Datasets:

DataHammer

/

scimrc

like 8

Quicksand, also known as sinking sand, is a colloid consisting of fine granular material (such as sand, silt or clay) and water. It forms in saturated loose sand when the sand is suddenly agitated. When water in the sand cannot escape, it creates a liquefied soil that loses strength and cannot support weight. Quicksand can form in standing water or in upward flowing water (as from an artesian spring). In the case of upward flowing water, forces oppose the force of gravity and suspend the soil particles.

What is Sinking Sand?

Sinking Sand is most commonly known as Quicksand.

With the steady growth in the commercial websites and social media venues, the access to users' reviews have become easier. As the amount of data that can be mined for opinion increased, commercial companies' interests for sentiment analysis increased as well. Sentiment analysis is an important part of understanding user behavior and opinions on products, places, or services. Sentiment analysis has long been studied by the research community, leading to several sentiment-related resources such as sentiment dictionaries that can be used as features for machine learning models BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . These resources help increase sentiment analysis accuracies; however, they are highly dependent on language and require researchers to build such resources for every language to process. Feature engineering is a large part of the model building phase for most sentiment analysis and emotion detection models BIBREF4 . Determining the correct set of features is a task that requires thorough investigation. Furthermore, these features are mostly language and dataset dependent making it even further challenging to build models for different languages. For example, the sentiment and emotion lexicons, as well as pre-trained word embeddings are not completely transferable to other languages which replicates the efforts for every language that users would like to build sentiment classification models on. For languages and tasks where the data is limited, extracting these features, building language models, training word embeddings, and creating lexicons are big challenges. In addition to the feature engineering effort, the machine learning models' parameters also need to be tuned separately for each language to get the optimal results. In this paper, we take a different approach. We build a reusable sentiment analysis model that does not utilize any lexicons. Our goal is to evaluate how well a generic model can be used to mine opinion in different languages where data is more limited than the language where the generic model is trained on. To that end, we build a training set that contains reviews from different domains in English (e.g., movie reviews, product reviews) and train a recurrent neural network (RNN) model to predict polarity of those reviews. Then focusing on a domain, we make the model specialized in that domain by using the trained weights from the larger data and further training with data on a specific domain. To evaluate the reusability of the sentiment analysis model, we test with non-English datasets. We first translate the test set to English and use the pre-trained model to score polarity in the translated text. In this way, our proposed approach eliminates the need to train language-dependent models, use of sentiment lexicons and word embeddings for each language. Our experiments show that a generalizable sentiment analysis model can be utilized successfully to perform opinion mining for languages that do not have enough resources to train specific models. The contributions of this study are; 1) a robust approach that utilizes machine translation to reuse a model trained on one language in other languages, 2) an RNN-based approach to eliminate feature extraction as well as resource requirements for sentiment analysis, and 3) a technique that statistically significantly outperforms baselines for multilingual sentiment analysis task when data is limited. To the best of our knowledge, this study is the first to apply a deep learning model to the multilingual sentiment analysis task. The contributions of this study are; 1) a robust approach that utilizes machine translation to reuse a model trained on one language in other languages, 2) an RNN-based approach to eliminate feature extraction as well as resource requirements for sentiment analysis, and 3) a technique that statistically significantly outperforms baselines for multilingual sentiment analysis task when data is limited.

What are the contributions of this study?

1) A robust approach that utilizes machine translation to reuse a model trained on one language in other languages. 2) An RNN-based approach to eliminate feature extraction as well as resource requirements for sentiment analysis. 3) A technique that statistically significantly outperforms baselines for multilingual sentiment analysis task when data is limited.

Prolonged lack of insulin can also result in diabetic ketoacidosis, characterized by persistent fatigue, dry or flushed skin, abdominal pain, nausea or vomiting, confusion, trouble breathing, and a fruity breath odor. Blood and urine tests reveal unusually high glucose and ketones in the blood and urine. Untreated ketoacidosis can rapidly progress to loss of consciousness, coma, and death. The percentage of children whose type 1 diabetes begins with an episode of diabetic ketoacidosis varies widely by geography, as low as 15% in parts of Europe and North America, and as high as 80% in the developing world.

What are the risks of untreated type 1 diabetes?

Untreated type 1 diabetes can rapidly result in diabetic ketoacidosis which may lead to loss of consciousness, coma and death.

We explored the improvement in the monolingual model in a semi-supervised setting. To this end, we randomly selected INLINEFORM0 of the sentences in the CoNLL dataset as “supervised sentences” and the rest INLINEFORM1 were kept unsupervised. Next, we clamped the role labels of the supervised sentences using the predefined mapping from Section SECREF29 . Sampling was done on the unsupervised sentences as usual. We then measured the clustering performance using the trained parameters. To access the contribution of partial supervision better, we constructed a “supervised baseline” as follows. For predicates seen in the supervised sentences, a MAP estimate of the parameters was calculated using the predefined mapping. For the unseen predicates, the standard baseline was used. Figures FIGREF33 and FIGREF33 show the performance variation with INLINEFORM0 . We make the following observations: [leftmargin=*] In both languages, at around INLINEFORM0 , the supervised baseline starts outperforming the semi-supervised model, which suggests that manually labeling about 10% of the sentences is a good enough alternative to our training procedure. Note that 10% amounts to about 3.6k sentences in German and 4k in English. We noticed that the proportion of seen predicates increases dramatically as we increase the proportion of supervised sentences. At 10% supervised sentences, the model has already seen 63% of predicates in German and 44% in English. This explains to some extent why only 10% labeled sentences are enough. For German, it takes about 3.5% or 1260 supervised sentences to have the same performance increase as 1.5M unlabeled sentences (Line 1 to Line 2 in Table TABREF27 ). Adding about 180 more supervised sentences also covers the benefit obtained by alignments in the multilingual model (Line 2 to Line 3 in Table TABREF27 ). There is no noticeable performance difference in English. We also evaluated the performance variation on a completely unseen CoNLL test set. Since the test set is very small compared to the training set, the clustering evaluation is not as reliable. Nonetheless, we broadly obtained the same pattern. To access the contribution of partial supervision better, we constructed a “supervised baseline” as follows. For predicates seen in the supervised sentences, a MAP estimate of the parameters was calculated using the predefined mapping. For the unseen predicates, the standard baseline was used.

What kind of supervised baseline has been constructed to access the contribution of partial supervision better?

For predicates seen in the supervised sentences, a MAP estimate of the parameters was calculated using the predefined mapping. For the unseen predicates, the standard baseline was used.

After performing entity linking to the input text using the ELS, we receive a sequential list of linked entities, arranged based on their location in the text. We embed these entities to $d$ -dimensional vectors $E = \lbrace e_1, e_2, ..., e_m\rbrace $ where $e_i \in \mathbb {R}^d$ . Since these entities may still contain ambiguity, it is necessary to resolve them before applying them to the base model. Based on the idea that an ambiguous entity can be disambiguated using its neighboring entities, we introduce two kinds of disambiguating encoders below. One way to disambiguate an entity is by using all the other entities, putting more importance to entities that are nearer. For this purpose, we employ an RNN-based model to globally disambiguate the entities. Specifically, we use BiGRU and concatenate the forward and backward hidden state vectors as the new entity vector: $ \overrightarrow{h}_i &= GRU(e_i, \overrightarrow{h}_{i-1}) \\ \overleftarrow{h}_i &= GRU(e_i, \overleftarrow{h}_{i+1}) \\ e^{\prime }_i &= [\overrightarrow{h}_i; \overleftarrow{h}_i] \nonumber $ Another way to disambiguate an entity is by using only the direct neighbors of the entity, putting no importance value to entities that are far. To do this, we employ a CNN-based model to locally disambiguate the entities. Specifically, we do the convolution operation using filter matrices $W_f \in \mathbb {R}^{h \times d}$ with filter size $h$ to a window of $h$ words. We do this for different sizes of $h$ . This produces new feature vectors $c_{i,h}$ as shown below, where $f(.)$ is a non-linear function: $ c_{i,h} = f([e_{i-(h-1)/2}; ...; e_{i+h(+1)/2}]^\top W_f + b_f) \nonumber $ The convolution operation reduces the number of entities differently depending on the filter size $h$ . To prevent loss of information and to produce the same amount of feature vectors $c_{i,h}$ , we pad the entity list dynamically such that when the filter size is $h$ , the number of paddings on each side is $(h-1)/2$ . The filter size $h$ therefore refers to the number of entities used to disambiguate a middle entity. Finally, we concatenate all feature vectors of different $h$ 's for each $i$ as the new entity vector: $ e^{\prime }_i = [c_{i,h_1}; c_{i, h_2}; ...] \nonumber $ The question on which disambiguating encoder is better has been a debate; some argued that using only the local context is appropriate BIBREF16 while some claimed that additionally using global context also helps BIBREF17 . The RNN-based encoder is good as it smartly makes use of all entities, however it may perform bad when there are many entities as it introduces noise when using a far entity during disambiguation. The CNN-based encoder is good as it minimizes the noise by totally ignoring far entities when disambiguating, however determining the appropriate filter sizes $h$ needs engineering. Overall, we argue that when the input text is short (e.g. a sentence), both encoders perform comparably, otherwise when the input text is long (e.g. a document), the CNN-based encoder performs better. It is obvious that not all entities need to be disambiguated. When a correctly linked and already adequately disambiguated entity is disambiguated again, it would make the entity very context-specific and might not be suitable for the summarization task. Our entity encoding submodule therefore uses a selective mechanism that decides whether to use the disambiguating encoder or not. This is done by introducing a selective disambiguation gate $d$ . The final entity vector $\tilde{e}_i$ is calculated as the linear transformation of $e_i$ and $e^{\prime }_i$ : $ e^{\prime }_i &= encoder(e_i) \\ d &= \sigma (W_d e^{\prime }_i + b_d) \\ \tilde{e}_i &= d \times f(W_x e_i + b_x) + \\ & \quad (1-d) \times f(W_y e^{\prime }_i + b_y) \nonumber $ The full entity encoding submodule is illustrated in Figure 3 . Ultimately, the submodule outputs the disambiguated entity vectors $\tilde{E} = \lbrace \tilde{e}_1, \tilde{e}_2, ..., \tilde{e}_m\rbrace $ . After performing entity linking to the input text using the ELS, we receive a sequential list of linked entities, arranged based on their location in the text. We embed these entities to $d$ -dimensional vectors $E = \lbrace e_1, e_2, ..., e_m\rbrace $ where $e_i \in \mathbb {R}^d$ . Since these entities may still contain ambiguity, it is necessary to resolve them before applying them to the base model. Based on the idea that an ambiguous entity can be disambiguated using its neighboring entities, we introduce two kinds of disambiguating encoders below. One way to disambiguate an entity is by using all the other entities, putting more importance to entities that are nearer. For this purpose, we employ an RNN-based model to globally disambiguate the entities. Specifically, we use BiGRU and concatenate the forward and backward hidden state vectors as the new entity vector: $ \overrightarrow{h}_i &= GRU(e_i, \overrightarrow{h}_{i-1}) \\ \overleftarrow{h}_i &= GRU(e_i, \overleftarrow{h}_{i+1}) \\ e^{\prime }_i &= [\overrightarrow{h}_i; \overleftarrow{h}_i] \nonumber $ Another way to disambiguate an entity is by using only the direct neighbors of the entity, putting no importance value to entities that are far. To do this, we employ a CNN-based model to locally disambiguate the entities. Specifically, we do the convolution operation using filter matrices $W_f \in \mathbb {R}^{h \times d}$ with filter size $h$ to a window of $h$ words. We do this for different sizes of $h$ . This produces new feature vectors $c_{i,h}$ as shown below, where $f(.)$ is a non-linear function: $ c_{i,h} = f([e_{i-(h-1)/2}; ...; e_{i+h(+1)/2}]^\top W_f + b_f) \nonumber $ The convolution operation reduces the number of entities differently depending on the filter size $h$ . To prevent loss of information and to produce the same amount of feature vectors $c_{i,h}$ , we pad the entity list dynamically such that when the filter size is $h$ , the number of paddings on each side is $(h-1)/2$ . The filter size $h$ therefore refers to the number of entities used to disambiguate a middle entity. Finally, we concatenate all feature vectors of different $h$ 's for each $i$ as the new entity vector: $ e^{\prime }_i = [c_{i,h_1}; c_{i, h_2}; ...] \nonumber $ The question on which disambiguating encoder is better has been a debate; some argued that using only the local context is appropriate BIBREF16 while some claimed that additionally using global context also helps BIBREF17 . The RNN-based encoder is good as it smartly makes use of all entities, however it may perform bad when there are many entities as it introduces noise when using a far entity during disambiguation. The CNN-based encoder is good as it minimizes the noise by totally ignoring far entities when disambiguating, however determining the appropriate filter sizes $h$ needs engineering. Overall, we argue that when the input text is short (e.g. a sentence), both encoders perform comparably, otherwise when the input text is long (e.g. a document), the CNN-based encoder performs better. It is obvious that not all entities need to be disambiguated. When a correctly linked and already adequately disambiguated entity is disambiguated again, it would make the entity very context-specific and might not be suitable for the summarization task. Our entity encoding submodule therefore uses a selective mechanism that decides whether to use the disambiguating encoder or not. This is done by introducing a selective disambiguation gate $d$ . The final entity vector $\tilde{e}_i$ is calculated as the linear transformation of $e_i$ and $e^{\prime }_i$ : $ e^{\prime }_i &= encoder(e_i) \\ d &= \sigma (W_d e^{\prime }_i + b_d) \\ \tilde{e}_i &= d \times f(W_x e_i + b_x) + \\ & \quad (1-d) \times f(W_y e^{\prime }_i + b_y) \nonumber $ The full entity encoding submodule is illustrated in Figure 3 . Ultimately, the submodule outputs the disambiguated entity vectors $\tilde{E} = \lbrace \tilde{e}_1, \tilde{e}_2, ..., \tilde{e}_m\rbrace $ . Based on the idea that an ambiguous entity can be disambiguated using its neighboring entities, we introduce two kinds of disambiguating encoders below.****Globally disambiguating encoder****Globally disambiguating encoder

Why disambiguating encoders are introduced?

They were introduced based on the idea that an ambiguous entity can be disambiguated using its neighboring entities.

The Osterville Baptist Church is an historic Baptist church building at 824 Main Street in the Osterville village of Barnstable, Massachusetts. The white clapboarded wood-frame structure was built in 1837 for a congregation formed two years earlier. It is one of the older buildings in Osterville, and is a fine example of the Greek Revival with Gothic Revival elements. The church was listed on the National Register of Historic Places in 1987. Description and history The Osterville Baptist Church is set prominently in the center of Osterville on the north side of the junction of Main Street and Wianno Avenue. It is rectangular single-story wood-frame structure, with a gable roof, vinyl siding, and a brick foundation. Its exterior features a mix of Greek Revival and Gothic Revival styling, with corner paneled pilasters rising to entablatures running along the sides of the building, and lancet-arched panels above its windows. Its facade is symmetrically arranged, with a pair of entrances, each framed by sidelight windows and pilasters, with an entablature and cornice on top, and sash windows above. A single lancet-arch panel adorns the center of the gable end. The church has a two-stage square tower, with a plain first stage topped by a belfry with lancet-arched louvered openings, and a steeple above. The church congregation was organized in 1835 as the Second Barnstable Baptist Church, and this church was dedicated in 1838, having been built on land given by Captain George Lovell. Its steeple is a reconstruction of the original, which was lost in a hurricane in 1945. The church interior was remodeled in 1889, at which time a heating system was added. Further additions have been made to the rear of the building, adding classroom, meeting, and office spaces.

