourist destination due to its romantic cityscape, including Heidelberg Castle, the Philosophers' Walk, and the Baroque old town.

Highlight relevant facts related to Heidelberg

Heidelberg has the oldest University in Germany. The city is a scientific hub and home to several internationally renowned research facilities. It was designated a "City of Literature" by the UNESCO Creative Cities Network.

Identify which animal species is alive or extinct: Platybelodon, Lion

Lion is alive, Platybelodon is extinct.

John Francis Bongiovi Jr. (born March 2, 1962), known professionally as Jon Bon Jovi, is an American singer, songwriter, guitarist, and actor. He is best known as the founder and frontman of the rock band Bon Jovi, which was formed in 1983. He has released 15 studio albums with his band as well as two solo albums. In the 1990s, Bon Jovi started an acting career, starring in the films Moonlight and Valentino and U-571 and appearing on television in Sex and the City, Ally McBeal, and The West Wing. As a songwriter, Bon Jovi was inducted into the Songwriters Hall of Fame in 2009. In 2012, he ranked number 50 on the list of Billboard magazine's "Power 100", a ranking of "The Most Powerful and Influential People in the Music Business". In 1996, People magazine named him one of the "50 Most Beautiful People in the World". In 2000, People awarded him the title "Sexiest Rock Star". Bon Jovi was a founder and former majority owner of the Arena Football League team, the Philadelphia Soul. He is the founder of The Jon Bon Jovi Soul Foundation, founded in 2006.

Who is Jon Bon Jovi

John Francis Bongiovi Jr. (born March 2, 1962), known professionally as Jon Bon Jovi, is an American singer, songwriter, guitarist, and actor. He is best known as the founder and frontman of the rock band Bon Jovi, which was formed in 1983. He has released 15 studio albums with his band as well as two solo albums.

This paper proposes a machine learning approach to part-of-speech tagging and named entity recognition for Greek, focusing on the extraction of morphological features and classification of tokens into a small set of classes for named entities. The architecture model that was used is introduced. The greek version of the spaCy platform was added into the source code, a feature that did not exist before our contribution, and was used for building the models. Additionally, a part of speech tagger was trained that can detect the morphology of the tokens and performs higher than the state-of-the-art results when classifying only the part of speech. For named entity recognition using spaCy, a model that extends the standard ENAMEX type (organization, location, person) was built. Certain experiments that were conducted indicate the need for flexibility in out-of-vocabulary words and there is an effort for resolving this issue. Finally, the evaluation results are discussed. The greek version of the spaCy platform was added into the source code, a feature that did not exist before our contribution, and was used for building the models.

Is the paper the first to develop a Greek POS tagger with morphological features?

Yes.

Controversies Ignoring and evading local regulations Uber has been criticized for its strategy of generally commencing operations in a city without regard for local regulations. If faced with regulatory opposition, Uber called for public support for its service and mounted a political campaign, supported by lobbying, to change regulations. Uber argued that it is "a technology company" and not a taxi company, and therefore it was not subject to regulations affecting taxi companies. Uber's strategy was generally to "seek forgiveness rather than permission". In 2014, with regards to airport pickups without a permit in California, drivers were actually told to ignore local regulations and that the company would pay for any citations. Uber's response to California Assembly Bill 5 (2019), whereby it announced that it would not comply with the law, then engaged lobbyists and mounted an expensive public opinion campaign to overturn it via a ballot, was cited as an example of this policy. Taxi companies sued Uber in numerous American cities, alleging that Uber's policy of violating taxi regulations was a form of unfair competition or a violation of antitrust law. Although some courts did find that Uber intentionally violated the taxi rules, Uber prevailed in every case, including the only case to proceed to trial. In March 2017, an investigation by The New York Times revealed that Uber developed a software tool called "Greyball" to avoid giving rides to known law enforcement officers in areas where its service was illegal such as in Portland, Oregon, Australia, South Korea, and China. The tool identified government officials using geofencing, mining credit card databases, identifying devices, and searches of social media. While at first, Uber stated that it only used the tool to identify riders that violated its terms of service, after investigations by Portland, Oregon, and the United States Department of Justice, Uber admitted to using the tool to skirt local regulations and promised not to use the tool for that purpose. The use of Greyball in London was cited by Transport for London as one of the reasons for its decision not to renew Uber's private hire operator licence in September 2017. A January 2018 report by Bloomberg News stated that Uber routinely used a "panic button" system, codenamed "Ripley", that locked, powered off and changed passwords on staff computers when those offices were subjected to government raids. Uber allegedly used this button at least 24 times, from spring 2015 until late 2016. Counter-intelligence research on class action plaintiffs In 2016 Uber hired the global security consulting firm Ergo to secretly investigate plaintiffs involved in a class action lawsuit. Ergo operatives posed as acquaintances of the plaintiff's counsel and tried to contact their associates to obtain information that could be used against them. The result of which was found out causing the judge to throw out evidence obtained as obtained in a fraudulent manner. Sexual harassment allegations and management shakeup (2017) On February 19, 2017, former Uber engineer Susan Fowler published on her website that she was propositioned for sex by a manager and subsequently threatened with termination of employment by another manager if she continued to report the incident. Kalanick was alleged to have been aware of the complaint. On February 27, 2017, Amit Singhal, Uber's Senior Vice President of Engineering, was forced to resign after he failed to disclose a sexual harassment claim against him that occurred while he served as Vice President of Google Search. After investigations led by former attorney general Eric Holder and Arianna Huffington, a member of Uber's board of directors, in June 2017, Uber fired over 20 employees. Kalanick took an indefinite leave of absence but, under pressure from investors, he resigned as CEO a week later. Also departing the company in June 2017 was Emil Michael, a senior vice president who suggested that Uber hire a team of opposition researchers and journalists, with a million-dollar budget, to "dig up dirt" on the personal lives and backgrounds of journalists who reported negatively on Uber, specifically targeting Sarah Lacy, editor of PandoDaily, who, in an article published in October 2014, accused Uber of sexism and misogyny in its advertising. In August 2018, Uber agreed to pay a total of $7 million to settle claims of gender discrimination, harassment, and hostile work environment, with 480 employees and former employees receiving $10,700 each and 56 of those employees and former employees receiving an additional $33,900 each. In December 2019, Kalanick resigned from the board of directors of the company and sold his shares. Delayed disclosure of data breaches On February 27, 2015, Uber admitted that it had suffered a data breach more than nine months prior. Names and license plate information from approximately 50,000 drivers were inadvertently disclosed. Uber discovered this leak in September 2014, but waited more than five months to notify the affected individuals. An announcement in November 2017 revealed that in 2016, a separate data breach had disclosed the personal information of 600,000 drivers and 57 million customers. This data included names, email addresses, phone numbers, and drivers' license information. Hackers used employees' usernames and passwords that had been compromised in previous breaches (a "credential stuffing" method) to gain access to a private GitHub repository used by Uber's developers. The hackers located credentials for the company's Amazon Web Services datastore in the repository files, and were able to obtain access to the account records of users and drivers, as well as other data contained in over 100 Amazon S3 buckets. Uber paid a $100,000 ransom to the hackers on the promise they would delete the stolen data. Uber was subsequently criticized for concealing this data breach. Khosrowshahi publicly apologized. In September 2018, in the largest multi-state settlement of a data breach, Uber paid $148 million to the Federal Trade Commission, admitted that its claim that internal access to consumers' personal information was closely monitored on an ongoing basis was false, and stated that it had failed to live up to its promise to provide reasonable security for consumer data. Also in November 2018, Uber's British divisions were fined £385,000 (reduced to £308,000) by the Information Commissioner's Office. In 2020, the US Department of Justice announced criminal charges against former Chief Security Officer Joe Sullivan for obstruction of justice. The criminal complaint said Sullivan arranged, with Kalanick's knowledge, to pay a ransom for the 2016 breach as a "bug bounty" to conceal its true nature, and for the hackers to falsify non-disclosure agreements to say they had not obtained any data.

Provide a bulleted list of the controversies regarding Uber and a brief summary of each

The following is a summary of the controversies around Uber: - Ignoring and Evading Local Regulations: Historically, Uber has launched in new cities without obtaining regulatory approval or following laws - Counter-intelligence Research on Class Action Plaintiffs: Uber secretly investigated plaintiffs to obtain information that could be used against them - Sexual Harassment Allegations and Management Shakeup: A former employee published a public letter exposing her experiences being sexually harassed while at Uber. This eventually lead to the ousting of the former CEO and an overhaul of the management team - Delayed Disclosure of Data Breaches: Uber experienced data breaches in the past, the company would not disclose the breach to authorities until multiple months later

Chadwick is a village in Carroll County, Illinois, United States. The population was 481 at the 2020 census, down from 551 at the 2010 census. Geography Chadwick is located at 42°0′51″N 89°53′21″W (42.014081, -89.889078). According to the 2021 census gazetteer files, Chadwick has a total area of 0.31 square miles (0.80 km2), all land. Demographics As of the 2020 census there were 481 people, 256 households, and 163 families residing in the village. The population density was 1,546.62 inhabitants per square mile (597.15/km2). There were 246 housing units at an average density of 791.00 per square mile (305.41/km2). The racial makeup of the village was 96.67% White, 0.42% African American, 0.62% Asian, and 2.29% from two or more races. Hispanic or Latino of any race were 2.49% of the population. There were 256 households, out of which 30.86% had children under the age of 18 living with them, 51.95% were married couples living together, 5.08% had a female householder with no husband present, and 36.33% were non-families. 31.25% of all households were made up of individuals, and 10.55% had someone living alone who was 65 years of age or older. The average household size was 2.61 and the average family size was 2.07. The village's age distribution consisted of 15.5% under the age of 18, 6.0% from 18 to 24, 19.2% from 25 to 44, 35.5% from 45 to 64, and 23.6% who were 65 years of age or older. The median age was 50.4 years. For every 100 females, there were 128.0 males. For every 100 females age 18 and over, there were 125.8 males. The median income for a household in the village was $50,000, and the median income for a family was $57,039. Males had a median income of $36,908 versus $25,652 for females. The per capita income for the village was $27,324. About 3.7% of families and 4.9% of the population were below the poverty line, including 11.0% of those under age 18 and 3.2% of those age 65 or over.

Given a reference text about Chadwick, Illinois, tell me median family income and the percent of families below the poverty line.

The median family income in Chadwick, Illinois is $57,039 and about 3.7% of families live below the poverty line.

1911.08829

We evaluate the quality of three idiom dictionaries by comparing them to each other and to three idiom corpora. Before we report on the comparison we first describe why we select and how we prepare these resources. We investigate the following six idiom resources: Wiktionary; the Oxford Dictionary of English Idioms (ODEI, BIBREF31); UsingEnglish.com (UE); the Sporleder corpus BIBREF10; the VNC dataset BIBREF9; There are four sizeable sense-annotated PIE corpora for English: the VNC-Tokens Dataset BIBREF9, the Gigaword dataset BIBREF14, the IDIX Corpus BIBREF10, and the SemEval-2013 Task 5 dataset BIBREF15. An overview of these corpora is presented in Table TABREF7. We investigate the following six idiom resources: Wiktionary; the Oxford Dictionary of English Idioms (ODEI, BIBREF31); UsingEnglish.com (UE); the Sporleder corpus BIBREF10; the VNC dataset BIBREF9; and the SemEval-2013 Task 5 dataset BIBREF15.

What dictionaries are used for automatic extraction of PIEs?

