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Is it wrong to split the infinitive?

First, let's establish the meaning of a split infinitive. In simple terms the infinitive is the grammatical construct in which a verb is preceded by the word 'to': hence, 'to walk', 'to talk', 'to reason'. As a side-note, it's probably worth exploring whether the 'verbification' of nouns is a good thing: personally, I take issue with this and feel that examples such as 'to medal', meaning 'to win a medal at a sporting event', should never have been visited on the English language. But that's another discussion entirely. Returning to the infinitive, the split occurs when another word (usually an adverb) is placed between the word 'to' and its corresponding verb. Probably the most famous split infinitive occurs in the opening sequence of every episode of the classic and original TV show, Star Trek, with the lines: "Space: The final frontier These are the voyages of the Starship, Enterprise; Its five year mission: to explore strange new worlds; to seek out new life and new civilizations; to boldly go where no man has gone before." Note that final line: 'to boldly go'. Never has an infinitive been split so boldly before or since. Those who are grammatical pedants will have shuddered at this every time (and probably ended up preferring Star Wars to Star Trek as a consequence) but through those original seventy-nine episodes and their many repeat showings, the split infinitive has subtly embedded itself in our lexicon. So, to return to the question, is it wrong to split the infinitive? Since language is constantly evolving, influenced by the whims and practices of each generation, the answer must be 'no'. On the whole sentences tend to flow more freely and with greater elan if infinitives aren't split and pesky adverbs are kept in their place either before or after ('to go boldly' or 'boldly to go') but sometimes one has to admit that splitting works, and we should be thankful for the colour that brings to the language we hear daily. Who nowadays wouldn't want to boldly go?

OpenRA is an open source game engine reimplementation and remake of several Command & Conquer games, including Red Alert. It is built using OpenGL and SDL, has support for HiDPI, and is cross platform, supporting Windows, Linux, macOS, and FreeBSD. The engine has support for custom mods via an SDK. The games are remade for modern computers with updated gameplay, new campaigns, user-created and curated maps, and online play. Deviations from the originals included a choice between “right click” and classic “left click” control schemes, an overhauled sidebar interfaces for managing productions, support for game replays and an observer interface designed for video game live streaming. Fog of war that obscures the battlefield outside your units’ line of sight, civilian structures that can be captured to provide benefits and units gain experience as they fight and improve when they earn new ranks. The game supports handicapping players, Discord integration and community ladders. Experimental support for the remastered assets was published in 2023 as a binary compatible yet separate mod.

Extract what Operating systems openRA supports from the below text and list them in a seperate line

Here are the operating systems supported by OpenRA: Windows Linux macOS FreeBSD

Write an email to the head of academic coaching at Services for Students with Disabilities sharing that I’ve applied to the coaching position. I have experience volunteering w children on the autism spectrum.

Hi, I hope you are having a wonderful day. My name is _, and I just applied for the Program Staff position at the Services for Students with Disabilities to empower students who have disabilities to reach their academic dreams. My background working in higher education and volunteering with kids on the Autism spectrum combine to bring an empathetic and impactful perspective. Please let me know if there is anything else I can do at this time to be considered for this position. With gratitude, _

Who wrote the music for the ballets Firebird and Rites of Spring

Igor Stravinsky

The first hammer forge was probably built in 1482 on the River Ohra. In 1592 the mill was bought by Tobias Albrecht, since when it has been named after him. The works originally had three huge iron hammers which were raised by the motion of a water wheel. The mill made iron products such as scythes, sickles, ploughshares, lances and swords. Later, it also carried out copper processing; washbowls, washtubs, kettle drums, brewing implements, pots, cans and pans were made. The kettle drums, which were produced into the late 20th century, were made from a single piece of copper, forged under the tilt hammer and still set a standard today in terms of their sound.

List me some important facts about Tobiashammer from given text

1. On the River Ohra, the first hammer forge was probably constructed around 1482. 2. Tobias Albrecht purchased the mill in 1592, and ever since, it has carried his name. 3. The factory's three enormous iron hammers, which were elevated by the rotation of a water wheel, were originally installed. 4. The mill produced iron tools like lances, scythes, sickles, and ploughshares. 5. Later, it also engaged in copper processing; pots, cans, pans, brewing equipment, kettle drums, washbowls, and washtubs were produced. 6. The single-piece copper kettle drums, which were forged with the tilt hammer and produced until the late 20th century, set a standard for sound even today.

Is the following statement true or false: the earth is the third planet from the sun?

The earth is the third planet from the sun in our solar system, so the statement is true.

Give me a list of free email service provider other than Gmail.

1. Outlook: It is offering from Microsoft, and is considered a powerful email service. It integrates with other Microsoft service ecosystem and used by large business enterprises. 2. Proton: Formerly called ProtonMail, has a unique offering when it comes to privacy protecting email service provided and hence has a very unique service offering. 3. Yahoo! Mail: Yahoo offers this very mature email service and has a lot of custom inbox and themes options. 4. Zoho Mail: Another email provider that provided fully encrypted data and privacy protection. On the top, it also provides a very easy to use email migration service.

Many machine learning models in question answering tasks often involve matching mechanism. For example, in factoid question answering such as SQuAD BIBREF1 , one needs to match between query and corpus in order to find out the most possible fragment as answer. In multiple choice question answering, such as MC Test BIBREF2 , matching mechanism can also help make the correct decision. The easiest way of matching is to calculate the cosine similarity between two vectors. It is generally done by two step: First, encode text into word vectors, sentence vectors or paragraph vectors. Second, simply calculate the cosine similarity between target vectors. This method performs well when applied to word-level matching. However, as for matching between sentences or paragraphs, a single vector is not sufficient to encode all the important information. In order to solve this problem, Wang and Jiang proposed a “compare-aggregate” BIBREF3 framework that performs word-level matching using multiple techniques followed by aggregation with convolutional neural network. In their work, they show that compare-aggregate framework can effectively match two sequences through a wide range. Although "compare-aggregate" matching mechanism performs well on multiple question answering tasks, it has two deficiencies. First, it tends to aggregate passively through the sequence rather than take the importance of each element into account. That is, "compare aggregate" model considers all the sequential contents equally. Second, "compare aggregate" can only take few neighboring elements into account at the same time because of the limitation of CNN kernel size. In this paper, we propose Query-based Attention CNN (QACNN) to deal with the deficiencies above. First, we add query-based attention mechanism into original "compare aggregate" model. Moreover, We re-design the aggregation mechanism in "compare aggregate" to a two-staged CNN architecture which comprises word-level aggregation and sentence-level aggregation. In this way, QACNN can efficiently extract features cross sentences. Our model consists of three components: 1) The similarity mapping layer which converts the input passage, query and choice into feature representation and perform a similarity operation to each other. 2) The attention-based CNN matching network composed of a two-staged CNN focusing on word-level and sentence-level matching respectively. 3) The prediction layer which makes the final decision. The main contributions of this work are three-fold. First, we introduce a two-staged CNN architecture which integrates information from word-level to sentence-level, and then from sentence-level to passage-level. Second, we introduce attention mechanism into this net. We use specially designed CNN structure and attention mechanism to recognize the pattern of similarity map and eventually identify specific syntactic structure of queries. By transforming passage-query feature into attention maps and applying it to passage-choice matching result, we reasonably give weight to every word in the passage. Lastly, our model reaches 79.99% accuracy on the MovieQA dataset which yields top 1 result on this dataset. Lastly, our model reaches 79.99% accuracy on the MovieQA dataset which yields top 1 result on this dataset.

What is the accuracy of the model on the MovieQA dataset?

The model reaches 79.99% accuracy on the MovieQA dataset.

What is nihilism and why do some people believe it? List some ways that philosophers have proposed to fight against nihilism. Explain to me in a casual tone

Nihilism is a belief that life is ultimately meaningless. Sounds pretty depressing right? Well, there are a number of reasons why one might feel this way. Do you ever feel like you are just going through the motions each day? You wake up, go to work, get off work, sleep, and the cycle continues. You chased after a goal, such as graduating college or getting that next promotion, thinking it would solve all your problems, only to be disappointed after finally getting it. Then you look around the universe that you lives in and find how insignificant your individuality is in the grand schemes of things, you can't help but think to yourself: man, is there any point to life? Does what I do actually matter at all, especially if I'm going to die anyways? If you ever feel this way, know that you are not alone. Many, many philosophers throughout human history have pondered upon this very idea. I'll present a select few philosophers and their proposals to fight against nihilism. One obvious solution, per Danish philosophers Kierkegaard, is religion. He believes that faith should be our purpose in life, following Him and doing good in this life will reap rewards in the afterlife. Now, German philosopher Nietzsche does not agree with that at all; in fact his most famous quote was "God is dead and we killed him". He's talking about how with new advances in science comes the death of religion -- each man must now create his own values and become an ubermensch. The idea of ubermensch, however, kind of throws morality out of window — who is to say one’s value is wrong? Enter Albert Camus and his idea of absurdism. He compares human life to Sisyphus — rolling a giant rock uphill, only for the rock to fall back down after reaching the top — an absurd and meaningless struggle. He suggests that we accept the absurdity of this world and human existence, and instead embrace the present, because “The struggle itself towards the heights is enough to fill a man's heart. One must imagine Sisyphus happy”. And there you have it, what nihilism is all about, why it happens and a few ways to fight against it. Note that this is a very extensive topic and what I covered here is only the tip of the iceberg, I’d suggest reading up more if you're interested. Hope it helped!

What is the difference between fruit and vegetables?

The distinction between fruit and vegetable is drawn from what part of the plant is eaten. Fruit are the flowering part of the plant and will usually have seeds. Vegetables, on the other hand, are usually another part of a plant such as the root, bulb, leaves or stem.

What famous Italian plumber brothers were featured in the Nintendo Entertainment System?

The Super Mario Brothers.

What are some common peer-to-peer money transfer apps in the United States?

Some of the most common peer-to-peer money transfer apps in the United States are Zelle, Venmo, CashApp, PayPal, and Apple/Google Pay (depending on the type of mobile device of the user).

1902.07285

Appropriate future directions on adversarial attacks and defenses: As an attacker, designing universal perturbations to catch better adversarial examples can be taken into consideration like it works in image BIBREF29 . A universal adversarial perturbation on any text is able to make a model misbehave with high probability. Moreover, more wonderful universal perturbations can fool multi-models or any model on any text. On the other hand, the work of enhancing the transferability of adversarial examples is meaningful in more practical back-box attacks. On the contrary, defenders prefer to completely revamp this vulnerability in DNNs, but it is no less difficult than redesigning a network and is also a long and arduous task with the common efforts of many people. At the moment defender can draw on methods from image area to text for improving the robustness of DNNs, e.g. adversarial training BIBREF107 , adding extra layer BIBREF113 , optimizing cross-entropy function BIBREF114 , BIBREF115 or weakening the transferability of adversarial examples. At the moment defender can draw on methods from image area to text for improving the robustness of DNNs, e.g. adversarial training BIBREF107 , adding extra layer BIBREF113 , optimizing cross-entropy function BIBREF114 , BIBREF115 or weakening the transferability of adversarial examples.

Which strategies show the most promise in deterring these attacks?

The answers are shown as follows: * At the moment defender can draw on methods from image area to text for improving the robustness of DNNs, e.g. adversarial training BIBREF107 , adding extra layer BIBREF113 , optimizing cross-entropy function BIBREF114 , BIBREF115 or weakening the transferability of adversarial examples.

Recent years, great efforts have been made on extracting relational fact from unstructured raw texts to build large structural knowledge bases. A relational fact is often represented as a triplet which consists of two entities (subject and object) and semantic relation between them. Early works BIBREF0, BIBREF1, BIBREF2 mainly focused on the task of relation classification which assumes the entity pair are identified beforehand. This limits their practical application since they neglect the extraction of entities. To extract both entities and their relation, existing methods can be divided into two categories : the pipelined framework, which first uses sequence labeling models to extract entities, and then uses relation classification models to identify the relation between each entity pair; and the joint approach, which combines the entity model and the relation model through different strategies, such as constraints or parameters sharing. To extract both entities and their relation, existing methods can be divided into two categories : the pipelined framework, which first uses sequence labeling models to extract entities, and then uses relation classification models to identify the relation between each entity pair; and the joint approach, which combines the entity model and the relation model through different strategies, such as constraints or parameters sharing.

What are the two categories of existing methods to extract both entities and their relation?

To extract both entities and their relation, existing methods can be divided into two categories : the pipelined framework, which first uses sequence labeling models to extract entities, and then uses relation classification models to identify the relation between each entity pair; and the joint approach, which combines the entity model and the relation model through different strategies, such as constraints or parameters sharing.

Pablo Ruiz Picasso (25 October 1881 – 8 April 1973) was a Spanish painter, sculptor, printmaker, ceramicist and theatre designer who spent most of his adult life in France. One of the most influential artists of the 20th century, he is known for co-founding the Cubist movement, the invention of constructed sculpture, the co-invention of collage, and for the wide variety of styles that he helped develop and explore. Among his most famous works are the proto-Cubist Les Demoiselles d'Avignon (1907), and the anti-war painting Guernica (1937), a dramatic portrayal of the bombing of Guernica by German and Italian air forces during the Spanish Civil War. Born: Pablo Diego José Francisco de Paula Juan Nepomuceno María de los Remedios Cipriano de la Santísima Trinidad Ruiz y Picasso 25 October 1881 Málaga, Kingdom of Spain

Given a reference text about Pablo Picasso, tell me when and where he was born, why he was famous, and what his most famous accomplishment was as a professional

Pablo Ruiz Picasso was born on 25 October 1881 in Málaga, Spain. He was a Spanish painter, sculptor, printmaker, ceramicist and theatre designer who spent most of his adult life in France. He is one of the most famous and influential artists of the 20th century because he co-founded the Cubist movement, the invention of constructed sculpture, co-invented collage, and he delveoped and explored a wide variety of other styles. Among his most famous works are the proto-Cubist Les Demoiselles d'Avignon (1907), and the anti-war painting Guernica (1937), a dramatic portrayal of the bombing of Guernica by German and Italian air forces during the Spanish Civil War.