Given a reference text about the Osterville Baptist Church, tell me why its steeple was rebuilt and when it was listed on the National Register of Historic Places.

The Osterville Baptist Church was listed on the National Register of Historic Places in 1987. The church's original steeple had to be replaced because it was damaged during a hurricane in 1945.

Write a review of Joe Abercrombie's book Before They Are Hanged

There are some amazing action sequences in this sequel. Logen and Ferro are the most epic swashbuckling fighting duo the circle has ever seen. Never doubt them. Is there a character arc in this book? Trick question! No one in the series changes or grows - you just get to see different shades of them. We empathize with the "villains" and the protagonists reveal their evil sides.

Leptodactylus tapiti is a species of frog in the family Leptodactylidae. It is endemic to Brazil. Its natural habitats are moist savanna, subtropical or tropical high-altitude grassland, and rivers.

What is Leptodactylus tapiti?

Leptodactylus tapiti is a species of frog and is native to Brazil.

1812.08879

Natural language generation (NLG) is an essential component of an SDS. Given a semantic representation (SR) consisting of a dialogue act and a set of slot-value pairs, the generator should produce natural language containing the desired information. Traditionally NLG was based on templates BIBREF3 , which produce grammatically-correct sentences that contain all desired information. However, the lack of variation of these sentences made these systems seem tedious and monotonic. Trainable generators BIBREF4 , BIBREF5 can generate several sentences for the same SR, but the dependence on pre-defined operations limits their potential. Corpus-based approaches BIBREF6 , BIBREF7 learn to generate natural language directly from data without pre-defined rules. However, they usually require alignment between the sentence and the SR. Recently, Wen et al. wensclstm15 proposed an RNN-based approach, which outperformed previous methods on several metrics. However, the generated sentences often did not include all desired attributes. Recently, Wen et al. wensclstm15 proposed an RNN-based approach, which outperformed previous methods on several metrics. However, the generated sentences often did not include all desired attributes.

How is some information lost in the RNN-based generation models?

The answers are shown as follows: * the generated sentences often did not include all desired attributes.

Identify the bird from the list: Lint, Linnet, Lining

Linnet

I need a bulleted list of a few things people like to do with trees.

- Read under one - Plant some in a park - Pick and eat its fruit - Cut it for lumber

Fix spelling, grammar and factual mistakes in the text below: Finland prime minister, Sana Marrin, has lost her battle to stay in power after her centre-left Social Democratic party (SDP) was narrowly beat into third place in a cliffhanger election by its conservative and far-right rivals. With 99.4% of votes counted on sunday, the right-wing National Coalition party was on course to secure 20.7% of the vote, with the populist, nation-first Finns party predicted to score 20.1%. Marin’s SDP was forecast to collect 19.9%. Marin congratulated the election winners during her concesion speech, but hailed an improvement in both her party’s vote share and its projected number of MPs. “It’s a really good achievement, even though I didn’t finish first today,” she told supporters in Helsinki. “Democrcy has spoken, the Finnish people have casted their vote and the celebration of democracy is always a wonderful thing,” she added. “We have good reason to be hapy about this result.”

Finland’s prime minister, Sanna Marin, has lost her battle to stay in power after her centre-left Social Democratic party (SDP) was narrowly beaten into third place in a cliffhanger election by its conservative and far-right rivals. With 99.4% of votes counted on Sunday, the right-wing National Coalition party was on course to secure 20.7% of the vote, with the populist, nation-first Finns party predicted to score 20.1%. Marin’s SDP was forecast to collect 19.9%. Marin congratulated the election winners during her concession speech, but hailed an improvement in both her party’s vote share and its projected number of MPs. “It’s a really good achievement, even though I didn’t finish first today,” she told supporters in Helsinki. “Democracy has spoken, the Finnish people have cast their vote, and the celebration of democracy is always a wonderful thing,” she added. “We have good reason to be happy about this result.”

Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation via the off-side rule. Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a "batteries included" language due to its comprehensive standard library. Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language and first released it in 1991 as Python 0.9.0. Python 2.0 was released in 2000. Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2. Python consistently ranks as one of the most popular programming languages.

Is Python a popular programming language?

Yes Python is a very popular general high level programming language. It is growing rapidly and is widely used in the data science community

1909.09986

While the plan generation stage is guaranteed to be faithful to the input, the translation process from plans to text is based on a neural seq2seq model and may suffer from known issues with such models: hallucinating facts that do not exist in the input, repeating facts, or dropping facts. While the clear mapping between plans and text helps to reduce these issues greatly, the system in BIBREF0 still has 2% errors of these kinds. Recent work in neural text generation and summarization attempt to address these issues by trying to map the textual outputs back to structured predicates, and comparing these predicates to the input data. BIBREF7 uses a neural checklist model to avoid the repetition of facts and improve coverage. BIBREF8 generate $k$-best output candidates with beam search, and then try to map each candidate output back to the input structure using a reverse seq2seq model trained on the same data. They then select the highest scoring output candidate that best translates back to the input. BIBREF9 reconstructs the input in training time, by jointly learning a back-translation model and enforcing the back-translation to reconstruct the input. Both of these approaches are “soft” in the sense that they crucially rely on the internal dynamics or on the output of a neural network module that may or may not be correct. While the plan generation stage is guaranteed to be faithful to the input, the translation process from plans to text is based on a neural seq2seq model and may suffer from known issues with such models: hallucinating facts that do not exist in the input, repeating facts, or dropping facts. While the clear mapping between plans and text helps to reduce these issues greatly, the system in BIBREF0 still has 2% errors of these kinds. Recent work in neural text generation and summarization attempt to address these issues by trying to map the textual outputs back to structured predicates, and comparing these predicates to the input data.

What is the effectiveness plan generation?

The answers are shown as follows: * clear mapping between plans and text helps to reduce these issues greatly, the system in BIBREF0 still has 2% errors * work in neural text generation and summarization attempt to address these issues

Privacy policies are the documents which disclose the ways in which a company gathers, uses, shares and manages a user's data. As legal documents, they function using the principle of notice and choice BIBREF0, where companies post their policies, and theoretically, users read the policies and decide to use a company's products or services only if they find the conditions outlined in its privacy policy acceptable. Many legal jurisdictions around the world accept this framework, including the United States and the European Union BIBREF1, BIBREF2. However, the legitimacy of this framework depends upon users actually reading and understanding privacy policies to determine whether company practices are acceptable to them BIBREF3. In practice this is seldom the case BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10. This is further complicated by the highly individual and nuanced compromises that users are willing to make with their data BIBREF11, discouraging a `one-size-fits-all' approach to notice of data practices in privacy documents. With devices constantly monitoring our environment, including our personal space and our bodies, lack of awareness of how our data is being used easily leads to problematic situations where users are outraged by information misuse, but companies insist that users have consented. The discovery of increasingly egregious uses of data by companies, such as the scandals involving Facebook and Cambridge Analytica BIBREF12, have further brought public attention to the privacy concerns of the internet and ubiquitous computing. This makes privacy a well-motivated application domain for NLP researchers, where advances in enabling users to quickly identify the privacy issues most salient to them can potentially have large real-world impact. [1]https://play.google.com/store/apps/details?id=com.gotokeep.keep.intl [2]https://play.google.com/store/apps/details?id=com.viber.voip [3]A question might not have any supporting evidence for an answer within the privacy policy. Motivated by this need, we contribute PrivacyQA, a corpus consisting of 1750 questions about the contents of privacy policies, paired with over 3500 expert annotations. The goal of this effort is to kickstart the development of question-answering methods for this domain, to address the (unrealistic) expectation that a large population should be reading many policies per day. In doing so, we identify several understudied challenges to our ability to answer these questions, with broad implications for systems seeking to serve users' information-seeking intent. By releasing this resource, we hope to provide an impetus to develop systems capable of language understanding in this increasingly important domain. Motivated by this need, we contribute PrivacyQA, a corpus consisting of 1750 questions about the contents of privacy policies, paired with over 3500 expert annotations.

What is the PrivacyQA in this paper?

It is a corpus consisting of 1750 questions about the contents of privacy policies, paired with over 3500 expert annotations.

Dutch is an official language of the Netherlands proper, Belgium, Suriname, the Dutch Caribbean municipalities (St. Eustatius, Saba and Bonaire), Aruba, Curaçao and Sint Maarten. Dutch is also an official language of several international organisations, such as the European Union, Union of South American Nations and the Caribbean Community. At an academic level, Dutch is taught in about 175 universities in 40 countries. About 15,000 students worldwide study Dutch at university. In Europe, most of the population of the Netherlands (where it is the only official language spoken countrywide) and about 60% of the population of Belgium (as one of three official languages) speak Dutch. Outside the Low Countries, Dutch is the native language of the majority of the population of the South American country of Suriname, a former Dutch colony, where it also holds an official status, as it does in the Caribbean island countries of Aruba, Curaçao and Sint Maarten, which are constituent countries of the Kingdom of the Netherlands. Up to half a million native speakers reside in the United States, Canada and Australia combined,[n 3] and historical linguistic minorities on the verge of extinction remain in parts of France, Germany and Indonesia.[n 4]

Where is Dutch spoken?

Dutch is mostly spoken in The Netherlands, Belgium and Suriname, where it is an official language. In addition, it is taught across 40 other countries around the world.

Tell me whether these songs are performed by Nick Cave & The Bad Seeds or by Radiohead: Karma Police, The Good Son, The Mercy Seat, Creep, Planet Telex, Deanna, Pyramid Song, Airbag, Tupelo, Into My Arms, The National Anthem, Morning Bell, Lament, Idioteque, Red Right Hand, Straight To You

The following songs are performed by Nick Cave & The Bad Seeds: The Good Son, The Mercy Seat, Deanna, Tupelo, Into My Arms, Lament, Red Right Hand, Straight To You The following songs are performed by Radiohead: Karma Police, Creep, Planet Telex, Pyramid Song, Airbag, The National Anthem, Morning Bell, Idioteque,

There exist a few limitations to this database. These data are unique to Beth Israel Deaconess Medical Center (BIDMC), and models resulting from these data may not generalize to notes generated at other hospitals. Admissions to hospitals not associated with BIDMC will not have been captured, and generalizability is limited due to the limited geographic distribution of patients which present to the hospital. We welcome opportunities to continue to expand this dataset with additional phenotypes sought in the unstructured text, patient subsets, and text originating from different sources, with the goal of expanding the utility of NLP methods to further structure patient note text for retrospective analyses. These data are unique to Beth Israel Deaconess Medical Center (BIDMC), and models resulting from these data may not generalize to notes generated at other hospitals. Admissions to hospitals not associated with BIDMC will not have been captured, and generalizability is limited due to the limited geographic distribution of patients which present to the hospital.

What are the limitations of this dataset?

These data are unique to Beth Israel Deaconess Medical Center (BIDMC), and models resulting from these data may not generalize to notes generated at other hospitals. Admissions to hospitals not associated with BIDMC will not have been captured, and generalizability is limited due to the limited geographic distribution of patients which present to the hospital.

Recently, text generation has been widely studied with neural models trained with maximum likelihood estimation BIBREF4 . However, MLE tends to generate universal text BIBREF18 . Various methods have been proposed to enhance the generation quality by refining the objective function BIBREF18 , BIBREF19 or modifying the generation distribution with external information like topic BIBREF20 , sentence type BIBREF21 , emotion BIBREF22 and knowledge BIBREF23 . As mentioned above, MLE suffers from the exposure bias problem BIBREF5 , BIBREF6 . Thus, reinforcement learning has been introduced to text generation tasks such as policy gradient BIBREF6 and actor-critic BIBREF24 . BIBREF17 proposed an efficient and stable approach called Reward Augmented Maximum Likelihood (RAML), which connects the log-likelihood and expected rewards to incorporate MLE training objective into RL framework. Since some text generation tasks have no explicit metrics to be directly optimized, adversarial training has been applied to generating discrete text samples with a discriminator to learn a proper reward. For instance, SeqGAN BIBREF7 devised a discriminator to distinguish the real data and generated samples, and a generator to maximize the reward from the discriminator via policy gradient. Other variants of GANs have been proposed to improve the generator or the discriminator. To improve the generator, MaliGAN BIBREF8 developed a normalized maximum likelihood optimization target for the generator to stably model the discrete sequences. LeakGAN BIBREF11 guided the generator with reward signals leaked from the discriminator at all generation steps to deal with long text generation task. MaskGAN BIBREF10 employed an actor-critic architecture to make the generator fill in missing text conditioned on the surrounding context, which is expected to mitigate the problem of mode collapse. As for the discriminator, RankGAN BIBREF9 replaced traditional discriminator with a ranker to learn the relative ranking information between the real texts and generated ones. Inverse reinforcement learning BIBREF12 used a trainable reward approximator as the discriminator to provide dense reward signals at each generation step. DPGAN BIBREF13 introduced a language model based discriminator and regarded cross-entropy as rewards to promote the diversity of generation results. The most similar works to our model are RAML BIBREF17 and MaliGAN BIBREF8 : 1) Compared with RAML, our model adds a discriminator to learn the reward signals instead of choosing existing metrics as rewards. We believe that our model can adapt to various text generation tasks, particularly those without explicit evaluation metrics. 2) Unlike MaliGAN, we acquire samples from a fixed distribution near the real data rather than the generator's distribution, which is expected to make the training process more stable. The most similar works to our model are RAML (Norouzi et al., 2016) and MaliGAN (Che et al., 2017): 1) Compared with RAML, our model adds a discriminator to learn the reward signals instead of choosing existing metrics as rewards.

Which models are the most similar works to the model proposed by authors?

The most similar works are RAML and MaliGAN.

What is Oz?

Oz is an American prison drama television series set at a fictional men's prison created and principally written by Tom Fontana. It was the first one-hour dramatic television series to be produced by the premium cable network HBO. Oz premiered on July 12, 1997, and ran for six seasons. The series finale aired on February 23, 2003.

Davos is an Alpine resort town and a municipality in the Praettigau/Davos Region in the canton of Graubuenden, Switzerland. It has a permanent population of 10,832 (2020). Davos is located on the river Landwasser, in the Rhaetian Alps, between the Plessur and Albula Ranges. The municipality covers nearly the entire valley of the Landwasser, and the centre of population, economic activity, and administration is two adjacent villages: Davos Dorf (German: Davos Village) and Davos Platz (Davos Place), at 1,560 m (5,120 ft) above sea level. Gaining prominence in the 19th century as a mountain health resort, Davos is perhaps best known today for hosting the World Economic Forum—often referred to simply as "Davos"—an annual meeting of global political and corporate leaders. With a long history of winter sport, Davos also has one of Switzerland's largest ski resorts, and hosts the international Spengler Cup ice hockey tournament each December. https://en.wikipedia.org/wiki/Davos

Is Davos worth traveling?