The answers are shown as follows: * Wiktionary * Oxford Dictionary of English Idioms * UsingEnglish.com (UE) * Sporleder corpus * VNC dataset * SemEval-2013 Task 5 dataset

In this section, we describe methods to explore combinatorially sized action spaces such as text-games—focusing especially on methods that can deal with their inherent bottleneck structure. We first describe our method that explicitly attempts to detect bottlenecks and then describe how an exploration algorithm such as Go Explore BIBREF9 can leverage knowledge graphs. KG-A2C-chained An example of a bottleneck can be seen in Figure FIGREF1. We extend the KG-A2C algorithm as follows. First, we detect bottlenecks as states where the agent is unable to progress any further. We set a patience parameter and if the agent has not seen a higher score in patience steps, the agent assumes it has been limited by a bottleneck. Second, when a bottleneck is found, we freeze the policy that gets the agent to the state with the highest score. The agent then begins training a new policy from that particular state. Simply freezing the policy that led to the bottleneck, however, can potentially result in a policy one that is globally sub-optimal. We therefore employ a backtracking strategy that restarts exploration from each of the $n$ previous steps—searching for a more optimal policy that reaches that bottleneck. At each step, we keep track of a buffer of $n$ states and admissible actions that led up to that locally optimal state. We force the agent to explore from this state to attempt to drive it out of the local optima. If it is further unable to find itself out of this local optima, we refresh the training process again, but starting at the state immediately before the agent reaches the local optima. If this continues to fail, we continue to iterate through this buffer of seen states states up to that local optima until we either find a more optimal state or we run out of states to refresh from, in which we terminate the training algorithm. KG-A2C-Explore Go-Explore BIBREF9 is an algorithm that is designed to keep track of sub-optimal and under-explored states in order to allow the agent to explore upon more optimal states that may be a result of sparse rewards. The Go-Explore algorithm consists of two phases, the first to continuously explore until a set of promising states and corresponding trajectories are found on the basis of total score, and the second to robustify this found policy against potential stochasticity in the game. Promising states are defined as those states when explored from will likely result in higher reward trajectories. Since the text games we are dealing with are mostly deterministic, with the exception of Zork in later stages, we only focus on using Phase 1 of the Go-Explore algorithm to find an optimal policy. BIBREF10 look at applying Go-Explore to text-games on a set of simpler games generated using the game generation framework TextWorld BIBREF1. Instead of training a policy network in parallel to generate actions used for exploration, they use a small set of “admissible actions”—actions guaranteed to change the world state at any given step during Phase 1—to explore and find high reward trajectories. This space of actions is relatively small (of the order of $10^2$ per step) and so finding high reward trajectories in larger action-spaces such as in Zork would be infeasible Go-Explore maintains an archive of cells—defined as a set of states that map to a single representation—to keep track of promising states. BIBREF9 simply encodes each cell by keeping track of the agent's position and BIBREF10 use the textual observations encoded by recurrent neural network as a cell representation. We improve on this implementation by training the KG-A2C network in parallel, using the snapshot of the knowledge graph in conjunction with the game state to further encode the current state and use this as a cell representation. At each step, Go-Explore chooses a cell to explore at random (weighted by score to prefer more advanced cells). The KG-A2C will run for a number of steps, starting with the knowledge graph state and the last seen state of the game from the cell. This will generate a trajectory for the agent while further training the KG-A2C at each iteration, creating a new representation for the knowledge graph as well as a new game state for the cell. After expanding a cell, Go-Explore will continue to sample cells by weight to continue expanding its known states. At the same time, KG-A2C will benefit from the heuristics of selecting preferred cells and be trained on promising states more often. We improve on this implementation by training the KG-A2C network in parallel, using the snapshot of the knowledge graph in conjunction with the game state to further encode the current state and use this as a cell representation.

How does the team improve the implementation of Ecoffet et al. and Madotto et al.?

They improve on this implementation by training the KG-A2C network in parallel, using the snapshot of the knowledge graph in conjunction with the game state to further encode the current state and use this as a cell representation.

1910.02754

Proposed Fusion Techniques ::: Step-Wise Decoder Fusion Our first proposed technique is the step-wise decoder fusion of visual features during every prediction step i.e. we concatenate the visual encoding as context at each step of the decoding process. This differs from the usual practice of passing the visual feature only at the beginning of the decoding process BIBREF5. Proposed Fusion Techniques ::: Multimodal Attention Modulation Similar to general attention BIBREF8, wherein a variable-length alignment vector $a_{th}(s)$, whose size equals the number of time steps on the source side, is derived by comparing the current target hidden state $h_{t}$ with each source hidden state $\overline{h_{s}}$; we consider a variant wherein the visual encoding $v_{t}$ is used to calculate an attention distribution $a_{tv}(s)$ over the source encodings as well. Then, the true attention distribution $a_{t}(s)$ is computed as an interpolation between the visual and text based attention scores. The score function is a content based scoring mechanism as usual. Proposed Fusion Techniques ::: Visual-Semantic (VS) Regularizer In terms of leveraging the visual modality for supervision, BIBREF1 use multi-task learning to learn grounded representations through image representation prediction. However, to our knowledge, visual-semantic supervision hasn't been much explored for multimodal translation in terms of loss functions. Proposed Fusion Techniques ::: Step-Wise Decoder Fusion Our first proposed technique is the step-wise decoder fusion of visual features during every prediction step i.e. we concatenate the visual encoding as context at each step of the decoding process. Proposed Fusion Techniques ::: Multimodal Attention Modulation Similar to general attention BIBREF8, wherein a variable-length alignment vector $a_{th}(s)$, whose size equals the number of time steps on the source side, is derived by comparing the current target hidden state $h_{t}$ with each source hidden state $\overline{h_{s}}$; we consider a variant wherein the visual encoding $v_{t}$ is used to calculate an attention distribution $a_{tv}(s)$ over the source encodings as well. Proposed Fusion Techniques ::: Visual-Semantic (VS) Regularizer In terms of leveraging the visual modality for supervision, BIBREF1 use multi-task learning to learn grounded representations through image representation prediction.

What are 3 novel fusion techniques that are proposed?

The answers are shown as follows: * Step-Wise Decoder Fusion * Multimodal Attention Modulation * Visual-Semantic (VS) Regularizer

1909.08752

Our model consists of two neural network modules, i.e. an extractor and abstractor. The extractor encodes a source document and chooses sentences from the document, and then the abstractor paraphrases the summary candidates. Formally, a single document consists of $n$ sentences $D=\lbrace s_1,s_2,\cdots ,s_n\rbrace $. We denote $i$-th sentence as $s_i=\lbrace w_{i1},w_{i2},\cdots ,w_{im}\rbrace $ where $w_{ij}$ is the $j$-th word in $s_i$. The extractor learns to pick out a subset of $D$ denoted as $\hat{D}=\lbrace \hat{s}_1,\hat{s}_2,\cdots ,\hat{s}_k|\hat{s}_i\in D\rbrace $ where $k$ sentences are selected. The abstractor rewrites each of the selected sentences to form a summary $S=\lbrace f(\hat{s}_1),f(\hat{s}_2),\cdots ,f(\hat{s}_k)\rbrace $, where $f$ is an abstracting function. And a gold summary consists of $l$ sentences $A=\lbrace a_1,a_2,\cdots ,a_l\rbrace $. The extractor is based on the encoder-decoder framework. We adapt BERT for the encoder to exploit contextualized representations from pre-trained transformers. BERT as the encoder maps the input sequence $D$ to sentence representation vectors $H=\lbrace h_1,h_2,\cdots ,h_n\rbrace $, where $h_i$ is for the $i$-th sentence in the document. Then, the decoder utilizes $H$ to extract $\hat{D}$ from $D$. We use LSTM Pointer Network BIBREF22 as the decoder to select the extracted sentences based on the above sentence representations. The decoder extracts sentences recurrently, producing a distribution over all of the remaining sentence representations excluding those already selected. Since we use the sequential model which selects one sentence at a time step, our decoder can consider the previously selected sentences. This property is needed to avoid selecting sentences that have overlapping information with the sentences extracted already. The abstractor network approximates $f$, which compresses and paraphrases an extracted document sentence to a concise summary sentence. We use the standard attention based sequence-to-sequence (seq2seq) model BIBREF23, BIBREF24 with the copying mechanism BIBREF25 for handling out-of-vocabulary (OOV) words. Our abstractor is practically identical to the one proposed in BIBREF8. Our model consists of two neural network modules, i.e. an extractor and abstractor. The extractor encodes a source document and chooses sentences from the document, and then the abstractor paraphrases the summary candidates. The extractor is based on the encoder-decoder framework. We adapt BERT for the encoder to exploit contextualized representations from pre-trained transformers. We use LSTM Pointer Network BIBREF22 as the decoder to select the extracted sentences based on the above sentence representations. Our abstractor is practically identical to the one proposed in BIBREF8.

What's the method used here?

Two neural networks: an extractor based on an encoder (BERT) and a decoder (LSTM Pointer Network BIBREF22) and an abstractor identical to the one proposed in BIBREF8.

What is Assembler?

An assembler program creates object code by translating combinations of mnemonics and syntax for operations and addressing modes into their numerical equivalents. This representation typically includes an operation code ("opcode") as well as other control bits and data. The assembler also calculates constant expressions and resolves symbolic names for memory locations and other entities. The use of symbolic references is a key feature of assemblers, saving tedious calculations and manual address updates after program modifications. Most assemblers also include macro facilities for performing textual substitution – e.g., to generate common short sequences of instructions as inline, instead of called subroutines. Some assemblers may also be able to perform some simple types of instruction set-specific optimizations. One concrete example of this may be the ubiquitous x86 assemblers from various vendors. Called jump-sizing, most of them are able to perform jump-instruction replacements (long jumps replaced by short or relative jumps) in any number of passes, on request. Others may even do simple rearrangement or insertion of instructions, such as some assemblers for RISC architectures that can help optimize a sensible instruction scheduling to exploit the CPU pipeline as efficiently as possible.

In Naples, he worked in 1572–1577 at the church of San Gregorio Armeno together with Giovanni Vincenzo della Monica. He helped design the Bank in Naples, called the second Monte di Pietà, started in 1539 with goal, according to Sasso, of liberating citizens from the supposed usury of the Jews. By 1597, the building was completed and unified all the banks in the city. In this project, he was aided by Giovanni Giacomo Di Conforto and Giovanni Cola di Franco.

Given this short summary of Giovan Battista Cavagna, how long did it take to build the second Monte di Pieta?

It took 58 years. Started in 1539, completed in 1597.

What is Power BI?

Power BI is a dashboard reporting tool that enables users and customers to interact with data using interactive and custom visualizations, custom calculations, and queries. You can build data models in Power BI, and you can run Power BI in direct query or import mode. Direct Query queries the data directly from the system of record it connects to and import mode makes a copy of that data locally in Power BI. Depending on the size of the dataset this can affect performance.

What are Salesforce's Inc current state and challenges?

Salesforce can be best described as being in the “Elaboration” stage within the organization's lifecycle. Salesforce, to date, has managed its growth very well. Despite growing to 17000 employees, Salesforce managed to retain its core culture. It also responded to external challenges, especially against the competition, as Salesforce today competes in the CRM industry and emerged as a viable alternative to AWS. Salesforce relies more heavily on bureaucratic control mechanisms than clan and market control. Salesforce divided itself into functional units, namely, Product, Customer, People, Strategy, and Revenue. It does have some core focus areas, like the public sector as an industry vertical. Even though Salesforce, when viewed through a rational lens, is a bureaucratic organization, it is designed for flexibility, i.e, organic design. The decision-making is pushed down to the business unit rather than being driven by the top management. Salesforce’s Tableau and Slack acquisitions are adaptive, quick, innovative, and much required in a changing and complex environment. With Slack and Tableau, Salesforce has an edge against other cloud providers in the platform space.

What is the typical math curriculum for a 5th grader

Typical math curriculum for 5th grade consists of 1. Numbers and operations on whole numbers, decimals, fractions, percentages, ratios and proportions 2. Loose introduction to algebra and abstract thinking 3. Areas and perimeters of shapes, introduction to triangles, lines, segments, angles etc. 4. Translating word problems into symbolic math to solve logic problems.

The Japan Rail Pass, also called the JR Pass, is a rail pass for overseas visitors sold by the Japan Railways Group, and is valid for travel on all major forms of transportation provided by the JR Group in Japan, with a few exceptions. The Rail Pass is designed to stimulate travel and tourism throughout the country. It is only cost effective for long-distance travel, particularly by bullet train. While the savings from extensive travel can be considerable, those who travel too little may in fact lose money on a rail pass. The Japan Rail Pass is of limited use within larger cities, as private operators generally do not accept the Rail Pass. In Tokyo, for instance, it covers the Yamanote Line which goes to several popular tourist areas, as well as in Osaka on the Osaka Loop Line, plus in Kyoto on the Nara Line and Sagano Line.

Given this paragraph about the Japan Rail Pass, tell me whether it is available for purchase to Japanese citizens.

No, the JR Pass is only intended for purchase by overseas visitors to Japan.

Provide a list of benefits of flying first class.

The following are benefits of flying first class: 1. Checked bags are free 2. More comfortable seats 3. Additional leg room 4. Free alcohol on the plane 5. Better food on the plane 6. Boarding and exiting the plane first

Neural Machine Translation (NMT) has shown its effectiveness in translation tasks when NMT systems perform best in recent machine translation campaigns BIBREF0 , BIBREF1 . Compared to phrase-based Statistical Machine Translation (SMT) which is basically an ensemble of different features trained and tuned separately, NMT directly modeling the translation relationship between source and target sentences. Unlike SMT, NMT does not require much linguistic information and large monolingual data to achieve good performances. An NMT consists of an encoder which recursively reads and represents the whole source sentence into a context vector and a recurrent decoder which takes the context vector and its previous state to predict the next target word. It is then trained in an end-to-end fashion to learn parameters which maximizes the likelihood between the outputs and the references. Recently, attention-based NMT has been featured in most state-of-the-art systems. First introduced by BIBREF2 , attention mechanism is integrated in decoder side as feedforward layers. It allows the NMT to decide which source words should take part in the predicting process of the next target words. It helps to improve NMTs significantly. Nevertheless, since the attention mechanism is specific to a particular source sentence and the considering target word, it is also specific to particular language pairs. Some recent work has focused on extending the NMT framework to multilingual scenarios. By training such network using parallel corpora in number of different languages, NMT could benefit from additional information embedded in a common semantic space across languages. Basically, the proposed NMT are required to employ multiple encoders or multiple decoders to deal with multilinguality. Furthermore, in order to avoid the tight dependency of the attention mechanism to specific language pairs, they also need to modify their architecture to combine either the encoders or the attention layers. These modifications are specific to the purpose of the tasks as well. Thus, those multilingual NMTs are more complicated, much more free parameters to learn and more difficult to perform standard trainings compared to the original NMT. In this paper, we introduce a unified approach to seamlessly extend the original NMT to multilingual settings. Our approach allows us to integrate any language in any side of the encoder-decoder architecture with only one encoder and one decoder for all the languages involved. Moreover, it is not necessary to do any network modification to enable attention mechanism in our NMT systems. We then apply our proprosed framework in two demanding scenarios: under-resourced translation and zero-resourced translation. The results show that bringing multilinguality to NMT helps to improve individual translations. With some insightful analyses of the results, we set our goal toward a fully multilingual NMT framework. The paper starts with a detailed introduction to attention-based NMT. In Section SECREF3 , related work about multi-task NMT is reviewed. Section SECREF5 describes our proposed approach and thorough comparisons to the related work. It is followed by a section of evaluating our systems in two aforementioned scenarios, in which different strategies have been employed under a unified approach (Section SECREF4 ). Finally, the paper ends with conclusion and future work. This work is licenced under a Creative Commons Attribution 4.0 International License. License details: http://creativecommons.org/licenses/by/4.0/ Compared to phrase-based Statistical Machine Translation (SMT) which is basically an ensemble of different features trained and tuned separately, NMT directly modeling the translation relationship between source and target sentences.

