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The growing popularity of online interactions through social media has been shown to have both positive and negative impacts. While social media improves information sharing, it also facilitates the propagation of online harassment, including hate speech. These negative experiences can have a measurable negative impact on users. Recently, the Pew Research Center BIBREF0 reported that “roughly four-in-ten Americans have personally experienced online harassment, and 63% consider it a major problem.” To address the growing problem of online hate, an extensive body of work has focused on developing automatic hate speech detection models and datasets BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8. However, simply detecting and blocking hate speech or suspicious users often has limited ability to prevent these users from simply turning to other social media platforms to continue to engage in hate speech as can be seen in the large move of individuals blocked from Twitter to Gab BIBREF9. What's more, such a strategy is often at odds with the concept of free speech. As reported by the Pew Research Center BIBREF0, “Despite this broad concern over online harassment, 45% of Americans say it is more important to let people speak their minds freely online; a slightly larger share (53%) feels that it is more important for people to feel welcome and safe online.” The special rapporteurs representing the Office of the United Nations High Commissioner for Human Rights (OHCHR) have recommended that “The strategic response to hate speech is more speech.” BIBREF10 They encourage to change what people think instead of merely changing what they do, so they advocate more speech that educates about cultural differences, diversity, and minorities as a better strategy to counter hate speech. Therefore, in order to encourage strategies of countering online hate speech, we propose a novel task of generative hate speech intervention and introduce two new datasets for this task. Figure FIGREF5 illustrates the task. Our datasets consist of 5K conversations retrieved from Reddit and 12k conversations retrieved from Gab. Distinct from existing hate speech datasets, our datasets retain their conversational context and introduce human-written intervention responses. The conversational context and intervention responses are critical in order to build generative models to automatically mitigate the spread of these types of conversations. To summarize, our contributions are three-fold: We introduce the generative hate speech intervention task and provide two fully-labeled hate speech datasets with human-written intervention responses. Our data is collected in the form of conversations, providing better context. The two data sources, Gab and Reddit, are not well studied for hate speech. Our datasets fill this gap. Due to our data collecting strategy, all the posts in our datasets are manually labeled as hate or non-hate speech by Mechanical Turk workers, so they can also be used for the hate speech detection task. The performance of commonly-used classifiers on our datasets is shown in Section SECREF6. Therefore, in order to encourage strategies of countering online hate speech, we propose a novel task of generative hate speech intervention and introduce two new datasets for this task.

What novel task do the authors propose?

A novel task of generative hate speech intervention.

What would be possible if a person had an oracle that could immediately provide the answer to any question about the visual world? Sight-impaired users could quickly and reliably figure out the denomination of their currency and so whether they spent the appropriate amount for a product BIBREF0 . Hikers could immediately learn about their bug bites and whether to seek out emergency medical care. Pilots could learn how many birds are in their path to decide whether to change course and so avoid costly, life-threatening collisions. These examples illustrate several of the interests from a visual question answering (VQA) system, including tackling problems that involve classification, detection, and counting. More generally, the goal for VQA is to have a single system that can accurately answer any natural language question about an image or video BIBREF1 , BIBREF2 , BIBREF3 . Entangled in the dream of a VQA system is an unavoidable issue that, when asking multiple people a visual question, sometimes they all agree on a single answer while other times they offer different answers (Figure FIGREF1 ). In fact, as we show in the paper, these two outcomes arise in approximately equal proportions in today's largest publicly-shared VQA benchmark that contains over 450,000 visual questions. Figure FIGREF1 illustrates that human disagreements arise for a variety of reasons including different descriptions of the same concept (e.g., “minor" and “underage"), different concepts (e.g., “ghost" and “photoshop"), and irrelevant responses (e.g., “no"). Our goal is to account for whether different people would agree on a single answer to a visual question to improve upon today's VQA systems. We propose multiple prediction systems to automatically decide whether a visual question will lead to human agreement and demonstrate the value of these predictions for a new task of capturing the diversity of all plausible answers with less human effort. Our work is partially inspired by the goal to improve how to employ crowds as the computing power at run-time. Towards satisfying existing users, gaining new users, and supporting a wide range of applications, a crowd-powered VQA system should be low cost, have fast response times, and yield high quality answers. Today's status quo is to assume a fixed number of human responses per visual question and so a fixed cost, delay, and potential diversity of answers for every visual question BIBREF2 , BIBREF0 , BIBREF4 . We instead propose to dynamically solicit the number of human responses based on each visual question. In particular, we aim to accrue additional costs and delays from collecting extra answers only when extra responses are needed to discover all plausible answers. We show in our experiments that our system saves 19 40-hour work weeks and $1800 to answer 121,512 visual questions, compared to today's status quo approach BIBREF0 . Our work is also inspired by the goal to improve how to employ crowds to produce the information needed to train and evaluate automated methods. Specifically, researchers in fields as diverse as computer vision BIBREF2 , computational linguistics BIBREF1 , and machine learning BIBREF3 rely on large datasets to improve their VQA algorithms. These datasets include visual questions and human-supplied answers. Such data is critical for teaching machine learning algorithms how to answer questions by example. Such data is also critical for evaluating how well VQA algorithms perform. In general, “bigger" data is better. Current methods to create these datasets assume a fixed number of human answers per visual question BIBREF2 , BIBREF4 , thereby either compromising on quality by not collecting all plausible answers or cost by collecting additional answers when they are redundant. We offer an economical way to spend a human budget to collect answers from crowd workers. In particular, we aim to actively allocate additional answers only to visual questions likely to have multiple answers. The key contributions of our work are as follows: We propose multiple prediction systems to automatically decide whether a visual question will lead to human agreement and demonstrate the value of these predictions for a new task of capturing the diversity of all plausible answers with less human effort.

What does the author use multiple prediction systems to do?

Multiple prediction systems.

Our underlying model architecture is a standard attentional encoder-decoder BIBREF1 . Let INLINEFORM0 and INLINEFORM1 denote the source and target sentences, respectively. We use a Bi-LSTM encoder to represent the source words as a matrix INLINEFORM2 . The conditional probability of the target sentence is given as DISPLAYFORM0 where INLINEFORM0 is computed by a softmax output layer that receives a decoder state INLINEFORM1 as input. This state is updated by an auto-regressive LSTM, INLINEFORM2 , where INLINEFORM3 is an input context vector. This vector is computed as INLINEFORM4 , where INLINEFORM5 is a probability distribution that represents the attention over the source words, commonly obtained as DISPLAYFORM0 where INLINEFORM0 is a vector of scores. We follow BIBREF14 and define INLINEFORM1 as a bilinear transformation of encoder and decoder states, where INLINEFORM2 is a model parameter. Our underlying model architecture is a stan- dard attentional encoder-decoder (Bahdanau et al., 2014).

What is the architecture of the underlying model？

Their underlying model architecture is a stan- dard attentional encoder-decoder (Bahdanau et al., 2014).

1909.00574

The dataset MSParS is published by NLPCC 2019 evaluation task. The whole dataset consists of 81,826 samples annotated by native English speakers. 80% of them are used as training set. 10% of them are used as validation set while the rest is used as test set. 3000 hard samples are selected from the test set. Metric for this dataset is the exactly matching accuracy on both full test set and hard test subset. Each sample is composed of the question, the logical form, the parameters(entity/value/type) and question type as the Table TABREF3 demonstrates. The dataset MSParS is published by NLPCC 2019 evaluation task. The whole dataset consists of 81,826 samples annotated by native English speakers. 80% of them are used as training set. 10% of them are used as validation set while the rest is used as test set. 3000 hard samples are selected from the test set. Metric for this dataset is the exactly matching accuracy on both full test set and hard test subset. Each sample is composed of the question, the logical form, the parameters(entity/value/type) and question type as the Table TABREF3 demonstrates.

Does the training dataset provide logical form supervision?

Yes.

What kind of plant is the Besleria modica

Besleria modica is a species of plant in the family Gesneriaceae. It is endemic to Ecuador. Its natural habitat is subtropical or tropical moist montane forests.

Why is my buttercream frosting not white?

Clear vanilla is one of the best ways to keep your buttercream frosting as close to white as possible. If you use traditional or even artificial vanilla, it will turn your buttercream frosting into more of a cream color rather than pure white. Pure white buttercream does not exist, as the color of butter is slightly yellow, which will give your buttercream frosting a small amount of color. However, the most likely culprit of your tan buttercream is your traditional vanilla extract.

How many players are there on each side of a water polo game, including the goalie

There are seven players on each side of a water polo game

1908.08593

Our experiments suggest that certain heads have a detrimental effect on the overall performance of BERT, and this trend holds for all the chosen tasks. Unexpectedly, disabling some heads leads not to a drop in accuracy, as one would expect, but to an increase in performance. This is effect is different across tasks and datasets. While disabling some heads improves the results, disabling the others hurts the results. However, it is important to note that across all tasks and datasets, disabling some heads leads to an increase in performance. The gain from disabling a single head is different for different tasks, ranging from the minimum absolute gain of 0.1% for STS-B, to the maximum of 1.2% for MRPC (see fig:disableheadsall). In fact, for some tasks, such as MRPC and RTE, disabling a random head gives, on average, an increase in performance. Furthermore, disabling a whole layer, that is, all 12 heads in a given layer, also improves the results. fig:disablelayers shows the resulting model performance on the target GLUE tasks when different layers are disabled. Notably, disabling the first layer in the RTE task gives a significant boost, resulting in an absolute performance gain of 3.2%. However, effects of this operation vary across tasks, and for QNLI and MNLI, it produces a performance drop of up to -0.2%. Notably, disabling the first layer in the RTE task gives a significant boost, resulting in an absolute performance gain of 3.2%. However, effects of this operation vary across tasks, and for QNLI and MNLI, it produces a performance drop of up to -0.2%.

How much is performance improved by disabling attention in certain heads?

The answers are shown as follows: * disabling the first layer in the RTE task gives a significant boost, resulting in an absolute performance gain of 3.2% * this operation vary across tasks

Forecasting is a critical task in time series analysis. Deep learning architectures used in the literature include Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Transformers, and Graph Neural Networks (GNNs). N-BEATS deeply stacks fully-connected layers with backward and forward residual links for univariate times series forecasting. TCN utilizes a deep CNN architecture with dilated causal convolutions. Considering both long-term dependencies and short-term trends in multivariate time series, LSTnet combines both CNNs and RNNS in a unified model. LogTrans brings the Transformer model to time series forecasting with causal convolution in its attention mechanism. Informer further proposes a sparse self-attention mechanism to reduce the time complexity and memory usage. StemGNN is a GNN based model that considers the intra-temporal and inter-series correlations simultaneously. Unlike these works, we aim to learn general representations for time series data that can not only be used for forecasting but also other tasks, such as classification. Besides, the proposed framework is compatible with various architectures as encoders. Time series forecasting aims to predict the future L y time stamps, with the last L x observations. We follow to train a linear model regularized with the L2 norm penalty to make predictions. The output has dimension L y in the univariate case and L y × F for the multivariate case, where F is the feature dimension. Datasets and Baselines. Four benchmark datasets for time series forecasting are adopted, including ETTh1, ETTh2, ETTm1, and the Electricity dataset. These datasets are used in both univariate and multivariate settings. We compare unsupervised In-foTS to the SOTA baselines, including TS2Vec, Informer, StemGNN, TCN, LogTrans, LSTnet, and N-BEATS. Among these methods, N-BEATS is merely designed for the univariate and StemGNN is for multivariate only. We refer to Performance. As shown in Tabel 1 and Tabel 4, comparison in both univariate and multivariate settings indicates that InfoTS consistently matches or outperforms the leading baselines. Some results of StemGNN are unavailable due to the out-of-memory issue. Specifically, we have the following observations. TS2Vec, another contrastive learning method with data augmentations, achieves the second-best performance in most cases. The consistent improvement of TS2Vec over other baselines indicates the effectiveness of contrastive learning for time series representations learning. However, such universal data augmentations may not be the most informative ones to generate positive pairs. Comparing to TS2Vec, InfoTS decreases the average MSE by 11.4%, and the average MAE by 8.6% in the univariate setting. In the multivariate setting, the MSE and MAE decrease by 4.6% and 2.3%, respectively. The reason is that InfoTS can adaptively select the most suitable augmentations in a data-driven manner with high variety and high fidelity. Encoders trained with such informative augmentations learn representations with higher quality. Specifically, we have the following observations. TS2Vec, another contrastive learning method with data augmentations, achieves the second-best performance in most cases. The consistent improvement of TS2Vec over other baselines indicates the effectiveness of contrastive learning for time series representations learning. However, such universal data augmentations may not be the most informative ones to generate positive pairs. Comparing to TS2Vec, InfoTS decreases the average MSE by 11.4%, and the average MAE by 8.6% in the univariate setting. In the multivariate setting, the MSE and MAE decrease by 4.6% and 2.3%, respectively.