Davos is a very scenic town in the Swiss Alps. Famous for winter sports like skiing, cross-country skiing, sledding, ice hockey and ice skating and also for the World Economic Forum that take place annually.

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

Does the 'translated semantic fingerprints' method outperform the method of 'orthographic translation'?

Yes.

1907.11907

We have evaluated the output of Nefnir against a reference corpus of 21,093 tokens and their correct lemmas. Samples for the reference corpus were extracted from two larger corpora, in order to obtain a diverse vocabulary: We have evaluated the output of Nefnir against a reference corpus of 21,093 tokens and their correct lemmas. Samples for the reference corpus were extracted from two larger corpora, in order to obtain a diverse vocabulary:

Which dataset do they use?

The answers are shown as follows: * a reference corpus of 21,093 tokens and their correct lemmas

Several species of shrub of the genus Coffea produce the berries from which coffee is extracted. The two main species commercially cultivated are Coffea canephora (predominantly a form known as 'robusta') and C. arabica. C. arabica, the most highly regarded species, is native to the southwestern highlands of Ethiopia and the Boma Plateau in southeastern Sudan and Mount Marsabit in northern Kenya. C. canephora is native to western and central Subsaharan Africa, from Guinea to Uganda and southern Sudan. Less popular species are C. liberica, C. stenophylla, C. mauritiana, and C. racemosa. All coffee plants are classified in the large family Rubiaceae. They are evergreen shrubs or trees that may grow 5 m (15 ft) tall when unpruned. The leaves are dark green and glossy, usually 10–15 cm (4–6 in) long and 6 cm (2.4 in) wide, simple, entire, and opposite. Petioles of opposite leaves fuse at the base to form interpetiolar stipules, characteristic of Rubiaceae. The flowers are axillary, and clusters of fragrant white flowers bloom simultaneously. Gynoecium consists of an inferior ovary, also characteristic of Rubiaceae. The flowers are followed by oval berries of about 1.5 cm (0.6 in). When immature, they are green, and they ripen to yellow, then crimson, before turning black on drying. Each berry usually contains two seeds, but 5–10% of the berries have only one; these are called peaberries. Arabica berries ripen in six to eight months, while robusta takes nine to eleven months. Coffea arabica is predominantly self-pollinating, and as a result, the seedlings are generally uniform and vary little from their parents. In contrast, Coffea canephora, and C. liberica are self-incompatible and require outcrossing. This means that useful forms and hybrids must be propagated vegetatively. Cuttings, grafting, and budding are the usual methods of vegetative propagation. On the other hand, there is great scope for experimentation in search of potential new strains.

Extract the species of coffee from the text. Separate them with a semi-colon

Coffea canephora; C. arabica; C. liberica; C. stenophylla; C. mauritiana; C. racemosa

Who was the first astronaut?

Yuri Gagarin, a Russian pilot, was the first person to travel into space. On April 12, 1961, he completed an orbit around the Earth in the Vostok 1 capsule. Gagarin famously said, "Our Earth is blue," while in Earth orbit.

1901.09755

Table TABREF7 shows the ABSA datasets from the restaurants domain for English, Spanish, French, Dutch, Russian and Turkish. From left to right each row displays the number of tokens, number of targets and the number of multiword targets for each training and test set. For English, it should be noted that the size of the 2015 set is less than half with respect to the 2014 dataset in terms of tokens, and only one third in number of targets. The French, Spanish and Dutch datasets are quite similar in terms of tokens although the number of targets in the Dutch dataset is comparatively smaller, possibly due to the tendency to construct compound terms in that language. The Russian dataset is the largest whereas the Turkish set is by far the smallest one. FLOAT SELECTED: Table 1: ABSA SemEval 2014-2016 datasets for the restaurant domain. B-target indicates the number of opinion targets in each set; I-target refers to the number of multiword targets. Apart from the manually annotated data, we also leveraged large, publicly available, unlabelled data to train the clusters: (i) Brown 1000 clusters and (ii) Clark and Word2vec clusters in the 100-800 range. In order to induce clusters from the restaurant domain we used the Yelp Academic Dataset, from which three versions were created. First, the full dataset, containing 225M tokens. Second, a subset consisting of filtering out those categories that do not correspond directly to food related reviews BIBREF29 . Thus, out of the 720 categories contained in the Yelp Academic Dataset, we kept the reviews from 173 of them. This Yelp food dataset contained 117M tokens in 997,721 reviews. Finally, we removed two more categories (Hotels and Hotels & Travel) from the Yelp food dataset to create the Yelp food-hotels subset containing around 102M tokens. For the rest of the languages we used their corresponding Wikipedia dumps. The pre-processing and tokenization is performed with the IXA pipes tools BIBREF30 . Table TABREF7 shows the ABSA datasets from the restaurants domain for English, Spanish, French, Dutch, Russian and Turkish. From left to right each row displays the number of tokens, number of targets and the number of multiword targets for each training and test set. For English, it should be noted that the size of the 2015 set is less than half with respect to the 2014 dataset in terms of tokens, and only one third in number of targets. The French, Spanish and Dutch datasets are quite similar in terms of tokens although the number of targets in the Dutch dataset is comparatively smaller, possibly due to the tendency to construct compound terms in that language. The Russian dataset is the largest whereas the Turkish set is by far the smallest one. FLOAT SELECTED: Table 1: ABSA SemEval 2014-2016 datasets for the restaurant domain. B-target indicates the number of opinion targets in each set; I-target refers to the number of multiword targets. Apart from the manually annotated data, we also leveraged large, publicly available, unlabelled data to train the clusters: (i) Brown 1000 clusters and (ii) Clark and Word2vec clusters in the 100-800 range. In order to induce clusters from the restaurant domain we used the Yelp Academic Dataset, from which three versions were created. First, the full dataset, containing 225M tokens. Second, a subset consisting of filtering out those categories that do not correspond directly to food related reviews BIBREF29 . Thus, out of the 720 categories contained in the Yelp Academic Dataset, we kept the reviews from 173 of them. This Yelp food dataset contained 117M tokens in 997,721 reviews. Finally, we removed two more categories (Hotels and Hotels & Travel) from the Yelp food dataset to create the Yelp food-hotels subset containing around 102M tokens. For the rest of the languages we used their corresponding Wikipedia dumps. The pre-processing and tokenization is performed with the IXA pipes tools BIBREF30 .

Which datasets are used?

ABSA SemEval 2014-2016 datasets Yelp Academic Dataset Wikipedia dumps

Block (data storage) In computing (specifically data transmission and data storage), a block, sometimes called a physical record, is a sequence of bytes or bits, usually containing some whole number of records, having a maximum length; a block size. Data thus structured are said to be blocked. The process of putting data into blocks is called blocking, while deblocking is the process of extracting data from blocks. Blocked data is normally stored in a data buffer, and read or written a whole block at a time. Blocking reduces the overhead and speeds up the handling of the data stream. For some devices, such as magnetic tape and CKD disk devices, blocking reduces the amount of external storage required for the data. Blocking is almost universally employed when storing data to 9-track magnetic tape, NAND flash memory, and rotating media such as floppy disks, hard disks, and optical discs. Most file systems are based on a block device, which is a level of abstraction for the hardware responsible for storing and retrieving specified blocks of data, though the block size in file systems may be a multiple of the physical block size. This leads to space inefficiency due to internal fragmentation, since file lengths are often not integer multiples of block size, and thus the last block of a file may remain partially empty. This will create slack space. Some newer file systems, such as Btrfs and FreeBSD UFS2, attempt to solve this through techniques called block suballocation and tail merging. Other file systems such as ZFS support variable block sizes. Block storage is normally abstracted by a file system or database management system (DBMS) for use by applications and end users. The physical or logical volumes accessed via block I/O may be devices internal to a server, directly attached via SCSI or Fibre Channel, or distant devices accessed via a storage area network (SAN) using a protocol such as iSCSI, or AoE. DBMSes often use their own block I/O for improved performance and recoverability as compared to layering the DBMS on top of a file system.

According to the text below, what is a block?

In computing -- and specifically in data transmission and data storage -- a block is a sequence of bytes or bits containing a number of records and having a maximum length. Putting data into blocks is called blocking, and reduces overhead and speeds up handling of a data stream. Block storage is typically abstracted by a file system or database management system for use by applications and end users.

To evaluate the unsupervised domain clustering we used the multi-domain corpus proposed by BIBREF8 which includes textual data in five diverse domains: subtitles, medical text (PDF documents from the European Medicines Agency), legal text (legislative text of the European Union), translations of the Koran, and IT-related text (manuals and localization files of open-source software). This dataset includes parallel sentences in English and German; for this experiment we used the English portion of the data. See more details on the dataset in Section SECREF22. We used 2000 distinct sentences from each domain. To evaluate whether the resulting clusters indeed capture the domains the data was drawn from we measure the clustering purity, which is a well-known metric for evaluating clustering BIBREF24. To measure the clustering purity, we assign each unsupervised cluster with the most common “true” domain in the sentences assigned to that cluster, and then compute the accuracy according to this majority-based cluster-domain assignment (note that in this case several unsupervised clusters can be assigned to the same domain). In cases where randomness is involved we run each experiment five times with different initializations and report the mean and variance of the purity metric for each model. To measure the clustering purity, we assign each unsupervised cluster with the most common “true” domain in the sentences assigned to that cluster, and then compute the accuracy according to this majority-based cluster-domain assignment.

What is used to measure the clustering purity?

They assign each unsupervised cluster with the most common “true”domain in the sentences assigned to that cluster.

Pasteur married Marie Pasteur (née Laurent) in 1849. She was the daughter of the rector of the University of Strasbourg, and was Pasteur's scientific assistant. They had five children together, three of whom died as children. Their eldest daughter, Jeanne, was born in 1850. She died from typhoid fever, aged 9, whilst at the boarding school Arbois in 1859. In 1865, 2-year-old Camille died of a liver tumour. Shortly after they decided to bring Cécile home from boarding school, but she too died of typhoid fever on 23 May 1866 at the age of 12. Only Jean Baptiste (b. 1851) and Marie Louise (b. 1858) survived to adulthood. Jean Baptiste would be a soldier in the Franco-Prussian War between France and Prussia.

How many children did Louis Pasteur have, and how many died of typhoid fever?

Louis Pasteur had 5 children, 2 of which died from Typhoid fever

The Island Bird is a Chesapeake Bay log canoe, built in 1882, by William Sidney Covington in Tilghman, Maryland. She is a 27 ft 4 in (8.33 m) sailing log canoe with a racing rig, a sharp stem with a longhead bow, and a sharp, raking stern. She is one of the smallest boats in the active racing fleet, with a beam of only 5 ft 6.5 in (1.689 m). The boat is privately owned by the descendants of Mr. Covington and has been racing every season since 1949. She one of the last 22 surviving traditional Chesapeake Bay racing log canoes that carry on a tradition of racing on the Eastern Shore of Maryland that has existed since the 1840s. She is located at St. Michaels, Talbot County, Maryland.

Who owns the canoe named The Island Bird?

The descendants of William Sidney Covington

What are the best places to visit in France?

France as a country has to offer an immense choice of travel and experience. Paris is certainly the city for those who like art, culture, and history. Its rich palaces, forts, and museums are evidence of France's culture & glorious history. For those who prefer a sunny day at a sun-kissed Mediterranean beach, the cities of Nice, Cannes, and Saint-Tropez are recommended. Anywhere you go in France, don't forget to taste its gourmet cheese and magnificent wine. And this goes without saying, the Eiffel Tower at night is absolutely gorgeous.

Mariano Sánchez Martínez (born 28 January 1978) is a Spanish former professional footballer who played as a defensive midfielder. He appeared in 108 Segunda División games over three seasons, scoring two goals for Cartagena. Club career Born in San Pedro del Pinatar, Region of Murcia, Sánchez did not reach the Segunda División B until he was 26, in 2004, arriving at CD Alcoyano from amateurs AD Mar Menor-San Javier. In the following year he moved to another club at that level, FC Cartagena, helping it promote to Segunda División in his fourth season. Sánchez made his debut in the competition on 29 August 2009 at the age of 31 years and seven months, playing the full 90 minutes in a 1–0 away win against Girona FC. He scored his first league goal on 22 May 2010 in the 3–5 home loss to Levante UD, and never appeared in less than 34 league matches during his three seasons in that tier, suffering relegation in his last and renewing his contract for a further two years in June 2012. On 14 May 2014, the 36-year-old Sánchez announced he would retire at the end of the campaign while hoping to help his team promote, which eventually did not befell. Personal life Sánchez rejected an offer to play youth football for Real Murcia when he was 18, after deciding to move to Madrid to study architecture. Not being able to enter Real Madrid's youth system, he chose to retire from football. After his playing days, Sánchez continued to work as an architect. Still as an active player, he was the figurehead behind the creation of the sports complex Pinatar Arena, in his hometown.

Who is Mariano Sánchez?

Mariano Sánchez Martínez, also known as Mariano Sánchez is a Spanish former professional footballer who played as a defensive midfielder.

While the demand for physical and manual labor is gradually declining, there is a growing need for a workforce with soft skills. Which soft skill do you think would be the most valuable in your daily life? According to an article in Forbes BIBREF0 , 70% of employed Americans agree that public speaking skills are critical to their success at work. Yet, it is one of the most dreaded acts. Many people rate the fear of public speaking even higher than the fear of death BIBREF1 . To alleviate the situation, several automated systems are now available that can quantify behavioral data for participants to reflect on BIBREF2 . Predicting the viewers' ratings from the speech transcripts would enable these systems to generate feedback on the potential audience behavior. Predicting human behavior, however, is challenging due to its huge variability and the way the variables interact with each other. Running Randomized Control Trials (RCT) to decouple each variable is not always feasible and also expensive. It is possible to collect a large amount of observational data due to the advent of content sharing platforms such as YouTube, Massive Open Online Courses (MOOC), or ted.com. However, the uncontrolled variables in the observational dataset always keep a possibility of incorporating the effects of the “data bias” into the prediction model. Recently, the problems of using biased datasets are becoming apparent. BIBREF3 showed that the error rates in the commercial face-detectors for the dark-skinned females are 43 times higher than the light-skinned males due to the bias in the training dataset. The unfortunate incident of Google's photo app tagging African-American people as “Gorilla” BIBREF4 also highlights the severity of this issue. We address the data bias issue as much as possible by carefully analyzing the relationships of different variables in the data generating process. We use a Causal Diagram BIBREF5 , BIBREF6 to analyze and remove the effects of the data bias (e.g., the speakers' reputations, popularity gained by publicity, etc.) in our prediction model. In order to make the prediction model less biased to the speakers' race and gender, we confine our analysis to the transcripts only. Besides, we normalize the ratings to remove the effects of the unwanted variables such as the speakers' reputations, publicity, contemporary hot topics, etc. For our analysis, we curate an observational dataset of public speech transcripts and other meta-data collected from the ted.com website. This website contains a large collection of high-quality public speeches that are freely available to watch, share, rate, and comment on. Every day, numerous people watch and annotate their perceptions about the talks. Our dataset contains 2231 public speech transcripts and over 5 million ratings from the spontaneous viewers of the talks. The viewers annotate each talk by 14 different labels—Beautiful, Confusing, Courageous, Fascinating, Funny, Informative, Ingenious, Inspiring, Jaw-Dropping, Long-winded, Obnoxious, OK, Persuasive, and Unconvincing. We use two neural network architectures in the prediction task. In the first architecture, we use LSTM BIBREF7 for a sequential input of the words within the sentences of the transcripts. In the second architecture, we use TreeLSTM BIBREF8 to represent the input sentences in the form of a dependency tree. Our experiments show that the dependency tree-based model can predict the TED talk ratings with slightly higher performance (average F-score 0.77) than the word sequence model (average F-score 0.76). To the best of our knowledge, this is the best performance in the literature on predicting the TED talk ratings. We compare the performances of these two models with a baseline of classical machine learning techniques using hand-engineered features. We find that the neural networks largely outperform the classical methods. We believe this gain in performance is achieved by the networks' ability to capture better the natural relationship of the words (as compared to the hand engineered feature selection approach in the baseline methods) and the correlations among different rating labels. In the first architecture, we use LSTM (Hochreiter and Schmidhuber, 1997) for a sequential input of the words within the sentences of the transcripts. In the second architecture, we use TreeLSTM (Tai et al., 2015) to represent the input sentences in the form of a dependency tree.