What's the difference between phrase-based Statistical Machine Translation and Neural Machine Translation?

NMT directly modeling the translation relationship between source and target sentences.

Given a piece of text and the span of an entity mention in this text, fine-grained entity typing (FET) is the task of assigning fine-grained type labels to the mention BIBREF0. The assigned labels should be context dependent BIBREF1. For example, in the sentence “Trump threatens to pull US out of World Trade Organization,” the mention “Trump” should be labeled as /person and /person/politician, although Donald Trump also had other occupations such as businessman, TV personality, etc. This task is challenging because it usually uses a relatively large tag set, and some mentions may require the understanding of the context to be correctly labeled. Moreover, since manual annotation is very labor-intensive, existing approaches have to rely on distant supervision to train models BIBREF0, BIBREF2. Thus, the use of extra information to help with the classification process becomes very important. In this paper, we improve FET with entity linking (EL). EL is helpful for a model to make typing decisions because if a mention is correctly linked to its target entity, we can directly obtain the type information about this entity in the knowledge base (KB). For example, in the sentence “There were some great discussions on a variety of issues facing Federal Way,” the mention “Federal Way” may be incorrectly labeled as a company by some FET models. Such a mistake can be avoided after linking it to the city Federal Way, Washington. For cases that require the understanding of the context, using entity linking results is also beneficial. In the aforementioned example where “Trump” is the mention, obtaining all the types of Donald Trump in the knowledge base (e.g., politician, businessman, TV personality, etc.) is still informative for inferring the correct type (i.e., politician) that fits the context, since they narrows the possible labels down. However, the information obtained through EL should not be fully trusted since it is not always accurate. Even when a mention is correctly linked to an entity, the type information of this entity in the KB may be incomplete or outdated. Thus, in this paper, we propose a deep neural fine-grained entity typing model that flexibly predicts labels based on the context, the mention string, and the type information from KB obtained with EL. Using EL also introduces a new problem for the training process. Currently, a widely used approach to create FET training samples is to use the anchor links in Wikipedia BIBREF0, BIBREF3. Each anchor link is regarded as a mention, and is weakly labeled with all the types of its referred entity (the Wikipedia page the anchor link points to) in KB. Our approach, when links the mention correctly, also uses all the types of the referred entity in KB as extra information. This may cause the trained model to overfit the weakly labeled data. We design a variant of the hinge loss and introduce noise during training to address this problem. We conduct experiments on two commonly used FET datasets. Experimental results show that introducing information obtained through entity linking and having a deep neural model both helps to improve FET performance. Our model achieves more than 5% absolute strict accuracy improvement over the state of the art on both datasets. Our contributions are summarized as follows: We propose a deep neural fine-grained entity typing model that utilizes type information from KB obtained through entity linking. We address the problem that our model may overfit the weakly labeled data by using a variant of the hinge-loss and introducing noise during training. We demonstrate the effectiveness of our approach with experimental results on commonly used FET datasets. Our code is available at https://github.com/HKUST-KnowComp/IFETEL. Experimental results show that introducing information obtained through entity linking and having a deep neural model both helps to improve FET performance.

Who can help to improve FET performance through their experiments? Introducing information obtained through entity linking or having a deep neural model?

Bothof them can help.

What type of rock climbing is best?

There are many different types of rock climbing and none of them are "best". Bouldering, Sport climbing and trad climbing are the main types. What type that you enjoy the most depends on many factors including how high you want to climb and how much gear you want to use.

Dean Dill (born 8 March 1947) was a magician and effects designer who lived and worked in Glendale, California, USA. He appeared on The Tonight Show in November 1990. His performance focused mostly on closeup effects and coin magic. He created effects, including "Dean's Box," "Blizzard", and "A New World" (created with magic innovator Michael Weber). He appeared on the cover of the October 2003 edition of The Linking Ring magazine. Dill died on 7 February 2015.

What media outlets did Dean Dill appear in? Please format this as a bulleted list, and include the date in the format "{Media Outlet} ({Date})" on each line

Sure, here are the media outlets Dean Dill appeared in: - The Tonight Show (November 1990) - The Linking Ring (October 2003)

We analyse the incremental effect of the different features in our system, focusing on the low-resource setting in Track 1 and using development data. Encoding the entire context with an LSTM highly increases the variance of the observed results. So we trained fifty models for each language and each architecture. Figure FIGREF23 visualises the means and standard deviations over the trained models. In addition, we visualise the average accuracy for the five best models for each language and architecture, as these are the models we use in the final ensemble prediction. Below we refer to these numbers only. The results indicate that encoding the full context with an LSTM highly enhances the performance of the model, by 11.15% on average. This observation explains the high results we obtain also for Track 2. Adding the auxiliary objective of MSD prediction has a variable effect: for four languages (de, en, es, and sv) the effect is positive, while for the rest it is negative. We consider this to be an issue of insufficient data for the training of the auxiliary component in the low resource setting we are working with. We indeed see results improving drastically with the introduction of multilingual training, with multilingual results being 7.96% higher than monolingual ones on average. We studied the five best models for each language as emerging from the multilingual training (listed in Table TABREF27 ) and found no strong linguistic patterns. The en–sv pairing seems to yield good models for these languages, which could be explained in terms of their common language family and similar morphology. The other natural pairings, however, fr–es, and de–sv, are not so frequent among the best models for these pairs of languages. Finally, monolingual finetuning improves accuracy across the board, as one would expect, by 2.72% on average. The final observation to be made based on this breakdown of results is that the multi-tasking approach paired with multilingual training and subsequent monolingual finetuning outperforms the other architectures for five out of seven languages: de, en, fr, ru and sv. For the other two languages in the dataset, es and fi, the difference between this approach and the approach that emerged as best for them is less than 1%. The overall improvement of the multilingual multi-tasking approach over the baseline is 18.30%. We indeed see results improving drastically with the introduction of multilingual training, with multilingual results being 7.96% higher than monolingual ones on average.

Are multilingual results higher than monolingual ones on average?

Yes.

Deep Learning approaches have achieved impressive results on various NLP tasks BIBREF0 , BIBREF1 , BIBREF2 and have become the de facto approach for any NLP task. However, these deep learning techniques have found to be less effective for low-resource languages when the available training data is very less BIBREF3 . Recently, several approaches like Multi-task learning BIBREF4 , multilingual learning BIBREF5 , semi-supervised learning BIBREF2 , BIBREF6 and transfer learning BIBREF7 , BIBREF3 have been explored by the deep learning community to overcome data sparsity in low-resource languages. Transfer learning trains a model for a parent task and fine-tunes the learned parent model weights (features) for a related child task BIBREF7 , BIBREF8 . This effectively reduces the requirement on training data for the child task as the model would have learned relevant features from the parent task data thereby, improving the performance on the child task. Transfer learning has also been explored in the multilingual Neural Machine Translation BIBREF3 , BIBREF9 , BIBREF10 . The goal is to improve the NMT performance on the source to target language pair (child task) using an assisting source language (assisting to target translation is the parent task). Here, the parent model is trained on the assisting and target language parallel corpus and the trained weights are used to initialize the child model. The child model can now be fine-tuned on the source-target language pairs, if parallel corpus is available. The divergence between the source and the assisting language can adversely impact the benefits obtained from transfer learning. Multiple studies have shown that transfer learning works best when the languages are related BIBREF3 , BIBREF10 , BIBREF9 . Several studies have tried to address lexical divergence between the source and the target languages BIBREF10 , BIBREF11 , BIBREF12 . However, the effect of word order divergence and its mitigation has not been explored. In a practical setting, it is not uncommon to have source and assisting languages with different word order. For instance, it is possible to find parallel corpora between English and some Indian languages, but very little parallel corpora between Indian languages. Hence, it is natural to use English as an assisting language for inter-Indian language translation. To see how word order divergence can be detrimental, let us consider the case of the standard RNN (Bi-LSTM) encoder-attention-decoder architecture BIBREF13 . The encoder generates contextual representations (annotation vectors) for each source word, which are used by the attention network to match the source words to the current decoder state. The contextual representation is word-order dependent. Hence, if the assisting and the source languages do not have similar word order the generated contextual representations will not be consistent. The attention network (and hence the decoder) sees different contextual representations for similar words in parallel sentences across different languages. This makes it difficult to transfer knowledge learned from the assisting language to the source language. We illustrate this by visualizing the contextual representations generated by the encoder of an English to Hindi NMT system for two versions of the English input: (a) original word order (SVO) (b) word order of the source language (SOV, for Bengali). Figure FIGREF1 shows that the encoder representations obtained are very different. The attention network and the decoder now have to work with very different representations. Note that the plot below does not take into account further lexical and other divergences between source and assisting languages, since we demonstrated word order divergence with the same language on the source side. To address this word order divergence, we propose to pre-order the assisting language sentences to match the word order of the source language. We consider an extremely resource constrained scenario, where we do not have any parallel corpus for the child task. We are limited to a bilingual dictionary for transfer information from the assisting to the source language. From our experiments, we show that there is a significant increase in the translation accuracy for the unseen source-target language pair. The weight initialization reduces the requirement on the training data for the source-target language pair by transferring knowledge from the parent task, thereby improving the performance on the child task.

How to improve the performance on the child task?

The weight initialization reduces the requirement on the training data for the source-target language pair by transferring knowledge from the parent task, thereby improving the performance on the child task.

1912.06670

The data presented in this paper was collected and validated via Mozilla's Common Voice initiative. Using either the Common Voice website or iPhone app, contributors record their voice by reading sentences displayed on the screen (see Figure (FIGREF5)). The recordings are later verified by other contributors using a simple voting system. Shown in Figure (FIGREF6), this validation interface has contributors mark $<$audio,transcript$>$ pairs as being either correct (up-vote) or incorrect (down-vote). The data presented in this paper was collected and validated via Mozilla's Common Voice initiative. Using either the Common Voice website or iPhone app, contributors record their voice by reading sentences displayed on the screen (see Figure (FIGREF5)). The recordings are later verified by other contributors using a simple voting system. Shown in Figure (FIGREF6), this validation interface has contributors mark $<$audio,transcript$>$ pairs as being either correct (up-vote) or incorrect (down-vote).

What crowdsourcing platform is used for data collection and data validation?

The answers are shown as follows: * the Common Voice website * iPhone app

1908.06151

The number of layers ($N_{src}$-$N_{mt}$-$N_{pe}$) in all encoders and the decoder for these results is fixed to 6-6-6. In Exp. 5.1, and 5.2 in Table TABREF5, we see the results of changing this setting to 6-6-4 and 6-4-6. This can be compared to the results of Exp. 2.3, since no fine-tuning or ensembling was performed for these three experiments. Exp. 5.1 shows that decreasing the number of layers on the decoder side does not hurt the performance. In fact, in the case of test2016, we got some improvement, while for test2017, the scores got slightly worse. In contrast, reducing the $enc_{src \rightarrow mt}$ encoder block's depth (Exp. 5.2) does indeed reduce the performance for all four scores, showing the importance of this second encoder. FLOAT SELECTED: Table 1: Evaluation results on the WMT APE test set 2016, and test set 2017 for the PBSMT task; (±X) value is the improvement over wmt18smtbest (x4). The last section of the table shows the impact of increasing and decreasing the depth of the encoders and the decoder. Exp. 5.1 shows that decreasing the number of layers on the decoder side does not hurt the performance. In fact, in the case of test2016, we got some improvement, while for test2017, the scores got slightly worse. In contrast, reducing the $enc_{src \rightarrow mt}$ encoder block's depth (Exp. 5.2) does indeed reduce the performance for all four scores, showing the importance of this second encoder. FLOAT SELECTED: Table 1: Evaluation results on the WMT APE test set 2016, and test set 2017 for the PBSMT task; (±X) value is the improvement over wmt18smtbest (x4). The last section of the table shows the impact of increasing and decreasing the depth of the encoders and the decoder.

