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1909.05890
| false
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We collected tweets related to five different DDoS attacks on three different American banks. For each attack, all the tweets containing the bank's name posted from one week before the attack until the attack day were collected. There are in total 35214 tweets in the dataset. Then the collected tweets were preprocessed as mentioned in the preprocessing section.
Only the tweets from the Bank of America attack on 09/19/2012 were used in this experiment. The tweets before the attack day and on the attack day were used to train the two LDA models mentioned in the approach section.
In this subsection we evaluate how good the model generalizes. To achieve that, the dataset is divided into two groups, one is about the attacks on Bank of America and the other group is about PNC and Wells Fargo. The only difference between this experiment and the experiment in section 4.4 is the dataset. In this experiment setting $D_a$ contains only the tweets collected on the days of attack on PNC and Wells Fargo. $D_b$ only contains the tweets collected before the Bank of America attack. There are 590 tweets in $D_a$ and 5229 tweets in $D_b$. In this experiment, we want to find out whether a model trained on Bank of America data can make good classification on PNC and Wells Fargo data.
We collected tweets related to five different DDoS attacks on three different American banks. For each attack, all the tweets containing the bank's name posted from one week before the attack until the attack day were collected. There are in total 35214 tweets in the dataset.
Only the tweets from the Bank of America attack on 09/19/2012 were used in this experiment.
In this subsection we evaluate how good the model generalizes. To achieve that, the dataset is divided into two groups, one is about the attacks on Bank of America and the other group is about PNC and Wells Fargo. The only difference between this experiment and the experiment in section 4.4 is the dataset. In this experiment setting $D_a$ contains only the tweets collected on the days of attack on PNC and Wells Fargo. $D_b$ only contains the tweets collected before the Bank of America attack.
|
What is the training and test data used?
|
Tweets related to a Bank of America DDos attack were used as training data. The test datasets contain tweets related to attacks to Bank of America, PNC and Wells Fargo.
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In autosports, what is the apex and racing line?
|
The racing line in autosports is an imaginary line in which when followed by the vehicle will provide the most efficient and quickest way through a turn or series of turns. The apex of the racing line is the ideal point in which the car should be positioned closest to the inside of a curve. The term, 'hitting the apex' is used when the ideal racing line is followed.
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Tell me if these animals are mammals or birds: Emu, Gorilla, Platypus, Kangaroo, Ostrich, Robin, Raven
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Emu: Bird, Gorilla: Mammal, Platypus: Mammal, Kangaroo: Mammal, Ostrich: Bird, Robin: Bird, Raven: Bird
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| 134
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We evaluate our method on natural language inference (NLI), paraphrase identification (PI), and sentiment classification. We also conduct analysis on gate values and experiments on model variants. For detailed experimental settings, we refer readers to the supplemental material.
For the NLI and PI tasks, there exists recent work specializing in sentence pair classification. However in this work we confine our model to the architecture that encodes each sentence using a shared encoder without any inter-sentence interaction, in order to focus on the effectiveness of the models in extracting semantics. But note that the applicability of CAS-LSTM is not limited to sentence encoding based approaches.
To inspect the effect of the additional forget gate, we investigate how the values of vertical forget gates are distributed. We sample 1,000 random sentences from the development set of the SNLI dataset, and use the 3-layer CAS-LSTM model trained on the SNLI dataset to compute gate values
|
How do the authors compute forget gate values?
|
They sample 1,000 random sentences from the development set of the SNLI dataset, and use the 3-layer CAS-LSTM model trained on the SNLI dataset to compute gate values
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Which national parks should I visit in Utah?
|
Utah has what is called the Mighty 5. This list includes Arches, Bryce Canyon, Canyonlands, Capitol Reef and Zion. With so many choices to choose from, it can be difficult to decide. Arches is known for it's famous "Delicate Arch" which you have seen on postcards and the like. Bryce Canyon has countless hoodoos, perfect for your picturesque moment. Canyonlands, may even remind you of the Grand Canyon, so if you've enjoyed that in the past, you might want to start here (it's close by to Arches). Capitol reef has a wide variety of landscapes including arches, cliffs, canyons, etc. Last but most certainly not least, Zion. This park may be the most well-traveled on the list. Zion is especially known for it's big wall climbs.