Just reporting only two of these 128 datasets does not tell me that in general the gap between TS2vec and InfoTS is as large as shown in the comment. So this comparison is the most important and can it be reported in the major parts of the work?

The results show that with information-aware adaptive augmentation, we can also consistently and significantly improve the performances of TS2vec. For example, with MAE as the metric, in the univariate setting, we decrease the average MAE by 8.6% by adding adaptive augmentation to TS2Vec. With the newly designed local and global contrastive loss, we can further reduce the error by 3.2%.

Fig. presents the architecture of NEUROSED. The input to our learning framework is a pair of graphs G Q (query), G T (target) along with the supervision data SED(G Q , G T ). Our objective is to train a model that can predict SED on unseen query and target graphs. The design of our model must be cognizant of the fact that computing SED is NP-hard and high quality training data is scarce. Thus, we use a Siamese architecture in which the weight sharing between the embedding models boosts learnability and generalization from low-volume data by imposing a strong prior that the same topological features must be extracted for both graphs. Siamese Networks: In these models, there are two networks with shared parameters applied to two inputs independently to compute representations. These representations are then passed through another module to compute a similarity score. Siamese Networks: In these models, there are two networks with shared parameters applied to two inputs independently to compute representations.****We note here that if different embedding models are used for G1 and G2, or if the distance computations are pair-dependent (Li et al., 2019), then Theorem 2 would not hold. It is easy to verify that the proof fails if EQ and ET are different embedding functions corresponding to the query and the target, or if E is a function of both G1 and G2. Hence, the siamese architecture is crucial.

Although the paper argues the model is inspired by Siamese networks, it is indeed just using a shared GNN to encode both target and query graphs. In the field of combinatorial optimization, encoding graphs using GNN is straightforward. Perhaps the paper is not novel enough?

A siamese network, by definition, means a neural network that contains one or more identical sub-networks. This is indeed the case in our architecture, where the sub-networks are GNNs. In our work, we do not claim to be the first to use siamese GNNs. However, shared weights due to siamese architecture lies at the core of our ability to preserve theoretical distance properties of the original graph space in the embedded space. We have emphasized this aspect in the updated version (see the discussion following Theorem 2 in Sec 3.2).

We present the results of our new learning process applied to the CIFAR-10, Celeb-A, and ImageNet benchmark datasets, using the image sizes 32 × 32, 64 × 64, and 128 × 128 respectively. A sketch of the hybrid learning algorithm is given in Appendix D. Besides standard deep learning techniques for stable optimization, our framework is fully described by the ML objective and MCMC initialization above. We use the SNGAN architectures for all models, where EBMs use the SNGAN discriminator with no normalization. The generator has batch normalization for the CIFAR-10 and Celeb-A experiments only. Table displays the FID scores achieved by our model in comparison with prior methods. When calculating FID scores, samples are first initialized from the generator then updated with the EBM using a number of Langevin updates tuned to provide optimal synthesis quality. As mentioned before, an appealing aspect of EBM learning with generator initialization is the ease of generating new images from scratch, in contrast with persistent initialization. We use 50K samples with the official FID code from to calculate all FID scores. In particular, our scores and framework are consistent with the GAN replication library from. We publicly release the checkpoints, learning code, and FID code for each model. The checkpoints are representative of what is achievable within an ordinary run of the code provided. Our CIFAR-10 results show a significant improvement over prior EBM synthesis and contribute to closing the gap between EBMs and other generative models. Surprisingly, our ImageNet results surpass the results of GANs such as SNGAN and SSGAN on unconditional synthesis using a similar magnitude of computational resources. We present the results of our new learning process applied to the CIFAR-10, Celeb-A, and ImageNet benchmark datasets, using the image sizes 32 × 32, 64 × 64, and 128 × 128 respectively. A sketch of the hybrid learning algorithm is given in Appendix D. Besides standard deep learning techniques for stable optimization, our framework is fully described by the ML objective and MCMC initialization above. We use the SNGAN architectures for all models, where EBMs use the SNGAN discriminator with no normalization. The generator has batch normalization for the CIFAR-10 and Celeb-A experiments only. Table displays the FID scores achieved by our model in comparison with prior methods. When calculating FID scores, samples are first initialized from the generator then updated with the EBM using a number of Langevin updates tuned to provide optimal synthesis quality. As mentioned before, an appealing aspect of EBM learning with generator initialization is the ease of generating new images from scratch, in contrast with persistent initialization. We use 50K samples with the official FID code from to calculate all FID scores. In particular, our scores and framework are consistent with the GAN replication library from. We publicly release the checkpoints, learning code, and FID code for each model. The checkpoints are representative of what is achievable within an ordinary run of the code provided. Our CIFAR-10 results show a significant improvement over prior EBM synthesis and contribute to closing the gap between EBMs and other generative models. Surprisingly, our ImageNet results surpass the results of GANs such as SNGAN and SSGAN on unconditional synthesis using a similar magnitude of computational resources. Table 1: Comparison of FID scores among representative generative models. (*=EBM, †=conditional)****Xie et al. (2018)* 33.6

Is there a comparison to Xie et al. (2018) needed?

The original publication of Xie et al. (2018) did not report FID on CIFAR-10. We have since found that the scores can be found in a separate submission [a]. The FID score for CIFAR-10 is 33. Our method yields significant improvement. We will include this in future versions.

Which parts of New York City are best to live in?

Neighborhoods like Battery Park City, West Village, Chelsea and the Upper East side are considered to be some of the best neighborhoods to live in New York City. These are also extremely expensive, with average per square meter cost being amongst the highest in the world.

Which characters belong to DC or Marvel Universe? Norman Osborne, Joker

Joker is DC, Norman Osborne is Marvel

What are some different types of natural disasters?

Types of natural disasters include: Forest Fires, Tsunamis, Earthquakes, Volcanic Eruptions, Hurricanes, Tornados, and Avalanches.

What is the difference between earphones and headphones?

Both earphones and headphones allow users to listen to audio from an input device. Their primary difference is in their form factor. Earphones tend to go inside your ears, while headphones sit on your ears (on your head). Earphones are usually smaller and more portable, while headphones tend to have better sound quality and battery life, as they are larger and can fit more parts and larger batteries inside. Depending on your needs, either would be a good choice for listening to audio.

Tell me whether each of these players is a quarterback or running back: Ezekiel Elliot, Peyton Manning, Lamar Jackson, Leonard Fournette, Sam Darnold, Derrick Henry

Ezekiel Elliot: Running Back Peyton Manning: Quarterback Lamar Jackson: Quarterback Leonard Fournette: Running Back Sam Darnold: Quarterback Derrick Henry: Running Back

Which of these US states either border the ocean or are land-locked: Colorado, Alaska, Wisconsin, Hawaii, Washington, Oregon, Indiana, Maine, Nebraska, Florida and California.

These US States have an ocean border: Alaska, Florida, Washington, Oregon, Maine, California, and Hawaii. These US States are landlocked: Nebraska, Colorado, Indiana, and Wisconsin.

The used architecture of the QA model is briefly summarized below. Here we choose QANet BIBREF2 as the base model due to the following reasons: 1) it achieves the second best performance on SQuAD, and 2) since there are completely no recurrent networks in QANet, its training speed is 5x faster than BiDAF BIBREF17 when reaching the same performance on SQuAD. The network architecture is illustrated in Figure FIGREF2 . The left blocks and the right blocks form two QANets, each of which takes a document and a question as the input and outputs an answer. In QANet, firstly, an embedding encoder obtains word and character embeddings for each word in INLINEFORM0 or INLINEFORM1 and then models the temporal interactions between words and refines word vectors to contextualized word representations. All encoder blocks used in QANet are composed exclusively of depth-wise separable convolutions and self-attention. The intuition here is that convolution components can model local interactions and self-attention components focus on modeling global interactions. The context-query attention layer generates the question-document similarity matrix and computes the question-aware vector representations of the context words. After that, a model encoder layer containing seven encoder blocks captures the interactions among the context words conditioned on the question. Finally, the output layer predicts a start position and an end position in the document to extract the answer span from the document. The used architecture of the QA model is briefly summarized below. Here we choose QANet [3] as the base model due to the following reasons:

What's the base model for the used architecture of the QA model?

QANet.

Why is it important to fill out post interview score card within three hours?

Because your impression of the candidate will be fresh in your mind, it's easy to get busy and the mix up impressions from multiple candidates get mixed up in your mind.

Classify each of the following as either a city, or a state/province, or neither: San Jose, Shanghai, Jiangsu, Texas, Japan, Shandong

The following are cities: San Jose, Shanghai. The following are States/Provinces: Jiangsu, Texas, Shandong. Japan is a country, so not a city or state/province.

Where should I keep my car keys when I'm home?

It's best to have a single designated spot for your car keys so you never lose them. Some good places in your home might be: - On a key rack near the door - On a console table near the door - In a decorative tray on your dresser - A knickknack bowl in the kitchen

When did California become a state?

California became a state on September 9, 1850.

Give me a list of top unicorns from India.

Flipkart, Swiggy, Ola, Meesho, Cred, Dream11, Paytm, Zomato

Landscape Arch is a natural arch in Arches National Park, Utah, United States. It is among the longest natural rock arches in the world. The arch is among many in the Devils Garden area in the north of the park. Landscape Arch was named by Frank Beckwith who explored the area in the winter of 1933–1934 as the leader of an Arches National Monument scientific expedition. The arch can be reached by a 0.8 mi (1.3 km) graded gravel trail. The Natural Arch and Bridge Society (NABS) considers Landscape Arch the fifth longest natural arch in the world, after four arches in China. In 2004, the span of Landscape Arch was measured at 290.1 ft (88.4 m), ±0.8 ft (0.24 m), with a height of 77.5 ft (23.6 m). NABS measured the span of the slightly shorter Kolob Arch in Zion National Park at 287 ft (87 m) in 2006. The most recent recorded rockfall events occurred in the 1990s when one large slab fell in 1991 and then two additional large rockfalls occurred in 1995. Since the rockfalls, the trail beneath the arch has been closed.