What did the TreeLSTM use for in the second architecture?

It is used to represent the input sentences in the form of a dependency tree.

Low dimensional word representations (embeddings) have become a key component in modern NLP systems for language modeling, parsing, sentiment classification, and many others. These embeddings are usually derived by employing the distributional hypothesis: that similar words appear in similar contexts BIBREF0 . The models that perform the word embedding can be divided into two classes: predictive, which learn a target or context word distribution, and counting, which use a raw, weighted, or factored word-context co-occurrence matrix BIBREF1 . The most well-known predictive model, which has become eponymous with word embedding, is word2vec BIBREF2 . Popular counting models include PPMI-SVD BIBREF3 , GloVe BIBREF4 , and LexVec BIBREF5 . These models all learn word-level representations, which presents two main problems: 1) Learned information is not explicitly shared among the representations as each word has an independent vector. 2) There is no clear way to represent out-of-vocabulary (OOV) words. fastText BIBREF6 addresses these issues in the Skip-gram word2vec model by representing a word by the sum of a unique vector and a set of shared character n-grams (from hereon simply referred to as n-grams) vectors. This addresses both issues above as learned information is shared through the n-gram vectors and from these OOV word representations can be constructed. In this paper we propose incorporating subword information into counting models using a strategy similar to fastText. We use LexVec as the counting model as it generally outperforms PPMI-SVD and GloVe on intrinsic and extrinsic evaluations BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , but the method proposed here should transfer to GloVe unchanged. The LexVec objective is modified such that a word's vector is the sum of all its subword vectors. We compare 1) the use of n-gram subwords, like fastText, and 2) unsupervised morphemes identified using Morfessor BIBREF11 to learn whether more linguistically motivated subwords offer any advantage over simple n-grams. To evaluate the impact subword information has on in-vocabulary (IV) word representations, we run intrinsic evaluations consisting of word similarity and word analogy tasks. The incorporation of subword information results in similar gains (and losses) to that of fastText over Skip-gram. Whereas incorporating n-gram subwords tends to capture more syntactic information, unsupervised morphemes better preserve semantics while also improving syntactic results. Given that intrinsic performance can correlate poorly with performance on downstream tasks BIBREF12 , we also conduct evaluation using the VecEval suite of tasks BIBREF13 , in which all subword models, including fastText, show no significant improvement over word-level models. We verify the model's ability to represent OOV words by quantitatively evaluating nearest-neighbors. Results show that, like fastText, both LexVec n-gram and (to a lesser degree) unsupervised morpheme models give coherent answers. This paper discusses related word ( $§$ "Related Work" ), introduces the subword LexVec model ( $§$ "Subword LexVec" ), describes experiments ( $§$ "Materials" ), analyzes results ( $§$ "Results" ), and concludes with ideas for future works ( $§$ "Conclusion and Future Work" ). To evaluate the impact subword information has on in-vocabulary (IV) word representations, we run intrinsic evaluations consisting of word similarity and word analogy tasks.

In what way does the team evaluate the impact subword information has on in-vocabulary word representations?

To evaluate the impact subword information has on in-vocabulary (IV) word representations, they run intrinsic evaluations consisting of word similarity and word analogy tasks.

The name "Pantheon" is from the Ancient Greek "Pantheion" meaning "of, relating to, or common to all the gods". Cassius Dio, a Roman senator who wrote in Greek, speculated that the name comes either from the statues of many gods placed around this building, or from the resemblance of the dome to the heavens. His uncertainty strongly suggests that "Pantheon" (or Pantheum) was merely a nickname, not the formal name of the building. In fact, the concept of a pantheon dedicated to all the gods is questionable. The only definite pantheon recorded earlier than Agrippa's was at Antioch in Syria, though it is only mentioned by a sixth-century source. Ziegler tried to collect evidence of pantheons, but his list consists of simple dedications "to all the gods" or "to the Twelve Gods", which are not necessarily true pantheons in the sense of a temple housing a cult that literally worships all the gods.

Given this paragraph about the Roman temple known as the Pantheon, tell me whether we definitively know why the temple was called the Pantheon.

We do not know why the temple was called the Pantheon. Even Cassius Dio, a Roman senator who would have had more context by nature of living and ruling in Roman society, was only able to speculate where the name came from.

What are some of the best vegan protein sources

Here are some of the most high protein vegan protein sources * Tofu * Tempeh * Seitan * Edamame Beans * Garbanzo Beans

Human-computer dialog/conversation is one of the most challenging problems in artificial intelligence. Given a user-issued utterance (called a query in this paper), the computer needs to provide a reply to the query. In early years, researchers have developed various domain-oriented dialogue systems, which are typically based on rules or templates BIBREF4 , BIBREF5 , BIBREF6 . Recently, open-domain conversation systems have attracted more and more attention in both academia and industry (e.g., XiaoBing from Microsoft and DuMi from Baidu). Due to high diversity, we can hardly design rules or templates in the open domain. Researchers have proposed information retrieval methods BIBREF7 and modern generative neural networks BIBREF8 , BIBREF9 to either search for a reply from a large conversation corpus or generate a new sentence as the reply. In open-domain conversations, context information (one or a few previous utterances) is particularly important to language understanding BIBREF1 , BIBREF9 , BIBREF10 , BIBREF11 . As dialogue sentences are usually casual and short, a single utterance (e.g., “Thank you.” in Figure FIGREF2 ) does not convey much meaning, but its previous utterance (“...writing an essay”) provides useful background information of the conversation. Using such context will certainly benefit the conversation system. However, tracking all previous utterances as the context is unwise. First, commercial chat-bots usually place high demands on efficiency. In a retrieval-based system, for example, performing a standard process of candidate retrieval and re-ranking for each previous utterance may well exceed the time limit (which is very short, e.g., 500ms). Second, we observe that not all sentences in the current conversation session are equally important. The sentence “Want to take a walk?” is irrelevant to the current context, and should not be considered when the computer synthesizes the reply. Therefore, it raises the question of session segmentation in conversation systems. Document segmentation for general-purpose corpora has been widely studied in NLP. For example, Hearst BIBREF12 proposes the TextTiling approach; she measures the similarity of neighboring sentences based on bag-of-words features, and performs segmentation by thresholding. However, such approaches are not tailored to the dialogue genre and may not be suitable for conversation session segmentation. In this paper, we address the problem of session segmentation for open-domain conversations. We leverage the classic TextTiling approach, but enhance it with modern embedding-based similarity measures. Compared with traditional bag-of-words features, embeddings map discrete words to real-valued vectors, capturing underlying meanings in a continuous vector space; hence, it is more robust for noisy conversation corpora. Further, we propose a tailored method for word embedding learning. In traditional word embedding learning, the interaction between two words in a query and a reply is weaker than that within an utterance. We propose to combine a query and its corresponding reply as a “virtual sentence,” so that it provides a better way of modeling utterances between two agents. Further, we propose a tailored method for word embedding learning.

What their method is tailored for?

Word embedding learning.

Name some players on the Golden State Warriors basketball team.

The current roster of the Golden State Warriors basketball team includes the following players: Stephen Curry, Draymond Green, Andrew Wiggins, Jordan Poole, Klay Thompson, Kevon Looney, Jonathan Kuminga.

What is a mammal?

A mammal is a warm blooded vertebrate animal that is distinguished by having mammary glands and hair. Typically mammals give birth to live babies that need the help of their parents to survive. Some fun facts include, 1) bats are the only flying mammals, and 2) blue whales, the largest animals on the planet, are also mammals.

Another major difference between our method and “Gaussian 3D”, is that our encodings have constant norm and rotation-invariant inner products, thus satisfying the NTK conditions for the outputs to approximate a convolution on the manifold (shift-invariance). This is appropriate for the tasks we consider, where the relative importance between training inputs (coordinates) should not depend on their absolute position. The Gaussian encoding does not satisfy these conditions and results in a translation-invariant encoding, which is not appropriate for points on the sphere. In this section, we show the effects of intentionally breaking these two properties in our model. We sample a vector, fixed during training, from a standard normal distribution with the same dimensions as the encoding. We then replace the positional encoding by its pointwise multiplication with this vector of random factors. The resulting encoding does not have constant norm nor rotation-invariant inner products.

Why is it achieving "shift-invariance" , "minimal parameterization" or even the orthonomality property matters for the purpose of positional encoding？

The desirable convolutional (shift-invariant) behavior is of the whole model when the task is coordinate-based regression, not particularly for the purpose of positional encoding. Since, according to the NTK results, shift-invariance is achieved given some properties of the inputs, and we are changing the inputs with the proposed encoding, we want to make sure our encoding satisfies those properties. Inspired by your suggestion regarding (sin, 2cos), we conduct an experiment to verify whether the 1) constant norm and 2) rotation-invariant inner products properties of the encodings matter in practice. The idea is to simply multiply each encoding element by a random factor, and the results show it causes a drop in performance. Please see Appendix C.3 for details.

Identify which instrument is string or percussion: Pandero jarocho, Sallaneh

Sallaneh is string, Pandero jarocho is percussion.

Question answering on tabular data is an important problem in natural language processing. Recently, a number of systems have been proposed for solving the problem using the WikiTableQuestions dataset BIBREF1 (henceforth called WTQ). This dataset consists of triples of the form INLINEFORM0 question, table, answer INLINEFORM1 where the tables are scraped from Wikipedia and questions and answers are gathered via crowdsourcing. The dataset is quite challenging, with the current best model BIBREF0 (henceforth called KDG) achieving a single model accuracy of only 43.3% . This is nonetheless a significant improvement compared to the 34.8% accuracy achieved by the previous best single model BIBREF2 . We sought to analyze the source of the improvement achieved by the KDG model. The KDG paper claims that the improvement stems from certain aspects of the model architecture. In this paper, we find that a large part of the improvement also stems from a certain pruning of the data used to train the model. The KDG system generates its training data using an algorithm proposed by BIBREF3 . This algorithm applies a pruning step (discussed in Section SECREF3 ) to eliminate spurious training data entries. We find that without this pruning of the training data, accuracy of the KDG model drops to 36.3%. We consider this an important finding as the pruning step not only accounts for a large fraction of the improvement in the state-of-the-art KDG model but may also be relevant to training other models. In what follows, we briefly discuss the pruning algorithm, how we identified its importance for the KDG model, and its relevance to further work. We find that without this pruning of the training data, accuracy of the KDG model drops to 36.3%.

Does the accuracy of the KDG model drop without this pruning of the training data?

Yes, it does.