How much is performance hurt when using too small amount of layers in encoder?

comparing to the results from reducing the number of layers in the decoder, the BLEU score was 69.93 which is less than 1% in case of test2016 and in case of test2017 it was less by 0.2 %. In terms of TER it had higher score by 0.7 in case of test2016 and 0.1 in case of test2017.

What are all the positions in hockey?

In hockey there are six distinct positions for players: center, left wing, right wing, left defense, right defense, and goalkeeper.

how many limbs are in yoga

8

1901.02222

In this paper, we propose the MIMN model for NLI task. Our model introduces a multi-turns inference mechanism to process multi-perspective matching features. Furthermore, the model employs the memory mechanism to carry proceeding inference information. In each turn, the inference is based on the current matching feature and previous memory. Experimental results on SNLI dataset show that the MIMN model is on par with the state-of-the-art models. Moreover, our model achieves new state-of-the-art results on the MPE and the SCITAL datasets. Experimental results prove that the MIMN model can extract important information from multiple premises for the final judgment. And the model is good at handling the relationships of entailment and contradiction. Our model introduces a multi-turns inference mechanism to process multi-perspective matching features.

Which matching features do they employ?

Matching features from matching sentences from various perspectives.

Before joining the Football League, Arsenal played briefly on Plumstead Common, then at the Manor Ground in Plumstead, then spent three years between 1890 and 1893 at the nearby Invicta Ground. Upon joining the Football League in 1893, the club returned to the Manor Ground and installed stands and terracing, upgrading it from just a field. Arsenal continued to play their home games there for the next twenty years (with two exceptions in the 1894–95 season), until the move to north London in 1913. Widely referred to as Highbury, Arsenal Stadium was the club's home from September 1913 until May 2006. The original stadium was designed by the renowned football architect Archibald Leitch, and had a design common to many football grounds in the UK at the time, with a single covered stand and three open-air banks of terracing. The entire stadium was given a massive overhaul in the 1930s: new Art Deco West and East stands were constructed, opening in 1932 and 1936 respectively, and a roof was added to the North Bank terrace, which was bombed during the Second World War and not restored until 1954. Highbury could hold more than 60,000 spectators at its peak, and had a capacity of 57,000 until the early 1990s. The Taylor Report and Premier League regulations obliged Arsenal to convert Highbury to an all-seater stadium in time for the 1993–94 season, thus reducing the capacity to 38,419 seated spectators. This capacity had to be reduced further during Champions League matches to accommodate additional advertising boards, so much so that for two seasons, from 1998 to 2000, Arsenal played Champions League home matches at Wembley, which could house more than 70,000 spectators. Expansion of Highbury was restricted because the East Stand had been designated as a Grade II listed building and the other three stands were close to residential properties. These limitations prevented the club from maximising matchday revenue during the 1990s and first decade of the 21st century, putting them in danger of being left behind in the football boom of that time. After considering various options, in 2000 Arsenal proposed building a new 60,361-capacity stadium at Ashburton Grove, since named the Emirates Stadium, about 500 metres south-west of Highbury. The project was initially delayed by red tape and rising costs, and construction was completed in July 2006, in time for the start of the 2006–07 season. The stadium was named after its sponsors, the airline company Emirates, with whom the club signed the largest sponsorship deal in English football history, worth around £100 million. Some fans referred to the ground as Ashburton Grove, or the Grove, as they did not agree with corporate sponsorship of stadium names. The stadium will be officially known as Emirates Stadium until at least 2028, and the airline will be the club's shirt sponsor until at least 2024. From the start of the 2010–11 season on, the stands of the stadium have been officially known as North Bank, East Stand, West Stand and Clock end. The capacity of the Emirates now stands at 60,704.

Give me a json array containing Arsenal stadiums and the year that they moved in based on the passage provided.

{ "Stadiums": [ { "name": "Plumstead Common" }, { "name": "Manor Ground" }, { "name": "Invicta Ground", "year": "1890" }, { "name": "Manor Ground", "year": "1893" }, { "name": "Arsenal Stadium", "year": "1913" }, { "name": "Emirates Stadium", "year": "2006" } ] }

1804.06506

In order to address the aforementioned problems we redesign the neural decoder in three different scenarios. In the first scenario we equip the decoder with an additional morphology table including target-side affixes. We place an attention module on top of the table which is controlled by the decoder. At each step, as the decoder samples a character, it searches the table to find the most relevant information which can enrich its state. Signals sent from the table can be interpreted as additional constraints. In the second scenario we share the decoder between two output channels. The first one samples the target character and the other one predicts the morphological annotation of the character. This multi-tasking approach forces the decoder to send morphology-aware information to the final layer which results in better predictions. In the third scenario we combine these two models. Section "Proposed Architecture" provides more details on our models. In the first scenario we equip the decoder with an additional morphology table including target-side affixes.

What type of morphological information is contained in the "morphology table"?

The answers are shown as follows: * target-side affixes

Which of the following are cities in Australia: Sydney, Melbourne, Adelaide, Perth, Canberra, Rio de Janeiro, Beijing, Shanghai, New York, Singapore, Germany, United States, Tokyo

Cities in Australia include Sydney, Melbourne, Adelaide, Perth, Canberra. However Rio de Janeiro is not in Australia, but in Brazil. Beijing, Shanghai are not in Australia as well, there are in China. Singapore, Germany, United States are not cities, they are countries. Even Singapore is both a country and its only city. New York is in United States. And Tokyo is in Japan.

What are some countries I could go backpacking in if I want to see the Alps?

Switzerland, France, Italy, Slovenia, Austria, Germany

On the morning of 6 December 1917, the French cargo ship SS Mont-Blanc collided with the Norwegian vessel SS Imo in the waters of Halifax, Nova Scotia, Canada. The Mont-Blanc, laden with high explosives, caught fire and exploded, devastating the Richmond district of Halifax. At least 1,782 people were killed, largely in Halifax and Dartmouth, by the blast, debris, fires, or collapsed buildings, and an estimated 9,000 others were injured. The blast was the largest human-made explosion at the time. It released the equivalent energy of roughly 2.9 kilotons of TNT (12 TJ). Mont-Blanc was under orders from the French government to carry her cargo from New York City via Halifax to Bordeaux, France. At roughly 8:45 am, she collided at low speed, approximately one knot (1.2 mph or 1.9 km/h), with the unladen Imo, chartered by the Commission for Relief in Belgium to pick up a cargo of relief supplies in New York. On the Mont-Blanc, the impact damaged benzol barrels stored on deck, leaking vapours which were ignited by sparks from the collision, setting off a fire on board that quickly grew out of control. Approximately 20 minutes later at 9:04:35 am, the Mont-Blanc exploded.

What role did barrels of benzol play in the explosion of the Mont-Blanc?

The impact of the SS Mont-Blanc and the SS Imo damaged barrels of benzol which were stored on the deck of the Mont-Blanc. The damaged barrels leaked vapours which were ignited by sparks which set off a fire.

Tell me five ways to build relationships with my neighbors.

There are several ways to build relationships and connections with your neighbors. Before proactively taking action to grow the relationship it is essential you first ensure you uphold exceptional neighborly behavior. For example, ensuring loud noises like vacuuming is done during day time and making an effort to upkeep your outdoor space to uplift the shared spaces of your homes. A thoughtful and polite neighbor is more likely to befriend those living around them. There are some ways to build relationships. First, ensure you greet your neighbor with a friendly gesture, such as a wave or a brief verbal greeting, every time you see them. Second, keep a watchful eye for the opportunity to do something friendly for your neighbor. For example, offer to help them if you see them attempting to carry a heavy item. Third, identify a proactive, planned gesture such as offering them a baked sweet around a holiday. Fourth, seek the opportunity to ask them about something they care about. For example, inquire how their work is going or a recent family event they may have mentioned. Fifth, invite them to your home for a meal. There are many ways to build a neighborly relationship and through a combination of these five suggestions, it is likely you will be off to a great start!

The series was launched in 1992 with Super Mario Kart on the Super Nintendo Entertainment System, to critical and commercial success. The Mario Kart series totals fourteen games, with six on home consoles, three on handheld consoles, four arcade games co-developed with Namco, and one for mobile phones. The latest game in the series, Mario Kart Live: Home Circuit, was released on the Nintendo Switch in October 2020. Over 164.43 million copies in the series have been sold worldwide.

When was Super Mario Kart launched?

Super Mario Kart was launched in 1992 on the Super Nintendo Entertainment System

Qualifying For much of the sport's history, qualifying sessions differed little from practice sessions; drivers would have one or more sessions in which to set their fastest time, with the grid order determined by each driver's best single lap, with the fastest getting first place on the grid, referred to as pole position. From 1996 to 2002, the format was a one-hour shootout. This approach lasted until the end of 2002 before the rules were changed again because the teams were not running in the early part of the session to take advantage of better track conditions later on. Grids were generally limited to 26 cars – if the race had more entries, qualification would also decide which drivers would start the race. During the early 1990s, the number of entries was so high that the worst-performing teams had to enter a pre-qualifying session, with the fastest cars allowed through to the main qualifying session. The qualifying format began to change in the early 2000s, with the FIA experimenting with limiting the number of laps, determining the aggregate time over two sessions, and allowing each driver only one qualifying lap. The current qualifying system was adopted in the 2006 season. Known as "knock-out" qualifying, it is split into three periods, known as Q1, Q2, and Q3. In each period, drivers run qualifying laps to attempt to advance to the next period, with the slowest drivers being "knocked out" of qualification (but not necessarily the race) at the end of the period and their grid positions set within the rearmost five based on their best lap times. Drivers are allowed as many laps as they wish within each period. After each period, all times are reset, and only a driver's fastest lap in that period (barring infractions) counts. Any timed lap started before the end of that period may be completed, and will count toward that driver's placement. The number of cars eliminated in each period is dependent on the total number of cars entered into the championship. Currently, with 20 cars, Q1 runs for 18 minutes, and eliminates the slowest five drivers. During this period, any driver whose best lap takes longer than 107% of the fastest time in Q1 will not be allowed to start the race without permission from the stewards. Otherwise, all drivers proceed to the race albeit in the worst starting positions. This rule does not affect drivers in Q2 or Q3. In Q2, the 15 remaining drivers have 15 minutes to set one of the ten fastest times and proceed to the next period. Finally, Q3 lasts 12 minutes and sees the remaining ten drivers decide the first ten grid positions. At the beginning of the 2016 Formula 1 season, the FIA introduced a new qualifying format, whereby drivers were knocked out every 90 seconds after a certain amount of time had passed in each session. The aim was to mix up grid positions for the race, but due to unpopularity, the FIA reverted to the above qualifying format for the Chinese GP, after running the format for only two races. Each car is allocated one set of the softest tyres for use in Q3. The cars that qualify for Q3 must return them after Q3; the cars that do not qualify for Q3 can use them during the race. As of 2022, all drivers are given a free choice of tyre to use at the start of the Grand Prix, whereas in previous years only the drivers that did not participate in Q3 had free tyre choice for the start of the race. Any penalties that affect grid position are applied at the end of qualifying. Grid penalties can be applied for driving infractions in the previous or current Grand Prix, or for changing a gearbox or engine component. If a car fails scrutineering, the driver will be excluded from qualifying but will be allowed to start the race from the back of the grid at the race steward's discretion. 2021 has seen the trialling of a 'sprint qualifying' race on the Saturday of three race weekends, with the intention of testing the new approach to qualifying.

Please explain about formula1 qualifying.