From the paragraph, tell me what is the fourth longest natural arch in the world

The article does not mention where the fourth longest natural arch in the world is. However, it does say that the top four are in China. The fifth longest one is the Landscape Arch in Utah, United States.

The University of Edinburgh (Scots: University o Edinburgh, Scottish Gaelic: Oilthigh Dhùn Èideann; abbreviated as Edin. in post-nominals) is a public research university based in Edinburgh, Scotland. Founded by the town council under the authority of a royal charter of King James VI in 1582 and officially opened in 1583, it is one of Scotland's four ancient universities and the sixth-oldest university in continuous operation in the English-speaking world. The university played an important role in Edinburgh becoming a chief intellectual centre during the Scottish Enlightenment and contributed to the city being nicknamed the "Athens of the North." Edinburgh is ranked among the top universities in the United Kingdom and the world. Edinburgh is a member of several associations of research-intensive universities, including the Coimbra Group, League of European Research Universities, Russell Group, Una Europa, and Universitas 21. In the fiscal year ending 31 July 2022, it had a total income of £1.262 billion, of which £331.6 million was from research grants and contracts. It has the third-largest endowment in the UK, behind only Cambridge and Oxford. The university has five main campuses in the city of Edinburgh, which include many buildings of historical and architectural significance such as those in the Old Town. Edinburgh is the seventh-largest university in the UK by enrolment and receives over 75,000 undergraduate applications per year, making it the second-most popular university in the UK by volume of applications. Edinburgh had the eighth-highest average UCAS points amongst British universities for new entrants in 2020. The university continues to have links to the royal family, having had Prince Philip, Duke of Edinburgh as its chancellor from 1953 to 2010 and Anne, Princess Royal since March 2011. The alumni of the university includes some of the major figures of modern history. Inventor Alexander Graham Bell, naturalist Charles Darwin, philosopher David Hume, and physicist James Clerk Maxwell studied at Edinburgh, as did writers such as Sir J. M. Barrie, Sir Arthur Conan Doyle, J. K. Rowling, Sir Walter Scott, and Robert Louis Stevenson. The university counts several heads of state and government amongst its graduates, including three British Prime Ministers. Three Supreme Court Justices of the UK were educated at Edinburgh. As of January 2023, 19 Nobel Prize laureates, four Pulitzer Prize winners, three Turing Award winners, and an Abel Prize laureate and Fields Medalist have been affiliated with Edinburgh as alumni or academic staff. Edinburgh alumni have won a total of ten Olympic gold medals.

Given these paragraphs about a well-known Scottish University of some import, tell me specific details about when and where it was founded, what it's most well known for, and who are some of its most famous alumni.

The University of Edinburgh is based out of Edinburgh, Scotland founded by the royal charter of King James the 6th in 1582, officially opening for students in 1583. The University of Edinburgh is famous for being the sixth oldest University in the English-speaking world and the seventh largest by enrollment in the United Kingdom where Scotland resides today. A top public research university often nicknamed the "Athens of the North" whose famous alumni were pivotal in contributing to the Scottish Enlightenment, the University of Edinburgh is known as one of the top-ranked universities in the United Kingdom. With such esteemed alumni as Alexander Graham Bell, Charles Darwin, and David Hume not to mention famous authors such as Sir Arthur Conan Doyle and J.K. Rowling, and even several heads of state in three British Prime Ministers.

What are the words of House Caron?

"No Song So Sweet"

where does the word dynamite come from?

The greek word for Power is where dynamite originates from

Previous work on multimodal machine translation has shown that visual information is only needed in very specific cases, for example in the presence of ambiguous words where the textual context is not sufficient. As a consequence, models tend to learn to ignore this information. We propose a translate-and-refine approach to this problem where images are only used by a second stage decoder. This approach is trained jointly to generate a good first draft translation and to improve over this draft by (i) making better use of the target language textual context (both left and right-side contexts) and (ii) making use of visual context. This approach leads to the state of the art results. Additionally, we show that it has the ability to recover from erroneous or missing words in the source language. We propose a translate-and-refine approach to this problem where images are only used by a second stage decoder.

What approach do the authors propose in the paper?

A translate-and-refine approach.

Classify each of the following as either a virtue or a vice: humility, kindness, patience, greed, anger, lust, envy, pride.

Humility, kindness, patience are the virtues. And greed, anger, lust, envy, pride are the vices.

Identify which instrument is string or percussion: Cuica, Kokles

Kokles is string, Cuica is percussion.

A few scholars give the start of the Golden Age as 711–718, the Muslim conquest of Iberia.

When did the golden age for Jewish culture start and where was it?

The golden age for jewish culture started somewhere around 711-718 and it was in Spain.

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

What approach does this paper propose for hate speech understanding?

The purpose of this paper is to propose a transfer learning approach for hate speech understanding using a combination of the unsupervised pre-trained model BERT and some new supervised fine-tuning strategies.

Tell me which of these are fire-type Pokemon: Arcanine, Lapras, Glalie, Spheal, Magmar, Houndour, Entei, Ho-oh, Torchic, Kyurem, Cubchoo, Beartic, Numel, Ninetales, Rotom, Mamoswine

Arcanine, Magmar, Houndour, Entei, Ho-oh, Torchic, Numel, Ninetales, Rotom

After Manny Pacquiao signed with Al Haymon in October 2018, reports circulated that Adrien Broner was the front-runner to face him in January 2019. Pacquiao confirmed on October 18 that the deal was almost complete. One month later on November 19, a press conference was held in New York City to confirm the fight between Pacquiao and Broner on January 19, 2019 for the WBA (Regular) welterweight title. Pacquiao reunited with long-time trainer Freddie Roach who supervised the whole training camp while Buboy Fernandez did the mitts and physical parts of the training due to Pacquiao's concern of Roach's health. The fight took place at the MGM Grand Garden Arena in Las Vegas, Nevada and was distributed by Showtime PPV.

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

• Manny Pacquiao • Al Haymon • Adrien Broner • Freddie Roach • Buboy Fernandez

What are the top 5 television sitcoms of all time?

1. Seinfeld 2. The Office 3. Mash 4. Cheers 5. Friends

Chatbots such as dialog and question-answering systems have a long history in AI and natural language processing. Early such systems were mostly built using markup languages such as AIML, handcrafted conversation generation rules, and/or information retrieval techniques BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Recent neural conversation models BIBREF4 , BIBREF5 , BIBREF6 are even able to perform open-ended conversations. However, since they do not use explicit knowledge bases and do not perform inference, they often suffer from generic and dull responses BIBREF5 , BIBREF7 . More recently, BIBREF8 and BIBREF9 proposed to use knowledge bases (KBs) to help generate responses for knowledge-grounded conversation. However, one major weakness of all existing chat systems is that they do not explicitly or implicitly learn new knowledge in the conversation process. This seriously limits the scope of their applications. In contrast, we humans constantly learn new knowledge in our conversations. Even if some existing systems can use very large knowledge bases either harvested from a large data source such as the Web or built manually, these KBs still miss a large number of facts (knowledge) BIBREF10 . It is thus important for a chatbot to continuously learn new knowledge in the conversation process to expand its KB and to improve its conversation ability. In recent years, researchers have studied the problem of KB completion, i.e., inferring new facts (knowledge) automatically from existing facts in a KB. KB completion (KBC) is defined as a binary classification problem: Given a query triple, ( INLINEFORM0 , INLINEFORM1 , INLINEFORM2 ), we want to predict whether the source entity INLINEFORM3 and target entity INLINEFORM4 can be linked by the relation INLINEFORM5 . However, existing approaches BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 solve this problem under the closed-world assumption, i.e., INLINEFORM6 , INLINEFORM7 and INLINEFORM8 are all known to exist in the KB. This is a major weakness because it means that no new knowledge or facts may contain unknown entities or relations. Due to this limitation, KBC is clearly not sufficient for knowledge learning in conversations because in a conversation, the user can say anything, which may contain entities and relations that are not already in the KB. In this paper, we remove this assumption of KBC, and allow all INLINEFORM0 , INLINEFORM1 and INLINEFORM2 to be unknown. We call the new problem open-world knowledge base completion (OKBC). OKBC generalizes KBC. Below, we show that solving OKBC naturally provides the ground for knowledge learning and inference in conversations. In essence, we formulate an abstract problem of knowledge learning and inference in conversations as a well-defined OKBC problem in the interactive setting. From the perspective of knowledge learning in conversations, essentially we can extract two key types of information, true facts and queries, from the user utterances. Queries are facts whose truth values need to be determined. Note that we do not study fact or relation extraction in this paper as there is an extensive work on the topic. (1) For a true fact, we will incorporate it into the KB. Here we need to make sure that it is not already in the KB, which involves relation resolution and entity linking. After a fact is added to the KB, we may predict that some related facts involving some existing relations in the KB may also be true (not logical implications as they can be automatically inferred). For example, if the user says “Obama was born in USA,” the system may guess that (Obama, CitizenOf, USA) (meaning that Obama is a citizen of USA) could also be true based on the current KB. To verify this fact, it needs to solve a KBC problem by treating (Obama, CitizenOf, USA) as a query. This is a KBC problem because the fact (Obama, BornIn, USA) extracted from the original sentence has been added to the KB. Then Obama and USA are in the KB. If the KBC problem is solved, it learns a new fact (Obama, CitizenOf, USA) in addition to the extracted fact (Obama, BornIn, USA). (2) For a query fact, e.g., (Obama, BornIn, USA) extracted from the user question “Was Obama born in USA?” we need to solve the OKBC problem if any of “Obama, “BornIn”, or “USA" is not already in the KB. We can see that OKBC is the core of a knowledge learning engine for conversation. Thus, in this paper, we focus on solving it. We assume that other tasks such as fact/relation extraction and resolution and guessing of related facts of an extracted fact are solved by other sub-systems. We solve the OKBC problem by mimicking how humans acquire knowledge and perform reasoning in an interactive conversation. Whenever we encounter an unknown concept or relation while answering a query, we perform inference using our existing knowledge. If our knowledge does not allow us to draw a conclusion, we typically ask questions to others to acquire related knowledge and use it in inference. The process typically involves an inference strategy (a sequence of actions), which interleaves a sequence of processing and interactive actions. A processing action can be the selection of related facts, deriving inference chain, etc., that advances the inference process. An interactive action can be deciding what to ask, formulating a suitable question, etc., that enable us to interact. The process helps grow the knowledge over time and the gained knowledge enables us to communicate better in the future. We call this lifelong interactive learning and inference (LiLi). Lifelong learning is reflected by the facts that the newly acquired facts are retained in the KB and used in inference for future queries, and that the accumulated knowledge in addition to the updated KB including past inference performances are leveraged to guide future interaction and learning. LiLi should have the following capabilities: This setting is ideal for many NLP applications like dialog and question-answering systems that naturally provide the scope for human interaction and demand real-time inference. LiLi starts with the closed-world KBC approach path-ranking (PR) BIBREF11 , BIBREF17 and extends KBC in a major way to open-world knowledge base completion (OKBC). For a relation INLINEFORM0 , PR works by enumerating paths (except single-link path INLINEFORM1 ) between entity-pairs linked by INLINEFORM2 in the KB and use them as features to train a binary classifier to predict whether a query INLINEFORM3 should be in the KB. Here, a path between two entities is a sequence of relations linking them. In our work, we adopt the latest PR method, C-PR BIBREF16 and extend it to make it work in the open-world setting. C-PR enumerates paths by performing bidirectional random walks over the KB graph while leveraging the context of the source-target entity-pair. We also adopt and extend the compositional vector space model BIBREF20 , BIBREF21 with continual learning capability for prediction. Given an OKBC query ( INLINEFORM0 , INLINEFORM1 , INLINEFORM2 ) (e.g., “(Obama, CitizenOf, USA), which means whether Obama a citizen of USA), LiLi interacts with the user (if needed) by dynamically formulating questions (see the interaction example in Figure 1, which will be further explained in §3) and leverages the interactively acquired knowledge (supporting facts (SFs) in the figure) for continued inference. To do so, LiLi formulates a query-specific inference strategy and executes it. We design LiLi in a Reinforcement Learning (RL) setting that performs sub-tasks like formulating and executing strategy, training a prediction model for inference, and knowledge retention for future use. To the best of our knowledge, our work is the first to address the OKBC problem and to propose an interactive learning mechanism to solve it in a continuous or lifelong manner. We empirically verify the effectiveness of LiLi on two standard real-world KBs: Freebase and WordNet. Experimental results show that LiLi is highly effective in terms of its predictive performance and strategy formulation ability. Below, we show that solving OKBC naturally provides the ground for knowledge learning and inference in conversations.