Tell me which of the following are tropical fruits: Passion fruit, Lychee, Apple, Strawberry

Sure! A tropical fruit is a fruit that typically grows in warm climates or equatorial areas. Passion fruit and Lychee are tropical fruits, whereas Apple and Strawberries are temperate fruits. Temperate fruits are those fruits which re adapted to the climate in middle latitude

Natural text generation, as a key task in NLP, has been advanced substantially thanks to the flourish of neural models BIBREF0 , BIBREF1 . Typical frameworks such as sequence-to-sequence (seq2seq) have been applied to various generation tasks, including machine translation BIBREF2 and dialogue generation BIBREF3 . The standard paradigm to train such neural models is maximum likelihood estimation (MLE), which maximizes the log-likelihood of observing each word in the text given the ground-truth proceeding context BIBREF4 . Although widely used, MLE suffers from the exposure bias problem BIBREF5 , BIBREF6 : during test, the model sequentially predicts the next word conditioned on its previous generated words while during training conditioned on ground-truth words. To tackle this problem, generative adversarial networks (GAN) with reinforcement learning (RL) training approaches have been introduced to text generation tasks BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , where the discriminator is trained to distinguish real and generated text samples to provide reward signals for the generator, and the generator is optimized via policy gradient BIBREF7 . However, recent studies have shown that potential issues of training GANs on discrete data are more severe than exposure bias BIBREF14 , BIBREF15 . One of the fundamental issues when generating discrete text samples with GANs is training instability. Updating the generator with policy gradient always leads to an unstable training process because it's difficult for the generator to derive positive and stable reward signals from the discriminator even with careful pre-training BIBREF8 . As a result, the generator gets lost due to the high variance of reward signals and the training process may finally collapse BIBREF16 . In this paper, we propose a novel adversarial training framework called Adversarial Reward Augmented Maximum Likelihood (ARAML) to deal with the instability issue of training GANs for text generation. At each iteration of adversarial training, we first train the discriminator to assign higher rewards to real data than to generated samples. Then, inspired by reward augmented maximum likelihood (RAML) BIBREF17 , the generator is updated on the samples acquired from a stationary distribution with maximum likelihood estimation (MLE), weighted by the discriminator's rewards. This stationary distribution is designed to guarantee that training samples are surrounding the real data, thus the exploration space of our generator is indeed restricted by the MLE training objective, resulting in more stable training. Compared to other text GANs with RL training techniques, our framework acquires samples from the stationary distribution rather than the generator's distribution, and uses RAML training paradigm to optimize the generator instead of policy gradient. Our contributions are mainly as follows: Compared to other text GANs with RL training techniques, our framework acquires samples from the stationary distribution rather than the generator's distribution, and uses RAML training paradigm to optimize the generator instead of policy gradient.

Compared to other text GANs with RL training techniques, what are the features of the frame proposed by the authors?

the framework acquires samples from the stationary distribution rather than the generator's distribution, and uses RAML training paradigm to optimize the generator instead of policy gradient.

In computer science, ACID (atomicity, consistency, isolation, durability) is a set of properties of database transactions intended to guarantee data validity despite errors, power failures, and other mishaps. In the context of databases, a sequence of database operations that satisfies the ACID properties (which can be perceived as a single logical operation on the data) is called a transaction. For example, a transfer of funds from one bank account to another, even involving multiple changes such as debiting one account and crediting another, is a single transaction. In 1983, Andreas Reuter and Theo Härder coined the acronym ACID, building on earlier work by Jim Gray who named atomicity, consistency, and durability, but not isolation, when characterizing the transaction concept. These four properties are the major guarantees of the transaction paradigm, which has influenced many aspects of development in database systems. According to Gray and Reuter, the IBM Information Management System supported ACID transactions as early as 1973 (although the acronym was created later). Atomicity Main article: Atomicity (database systems) Transactions are often composed of multiple statements. Atomicity guarantees that each transaction is treated as a single "unit", which either succeeds completely or fails completely: if any of the statements constituting a transaction fails to complete, the entire transaction fails and the database is left unchanged. An atomic system must guarantee atomicity in each and every situation, including power failures, errors, and crashes. A guarantee of atomicity prevents updates to the database from occurring only partially, which can cause greater problems than rejecting the whole series outright. As a consequence, the transaction cannot be observed to be in progress by another database client. At one moment in time, it has not yet happened, and at the next, it has already occurred in whole (or nothing happened if the transaction was canceled in progress). Consistency Main article: Consistency (database systems) Consistency ensures that a transaction can only bring the database from one consistent state to another, preserving database invariants: any data written to the database must be valid according to all defined rules, including constraints, cascades, triggers, and any combination thereof. This prevents database corruption by an illegal transaction. Referential integrity guarantees the primary key–foreign key relationship. Isolation Main article: Isolation (database systems) Transactions are often executed concurrently (e.g., multiple transactions reading and writing to a table at the same time). Isolation ensures that concurrent execution of transactions leaves the database in the same state that would have been obtained if the transactions were executed sequentially. Isolation is the main goal of concurrency control; depending on the isolation level used, the effects of an incomplete transaction might not be visible to other transactions. Durability Main article: Durability (database systems) Durability guarantees that once a transaction has been committed, it will remain committed even in the case of a system failure (e.g., power outage or crash). This usually means that completed transactions (or their effects) are recorded in non-volatile memory.

What does it mean if a database is ACID compliant?

ACID is an acronym that stands for Atomicity, Consistency, Isolation, and Durability. Atomicity means that actions occur either entirely or not at all; there is no partial write or situation where only part of an action was completed. For a database, this means that if data was written in a single transaction, that transaction either completes entirely or fails completely with no data left behind in a partial or corrupt state. Consistency means that transactions take the state from one valid state to another valid state, with no way to enter an invalid state. Isolation means that multiple readers/writers to the same database do not interfere with each other and can proceed without worrying about the effects of another action occurring. Durability means that state is preserved across failures and shutdowns, e.g. that state is persisted to permanent storage.

After completing his studies at the Baghdad University, he entered the Iraqi Army, where he served shortly as a tank unit commander. In the early 1960, he was made the head of the Polit bureau of the Kurdistan Democratic Party (KDP). When in September 1961, the Kurdish uprising for the rights of the Kurds in northern Iraq was declared against the Baghdad government of Abd al-Karim Qasim, Talabani took charge of the Kirkuk and Silemani battlefronts and organized and led separatist movements in Mawat, Rezan and the Qaradagh regions. In March 1962, he led a coordinated offensive that brought about the liberation of the district of Sharbazher from Iraqi government forces. When not engaged in fighting in the early and mid-1960s, Talabani undertook numerous diplomatic missions, representing the Kurdish leadership at meetings in Europe and the Middle East. In 1964, he and the Barzani family had a dispute over the direction of the KDP and Talabani left Iraq and settled in Iran. In Iran he purchased weapons without the knowledge of the Barzanis, following which he was expelled from the KDP in summer 1964. After the March 1970 agreement between the Iraqi government and the Kurdish rebels, Talabani returned to Iraqi Kurdistan, and rejoined the KDP, even though he wouldn't hold an office at the time. The Kurdish separatist movement collapsed in March 1975, after Iran ended their support in exchange for a border agreement with Iraq. This agreement was the 1975 Algiers Agreement, where Iraq gave up claims to the Shatt al-Arab (Arvand Rūd) waterway and Khuzestan, which later became the basis for the Iran–Iraq War. Believing it was time to give a new direction to the Kurdish separatists and to the Kurdish society, Talabani, with a group of Kurdish intellectuals and activists, founded the Kurdish Patriotic Union of Kurdistan (Yekiaiti Nishtimani Kurdistan). In 1976, he began organizing an armed campaign for Kurdish independence inside Iraqi Kurdistan. From 1977 onwards, he established the PUK base within Iranian Kurdistan in Nawkhan and another one in Iraqi Kurdistan in Qandil. During the 1980s, Talabani sided with Iran and led a Kurdish struggle from bases inside Iraq until the crackdown against Kurdish separatists from 1987 to 1988. Following the invasion of Kuwait by Iraq in August 1990, he travelled to the United States, in order to offer his services and troops to the United States and raise support for the PUK. But his attempts did not bear the success he expected at the time. In 1991, he helped inspire a renewed effort for Kurdish independence. He negotiated a ceasefire with the Iraqi Ba'athist government that saved the lives of many Kurds and worked closely with the United States, United Kingdom, France and other countries to set up the safe haven in Iraqi Kurdistan. In 1992 the Kurdistan Regional Government was founded. He was also supportive of peace negotiations between the Kurdistan Workers' Party and Turkey, and was also present as Abdullah Öcalan announced the ceasefire of the PKK on the 17 March and prolonged it indefinitely on the 16 April 1993. Talabani pursued a negotiated settlement to the Iraqi Kurdish Civil War, as well as the larger issue of Kurdish rights in the current regional context. He worked closely with other Kurdish politicians as well as the rest of the Iraqi opposition factions. In close coordination with Masoud Barzani, Talabani and the Kurds played a key role as a partner of the U.S. led Coalition in the invasion of Iraq. Talabani was a member of the Iraqi Governing Council which negotiated the Transitional Administrative Law (TAL), Iraq's interim constitution. The TAL governed all politics in Iraq and the process of writing and adopting the final constitution.

Using the passage, summarise the key events of Jalal Talabani's career as a bullet point list

Sure, Jalal Talabani was involved in the following key events during the 1970's: * March 1970: Talabani returned to Iraqi Kurdistan and rejoined the Kurdistan Democratic Party * March 1975: Talabani founded the Kurdish Patriotic Union of Kurdistan (PUK) * 1976: Talabani orchestrated an armed campaign for Kurdish independence inside Iraqi Kurdistan * 1977: Talabani founded two PUK bases, one in Nawkhan in Iranian Kurdistan and the other in Qandil in Iraqi Kurdistan

Greater London's population declined in the decades after the Second World War, from an estimated peak of 8.6 million in 1939 to around 6.8 million in the 1980s. The principal ports for London moved downstream to Felixstowe and Tilbury, with the London Docklands area becoming a focus for regeneration, including the Canary Wharf development. This was born out of London's increasing role as an international financial centre in the 1980s. The Thames Barrier was completed in the 1980s to protect London against tidal surges from the North Sea.

Given this paragraph about London, give me a reason why the London Docklands became a focus for regeneration in the 1980s.

One reason why the London Docklands became a focus for regeneration in the 1980s is that London had an increasing role as an international financial centre.

Who is Lee Jung-sik

Lee Jung-sik (born July 6, 1995) is a South Korean actor and model. He is best known for his starring roles in the web series I Have Three Boyfriends (2019) and Summer Guys (2021).

1607.03542

Much recent work on semantic parsing has been evaluated using the WebQuestions dataset BIBREF3 . This dataset is not suitable for evaluating our model because it was filtered to only questions that are mappable to Freebase queries. In contrast, our focus is on language that is not directly mappable to Freebase. We thus use the dataset introduced by Krishnamurthy and Mitchell krishnamurthy-2015-semparse-open-vocabulary, which consists of the ClueWeb09 web corpus along with Google's FACC entity linking of that corpus to Freebase BIBREF9 . For training data, 3 million webpages from this corpus were processed with a CCG parser to produce logical forms BIBREF10 . This produced 2.1m predicate instances involving 142k entity pairs and 184k entities. After removing infrequently-seen predicates (seen fewer than 6 times), there were 25k categories and 4.2k relations. We thus use the dataset introduced by Krishnamurthy and Mitchell krishnamurthy-2015-semparse-open-vocabulary, which consists of the ClueWeb09 web corpus along with Google's FACC entity linking of that corpus to Freebase BIBREF9 .

What knowledge base do they use?

The answers are shown as follows: * Freebase

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FLOAT SELECTED: Table 1: Example Twitter Customer Service Conversation FLOAT SELECTED: Table 1: Example Twitter Customer Service Conversation

Do they evaluate only on English datasets?

Yes.

Propaganda has been tackled mostly at the article level. BIBREF3 created a corpus of news articles labelled as propaganda, trusted, hoax, or satire. BIBREF4 experimented with a binarized version of that corpus: propaganda vs. the other three categories. BIBREF5 annotated a large binary corpus of propagandist vs. non-propagandist articles and proposed a feature-based system for discriminating between them. In all these cases, the labels were obtained using distant supervision, assuming that all articles from a given news outlet share the label of that outlet, which inevitably introduces noise BIBREF6. A related field is that of computational argumentation which, among others, deals with some logical fallacies related to propaganda. BIBREF7 presented a corpus of Web forum discussions with instances of ad hominem fallacy. BIBREF8, BIBREF9 introduced Argotario, a game to educate people to recognize and create fallacies, a by-product of which is a corpus with $1.3k$ arguments annotated with five fallacies such as ad hominem, red herring and irrelevant authority, which directly relate to propaganda. Unlike BIBREF8, BIBREF9, BIBREF7, our corpus uses 18 techniques annotated on the same set of news articles. Moreover, our annotations aim at identifying the minimal fragments related to a technique instead of flagging entire arguments. The most relevant related work is our own, which is published in parallel to this paper at EMNLP-IJCNLP 2019 BIBREF10 and describes a corpus that is a subset of the one used for this shared task. The most relevant related work is our own, which is published in parallel to this paper at EMNLP-IJCNLP 2019 and describes a corpus that is a subset of the one used for this shared task.