Deep networks achieve tremendous success on various visual tasks at the expense of massive data collection and annotation efforts. Even more data is needed when training (source) and testing (target) data differ, as the model must be adapted on the new data to maintain accuracy. To reduce the annotation effort on new data, unsupervised domain adaptation (UDA) approaches transfer knowledge from labeled source data to unlabeled target data. Standard UDA requires simultaneous optimization on the source and target data to do so. However, this requirement may not be entirely practical, in that shifted or future target data may not be available during training. Furthermore, (re-)processing source data during testing may be limited by computation, bandwidth, and privacy. Most importantly, it is the target data that ultimately matters for testing. In this work, we therefore turn our attention from source to target, and how to learn more from it. Recent work adapts to the target data without the source data or even adapts during testing. However, these "source-free" and "test-time" approaches still rely heavily on the source parameters for finetuning. Source-free adaptation initializes from source parameters then optimizes on target data without the joint use of source data. Testtime adaptation partially updates source parameters on the target data while testing. Such approaches reduce reliance on the source data, and can even improve accuracy, but have they made full use of the target data? Many of the model parameters are fixed or regularized toward the source parameters. We investigate whether more can be learned from target, and more accuracy gained, by not transferring the source parameters. We propose on-target adaptation to unshackle the target representation from the source representation. To do so, we (1) factorize the representation from the classifier and (2) separate the source parameters from the source predictions. By factorizing the representation from the classifier, we can train the representation entirely on the target data by self-supervision. Given this on-target representation, we can then supervise a new classifier from source predictions by distillation, without transferring the source parameters. Not transferring parameters frees our target model from the constraints of the source architecture, so that we can experiment with distinct target architectures. In this way, we can even change the model size to optimize a target-specific model that is more accurate and more efficient. In contrast to prior work on adaptation, this uniquely allows for learning 100% of the target model parameters on target data, as illustrated by Figure. Figure: Domain adaptation adjusts a model trained on source data for testing on target data. We contrast methods by their updates on source and target. Unsupervised domain adaptation (UDA) jointly learns 50/50 on source/target. Source-free adaptation transfers source parameters, then selectively learns on target. Our on-target approach learns 100% of the testing model parameters on target by neither sharing nor transferring source parameters, but instead distilling source predictions. To realize our proposed factorization and separation, we employ contrastive learning, sourcefree adaptation, and teacher-student distillation. We initialize the target representation by selfsupervision with contrastive learning. We turn the source model into a teacher model by sourcefree adaptation, and then generate pseudo-labels to supervise distillation. We lastly train the student model on the teacher supervision, starting from the target representation and new classifier parameters, and repeat this teacher-student cycle by resetting the student classifier parameters between epochs. Contrastive learning has recently enabled self-supervised representations to compete with or even surpass supervised representations. We show it provides a sufficient target representation. Our experiments show on-target adaptation achieves state-of-the-art accuracy and computational efficiency on common domain adaptation benchmarks. For model accuracy, our method brings ∼3% absolute improvement compared to state-of-the-art unsupervised and source-free domain adaptation methods on VisDA-C and ImageNet Sketch while reducing 50%+ of parameters. For computation, our method reduces FLOPs by 50+% and memory by 75+% for each forward pass of the target model. In the long-tailed classification setting, on-target class distribution learning equals the state-of-the-art learnable weight scaling without needing source data. Ablation experiments support the generality of on-target representation learning across architectures, contrastive learning methods, losses, and amount of optimization. Our contribution is to investigate whether the source data should be the primary source of target model parameters, and to propose an alternative: on-target adaptation. Our insight is that the source representation can be fully decoupled from source supervision. Domain adaptation normally emphasizes the representation of source data, by either jointly optimizing on source data or transferring source parameters. On-target adaptation emphasizes the representation of target data instead, by distilling source predictions into a self-supervised target representation. We are the first to show this is feasible, as a new kind of source-free adaptation. Furthermore we show it improves accuracy and reduces computation on standard benchmarks like VisDA-C. Deep networks achieve tremendous success on various visual tasks at the expense of massive data collection and annotation efforts. Even more data is needed when training (source) and testing (target) data differ, as the model must be adapted on the new data to maintain accuracy. To reduce the annotation effort on new data, unsupervised domain adaptation (UDA) approaches transfer knowledge from labeled source data to unlabeled target data. Standard UDA requires simultaneous optimization on the source and target data to do so. However, this requirement may not be entirely practical, in that shifted or future target data may not be available during training. Furthermore, (re-)processing source data during testing may be limited by computation, bandwidth, and privacy. Most importantly, it is the target data that ultimately matters for testing. In this work, we therefore turn our attention from source to target, and how to learn more from it. Recent work adapts to the target data without the source data or even adapts during testing. However, these "source-free" and "test-time" approaches still rely heavily on the source parameters for finetuning. Source-free adaptation initializes from source parameters then optimizes on target data without the joint use of source data. Testtime adaptation partially updates source parameters on the target data while testing. Such approaches reduce reliance on the source data, and can even improve accuracy, but have they made full use of the target data? Many of the model parameters are fixed or regularized toward the source parameters. We investigate whether more can be learned from target, and more accuracy gained, by not transferring the source parameters. We propose on-target adaptation to unshackle the target representation from the source representation. To do so, we (1) factorize the representation from the classifier and (2) separate the source parameters from the source predictions. By factorizing the representation from the classifier, we can train the representation entirely on the target data by self-supervision. Given this on-target representation, we can then supervise a new classifier from source predictions by distillation, without transferring the source parameters. Not transferring parameters frees our target model from the constraints of the source architecture, so that we can experiment with distinct target architectures. In this way, we can even change the model size to optimize a target-specific model that is more accurate and more efficient. In contrast to prior work on adaptation, this uniquely allows for learning 100% of the target model parameters on target data, as illustrated by Figure. Figure: Domain adaptation adjusts a model trained on source data for testing on target data. We contrast methods by their updates on source and target. Unsupervised domain adaptation (UDA) jointly learns 50/50 on source/target. Source-free adaptation transfers source parameters, then selectively learns on target. Our on-target approach learns 100% of the testing model parameters on target by neither sharing nor transferring source parameters, but instead distilling source predictions. To realize our proposed factorization and separation, we employ contrastive learning, sourcefree adaptation, and teacher-student distillation. We initialize the target representation by selfsupervision with contrastive learning. We turn the source model into a teacher model by sourcefree adaptation, and then generate pseudo-labels to supervise distillation. We lastly train the student model on the teacher supervision, starting from the target representation and new classifier parameters, and repeat this teacher-student cycle by resetting the student classifier parameters between epochs. Contrastive learning has recently enabled self-supervised representations to compete with or even surpass supervised representations. We show it provides a sufficient target representation. Our experiments show on-target adaptation achieves state-of-the-art accuracy and computational efficiency on common domain adaptation benchmarks. For model accuracy, our method brings ∼3% absolute improvement compared to state-of-the-art unsupervised and source-free domain adaptation methods on VisDA-C and ImageNet Sketch while reducing 50%+ of parameters. For computation, our method reduces FLOPs by 50+% and memory by 75+% for each forward pass of the target model. In the long-tailed classification setting, on-target class distribution learning equals the state-of-the-art learnable weight scaling without needing source data. Ablation experiments support the generality of on-target representation learning across architectures, contrastive learning methods, losses, and amount of optimization. Our contribution is to investigate whether the source data should be the primary source of target model parameters, and to propose an alternative: on-target adaptation. Our insight is that the source representation can be fully decoupled from source supervision. Domain adaptation normally emphasizes the representation of source data, by either jointly optimizing on source data or transferring source parameters. On-target adaptation emphasizes the representation of target data instead, by distilling source predictions into a self-supervised target representation. We are the first to show this is feasible, as a new kind of source-free adaptation. Furthermore we show it improves accuracy and reduces computation on standard benchmarks like VisDA-C. Deep networks achieve tremendous success on various visual tasks at the expense of massive data collection and annotation efforts. Even more data is needed when training (source) and testing (target) data differ, as the model must be adapted on the new data to maintain accuracy. To reduce the annotation effort on new data, unsupervised domain adaptation (UDA) approaches transfer knowledge from labeled source data to unlabeled target data. Standard UDA requires simultaneous optimization on the source and target data to do so. However, this requirement may not be entirely practical, in that shifted or future target data may not be available during training. Furthermore, (re-)processing source data during testing may be limited by computation, bandwidth, and privacy. Most importantly, it is the target data that ultimately matters for testing. In this work, we therefore turn our attention from source to target, and how to learn more from it. Recent work adapts to the target data without the source data or even adapts during testing. However, these "source-free" and "test-time" approaches still rely heavily on the source parameters for finetuning. Source-free adaptation initializes from source parameters then optimizes on target data without the joint use of source data. Testtime adaptation partially updates source parameters on the target data while testing. Such approaches reduce reliance on the source data, and can even improve accuracy, but have they made full use of the target data? Many of the model parameters are fixed or regularized toward the source parameters. We investigate whether more can be learned from target, and more accuracy gained, by not transferring the source parameters. We propose on-target adaptation to unshackle the target representation from the source representation. To do so, we (1) factorize the representation from the classifier and (2) separate the source parameters from the source predictions. By factorizing the representation from the classifier, we can train the representation entirely on the target data by self-supervision. Given this on-target representation, we can then supervise a new classifier from source predictions by distillation, without transferring the source parameters. Not transferring parameters frees our target model from the constraints of the source architecture, so that we can experiment with distinct target architectures. In this way, we can even change the model size to optimize a target-specific model that is more accurate and more efficient. In contrast to prior work on adaptation, this uniquely allows for learning 100% of the target model parameters on target data, as illustrated by Figure. Figure: Domain adaptation adjusts a model trained on source data for testing on target data. We contrast methods by their updates on source and target. Unsupervised domain adaptation (UDA) jointly learns 50/50 on source/target. Source-free adaptation transfers source parameters, then selectively learns on target. Our on-target approach learns 100% of the testing model parameters on target by neither sharing nor transferring source parameters, but instead distilling source predictions. To realize our proposed factorization and separation, we employ contrastive learning, sourcefree adaptation, and teacher-student distillation. We initialize the target representation by selfsupervision with contrastive learning. We turn the source model into a teacher model by sourcefree adaptation, and then generate pseudo-labels to supervise distillation. We lastly train the student model on the teacher supervision, starting from the target representation and new classifier parameters, and repeat this teacher-student cycle by resetting the student classifier parameters between epochs. Contrastive learning has recently enabled self-supervised representations to compete with or even surpass supervised representations. We show it provides a sufficient target representation. Our experiments show on-target adaptation achieves state-of-the-art accuracy and computational efficiency on common domain adaptation benchmarks. For model accuracy, our method brings ∼3% absolute improvement compared to state-of-the-art unsupervised and source-free domain adaptation methods on VisDA-C and ImageNet Sketch while reducing 50%+ of parameters. For computation, our method reduces FLOPs by 50+% and memory by 75+% for each forward pass of the target model. In the long-tailed classification setting, on-target class distribution learning equals the state-of-the-art learnable weight scaling without needing source data. Ablation experiments support the generality of on-target representation learning across architectures, contrastive learning methods, losses, and amount of optimization. Our contribution is to investigate whether the source data should be the primary source of target model parameters, and to propose an alternative: on-target adaptation. Our insight is that the source representation can be fully decoupled from source supervision. Domain adaptation normally emphasizes the representation of source data, by either jointly optimizing on source data or transferring source parameters. On-target adaptation emphasizes the representation of target data instead, by distilling source predictions into a self-supervised target representation. We are the first to show this is feasible, as a new kind of source-free adaptation. Furthermore we show it improves accuracy and reduces computation on standard benchmarks like VisDA-C. Furthermore, (re-)processing source data during testing may be limited by computation, bandwidth, and privacy. Most importantly, it is the target data that ultimately matters for testing. In this work, we therefore turn our attention from source to target, and how to learn more from it.****Our experiments show on-target adaptation achieves state-of-the-art accuracy and computational efficiency on common domain adaptation benchmarks.****Our on-target approach learns 100% of the testing model parameters on target by neither sharing nor transferring source parameters, but instead distilling source predictions.

Does the novelty seem a bit limited? The paper combines some standard ideas (target adaptation, distillation, contrastive pre-train on target, fix-match), the main novelty seems to be in the system - combining these for source free adaptation.

We agree, and underline in our general response (Novelty) that the novelty is one part conceptual—in how a system can learn more on target—and one part empirical—in reporting results that show the system can improve both accuracy and computational efficiency by contrastive learning and altering the target architecture. While some parts of our system are standard, its overal characteristic in training 100% of the target model parameters on target data is not (as illustrated in Figure 1).

Ella Tromp's political career began in 1989. She worked as an employee in the cabinet of the Prime Minister. On 9 March 1991 she was appointed Minister Plenipotentiary in the first cabinet of Nelson Oduber, a position which she held until 1 March 1993. She was the first woman in the history of the country to hold this position. She was also the first woman to be appointed as Minister of Finance, which she served as during Oduber's second cabinet. During her tenure as Minister of Finance, from 1993 to 1994, there was a quarrel between coalition members of the ruling government, which led to early elections and a shortened term. Despite this, she maintained firm control over government spending and for the first time in Aruba's status aparte period the country had a budget surplus.

How did Ella Tromp begin her political career?

Ella Tromp began her political career working in the cabinet of the Prime Minister in 1989. After two years she was appointed Minister Plenipotentiary in the first cabinet of Nelson Oduber, and was the first woman in the history of the country to hold that position. She then became the Minister of Finance in 1993, a role she served in until 1994.

Shah Rukh Khan (pronounced [ˈʃɑːɦɾʊx xɑːn]; born 2 November 1965), also known by the initialism SRK, is an Indian actor and film producer who works in Hindi films. Referred to in the media as the "Baadshah of Bollywood" and "King Khan", he has appeared in more than 90 films, and earned numerous accolades, including 14 Filmfare Awards. He has been awarded the Padma Shri by the Government of India, as well as the Ordre des Arts et des Lettres and Legion of Honour by the Government of France. Khan has a significant following in Asia and the Indian diaspora worldwide. In terms of audience size and income, he has been described as one of the most successful film stars in the world. Many of his films thematise Indian national identity and connections with diaspora communities, or gender, racial, social and religious differences and grievances. Khan began his career with appearances in several television series in the late 1980s, and had his Bollywood debut in 1992 with Deewana. He was initially recognised for playing villainous roles in the films Baazigar (1993) and Darr (1993). Khan established himself by starring in a series of top-grossing romantic films, including Dilwale Dulhania Le Jayenge (1995), Dil To Pagal Hai (1997), Kuch Kuch Hota Hai (1998), Mohabbatein (2000), Kabhi Khushi Kabhie Gham... (2001), Kal Ho Naa Ho (2003) and Veer-Zaara (2004). He earned critical acclaim for his portrayal of an alcoholic in Devdas (2002), a NASA scientist in Swades (2004), a hockey coach in Chak De! India (2007) and a man with Asperger syndrome in My Name Is Khan (2010). Further commercial successes came with the romantic dramas Om Shanti Om (2007) and Rab Ne Bana Di Jodi (2008), and with his expansion to comedies in Chennai Express (2013) and Happy New Year (2014). Following a brief setback and hiatus, Khan made a career comeback with the action film Pathaan (2023), which became the second-highest-grossing Hindi release.

From the given text, list the movies of Shah Rukh Khan in a chronological manner.

Deewana (1992), Baazigar (1993), Darr (1993), Dilwale Dulhania Le Jayenge (1995), Dil To Pagal Hai (1997), Kuch Kuch Hota Hai (1998), Mohabbatein (2000), Kabhi Khushi Kabhie Gham... (2001), Devdas (2002), Kal Ho Naa Ho (2003), Swades (2004), Veer-Zaara (2004), Chak De! India (2007), Om Shanti Om (2007), Rab Ne Bana Di Jodi (2008), My Name Is Khan (2010), Chennai Express (2013), Happy New Year (2014), Pathaan (2023)

How do you reverse sear a steak on the Big Green Egg?

How to reverse Sear? The reverse sear is one of the most popular ways to grill a steak on the Big Green Egg, and my personal favorite. The key to the Reverse Sear is to start low and slow. It is much easier to raise the temp on the BGE than lower the temp. Ideally, the dome temp for a reverse sear is 250. However, I've had good success with the temp as low as 225 and as high as 300. Fill your BGE up with your favorite lump charcoal up to the fire ring , light the fire and let it burn for about 9 minutes. Stir the coals so that the lit coals mix with unlit coals for an even cook.I prefer to cook my steak without adding wood chips as I don't want to overpower the meat...but if I were going to add chips I prefer post oak. Put the plate setter in with the legs up. Close the top and open the bottom vent and top vent with the opening about the size of a quarter. With the plate setter in, give it about 30 minutes to heat up the plate setter and stabilize at 250. Now that the Egg is stabilized at 250, time to add your steaks. You can pretty much reverse sear almost any cut but I prefer a ribeye, strip, or filet. The quintessential reverse sear cut is the bone-in ribeye, aka the cowboy or Tomahawk. The reverse sear works best with thick cuts. I would say the minimum thickness for a reverse sear is about 1 and a half inch, but the thicker the better. I look for the thickest cut possible for best results. I personally prefer to season my steaks with salt and pepper. My theory is that if you just spent $50 on a prime steak, then no need to overpower the meat with fancy seasoning. I want like the steaks to speak for themselves. The key to this stage is a good leave in thermometer. You want to roast the steaks until internal temp hits 113 degrees. No more no less. When the internal temp hits 113, remove your steak to a cutting board and tent with foil. At this point you should have pulled the steak with an internal temp of 113, and it should be resting on a tented cutting board. Final step of the reverse sear is the sear. Open up the top and remove the plate setter and grates.Open the bottom vent all the way and let the oxygen in to stoke the fire. I will give the coals another stir and about 5 minutes with the top open to gas up the fire. I then replace the grate, close the top and open all vents. The dome temp should climb rapidly. For a good sear you want it at least 450, with the ideal dome temp about 500-550. At this point you need to make a choice. You can either sear directly over the coals, or you can use a cast iron skillet with a butter bath. if you are going directly over the coals, its pretty simple. Place the steaks on the grate, and sear for about 90 seconds per side. If you use a cast iron skillet, I use a 50/50 mix of salted butter and olive oil. Butter has a lower scorch point, so if your skillet gets too hot you will burn the butter. I've also had good success with Avocado or Peanut oil that has a much higher scorch point. Add some garlic and thyme, sear for 90 seconds a side and don't forget to spoon the butter over the steaks for a little extra love. Remove the steaks and let them rest for 10 minutes. This is a critical step that will ensure the juices will stay in the meat. Slice and serve.