Qualifying split into three period, known as Q1, Q2, and Q3. Q1 - runs 20 cars for 18min. slowest five drivers are eliminated Q2- the remaining 15 drivers have 15 minutes. another five slowest drivers are eliminated Q3 - remaining 10 drivers decide the first 10 grid positions for the race

The dataset consists of 66,723 sentences with 2,157,048 tokens (incl. punctuation), see Table . The sizes of the seven court-specific datasets varies between 5,858 and 12,791 sentences, and 177,835 to 404,041 tokens. The distribution of annotations on a per-token basis corresponds to approx. 19–23 %. The Federal Patent Court (BPatG) dataset contains the lowest number of annotated entities (10.41 %). The dataset includes two different versions of annotations, one with a set of 19 fine-grained semantic classes and another one with a set of 7 coarse-grained classes (Table ). There are 53,632 annotated entities in total, the majority of which (74.34 %) are legal entities, the others are person, location and organization (25.66 %). Overall, the most frequent entities are law GS (34.53 %) and court decision RS (23.46 %). The other legal classes (ordinance VO, European legal norm EUN, regulation VS, contract VT, and legal literature LIT) are much less frequent (1–6 % each). Even less frequent (less than 1 %) are lawyer AN, street STR, landscape LDS, and brand MRK. The classes person, lawyer and company are heavily affected by the anonymisation process (80 %, 95 % and 70 % respectively). More than half of city and street, about 55 %, have also been modified. Landscape and organization are affected as well, with 40 % and 15 % of the occurrences edited accordingly. However, anonymisation is typically not applied to judge, country, institution and court (1–5 %). The dataset was originally annotated by the first author. To evaluate and potentially improve the quality of the annotations, part of the dataset was annotated by a second linguist (using the annotation guidelines specifically prepared for its construction). We selected a small part that could be annotated in approx. two weeks. For the sentence extraction we paid special attention to the anonymised mentions of person, location or organization entities, because these are usually explained at their first mention. The resulting sample consisted of 2005 sentences with a broad variety of different entities (3 % of all sentences from each federal court). The agreement between the two annotators was measured using Kappa on a token basis. All class labels were taken into account in accordance with the IOB2 scheme BIBREF18. The inter-annotator agreement is 0.89, i. e., there is mostly very good agreement between the two annotators. Differences were in the identification of court decision and legal literature. Some unusual references of court decision (consisting only of decision type, court, date, file number) were not annotated such as `Urteil des Landgerichts Darmstadt vom 16. April 2014 – 7 S 8/13 –'. Apart from missing legal literature annotations, author names and law designations were annotated according to their categories (i. e., `Schoch, in: Schoch/Schneider/Bier, VwGO § 123 Rn. 35', `Bekanntmachung des BMG gemäß §§ 295 und 301 SGB V zur Anwendung des OPS vom 21.10.2010'). The second annotator had difficulties annotating the class law, not all instances were identified (`§ 272 Abs. 1a und 1b HGB', `§ 3c Abs. 2 Satz 1 EStG'), others only partially (`§ 716 in Verbindung mit' in `§ 716 in Verbindung mit §§ 321 , 711 ZPO'). Some titles of contract were not recognised and annotated (`BAT', `TV-L', `TVÜ-Länder' etc.). This evaluation has revealed deficiencies in the annotation guidelines, especially regarding court decision and legal literature as well as non-entities. It would also be helpful for the identification and classification to list well-known sources of law, court decision, legal literature etc. The dataset was originally annotated by the first author. To evaluate and potentially improve the quality of the annotations, part of the dataset was annotated by a second linguist (using the annotation guidelines specifically prepared for its construction).

Who has annotated the dataset?

The dataset was originally annotated by the first author and part of the dataset was annotated by a second linguist.

Andhra Pradesh comprises three major regions namely Rayalaseema in the south-west, Coastal Andhra bordering the Bay of Bengal in the east and Uttarandhra at north-east. The state has 26 districts, 6 in Uttarandhra, 12 in Coastal Andhra and 8 in Rayalaseema. The state also borders a union territory, Yanam – a district of Puducherry, which lies to the south of Kakinada in the Godavari delta on the eastern side of the state. The economy of Andhra Pradesh is the 8th largest in India, with a gross state domestic product (GSDP) of ₹8.84 trillion (US$110 billion) and has the country's 17th-highest GSDP per capita of ₹170,000 (US$2,100). Andhra Pradesh ranks 27th among Indian states in Human Development Index (HDI). It has a jurisdiction over almost 15,000 square kilometres (5,800 sq mi) of territorial waters.

From the passage provided, extract the GSDP of Andhra Pradesh

GSDP of Andhra Pradesh is US$110 billion

Word embeddings are fixed-length vector representations for words BIBREF0 , BIBREF1 . In recent years, the morphology of words is drawing more and more attention BIBREF2 , especially for Chinese whose writing system is based on logograms. UTF8gbsn With the gradual exploration of the semantic features of Chinese, scholars have found that not only words and characters are important semantic carriers, but also stroke feature of Chinese characters is crucial for inferring semantics BIBREF3 . Actually, a Chinese word usually consists of several characters, and each character can be further decomposed into a stroke sequence which is certain and changeless, and this kind of stroke sequence is very similar to the construction of English words. In Chinese, a particular sequence of strokes can reflect the inherent semantics. As shown in the upper half of Figure FIGREF3 , the Chinese character “驾" (drive) can be decomposed into a sequence of eight strokes, where the last three strokes together correspond to a root character “马" (horse) similar to the root “clar" of English word “declare" and “clarify". Moreover, Chinese is a language originated from Oracle Bone Inscriptions (a kind of hieroglyphics). Its character glyphs have a spatial structure similar to graphs which can convey abundant semantics BIBREF4 . Additionally, the critical reason why Chinese characters are so rich in morphological information is that they are composed of basic strokes in a 2-D spatial order. However, different spatial configurations of strokes may lead to different semantics. As shown in the lower half of Figure 1, three Chinese characters “入" (enter), “八" (eight) and “人" (man) share exactly a common stroke sequence, but they have completely different semantics because of their different spatial configurations. In addition, some biological investigations have confirmed that there are actually two processing channels for Chinese language. Specifically, Chinese readers not only activate the left brain which is a dominant hemisphere in processing alphabetic languages BIBREF5 , BIBREF6 , BIBREF7 , but also activate the areas of the right brain that are responsible for image processing and spatial information at the same time BIBREF8 . Therefore, we argue that the morphological information of characters in Chinese consists of two parts, i.e., the sequential information hidden in root-like strokes order, and the spatial information hidden in graph-like character glyphs. Along this line, we propose a novel Dual-channel Word Embedding (DWE) model for Chinese to realize the joint learning of sequential and spatial information in characters. Finally, we evaluate DWE on two representative tasks, where the experimental results exactly validate the superiority of DWE in capturing the morphological information of Chinese. Finally, we evaluate DWE on two representative tasks, where the experimental results exactly validate the superiority of DWE in capturing the morphological information of Chinese.

Is there superiority of DWE in capturing the morphological information of Chinese?

Yes, there is.

What is genomic medicine?

Next-generation genomic technologies allow clinicians and biomedical researchers to drastically increase the amount of genomic data collected on large study populations.[When combined with new informatics approaches that integrate many kinds of data with genomic data in disease research, this allows researchers to better understand the genetic bases of drug response and disease. Early efforts to apply the genome to medicine included those by a Stanford team led by Euan Ashley who developed the first tools for the medical interpretation of a human genome. The Genomes2People research program at Brigham and Women’s Hospital, Broad Institute and Harvard Medical School was established in 2012 to conduct empirical research in translating genomics into health. Brigham and Women's Hospital opened a Preventive Genomics Clinic in August 2019, with Massachusetts General Hospital following a month later. The All of Us research program aims to collect genome sequence data from 1 million participants to become a critical component of the precision medicine research platform.

What is a "frunk" in an electric vehicle?

The frunk is the front trunk, which in an electric vehicle is typically used as additional storage space.

What is the current world population? Is it growing?

In 2022 the world's population hit eight billion. But the number of people on earth will likely top out during this century. Population growth has been steady over past few decades, with billion person marks coming every dozen years or so. But the pattern is changing. Growth is beginning to slow and experts predict worlds population will top out sometime in 2080's at about 11 billion.

Isthmohyla debilis is a rare species of frog in the family Hylidae. It occurs in the Atlantic slopes of the Cordillera Central and Cordillera Talamanca in Costa Rica and western Panama as well as on the Pacific slopes in southwestern Panama. Common name Isla Bonita treefrog has been suggested for it (the type locality is "Isla Bonita" in Costa Rica).

Given the reference text below, what is Isthmohyla debilis?

Isthmohyla debilis is a rare species of Frog that is found in Costa Rica and Panama.

Tell me about the Cincinnati Bengals

The Cincinnati Bengals are in the National Football League (NFL) , and as of 2023, are one of the best teams in the league. They've made the Super Bowl and the AFC title game in the last 2 years, respectively. The team is led by the best quarterback in the NFL, Joe Burrow, former #1 overall NFL draft pick and Heisman Trophy winner from Louisiana State University. The Bengals are expected to win multiple Super Bowls as long as Joe Burrow is the quarterback.

Which actor is common to Magnificent 7 and Dirty Dozen

Charles Bronson

Ayahuasca[note 1] is a South American psychoactive and entheogenic brewed drink traditionally used both socially and as a ceremonial or shamanic spiritual medicine among the indigenous peoples of the Amazon basin, and more recently in North America and Europe. The tea causes altered states of consciousness often known as "psychedelic experiences" which include visual hallucinations and altered perceptions of reality.

What types of ritual use Ayahuasca?

Ayahuasca is used both socially and as a ceremonial or shamanic spiritual medicine

When did Singapore achieve independence?

Singapore first achieved independence from British rule on 16 September 1963, through a merger with Malaya, North Borneo and Sarawak. However, due to racial tensions, Singapore was abruptly expelled from this union on 9 August 1965. This decision was made through a 126-0 vote for a constitutional amendment at the Parliament of Malaysia. Lee Kuan Yew, the first prime minister of Singapore, tearfully announced that Singapore had become an independent and sovereign state.

What are the steps to care for a houseplant?

First, be sure to provide water, sunlight and humidity levels necessary for the variety of plant you have. For example, succulent-type plants may require less frequent waterings than their green-leafy counterparts. Research your new plants' preferred sunlight, water, and humidity requirements so you can provide the best care. Second, be sure to review your newly acquired houseplant for any pests before introducing them into your home. You’ll want to check the leaves for mealy bugs, spider mites, etc. and the soil for pests such as fungus gnats. If you find any of these be sure to quarantine your new plant away from your current plants and treat with a pesticide of your choosing. Many houseplant owners use neem oil to treat pests. Third, upsize or repot your plant only when it is ready. Your new plant likely will be happy for a while in its current nursery (plastic) container that it came home from the shop in. You can choose to upsize the pot once you notice signs of the plant being root bound (the roots take up a majority of the pot) such as water draining from the pot quickly and the soil not becoming saturated or the plant slows its growth of new leaves (unless its the winter season at which time plants naturally slow or stop new growth). Spring time is the best time to repot your plants.

Give me a list of amazing noise cancelling headphones in 2023

1. Sony WH-1000XM5 - Amazing for great music and also for taking office video calls 2. Apple AirPods Max - Ideal for apple fanatics 3. Sennheiser Momentum 4 Wireless - Great brand and product 4. Sony WH-1000XM4 - yes previous version are equally good and much more affordable on your pocket. 5. Bowers & Wilkins PX7 S2 6. Bose QuietComfort 45 - State of the art noise cancellation 7. Anker SoundCore Space Q45 - Great price and quality.

As a leader, how do you build an environment of trust?

In order to build an environment of trust, a leader should should take a bottom-up approach to identify and empathize on efforts being taken by the team in the ground, and be able to relate to it. One of the main pillars of strong leadership is built on trust, which can flourish over a period of time by nurturing the team, keeping faith on them and holding their hands during difficult times.

If you were given 1 million dollars in your fidelity account provide a list of which stocks you would buy

1. Apple 2. Google 3. Microsoft 4. Amazon 5. NVIDIA 6. Netflix 7. PayPal 8. Disney 9. Meta 10. Tesla

Polly, a 51-year-old female, visits your fitness facility. She is dissatisfied with her body and is responding to a series of ads your facility uses to attract people interested in weight loss. Can we think of ways she can become more accepting of her body?

Instead of jumping to intense workout regimens or strict diets, we can coax her to focus more on what her body can accomplish physically versus just how it looks. We wish to encourage her to accept her body and cherish her progress in her athletic endeavors, e.g. if she is consistently performing resistance exercises, remind her that her body can now lift much more weight than when she first started. She can focus more on that, instead of purely a number on a scale. Moreover, we can also dig into where her body insecurities came from, and take steps so that she can work through this trauma. Suggestions include renaming a specific disliked body part, focusing on the great parts of her own body, and appreciating the inherent strength and functionality of her body.

What are ways to calm myself down when I get upset?

If you become upset or agitated, a great way to calm down is take a deep breath, like a “dragon breath.” Breathe in as deeply as you can and then breathe out slowly. Repeat this several times. You can also lay on the floor for several minutes. Laying on the floor can be particularly grounding. Another way to relax when you get upset is to go outside and look at the sky or a body of water if you can get to one. Look for a flying bird, or focus on a small flower or piece of grass. A long walk can also help calm you down.

The one-hidden layer convolutional neural network with a binary activation function is realized as where σ(•) is the step function, Z ∈ R m×n is constructed by deividing the input data, e.g. an image, into m patches with size n, and w ∈ R n and v ∈ R m are the trainable weights, i.e. kernels, in the first and second layers, respectively). We assume that Z is generated by a continuous distribution P (Z). Due to the property of the step function, the network is invariant under the scale transformation for w, w → kw (k > 0). Using the loss for each sample, (y(v, w, Z); y * ) with y * being the labels generated by the true distribution q(y * |Z), we can express the population loss function as L(v, w; y * ) = E Z [ (y(v, w, Z); y * )] . (3) σ(x) = 1 x > 0, 0 x ≤ 0,

What is the defination of the σ?

Thank you very much for the comment. To make the definition of σ clear, we have added Eq.(2) in Sec.2.1.

How does a transistor amplify a signal?