What does the most relevant related work describe?

Describes a corpus that is a subset of the one used for this shared task.

what the most popular books written by Malcom Gladwell?

Malcolm Timothy Gladwell CM is an English-born Canadian journalist, author, and public speaker. He has been a staff writer for The New Yorker since 1996. He has published 7 books including: The Tipping Point, Blink, Outliers, What the Dog Saw, David and Goliath, Talking to Strangers and The Bomber Mafia. A few of them are NYT bestsellers.

Identify which animal species is alive or extinct: Pliosaur, Marine Iguana

Pliosaur is extinct, Marine Iguana is alive.

What are some common character types in D&D?

Dungeons & Dragons (commonly D&D) has many character classes, and those classes have evolved throughout the different releases. The core character classes in the 3rd edition are Barbarian, Bard, Cleric, Druid, Fighter, Monk, Paladin, Ranger, Rogue, Sorcerer, and Wizard.

Which US state produces the most wine?

California produces the most wine in the United States, followed by Washington State.

Name top 10 companies from the Fortune 500 list in 2022

1. Walmart   2. Amazon.com   3. Apple  4. CVS Health 5. UnitedHealth Group  6. Exxon Mobil 7. Berkshire Hathaway   8. Alphabet  9. McKesson   10. AmerisourceBergen

Wikipedia is the largest source of open and collaboratively curated knowledge in the world. Introduced in 2001, it has evolved into a reference work with around 5m pages for the English Wikipedia alone. In addition, entities and event pages are updated quickly via collaborative editing and all edits are encouraged to include source citations, creating a knowledge base which aims at being both timely as well as authoritative. As a result, it has become the preferred source of information consumption about entities and events. Moreso, this knowledge is harvested and utilized in building knowledge bases like YAGO BIBREF0 and DBpedia BIBREF1 , and used in applications like text categorization BIBREF2 , entity disambiguation BIBREF3 , entity ranking BIBREF4 and distant supervision BIBREF5 , BIBREF6 . However, not all Wikipedia pages referring to entities (entity pages) are comprehensive: relevant information can either be missing or added with a delay. Consider the city of New Orleans and the state of Odisha which were severely affected by cyclones Hurricane Katrina and Odisha Cyclone, respectively. While Katrina finds extensive mention in the entity page for New Orleans, Odisha Cyclone which has 5 times more human casualties (cf. Figure FIGREF2 ) is not mentioned in the page for Odisha. Arguably Katrina and New Orleans are more popular entities, but Odisha Cyclone was also reported extensively in national and international news outlets. This highlights the lack of important facts in trunk and long-tail entity pages, even in the presence of relevant sources. In addition, previous studies have shown that there is an inherent delay or lag when facts are added to entity pages BIBREF7 . To remedy these problems, it is important to identify information sources that contain novel and salient facts to a given entity page. However, not all information sources are equal. The online presence of major news outlets is an authoritative source due to active editorial control and their articles are also a timely container of facts. In addition, their use is in line with current Wikipedia editing practice, as is shown in BIBREF7 that almost 20% of current citations in all entity pages are news articles. We therefore propose news suggestion as a novel task that enhances entity pages and reduces delay while keeping its pages authoritative. Existing efforts to populate Wikipedia BIBREF8 start from an entity page and then generate candidate documents about this entity using an external search engine (and then post-process them). However, such an approach lacks in (a) reproducibility since rankings vary with time with obvious bias to recent news (b) maintainability since document acquisition for each entity has to be periodically performed. To this effect, our news suggestion considers a news article as input, and determines if it is valuable for Wikipedia. Specifically, given an input news article INLINEFORM0 and a state of Wikipedia, the news suggestion problem identifies the entities mentioned in INLINEFORM1 whose entity pages can improve upon suggesting INLINEFORM2 . Most of the works on knowledge base acceleration BIBREF9 , BIBREF10 , BIBREF11 , or Wikipedia page generation BIBREF8 rely on high quality input sources which are then utilized to extract textual facts for Wikipedia page population. In this work, we do not suggest snippets or paraphrases but rather entire articles which have a high potential importance for entity pages. These suggested news articles could be consequently used for extraction, summarization or population either manually or automatically – all of which rely on high quality and relevant input sources. We identify four properties of good news recommendations: salience, relative authority, novelty and placement. First, we need to identify the most salient entities in a news article. This is done to avoid pollution of entity pages with only marginally related news. Second, we need to determine whether the news is important to the entity as only the most relevant news should be added to a precise reference work. To do this, we compute the relative authority of all entities in the news article: we call an entity more authoritative than another if it is more popular or noteworthy in the real world. Entities with very high authority have many news items associated with them and only the most relevant of these should be included in Wikipedia whereas for entities of lower authority the threshold for inclusion of a news article will be lower. Third, a good recommendation should be able to identify novel news by minimizing redundancy coming from multiple news articles. Finally, addition of facts is facilitated if the recommendations are fine-grained, i.e., recommendations are made on the section level rather than the page level (placement). Approach and Contributions. We propose a two-stage news suggestion approach to entity pages. In the first stage, we determine whether a news article should be suggested for an entity, based on the entity's salience in the news article, its relative authority and the novelty of the article to the entity page. The second stage takes into account the class of the entity for which the news is suggested and constructs section templates from entities of the same class. The generation of such templates has the advantage of suggesting and expanding entity pages that do not have a complete section structure in Wikipedia, explicitly addressing long-tail and trunk entities. Afterwards, based on the constructed template our method determines the best fit for the news article with one of the sections. We evaluate the proposed approach on a news corpus consisting of 351,982 articles crawled from the news external references in Wikipedia from 73,734 entity pages. Given the Wikipedia snapshot at a given year (in our case [2009-2014]), we suggest news articles that might be cited in the coming years. The existing news references in the entity pages along with their reference date act as our ground-truth to evaluate our approach. In summary, we make the following contributions. We therefore propose news suggestion as a novel task that enhances entity pages and reduces delay while keeping its pages authoritative.

What is the novel task proposed by the authors in this paper?

The novel task enhances entity pages and reduces delay while keeping its pages authoritative.

If we didn't want to risk a having a flat tire, which of these vehicles would we avoid: bus, car, truck, plane, train, kayak, helicopter.

Flat tires are only possible on a car, bus and truck, so avoid those. You can take a plane, train, kayak or helicopter without risking having a flat tire.

What's the best BBQ place in Austin

Texas BBQ is one of the most famous and popular food choices for both visitors and locals. The Franklin BBQ in Austin gets the highest hypes all these years after former President Obama's visit. Its long waiting line has not pushed foodies and pilgrims away. Other popular but less crowded places includes Terry & Blacks BBQ, County Line, and Rudy's BBQ.

My parents are staying with me this weekend. What should I do with them?

Your parents are probably just happy to spend time with you. Still, it can be difficult to know how to entertain them in your city. Here are some ideas for things to do with your parents: - Take them to a local brewery or winery - Find an easy hike to do one morning - Cook them a meal at home and watch a parent-friendly movie - Go to a local sporting event - Have some people over for food and games so your parents can get to know your friends

Mushroom power-ups appear in almost every Super Mario game. The most iconic of these is the Super Mushroom. The Super Mushroom increases the character's size, turning them into a "Super" variant, and allows them to break certain blocks. When hit by an enemy, the character reverts to their smaller size instead of losing a life. When the character is in their "Super" form, most blocks that would contain a Super Mushroom instead offer a more powerful power-up such as the Fire Flower. The Super Mushroom is similar in appearance to the Amanita muscaria, with an ivory stalk below a most commonly red and white (originally red and orange) spotted cap. Created by chance, Shigeru Miyamoto stated in an interview that beta tests of Super Mario Bros. proved Mario too tall, so the development team implemented mushrooms to grow and shrink Mario. Different variants of mushroom power-ups appear in the series. For example, Super Mario Bros.: The Lost Levels introduces the Poison Mushroom, which causes damage when collected; and New Super Mario Bros. introduces the Mini Mushroom, which shrinks the character to miniature size; and the Mega Mushroom, grows the character into a towering, invulnerable giant who destroys enemies and the environment by running through them. Super Mario Bros. 3 introduces suits to the Super Mario series, many of which are based on animals or Mario enemies. The Raccoon Suit (provisioned by a Super Leaf) and the Tanooki Suit each provide the character with a tail that enables flight. In addition, the Tanooki Suit lets the character spontaneously change into an invincible statue for about five seconds. Super Mario Bros. 3 includes a Hammer Bros. suit, which allows Mario and Luigi to throw hammers as projectiles to defeat enemies at a distance. Other suits in later games in the series include the Frog Suit, Penguin Suit, Cat Suit, Boomerang Suit, and Bee Suit.

Given the text, write a summary of the effects of the Super Mushroom power-up in the Super Mario videogame series

The Super Mushroom is one of the main power-ups in the Super Mario games and appear in almost every game of the series. The Super Mushroom makes Mario bigger, more stronger and therefore able to break certain blocks. When Mario takes damage, for example when hit by an enemy. he loses the power-up, and turns back to its original small size. There are other variants of the mushroom power-up throughout the Super Mario series.

the first person was on the moon?

Apollo 11 was the American spaceflight that first landed humans on the Moon. Commander Neil Armstrong and lunar module pilot Buzz Aldrin landed the Apollo Lunar Module Eagle on July 20, 1969, at 20:17, and Armstrong became the first person to step onto the Moon's surface six hours and 39 minutes later, on July 21 at 02:56 UTC. Aldrin joined him 19 minutes later, and they spent about two and a quarter hours together exploring the site they had named Tranquility Base upon landing. Armstrong and Aldrin collected 47.5 pounds (21.5 kg) of lunar material to bring back to Earth as pilot Michael Collins flew the Command Module Columbia in lunar orbit, and were on the Moon's surface for 21 hours, 36 minutes before lifting off to rejoin Columbia.

What is the best kind of bread?

Sourdough is the best type of bread because it is the most multi-faceted bread. You can have it as grilled cheese, with eggs and avocado for breakfast, even as a bowl for artichoke dip. You can also use old sourdough as croutons, or use the leftover discard to make waffles or crackers. With sourdough dough you can make focaccia, sandwich bread, and pizza! Sourdough also doesn't require much ingredients, you only need flour, salt, and water.

1909.00694

FLOAT SELECTED: Table 4: Results for small labeled training data. Given the performance with the full dataset, we show BERT trained only with the AL data. FLOAT SELECTED: Table 4: Results for small labeled training data. Given the performance with the full dataset, we show BERT trained only with the AL data.

How big are improvements of supervszed learning results trained on smalled labeled data enhanced with proposed approach copared to basic approach?

3%

1709.08299

The previously mentioned datasets are all in English. To add diversities to the reading comprehension datasets, Cui et al. cui-etal-2016 proposed the first Chinese cloze-style reading comprehension dataset: People Daily & Children's Fairy Tale, including People Daily news datasets and Children's Fairy Tale datasets. They also generate the data in an automatic manner, which is similar to the previous datasets. They choose short articles (several hundreds of words) as Document and remove a word from it, whose type is mostly named entities and common nouns. Then the sentence that contains the removed word will be regarded as Query. To add difficulties to the dataset, along with the automatically generated evaluation sets (validation/test), they also release a human-annotated evaluation set. The experimental results show that the human-annotated evaluation set is significantly harder than the automatically generated questions. The reason would be that the automatically generated data is accordance with the training data which is also automatically generated and they share many similar characteristics, which is not the case when it comes to human-annotated data. The previously mentioned datasets are all in English

For which languages most of the existing MRC datasets are created?

English

The Driftwood River is a 15.5-mile-long (24.9 km) tributary of the East Fork of the White River in central Indiana in the United States. Via the White, Wabash and Ohio rivers, it is part of the watershed of the Mississippi River. It is a short river, formed by the confluence of two longer streams a short distance upstream of its mouth. Course The Driftwood River is formed in southeastern Johnson County, 1 mile (1.6 km) west of Edinburgh, by the confluence of Sugar Creek and the Big Blue River. It flows generally southwardly through northwestern Bartholomew County to Columbus, where it joins the Flatrock River to form the East Fork of the White River. Near Edinburgh, IN, Driftwood River has a mean annual discharge of 1,210 cubic feet per second.[

Given a reference text about the Driftwood River, tell me what main river it is part of.

The Driftwood River is part of the Mississippi River.