What is C#?

C# (pronounced C sharp)is a general-purpose high-level programming language supporting multiple paradigms. C# encompasses static typing, strong typing, lexically scoped, imperative, declarative, functional, generic, object-oriented (class-based), and component-oriented programming disciplines.

Think up some openings for beginners to the game of Chess

Chess openings are the most important part of the game, there are some advanced ones, some common ones, and some easy ones. This is a list of openings for beginners to the game: - The King's Pawn Opening: This is a very common opening, you move your king's pawn 2 spaces forward, so does the opponent. This is followed by developing your knights. - The Fianchetto defense. You move your kingside knight's pawn forward 1 space, then you develop the kingside knight. You then put your kingside bishop in the square that the pawn used to be in, followed by castling. This opening is common among lower ranked players. - Four Knights Game: This doesn’t happen too much. Both sides start by developing their knights, the pawns take it over from there. - The French Defense: You move your king's pawn 1 space forward, followed by developing all of your kingside pieces, and finishing by castling.

You stumble upon a new animal, one the world has never seen. What is it and how did you come up with it's name?

I can't believe I found the worlds first ever Micophant. I thought for sure what I had stumbled upon was a very tiny, baby elephant but I know baby elephants can weigh up to 200lbs at birth. This little 3lb creature with all the features of a full grown elephant was truly one of a kind. The most notable item my Microphant had was a tiny set of functional wings. So not only could this small creature move by foot, it could also fly! I was so astonished by this discovery I contacted my local Zoo which helped me identify this animal and notify the appropriate scientific community of this incredible discovery.

How does a push rod internal combustion engine work?

The heart of a push rod internal combustion engine (ICE) is the crankshaft. Rods connect the engines pistons to the journals of the crankshaft. The crankshaft is evenly balanced by weights on the opposite side of each journal to ensure that at high revolutions per minute (RPM) the motor is stable. As the crankshaft rotates it pushes the rods up and down that are connected to pistons. At the front of the crankshaft is a gear, the gear is typically connected via a chain to the camshaft. If the crankshaft is the heart of the engine, a camshaft is the brain of the engine. The camshaft determines when to open and close valves that let in air and fuel into each cylinder and let out exhaust from the internal combustion. The camshaft is a shaft that runs the length of the engine and has lobes on it, small metal cylinders called lifters ride on these lobes and are connected to push rods, the push rods are connected to rockers which rock back and forth opening and closing valves in the cylinder heads. The valves are located in the cylinder heads that sit on top of the cylinders that the pistons are in. As the pistons rotate up, a valve (being driven by the camshaft) opens to bring fuel into the cylinder, when the valve closes a spark from a spark plug, ignites the fuel causing a mini explosion pushing the cylinder down, a second valve opens letting the exhaust from the mini explosion exit the engine. This in turn rotates the crankshaft, pushing another piston up where the process continues. An intake manifold is like the lungs of a motor, it breaths in air from the atmosphere and mixes it with just the right amount of fuel to create that mini explosion in the cylinder. The timing of the air, fuel, spark delivery is critical if it is off just the smallest of margins the engine will not run at all.

Seattle came in at No. 39 in the 2023 World's Best Cities Report, a comprehensive annual study by Resonance Consultancy. Seattle is a seaport city on the West Coast of the United States. The culture of Seattle is heavily defined by its significant musical history. Between 1918 and 1951, nearly 24 jazz nightclubs existed along Jackson Street, from the current Chinatown/International District to the Central District. The jazz scene nurtured the early careers of Ernestine Anderson, Ray Charles, Quincy Jones, and others. The city is also the origin of rock acts such as Foo Fighters, Heart, and Jimi Hendrix, as well as the grunge subgenre of rock and its pioneering bands Alice in Chains, Pearl Jam, Nirvana, and Soundgarden. The city itself is hilly, though not uniformly so. Like Rome, the city is said to lie on seven hills; the lists vary but typically include Capitol Hill, First Hill, West Seattle, Beacon Hill, Queen Anne, Magnolia, and the former Denny Hill. Due to its location in the Pacific Ring of Fire, Seattle is in a major earthquake zone. Temperature extremes are moderated by the adjacent Puget Sound, greater Pacific Ocean, and Lake Washington. Thus extreme heat waves are rare in the Seattle area, as are very cold temperatures (below about 15 °F (−9 °C)). The Seattle area is the cloudiest region of the United States, due in part to frequent storms and lows moving in from the adjacent Pacific Ocean. With many more "rain days" than other major American cities, Seattle has a well-earned reputation for frequent rain. Seattle's economy is driven by a mix of older industrial companies and "new economy" internet and technology companies, as well as service, design, and clean technology companies.

Is Seattle a nice place to live?

Yes

The ability of semantic reasoning is essential for advanced natural language understanding (NLU) systems. Many NLU tasks that take sentence pairs as input, such as natural language inference (NLI) and machine reading comprehension (MRC), heavily rely on the ability of sophisticated semantic reasoning. For instance, the NLI task aims to determine whether the hypothesis sentence (e.g., a woman is sleeping) can be inferred from the premise sentence (e.g., a woman is talking on the phone). This requires the model to read and understand sentence pairs to make the specific semantic inference. Bidirectional Encoder Representations from Transformer (BERT) BIBREF1 has shown strong ability in semantic reasoning. It was recently proposed and obtained impressive results on many tasks, ranging from text classification, natural language inference, and machine reading comprehension. BERT achieves this by employing two objectives in the pre-training, i.e., the masked language modeling (Masked LM) and the next sentence prediction (NSP). Intuitively, the Masked LM task concerns word-level knowledge, and the NSP task captures the global document-level information. The goal of NSP is to identify whether an input sentence is next to another input sentence. From the ablation study BIBREF1, the NSP task is quite useful for the downstream NLI and MRC tasks (e.g., +3.5% absolute gain on the Question NLI (QNLI) BIBREF2 task). Despite its usefulness, we suggest that BERT has not made full use of the document-level knowledge. The sentences in the negative samples used in NSP are randomly drawn from other documents. Therefore, to discriminate against these sentences, BERT is prone to aggregating the shallow semantic, e.g., topic, neglecting context clues useful for detailed reasoning. In other words, the canonical NSP task would encourage the model to recognize the correlation between sentences, rather than obtaining the ability of semantic entailment. This setting weakens the BERT model from learning specific semantic for inference. Another issue that renders NSP less effective is that BERT is order-sensitive. Performance degradation was observed on typical NLI tasks when the order of two input sentences are reversed during the BERT fine-tuning phase. It is reasonable as the NSP task can be roughly analogy to the NLI task when the input comes as (premise, hypothesis), considering the causal order among sentences. However, this identity between NSP and NLI is compromised when the sentences are swapped. Based on these considerations, we propose a simple yet effective method, i.e., introducing a IsPrev category to the classification task, which is a symmetric label of IsNext of NSP. The input of samples with IsPrev is the reverse of those with IsNext label. The advantages of using this previous sentence prediction (PSP) are three folds. (1) Learning the contrast between NSP and PSP forces the model to extract more detailed semantic, thereby the model is more capable of discriminating the correlation and entailment. (2) NSP and PSP are symmetric. This symmetric regularization alleviates the influence of the order of the input pair. (3) Empirical results indicate that our method is beneficial for all the semantic reasoning tasks that take sentence pair as input. In addition, to further incorporating the document-level knowledge, NSP and PSP are extended with non-successive sentences, where the label smoothing technique is adopted. The proposed method yields a considerable improvement in our experiments. We evaluate the ability of semantic reasoning on standard NLI and MRC benchmarks, including the challenging HANS dataset BIBREF0. Analytical work on the HANS dataset provides a more comprehensible perspective towards the proposed method. Furthermore, the results on the Chinese benchmarks are provided to demonstrate its generality. In summary, this work makes the following contributions: The supervision signal from the original NSP task is weak for semantic inference. Therefore, a novel method is proposed to remedy the asymmetric issue and enhance the reasoning ability. Both empirical and analytical evaluations are provided on the NLI and MRC datasets, which verifies the effectiveness of using more document-level knowledge. Based on these considerations, we propose a simple yet effective method, i.e., introducing a IsPrev category to the classification task, which is a symmetric label of IsNext of NSP.

What is a symmetric label of IsNext of NSP?

The authors propose a novel method, i.e., introducing a symmetric label of IsNext to the classification task, which is a symmetric label of IsNext of NSP.

Victoria Inn is a heritage-listed former hotel and restaurant at 20-22 Jellore Street, Berrima, Wingecarribee Shire, New South Wales, Australia. It is also known as Queen Victoria Inn and Allington. It was added to the New South Wales State Heritage Register on 2 April 1999. History The inn was built by Joseph Levy, an ex-convict turned prominent businessman, and was first licensed in 1840, operating as both an inn and brewery. Philip Solomon was the initial licensee. There is evidence in an early painting that the building once had a verandah. It was sold early in 1863, and no record of it operating as an inn in its original incarnation appears thereafter. An 1868 report refers to it in the past-tense, but notes that its stables were still in use by Cobb and Co. It later became a residence known as Allington and became the surgery of Dr. George Lambert in 1876. It operated as "Arlington House" or "Arlington Inn" for a period c. 1960s, but had resumed its original name by the late 1970s. The stables and boarding house were later refurbished as a restaurant, operating as the Victoria Inn Restaurant. It hosted Governor-General of Australia Zelman Cowen in 1986. It is no longer in operation.

Given a reference text about the Victoria Inn, who built it and when was it sold?

The Victoria Inn was built by Joseph Levy and was sold in early 1863.

What was the 2008 Scottish Labour Party deputy leadership election?

What is the 2008 Scottish Labour deputy leadership election?

The 2008 Scottish Labour Party deputy leadership election was an internal party election to choose a new deputy leader of the Labour Party in the Scottish Parliament, which caused the resignation of Cathy Jamieson. Johann Lamont won the election and was elected deputy leader on Saturday 13 September.

How many languages are spoken in India?

There are several languages and dialects spoken across various parts of India. Currently, the Indian constitution recognises 22 languages. However, there are other prominent languages in certain regions that are not recognised. One such example is Marwari which is spoken in several parts of the state named Rajasthan but is not constitutionally recognised.

How can I change the type of a column?

Changing a column’s type or dropping a column requires rewriting the table. For an example, see Change column type.

The final of Asia cup was held in UAE, was won by Sri Lanka from which country

Pakistan by 23 runs

Lionel Danthanarayana (born 24 August 1940 – died 30 July 1994 as ලයනල් දැරණියගල) [Sinhala]), popularly as stage name Lionel Deraniyagala, was an actor in Sri Lankan cinema and theater. One of the most popular villain artists ever in Sri Lankan film history, he had a career for more than three decades as the antagonist.

Who was Lionel Danthanarayana ?

Lionel Danthanarayana was bor on August 24, 1940. He was a famous Sri Lankan Cinema and theatre actor. He is considered one of the most famous Sri Lankan viillain artists, and spent more than three decades as the antagonist.

Przystań [ˈpʂɨstaɲ] (German: Pristanien, from 1938-45 Passdorf) is a village in the administrative district of Gmina Węgorzewo, within Węgorzewo County, Warmian-Masurian Voivodeship, in northern Poland, close to the border with the Kaliningrad Oblast of Russia. It lies approximately 8 kilometres (5 mi) west of Węgorzewo and 89 km (55 mi) north-east of the regional capital Olsztyn. The village has a population of 80.

Where is Przystań?

Przystań is a small village in Poland close to the Russia border. It has a population of 80

2003.06279

Our findings paves the way for research in several new directions. While we probed the effectiveness of virtual edges in a specific text classification task, we could extend this approach for general classification tasks. A systematic comparison of embeddings techniques could also be performed to include other recent techniques BIBREF54, BIBREF55. We could also identify other relevant techniques to create virtual edges, allowing thus the use of the methodology in other networked systems other than texts. For example, a network could be enriched with embeddings obtained from graph embeddings techniques. A simpler approach could also consider link prediction BIBREF56 to create virtual edges. Finally, other interesting family of studies concerns the discrimination between co-occurrence and virtual edges, possibly by creating novel network measurements considering heterogeneous links. Our findings paves the way for research in several new directions. While we probed the effectiveness of virtual edges in a specific text classification task, we could extend this approach for general classification tasks. A systematic comparison of embeddings techniques could also be performed to include other recent techniques BIBREF54, BIBREF55. We could also identify other relevant techniques to create virtual edges, allowing thus the use of the methodology in other networked systems other than texts. For example, a network could be enriched with embeddings obtained from graph embeddings techniques. A simpler approach could also consider link prediction BIBREF56 to create virtual edges. Finally, other interesting family of studies concerns the discrimination between co-occurrence and virtual edges, possibly by creating novel network measurements considering heterogeneous links.

What other natural processing tasks authors think could be studied by using word embeddings?

The answers are shown as follows: * general classification tasks * use of the methodology in other networked systems * a network could be enriched with embeddings obtained from graph embeddings techniques

what are the benefits of swimming?

Swimming is a gravity-free, full-body cardio workout; swimmers can determine the intensity of the workout on their own with different exercise strategies. Swimming can improve your cardio level, get better heart-blood circulation, and have stronger lungs. Like any other sport or exercises, swimming also reduces stress, boosts mood, and helps with sleep quality. If you swim fast pace sprints, it can build muscles. On the other hand, if you swim steady-paced, long laps, it improves endurance and reduces your resting heart rate with practice.

How many syllables are in the word reverberate?

The word reverberate has 4 syllables: re-ver-ber-ate

How many syllables are in the word smarter?

There are two syllables in the word smarter: smart-er.

The history of ITV, the United Kingdom "Independent Television" commercial network, goes back to 1955. Independent Television began as a network of independently-owned regional companies that were both broadcasters and programme makers, beginning with four companies operating six stations in three large regions in 1955–1956, and gradually expanding to 17 stations in 14 regions by 1962. Each regional station was responsible for its own branding, scheduling and advertising, with many peak-time programmes shared simultaneously across the whole network. By 29 February 2016, 12 regions in England and Wales shared national ITV branding and scheduling, and, together with a 13th region UTV in Northern Ireland, were owned by a single company, ITV plc. A further two regions in Scotland carry STV branding and are owned by the STV Group.

How many regions make up the ITV network?

ITV started with three large regions in 1955. This expanded to 14 regions in 1962. In 2016 12 of the 14 regions shared national ITV branding in England and Wales with Scotland branded as STV and Northern Ireland as UTV.