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| 134
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We evaluate our method on natural language inference (NLI), paraphrase identification (PI), and sentiment classification. We also conduct analysis on gate values and experiments on model variants. For detailed experimental settings, we refer readers to the supplemental material.
For the NLI and PI tasks, there exists recent work specializing in sentence pair classification. However in this work we confine our model to the architecture that encodes each sentence using a shared encoder without any inter-sentence interaction, in order to focus on the effectiveness of the models in extracting semantics. But note that the applicability of CAS-LSTM is not limited to sentence encoding based approaches.
However in this work we confine our model to the architecture that encodes each sentence using a shared encoder without any inter-sentence interaction, in order to focus on the effectiveness of the architectures in extracting semantics.
|
To focus on the effectiveness of the architectures in extracting semantics, what method do the authors take?
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Confine their model to the architecture that encodes each sentence using a shared encoder without any inter-sentence interaction
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Against the Cincinnati Bengals in Super Bowl LVI, Stafford and the Rams came away victorious on their home field with a final score of 23–20. Scoring started early in the first quarter thanks to a long drive that culminated in a Odell Beckham Jr. touchdown reception. The Bengals managed a field goal to make the score 7–3. Stafford then tossed another touchdown to wide receiver Cooper Kupp on the next possession to extend the lead to 13–3. On the first play of the second half, however, the Bengals retook the lead, 17–13. After field goals by both teams, the Rams trailed by a score of 20–16 in the fourth quarter. Stafford orchestrated a drive in the final minutes that ended with another touchdown pass to Kupp to regain the lead at 23–20. Overall, Stafford completed 26 of his 40 pass attempts for 283 yards, three passing touchdowns, and two interceptions as he earned his first championship ring in his first season with the Rams.
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Who did Stafford throw touchdown passes to in Super Bowl LV?
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The text provided does not mention information about Super Bowl LV. However, it does provide information regarding Super Bowl LVI.
In Super Bowl LVI, Stafford threw 3 touchdown passes, two to Cooper Kupp and one to Odell Beckham Jr.
In the first quarter, Stafford led a long drive that culminated in a Odell Beckham Jr. touchdown reception. Stafford later tossed a touchdown to wide receiver Cooper Kupp. In the fourth quarter, Stafford orchestrated a drive in the final minutes that ended with another touchdown pass to Kupp.
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If you are traveling outside the country, categorize each of the following as either: ‘Mandatory’, ‘Good to have’, ‘Least important’. Passport, Cash of the country visiting, Power bank, Book, Pen, Laptop
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Mandatory: Passport
Good to have: Cash of the country visiting, Powerbank
Least important: Book, Pen, Laptop
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| 233
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Speech-enabled natural-language question-answering interfaces to enterprise application systems, such as Incident-logging systems, Customer-support systems, Marketing-opportunities systems, Sales data systems etc., are designed to allow end-users to speak-out the problems/questions that they encounter and get automatic responses. The process of converting human spoken speech into text is performed by an Automatic Speech Recognition (ASR) engine. While functional examples of ASR with enterprise systems can be seen in day-to-day use, most of these work under constraints of a limited domain, and/or use of additional domain-specific cues to enhance the speech-to-text conversion process. Prior speech-and-natural language interfaces for such purposes have been rather restricted to either Interactive Voice Recognition (IVR) technology, or have focused on building a very specialized speech engine with domain specific terminology that recognizes key-words in that domain through an extensively customized language model, and trigger specific tasks in the enterprise application system. This makes the interface extremely specialized, rather cumbersome and non-adaptable for other domains. Further, every time a new enterprise application requires a speech and natural language interface, one has to redevelop the entire interface again.