What does solving OKBC provide?

Solving OKBC naturally provides the ground for knowledge learning and inference in conversations.

Speech-to-Text translation (ST) is essential for a wide range of scenarios: for example in emergency calls, where agents have to respond emergent requests in a foreign language BIBREF0; or in online courses, where audiences and speakers use different languages BIBREF1. To tackle this problem, existing approaches can be categorized into cascaded method BIBREF2, BIBREF3, where a machine translation (MT) model translates outputs of an automatic speech recognition (ASR) system into target language, and end-to-end method BIBREF4, BIBREF5, where a single model learns acoustic frames to target word sequence mappings in one step towards the final objective of interest. Although the cascaded model remains the dominant approach due to its better performance, the end-to-end method becomes more and more popular because it has lower latency by avoiding inferences with two models and rectifies the error propagation in theory. Since it is hard to obtain a large-scale ST dataset, multi-task learning BIBREF5, BIBREF6 and pre-training techniques BIBREF7 have been applied to end-to-end ST model to leverage large-scale datasets of ASR and MT. A common practice is to pre-train two encoder-decoder models for ASR and MT respectively, and then initialize the ST model with the encoder of the ASR model and the decoder of the MT model. Subsequently, the ST model is optimized with the multi-task learning by weighing the losses of ASR, MT, and ST. This approach, however, causes a huge gap between pre-training and fine-tuning, which are summarized into three folds: Subnet Waste: The ST system just reuses the ASR encoder and the MT decoder, while discards other pre-trained subnets, such as the MT encoder. Consequently, valuable semantic information captured by the MT encoder cannot be inherited by the final ST system. Role Mismatch: The speech encoder plays different roles in pre-training and fine-tuning. The encoder is a pure acoustic model in pre-training, while it has to extract semantic and linguistic features additionally in fine-tuning, which significantly increases the learning difficulty. Non-pre-trained Attention Module: Previous work BIBREF6 trains attention modules for ASR, MT and ST respectively, hence, the attention module of ST does not benefit from the pre-training. To address these issues, we propose a Tandem Connectionist Encoding Network (TCEN), which is able to reuse all subnets in pre-training, keep the roles of subnets consistent, and pre-train the attention module. Concretely, the TCEN consists of three components, a speech encoder, a text encoder, and a target text decoder. Different from the previous work that pre-trains an encoder-decoder based ASR model, we only pre-train an ASR encoder by optimizing the Connectionist Temporal Classification (CTC) BIBREF8 objective function. In this way, the additional decoder of ASR is not required while keeping the ability to read acoustic features into the source language space by the speech encoder. Besides, the text encoder and decoder can be pre-trained on a large MT dataset. After that, we employ common used multi-task learning method to jointly learn ASR, MT and ST tasks. Compared to prior works, the encoder of TCEN is a concatenation of an ASR encoder and an MT encoder and our model does not have an ASR decoder, so the subnet waste issue is solved. Furthermore, the two encoders work at tandem, disentangling acoustic feature extraction and linguistic feature extraction, ensuring the role consistency between pre-training and fine-tuning. Moreover, we reuse the pre-trained MT attention module in ST, so we can leverage the alignment information learned in pre-training. Since the text encoder consumes word embeddings of plausible texts in MT task but uses speech encoder outputs in ST task, another question is how one guarantees the speech encoder outputs are consistent with the word embeddings. We further modify our model to achieve semantic consistency and length consistency. Specifically, (1) the projection matrix at the CTC classification layer for ASR is shared with the word embedding matrix, ensuring that they are mapped to the same latent space, and (2) the length of the speech encoder output is proportional to the length of the input frame, so it is much longer than a natural sentence. To bridge the length gap, source sentences in MT are lengthened by adding word repetitions and blank tokens to mimic the CTC output sequences. We conduct comprehensive experiments on the IWSLT18 speech translation benchmark BIBREF1, demonstrating the effectiveness of each component. Our model is significantly better than previous methods by 3.6 and 2.2 BLEU scores for the subword-level decoding and character-level decoding strategies, respectively. Our contributions are three-folds: 1) we shed light on why previous ST models cannot sufficiently utilize the knowledge learned from the pre-training process; 2) we propose a new ST model, which alleviates shortcomings in existing methods; and 3) we empirically evaluate the proposed model on a large-scale public dataset. Different from the previous work that pre-trains an encoder-decoder based ASR model, we only pre-train an ASR encoder by optimizing the Connectionist Temporal Classification (CTC) (Graves et al. 2006) objective function.

How do they pre-train an ASR encoder?

By optimizing the Connectionist Temporal Classification (CTC) (Graves et al. 2006) objective function.

What is Bayes theorem mathematical formula

P(A/B) = P(B/A) * P(A) / P(B)

Why are the Georgia Bulldogs back to back National Champions in Football?

Coach Kirby Smart brought to Georgia what he learned as a longtime assistant with Nick Saban at Alabama and made his own process for Championship success. Smart has recruited the top high school players in the country to play for the Bulldogs and he has created a culture of brotherhood and accountability. The Georgia Bulldogs are now the standard of excellence when it comes to College Football.

We evaluate our model on the zero-shot cross-lingual answer-aware question generation task. The goal of question generation (QG) is to generate a question that asks towards the answer with the given passage and the expected answer. In the following experiments, we extend the QG task to the cross-lingual setting. By only using English QG training data, our goal is to generate questions in English or Chinese with the given passage-answer pair in English or Chinese. We use SQuAD 1.1 BIBREF30 as the English QG dataset. It is a popular English question answering dataset containing over 100,000 questions and their corresponding annotated passages. Following BIBREF31, we regard the original development set as the test set, and sample 5000 examples from the training data of two datasets as the development sets. For Chinese QG, we follow the default data splits of WebQA BIBREF32. We regard the provided annotated evidence sentences as the input passages instead of entire documents. To construct the input sequence, we view the whole input passage as a single sentence, and concatenate the passage and the answer into one sequence with a special token S between them. During decoding Chinese, we utilize a subset of vocabulary, which is obtained from the passage sentences of the WebQA dataset. We first conduct experiments on the supervised English-English QG setting. We compare our model to the following baselines: CorefNqg BIBREF33 A sequence-to-sequence model with attention mechanism and a feature-rich encoder. Mp-Gsn BIBREF31 A sequence-to-sequence model with gated self-attention and maxout pointer mechanism. Xlm BIBREF5 The current state-of-the-art cross-lingual pre-training model. We initialize the Transformer-based sequence-to-sequence model with pre-trained XLM. We evaluate models with BLEU-4 (BL-4), ROUGE (RG) and METEOR (MTR) metrics. As shown in Table TABREF16, our model outperforms the baselines, which demonstrates that our pre-trained model provides a good initialization for NLG. We conduct experiments on the zero-shot Chinese-Chinese QG task to evaluate the cross-lingual transfer ability. In this task, models are trained with English QG data but evaluated with Chinese QG examples. We include the following models as our baselines: Xlm Fine-tuning XLM with the English QG data. Pipeline (Xlm) The pipeline of translating input Chinese sentences into English first, then performing En-En-QG with the XLM model, and finally translating back to the Chinese. We use the Transformer as the translator, which is also trained on the MultiUN dataset. Pipeline (Xlm) with Google Translator Same to Pipeline (Xlm) but using Google Translator to translate the texts. We evaluate models by both automatic evaluation metrics and human experts. The automatic metrics scores are computed by regarding each Chinese character as a token. For human evaluation, we consider three metrics for the generated questions: relatedness, fluency, and correctness, which are represented as integers ranged from 1 to 3. We randomly select 100 passage-answer pairs from the English QG test set, and use the models to generate questions. Then we present these examples to three experts to ask for the above scores. In Table TABREF17 and Table TABREF18, we present the results for the zero-shot Zh-Zh-QG. The results of monolingual supervised models are also reported in Table TABREF16 as reference. In the automatic evaluation, our model consistently performs better than baselines in both zero-shot and monolingual supervised setting. In the human evaluation, our model also obtains significant improvements in terms of relatedness and correctness. In the zero-shot English-Chinese question generation experiments, we use Xlm and Pipeline (Xlm) as our baselines. Pipeline (Xlm) is a pipeline method that uses En-En-QG with Xlm to generate questions, and then translates the results to Chinese. Because there is no annotations for En-Zh-QG, we perform human evaluation studies for this setting. Table TABREF19 shows the human evaluation results, where our model surpasses all the baselines especially in terms of relatedness and correctness. We also conduct experiments for zero-shot Chinese-English question generation, and adopt the same evaluation procedure to En-Zh-QG. Pipeline (Xlm) first translates Chinese input to English, and then conduct En-En-QG with Xlm. As shown in Table TABREF20, human evaluation results indicate that Xnlg achieves significant improvements on the three metrics. PIPELINE (XLM) is a pipeline method that uses En-En-QG with XLM to generate questions, and then translates the results to Chinese.

What is PIPELINE (XLM)?

A pipeline method that uses En-En-QG with XLM to generate questions, and then translates the results to Chinese.

What does BATNA stand for?

Best - Alternative - To - A - Negotiated - Agreement

What is a Fixed asset in finance?

A fixed asset is one which is intended to be used for several years. Examples are buildings, machinery and vehicles.

The bull shark (Carcharhinus leucas), also known as the Zambezi shark (informally zambi) in Africa and Lake Nicaragua shark in Nicaragua, is a species of requiem shark commonly found worldwide in warm, shallow waters along coasts and in rivers. It is known for its aggressive nature, and presence mainly in warm, shallow brackish and freshwater systems including estuaries and (usually) lower reaches of rivers. Bull sharks are euryhaline and can thrive in both salt and fresh water. They are known to travel far up rivers, and have been known to travel up the Mississippi River as far as Alton, Illinois, about 1,100 kilometres (700 mi) from the ocean, but few freshwater interactions with humans have been recorded. Larger-sized bull sharks are probably responsible for the majority of nearshore shark attacks, including many incidents of shark bites attributed to other species. Unlike the river sharks of the genus Glyphis, bull sharks are not true freshwater sharks, despite their ability to survive in freshwater habitats.

Based on this reference text about bull sharks, why can bull sharks live in both salt and fresh water?

Bull sharks can live in both salt and fresh water because they are euryhaline.