Using GANs, we can train generative models in a two-player game setting between a discriminator and a generator, where the discriminator (a binary classifier) learns to distinguish between real and fake data samples and the generator tries to fool the discriminator by generating authentic and high quality output BIBREF16. GANs have shown to be successful in image generation tasks BIBREF17 and recently, some progress has been observed in text generation BIBREF13, BIBREF12, BIBREF15. Our generator is a language model trained using backpropagation through time BIBREF18. During the pre-training phase we optimize for MLE and during the GAN training phase, we optimize on the creativity reward from the discriminator. The discriminator's encoder has the same architecture as the generator encoder module with the addition of a pooled decoder layer. The decoder contains 3 $[Dense Batch Normalization,ReLU]$ blocks and an addtional $Sigmoid$ layer. The discriminator decoder takes the hidden state at the last time step of a sequence concatenated with both the max-pooled and mean-pooled representation of the hidden states BIBREF19 and outputs a number in the range $[0,1]$. The difficulty of using GANs in text generation comes from the discrete nature of text, making the model non-differentiable hence, we update parameters for the generator model with policy gradients as described in Yu BIBREF15. We utilize AWD-LSTM BIBREF20 and TransformerXL BIBREF21 based language models. For model hyperparameters please to refer to Supplementary Section Table TABREF5. We use Adam optimizer BIBREF22 with $\beta 1= 0.7$ and $\beta 2= 0.8$ similar to BIBREF19 and use a batch size of 50. Other practices for LM training were the same as BIBREF21 and BIBREF20 for Transformer-XL and AWD-LSTM respectively. We refer to our proposed GAN as Creative-GAN and compare it to a baseline (a language model equivalent to our pre-trained generator) and a GumbelGAN model BIBREF14 across all proposed datasets. We use three creative English datasets with distinct linguistic characteristics: (1) A corpus of 740 classical and contemporary English poems, (2) a corpus of 14950 metaphor sentences retrieved from a metaphor database website and (3) a corpus of 1500 song lyrics ranging across genres. The mix of linguistic styles within this corpus offers the potential for interesting variation during the generation phase. We use the same pre-processing as in earlier work BIBREF19, BIBREF23. We reserve 10% of our data for test set and another 10% for our validation set. We first pre-train our generator on the Gutenberg dataset BIBREF24 for 20 epochs and then fine-tune BIBREF19 them to our target datasets with a language modeling objective. The discriminator's encoder is initialized to the same weights as our fine-tuned language model. Once we have our fine-tuned encoders for each target dataset, we train in an adversarial manner. The discriminator objective here is to score the quality of the creative text. The discriminator is trained for 3 iterations for every iteration of the generator, a practice seen in previous work BIBREF25. Creative-GAN relies on using the reward from the discriminator BIBREF12, BIBREF15 for backpropagation. We follow a similar training procedure for GumbelGAN. Outputs are generated through sampling over a multinomial distribution for all methods, instead of $argmax$ on the log-likelihood probabilities, as sampling has shown to produce better output quality BIBREF4. Please refer to Supplementary Section Table TABREF6 for training parameters of each dataset and Table TABREF5 for hyperparameters of each encoder. We pick these values after experimentation with our validation set. Training and output generation code can be found online. We use three creative English datasets with distinct linguistic characteristics: (1) A corpus of 740 classical and contemporary English poems, (2) a corpus of 14950 metaphor sentences retrieved from a metaphor database website 1 and (3) a corpus of 1500 song lyrics ranging across genres.

Is the dataset English or Chinese?

English.

What are the words of House Bulwer?

"Death Before Disgrace"

Nomadism is a way of life in which people don't have a permanent place to live. Instead, they move around a certain area in search of food and grazing land.

What is the meaning of "tidal coupling"?

Tidal coupling or synchronous rotation is what causes one astronomical object's face to be fixed pointing to another, just as the Moon's visible face is always pointing to the Earth. A coupled object takes the same amount of time to rotate about its axis as it does to translate around its companion. This synchronous rotation, also known as captured rotation or synchronous rotation, causes one hemisphere to point continuously toward the companion object. Normally, only the satellite couples around a larger planet, but if the mass difference between the two bodies and the distance between them is small, both objects may have a reciprocal tidal coupling, as Pluto and Charon do.

Putlacheruvu is a village in the Mandavalli Mandal, Krishna district, Andhra Pradesh state, India. According to the 2011 census it has a population of 1873 living in 583 households. Geography The village of Putlacheruvu is 6 km from Kolleru Lake and consists of three areas: Krishnapuram, Ramapuram, and Putlacheruvu proper. It features a 7.5 acre freshwater lake with a Naandi Foundation water treatment plant (which is a small failure )and a 12-foot tall statue of Y. S. Rajasekhara Reddy at the south end of the lake. There are three large canals off the Krishna river on both ends of the village. One of these is used for drinking water and the other two are sewage canals used for pumping water into the fish tanks. The soil is very rich and suitable for many crops including rice and coconut. The local wildlife includes parrots, sparrows, minas, and black crows. In the evenings and early mornings, snake birds form in large migrations.

Given a reference text about Putlacheruvu, how many canals are there and what are they used for?

There are three canals in Putlacheruvu. One is used for drinking water and the other two are sewage canals used for pumping water into the fish tanks.

What are the best complex board games for two players?

Here are some of the best complex, strategic games that play well with two players: - Chess - Hive - Go - Targi - Star Wars: Rebellion - Twilight Struggle - Agricola - Food Chain Magnate - Brass: Birmingham - The Gallerist - Innovation - Fields of Arle - War of the Ring - Madeira

What does SRO mean for concert tickets?

SRO refers to Standing Room Only and describes tickets for a section with no seating. These tickets are sometimes sold at a discount at concerts or broadway shows. People may prefer standing sections as they are often closer to the stage and give a more energetic experience.

The building is the former National Union Fire Insurance Company building built circa 1923–1925 in the Early Classical style. The building was purchased by the university in 1968 for $1.875 million ($14.6 million in 2021 dollars), and was originally purposed for faculty offices. It was known as the Social Sciences Building until 1972 when it was renamed Mervis Hall and designated as the home of the Graduate School of Business. In 1983, when the Graduate School of Business moved into a new building also named Mervis Hall, it was renamed Thackeray Hall.

Give me a summary of the history of Thackeray Hall at the University of Pittsburgh based on this passage.

Thackeray Hall used to be the building for the National Union Fire Insurance Company until it was purchased by the University of Pittsburgh in 1968 for $1.875 million. First, it was used for faculty offices, then later known as the Social Sciences Building until it was renamed Mervis Hall. In 1983, it was renamed Thackeray Hall and has housed the university's Department of Mathematics since then.

WMT provides task participants with large amounts of monolingual data, both in-domain and out-of-domain. We exploit this monolingual data for training as described in BIBREF2 . Specifically, we sample a subset of the available target-side monolingual corpora, translate it automatically into the source side of the respective language pair, and then use this synthetic parallel data for training. For example, for EN INLINEFORM0 RO, the back-translation is performed with a RO INLINEFORM1 EN system, and vice-versa. 2015arXiv151106709S motivate the use of monolingual data with domain adaptation, reducing overfitting, and better modelling of fluency. We sample monolingual data from the News Crawl corpora, which is in-domain with respect to the test set. The amount of monolingual data back-translated for each translation direction ranges from 2 million to 10 million sentences. Statistics about the amount of parallel and synthetic training data are shown in Table TABREF9 . With dl4mt, we observed a translation speed of about 200000 sentences per day (on a single Titan X GPU). We sample monolingual data from the News Crawl corpora6, which is in-domain with respect to the test set.

Where is the monolingual data extracted from?

The News Crawl corpora.

Long before any knowledge of electricity existed, people were aware of shocks from electric fish. Ancient Egyptian texts dating from 2750 BCE referred to these fish as the "Thunderer of the Nile", and described them as the "protectors" of all other fish. Electric fish were again reported millennia later by ancient Greek, Roman and Arabic naturalists and physicians. Several ancient writers, such as Pliny the Elder and Scribonius Largus, attested to the numbing effect of electric shocks delivered by electric catfish and electric rays, and knew that such shocks could travel along conducting objects. Patients with ailments such as gout or headache were directed to touch electric fish in the hope that the powerful jolt might cure them. Ancient cultures around the Mediterranean knew that certain objects, such as rods of amber, could be rubbed with cat's fur to attract light objects like feathers. Thales of Miletus made a series of observations on static electricity around 600 BCE, from which he believed that friction rendered amber magnetic, in contrast to minerals such as magnetite, which needed no rubbing. Thales was incorrect in believing the attraction was due to a magnetic effect, but later science would prove a link between magnetism and electricity. According to a controversial theory, the Parthians may have had knowledge of electroplating, based on the 1936 discovery of the Baghdad Battery, which resembles a galvanic cell, though it is uncertain whether the artifact was electrical in nature. Electricity would remain little more than an intellectual curiosity for millennia until 1600, when the English scientist William Gilbert wrote De Magnete, in which he made a careful study of electricity and magnetism, distinguishing the lodestone effect from static electricity produced by rubbing amber. He coined the New Latin word electricus ("of amber" or "like amber",, elektron, the Greek word for "amber") to refer to the property of attracting small objects after being rubbed. This association gave rise to the English words "electric" and "electricity", which made their first appearance in print in Thomas Browne's Pseudodoxia Epidemica of 1646. Further work was conducted in the 17th and early 18th centuries by Otto von Guericke, Robert Boyle, Stephen Gray and C. F. du Fay. Later in the 18th century, Benjamin Franklin conducted extensive research in electricity, selling his possessions to fund his work. In June 1752 he is reputed to have attached a metal key to the bottom of a dampened kite string and flown the kite in a storm-threatened sky. A succession of sparks jumping from the key to the back of his hand showed that lightning was indeed electrical in nature. He also explained the apparently paradoxical behavior of the Leyden jar as a device for storing large amounts of electrical charge in terms of electricity consisting of both positive and negative charges In 1775, Hugh Williamson reported a series of experiments to the Royal Society on the shocks delivered by the electric eel; that same year the surgeon and anatomist John Hunter described the structure of the fish's electric organs. In 1791, Luigi Galvani published his discovery of bioelectromagnetics, demonstrating that electricity was the medium by which neurons passed signals to the muscles. Alessandro Volta's battery, or voltaic pile, of 1800, made from alternating layers of zinc and copper, provided scientists with a more reliable source of electrical energy than the electrostatic machines previously used. The recognition of electromagnetism, the unity of electric and magnetic phenomena, is due to Hans Christian Ørsted and André-Marie Ampère in 1819–1820. Michael Faraday invented the electric motor in 1821, and Georg Ohm mathematically analysed the electrical circuit in 1827. Electricity and magnetism (and light) were definitively linked by James Clerk Maxwell, in particular in his "On Physical Lines of Force" in 1861 and 1862.  While the early 19th century had seen rapid progress in electrical science, the late 19th century would see the greatest progress in electrical engineering. Through such people as Alexander Graham Bell, Ottó Bláthy, Thomas Edison, Galileo Ferraris, Oliver Heaviside, Ányos Jedlik, William Thomson, 1st Baron Kelvin, Charles Algernon Parsons, Werner von Siemens, Joseph Swan, Reginald Fessenden, Nikola Tesla and George Westinghouse, electricity turned from a scientific curiosity into an essential tool for modern life. In 1887, Heinrich Hertz discovered that electrodes illuminated with ultraviolet light create electric sparks more easily. In 1905, Albert Einstein published a paper that explained experimental data from the photoelectric effect as being the result of light energy being carried in discrete quantized packets, energising electrons. This discovery led to the quantum revolution. Einstein was awarded the Nobel Prize in Physics in 1921 for "his discovery of the law of the photoelectric effect". The photoelectric effect is also employed in photocells such as can be found in solar panels. The first solid-state device was the "cat's-whisker detector" first used in the 1900s in radio receivers. A whisker-like wire is placed lightly in contact with a solid crystal (such as a germanium crystal) to detect a radio signal by the contact junction effect. In a solid-state component, the current is confined to solid elements and compounds engineered specifically to switch and amplify it. Current flow can be understood in two forms: as negatively charged electrons, and as positively charged electron deficiencies called holes. These charges and holes are understood in terms of quantum physics. The building material is most often a crystalline semiconductor. Solid-state electronics came into its own with the emergence of transistor technology. The first working transistor, a germanium-based point-contact transistor, was invented by John Bardeen and Walter Houser Brattain at Bell Labs in 1947, followed by the bipolar junction transistor in 1948.

Extract references to electric fish in ancient texts from the following passage.

Long before any knowledge of electricity existed, people were aware of shocks from electric fish. Ancient Egyptian texts dating from 2750 BCE referred to these fish as the "Thunderer of the Nile", and described them as the "protectors" of all other fish. Electric fish were again reported millennia later by ancient Greek, Roman and Arabic naturalists and physicians. Several ancient writers, such as Pliny the Elder and Scribonius Largus, attested to the numbing effect of electric shocks delivered by electric catfish and electric rays, and knew that such shocks could travel along conducting objects.

Review aggregator Rotten Tomatoes reported an approval rating of 92% based on 74 reviews, with an average rating of 8.2/10. The website's critics consensus reads, "Warm and winsome, if not particularly hilarious, Ted Lasso fleshes out its promo premise with unrelenting optimism and a charming turn from Jason Sudeikis." Metacritic gave the first season a weighted average score of 71 out of 100 based on 21 reviews, indicating "generally favorable reviews". Kristen Baldwin of Entertainment Weekly gave the series an A− and wrote, "There's nothing groundbreaking about the way Ted Lasso's story beats play out, but the show—a mix of workplace antics, sentimental sports inspo, and soapy romance—is undeniably winning." Reviewing the series for Rolling Stone, Alan Sepinwall described the series as "extremely likable throughout, but it's more a hypothetical comedy than an actual one. There are long stretches where Juno Temple is the only actor even trying to sell what few jokes are in the scripts." and gave a rating of 3/5. Writing for The Guardian, Benjamin Lee gave it 2/5, describing it as "a show that isn't unwatchably bad but isn't really much of anything", and suggesting that some of its humor was "rooted in some questionable and uneasy stereotypes". As the season went on, critical appreciation for the show increased. After the eighth episode aired, Caroline Framke of Variety published a review with the headline "For Your Reconsideration: Ted Lasso". She went on to say, "Above all odds, Ted Lasso chipped away at my skepticism until there was none left—just like the character himself does to everyone he meets", adding, "At a time when just about everything feels catastrophic, there's something undeniably satisfying about spending some time with good people who are just trying to be the best they can, on and off the field." Keri Lumm of Paste said, after the airing of the penultimate episode, "Ted Lasso is the wholesome American hero we need", going on to say "... the landscape of television has felt kind of gloomy, so imagine my surprise when I turned on the TV to Ted Lasso and felt a swelling of a now unfamiliar emotion—hope." And after the finale aired, Lea Palmieri from Decider said: "Every step of the way, Ted Lasso proves to be comforting and entertaining and somehow both a distraction and a reminder that kindness is out there, not just on this fictional show, not just across the pond, but deep in the heart of America too."