An alternative to domain-specific speech recognition engines has been to re-purpose general-purpose speech recognition engines, such as Google Speech API, IBM Watson Speech to text API which can be used across domains with natural language question answering systems. Such general-purpose automatic speech engines (gp-ASR) are deep trained on very large general corpus using deep neural network (DNN) techniques. The deep learnt acoustic and language models enhance the performance of a ASR. However, this comes with its own limitations. For freely spoken natural language sentences, the typical recognition accuracy achievable even for state-of-the-art speech recognition systems have been observed to be about 60% to 90% in real-world environments BIBREF0 . The recognition is worse if we consider factors such as domain-specific words, environmental noise, variations in accent, poor ability to express on the part of the user, or inadequate speech and language resources from the domain to train such speech recognition systems. The subsequent natural language processing, such as that in a question answering system, of such erroneously and partially recognized text becomes rather problematic, as the domain terms may be inaccurately recognized or linguistic errors may creep into the sentence. It is, hence, important to improve the accuracy of the ASR output text.
In this paper, we focus on the issues of using a readily available gp-ASR and adapting its output for domain-specific natural language question answering BIBREF1 . We present two mechanisms for adaptation, namely
We present the results of these two adaptation and gauge the usefulness of each mechanism. The rest of the paper is organized as follows, in Section SECREF2 we briefly describe the work done in this area which motivates our contribution. The main contribution of our work is captured in Section SECREF3 and we show the performance of our approach through experiments in Section SECREF4 . We conclude in Section SECREF5 .
In this paper, we focus on the issues of using a readily available gp-ASR and adapting its output for domain-specific natural language question answering (Anantaram et al., 2015a).
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What issues do the authors focus on in this paper?
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The issues of using a readily available gp-ASR and adapting its output for domain-specific natural language question answering.
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What are the merits of skipping breakfast? Can you target your answer to a typical American and describe in layman's terms rather than detailed scientific jargon?
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A truism instilled in youths is "Breakfast is the most important meal of the day". A well-rounded breakfast may indeed lead to a productive and energetic day. However, advertisements often position breakfast as a dessert rather than a well-rounded meal. For example, many eat sugary cereal with large servings of milk to kickstart their day. This is ill-advised. One should consider a glass of water or cup of coffee in place of breakfast. Skipping breakfast creates a "fast" in the body, which allows restoration of vital biological functions without spending cycles digesting new nutrients. In short, if one skips breakfast, additional time and focused energy may be awarded, especially if the breakfast has a poor nutrient profile.
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Whats the name of the person who wrote the US declaration of independence?
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Thomas Jefferson
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The Skate series distinguishes itself from other skateboarding video games with its unique control system, called "Flick-it", in which players execute tricks with brief, rapid movements of either or both analog sticks of a twin-stick gamepad. Players assume the role of a customizable skater and freely explore a fictional city in an open world setting, completing diverse and increasingly difficult challenges while being filmed by cameraman Giovanni Reda to unlock new areas, obtain new cosmetics and eventually rise to stardom. Along the way, they can encounter, compete with and even play as famous real-life skaters, including Danny Way, Terry Kennedy and Rob Dyrdek, and must eventually choose from among several authentic licensed skateboard gear firms to strike an endorsement with for exclusive skateboard parts and gear. Players can make use of session markers to quickly travel to or return to a location of their choosing. A special injury system called the "Hall of Meat" tracks injuries sustained during wipeouts, particularly broken bones, which is key to completing certain challenges.
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Tell me why people like the Skate video game series in one sentence
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People like the Skate series because of its unique "Flick-it" controls, authenticity, and similarity to real world skateboarding.
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| 137
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Pre-trained models BIBREF0, BIBREF1 have received much of attention recently thanks to their impressive results in many down stream NLP tasks. Additionally, multilingual pre-trained models enable many NLP applications for other languages via zero-short cross-lingual transfer. Zero-shot cross-lingual transfer has shown promising results for rapidly building applications for low resource languages. BIBREF2 show the potential of multilingual-BERT BIBREF0 in zero-shot transfer for a large number of languages from different language families on five NLP tasks, namely, natural language inference, document classification, named entity recognition, part-of-speech tagging, and dependency parsing.