We adopt two evaluation metrics: segment-based and event-based metric. Segment-based metric is a widely adopted evaluation metric, it couples semantic label and spatial location together: a semantic-correctly detected sound source needs to be spatially close enough to its ground truth location in order to be regarded as a true positive detection. Event-based based metric is newly proposed by to comprehensively evaluate under different confidence scores. Like object detection from images, it computes mean average precision (mAP) and mean average recall (mAR) score. We call our framework SoundSynp and compare it with four most recent methods: SELD-Net, EIN, SoundDet and Utsc-Iflytek. SELDNet is the baseline model and it jointly trains sound source's semantic label and spatial location with a convolutional recurrent neural network (CRNN). EIN) is a very recent work. It adopts multi-heads self-attention to model temporal dependency and trackwise permutation-invariant training to train the model. SoundDet directly learns from raw waveform with MaxCorr kernels, followed by an encoder-decoder neural network. Utsc-Iflytek is ranked first in DECASE2020 challenge leaderboard 1 , it combines MIC and FOA features and ensembles different models like ResNet and Xception(Chollet, 2017) to give final prediction. The network architecture is shown in table in Appendix material. To train the neural network, we evenly divide the one minute long four-channel raw waveform into non-overlapping 4s short snippets. The raw waveform is first normalized to r´1, 1s before feeding the neural network. We adopt Adam optimizer with an initial learning rate 0.0002 in the first 100 epochs and 0.00007 in the following 50 epochs. Batchsize is 16. The loss combination weight between classification head and regression head is 1 : 2. During training, data augmentation method SpecAugment is applied. For DoA task, we regress direction of arrival angle in Cartesian coordinates rx, y, zs. In synperiodic filter bank groups, the filter length is 1025, each group's filter number is 256 and the step size is 600. Particularly, we have observed the initialized learnable synperiodic filter banks update its parameters intensively during the very early several epochs, and then gradually becomes stable. The final learned parameters are close to their initialization, one group's learned parameters (like frequency response and filter window length) is different from those learned by other groups, although they are initialized as the same in the start. We train each model five times independently and report the average score. The standard deviation is within 0.04 (for recall) and 0.2 ˝for angle, 0.003 for mAP and mAR. The result is given in Table1, from which we see that SoundSynp achieves the best performance over all comparing methods by a large margin, under both segment-based and event-based metrics. Both SELDNet, EIN and UTSC-Iflytek use pre-extracted hand-engineered sound features, such as Logmel, GCC-Phat and Intensity Vector. SoundDet and SoundSynp are the only two methods that directly learn from raw waveforms. At the same time, SoundSynp obtains better performance on FOA than MIC format, the same phenomena has been observed by all other methods. It thus shows FOA better fits for sound source detection than MIC. It is worth noting that Utsc-Iflytek ensembles different powerful image-based 2D models to detect sound sources. However, our proposed SoundSynp still outperforms Utsc-Iflytek by a large margin. We don't report of mAP/mAR value for Utsc-Iflytek because it is a complex system and no detail about their system is available. Ablation Study To disentangle the individual contribution of each part of our SoundSynp framework to the whole performance improvement, we conduct three ablation studies. First, the individual contribution of Synperiodic filter bank. We replace hand-engineered sound feature head of SELDNet, EIN and learnable MaxCorr filter bank with our proposed Synperiodic filter bank to test their corresponding performance. It helps to remove the influence of the backbone neural network of different models and thus helps to get direct comparison of synperiodic filter bank with other front-end. Second, we replace SoundSynp's synperiodic filter bank group with widely-used MFCC and Log-Mel that is used by and EIN, respectively. It disentangles synperiodic filter bank group with Transformer-like backbone, thus helps to figure out if the performance gain is simply brought by backbone network. Third, internally, we test five synperiodic variants: (1) synperiodic filter bank with just multiscale perception in frequency domain (SoundSynp MSFreq), (2) just multi-scale perception in time domain (SoundSynp MSTime), (3) Synperiodic filter bank with frequency responses linearly initialized in Nyquist frequency range (SoundSynp Linear, compare with our mel-scale initialization), (4) just one synperiodic filter bank without multi-scale perception neither in time nor frequency domain (SoundSynp SingleScale). () synperiodic filter bank with rectangular band-pass frequency response initialization (SoundSynp Sinc), like SincNet does. The internal comparison helps us to figure out the necessity of each part of synperiodic filter bank design. The ablation study result is shown in Table. We can observe that: First, using synperiodic filter bank as a replacement of existing filter bank can help to improve the corresponding performance. Second, replacing SoundSynp's synperiodic filter bank with classic hand-engineered features inevitably reduces the performance under all evaluation metrics, its thus shows learning from pre-extracted fixed and single-scale sound representation leads to inferior performance than our proposed multiscale synperiodic filter bank. Third, the absence of multi-scale perception in either frequency domain or time domain inevitably reduces the performance. We find sound source semantic label detection suffers more in single-scale perception in time domain than in frequency domain (see better performance on ER 20 ˝, and F 20 ˝score), which shows frequency domain multi-scale perception is vital for semantic label estimation. Similarly, we can observe that multi-scale perception in time domain is vital for sound source spatial location estimation (see better performance on LE and LR score). Linearly initialized filter bank frequency response reduces the performance, which shows assigning more filters to the lower frequency range is important. But this conclusion might be data-dependent because we find DCASE dataset contains many low-frequency sounds like burning fire and footsteps. Moreover, reducing the synperiodic filter bank group to one groups with just singlescale perception leads to comparably the worst performance, it thus shows multi-scale perception in both time and frequency domain is essential for DoA-based sound source detection. Lastly, SoundSynp Sinc leads to slightly inferior performance than our used mel-scale initialization strategy, it shows our proposed synperiodic filter bank is a general filter bank that can be adopted to other frequency sensitive filter bank. One qualitative comparison is shown in Fig.. We can clearly see that SELDNet generates mixed prediction at different time steps and DoA locations. SoundDet and EIN give non-existing sound sources (orange color). When multiple sound sources happen at the same time (polyphonicity), SoundDet and EIN are easily failed to predict the right spatial location (discretized blue and red color). Our method predicts more spatially and temporally consistent sound sources by maximally keeping sound source's continuity and consistency. We train each model five times independently and report the average score. The standard deviation is within 0.04 (for recall) and 0.2 ˝ for angle, 0.003 for mAP and mAR.

how reliable are these numbers?

In our experiment, we train each model five times independently and report the averaging performance score. We did not report the standard deviation because many comparing methods did not report it. In our revised version, we report the average standard deviation.

1809.02731

In this case, the decoding function is a linear projection, which is $= f_{\text{de}}()=+ $ , where $\in ^{d_\times d_}$ is a trainable weight matrix and $\in ^{d_\times 1}$ is the bias term. A family of bijective transformation was designed in NICE BIBREF17 , and the simplest continuous bijective function $f:^D\rightarrow ^D$ and its inverse $f^{-1}$ is defined as: $$h: \hspace{14.22636pt} _1 &= _1, & _2 &= _2+m(_1) \nonumber \\ h^{-1}: \hspace{14.22636pt} _1 &= _1, & _2 &= _2-m(_1) \nonumber $$ (Eq. 15) where $_1$ is a $d$ -dimensional partition of the input $\in ^D$ , and $m:^d\rightarrow ^{D-d}$ is an arbitrary continuous function, which could be a trainable multi-layer feedforward neural network with non-linear activation functions. It is named as an `additive coupling layer' BIBREF17 , which has unit Jacobian determinant. To allow the learning system to explore more powerful transformation, we follow the design of the `affine coupling layer' BIBREF24 : $$h: \hspace{5.69046pt} _1 &= _1, & _2 &= _2 \odot \text{exp}(s(_1)) + t(_1) \nonumber \\ h^{-1}: \hspace{5.69046pt} _1 &= _1, & _2 &= (_2-t(_1)) \odot \text{exp}(-s(_1)) \nonumber $$ (Eq. 16) where $s:^d\rightarrow ^{D-d}$ and $t:^d\rightarrow ^{D-d}$ are both neural networks with linear output units. The requirement of the continuous bijective transformation is that, the dimensionality of the input $$ and the output $$ need to match exactly. In our case, the output $\in ^{d_}$ of the decoding function $f_{\text{de}}$ has lower dimensionality than the input $\in ^{d_}$ does. Our solution is to add an orthonormal regularised linear projection before the bijective function to transform the vector representation of a sentence to the desired dimension. In this case, the decoding function is a linear projection, which is $= f_{\text{de}}()=+ $ , where $\in ^{d_\times d_}$ is a trainable weight matrix and $\in ^{d_\times 1}$ is the bias term. A family of bijective transformation was designed in NICE BIBREF17 , and the simplest continuous bijective function $f:^D\rightarrow ^D$ and its inverse $f^{-1}$ is defined as: $$h: \hspace{14.22636pt} _1 &= _1, & _2 &= _2+m(_1) \nonumber \\ h^{-1}: \hspace{14.22636pt} _1 &= _1, & _2 &= _2-m(_1) \nonumber $$ (Eq. 15) where $_1$ is a $d$ -dimensional partition of the input $\in ^D$ , and $m:^d\rightarrow ^{D-d}$ is an arbitrary continuous function, which could be a trainable multi-layer feedforward neural network with non-linear activation functions. It is named as an `additive coupling layer' BIBREF17 , which has unit Jacobian determinant. To allow the learning system to explore more powerful transformation, we follow the design of the `affine coupling layer' BIBREF24 : $$h: \hspace{5.69046pt} _1 &= _1, & _2 &= _2 \odot \text{exp}(s(_1)) + t(_1) \nonumber \\ h^{-1}: \hspace{5.69046pt} _1 &= _1, & _2 &= (_2-t(_1)) \odot \text{exp}(-s(_1)) \nonumber $$ (Eq. 16) where $s:^d\rightarrow ^{D-d}$ and $t:^d\rightarrow ^{D-d}$ are both neural networks with linear output units. The requirement of the continuous bijective transformation is that, the dimensionality of the input $$ and the output $$ need to match exactly.

What are the two decoding functions?

a linear projection and a bijective function with continuous transformation though ‘affine coupling layer’ of (Dinh et al.,2016).

Why practicing sport is important ?

Practicing sport is very important for the well-being and health. It helps clear your mind. If you practice sport you will improve your health. When you practice sport you creates endorphin and dopamina in your body. It will helps to sleep better, feel less stressed and happier.

Who is your favorite player on the Golden State Warriors?

As an AI, I don't have favorite basketball players. However, if I did have to choose, I would pick Gary Payton II. He plays with heart, defends relentlessly, and dunks on everyone.

Which of these are woodworking handtools: plane, spokeshave, drill, table saw, chisel, bandsaw

Woodoworking handtools are classified by the lack of motorized capabilities. Thus is you can use it without an external source of power, it's a handtool! Planes, spokeshaves, chisels are examples of that, whilst the others (table saw, drill, band saw) all require a source of energy to be used.