A transistor is a constructed from materials that behave as semiconductors--which is to say that these materials conduct electrical current but only when certain conditions are met. By combining these materials into a device called a transistor we are able to create an electronic component that will only conduct electricity when a small electric current is applied to the so-called Base pin of the transistor. One obvious use of such behavior is to use the transistor as a switch--by applying or removing current to the base pin we are able to control whether the transistor conducts electricity via its collector and emitter pins. When no current is present at the Base pin the transistor will block electrical transmission through its emitter and collector pins. Generally speaking, the electrical current required to control the transistor at the base pin is very small. Much larger currents can be transferred through the device through the collector and emitter pins. Thus, in effect, a transistor uses a very small input current to control the flow of a much larger current. An audio signal, for example, is transmitted as a very low power electrical current--in it's original form it is too small to be able to drive a loudspeaker to make sounds that are audible to the human ear. To amplify this signal so that is usable for audio playback we can apply the audio signal to a transistor's base pin--this will allow a much larger current to flow between the emitter and collector pins and through the transistor. Since the flow of this large current will mirror the much smaller current of the audio signal we will have effectively created a much stronger version of the original input signal.

Which NBA team has the longest playoff drought?

The Sacramento Kings have the longest playoff drought in the NBA. The Sacramento Kings have a 16-year playoff drought and the team last appeared in the post-season in 2006. In that series, the San Antonio Spurs eliminated the Sacramento Kings in the first round after winning the series 4-2.

Belgrandia varica is a species of minute freshwater snail with an operculum, an aquatic gastropod mollusc or micromollusc in the family Hydrobiidae.

Does Belgrandia varica live in the ocean?

No, Belgrandia varica lives in freshwater.

List all the persons in the text in alphabetical order using their surname: At acute times where there is a lot of pressure crossing the Channel, whether that’s on tunnel or ferries, then I think there’s always going to be a backup. I just urge everybody to be a bit patient while the ferry companies work their way through the backlog.” Later, speaking on the Laura Kuenssberg programme on BBC One, Braverman denied the situation at Dover would repeat itself and blamed “bad weather”. But her comments variously attracted ire and ridicule. The Liberal Democrats’ home affairs spokesperson, Alistair Carmichael, said: “These comments show Suella Braverman is in complete denial about the impact of the Conservative government’s botched deal with Europe on our borders. For Conservative ministers like Braverman, it is always someone else’s fault. “Businesses and travellers are being tied up in reams of red tape but ministers are refusing to lift a finger. It shows the Conservative party is out of touch, out of excuses and should be out of power.” The former Conservative cabinet minister David Gauke, who was stripped of the Tory whip for rebelling in a Brexit vote, tweeted: “If we were in the EU, the French would not need to do individual passport checks. If no individual passport checks, the process at Dover would be quicker. The Dover queues are, therefore, partly caused by Brexit. Not a contentious point, surely?” Caroline Lucas, the Green party MP, described Braverman as being “on another planet as usual”. She said that the home secretary’s comments regarding no Brexit link contradicted those expressed by the port’s chief executive. In an interview with the Observer a year ago, Doug Bannister, admitted Brexit was causing longer processing times at the border.

- Doug Bannister - Suella Braverman - Alistair Carmichael - David Gauke - Laura Kuenssberg - Caroline Lucas

Which characters belong to DC or Marvel Universe? Catwoman, Black Cat

Catwoman is DC, Black Cat is Marvel

1705.02394

Within this work, we particularly target emotional valence as the primary task, as it has been shown to be the most challenging emotional dimension for acoustic analyses in a number of studies BIBREF10 , BIBREF11 . Apart from solely targeting valence classification, we further investigate the principle of multitask learning. In multitask learning, a set of related tasks are learned (e.g., emotional activation), along with a primary task (e.g., emotional valence); both tasks share parts of the network topology and are hence jointly trained, as depicted in Figure FIGREF4 . It is expected that data for the secondary task models information, which would also be discriminative in learning the primary task. In fact, this approach has been shown to improve generalizability across corpora BIBREF12 . In multitask learning, a set of related tasks are learned (e.g., emotional activation), along with a primary task (e.g., emotional valence); both tasks share parts of the network topology and are hence jointly trained, as depicted in Figure FIGREF4 .

What are the tasks in the multitask learning setup?

The answers are shown as follows: * set of related tasks are learned (e.g., emotional activation) * primary task (e.g., emotional valence)

What format does Delta Lake use to store data?

Delta Lake uses versioned Parquet files to store your data in your cloud storage. Apart from the versions, Delta Lake also stores a transaction log to keep track of all the commits made to the table or blob store directory to provide ACID transactions.

Lauren is a 32-year-old women who weighs 166 lbs and has never participated in resistance training. She participated in an initial fitness assessment and wants your advice about starting a muscular fitness program at your exercise facility. Design a resistance training routine for Lauren to help her achieve moderate gains in both muscular strength and endurance.

First, we design the resistance training routine according to the FITT principle: she should be exercising at least 2 non-consecutive days per week, 1-3 sets per exercise, 8-10 exercises, resting 30 seconds to 2 minutes between exercises, and targeting large muscle groups with her exercises. When she first starts, we’re going to begin with more weight training machines versus free weights, as she’s a beginner, and free weights may be more dangerous. We’ll prescribe machines such as a horizontal chest press, lat pull-down, leg press. As she progresses with her experience lifting weights, we will prescribe more and more free weight exercises under the careful guidance of a spotter, such as squat, deadlift, bench press, overhead press, barbell rows, etc. To achieve gains in both muscular strength and endurance, we’ll suggest alternating days targeted for hypertrophy (1-2 sets, 10 exercises, low resting times e.g. 30 seconds, high number of repetitions, and lower weight) versus muscular strength (1-4 sets, 8 exercises, longer resting times e.g. 2 minutes, lower reps and high weight).

Brazil is the world's largest exporter of chicken meat: 3.77 million tonnes in 2019. The country is the holder of the second largest herd of cattle in the world, 22.2% of the world herd. The country was the second largest producer of beef in 2019, responsible for 15.4% of global production. It was also the 3rd largest world producer of milk in 2018. This year, the country produced 35.1 billion liters. In 2019, Brazil was the 4th largest pork producer in the world, with almost 4 million tonnes.

From the passage provided, extract the total amount of milk produced by Brazil in 2018

Brazil produced 35.1 billion litres of milk in 2018

What 5-letter word spelt in all capital letters can be read the exact same upside down?

SWIMS

1902.09087

Word Lattice As shown in Figure FIGREF4 , a word lattice is a directed graph INLINEFORM0 , where INLINEFORM1 represents a node set and INLINEFORM2 represents a edge set. For a sentence in Chinese, which is a sequence of Chinese characters INLINEFORM3 , all of its possible substrings that can be considered as words are treated as vertexes, i.e. INLINEFORM4 . Then, all neighbor words are connected by directed edges according to their positions in the original sentence, i.e. INLINEFORM5 . Word Lattice As shown in Figure FIGREF4 , a word lattice is a directed graph INLINEFORM0 , where INLINEFORM1 represents a node set and INLINEFORM2 represents a edge set. For a sentence in Chinese, which is a sequence of Chinese characters INLINEFORM3 , all of its possible substrings that can be considered as words are treated as vertexes, i.e. INLINEFORM4 . Then, all neighbor words are connected by directed edges according to their positions in the original sentence, i.e. INLINEFORM5 .

How do they obtain word lattices from words?

By considering words as vertices and generating directed edges between neighboring words within a sentence

Kingdom Hearts is a fantasy action role-playing game franchise created by Japanese game designers Tetsuya Nomura and Shinji Hashimoto, being developed and published by Square Enix (originally by Square). It is a collaboration between Square Enix and The Walt Disney Company and is under the leadership of Nomura, a longtime Square Enix employee.

Based on this questio about Kingdom Hearts, what company published it?

Square Enix

Trolls World Tour: Original Motion Picture Soundtrack is the soundtrack album to the 2020 DreamWorks Animation film Trolls World Tour, released by RCA Records on March 13, 2020. The soundtrack is produced primarily by singer-songwriter Justin Timberlake. The singles "The Other Side" by SZA and Timberlake and "Don't Slack" by Anderson .Paak and Timberlake were released prior to the album. Background As well as reprising his voice role as Branch in the sequel, Justin Timberlake also served as executive producer for its soundtrack, as he did on the original film's soundtrack, released in 2016. He revealed a handwritten list of the tracks on the soundtrack on his social media on February 13, also tagging the major artists featured on it. Following the plot of the film, in which the Trolls from the first film discover that Trolls around the world are divided by six different types of music (pop, funk, classical, techno, country, and rock), the soundtrack features songs in those genres. Track listing No. Title Writer(s) Producer(s) Length 1. "The Other Side" (SZA and Justin Timberlake) Solána RoweSarah AaronsJustin TimberlakeLudwig GöranssonMax Martin TimberlakeGöransson 3:08 2. "Trolls Wanna Have Good Times" (Anna Kendrick, Justin Timberlake, James Corden, Ester Dean, Icona Pop, Kenan Thompson and The Pop Trolls) ThompsonBernard EdwardsChristopher HartzDmitry BrillHerbie HancockLady Miss KierGöranssonNile RodgersQ-TipRobert HazardTowa Tei Göransson 3:25 3. "Don't Slack" (Anderson .Paak and Justin Timberlake) TimberlakeBrandon AndersonGöransson TimberlakeAnderson .PaakGöransson 2:54 4. "It's All Love" (Anderson .Paak, Justin Timberlake, Mary J. Blige and George Clinton) AndersonJames FauntleroyJoseph ShirleyGöransson ShirleyGöransson 3:35 5. "Just Sing (Trolls World Tour)" (Justin Timberlake, Anna Kendrick, Kelly Clarkson, Mary J. Blige, Anderson .Paak and Kenan Thompson) TimberlakeAaronsGöranssonMartin TimberlakeGöransson 3:34 6. "One More Time" (Anthony Ramos) Thomas BangalterGuy-Manuel de Homem-ChristoAnthony Moore Göransson 2:42 7. "Atomic Dog World Tour Remix" (George Clinton and Parliament-Funkadelic, Anderson .Paak and Mary J. Blige) ClintonDavid SpradleyGarry ShiderAnderson ClintonShirleyGöransson 4:17 8. "Rainbows, Unicorns, Everything Nice" (Walt Dohrn and Joseph Shirley) Aidan Jensen Göransson 0:12 9. "Rock N Roll Rules" (Haim and Ludwig Göransson) Alana HaimDanielle HaimEste HaimGöransson Göransson 3:10 10. "Leaving Lonesome Flats" (Dierks Bentley) Chris StapletonTimberlake TimberlakeGöransson 3:10 11. "Born to Die" (Kelly Clarkson) StapletonTimberlake TimberlakeGöransson 3:26 12. "Trolls 2 Many Hits Mashup" (Anna Kendrick, Justin Timberlake, James Corden, Icona Pop and The Pop Trolls) Anslem DouglasArmando PerezDonnie WahlbergDan HartmanEmma BuntonYoo Gun-hyungPark Jai-sangDavid ListenbeeMark WahlbergMatthew RoweMelanie BrownMelanie ChrisholmPeter SchroederBiff StannardSandy WilhelmStefan GordySkyler GordyFaheem Najm Göransson 1:01 13. "Barracuda" (Rachel Bloom) Ann WilsonMichael DerosierNancy WilsonRoger Fisher Göransson 4:06 14. "Yodel Beat" (Ludwig Göransson) Göransson Göransson 2:50 15. "Crazy Train" (Rachel Bloom) Ozzy OsbourneRandy RhoadsBob Daisley Göransson 3:15 16. "I Fall to Pieces" (Sam Rockwell) Hank CochranHarlan Howard Göransson 2:14 17. "Perfect for Me" (Justin Timberlake) TimberlakeKenyon DixonGöransson TimberlakeGöransson 3:47 18. "Rock You Like a Hurricane" (Bloom) Herman RarebellKlaus MeineRudolf Schenker Göransson 3:05 19. "It's All Love (History of Funk)" (George Clinton, Mary J. Blige, Anderson .Paak) AndersonFauntleroyShirleyGöransson ShirleyGöransson 2:10 20. "Just Sing (Trolls World Tour)" (Justin Timberlake, Anna Kendrick, James Corden, Kelly Clarkson, George Clinton, Mary J. Blige, Anderson .Paak, Rachel Bloom, Kenan Thompson, Anthony Ramos, Red Velvet, Icona Pop and Sam Rockwell) S. RoweTimberlakeAaronsGöranssonMartin TimberlakeGöransson 4:00 Total length: 60:00

Give me a bulleted list of all artists that performers on the Trolls World Tour Soundtrack.