Please give me a short summary of the critical response received by Season 1 of the Apple TV Series "Ted Lasso"

Season 1 of Ted Lasso was rated 92% on Rotten Tomatoes (74 reviews) and 71/100 on Metacritic (21 reviews). Some of the positive reviews described the show as winsome, comforting, entertaining and hopeful. Some viewers enjoyed the good-hearted cheer and optimism the show embodied during the current times of despair and hopelessness. One critic rated the show 2/5 and described it to be " not unwatchably bad".

Shared-private feature extractor is mainly used for extracting shared features and private features among different tasks. In this paper, we apply the encoder module of transformer BIBREF16 (henceforth, transformer encoder) to the shared-private extractor of our model. Specially, we employ two transformer encoders to encode the input embeddings of the two tasks as their respective private features. A transformer encoder is used to encode simultaneously the input embeddings of the two tasks as shared features of both tasks. This process is illustrated by the shared-private layer of Figure FIGREF6. The red box in the middle denotes the extraction of shared features and the left and right boxes represent the extraction of private features of two tasks. Next, we take the extraction of the private feature of fake news detection as an example to elaborate on the process of transformer encoder. The kernel of transformer encoder is the scaled dot-product attention, which is a special case of attention mechanism. It can be precisely described as follows: where ${\rm \textbf {Q}} \in \mathbb {R}^{l \times (d_p+d_w)}$, ${\rm \textbf {K}} \in \mathbb {R}^{l \times (d_p+d_w)}$, and ${\rm \textbf {V}} \in \mathbb {R}^{l \times (d_p+d_w)}$ are query matrix, key matrix, and value matrix, respectively. In our setting, the query ${\rm \textbf {Q}}$ stems from the inputs itself, i.e., ${\rm \textbf {Q}}={\rm \textbf {K}}={\rm \textbf {V}}={\rm \textbf {E}}$. To explore the high parallelizability of attention, transformer encoder designs a multi-head attention mechanism based on the scaled dot-product attention. More concretely, multi-head attention first linearly projects the queries, keys and values $h$ times by using different linear projections. Then $h$ projections perform the scaled dot-product attention in parallel. Finally, these results of attention are concatenated and once again projected to get the new representation. Formally, the multi-head attention can be formulated as follows: where ${\rm \textbf {W}}_i^Q \in \mathbb {R}^{(d_p+d_w) \times d_k}$, ${\rm \textbf {W}}_i^K \in \mathbb {R}^{(d_p+d_w) \times d_k}$, ${\rm \textbf {W}}_i^V \in \mathbb {R}^{(d_p+d_w) \times d_k}$ are trainable projection parameters. $d_k$ is $(d_p+d_w)/h$, $h$ is the number of heads. In Eq.(DISPLAY_FORM11), ${\rm \textbf {W}}^o \in \mathbb {R}^{(d_p+d_w) \times (d_p+d_w)}$ is also trainable parameter. To explore the high parallelizability of attention, transformer encoder designs a multi-head attention mechanism based on the scaled dot-product attention.

What mechanism does the transformer encoder design to explore the high parallelizability of attention?

A multi-head attention mechanism based on the scaled dot-product attention.

James A. Garfield, the 20th president of the United States, was shot at the Baltimore and Potomac Railroad Station in Washington, D.C., at 9:30 am on Saturday, July 2, 1881. He died in Elberon, New Jersey, 79 days later on September 19, 1881. The shooting occurred less than four months into his term as president. Garfield's assassin was Charles J. Guiteau, whose motive was revenge against Garfield for an imagined political debt, and getting Chester A. Arthur elevated to president. Guiteau was convicted of Garfield's murder and executed by hanging one year after the shooting. Part of Guiteau's preserved brain is on display at the Mütter Museum at the College of Physicians of Philadelphia. Guiteau's bones and more of his brain, along with Garfield's backbone and a few ribs, are kept at the National Museum of Health and Medicine, at the Army's Forest Glen Annex in Silver Spring, Maryland. Garfield's assassination was instrumental to the passage of the Pendleton Civil Service Reform Act on January 16, 1883. Garfield himself had called for civil service reform in his inaugural address and supported it as president in the belief that it would make government more efficient. It was passed as something of a memorial to the fallen President. Arthur lost the Republican nomination in 1884 to Blaine, who went on to lose a close election to Democrat Grover Cleveland.

Given that preserving human brains for more than a century is fairly uncommon, what do you think was the motivation for preserving Guiteau's brain?

One reason for preserving the brain may have been to study structural abnormalities that could have explained Guiteau's violent behavior. Alternatively, because Guiteau was well-known and infamous, his brain may have been preserved for use as a memento or as a warning to discourage presidential assassinations in the future.

Who betrayed Jesus to the Romans

Judas Escariot

Balloon was a brush script commonly used for signage or display purposes. It was designed in 1939 by Max R. Kaufmann, for American Type Founders, in response to Howard Allen Trafton's Cartoon, cut for Bauer Type Foundry in 1936. It had no lowercase letters and was cast in Light, Bold, and Extra Bold. The two lighter weights were identical with Kaufmann Script and so could be used as alternate capitals for that face. It was most notably used in the Madeline books, TV series and film. The font was most famously known for the typeface of the Nickelodeon logo from late 1984 to 2009 in white letters with an orange splat background. This font was digitized by the now-defunct Bitstream Inc.

Extract the year that the Balloon typeface was created from the following Wikipedia article.

Balloon typeface was created in 1939 by Max R. Kaufmann.

The SemEval-2010 benchmark dataset BIBREF0 is composed of 244 scientific articles collected from the ACM Digital Library (conference and workshop papers). The input papers ranged from 6 to 8 pages and were converted from PDF format to plain text using an off-the-shelf tool. The only preprocessing applied is a systematic dehyphenation at line breaks and removal of author-assigned keyphrases. Scientific articles were selected from four different research areas as defined in the ACM classification, and were equally distributed into training (144 articles) and test (100 articles) sets. Gold standard keyphrases are composed of both author-assigned keyphrases collected from the original PDF files and reader-assigned keyphrases provided by student annotators. Long documents such as those in the SemEval-2010 benchmark dataset are notoriously difficult to handle due to the large number of keyphrase candidates (i.e. phrases that are eligible to be keyphrases) that the systems have to cope with BIBREF6 . Furthermore, noisy textual content, whether due to format conversion errors or to unusable elements (e.g. equations), yield many spurious keyphrase candidates that negatively affect keyphrase extraction performance. This is particularly true for systems that make use of core NLP tools to select candidates, that in turn exhibit poor performance on degraded text. Filtering out irrelevant text is therefore needed for addressing these issues. In this study, we concentrate our effort on re-assessing keyphrase extraction performance on three increasingly sophisticated levels of document preprocessing described below. Table shows the average number of sentences and words along with the maximum possible recall for each level of preprocessing. The maximum recall is obtained by computing the fraction of the reference keyphrases that occur in the documents. We observe that the level 2 preprocessing succeeds in eliminating irrelevant text by significantly reducing the number of words (-19%) while maintaining a high maximum recall (-2%). Level 3 preprocessing drastically reduce the number of words to less than a quarter of the original amount while interestingly still preserving high recall. Long documents such as those in the SemEval-2010 benchmark dataset are notoriously difficult to handle due to the large number of keyphrase candidates (i.e. phrases that are eligible to be keyphrases) that the systems have to cope with (Hasan and Ng, 2014). Furthermore, noisy textual content, whether due to format conversion errors or to unusable elements (e.g. equations), yield many spurious keyphrase candidates that negatively affect keyphrase extraction performance.

Why are there so many keyphrase candidates in the dataset?

They are long with noisy textual content.

Why do people like to run very long distances like marathons?

As with all endurance sports, long distance running can pose an enjoyable challenge for some people. Some athletes are known to like running for its health benefits, its feeling of calm or meditative relaxation, or companionship with other runners. Whether running for fun or in a competitive race event, the feeling of accomplishment that comes from tackling a long distance by foot brings joy to many people. It may not be the best activity for all people though, and some may find activities such as bike riding or water-sports a more enjoyable way to achieve the same feeling.

The English rock band the Beatles are commonly regarded as the foremost and most influential band in popular music history. With a line-up comprising John Lennon, Paul McCartney, George Harrison and Ringo Starr, they sparked the "Beatlemania" phenomenon in 1963, gained international superstardom in 1964, and remained active until their break-up in 1970. Over the latter half of the decade, they were often viewed as orchestrators of society's developments. Their recognition concerns their effect on the era's youth and counterculture, British identity, popular music's evolution into an art form, and their unprecedented following. Many cultural movements of the 1960s were assisted or inspired by the Beatles. In Britain, their rise to national prominence signalled the youth-driven changes in postwar society, with respect to social mobility, teenagers' commercial influence, and informality. They spearheaded the shift from American artists' global dominance of rock and roll to British acts (known in the US as the British Invasion) and inspired many young people to pursue music careers. From 1964 to 1970, the group had the top-selling US single one out of every six weeks, and the top-selling US album one out of every three weeks. In 1965, they were awarded MBEs, the first time such an honour was bestowed on a British pop act. A year later, Lennon controversially remarked that the band were "more popular than Jesus now". The Beatles often incorporated classical elements, traditional pop forms and unconventional recording techniques in innovative ways, especially with the albums Rubber Soul (1965), Revolver (1966) and Sgt. Pepper's Lonely Hearts Club Band (1967). Many of their advances in production, writing, and artistic presentation were soon widespread. Other cultural changes initiated by the group include the elevation of the album to become the dominant form of record consumption over singles, a wider interest in psychedelic drugs and Eastern spirituality, and several fashion trends. The band also pioneered with their record sleeves and music videos, as well as informed music styles such as jangle, folk rock, power pop, psychedelia, progressive rock and heavy metal. By the end of the decade, the group were seen as an embodiment of the era's sociocultural movements, exemplified by the sentiment of their 1967 song "All You Need Is Love". Over the 1960s as a whole, the Beatles were the dominant youth-centred pop act on the sales charts. They broke numerous sales and attendance records, many of which they have or had maintained for decades, and continue to enjoy a canonised status unprecedented for popular musicians. Their songs are among the most recorded in history, with cover versions of "Yesterday" exceeding thousands. As of 2009, they were the best-selling band in history, with estimated sales of over 600 million records worldwide. Time included them in its list of the twentieth century's 100 most important people.

Why were the Beatles so popular?

The Beatles were the foremost rock band in popular music history. They broke numerous sales and many of these have maintained for decades.

1910.09399

Following the above definition, the $\min \max $ objective function in Eq. (DISPLAY_FORM10) aims to learn parameters for the discriminator ($\theta _d$) and generator ($\theta _g$) to reach an optimization goal: The discriminator intends to differentiate true vs. fake images with maximum capability $\max _{\theta _d}$ whereas the generator intends to minimize the difference between a fake image vs. a true image $\min _{\theta _g}$. In other words, the discriminator sets the characteristics and the generator produces elements, often images, iteratively until it meets the attributes set forth by the discriminator. GANs are often used with images and other visual elements and are notoriously efficient in generating compelling and convincing photorealistic images. Most recently, GANs were used to generate an original painting in an unsupervised fashion BIBREF24. The following sections go into further detail regarding how the generator and discriminator are trained in GANs. Most recently, GANs were used to generate an original painting in an unsupervised fashion BIBREF24.

Is text-to-image synthesis trained is suppervized or unsuppervized manner?

The answers are shown as follows: * unsupervised

If you were to design a pantheon, how many gods or deities would you include?

The beauty of polytheism is that there is no limit on the number of deities. The gods and goddesses can represent different forces of nature, human characteristics, ancestral principles, natural elements or animals. These can interact with each other and have children representing new concepts. Throughout the human history polytheism has been the most common form of religion and seems to be the most natural.

What is the least common birthday?

The least common birthday in the modern Gregorian calendar is February 29. This is because of leap years, as February 29 occurs only a bit more than a quarter of the time in any given calendar year. However excluding February 29, in most Western countries the least common birthday is December 25. For instance, in the United States between 1973 and 1999, September 16 is the most common birthday and December 25 the least common birthday. Some other uncommon birthdays in the West are January 1, December 24, and April 1. The least common birthday is usually a holiday when most people take time off work. There are multiple reasons why these days have fewer births, but one reason is that doctors are less likely to schedule cesarean births on such days. April 1 is an exception, and is likely because parents who are able to choose not to give birth on that day, to prevent their children from being bullied for having their birthday on April Fool's Day. This question is harder to answer in many non-Western countries as there is less easily accessible data. For example in a survey of students in India between 2007 and 2012, the statistics show much more births just before school opens, and on round days like the 5th, 10th, 15th, and 20th. This indicates much of the data may be faked.

Where can I find high-resolution versions of the Spark logo?

We provide versions here: black logo, white logo. Please be aware that Spark, Apache Spark and the Spark logo are trademarks of the Apache Software Foundation, and follow the Foundation's trademark policy in all uses of these logos.

2003.07433

A threshold of 1 for $s-score$ divides scores into positive and negative classes. In a multi-class setting, the algorithm minimizes the cross entropy, selecting the model with the highest probability. For each user, we calculate the proportion of tweets scored positively by each LIWC category. These proportions are used as a feature vector in a loglinear regression model BIBREF20. Prior to training, we preprocess the text of each tweet: we replace all usernames with a single token (USER), lowercase all text, and remove extraneous whitespace. We also exclude any tweet that contained a URL, as these often pertain to events external to the user. For each user, we calculate the proportion of tweets scored positively by each LIWC category.