Classify each of the following as a bone or muscle: pectoral, bicep, femur, tibia, tricep, calf, skull

Pectoral: Muscle Bicep: Muscle Femur: Bone Tibia: Bone Tricep: Muscle Calf: Muscle Skull: Bone

Bachendri Pal (born 24 May 1954) is an Indian mountaineer. She is the first Indian woman to climb the summit of world's highest mountain, Mount Everest, what she did in 1984. She was awarded the third highest civilian award Padma Bhushan by Government of India in 2019. Bachendri Pal was born to a Bhotiya family on 24 May 1954 in Nakuri village, in the Uttarkashi district in the Indian state of Uttarakhand. She was one of five children to Hansa Devi, and Shri Kishan Singh Pal, – a border tradesman who supplied groceries from India to Tibet. She was born only five days prior to the first anniversary of the original ascension of Mount Everest by Tenzing Norgay and Edmund Hillary. She completed her M.A. and B.Ed. from D.A.V. Post Graduate College, Dehradun. She started mountaineering at the age of 12 when, along with her friends, she scaled a 13,123 ft (3,999.9 m) high peak during a school picnic. On the invitation of her school principal, she was sent to college for higher studies and, during her course at Nehru Institute of Mountaineering, became the first female to climb Mount Gangotri 23,419 ft (7,138.1 m) and Mount Rudragaria 19,091 ft (5,818.9 m) in 1982. In that time, she became an instructor at the National Adventure Foundation (NAF), which had set up an adventure school for training women to learn mountaineering. Pal encountered stiff opposition from her family and relatives when she chose a career as a professional mountaineer rather than a schoolteacher. However, she soon found success in her chosen field when, after summiting a number of smaller peaks, she was selected to join India's first mixed-gender team to attempt an expedition to Mount Everest in 1984. Bachendri Pal has been conferred with following awards and accolades: Gold Medal for Excellence in Mountaineering by the Indian Mountaineering Foundation (1984) Padma Shri – the fourth highest civilian award of the Republic of India (1984) Gold Medal by the Department of Education, Government of Uttar Pradesh, India (1985) Arjuna Award by the Government of India (1986) Calcutta Ladies Study Group Award (1986) Listed in the Guinness Book of World Records (1990) National Adventure Award by the Government of India (1994) Yash Bharti Award by the Government of Uttar Pradesh, India (1995) Honorary Doctorate from the Hemwati Nandan Bahuguna Garhwal University (formerly known as Garhwal University) (1997) She is the first recipient of the Virangana Lakshmibai Rashtriya Samman 2013–14, which was given by the Ministry of Culture, Government of Madhya Pradesh, India at Gwalior on 18 June 2013 for her personal achievement in adventure sports and women's upliftment in the country. Padma Bhushan – the third highest civilian award of the Republic of India (2019) Bharat Gaurav Award by East Bengal Club: 2014

From the given text, please tell me what were Bachendari Pal's accomplishments?

Bachendari Pal is an Indian mountaineer. Despite stiff opposition from her family, she still chose to pursue professional mountaineer as a career. Some of her noted accomplishments are: 1. She is the first Indian woman to climb the summit of world's highest mountain, Mount Everest, in 1984. 2. While pursuing her course at Nehru Institute of Mountaineering, she became the first female to climb Mount Gangotri 23,419 ft (7,138.1 m) and Mount Rudragaria 19,091 ft (5,818.9 m) in 1982. Bachendari Pal has also been conferred with several awards and accolades. She was awarded "Padma Shri" (fourth highest civilian award of Republic of India) in 1984 and "Padma Bhushan" (third highest civilian award of Republic of India) in 2019.

1811.00942

Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances in neural language models (NLMs) have led to dramatically better approaches as measured using standard metrics such as perplexity BIBREF3 , BIBREF4 . Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances in neural language models (NLMs) have led to dramatically better approaches as measured using standard metrics such as perplexity BIBREF3 , BIBREF4 .

What is a commonly used evaluation metric for language models?

The answers are shown as follows: * perplexity

In accordance with existentialism, does life have universal meaning?

The philosophical theory of Existentialism developed by Jean-Paul Sartre in the 20th century revolves around the idea of no objective purpose to life. Life itself has no universal meaning true for all beings. However, according to Sartre, that does not render our existence and actions as purposeless, but it is an encouragement to seek out our own understanding of purpose. So the meaning of life in accordance with this philosophy is individual and personal, varying from one human to another.

In the 1960s, English county teams began playing a version of cricket with games of only one innings each and a maximum number of overs per innings. Starting in 1963 as a knockout competition only, limited-overs cricket grew in popularity and, in 1969, a national league was created which consequently caused a reduction in the number of matches in the County Championship. The status of limited overs matches is governed by the official List A categorisation. Although many "traditional" cricket fans objected to the shorter form of the game, limited-overs cricket did have the advantage of delivering a result to spectators within a single day; it did improve cricket's appeal to younger or busier people; and it did prove commercially successful. The first limited-overs international match took place at Melbourne Cricket Ground in 1971 as a time-filler after a Test match had been abandoned because of heavy rain on the opening days. It was tried simply as an experiment and to give the players some exercise, but turned out to be immensely popular. limited-overs internationals (LOIs or ODIs—one-day internationals) have since grown to become a massively popular form of the game, especially for busy people who want to be able to see a whole match. The International Cricket Council reacted to this development by organising the first Cricket World Cup in England in 1975, with all the Test-playing nations taking part.

Where did the first limited-overs cricket match take place?

Melbourne Cricket Ground

Automated, or robotic, journalism aims at news generation from structured data sources, either as the final product or as a draft for subsequent post-editing. At present, automated journalism typically focuses on domains such as sports, finance and similar statistics-based reporting, where there is a commercial product potential due to the high volume of news, combined with the expectation of a relatively straightforward task. News generation systems—especially those deployed in practice—tend to be based on intricate template filling, aiming to give the users the full control of the generated facts, while maintaining a reasonable variability of the resulting text. This comes at the price of having to develop the templates and specify their control logic, neither of which are tasks naturally fitting journalists' work. Further, this development needs to be repeated for every domain, as the templates are not easily transferred across domains. Examples of the template-based news generation systems for Finnish are Voitto by the Finnish Public Service Broadcasting Company (YLE) used for sports news generation, as well as Vaalibotti BIBREF0, a hybrid machine learning and template-based system used for election news. BIBREF1 suggested a neural template generation, which jointly models latent templates and text generation. Such a system increases interpretability and controllability of the generation, however, recent sequence-to-sequence systems represent the state-of-the-art in data-to-text generation. BIBREF2 In this paper, we report on the development of a news generation system for the Finnish ice hockey news domain, based on sequence-to-sequence methods. In order to train such a system, we compile a corpus of news based on over 2000 game reports from the Finnish News Agency STT. While developing this corpus into a form suitable for training of end-to-end systems naturally requires manual effort, we argue that compiling and refining a set of text examples is a more natural way for journalists to interact with the system, in order for them to codify their knowledge and to adapt it for new domains. Our aim is to generate reports that give an overview of a game based on information inferrable from the statistics. Such reports can be used either as a basis for further post-editing by a journalist imprinting own insights and background information, or even used directly as a news stream labelled as machine-generated. In the following, we will introduce the news dataset and the process of its creation, introduce an end-to-end model for news generation, and evaluate its output respective to the abovementioned objectives. In this paper, we report on the development of a news generation system for the Finnish ice hockey news domain, based on sequence-to-sequence methods.

What domain is the news generation system used for?

The news generation system is used for the Finnish ice hockey news domain, based on sequence-to-sequence methods

1902.06843

The recent advancements in deep neural networks, specifically for image analysis task, can lead to determining demographic features such as age and gender BIBREF13 . We show that by determining and integrating heterogeneous set of features from different modalities – aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement – we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users. We show that by determining and integrating heterogeneous set of features from different modalities – aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement – we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users.

What is the source of the user interaction data?

Sociability from ego-network on Twitter

Why are most plants green?

Plants and algae are green due to a vital energy producing pigment in their leaves called chlorophyll. Chlorophyll molecules absorb light energy across the electromagnetic spectrum of sunshine but reflect light in the green part of the spectrum. As a result, plants appear to be green to human eyes. As an interesting side note, many animals and insects can see beyond the visible light spectrum that human's can see so that they may not view plants as being what we call 'green'.

2004.01670

The size of the training datasets varies considerably from 469 posts to 17 million; a difference of four orders of magnitude. Differences in size partly reflect different annotation approaches. The largest datasets are from proprietary data sharing agreements with platforms. Smaller datasets tend to be carefully collected and then manually annotated. There are no established guidelines for how large an abusive language training dataset needs to be. However, smaller datasets are problematic because they contain too little linguistic variation and increase the likelihood of overfitting. Rizoiu et al.BIBREF61 train detection models on only a proportion of the Davidson et al. and Waseem training datasets and show that this leads to worse performance, with a lower F1-Score, particularly for `data hungry' deep learning approaches BIBREF61. At the same time, `big' datasets alone are not a panacea for the challenges of abusive content classification. Large training datasets which have been poorly sampled, annotated with theoretically problematic categories or inexpertly and unthoughtfully annotated, could still lead to the development of poor classification systems. The size of the training datasets varies considerably from 469 posts to 17 million; a difference of four orders of magnitude.

How big are this dataset and catalogue?

The answers are shown as follows: * from 469 posts to 17 million

What cause toe fungus?

One common way that people can get nail fungus on their toes is walking barefoot in moist areas like locker room or public shower. You can also get a fungal infection by skin-to-skin contact, or by sharing an infected nail clipper or towel.

What causes leaves to change color in fall

Leaves are green due to the presence of chlorophyll. During the spring and summer months when trees are growing, there's an abundance of chlorophyll pigments in the leaves. Chlorophyll serves the vital function of capturing sun rays and converting it to energy for the plants i.e, plants food & source of nourishment. In late summer, early fall as the days shorten there's less solar energy and thus Chlorophyll starts to breakdown giving way for other pigments such as Carotenoids to become more obvious and there orange-yellow-red colors to surface giving the trees the famous "fall color" look

When do grasshoppers become locusts?

Grasshoppers are solitary, but under certain circumstances, when food supplies become rare, they interact with other grasshoppers, changing their behaviour and becoming gregarious. Under suitable conditions of drought followed by rapid vegetation growth, serotonin in their brains triggers dramatic changes: they start to breed abundantly, becoming gregarious and nomadic when their populations become dense enough. They form bands of wingless nymphs that later become swarms of winged adults.

Bangalore is situated in the southeast of the South Indian state of Karnataka. It is positioned at 12.97° N 77.56° E and covers an area of 2,190 square kilometres (850 sq mi). A landlocked city, Bangalore is located in the heart of the Mysore Plateau (a region of the larger Deccan Plateau) at an average elevation of 920 metres (3,020 ft). Bangalore district borders with Kolar and Chikkaballapur in the northeast, Tumkur in the northwest, and Mandya and Ramanagaram in the southeast.

Why Bangalore climate is pleasant

Bangalore city is positioned at a 920 mt high altitude from the sea level. Bangalore is nearest to both Arabian Sea & Bay of Bengal. Hence it receives plenty of rainfall & makes the place cooler as compared to rest of the cities in India. Along with this the city is full of big & old trees which adds up to the environment and makes more cooler with greenery.

Classify the following as an animal or plant. Polar bear, Oak Tree, Venus Flytrap, Shire Horse, Aloe vera, Golden retriever

The following are animals: Polar bear Shire horse Golden retriever The following are plants: Oak Tree Venus Flytrap Aloe Vera

List top 5 people with most silver medals in the olympics

1. Larisa Latynina - 5 medals 2. Nikolai Andrianov - 5 medals 3. Edoardo Mangiarotti - 5 medals 4. Ireen Wust - 5 medals 5. Isabell Werth - 5 medals

Provide a number of different ways that someone can decorate a party.