• (SZA and Justin Timberlake) • (Anna Kendrick, Justin Timberlake, James Corden, Ester Dean, Icona Pop, Kenan Thompson and The Pop Trolls) • (Anderson .Paak and Justin Timberlake) • (Anderson .Paak, Justin Timberlake, Mary J. Blige and George Clinton) • (Trolls World Tour)" (Justin Timberlake, Anna Kendrick, Kelly Clarkson, Mary J. Blige, Anderson .Paak and Kenan Thompson) • (Anthony Ramos) • (George Clinton and Parliament-Funkadelic, Anderson .Paak and Mary J. Blige) • (Walt Dohrn and Joseph Shirley) • (Haim and Ludwig Göransson) • (Dierks Bentley) • (Kelly Clarkson) • (Anna Kendrick, Justin Timberlake, James Corden, Icona Pop and The Pop Trolls) • (Rachel Bloom) • (Ludwig Göransson) • (Rachel Bloom) • (Sam Rockwell) • (Justin Timberlake) • (Bloom) • (George Clinton, Mary J. Blige, Anderson .Paak) • (Justin Timberlake, Anna Kendrick, James Corden, Kelly Clarkson, George Clinton, Mary J. Blige, Anderson .Paak, Rachel Bloom, Kenan Thompson, Anthony Ramos, Red Velvet, Icona Pop and Sam Rockwell)

1911.05153

The performance of the base model described in the previous section is shown in the first row of Table TABREF8 for the Nematus cs-en ($\bar{cs}$), FB MT system cs-en (cs) and es-en (es), sequence autoencoder (seq2seq), and the average of the adversarial sets (avg). We also included the results for the ensemble model, which combines the decisions of five separate baseline models that differ in batch order, initialization, and dropout masking. We can see that, similar to the case in computer vision BIBREF4, the adversarial examples seem to stem from fundamental properties of the neural networks and ensembling helps only a little. FLOAT SELECTED: Table 3: Accuracy over clean and adversarial test sets. Note that data augmentation and logit pairing loss decrease accuracy on clean test sets and increase accuracy on the adversarial test sets. The performance of the base model described in the previous section is shown in the first row of Table TABREF8 for the Nematus cs-en ($\bar{cs}$), FB MT system cs-en (cs) and es-en (es), sequence autoencoder (seq2seq), and the average of the adversarial sets (avg). FLOAT SELECTED: Table 3: Accuracy over clean and adversarial test sets. Note that data augmentation and logit pairing loss decrease accuracy on clean test sets and increase accuracy on the adversarial test sets.

How big is performance improvement proposed methods are used?

Data augmentation (es) improved Adv es by 20% comparing to baseline Data augmentation (cs) improved Adv cs by 16.5% comparing to baseline Data augmentation (cs+es) improved both Adv cs and Adv es by at least 10% comparing to baseline All models show improvements over adversarial sets

What is ice cream

Ice cream is a frozen dessert, typically made from milk or cream and flavored with a sweetener, either sugar or an alternative, and a spice, such as cocoa or vanilla, or with fruit such as strawberries or peaches. It can also be made by whisking a flavored cream base and liquid nitrogen together. Food coloring is sometimes added, in addition to stabilizers. The mixture is cooled below the freezing point of water and stirred to incorporate air spaces and to prevent detectable ice crystals from forming. The result is a smooth, semi-solid foam that is solid at very low temperatures (below 2 °C or 35 °F). It becomes more malleable as its temperature increases.

Should I use a dynamic microphone or a condenser microphone for recording a podcast? What about streaming on twitch or making youtube videos? Should I ever use a ribbon microphone?

The most important factor when deciding on a microphone is the environment where you will be recording. Each microphone technology was developed for a certain kind of environment. Condenser microphones like the Blue Yeti are designed to record their entire environment with the utmost clarity. If your environment is noisy then the microphone audio will be noisy. But if the environment has been acoustically treated then the recording will sound detailed and clear. Dynamic microphones like the Shure SM7B are designed to record sounds spoken within a 18 inches of the front of the microphone. All other sounds will be quieter and less detailed. If the environment is noisy then the microphone audio will tend to mute out sound that comes from off-axis. If the environment is quiet then the audio will sound good, but only if the person or instrument being recorded is within 18 inches of the microphone. A ribbon microphone is designed to capture extremely loud percussion sounds, usually from drums or brass instruments. They are not suitable for recording human voices as they tend to emphasize frequencies not usually associated with the human voice.

Rotisserie baseball, nicknamed roto, proved to be popular despite the difficulties of compiling statistics by hand, which was an early drawback to participation. The traditional statistics used in early rotisserie leagues were often chosen because they were easy to compile from newspaper box scores or weekly information published in USA Today.[citation needed] Okrent credits the idea's rapid spread to the fact that the initial league was created by sports journalists, telling Vanity Fair in 2008 that "most of us in the league were in the media, and we got a lot of press coverage that first season. The second season, there were rotisserie leagues in every Major League press box." According to Okrent, rotisserie baseball afforded sportswriters the opportunity to write about baseball-related material during the 1981 Major League Baseball strike, saying "the writers who were covering baseball had nothing to write about, so they began writing about the teams they had assembled in their own leagues. And that was what popularized it and spread it around very, very widely."

How did Rotisserie baseball become popular?

Rotisserie baseball became popular because it allowed sportswriters to write about baseball during the 1981 Major League Baseball strike.

How do I get to Juneau, Alaska?

You really only have two options, by boat or by plane. No roads lead directly to Juneau, Alaska, due to the surrounding geography. Many cruise ships visit Juneau during summer, which is how it receives most of its visitors.

1910.03814

Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available. $TT$ refers to the tweet text, $IT$ to the image text and $I$ to the image. It also shows results for the LSTM, for the Davison method proposed in BIBREF7 trained with MMHS150K, and for random scores. Fig. FIGREF32 shows the Precision vs Recall plot and the ROC curve (which plots the True Positive Rate vs the False Positive Rate) of the different models. Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available. Fig. FIGREF32 shows the Precision vs Recall plot and the ROC curve (which plots the True Positive Rate vs the False Positive Rate) of the different models.

What metrics are used to benchmark the results?

The answers are shown as follows: * F-score * Area Under the ROC Curve (AUC) * mean accuracy (ACC) * Precision vs Recall plot * ROC curve (which plots the True Positive Rate vs the False Positive Rate)

1910.02339

Given a natural-language math problem, we need to generate a sequence of operations (operators and corresponding arguments) from a set of operators and arguments to solve the given problem. Each operation is regarded as a relational tuple by viewing the operator as relation, e.g., $(add, n1, n2)$. We test TP-N2F for this task on the MathQA dataset BIBREF16. The MathQA dataset consists of about 37k math word problems, each with a corresponding list of multi-choice options and the corresponding operation sequence. In this task, TP-N2F is deployed to generate the operation sequence given the question. The generated operations are executed with the execution script from BIBREF16 to select a multi-choice answer. As there are about 30% noisy data (where the execution script returns the wrong answer when given the ground-truth program; see Sec. SECREF20 of the Appendix), we report both execution accuracy (of the final multi-choice answer after running the execution engine) and operation sequence accuracy (where the generated operation sequence must match the ground truth sequence exactly). TP-N2F is compared to a baseline provided by the seq2prog model in BIBREF16, an LSTM-based seq2seq model with attention. Our model outperforms both the original seq2prog, designated SEQ2PROG-orig, and the best reimplemented seq2prog after an extensive hyperparameter search, designated SEQ2PROG-best. Table TABREF16 presents the results. To verify the importance of the TP-N2F encoder and decoder, we conducted experiments to replace either the encoder with a standard LSTM (denoted LSTM2TP) or the decoder with a standard attentional LSTM (denoted TP2LSTM). We observe that both the TPR components of TP-N2F are important for achieving the observed performance gain relative to the baseline. FLOAT SELECTED: Table 1: Results on MathQA dataset testing set Our model outperforms both the original seq2prog, designated SEQ2PROG-orig, and the best reimplemented seq2prog after an extensive hyperparameter search, designated SEQ2PROG-best. Table TABREF16 presents the results. FLOAT SELECTED: Table 1: Results on MathQA dataset testing set

What is the performance proposed model achieved on MathQA?

Operation accuracy: 71.89 Execution accuracy: 55.95

The training objective with adversarial neural network is different from the baseline model, as it includes the extra worker discriminator. Thus the new objective includes two parts, one being the negative log-likelihood from NER which is the same as the baseline, and the other being the negative the log-likelihood from the worker discriminator. In order to obtain the negative log-likelihood of the worker discriminator, we use softmax to compute the probability of the actual worker $\bar{z}$ as well, which is defined by: $$p(\bar{z}|\mathbf {X}, \mathbf {\bar{y}}) = \frac{\exp (\mathbf {o}^{\text{worker}}_{\bar{z}})}{\sum _{z} \exp (\mathbf {o}^{\text{worker}}_z)},$$ (Eq. 22) where $z$ should enumerate all workers. Based on the above definition of probability, our new objective is defined as follows: $$\begin{split} \text{R}(\Theta , \Theta ^{\prime }, \mathbf {X}, \mathbf {\bar{y}}, \bar{z}) &= \text{loss}(\Theta , \mathbf {X}, \mathbf {\bar{y}}) - \text{loss}(\Theta , \Theta ^{\prime }, \mathbf {X}) \\ \text{~~~~~~} &= -\log p(\mathbf {\bar{y}}|\mathbf {X}) + \log p(\bar{z}|\mathbf {X}, \mathbf {\bar{y}}), \end{split}$$ (Eq. 23) where $\Theta $ is the set of all model parameters related to NER, and $\Theta ^{\prime }$ is the set of the remaining parameters which are only related to the worker discriminator, $\mathbf {X}$ , $\mathbf {\bar{y}}$ and $\bar{z}$ are the input sentence, the crowd-annotated NE labels and the corresponding annotator for this annotation, respectively. It is worth noting that the parameters of the common Bi-LSTM are included in the set of $\Theta $ by definition. In particular, our goal is not to simply minimize the new objective. Actually, we aim for a saddle point, finding the parameters $\Theta $ and $\Theta ^{\prime }$ satisfying the following conditions: $$\begin{split} \hat{\Theta } &= \mathop {arg~min}_{\Theta }\text{R}(\Theta , \Theta ^{\prime }, \mathbf {X}, \mathbf {\bar{y}}, \bar{z}) \\ \hat{\Theta }^{\prime } &= \mathop {arg~max}_{\Theta ^{\prime }}\text{R}(\hat{\Theta }, \Theta ^{\prime }, \mathbf {X}, \mathbf {\bar{y}}, \bar{z}) \\ \end{split}$$ (Eq. 24) where the first equation aims to find one $\Theta $ that minimizes our new objective $\text{R}(\cdot )$ , and the second equation aims to find one $\Theta ^{\prime }$ maximizing the same objective. Intuitively, the first equation of Formula 24 tries to minimize the NER loss, but at the same time maximize the worker discriminator loss by the shared parameters of the common Bi-LSTM. Thus the resulting features of common Bi-LSTM actually attempt to hurt the worker discriminator, which makes these features worker independent since they are unable to distinguish different workers. The second equation tries to minimize the worker discriminator loss by its own parameter $\Theta ^{\prime }$ . We use the standard back-propagation method to train the model parameters, the same as the baseline model. In order to incorporate the term of the argmax part of Formula 24 , we follow the previous work of adversarial training BIBREF13 , BIBREF15 , BIBREF17 , by introducing a gradient reverse layer between the common Bi-LSTM and the CNN module, whose forward does nothing but the backward simply negates the gradients. We use the standard back-propagation method to train the model parameters, the same as the baseline model.

What do the authors use to train the model parameters?

The authors use the standard back-propagation method to train the model parameters, the same as the baseline model.

According to preliminary data from the US Energy Information Administration, renewable energy accounted for about 12.6% of total primary energy consumption and about 19.8% of the domestically produced electricity in the United States in 2020.

How much renewable energy is produced and consumed in the United States?

Since 2019, wind power has been the largest producer of renewable electricity in the country. Wind power generated 337.9 terawatt-hours of electricity in 2020, which accounted for 8.4% of the nation's total electricity generation and 43.2% of the total renewable electricity generation. By October 2021, the United States nameplate generating capacity for wind power was 129,256 megawatts (MW). Texas remained firmly established as the leader in wind power deployment, followed by Iowa and Oklahoma as of year end 2020. Hydroelectric power is the second-largest producer of renewable electricity in the country, generating around 7.3% of the nation's total electricity in 2020 as well as 36.4% of the total renewable electricity generation. The United States is the fourth largest producer of hydroelectricity in the world after China, Canada and Brazil. Solar power provides a growing share of electricity in the country, with over 50 GW of installed capacity generating about 1.3% of the country's total electricity supply in 2017, up from 0.9% the previous year. As of 2016, more than 260,000 people worked in the solar industry and 43 states deployed net metering, where energy utilities bought back excess power generated by solar arrays. Large photovoltaic power plants in the United States include Mount Signal Solar (600 MW) and Solar Star (579 MW). Since the United States pioneered solar thermal power technology in the 1980s with Solar One, several more such power stations have been built. The largest of these solar thermal power stations are the Ivanpah Solar Power Facility (392 MW), southwest of Las Vegas, and the SEGS group of plants in the Mojave Desert, with a total generating capacity of 354 MW. Other renewable energy sources include geothermal, with The Geysers in Northern California the largest geothermal complex in the world. The development of renewable energy and energy efficiency marked "a new era of energy exploration" in the United States, according to former President Barack Obama. In a joint address to the Congress on February 24, 2009, President Obama called for doubling renewable energy within the following three years. Renewable energy reached a major milestone in the first quarter of 2011, when it contributed 11.7% of total national energy production (660 TWh), surpassing energy production from nuclear power (620 TWh) for the first time since 1997. In his 2012 State of the Union address, President Barack Obama restated his commitment to renewable energy and mentioned the long-standing Interior Department commitment to permit 10,000 MW of renewable energy projects on public land in 2012.