How is LIWC incorporated into this system?

The answers are shown as follows: * For each user, we calculate the proportion of tweets scored positively by each LIWC category.

Who is Gillian Anderson?

Gillian Anderson is an American actress, born August 9, 1968. Some of her best known television roles include Special Agent Dana Scully on "The X-Files," Jean Milburn on "Sex Education," DSU Stella Gibson on "The Fall," and Margaret Thatcher on "The Crown." She was born in Chicago, Illinois, but spent many years living in England as a child, and can shift between American and British accents. She has resided in London now for many years. She has a younger brother, Aaron, who suffered from neurofibromatosis and died of a brain tumor in 2011, and a younger sister, Zoe, who is an artist. She has three children: daughter Piper Maru Klotz and sons Oscar and Felix Griffiths.

Which of these are car brands: Ferrari, Fiat, Batavus, Philips, Renault, BMW, IKEA and MG

Fiat, Ferrari, Renault, BMW and MG are car brands.

10 best cars to own

Benz Motorwagen Waverly Electric Ford Model T VW Beetle Cord L-29 Ford 150 Pickup Truck Oldsmobile F-88 BMC Mini Ford Mustang Porsche 911

Single-relation factoid questions are the most common form of questions found in search query logs and community question answering websites BIBREF1 , BIBREF2 . A knowledge-base (KB) such as Freebase, DBpedia, or Wikidata can help answer such questions after users reformulate them as queries. For instance, the question Where was Barack Obama born? can be answered by issuing the following KB query: $ \lambda (x).place\_of\_birth(Barack\_Obama, x) $ However, automatically mapping a natural language question such as Where was Barack Obama born? to its corresponding KB query remains a challenging task. There are three key issues that make learning this mapping non-trivial. First, there are many paraphrases of the same question. Second, many of the KB entries are unseen during training time; however, we still need to correctly predict them at test time. Third, a KB such as Freebase typically contains millions of entities and thousands of predicates, making it difficult for a system to predict these entities at scale BIBREF1 , BIBREF3 , BIBREF0 . In this paper, we address all three of these issues with a character-level encoder-decoder framework that significantly improves performance over state-of-the-art word-level neural models, while also providing a much more compact model that can be learned from less data. First, we use a long short-term memory (LSTM) BIBREF4 encoder to embed the question. Second, to make our model robust to unseen KB entries, we extract embeddings for questions, predicates and entities purely from their character-level representations. Character-level modeling has been previously shown to generalize well to new words not seen during training BIBREF5 , BIBREF6 , which makes it ideal for this task. Third, to scale our model to handle the millions of entities and thousands of predicates in the KB, instead of using a large output layer in the decoder to directly predict the entity and predicate, we use a general interaction function between the question embeddings and KB embeddings that measures their semantic relevance to determine the output. The combined use of character-level modeling and a semantic relevance function allows us to successfully produce likelihood scores for the KB entries that are not present in our vocabulary, a challenging task for standard encoder-decoder frameworks. Our novel, character-level encoder-decoder model is compact, requires significantly less data to train than previous work, and is able to generalize well to unseen entities in test time. In particular, without use of ensembles, we achieve 70.9% accuracy in the Freebase2M setting and 70.3% accuracy in the Freebase5M setting on the SimpleQuestions dataset, outperforming the previous state-of-arts of 62.7% and 63.9% BIBREF0 by 8.2% and 6.4% respectively. Moreover, we only use the training questions provided in SimpleQuestions to train our model, which cover about 24% of words in entity aliases on the test set. This demonstrates the robustness of the character-level model to unseen entities. In contrast, data augmentation is usually necessary to provide more coverage for unseen entities and predicates, as done in previous work BIBREF0 , BIBREF1 . In particular, without use of ensembles, the authors achieve 70.9% accuracy in the Freebase2M setting and 70.3% accuracy in the Freebase5M setting on the SimpleQuestions dataset, outperforming the previous state-of-arts of 62.7% and 63.9% by 8.2% and 6.4% respectively.

How about the accuracy the authors achieve in SimpleQuestions dataset?

The authors achieve 70.9% accuracy in the Freebase2M setting and 70.3% accuracy in the Freebase5M setting on the SimpleQuestions dataset, outperforming the previous state-of-arts of 62.7% and 63.9% BIBREF0 by 8.2% and 6.4% respectively.

List five titles by Jules Vernes.

Around the World in Eighty Days, From the Earth to the Moon, Martin Paz, Journey to the Centre of the Earth, A Drama in the Air

RMS Adriatic was a British ocean liner of the White Star Line. She was the fourth of a quartet of ships of more than 20,000 GRT, dubbed The Big Four. The ship was the only one of the four which was never the world's largest ship. However, she was the largest, the fastest, and the most luxurious of the Big Four, being the first ocean liner to have an indoor swimming pool and a Turkish bath. She began her career on the brand new route from Southampton to New York before joining, from 1911, her sister ships on the secondary route from Liverpool. They were in fact slow liners intended to provide a service at moderate prices. When World War I broke out, the Adriatic was among the ships that continued their civilian transatlantic service, while carrying many provisions. In 1917, she was requisitioned and served as a troop transport. After the war, she was refitted several times, and was gradually used for cruises, which became her main services in the 1930s. When Cunard Line and White Star Line merged in 1934, she was quickly deemed unnecessary and sold at the end of the year. She was scrapped in Osaka in 1935.

Tell me about the RMS Adriatic

The RMS Adriatic was a British ocean liner of the White Star Line. She was the fourth of a quartet of ships of more than 20,000 GRT, dubbed The Big Four. The ship was the only one of the four which was never the world's largest ship. However, she was the largest, the fastest, and the most luxurious of the Big Four, being the first ocean liner to have an indoor swimming pool and a Turkish bath.

We conduct ablation studies for pre-training objectives, and the results can be seen in Table TABREF40. We observe that our model greatly benefits from the DAE objective for the zero-shot Chinese question generation task. The results also demonstrate that combining DAE and XAE can alleviate the spurious correlation issue and improves cross-lingual NLG. As shown in Table TABREF41, we use the En-En-QG and Zh-Zh-QG tasks to analyze the effects of using different fine-tuning strategies. It can be observed that fine-tuning encoder parameters, our model obtain an impressive performance for both English and Chinese QG, which shows the strong cross-lingual transfer ability of our model. When fine-tuning all the parameters, the model achieves the best score for English QG, but it suffers a performance drop when evaluating on Chinese QG. We find that fine-tuning decoder hurts cross-lingual decoding, and the model learns to only decodes English words. For only fine-tuning decoder, the performance degrades by a large margin for both languages because of the underfitting issue, which indicates the necessity of fine-tuning encoder. We examine whether low-resource NLG can benefit from cross-lingual transfer. We consider English as the rich-resource language, and conduct experiments for few-shot French/Chinese AS. Specifically, we first fine-tune Xnlg on the English AS data, and then fine-tune it on the French or Chinese AS data. We compare with the monolingual supervised model that Xnlg is only fine-tuned on the dataset of the target language. As shown in Figure FIGREF49, we can observe that the cross-lingual supervision improves performance for few-shot abstractive summarization. As the training data size becomes larger, the performance of two models is getting closer. As shown in Table 10, we use the En-En-QG and Zh-Zh-QG tasks to analyze the effects of using different fine-tuning strategies. It can be observed that fine-tuning encoder parameters, our model obtain an impressive performance for both English and Chinese QG, which shows the strong cross-lingual transfer ability of our model.

Does their model have transferability?

Yes, it has a cross-lingual transferability .

The Austrian Cup (German: ÖFB-Cup), known as UNIQA ÖFB Cup for sponsorship purposes, is an annual football competition held by the Austrian Football Association, the ÖFB. During the 2008–09 season, Austria Wien won the tournament for 27th time, a record. Red Bull Salzburg are the current holders, winning the 2021-2022 tournament, making it their 8th successive win in the tournament and their 9th overall.

Please note down some points regarding Austrian Cup from the given text

1. The Austrian Football Association, or FB, holds an annual football competition known as the Austrian Cup (German: FB-Cup), often known as the UNIQA FB Cup for sponsorship reasons. Austria Wien set a record by winning the competition for the 27th time during the 2008–09 season. 2. Red Bull Salzburg is the current champion, having won the competition for the eighth time in a row and nine times total.

What does the idiom burning your bridges mean?

Burning your bridges means don't do something that can be regretted later. An example of this would be a person who does not exhibit good character in a relationship and later on tries to take advantage of that relationship as if nothing bad ever happened. This idiom can be used in sentences describing many different types of relationships, such as friendships and employment, that do not end well.

Tritonia auriculata is a species of dendronotid nudibranch. It is a marine gastropod mollusc in the family Tritoniidae.

What is the Tritonia auriculata

Tritonia auriculata is a species of dendronotid nudibranch. It is a marine gastropod mollusc in the family Tritoniidae

What can be done about NRA and why gun control is such an issue?

We all know what America stands for. There is no reason to discuss love for guns, indivdual freedom, right to bear firearms, self defence, gun lobby funded by NRA, and how republicans love guns etc etc. These are all facts but it doesn't change that people die everyday and lives are wasted or impacted as a result of poor gun laws. What can be done about this? 1. People who vote for gun laws have not had someone in their family shot or impacted. We don't want a personal event or death of a closed one to create a new law. An eye for an eye doesn't bring lives back. Educate the masses. Also, women tend to vote more positive on banning rifles as compared to men. Get more women out to change the law. 2. There is license for fishing and hunting. Guns should be rented and not owned. If you're into recreational things make it possible to rent guns and not own. Do background check, and let the person enjoy his time with the toys but cannot own it. This would allow the manufacture and NRA in business. 3. Never sell assault rifles to general public. A person who needs to have assault rifles are insecure by nature. They fear that the public is trying to get at them and they need to protect. Form community monitoring and have more police funding to protect our families.

Words with multiple senses commonly exist in many languages. For example, the word bank can either mean a “financial establishment” or “the land alongside or sloping down to a river or lake”, based on different contexts. Such a word is called a “polyseme”. The task to identify the meaning of a polyseme in its surrounding context is called word sense disambiguation (WSD). Word sense disambiguation is a long-standing problem in natural language processing (NLP), and has broad applications in other NLP problems such as machine translation BIBREF0 . Lexical sample task and all-word task are the two main branches of WSD problem. The former focuses on only a pre-selected set of polysemes whereas the later intends to disambiguate every polyseme in the entire text. Numerous works have been devoted in WSD task, including supervised, unsupervised, semi-supervised and knowledge based learning BIBREF1 . Our work focuses on using supervised learning to solve all-word WSD problem. Most supervised approaches focus on extracting features from words in the context. Early approaches mostly depend on hand-crafted features. For example, IMS by BIBREF2 uses POS tags, surrounding words and collections of local words as features. These approaches are later improved by combining with word embedding features BIBREF0 , which better represents the words' semantic information in a real-value space. However, these methods neglect the valuable positional information between the words in the sequence BIBREF3 . The bi-directional Long-Short-Term-Memory (LSTM) approach by BIBREF3 provides one way to leverage the order of words. Recently, BIBREF4 improved the performance by pre-training a LSTM language model with a large unlabelled corpus, and using this model to generate sense vectors for further WSD predictions. However, LSTM significantly increases the computational complexity during the training process. The development of the so called “fixed-size ordinally forgetting encoding” (FOFE) has enabled us to consider more efficient method. As firstly proposed in BIBREF5 , FOFE provides a way to encode the entire sequence of words of variable length into an almost unique fixed-size representation, while also retain the positional information for words in the sequence. FOFE has been applied to several NLP problems in the past, such as language model BIBREF5 , named entity recognition BIBREF6 , and word embedding BIBREF7 . The promising results demonstrated by the FOFE approach in these areas inspired us to apply FOFE in solving the WSD problem. In this paper, we will first describe how FOFE is used to encode sequence of any length into a fixed-size representation. Next, we elaborate on how a pseudo language model is trained with the FOFE encoding from unlabelled data for the purpose of context abstraction, and how a classifier for each polyseme is built from context abstractions of its labelled training data. Lastly, we provide the experiment results of our method on several WSD data sets to justify the equivalent performance as the state-of-the-art approach. We observe that combining different auxiliary penalty terms results in the best performance, outperforming benchmark methods that require source data.

Does their method perform better than benchmark methods?

Yes.

What type of battery should I get to power my trolling motor?

Traditionally the answer is to utilize a deep cycle marine lead acid battery. These batteries are similar to a car battery but instead of having a high amount of cold cranking amps they have the ability to be run to a lower state of charge without damage. A better solution is to use a LiFePO4 battery. These batteries may have a higher initial cost, but will generally last longer than the traditional battery while providing more usable capacity and having a substantially lower weight.

The leaves are usually large and imposing, often lobed or deeply cut, and may be more or less pinnate. They can also be oval, spear-shaped, or in many other possible shape variations. The leaves are borne alternately on the stem. A quality of philodendrons is that they do not have a single type of leaf on the same plant. Instead, they have juvenile leaves and adult leaves, which can be drastically different from one another. The leaves of seedling philodendrons are usually heart-shaped early in the life of the plant. But after it has matured past the seedling stage, the leaves will acquire the typical juvenile leaf's shape and size. Later in the philodendron's life, it starts producing adult leaves, a process called metamorphosis. Most philodendrons go through metamorphosis gradually; there is no immediately distinct difference between juvenile and adult leaves. Aside from being typically much bigger than the juvenile leaves, the shape of adult leaves can be significantly different. In fact, considerable taxonomic difficulty has occurred in the past due to these differences, causing juvenile and adult plants to mistakenly be classified as different species.