Although multilingual models are an important ingredient for enhancing language technology in many languages, recent research on improving pre-trained models puts much emphasis on English BIBREF3, BIBREF4, BIBREF5. The current state of affairs makes it difficult to translate advancements in pre-training from English to non-English languages. To our best knowledge, there are only three available multilingual pre-trained models to date: (1) the multilingual-BERT (mBERT) that supports 104 languages, (2) cross-lingual language model BIBREF6 that supports 100 languages, and (3) Language Agnostic SEntence Representations BIBREF7 that supports 93 languages. Among the three models, LASER is based on neural machine translation approach and strictly requires parallel data to train.
Do multilingual models always need to be trained from scratch? Can we transfer linguistic knowledge learned by English pre-trained models to other languages? In this work, we develop a technique to rapidly transfer an existing pre-trained model from English to other languages in an energy efficient way BIBREF8. As the first step, we focus on building a bilingual language model (LM) of English and a target language. Starting from a pre-trained English LM, we learn the target language specific parameters (i.e., word embeddings), while keeping the encoder layers of the pre-trained English LM fixed. We then fine-tune both English and target model to obtain the bilingual LM. We apply our approach to autoencoding language models with masked language model objective and show the advantage of the proposed approach in zero-shot transfer. Our main contributions in this work are:
We propose a fast adaptation method for obtaining a bilingual BERT$_{\textsc {base}}$ of English and a target language within a day using one Tesla V100 16GB GPU.
We evaluate our bilingual LMs for six languages on two zero-shot cross-lingual transfer tasks, namely natural language inference BIBREF9 and universal dependency parsing. We show that our models offer competitive performance or even better that mBERT.
We illustrate that our bilingual LMs can serve as an excellent feature extractor in supervised dependency parsing task.
As the first step, we focus on building a bilingual language model (LM) of English and a target language. Starting from a pretrained English LM, we learn the target language specific parameters (i.e., word embeddings), while keeping the encoder layers of the pretrained English LM fixed. We then fine-tune both English and target model to obtain the bilingual LM. We apply our approach to autoencoding language models with masked language model objective and show the advantage of the proposed approach in zero-shot transfer.
|
How to develop the technique?
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First, focus on building a bilingual language model (LM) of English and a target language. Then, fine-tune both English and target model to obtain the bilingual LM.
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What is a derivative in finance?
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In finance, a derivative is a contract that derives its value from the performance of an underlying entity. This underlying entity can be an asset, index, or interest rate, and is often simply called the "underlying". Derivatives can be used for a number of purposes, including insuring against price movements (hedging), increasing exposure to price movements for speculation, or getting access to otherwise hard-to-trade assets or markets.
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| 214
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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.
Our data is collected in the form of conversations, providing better context.
|
How do the authors collect datasets?
|
The data is collected in the form of conversations, providing better context.
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1909.06762
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Follow the prior works BIBREF6, BIBREF7, BIBREF9, we adopt the BLEU and the Micro Entity F1 to evaluate our model performance. The experimental results are illustrated in Table TABREF30.
We provide human evaluation on our framework and the compared models. These responses are based on distinct dialogue history. We hire several human experts and ask them to judge the quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5. In each judgment, the expert is presented with the dialogue history, an output of a system with the name anonymized, and the gold response.
Follow the prior works BIBREF6, BIBREF7, BIBREF9, we adopt the BLEU and the Micro Entity F1 to evaluate our model performance.
We provide human evaluation on our framework and the compared models.
We hire several human experts and ask them to judge the quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5.
|
What were the evaluation metrics?
|
The answers are shown as follows:
* BLEU
* Micro Entity F1
* quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5
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How do electric cars work?
|
An electric car, battery electric car, or all-electric car is an automobile that is propelled by one or more electric motors, using only energy stored in batteries. Compared to internal combustion engine (ICE) vehicles, electric cars are quieter, have no exhaust emissions, and lower emissions overall. In the United States and the European Union, as of 2020, the total cost of ownership of recent electric vehicles is cheaper than that of equivalent ICE cars, due to lower fueling and maintenance costs. Charging an electric car can be done at a variety of charging stations; these charging stations can be installed in both houses and public areas.