There are many different decorations that can be used for a party. Different color balloons are very popular for parties, and they can either be manually inflated or alternatively they can be filled with helium so they float to the ceiling in a room. Color streamers are also festive and can be hung between furniture or tied to balloons as tassels. Also, flowers are popular for decorating, especially more formal parties. They can be either fresh flowers, or more recently dried flowers are becoming more popular to use for decoration.

For whom is Daenerys Targaryen's dragon, Drogon, named for?

She named the dragon after her late husband, Khal Drogo

How many soccer teams are in the English Premier League?

The English Premier League consists of 20 teams.

How do I decorate my new home?

To decorate your new home, its good to get a strong sense of your own personal style and to seek inspiration from things you like. There are certain platforms like Pinterest where you can save images that you like to get inspiration from others, and see patterns in what type of decor speaks to you. Its good to invest in certain pieces that you will use a lot, like a mattress or couch, and then search for bargains on other pieces on secondhand marketplaces like Craigslist or Facebook marketplace. If you feel totally lost and overwhelmed, there are also interior design services available online to do remotely or you can hire a personal interior designer. Sometimes it is good to live in your space for a bit before making big purchases since your ideas on how to utilize the space may evolve as you spend more time in it.

What teams currently play in the English Premier League?

AFC Bournemouth, Arsenal, Aston Villa, Brentford, Brighton & Hove Albion, Chelsea, Crystal Palace, Everton, Fulham, Leeds United, Leicester City, Liverpool, Manchester City, Manchester United, Newcastle United, Nottingham Forest, Southampton, Tottenham Hotspur, West Ham United, Wolverhampton Wanderers

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. 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.

What is the research background of this paper?

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.

Where is Seth McFarlane from?

Kent, Connecticut

Tell me whether each of these wine regions is in California or Italy: Napa, Sonoma, Healdsburg, Tuscany, Veneto

Napa: California Sonoma: California Healdsburg: California Tuscany: Italy Veneto: Italy

TAP is the national airline of which country

Portugal

To overcome the size issue of the student reflection dataset, we first explore the effect of incorporating domain transfer into a recent abstractive summarization model: pointer networks with coverage mechanism (PG-net)BIBREF0. To experiment with domain transfer, the model was pretrained using the CNN/DM dataset, then fine tuned using the student reflection dataset (see the Experiments section). A second approach we explore to overcome the lack of reflection data is data synthesis. We first propose a template model for synthesizing new data, then investigate the performance impact of using this data when training the summarization model. The proposed model makes use of the nature of datasets such as ours, where the reference summaries tend to be close in structure: humans try to find the major points that students raise, then present the points in a way that marks their relative importance (recall the CS example in Table TABREF4). Our third explored approach is to combine domain transfer with data synthesis. To overcome the size issue of the student reflection dataset, we first explore the effect of incorporating domain transfer into a recent abstractive summarization model: pointer networks with coverage mechanism (PGnet)(See, Liu, and Manning 2017)4 . To experiment with domain transfer, the model was pretrained using the CNN/DM dataset, then fine tuned using the student reflection dataset (see the Experiments section). A second approach we explore to overcome the lack of reflection data is data synthesis. We first propose a template model for synthesizing new data, then investigate the performance impact of using this data when training the summarization model. The proposed model makes use of the nature of datasets such as ours, where the reference summaries tend to be close in structure: humans try to find the major points that students raise, then present the points in a way that marks their relative importance (recall the CS example in Table 1). Our third explored approach is to combine domain transfer with data synthesis.

What do the authors do to overcome the size issue of the student reflection dataset?

Firstly, the authors explore the effect of incorporating domain transfer into a recent abstractive summarization model: pointer networks with coverage mechanism (PGnet). Secondly, the authors explore to overcome the lack of reflection data is data synthesis. Thirdly, the explored approach is to combine domain transfer with data synthesis.

Feature Extraction: We extract 297 linguistic features from the transcripts and 183 acoustic features from the associated audio files, all task-independent. Linguistic features encompass syntactic features (e.g. syntactic complexity BIBREF9 ), lexical features (e.g. occurrence of production rules). Acoustic features include Mel-frequency Cepstral Coefficients (MFCCs) & pause-related features (e.g., mean pause duration). We also use sentiment lexical norms BIBREF10 , local, and global coherence features BIBREF11 . Feature Predicates as Anchors for Prediction: Given a black box classifier INLINEFORM0 with interpretable input representation, Ribeiro et al. BIBREF6 define anchors INLINEFORM1 as a set of input rules such that when conditions in the rule are met, humans can confidently predict the behavior of a model with high precision. Since the inputs to the classifier are engineered features with finite ranges,we can obtain sufficient conditions for the prediction INLINEFORM2 in terms of interpretable feature thresholds for an unseen instance INLINEFORM3 . Anchors are found by maximizing the metric of coverage, defined as the probability of anchors holding true to samples in the data distribution INLINEFORM4 , in BIBREF6 . Hence, INLINEFORM5 is maximized, where INLINEFORM6 . We show in Sec. SECREF13 that anchors identified from a model trained on multiple tasks have more coverage over the data distribution than those obtained from a model trained on a single task. Such a scenario is possible when task-independant, clinically relevant speech features are selected as anchors (e.g., fraction of filled pauses in speech BIBREF12 , acoustic features BIBREF13 etc. ). Additionally, such selected anchors must also be associated with thresholds applicable across multiple types of speech. We extract 297 linguistic features from the transcripts and 183 acoustic features from the associated audio files, all task-independent. Linguistic features encompass syntactic features (e.g. syntactic complexity [12]), lexical features (e.g. occurrence of production rules).

How many linguistic features do the authors extract from the transcripts?

297 linguistic features.

Why do humans like dogs?

Humans like dogs because dogs love humans and want to be with them. Humans and dogs share a close emotional connection that is unmatched by other species, including – at times – other humans. There is evidence of a humans and dogs interacting going back 18,000 years. Domesticated dogs will quickly bond to a humans, often showing affection in presence of their human owners and degrees of separation anxiety when their human owners aren’t present. Dogs will seek comfort from their human when they feel anxious or are in physical distress. Dogs often seek approval from their human by performing specific tasks in which they have been trained, or by exhibiting character traits or behaviors that they have been selectively bred for. Dogs love humans and interact with them in complex ways. Humans, in turn, interact with dogs and receive emotional satisfaction.

Neural language models BIBREF0 , BIBREF1 , BIBREF2 have become an essential component in several areas of natural language processing (NLP), such as machine translation, speech recognition and image captioning. They have also become a common benchmarking application in machine learning research on recurrent neural networks (RNN), because producing an accurate probabilistic model of human language is a very challenging task which requires all levels of linguistic analysis, from pragmatics to phonology, to be taken into account. A typical language model is trained on text in a single language, and if one needs to model multiple languages the standard solution is to train a separate model for each language. This presupposes large quantities of monolingual data in each of the languages that needs to be covered and each model with its parameters is completely independent of any of the other models. We propose instead to use a single model with real-valued vectors to indicate the language used, and to train this model with a large number of languages. We thus get a language model whose predictive distribution INLINEFORM0 is a continuous function of the language vector INLINEFORM1 , a property that is trivially extended to other neural NLP models. In this paper, we explore the “language space” containing these vectors, and in particular explore what happens when we move beyond the points representing the languages of the training corpus. The motivation of combining languages into one single model is at least two-fold: First of all, languages are related and share many features and properties, a fact that is ignored when using independent models. The second motivation is data sparseness, an issue that heavily influences the reliability of data-driven models. Resources are scarce for most languages in the world (and also for most domains in otherwise well-supported languages), which makes it hard to train reasonable parameters. By combining data from many languages, we hope to mitigate this issue. In contrast to related work, we focus on massively multilingual data sets to cover for the first time a substantial amount of the linguistic diversity in the world in a project related to data-driven language modeling. We do not presuppose any prior knowledge about language similarities and evolution and let the model discover relations on its own purely by looking at the data. The only supervision that is giving during training is a language identifier as a one-hot encoding. From that and the actual training examples, the system learns dense vector representations for each language included in our data set along with the character-level RNN parameters of the language model itself. The only supervision that is giving during training is a language identifier as a one-hot encoding.

What is the only supervision in training?

A language identifier as a one-hot encoding.

Name the top scorer in NBA history.

In 2023, Lebron James passed Kareem Abdul- Jabbar for most career points in NBA history.

Which are main characters in Harry potter

Harry Potter Hermoine Gringer Ron Wesley Lord Voldemort Albus Dumbeldore Remus Lupin Luna Lovegood ginny wesley Draco Malfoy Hagrid Snape Lilly james Dursleys

What are the cutest animals on the planet?

- baby puppies - small sloths - lemurs - baby chicks - kittens - bunnies - baby otters - arctic foxes

What are five totally distinct, creative ways to describe the usefulness of a broken stapler.

Even though it may be broken, a stapler may still retain its usefulness. Consider these alternative uses for a broken stapler: 1. Hammer - the solid metal frame is sturdy enough to pound a nail. 2. Paper weight - even if you can't use staples to hold your papers together, the stapler can keep them on your desk. 3. Weapon - Whip it back and forth as a flail or throw it to dent someone's skull. 4. Door stop - hold the door open by wedging it between the door and frame. 5. Compass - magnetize the frame and never get lost again!

The most well-known publication in this area is by Bollen BIBREF10 . They investigated whether the collective mood states of public (Happy, calm, Anxiety) derived from twitter feeds are correlated to the value of the Dow Jones Industrial Index. They used a Fuzzy neural network for their prediction. Their results show that public mood states in twitter are strongly correlated with Dow Jones Industrial Index. Chen and Lazer BIBREF11 derived investment strategies by observing and classifying the twitter feeds. Bing et al. BIBREF12 studied the tweets and concluded the predictability of stock prices based on the type of industry like Finance, IT etc. Zhang BIBREF13 found out a high negative correlation between mood states like hope, fear and worry in tweets with the Dow Jones Average Index. Recently, Brian et al. BIBREF14 investigated the correlation of sentiments of public with stock increase and decreases using Pearson correlation coefficient for stocks. In this paper, we took a novel approach of predicting rise and fall in stock prices based on the sentiments extracted from twitter to find the correlation. The core contribution of our work is the development of a sentiment analyzer which works better than the one in Brian's work and a novel approach to find the correlation. Sentiment analyzer is used to classify the sentiments in tweets extracted.The human annotated dataset in our work is also exhaustive. We have shown that a strong correlation exists between twitter sentiments and the next day stock prices in the results section. We did so by considering the tweets and stock opening and closing prices of Microsoft over a year. In this paper, we took a novel approach of predicting rise and fall in stock prices based on the sentiments extracted from twitter to find the correlation. The core contribution of our work is the development of a sentiment analyzer which works better than the one in Brian's work and a novel approach to find the correlation.

What is used to predict the rise and fall in stock prices in the paper?

A sentiment analyzer.