What is MGMT's most popular album

Oracular Spectacular was named by Rolling Stone as one of their 500 Greatest Albums of All Time

A growing amount of literature has been devoted to address the problem of HTE estimation with continuous treatments. The algorithms in the context of DRF include methods targeting at confounding bias, kernel-based or ML-based methods for regression bias reduction, and techniques of DRE or DML that balance the trade-off between the two biases. DRF combined with IPW can achieve consistent estimators by weighting the estimation with probability density of treatment. For the regression bias, employ parametric estimations and model DRF as non-parametric functions using kernel regression. To advance, propose a doubly robust estimators for DRF by combine the estimation of generalized propensity score with the estimation of outcome using kernel regression. Thus far, these approaches only provide global estimations and cannot handle massive amounts of high-dimensional data. Recently, the great efficiency of ML methods motivates their generalization to the problem of HTE estimation. Towards that end, estimating DRF with ML-based algorithms is developed and among which tree-based models are a great candidate since it partitions the feature space for dimensionality reduction and maximizes the heterogeneity as well. Causal Forest (CF) proposed in utilizes a subset of training samples for growing trees by recursively partitioning via a splitting criterion. Then HTE estimation is given by weighted average over the outcomes of the remaining training samples, known as honesty principle. The final estimator obtained from CF further exhibits a lower degree of bias by tree ensembles. However, their splitting criterion relies on a linear model assumption and is formulated as the difference between the slopes of linear models. The same limitation applies to Orthogonal Random Forest and Bayesian Additive Regression Tress. Nevertheless, the complexity of HTE estimation may not be fully captured by linear or general parametric models, which brings the necessity of non-parametric ML models. Kernel-based DML estimates the nuisance functions with cross-fitting, constructs a non-parametric DML estimator by Gaeutax Derivative. This motivates the utilization of DML and non-parametric estimation in our work, though it only provide global estimators with the limited capacity of localization. A fully non-parametric DML with locally weighted estimations for continuous treatments has not been considered yet. Building on prior art, we point out that our integration of DRF estimation into CF overcome the challenges aforementioned by using local non-parametric DRF to constructing splitting criterion. We introduce the conceptual partial DRF as a component of the splitting criterion and employ the distance in the functional space of Partial DRF as a proxy for heterogeneity instead of the difference of slopes. Moreover, the partial DRF can be estimated precisely by the robust kernel-based DML estimators in our splitting criterion. Then HTE estimation is given by weighted average over the outcomes of the remaining training samples, known as honesty principle.

What is the meaning of 'honesty' in Algorithm 1?

We have included the details on causal trees and the associated honesty principle in the related work of the paper. To have an overview of the honesty principle, when we grow a causal tree, we only utilize a subset of training samples. The remaining training samples only contribute to computing the ATE estimators on each leaf node of the tree at the prediction stage. There is no intersection between the two subsets. This principle is vital in two aspects. On the one hand, it deals with overfitting and thereby achieving bias reduction. On the other side, it leads to the convergence of the estimator.

Lyft was launched in the summer of 2012 by computer programmers Logan Green and John Zimmer as a service of Zimride, a long-distance intercity carpooling company focused on college transport that they founded in 2007 after Green shared rides from the University of California, Santa Barbara campus to visit his girlfriend in Los Angeles and was seeking an easier way to share rides. In May 2013, the company made the decision to change its name from Zimride to Lyft. Later that year, Lyft sold the original Zimride service to Enterprise Holdings, the parent company of Enterprise Rent-A-Car, to enable the company to focus exclusively on the growth of Lyft. Lyft's marketing strategy included large pink furry mustaches that drivers attached to the front of their cars and encouraging riders to sit in the front seat and fist bump with drivers upon meeting. In November 2014, the company distanced itself from the fist bump. In January 2015, Lyft introduced a small, glowing plastic dashboard mustache it called a "glowstache" as an alternative to the large fuzzy mustaches on the front of cars. The transition was to help overcome the resistance of some riders to arrive at destinations, such as business meetings, in a car with a giant mustache. In April 2014, Lyft hired two lobbying firms, TwinLogic Strategies, and Jochum Shore & Trossevin, to address the regulatory barriers and opposition it had received since its launch. Due to regulatory hurdles in New York City, the company altered its business model when establishing Lyft on the East Coast of the United States. Lyft's launch in New York City occurred on the evening of July 25, 2014, and, by the Taxi and Limousine Commission (TLC) and the approval of the Manhattan Supreme Court, only drivers registered with the TLC were permitted to drive Lyft-branded vehicles in New York City. In August 2014, the company introduced a shared ride concept, which provides cheaper fares. In December 2017, Lyft expanded into Canada, with operations in the Toronto, Hamilton and Ottawa metropolitan areas. In March 2018, Lyft partnered with Allscripts to create a platform allowing healthcare providers to arrange rides for patients who lack transportation to appointments. The service would be available to 2,500 hospitals, 180,000 physicians, and approximately 7 million patients. Lyft acquired Motivate, a bicycle-sharing system and the operator of Capital Bikeshare and Citi Bike, in November 2018. The company also announced plans to add 28,000 Citi Bikes and expand its service. In March 2019, Lyft became a public company via an initial public offering, raising $2.34 billion at a valuation of $24.3 billion. The company set aside some shares to be given to long-time drivers. In March 2020, Lyft acquired Halo Cars which pays drivers to display digital advertisements on their vehicles. In April 2020, Lyft laid off 982 employees and furloughed an additional 288 to reduce operating expenses and adjust cash flows due to the COVID-19 pandemic in the United States. The company continued to offer scooters for rent in San Francisco, while Miami government asked Lyft to halt operations. In August 2020, Lyft partnered with rental car company Sixt to let users access rental cars. Most of the rental cars are owned and operated by Sixt, with 85 locations in the US. Lyft receives commissions from rentals. In December 2020, Lyft announced plans to launch a multi-city U.S. robotaxi service in 2023 with Motional. Lyft sold its self-driving car division to Toyota for $550 million in April 2021. The division had partnerships with General Motors, NuTonomy, Ford Motor Company, GoMentum Station, and Magna International. It also owned Blue Vision Labs, a London-based augmented reality startup, acquired in 2018 for $72 million. In April 2022, Lyft announced an agreement to acquire PBSC Urban Solutions, a Canadian bike-share equipment and technology supplier. In November 2022, the company announced layoffs of approximately 700 employees, or about 13% of its staff. In March 2023, amid "a battered stock price," Lyft announced David Risher as its next CEO.

Provide a bulleted list of 5 key moments in Lyft's history

- Lyft was launched in 2012 my John Zimmer and Logan Green - In 2014, Lyft hired two lobbying firms to address regulatory headwinds it faced since launching - In 2017, Lyft expanded to Canada - In 2019, Lyft became a public company via IPO - In 2023, while the stock had been performing poorly, Lyft's founders resigned and were replaced by David Risher

Tell me whether these are states or countries: Canada, South Carolina, New York, New Jersey, Japan, Germany, Australia, USA, Georgia, United Kingdom.

Canada is a country. South Carolina is a state. New York is a state. New Jersey is a state. Japan is a country. Germany is a country. Australia is a country. USA is a country. Georgia is a state. United Kingdom is a country.

With the rapid growth of social network platforms, more and more people tend to share their experiences and emotions online.[2]Corresponding Author: xuruifeng@hit.edu.cn Emotion analysis of online text becomes a new challenge in Natural Language Processing (NLP). In recent years, studies in emotion analysis largely focus on emotion classification including detection of writers' emotions BIBREF0 as well as readers' emotions BIBREF1 . There are also some information extraction tasks defined in emotion analysis BIBREF2 , BIBREF3 , such as extracting the feeler of an emotion BIBREF4 . These methods assume that emotion expressions are already observed. Sometimes, however, we care more about the stimuli, or the cause of an emotion. For instance, Samsung wants to know why people love or hate Note 7 rather than the distribution of different emotions. Ex.1 我的手机昨天丢了，我现在很难过。 Ex.1 Because I lost my phone yesterday, I feel sad now. In an example shown above, “sad” is an emotion word, and the cause of “sad” is “I lost my phone”. The emotion cause extraction task aims to identify the reason behind an emotion expression. It is a more difficult task compared to emotion classification since it requires a deep understanding of the text that conveys an emotions. Existing approaches to emotion cause extraction mostly rely on methods typically used in information extraction, such as rule based template matching, sequence labeling and classification based methods. Most of them use linguistic rules or lexicon features, but do not consider the semantic information and ignore the relation between the emotion word and emotion cause. In this paper, we present a new method for emotion cause extraction. We consider emotion cause extraction as a question answering (QA) task. Given a text containing the description of an event which [id=lq]may or may not cause a certain emotion, we take [id=lq]an emotion word [id=lq]in context, such as “sad”, as a query. The question to the QA system is: “Does the described event cause the emotion of sadness?”. The [id=lq]expected answer [id=lq]is either “yes” or “no”. (see Figure FIGREF1 ). We build our QA system based on a deep memory network. The memory network has two inputs: a piece of text, [id=lq]referred to as a story in QA systems, and a query. The [id=lq]story is represented using a sequence of word embeddings. [id=lq]A recurrent structure is implemented to mine the deep relation between a query and a text. It measure[id=lq]s the [id=lq]importance of each word in the text by [id=lq]an attention mechanism. Based on the [id=lq]learned attention result, the network maps the text into a low dimensional vector space. This vector is [id=lq]then used to generate an answer. Existing memory network based approaches to QA use weighted sum of attentions to jointly consider short text segments stored in memory. However, they do not explicitly model [id=lq]sequential information in the context. In this paper, we propose a new deep memory network architecture to model the context of each word simultaneously by multiple memory slots which capture sequential information using convolutional operations BIBREF5 , and achieves the state-of-the-art performance compared to existing methods which use manual rules, common sense knowledge bases or other machine learning models. The rest of the paper is organized as follows. Section SECREF2 gives a review of related works on emotion analysis. Section SECREF3 presents our proposed deep memory network based model for emotion cause extraction. Section SECREF4 discusses evaluation results. Finally, Section SECREF5 concludes the work and outlines the future directions. The emotion cause extraction task aims to identify the reason behind an emotion expression. It is a more difficult task compared to emotion classification since it requires a deep understanding of the text that conveys an emotions.

Why the emotion cause extraction task is more difficult compared to emotion classification?

Because it requires a deep understanding of the text that conveys an emotions.

Word taboo, also called taboo language, language taboo or linguistic taboo is a kind of taboo that involves restricting the use of words or other parts of language due to social constraints. This may be due to a taboo on specific parts of the language itself (such as certain words, or sounds), or due to the need to avoid a taboo topic. The taboo against naming the dead in parts of the world is an example. Taboo words are commonly avoided with euphemisms, such as the English euphemism pass away, meaning "die". It is a common source of neologisms and lexical replacement.

Explain the word taboo

Word taboo, also called taboo language, language taboo or linguistic taboo is a kind of taboo that involves restricting the use of words or other parts of language due to social constraints. This may be due to a taboo on specific parts of the language itself (such as certain words, or sounds), or due to the need to avoid a taboo topic. The taboo against naming the dead in parts of the world is an example.

Maskavas Forštate (German: Moskauer Vorstadt) also known as Maskavas priekšpilsēta and colloquially as Maskačka, is a suburb of Riga situated on the right bank of the Daugava. The neighborhood is located to the south of Old Riga along the road historically connecting Riga to Moscow, from which its name is derived. History See also: Riga Ghetto The history of Maskavas Forštate, whose name in English literally means Moscow Suburb goes back to at least the 14th century, in some parts the medieval street network has been preserved. However, the area is first mentioned in 1348 by the name of Lastādija (German: Lastadie). Architecturally, the neighborhood reflects its history as an area of Russian, Belarusian and Jewish migration, especially characteristic wooden homes. During the Nazi occupation of Riga, the neighborhood was turned into a ghetto for Jews. Today, there are memorials on the site of the Great Choral Synagogue and the Old Jewish cemetery. Most of the prewar buildings remain standing.

What's the architecture in Maskavas Forstate look like?

The architecture of Maskavas Forštate reflects the history of Russian, Belarusian, and Jewish migration.