The Voyager Golden Records are two phonograph records that were included aboard both Voyager spacecraft launched in 1977. The records contain sounds and images selected to portray the diversity of life and culture on Earth, and are intended for any intelligent extraterrestrial life form who may find them. The records are a time capsule. Although neither Voyager spacecraft is heading toward any particular star, Voyager 1 will pass within 1.6 light-years' distance of the star Gliese 445, currently in the constellation Camelopardalis, in about 40,000 years. Carl Sagan noted that "The spacecraft will be encountered and the record played only if there are advanced space-faring civilizations in interstellar space, but the launching of this 'bottle' into the cosmic 'ocean' says something very hopeful about life on this planet." The contents of the record were selected for NASA by a committee chaired by Carl Sagan of Cornell University. The selection of content for the record took almost a year. Sagan and his associates assembled 115 images and a variety of natural sounds, such as those made by surf, wind, thunder and animals (including the songs of birds and whales). To this they added audio content to represent humanity: spoken greetings in 55 ancient and modern languages, including a spoken greeting in English by U.N. Secretary-General Kurt Waldheim and a greeting by Sagan's six-year-old son, Nick; other human sounds, like footsteps and laughter (Sagan's); the inspirational message Per aspera ad astra in Morse code; and musical selections from different cultures and eras. The record also includes a printed message from U.S. president Jimmy Carter.

Given this paragraph about the Golden Records, tell me what it is, where it is heading, who/when created it.

The Voyager Golden Records was created by NASA committee and chaired by Carl Sagan launched in 1977 heading to any particular star passing within 1.6 light-years' distance of the star Gliese 445, currently in the constellation Camelopardalis, in about 40,000 years. The contents provided depict diversity of life and culture on Earth through sounds and images.

Tell me whether these operation systems are open-source of proprietary; Ubuntu, iOS , Debian, Fedora, CentOS, macOS, Open Suse, Windows

Definitely I can do that. Ubuntu, Fedora, Debian, CentOS and Open Suse are open-source operating systems. On the other hand, Windows, macOS and iOS are proprietary.

Tell me whether these are insects: ants, cicadas, flies, mosquitos, spiders, scorpions.

ants, cicadas, flies and mosquitos are insects, spiders and scorpions are animals (a class of joint-legged invertebrate animals, i.e. arthropods).

How long should a child's carseat remain rear facing?

Extended rear facing until the child reaches the maximum height or weight limits of the carseat is the safest option. When a child is sitting forward facing in a frontal collision a large amount of force is exerted onto the head, neck and shoulders leading to serious injury like paralysis or death. If the child is sitting rear facing during a collision, the force is spread over a greater area of the body with less pressure on the neck, head, and inner organs. Children should sit rear facing as long as possible, in case of collision it reduces the risk of serious injury or death by more than 90%. You should consult your car seat's manual to determine the maximum height and weight restrictions.

When was the Philippine House Committee elevated into a standing committee

The Philippine House Committee on Sustainable Development Goals, or House Sustainable Development Goals Committee is a standing committee of the Philippine House of Representatives. Originally designated as a special committee, it was elevated into a standing committee on March 10, 2020.

who are the greatest Raider players

Jim Otto, Ken Stabler, Cliff Branch, Tim Brown, Marcus Allen

What is the difference between affect and effect?

Affect is a verb, and it means to impact or change. Effect is a noun, an effect is the result of a change. An example of each is as follows; "Exercising is positively affecting my health” “Exercise can have positive effects on your health”

What are some words that rhyme with orange

Sporange Storage Courage Warrant Abhorrent Forage Porridge

Neural machine translation (NMT) has recently become popular in the field of machine translation. However, NMT suffers from the problem of repeating or missing words in the translation. To address this problem, Tu et al. (2017) proposed an encoder-decoder-reconstructor framework for NMT using back-translation. In this method, they selected the best forward translation model in the same manner as Bahdanau et al. (2015), and then trained a bi-directional translation model as fine-tuning. Their experiments show that it offers significant improvement in BLEU scores in Chinese-English translation task. We confirm that our re-implementation also shows the same tendency and alleviates the problem of repeating and missing words in the translation on a English-Japanese task too. In addition, we evaluate the effectiveness of pre-training by comparing it with a jointly-trained model of forward translation and back-translation. To address this problem, Tu et al. (2017) proposed an encoder-decoder-reconstructor framework for NMT using back-translation.

What framework do they introduce to optimize NMT by?

By back-translation from the output sentences into the original source sentences.

How much of American wine is produced in California?

California is responsibly for roughly 90% of American wine production

Does having a real cork, artificial cork, or a screw-top have any impact on the quality of a bottle of wine?

Generally, no. There is little evidence to suggest any one closure is more superior than another for most wines. It is more important to have an airtight seal on a bottle of wine so as to prevent introduction of air into the bottle which would cause oxidation of the wine inside.

Second-stage boot loaders, such as GNU GRUB, rEFInd, BOOTMGR, Syslinux, NTLDR or iBoot, are not themselves operating systems, but are able to load an operating system properly and transfer execution to it; the operating system subsequently initializes itself and may load extra device drivers. The second-stage boot loader does not need drivers for its own operation, but may instead use generic storage access methods provided by system firmware such as the BIOS or Open Firmware, though typically with restricted hardware functionality and lower performance. Many boot loaders can be configured to give the user multiple booting choices. These choices can include different operating systems (for dual or multi-booting from different partitions or drives), different versions of the same operating system (in case a new version has unexpected problems), different operating system loading options (e.g., booting into a rescue or safe mode), and some standalone programs that can function without an operating system, such as memory testers (e.g., memtest86+), a basic shell (as in GNU GRUB), or even games (see List of PC Booter games).Some boot loaders can also load other boot loaders; for example, GRUB loads BOOTMGR instead of loading Windows directly. Usually, a default choice is preselected with a time delay during which a user can press a key to change the choice; after this delay, the default choice is automatically run so normal booting can occur without interaction. The boot process can be considered complete when the computer is ready to interact with the user, or the operating system is capable of running system programs or application programs. Many embedded systems must boot immediately. For example, waiting a minute for a digital television or a GPS navigation device to start is generally unacceptable. Therefore, such devices have software systems in ROM or flash memory so the device can begin functioning immediately; little or no loading is necessary, because the loading can be precomputed and stored on the ROM when the device is made. Large and complex systems may have boot procedures that proceed in multiple phases until finally the operating system and other programs are loaded and ready to execute. Because operating systems are designed as if they never start or stop, a boot loader might load the operating system, configure itself as a mere process within that system, and then irrevocably transfer control to the operating system. The boot loader then terminates normally as any other process would.

Linux Second-stage boot loader

A two-stage bootloader actually consists of two bootloaders after each other. The first being small with the sole purpose of loading the second one. The second one can then contain all the code needed for loading the kernel. GRUB uses two (or arguably, three) stages.

Classify the below energy sources based on their type. NUCLEAR, SOLAR ENERGY, COAL, HYDROPOWER, GEOTHERMAL, WIND ENERGY, NATURAL GAS

SOLAR ENERGY, WIND ENERGY, GEOTHERMAL, HYDROPOWER - Renewable energy. COAL, NATURAL GAS, NUCLEAR - Non Renewable energy.

Name the BRICS nations.

Brazil, Russia, India, China & S.Africa

Question answering (QA) has been a blooming research field for the last decade. Selection-based QA implies a family of tasks that find answer contexts from large data given questions in natural language. Three tasks have been proposed for selection-based QA. Given a document, answer extraction BIBREF0 , BIBREF1 finds answer phrases whereas answer selection BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 and answer triggering BIBREF6 , BIBREF7 find answer sentences instead, although the presence of the answer context is not assumed within the provided document for answer triggering but it is for the other two tasks. Recently, various QA tasks that are not selection-based have been proposed BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 ; however, selection-based QA remains still important because of its practical value to real applications (e.g., IBM Watson, MIT Start). Several datasets have been released for selection-based QA. wang:07a created the QASent dataset consisting of 277 questions, which has been widely used for benchmarking the answer selection task. feng:15a presented InsuranceQA comprising 16K+ questions on insurance contexts. yang:15a introduced WikiQA for answer selection and triggering. jurczyk:16 created SelQA for large real-scale answer triggering. rajpurkar2016squad presented SQuAD for answer extraction and selection as well as for reading comprehension. Finally, morales-EtAl:2016:EMNLP2016 provided InfoboxQA for answer selection. These corpora make it possible to evaluate the robustness of statistical question answering learning. Although all of these corpora target on selection-based QA, they are designed for different purposes such that it is important to understand the nature of these corpora so a better use of them can be made. In this paper, we make both intrinsic and extrinsic analyses of four latest corpora based on Wikipedia, WikiQA, SelQA, SQuAD, and InfoboxQA. We first give a thorough intrinsic analysis regarding contextual similarities, question types, and answer categories (Section SECREF2 ). We then map questions in all corpora to the current version of English Wikipedia and benchmark another selection-based QA task, answer retrieval (Section SECREF3 ). Finally, we present an extrinsic analysis through a set of experiments cross-testing these corpora using a convolutional neural network architecture (Section SECREF4 ). Wang et al. (2007) created the QASENT dataset consisting of 277 questions, which has been widely used for benchmarking the answer selection task.

What is the QASENT dataset used for?

The QASENT dataset has been widely used for benchmarking the answer selection task.

"The sauna in Finland is an old phenomenon and its roots are difficult to trace, but its earliest versions are believed to be from 7000 BC.[citation needed] Bath houses were recorded in Europe during the same time period, but Finnish bathing habits were poorly documented for most of history. One of the first written mentions of what is believed to be the sauna customs of the forefathers of the Finns was written by Nestor the Chronicler in 1112. He told of ""hot wooden saunas in which naked bathers beat themselves with branches and finally pour cold water over themselves"". During the Reformation in Scandinavia the popularity of saunas expanded to other countries because the European bath houses were being destroyed. One reason the sauna culture has always flourished in Finland has been because of the versatility of the sauna. When people were moving, the first thing they did was to build a sauna. Finns have used the sauna to live in, eat, address matters of hygiene, and, most importantly, give birth in an almost sterile environment. Unlike many other, more densely populated places in Europe, the availability of wood needed to build and warm the sauna has never been an issue. Another reason for its popularity is that in such a cold climate, the sauna allows people warmth for at least a short period of time. However, it is just as popular in the summer as in the winter."

What is one of the first recorded mentions of the sauna in Finnish literature?

Nestor the Chronicler in 1112. He told of "hot wooden saunas in which naked bathers beat themselves with branches and finally pour cold water over themselves"".

Why Men love whisky

Because whisky is Manly.

What are the ways to travel from Delhi to Varanasi? And how long does each route take?

One can use any of the following 3 ways to travel from Delhi to Varanasi. 1. Travel by flight from Delhi airport to Varanasi. Depending on where you stay in Delhi, your travel time to Delhi airport could be between 45 mins to 2 hours. The flight time from Delhi to Varanasi is 1 hour 30 minutes. On arrival at Varanasi airport, it takes about 1 hour to get to the city. The total time that this route takes is about 7- 8 hours taking into account the airport security time. 2. You could take a train from New Delhi railway station to go to Varanasi. The fastest train travel time is about 6 hours and the Varanasi railway station is in the heart of the city. The total time this route takes is also between 7-8 hours depending on how far one is from the New Delhi railway station. 3. The last option one could consider is driving by road from Delhi to Varanasi. The total travel time by this route is bout 11-12 hours with a couple of breaks on the way.

The concept of the electric field was introduced by Michael Faraday. An electric field is created by a charged body in the space that surrounds it, and results in a force exerted on any other charges placed within the field. The electric field acts between two charges in a similar manner to the way that the gravitational field acts between two masses, and like it, extends towards infinity and shows an inverse square relationship with distance. However, there is an important difference. Gravity always acts in attraction, drawing two masses together, while the electric field can result in either attraction or repulsion. Since large bodies such as planets generally carry no net charge, the electric field at a distance is usually zero. Thus gravity is the dominant force at distance in the universe, despite being much weaker. An electric field generally varies in space and its strength at any one point is defined as the force (per unit charge) that would be felt by a stationary, negligible charge if placed at that point. The conceptual charge, termed a 'test charge', must be vanishingly small to prevent its own electric field disturbing the main field and must also be stationary to prevent the effect of magnetic fields. As the electric field is defined in terms of force, and force is a vector, having both magnitude and direction, so it follows that an electric field is a vector field. The study of electric fields created by stationary charges is called electrostatics. The field may be visualised by a set of imaginary lines whose direction at any point is the same as that of the field. This concept was introduced by Faraday, whose term 'lines of force' still sometimes sees use. The field lines are the paths that a point positive charge would seek to make as it was forced to move within the field; they are however an imaginary concept with no physical existence, and the field permeates all the intervening space between the lines. Field lines emanating from stationary charges have several key properties: first, that they originate at positive charges and terminate at negative charges; second, that they must enter any good conductor at right angles, and third, that they may never cross nor close in on themselves.

Who introduced the concept of the electric field and what is it?

The concept of the electric field was introduced by Michael Faraday. An electric field is created by a charged body in the space that surrounds it, and results in a force exerted on any other charges placed within the field.

According to the U.S. Census Bureau, the township had a total area of 82.92 square miles (214.77 km2), including 70.24 square miles (181.92 km2) of land and 12.69 square miles (32.86 km2) of water (15.30%). Burleigh (with a 2010 Census population of 725), Cape May Court House (5,338), Rio Grande (2,670) and Whitesboro (2,205) are unincorporated communities and census-designated places (CDPs) located within Middle Township; Whitesboro and Burleigh had previously been combined for statistical purposes by the Census Bureau as Whitesboro-Burleigh through the 2000 Census. Other unincorporated communities, localities and place names located partially or completely within the township include Del Haven, Dias Creek, Goshen, Goshen Landing, Green Creek, Holly Beach, Mayville, Norburys Landing, Nummytown, Pierces, Pierces Point, Reeds Beach, Shellbed Landing, Swain Point, Swainton, Wildwood Gardens and Wildwood Junction.

Extract the unincorporated communities in the below text that begin with starting with the letter R, W and G and put them in a pipe separated list

Reeds Beach | Wildwood Gardens | Wildwood Junction | Goshen | Goshen Landing | Green Creek

NATO formed with twelve founding members and has added new members eight times, most recently when North Macedonia joined the alliance in March 2020. Following the acceptance of their applications for membership in June 2022, Finland and Sweden are anticipated to become the 31st and 32nd members, with their Accession Protocols to the North Atlantic Treaty now in the process of being ratified by the existing members. In addition, NATO currently recognizes Bosnia and Herzegovina, Georgia, and Ukraine as aspiring members. Enlargement has led to tensions with non-member Russia, one of the twenty additional countries participating in NATO's Partnership for Peace programme. Another nineteen countries are involved in institutionalized dialogue programmes with NATO.

How many members will NATO have once Finland and Sweden join the organization?

32

What are the top Sports Movies ever?

The top sports movies are somewhat subjective but certainly could include the following movies. 1. Miracle 2. Caddyshack 3. The Blind Side 4. Rocky 1 5. The Blind Side 6. Hooziers 7. Slap Shot 8. A field of Dreams 9. Prefontaine 10. Moneyball

Give me 4 words to describe designer shoes

creative, exquisite, expensive, fashionable

Make a list of 10 things I should buy at the grocery store.