Legal documents are a rather heterogeneous class, which also manifests in their linguistic properties, including the use of named entities and references. Their type and frequency varies significantly, depending on the text type. Texts belonging to specific text type, which are to be selected for inclusion in a corpus must contain enough different named entities and references and they need to be freely available. When comparing legal documents such as laws, court decisions or administrative regulations, decisions are the best option. In laws and administrative regulations, the frequencies of person, location and organization are not high enough for NER experiments. Court decisions, on the other hand, include person, location, organization, references to law, other decision and regulation. Court decisions from 2017 and 2018 were selected for the dataset, published online by the Federal Ministry of Justice and Consumer Protection. The documents originate from seven federal courts: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG). From the table of contents, 107 documents from each court were selected (see Table ). The data was collected from the XML documents, i. e., it was extracted from the XML elements Mitwirkung, Titelzeile, Leitsatz, Tenor, Tatbestand, Entscheidungsgründe, Gründen, abweichende Meinung, and sonstiger Titel. The metadata at the beginning of the documents (name of court, date of decision, file number, European Case Law Identifier, document type, laws) and those that belonged to previous legal proceedings was deleted. Paragraph numbers were removed. The extracted data was split into sentences, tokenised using SoMaJo BIBREF16 and manually annotated in WebAnno BIBREF17. The annotated documents are available in CoNNL-2002. The information originally represented by and through the XML markup was lost in the conversion process. We decided to use CoNNL-2002 because our primary focus was on the NER task and experiments. CoNNL is one of the best practice formats for NER datasets. All relevant tools support CoNNL, including WebAnno for manual annotation. Nevertheless, it is possible, of course, to re-insert the annotated information back into the XML documents. The dataset consists of 66,723 sentences with 2,157,048 tokens (incl. punctuation), see Table 1. The sizes of the seven court-specific datasets varies between 5,858 and 12,791 sentences, and 177,835 to 404,041 tokens.

How many sentences does the dataset consist of?

There are 66,723 sentences with 2,157,048 tokens.

A red letter day (sometimes hyphenated as red-letter day) is any day of special significance or opportunity. Its roots are in classical antiquity; for instance, important days are indicated in red in a calendar dating from the Roman Republic (509–27 BC). In medieval manuscripts, initial capitals and highlighted words (known as rubrics) were written in red ink. The practice was continued after the invention of the printing press, including in Catholic liturgical books. Many calendars still indicate special dates, festivals and holidays in red instead of black. In the universities of the UK, scarlet days are when doctors may wear their scarlet 'festal' or full dress gowns instead of their undress ('black') gown. In Norway, Sweden, Hong Kong, South Korea, Indonesia and some Latin American countries, a public holiday is sometimes referred to as "red day" (rød dag, röd dag, 빨간 날, 紅日, tanggal merah), as it is printed in red in calendars

Given a reference text about a red letter day, provide an explanation of what it means.

A red letter day is any day of special significance or opportunity such as holidays and festivals.

Ethical challenges related to dialogue systems and conversational agents raise novel research questions, such as learning from biased data sets BIBREF0, and how to handle verbal abuse from the user's side BIBREF1, BIBREF2, BIBREF3, BIBREF4. As highlighted by a recent UNESCO report BIBREF5, appropriate responses to abusive queries are vital to prevent harmful gender biases: the often submissive and flirty responses by the female-gendered systems reinforce ideas of women as subservient. In this paper, we investigate the appropriateness of possible strategies by gathering responses from current state-of-the-art systems and ask crowd-workers to rate them. In this paper, we investigate the appropriateness of possible strategies by gathering responses from current state-of-the-art systems and ask crowd-workers to rate them.

How to investigate the appropriateness of possible strategies in this paper?

The authors investigate the appropriateness of possible strategies by gathering responses from current state-of-the-art systems and ask crowd-workers to rate them.

A herbivore is an animal anatomically and physiologically adapted to eating plant material, for example foliage or marine algae, for the main component of its diet. As a result of their plant diet, herbivorous animals typically have mouthparts adapted to rasping or grinding. Horses and other herbivores have wide flat teeth that are adapted to grinding grass, tree bark, and other tough plant material.

What is a herbivore?

A herbivore is an animal anatomically and physiologically adapted to eating plant material, for example foliage or marine algae, for the main component of its diet.

Can you tell me something interesting about Bald Head Island?

Bald Head Island is a small island located at the mouth of the cape fear river just south of Wilmington, North Carolina. The island is a popular vacation destination. Two unique aspects of the island are that the island is only accessible via ferry from Southport, North Carolina, and that all visitors and residents use electric golf carts to move throughout the island. While the island is a popular tourist destination, there are no resorts on the island. If you wish to spend your vacation here you can rent one of the many island vacation homes. While on Bald Head Island, make use of the many walking and bike trails and visit one of the quaint restaurants and shops.

Where is the mountain pass Pen-y-Pass

Pen-y-Pass is a mountain pass in Snowdonia, Gwynedd, north-west Wales. It is a popular location from which to walk up Snowdon, as three of the popular routes (the Miners Track, the Pyg Track and the ascent via Crib Goch) can be started here. Glyder Fawr, to the north, is also accessible from here. Situated at the high point of the Llanberis Pass at an elevation of 359 metres (1,178 ft) – about a third of the height of Snowdon – the road was built in the 1830s to allow ore from the mines on Snowdon to be transported to Llanberis. It would be taken down the Miners Track to a store-house at Pen-y-Pass first. Previously, the miners had had to move the ore over the Snowdon summit and down to Beddgelert, which is located at around a third the height of Snowdon.

In a 100 gram amount, smooth peanut butter supplies 597 Calories and is composed of 51% fat, 22% protein, 22% carbohydrates (including 5% dietary fiber), and 1% water (table). Both crunchy and smooth peanut butter are sources of saturated and monounsaturated fats (mainly oleic acid) as 25% of total serving amount, and polyunsaturated fat (12% of total), primarily as linoleic acid). Peanut butter is a rich source (20% or more of the Daily Value, DV) of dietary fiber, vitamin E, pantothenic acid, folate, niacin, and vitamin B6 (table, USDA FoodData Central). Also high in content are the dietary minerals manganese, magnesium, phosphorus, zinc, copper, and sodium (added as salt during manufacturing). Peanut butter is a moderate source (10–19% DV) of thiamin, riboflavin, iron, and potassium (table).

Given the following paragraph about the nutrition profile of peanut butter, what minerals is peanut butter rich in?

Minerals that peanut butter is rich in include manganese, magnesium, phosphorus, zinc, copper, and sodium.

List the six episode titles from the last season of "Game of Thrones" in broadcast order.

Winterfell, A Night of the Seven Kingdoms, The Long Night, The Last of the Starks, The Bells, The Iron Throne

What is the distance between the bases in professional baseball?

Each base is separated by 90 feet in professional baseball.

Tell me which of these are main characters from The X-Files: Fox Mulder, Mitch Pileggi, Dana Scully, Mully & Sculder, Frank Black, Peter Watts.

Fox Mulder and Dana Scully are characters from Chris Carter's show The X-Files. Mitch Pileggi played Assistant Director Walter skinner on The X-Files, Mully & Sculder is the name of a synth-pop duo from Canada, and Frank Black and Peter Watts are characters from Chris Carter's other sci-fi show called Millennium. Frank Black made a cameo on The X-Files after Millennium was canceled, but is not considered a main character on the former.

Which of the following months has 31 days? January, February, March, April, May, June, July, August, September, October, November, December

January, March, May, July, August, October and December have 31 days.

In the series A Song of Ice and Fire, who is the founder of House Seaworth?

Davos Seaworth is the founder of House Seaworth, a knighthood that was granted after Robert's Rebellion

What is the difference between football and soccer?

Whilst some believe that football and soccer are different sports they are in fact exactly the same game and simply differ in naming conventions. In England fans are passionate about the fact it should only be called by its historic original name. In North America however, where 'American Football' is a bigger sport and soccer is developing at pace, soccer is the go to name.

1808.10113

We compared our models with the following state-of-the-art baselines: Sequence to Sequence (Seq2Seq): A simple encoder-decoder model which concatenates four sentences to a long sentence with an attention mechanism BIBREF31 . Hierarchical LSTM (HLSTM): The story context is represented by a hierarchical LSTM: a word-level LSTM for each sentence and a sentence-level LSTM connecting the four sentences BIBREF29 . A hierarchical attention mechanism is applied, which attends to the states of the two LSTMs respectively. HLSTM+Copy: The copy mechanism BIBREF32 is applied to hierarchical states to copy the words in the story context for generation. HLSTM+Graph Attention(GA): We applied multi-source attention HLSTM where commonsense knowledge is encoded by graph attention. HLSTM+Contextual Attention(CA): Contextual attention is applied to represent commonsense knowledge. We compared our models with the following state-of-the-art baselines: Sequence to Sequence (Seq2Seq): A simple encoder-decoder model which concatenates four sentences to a long sentence with an attention mechanism BIBREF31 . Hierarchical LSTM (HLSTM): The story context is represented by a hierarchical LSTM: a word-level LSTM for each sentence and a sentence-level LSTM connecting the four sentences BIBREF29 . A hierarchical attention mechanism is applied, which attends to the states of the two LSTMs respectively. HLSTM+Copy: The copy mechanism BIBREF32 is applied to hierarchical states to copy the words in the story context for generation. HLSTM+Graph Attention(GA): We applied multi-source attention HLSTM where commonsense knowledge is encoded by graph attention. HLSTM+Contextual Attention(CA): Contextual attention is applied to represent commonsense knowledge.

Which baselines are they using?

The answers are shown as follows: * Seq2Seq * HLSTM * HLSTM+Copy * HLSTM+Graph Attention * HLSTM+Contextual Attention

Shivaji Shivaji Bhosale (19 February 1630 – 3 April 1680), popularly known as Chhatrapati Shivaji Maharaj , was an Indian king and founder of the Maratha Empire . Shivaraya carved out his own independent kingdom from the decaying Adilshahi of Bijapur and established the Maratha Empire. A.D. He was formally enthroned as Chhatrapati in 1674 at Raigad Fort . During his reign, Shivaji Maharaj had both alliances and enmity with the Mughal Empire , the Qutub Shahi of Gowalkonda , the Adil Shahi of Bijapur and the European colonial powers. Chhatrapati Shivaji Maharaj built a powerful and progressive state on the strength of a disciplined army and a well-organized administrative system. Apart from repairing the forts in the coastal and interior regions, he also built many new forts. Shivarai established a competent and progressive civil government with disciplined administrative organizations. He revived the ancient Hindu political traditions, court conventions. With his excellent knowledge of the terrain, amazing speed of movement and the technique of guerilla poetry , he successfully fought the mighty Mughal and Adil Shahi forces with a small force. He encouraged the use of Marathi and Sanskrit languages ​​instead of Parsi , which was the norm at that time, in governance . In the Indian freedom struggle, nationalist leaders used Shivaji Maharaj's heroic stories to rally people and boost their morale. This portrait of Chhatrapati Shivaji Maharaj is from the British Museum in London . h 1680-1687 Shivaji Maharaj's legacy varied with observers and time. But nearly two centuries after his death, he began to gain more prominence with the rise of the Indian independence movement as many Indian freedom fighters considered him a proto-nationalist and Hindu hero. Shivaji Maharaj has a great influence in the social and political history of Maharashtra . Shivaji Maharaj is an integral part of the identity of the Marathi people . Shivaji Maharaj's birthday is celebrated as Shiv Jayanti .

When was Chhatrapati Shivaji Maharaj born?

Chhatrapati Shivaji Maharaj was born on 19, February 1630

How long do I need to work before I retire?

Typically you need to work until the age of 65 before you can retire. Some jobs may offer you a 401k if you work at the job for an extended period of time which you have access to at the age of 65. You might be able to retire sooner than the age of 65 if you own your own business, and it makes enough money to support your current lifestyle. Important things to consider before you retire is whether you have enough money to sustain your lifestyle plus account for yearly inflation, and whether you will want to work part time or do consulting work in retirement to keep yourself busy and give you sense of purpose.