Data: We evaluate the spell correctors from § "Robust Word Recognition" on movie reviews from the Stanford Sentiment Treebank (SST) BIBREF24 . The SST dataset consists of 8544 movie reviews, with a vocabulary of over 16K words. As a background corpus, we use the IMDB movie reviews BIBREF25 , which contain 54K movie reviews, and a vocabulary of over 78K words. The two datasets do not share any reviews in common. The spell-correction models are evaluated on their ability to correct misspellings. The test setting consists of reviews where each word (with length $\ge 4$ , barring stopwords) is attacked by one of the attack types (from swap, add, drop and keyboard attacks). In the all attack setting, we mix all attacks by randomly choosing one for each word. This most closely resembles a real world attack setting. In addition to our word recognition models, we also compare to After The Deadline (ATD), an open-source spell corrector. We found ATD to be the best freely-available corrector. We refer the reader to BIBREF7 for comparisons of ScRNN to other anonymized commercial spell checkers. For the ScRNN model, we use a single-layer Bi-LSTM with a hidden dimension size of 50. The input representation consists of 198 dimensions, which is thrice the number of unique characters (66) in the vocabulary. We cap the vocabulary size to 10K words, whereas we use the entire vocabulary of 78470 words when we backoff to the background model. For training these networks, we corrupt the movie reviews according to all attack types, i.e., applying one of the 4 attack types to each word, and trying to reconstruct the original words via cross entropy loss. We calculate the word error rates (WER) of each of the models for different attacks and present our findings in Table 2 . Note that ATD incorrectly predicts $11.2$ words for every 100 words (in the `all' setting), whereas, all of the backoff variations of the ScRNN reconstruct better. The most accurate variant involves backing off to the background model, resulting in a low error rate of $6.9\%$ , leading to the best performance on word recognition. This is a $32\%$ relative error reduction compared to the vanilla ScRNN model with a pass-through backoff strategy. We can attribute the improved performance to the fact that there are $5.25\%$ words in the test corpus that are unseen in the training corpus, and are thus only recoverable by backing off to a larger corpus. Notably, only training on the larger background corpus does worse, at $8.7\%$ , since the distribution of word frequencies is different in the background corpus compared to the foreground corpus. We calculate the word error rates (WER) of each of the models for different attacks and present our findings in Table 2. Note that ATD incorrectly predicts 11.2 words for every 100 words (in the ‘all’ setting), whereas, all of the backoff variations of the ScRNN reconstruct better. The most accurate variant involves backing off to the background model, resulting in a low error rate of 6.9%, leading to the best performance on word recognition. This is a 32% relative error reduction compared to the vanilla ScRNN model with a pass-through backoff strategy. We can attribute the improved performance to the fact that there are 5.25% words in the test corpus that are unseen in the training corpus, and are thus only recoverable by backing off to a larger corpus. Notably, only training on the larger background corpus does worse, at 8.7%, since the distribution of word frequencies is different in the background corpus compared to the foreground corpus.

Why is the error rate of ATD lower than that of ScRNN?

Because there are 5.25% words in the test corpus that are unseen in the training corpus, and are thus only recoverable by backing off to a larger corpus.

Why should people drink water?

The body is composed of anywhere between 55-75% water and many bodily functions rely on being sufficiently hydrated. One of the best ways to stay hydrated is to drink water. Drinking water has many benefits such as helping regulate your body temperature, protecting your joints and organs and keeping your skin healthy and clear.

People are increasingly using social networking platforms such as Twitter, Facebook, YouTube, etc. to communicate their opinions and share information. Although the interactions among users on these platforms can lead to constructive conversations, they have been increasingly exploited for the propagation of abusive language and the organization of hate-based activities BIBREF0, BIBREF1, especially due to the mobility and anonymous environment of these online platforms. Violence attributed to online hate speech has increased worldwide. For example, in the UK, there has been a significant increase in hate speech towards the immigrant and Muslim communities following the UK's leaving the EU and the Manchester and London attacks. The US also has been a marked increase in hate speech and related crime following the Trump election. Therefore, governments and social network platforms confronting the trend must have tools to detect aggressive behavior in general, and hate speech in particular, as these forms of online aggression not only poison the social climate of the online communities that experience it, but can also provoke physical violence and serious harm BIBREF1. Recently, the problem of online abusive detection has attracted scientific attention. Proof of this is the creation of the third Workshop on Abusive Language Online or Kaggle’s Toxic Comment Classification Challenge that gathered 4,551 teams in 2018 to detect different types of toxicities (threats, obscenity, etc.). In the scope of this work, we mainly focus on the term hate speech as abusive content in social media, since it can be considered a broad umbrella term for numerous kinds of insulting user-generated content. Hate speech is commonly defined as any communication criticizing a person or a group based on some characteristics such as gender, sexual orientation, nationality, religion, race, etc. Hate speech detection is not a stable or simple target because misclassification of regular conversation as hate speech can severely affect users’ freedom of expression and reputation, while misclassification of hateful conversations as unproblematic would maintain the status of online communities as unsafe environments BIBREF2. To detect online hate speech, a large number of scientific studies have been dedicated by using Natural Language Processing (NLP) in combination with Machine Learning (ML) and Deep Learning (DL) methods BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF0. Although supervised machine learning-based approaches have used different text mining-based features such as surface features, sentiment analysis, lexical resources, linguistic features, knowledge-based features or user-based and platform-based metadata BIBREF8, BIBREF9, BIBREF10, they necessitate a well-defined feature extraction approach. The trend now seems to be changing direction, with deep learning models being used for both feature extraction and the training of classifiers. These newer models are applying deep learning approaches such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), etc.BIBREF6, BIBREF0 to enhance the performance of hate speech detection models, however, they still suffer from lack of labelled data or inability to improve generalization property. Here, we propose a transfer learning approach for hate speech understanding using a combination of the unsupervised pre-trained model BERT BIBREF11 and some new supervised fine-tuning strategies. As far as we know, it is the first time that such exhaustive fine-tuning strategies are proposed along with a generative pre-trained language model to transfer learning to low-resource hate speech languages and improve performance of the task. In summary: We propose a transfer learning approach using the pre-trained language model BERT learned on English Wikipedia and BookCorpus to enhance hate speech detection on publicly available benchmark datasets. Toward that end, for the first time, we introduce new fine-tuning strategies to examine the effect of different embedding layers of BERT in hate speech detection. Our experiment results show that using the pre-trained BERT model and fine-tuning it on the downstream task by leveraging syntactical and contextual information of all BERT's transformers outperforms previous works in terms of precision, recall, and F1-score. Furthermore, examining the results shows the ability of our model to detect some biases in the process of collecting or annotating datasets. It can be a valuable clue in using pre-trained BERT model for debiasing hate speech datasets in future studies. To detect online hate speech, a large number of scientific studies have been dedicated by using Natural Language Processing (NLP) in combination with Machine Learning (ML) and Deep Learning (DL) methods

To detect online hate speech, what method has been used in scientific studies?

To detect online hate speech, a large number of scientific studies have been dedicated by using Natural Language Processing (NLP) in combination with Machine Learning (ML) and Deep Learning (DL) methods.

We design a simple non-learning-based goal set generator by exploiting the KG-based observation in the cooking theme. Algorithm 2 shows the pipeline for obtaining the goal set G_t. We first obtain the ingredient set I. For each ingredient i ∈ I, we first check whether it has been collected, then obtain its status set Si and requirement set R_i . We consider three types of goals: 1) “find” requires the agent to find and collect an uncollected ingredient, 2) “prepare” requires the agent to prepare an ingredient to satisfy a requirement, and 3) “eat”, that the agent is required to prepare and eat the final meal. Algorithm 3 shows the pipeline for assigning the goal-conditioned reward r^goal_t . We first obtain the type of a goal g, then check whether this goal has been accomplished given at and o^KG_(t+1). Some functions in Algorithm 2 can be reused here. r^goal_t is a binary reward that we will assign r_goal t = r_max if g is accomplished successfully, otherwise r_min (still not finished, or failed). Algorithm 2 and Algorithm 3 can also be implemented via learning-based methods. For example, the functions can be achieved by a QA model by answering specific questions.

How are goals extracted from the knowledge graph? How does the agent know what objects are ingredients versus other items in the environment like the stove? How are the requirements for preparing ingredients understood by the agent?

We use common sense rules to generate goals. We provide more details in in Appendix B in our paper revision and the pipeline is as follows: For obtaining the goal set Gt, we first obtain the ingredient set I. For each ingredient i \in I, we first check whether it has been collected, then obtain its status set Si and requirement set Ri. We consider three types of goals: 1) “find” requires the agent to find and collect an uncollected ingredient, 2) “prepare” requires the agent to prepare an ingredient to satisfy a requirement, and 3) “eat”, that the agent is required to prepare and eat the final meal.

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

What are the features of the approach for the identification of complaint-based tweets?

It is an iterative, semi-supervised for identification of complaint based tweets, which having the ability to be replicated for stream of information flow.

classify the following as mammals vs reptiles: goat snake cockroach pig dog caterpillar lion

mammals: goat, pig, dog, lion reptiles: snack, cockroach, caterpillar

Where is the band Portugal. The Man originally from?

The band members are originally from Wasilla, Alaska

Teodorów [tɛɔˈdɔruf] is a village in the administrative district of Gmina Koniecpol, within Częstochowa County, Silesian Voivodeship, in southern Poland. It lies approximately 7 kilometres (4 mi) east of Koniecpol, 46 km (29 mi) east of Częstochowa, and 82 km (51 mi) north-east of the regional capital Katowice. The village has a population of 107.

Extract the population of Teodorów from the following text.

The village of Teodorów has a population of 107.

How are cats different than dogs?

Dogs are domesticated animals that usually are more active and rely on their owner. Cats, on average, are more solitary creatures. They can be left alone longer and are more self sufficient. Dogs need lots of attention and activity while cats can entertain themselves.

Unicorns, sharks, dragons, pythons, minotaurs, alligators, platypuses and griffins. Which of these creatures are real, and which are imaginary?

Sharks, pythons, alligators and platypuses are all real creatures. Unicorns, dragons, minotaurs and griffins are all imaginary creatures.

Classify the countries based on their continents. Ethiopia, Nigeria, Angola, India, China, Brazil, Mexico

Africa - Ethiopia, Nigeria, Angola Asia - India, China America - Brazil, Mexico

What are some important devices you would see in a kitchen? And what are they used for?

You would see the following devices: Stove used to heat things Oven used to bake things Toaster to heat bread and make toast Mixer to mix things for baking like flour, eggs etc A refrigerator (also known as fridge) to keep things cold

1808.05077

To evaluate the performance of the proposed approach, precision (1), recall (2), f-Measure (3), and prediction accuracy (4) have been used as a performance matrices. The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%. DISPLAYFORM0 DISPLAYFORM1 The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%.

What was their performance on the dataset?

The answers are shown as follows: * accuracy of 82.6%

Give me a list of the top six premier league football clubs

While clubs participating in the Premier League table can end in different positions in each season, the historical top six, known commonly as the "Big Six", have been: - Arsenal - Chelsea - Liverpool - Manchester City - Manchester United - Tottenham These are considered the top six clubs due to their consistency and success within the Premier League and continental competitions like the UEFA Champions League and the UEFA Europa League.

Atlantis Princess is the third Korean-language studio album by South Korean singer BoA, released on May 30, 2003 by SM Entertainment. Several singles were promoted off the album, including the promotional single "Lights of Seoul", "Atlantis Princess", and "Milky Way", with accompanying music videos produced for the latter two songs. The album was also released in some parts of Asia, such as the Philippines, Hong Kong and Taiwan, with the overseas version containing a bonus VCD. Commercially, the album debuted at number one on the top 50 Korean monthly charts, with initial monthly sales of nearly 128,000 in from two days of shipments in May. It was the 4th best-selling album of the year in South Korea with sales of over 345,000 copies.

Extract the names of the singles from the text as a bulleted list.

* Lights of Seoul * Atlantis Princess * Milky Way

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In the word embedding literature, the problem of interpretability has been approached via several different routes. For learning sparse, interpretable word representations from co-occurrence variant matrices, BIBREF21 suggested algorithms based on non-negative matrix factorization (NMF) and the resulting representations are called non-negative sparse embeddings (NNSE). To address memory and scale issues of the algorithms in BIBREF21 , BIBREF22 proposed an online method of learning interpretable word embeddings. In both studies, interpretability was evaluated using a word intrusion test introduced in BIBREF20 . The word intrusion test is expensive to apply since it requires manual evaluations by human observers separately for each embedding dimension. As an alternative method to incorporate human judgement, BIBREF23 proposed joint non-negative sparse embedding (JNNSE), where the aim is to combine text-based similarity information among words with brain activity based similarity information to improve interpretability. Yet, this approach still requires labor-intensive collection of neuroimaging data from multiple subjects. In addition to investigating the semantic distribution in the embedding space, a word category dataset can be also used to quantify the interpretability of the word embeddings. In several studies, BIBREF21 , BIBREF22 , BIBREF20 , interpretability is evaluated using the word intrusion test. In the word intrusion test, for each embedding dimension, a word set is generated including the top 5 words in the top ranks and a noisy word (intruder) in the bottom ranks of that dimension. The intruder is selected such that it is in the top ranks of a separate dimension. Then, human editors are asked to determine the intruder word within the generated set. The editors' performances are used to quantify the interpretability of the embedding. Although evaluating interpretability based on human judgements is an effective approach, word intrusion is an expensive method since it requires human effort for each evaluation. Furthermore, the word intrusion test does not quantify the interpretability levels of the embedding dimensions, instead it yields a binary decision as to whether a dimension is interpretable or not. However, using continuous values is more adequate than making binary evaluations since interpretability levels may vary gradually across dimensions. The word intrusion test is expensive to apply since it requires manual evaluations by human observers separately for each embedding dimension. Furthermore, the word intrusion test does not quantify the interpretability levels of the embedding dimensions, instead it yields a binary decision as to whether a dimension is interpretable or not. However, using continuous values is more adequate than making binary evaluations since interpretability levels may vary gradually across dimensions.

What advantages does their proposed method of quantifying interpretability have over the human-in-the-loop evaluation they compare to?

it is less expensive and quantifies interpretability using continuous values rather than binary evaluations

What are the best books of the 19th century?

1. Pride and Prejudice by Jane Austen 2. Jane Eyre by Charlotte Brontë 3. The Picture of Dorian Gray by Oscar Wilde 4. Wuthering Heights by Emily Brontë 5. Crime and Punishment by Fyodor Dostoevsky

What are the most powerful server CPUs?

AMD EPYC and Intel Xeon are among the most powerful server CPUs these days.

What is backgammon?

Backgammon is a two-player board game that is thousands of years old. Each player has fifteen pieces. The objective is to be the first player to move your pieces off of the board. The players take turns rolling dice that determine how many places they can move their pieces. The game is a combination of skill and luck.