Eggs, milk, yogurt, bread, deli meat, cheese, spinach, apples, chicken breast, chocolate

Bitcoin is a protocol which implements a highly available, public, permanent, and decentralized ledger. In order to add to the ledger, a user must prove they control an entry in the ledger. The protocol specifies that the entry indicates an amount of a token, bitcoin with a minuscule b. The user can update the ledger, assigning some of their bitcoin to another entry in the ledger. Because the token has characteristics of money, it can be thought of as a digital currency.

Extract the most commonly used term to describe bitcoin and simplify it in one sentence

Bitcoin is basically a secure way to maintain a virtual ledger

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. Our model is based on this framework and make three improvements: (1) BERT [1] is introduced as a feature extraction layer in place of BiLSTM. We also optimize the pre-training process of BERT by introducing a semanticenhanced task. (2) A large-scale Baidu Baike corpus is introduced for entity recognition pretraining, which is of weekly supervised learning since there is no actual named entity label. (3) Soft label embedding is proposed to effectively transmit information between entity recognition and relation extraction.

What are the improvements of the authors' model?

(1) BERT is introduced as a feature extraction layer in place of BiLSTM. The authors also optimize the pre-training process of BERT by introducing a semanticenhanced task. (2) A large-scale Baidu Baike corpus is introduced for entity recognition pretraining, which is of weekly supervised learning since there is no actual named entity label. (3) Soft label embedding is proposed to effectively transmit information between entity recognition and relation extraction.

1905.10247

The goal of this paper is to propose a novel OOD detection method that does not require OOD data by utilizing counterfeit OOD turns in the context of a dialog. Most prior approaches do not consider dialog context and make predictions for each utterance independently. We will show that this independent decision leads to suboptimal performance even when actual OOD utterances are given to optimize the model and that the use of dialog context helps reduce OOD detection errors. To consider dialog context, we need to connect the OOD detection task with the overall dialog task. Thus, for this work, we build upon Hybrid Code Networks (HCN) BIBREF4 since HCNs achieve state-of-the-art performance in a data-efficient way for task-oriented dialogs, and propose AE-HCNs which extend HCNs with an autoencoder (Figure FIGREF8 ). Furthermore, we release new dialog datasets which are three publicly available dialog corpora augmented with OOD turns in a controlled way (exemplified in Table TABREF2 ) to foster further research. The result is shown in Table TABREF23 . Since there are multiple actions that are appropriate for a given dialog context, we use per-utterance Precision@K as performance metric. We also report f1-score for OOD detection to measure the balance between precision and recall. The performances of HCN on Test-OOD are about 15 points down on average from those on Test, showing the detrimental impact of OOD utterances to such models only trained on in-domain training data. AE-HCN(-CNN) outperforms HCN on Test-OOD by a large margin about 17(20) points on average while keeping the minimum performance trade-off compared to Test. Interestingly, AE-HCN-CNN has even better performance than HCN on Test, indicating that, with the CNN encoder, counterfeit OOD augmentation acts as an effective regularization. In contrast, AE-HCN-Indep failed to robustly detect OOD utterances, resulting in much lower numbers for both metrics on Test-OOD as well as hurting the performance on Test. This result indicates two crucial points: 1) the inherent difficulty of finding an appropriate threshold value without actually seeing OOD data; 2) the limitation of the models which do not consider context. For the first point, Figure FIGREF24 plots histograms of reconstruction scores for IND and OOD utterances of bAbI6 Test-OOD. If OOD utterances had been known a priori, the threshold should have been set to a much higher value than the maximum reconstruction score of IND training data (6.16 in this case). Thus, for this work, we build upon Hybrid Code Networks (HCN) BIBREF4 since HCNs achieve state-of-the-art performance in a data-efficient way for task-oriented dialogs, and propose AE-HCNs which extend HCNs with an autoencoder (Figure FIGREF8 ). AE-HCN(-CNN) outperforms HCN on Test-OOD by a large margin about 17(20) points on average while keeping the minimum performance trade-off compared to Test.

By how much does their method outperform state-of-the-art OOD detection?

AE-HCN outperforms by 17%, AE-HCN-CNN outperforms by 20% on average

What day is Halloween?

Halloween is October 31st

Who's the protagonist in the book "Gone with the wind"

Scarlett O Hara

Where can I see the remnants of a meteor strike on earth?

Meteor Crater located in Arizona, USA is one of the best preserved impact crater site on earth. The crater width is more than one kilometer and is visible from passenger airplanes flying overhead. The site is a best example of the destruction the meteors can cause. The site contains a visitor center that has various educational shows about space, meteor and human space flight history, remnants of the meteors the visitors can touch and feel, observation deck and guided tours of the crater outer rim. The site is owned by a private family who generates money by selling visitor tickets.

Skydiving includes free falling (usually from an airplane) through the air prior to opening a parachute. Typically skydives are carried out from around 4,000m (or 12,500ft) offering 40 to 50 seconds of freefall time. Longer free fall times can be achieved by exiting an aircraft at altitudes much higher than 4,000m, but very high jumps require pressurized air within the aircraft, and bottled oxygen for the diver.

Given this paragraph about skydiving, give me the average time skydiver spends in freefall.

Typically skydives are carried out from around 4,000m (or 12,500ft) offering 40 to 50 seconds of freefall time.

The relative size of a Foley catheter is described using French units (F). Alternatively, the size of a 10 F catheter might be expressed as 10 Ch (Charriere units – named after a 19th century French scientific instrument maker, Joseph-Frédéric-Benoît Charrière). The most common sizes are 10 F to 28 F. 1 F is equivalent to 0.33 mm = .013" = 1/77" of diameter. Foley catheters are usually color coded by size with a solid color band at the external end of the balloon inflation tube, allowing for easy identification of the size. Note: Colors for French sizes 5, 6, 8, 10 may vary significantly if intended for pediatric patients. Color for French size 26 may also be pink instead of black. https://en.wikipedia.org/wiki/Foley_catheter

Based on this paragraph, what is the diameter of a 10 F Foley catheter in mm?

According to this paragraph, the diameter of a 1 F catheter is 1/77mm so the diameter of a 10 F catheter is 10/77mm.

1907.04152

Three most common classic non-contextual approaches to obtain word embeddings are skip-gram, Continuous Bag of Words (two algorithms from BIBREF19 ) and GloVe (Global Vectors, BIBREF20 , where higher accuracy than in previous algorithms was proved). Some authors use pretrained embeddings (especially when their data set is too small to train their own embeddings) or try to modify these embeddings and adjust to their set. But the biggest drawback of these approaches is that the corpus for training embeddings can be not related to the specific task where embeddings are utilized. A lot of medical concepts are not contained in well-known embeddings bases. Furthermore, the similarity of words may vary in different contexts. Then, we compute embeddings of concepts (by GloVe) for interview descriptions and for examination descriptions separately. We compute two separate embeddings, because we want to catch the similarity between terms in their specific context, i.e. words similar in the interview may not be similar in the examination description (for example we computed that the nearest words to cough in interview descriptions was runny nose, sore throat, fever, dry cough but in examination description it was rash, sunny, laryngeal, dry cough). Some authors use pretrained embeddings (especially when their data set is too small to train their own embeddings) or try to modify these embeddings and adjust to their set. But the biggest drawback of these approaches is that the corpus for training embeddings can be not related to the specific task where embeddings are utilized. A lot of medical concepts are not contained in well-known embeddings bases. Furthermore, the similarity of words may vary in different contexts. Then, we compute embeddings of concepts (by GloVe) for interview descriptions and for examination descriptions separately.

Do they fine-tune the used word embeddings on their medical texts?

No.

How humans process language has become increasingly relevant in natural language processing since physiological data during language understanding is more accessible and recorded with less effort. In this work, we focus on eye-tracking and electroencephalography (EEG) recordings to capture the reading process. On one hand, eye movement data provides millisecond-accurate records about where humans look when they are reading, and is highly correlated with the cognitive load associated with different stages of text processing. On the other hand, EEG records electrical brain activity across the scalp and is a direct measure of physiological processes, including language processing. The combination of both measurement methods enables us to study the language understanding process in a more natural setting, where participants read full sentences at a time, in their own speed. Eye-tracking then permits us to define exact word boundaries in the timeline of a subject reading a sentence, allowing the extraction of brain activity signals for each word. Human cognitive language processing data is immensely useful for NLP: Not only can it be leveraged to improve NLP applications (e.g. barrett2016weakly for part-of-speech tagging or klerke2016improving for sentence compression), but also to evaluate state-of-the-art machine learning systems. For example, hollenstein2019cognival evaluate word embeddings, or schwartz2019inducing fine-tune language models with brain-relevant bias. Additionally, the availability of labelled data plays a crucial role in all supervised machine learning applications. Physiological data can be used to understand and improve the labelling process (e.g. tokunaga2017eye), and, for instance, to build cost models for active learning scenarios BIBREF0. Is it possible to replace this expensive manual work with models trained on physiological activity data recorded from humans while reading? That is to say, can we find and extract relevant aspects of text understanding and annotation directly from the source, i.e. eye-tracking and brain activity signals during reading? Motivated by these questions and our previously released dataset, ZuCo 1.0 BIBREF1, we developed this new corpus, where we specifically aim to collect recordings during natural reading as well as during annotation. We provide the first dataset of simultaneous eye movement and brain activity recordings to analyze and compare normal reading to task-specific reading during annotation. The Zurich Cognitive Language Processing Corpus (ZuCo) 2.0, including raw and preprocessed eye-tracking and electroencephalography (EEG) data of 18 subjects, as well as the recording and preprocessing scripts, is publicly available at https://osf.io/2urht/. It contains physiological data of each subject reading 739 English sentences from Wikipedia (see example in Figure FIGREF1). We want to highlight the re-use potential of this data. In addition to the psycholinguistic motivation, this corpus is especially tailored for training and evaluating machine learning algorithms for NLP purposes. We conduct a detailed technical validation of the data as proof of the quality of the recordings. We provide the first dataset of simultaneous eye movement and brain activity recordings to analyze and compare normal reading to task-specific reading during annotation.

What do they want to do by using their dataset of EEG recordings?

To analyze and compare normal reading to task-specific reading during annotation.

Classify each of the following as old or young: baby, child, grandma, elderly, grandpa, octogenarian, infant

Baby: Young Child: Young Grandma: Old Elderly: Old Grandpa: Old Octogenarian: Old Infant: Young

The PlayStation 5 (PS5) is a home video game console developed by Sony Interactive Entertainment. It was announced as the successor to the PlayStation 4 in April 2019, was launched on November 12, 2020, in Australia, Japan, New Zealand, North America, and South Korea, and was released worldwide one week later. The PS5 is part of the ninth generation of video game consoles, along with Microsoft's Xbox Series X/S consoles, which were released in the same month. The base model includes an optical disc drive compatible with Ultra HD Blu-ray discs. The Digital Edition lacks this drive, as a lower-cost model for buying games only through download. The two variants were launched simultaneously. The PlayStation 5's main hardware features include a solid-state drive customized for high-speed data streaming to enable significant improvements in storage performance, an AMD GPU capable of 4K resolution display at up to 120 frames per second, hardware-accelerated ray tracing for realistic lighting and reflections, and the Tempest Engine for hardware-accelerated 3D audio effects. Other features include the DualSense controller with haptic feedback, backward compatibility with the majority of PlayStation 4 and PlayStation VR games, and the PlayStation VR2 headset. History Development Mark Cerny, the PlayStation 5's chief architect The lead architect of the PlayStation console line, Mark Cerny, implemented a two-year feedback cycle after the launch of the PlayStation 4. This entailed regularly visiting Sony's first-party developers at two-year intervals to find out what concerns they had with shortcomings in Sony's current hardware and how such hardware could be improved in console refreshes or for the next generation. This feedback was fed into the priorities for the console development team. In the development of the PlayStation 5, a key issue was the length of loading times for games. Cerny said several developers, including Epic Games' Tim Sweeney, told him that standard I/O speed of hard disk drives was now a limiting factor in pushing game development. Slow data rates placed limits on the size of data being loaded into the game, the physical location of data on the storage medium, and the duplication of data across the medium in order to reduce load times. An important goal was to find ways to reduce loading time, particularly in games that stream or dynamically load new game areas as the player moves through the game world. Jim Ryan, the CEO of Sony Interactive Entertainment, stated that Sony had researched the feasibility of a "low priced, reduced spec" version of the PlayStation 5, like what Microsoft had done with its Xbox Series X and its lower-power counterpart the Xbox Series S; and concluded that they believed such consoles do not fare well, becoming obsolete too fast. Marketing and release Cerny first publicly described the new console in an interview with Wired magazine in April 2019. In early 2019, Sony's financial report for the quarter ending March 31, 2019, affirmed that new next-generation hardware was in development but would ship no earlier than April 2020. In a second Wired magazine interview in October 2019, Sony said it intended to ship its next-generation console worldwide by the end of 2020. The current hardware specifications were revealed in October 2019. At CES 2020, Sony unveiled the official logo for the platform, which follows the similar minimalist styling of the previous PlayStation consoles and brand. Full specifications were given in an online presentation by Cerny and published by Sony and Digital Foundry on March 18, 2020. Digital Foundry spoke with Cerny in detail and published a "deep dive" on April 2. A major game library showcase had been planned for June 4, 2020, but was postponed until June 11 due to the George Floyd protests. This presentation was also the premiere of the console's external hardware design. Event lighting being set up at SIE headquarters on the evening of November 8, four days before the launch on November 12, 2020. Sony planned to launch the PlayStation 5 by the 2020 end-of-year holiday period. The date and pricing was confirmed as part of a game showcase presentation on September 16, 2020; the release date in Australia, Japan, New Zealand, North America, and South Korea was confirmed for November 12, 2020, and for most of the rest of the world on November 19, 2020. PlayStation 5's release in India was delayed, leading to speculation that a trademark dispute was the reason; the name "PS5" was briefly trademarked by a different person; eventually the dispute was resolved and the system released there on February 2, 2021. The console launched in Indonesia on January 22, 2021. The system launched in China on May 15, 2021. The console launched with two models: a base version with an Ultra HD Blu-ray compatible optical disc drive for retail game support alongside online distribution via the PlayStation Store, and a lower-cost variant lacking the disc drive and retaining digital download support. Following the September 16, 2020, presentation, Sony stated that pre-orders for the console were to open at various retailers on the following day. However, several retailers in the United States and the United Kingdom launched pre-orders that evening, causing a rush on pre-orders, including scalping as many stores' inventories were quickly sold out, and creating confusion. Sony apologized for the incident on September 19, 2020, and promised to increase more pre-order deliveries over the coming days and stock through the end of the year. Worldwide supply of the console has remained low due to a global chip shortage since 2020. Sony expects a short retail stock until 2023. In August 2022, Sony announced a price increase by up to 20% in most of its markets except the US, citing global economic, inflationary, and supply chain pressures.

when was the Playstation 5 announced?

The PlayStation 5 (PS5) is a home video game console developed by Sony Interactive Entertainment. It was announced as the successor to the PlayStation 4 in April 2019, was launched on November 12, 2020, in Australia, Japan, New Zealand, North America, and South Korea, and was released worldwide one week later.