Is Blind a reliable source for information?

Blind is an app where the tech community can anonymously post opinions and polls. Because of the anonymity of the users, it can be reliable because people can post opinions unfiltered without the fear of repercussions (although there is an anti bullying policy that is enforced by banning users if they violate the policy). Ironically, the anonymity is also what can lead to unreliable information as many opinions are users trolling each other and giving exaggerated and false responses.

When was the newest country created?

South Sudan became a country in July 2011.

1809.06963

We observe that merely adding more tasks cannot provide much improvement on the target task. Thus, we propose two MTL training algorithms to improve the performance. The first method simply adopts a sampling scheme, which randomly selects training data from the auxiliary tasks controlled by a ratio hyperparameter; The second algorithm incorporates recent ideas of data selection in machine translation BIBREF7 . It learns the sample weights from the auxiliary tasks automatically through language models. Prior to this work, many studies have used upstream datasets to augment the performance of MRC models, including word embedding BIBREF5 , language models (ELMo) BIBREF8 and machine translation BIBREF1 . These methods aim to obtain a robust semantic encoding of both passages and questions. Our MTL method is orthogonal to these methods: rather than enriching semantic embedding with external knowledge, we leverage existing MRC datasets across different domains, which help make the whole comprehension process more robust and universal. Our experiments show that MTL can bring further performance boost when combined with contextual representations from pre-trained language models, e.g., ELMo BIBREF8 . We develop a novel re-weighting method to resolve these problems, using ideas inspired by data selection in machine translation BIBREF26 , BIBREF7 . We use $(Q^{k},P^{k},A^{k})$ to represent a data point from the $k$ -th task for $1\le k\le K$ , with $k=1$ being the target task. Since the passage styles are hard to evaluate, we only evaluate data points based on $Q^{k}$ and $A^k$ . Note that only data from auxiliary task ( $2\le k\le K$ ) is re-weighted; target task data always have weight 1. The second algorithm incorporates recent ideas of data selection in machine translation BIBREF7 . We develop a novel re-weighting method to resolve these problems, using ideas inspired by data selection in machine translation BIBREF26 , BIBREF7 .

What is the data selection paper in machine translation

The answers are shown as follows: * BIBREF7 * BIBREF26

why do human beings have a tendency to think they are always right?

As a first attempt to answer the question of why human beings have a tendency to think they are always right, we could point to cognitive biases, and in particular to one called "confirmation bias". This bias conditions human beings to use any new evidence to confirm their pre-existing beliefs, and to reject any new evidence that contradicts these beliefs. People may also experience the so-called illusory superiority, another bias that conditions them to overestimate their skills and make them believe they are superior to the rest. However these biases, as concrete as well-proven as they are, are only part of the explanation. The book "Being Wrong: Adventures in the Margin of Error" by Kathryn Schulz is an exploration on why humans have the tendency to disregard errors and think they are always right. Shulz describes how people start with the assumption that they are right in everything they believe, and when they realise they have made a mistake, instead of challenging this assumption, they actually feel relief and think: "I was right except for this particular error I was making, but now that I have realised it I can certainly say that I am 100% right in everything".

which weighs more, a pound of bricks or a ton of feathers?

A ton is more than a pound, so a ton of feathers weighs more than a pound of bricks.

A companion dog is a dog that does not work, providing only companionship as a pet, rather than usefulness by doing specific tasks. Many of the toy dog breeds are used only for the pleasure of their company, not as workers. Any dog can be a companion dog, and many working types such as retrievers are enjoyed primarily for their friendly nature as a family pet, as are mixed breed dogs. The American Kennel Club also offers a Companion dog title for judged dog obedience competitions.

Based on this paragraph about dogs, what is a companion dog?

A companion dog is a dog that provides only companionship as a pet, rather than usefulness by doing specific tasks.

What’s the difference between Delta Live Tables and Workflows?

These are some of the main differences between Delta Live Tables and Workflows: - Workflows is complementary to Delta Live Tables - Workflows is a control flow that allows end-to-end orchestration of tasks that might include one or more DLT Pipelines. - A workflow consists of one or more tasks, which could be a Spark job, a DLT Pipeline, an ML job, some random python code, etc. - DLT Pipelines is a data flow. You declare your data transformations, and Live Tables takes care of actually running your pipeline for you.

What are some of the highest quality, full flavored cigars I can buy in the united states?

Due to the US embargo banning importation of products from Cuba in the mid 20th century, the storied Cuban cigar has not been able to be legally purchased by US consumers.With that said, there are numerous highly regarded cigar makers in countries like Nicargua, the Dominican Republic, and Honduras whose cigars rival the best Cuban cigars, many of them full flavored. Here are a few options to consider: Arturo Fuente Opus X - this is a highly complex, highly sought after, full-flavored cigar from the Dominican Republic that retails for ~$25 for a single, making it an expensive choice for a regular cigar smoker. With that said, it is near the top of the list of favorites for most cigar aficianados. Padron Serie 1926 - this is a creamy, chocolatey cigar from Nicaragua that retails for around $17, making it a slightly more affordable option, though still a premium selection. These are produced in higher volume than the Opus, which makes them easier to fine. Ashton VSG - this is a Dominican cigar, with VSG standing for "Virgin Sun Grown." The flavor profile is very unique due to the sun grown wrapper. These are about $13 for a single at retail depending on size. There are dozens of reputable cigar makers making thousands of cigars, but those above are longstanding staples of many avid cigar smokers in the US.

Name the spyware used by Israel to hack Whatsapp?

Pegasus

1911.13066

We re-implement the model proposed in BIBREF3, and use it as a baseline for our problem. The rationale behind choosing this particular model as a baseline is it's proven good predictive performance on multilingual text classification. For McM, the choices of number of convolutional filters, number of hidden units in first dense layer, number of hidden units in second dense layer, and recurrent units for LSTM are made empirically. Rest of the hyperparameters were selected by performing grid search using $20\%$ stratified validation set from training set on McM$_\textsubscript {R}$. Available choices and final selected parameters are mentioned in Table TABREF18. These choices remained same for all experiments and the validation set was merged back into training set. We re-implement the model proposed in BIBREF3, and use it as a baseline for our problem. The rationale behind choosing this particular model as a baseline is it's proven good predictive performance on multilingual text classification.

What is their baseline model?

The answers are shown as follows: * the model proposed in BIBREF3

1906.01840

We further explore the effect of INLINEFORM0 -gram length in our model (i.e., the filter size for the covolutional layers used by the attention parsing module). In Figure FIGREF39 we plot the AUC scores for link prediction on the Cora dataset against varying INLINEFORM1 -gram length. The performance peaked around length 20, then starts to drop, indicating a moderate attention span is more preferable. Similar results are observed on other datasets (results not shown). Experimental details on the ablation study can be found in the SM. We further explore the effect of INLINEFORM0 -gram length in our model (i.e., the filter size for the covolutional layers used by the attention parsing module). In Figure FIGREF39 we plot the AUC scores for link prediction on the Cora dataset against varying INLINEFORM1 -gram length.

Do they measure how well they perform on longer sequences specifically?

Yes.

1906.01946

The UNGA speeches dataset, compiled by Baturo et al. UNGAspeeches, contains the text from 7,507 speeches given between 1970-2015 inclusive. Over the course of this period a variety of topics are discussed, with many debated throughout (such as nuclear disarmament). Although the linguistic style has changed over this period, the context of these speeches constrains the variability to the formal domain. Before training the model, the dataset is split into 283,593 paragraphs, cleaned by removing paragraph deliminators and other excess noise, and tokenized using the spaCy tokenizer BIBREF4 . The UNGA speeches dataset, compiled by Baturo et al. UNGAspeeches, contains the text from 7,507 speeches given between 1970-2015 inclusive.

how many speeches are in the dataset?

The answers are shown as follows: * 7,507

classify each of the following as living or non-living: dog, table, cat, tree, pen, book.

living: dog, cat, tree non-living: table, pen, book

1704.02686

For a fair comparison, we trained each model on the same corpus of 10 million sentences gathered from Wikipedia. We removed stopwords and words appearing fewer than 2,000 times (130 million tokens total) to reduce noise and uninformative words. Our word2vec and NNSE baselines were trained using the recommended hyperparameters from their original publications, and all optimizers were using using the default settings. Hyperparameters are always consistent across evaluations. For a fair comparison, we trained each model on the same corpus of 10 million sentences gathered from Wikipedia.

On which dataset(s) do they compute their word embeddings?

The answers are shown as follows: * 10 million sentences gathered from Wikipedia

This work is most closely related to the paper from which we get the ERP data: BIBREF0 . In that work, the authors relate the surprisal of a word, i.e. the (negative log) probability of the word appearing in its context, to each of the ERP signals we consider here. The authors do not directly train a model to predict ERPs. Instead, models of the probability distribution of each word in context are used to compute a surprisal for each word, which is input into a mixed effects regression along with word frequency, word length, word position in the sentence, and sentence position in the experiment. The effect of the surprisal is assessed using a likelihood-ratio test. In BIBREF7 , the authors take an approach similar to BIBREF0 . The authors compare the explanatory power of surprisal (as computed by an LSTM or a Recurrent Neural Network Grammar (RNNG) language model) to a measure of syntactic complexity they call “distance" that counts the number of parser actions in the RNNG language model. The authors find that surprisal (as predicted by the RNNG) and distance are both significant factors in a mixed effects regression which predicts the P600, while the surprisal as computed by an LSTM is not. Unlike BIBREF0 and BIBREF7 , we do not use a linking function (e.g. surprisal) to relate a language model to ERPs. We thus lose the interpretability provided by the linking function, but we are able to predict a significant proportion of the variance for all of the ERP components, where prior work could not. We interpret our results through characterization of the ERPs in terms of how they relate to each other and to eye-tracking data rather than through a linking function. The authors in BIBREF8 also use a recurrent neural network to predict neural activity directly. In that work the authors predict magnetoencephalography (MEG) activity, a close cousin to EEG, recorded while participants read a chapter of Harry Potter and the Sorcerer’s Stone BIBREF9 . Their approach to characterization of processing at each MEG sensor location is to determine whether it is best predicted by the context vector of the recurrent network (prior to the current word being processed), the embedding of the current word, or the probability of the current word given the context. In future work we also intend to add these types of studies to the ERP predictions. In Hale et al. (2018), the authors take an approach similar to Frank et al. (2015). The authors compare the explanatory power of surprisal (as computed by an LSTM or a Recurrent Neural Network Grammar (RNNG) language model) to a measure of syntactic complexity they call “distance” that counts the number of parser actions in the RNNG language model.

What is the method used by Hale et al.?

The authors compare the explanatory power of surprisal (as computed by an LSTM or a Recurrent Neural Network Grammar (RNNG) language model) to a measure of syntactic complexity they call “distance” that counts the number of parser actions in the RNNG language model.

Which characters belong to DC or Marvel Universe? She-Hulk, Wonder Woman

She-Hulk is Marvel, Wonder Woman is DC

Ayrton Senna da Silva (21 March 1960 – 1 May 1994) was a Brazilian racing driver who won the Formula One World Drivers' Championship in 1988, 1990, and 1991. Senna is one of three Formula One drivers from Brazil to win the World Championship and won 41 Grands Prix and 65 pole positions, with the latter being the record until 2006.

Extract the years that Ayrton Senna da Silva won the Formula One World Championship, and put them separated by comma.

1988, 1990, 1991

What is the area of Texas in square feet?

7.5 Trillion

Write a few sentences explaining the criteria you used to choose your favorite fast food restaurant.

The most important piece of criteria I use is taste of the food and drinks. Although there are other important factors like price, and variety of menu options ultimately the tastiness of the food is the most important factor.

1910.12618

The initial analyses of the embedding matrices for both the UK and France revealed that in general, words were grouped by context or influence on the electricity consumption. For instance, we observed that winter words were together and far away from summer ones. Week days were grouped as well and far from week-end days. However considering the vocabulary was reduced to $V^* = 52$ words, those results lacked of consistency. Therefore for both languages we decided to re-train the RNNs using the same architecture, but with a larger vocabulary of the $V=300$ most relevant words (still in the RF sense) and on all the available data (i.e. everything is used as training) to compensate for the increased size of the vocabulary. We then calculated the distance of a few prominent words to the others. The analysis of the average cosine distance over $B=10$ runs for three major words is given by tables TABREF38 and TABREF39, and three other examples are given in the appendix tables TABREF57 and TABREF58. The first row corresponds to the reference word vector $\overrightarrow{w_1}$ used to calculate the distance from (thus the distance is always zero), while the following ones are the 9 closest to it. The two last rows correspond to words we deemed important to check the distance with (an antagonistic one or relevant one not in the top 9 for instance). The initial analyses of the embedding matrices for both the UK and France revealed that in general, words were grouped by context or influence on the electricity consumption. For instance, we observed that winter words were together and far away from summer ones.

Is there any example where geometric property is visible for context similarity between words?

Yes.

In what city was the first season of MTV's The Real World filmed?

The first season of MTV's The Real World was filmed in New York.

Identify which animal species is alive or extinct: Megalania, Sea Turtle

Sea Turtle is alive, Megalania is extinct.