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Why is sleep training your baby important?
|
Sleep training your baby is important because it teaches your child the fundamental skill of self-soothing themselves to sleep. If your baby is not sleep trained, it will likely result in them waking up more often at night and needing the parent's help to soothe or rock them back to sleep. There are several methods you can use to sleep train your baby including the "cry it out" method, which works by allowing your child to cry while checking in on them incrementally to make sure they know you're there. When using the cry it out method, you need to ensure that all of your baby's needs have been met. Make sure your child is fed, changed, burped, and is overall comfortable. You can use words like "it's sleepy time" to signal your baby to sleep. This method takes about 4-7 days to work and has been proven to be the most effective and completely safe to use on your child.
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| 284
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With the massive increase in social interactions on online social networks, there has also been an increase of hateful activities that exploit such infrastructure. On Twitter, hateful tweets are those that contain abusive speech targeting individuals (cyber-bullying, a politician, a celebrity, a product) or particular groups (a country, LGBT, a religion, gender, an organization, etc.). Detecting such hateful speech is important for analyzing public sentiment of a group of users towards another group, and for discouraging associated wrongful activities. It is also useful to filter tweets before content recommendation, or learning AI chatterbots from tweets.
The manual way of filtering out hateful tweets is not scalable, motivating researchers to identify automated ways. In this work, we focus on the problem of classifying a tweet as racist, sexist or neither. The task is quite challenging due to the inherent complexity of the natural language constructs – different forms of hatred, different kinds of targets, different ways of representing the same meaning. Most of the earlier work revolves either around manual feature extraction BIBREF0 or use representation learning methods followed by a linear classifier BIBREF1 , BIBREF2 . However, recently deep learning methods have shown accuracy improvements across a large number of complex problems in speech, vision and text applications. To the best of our knowledge, we are the first to experiment with deep learning architectures for the hate speech detection task.
In this paper, we experiment with multiple classifiers such as Logistic Regression, Random Forest, SVMs, Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks(DNNs). The feature spaces for these classifiers are in turn defined by task-specific embeddings learned using three deep learning architectures: FastText, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs). As baselines, we compare with feature spaces comprising of char n-grams BIBREF0 , TF-IDF vectors, and Bag of Words vectors (BoWV).
Main contributions of our paper are as follows: (1) We investigate the application of deep learning methods for the task of hate speech detection. (2) We explore various tweet semantic embeddings like char n-grams, word Term Frequency-Inverse Document Frequency (TF-IDF) values, Bag of Words Vectors (BoWV) over Global Vectors for Word Representation (GloVe), and task-specific embeddings learned using FastText, CNNs and LSTMs. (3) Our methods beat state-of-the-art methods by a large margin ( INLINEFORM0 18 F1 points better).
(1) We investigate the application of deep learning methods for the task of hate speech detection.
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What does the paper investigate?
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The application of deep learning methods for the task of hate speech detection.
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Wines and spirits
Ao Yun
Ardbeg
Belvedere
Bodega Numanthia
Cape Mentelle
Chandon
Château d'Esclans
Château Cheval Blanc
Château d'Yquem
Cheval des Andes
Clos des Lambrays
Cloudy Bay
Colgin Cellars
Dom Pérignon
Glenmorangie
Hennessy
Krug
Mercier
Moët & Chandon
Newton Vineyard
Ruinart
Terrazas de los Andes
Veuve Clicquot
Volcan de mi Tierra
Woodinville
Fashion and leather goods
Berluti
Birkenstock
Celine
Christian Dior
Emilio Pucci
Fendi
Givenchy
JW Anderson
Kenzo
Loewe
Loro Piana
Louis Vuitton
Marc Jacobs
Moynat
Off-White
Patou
Phoebe Philo
Rimowa
Stella McCartney
Perfumes and cosmetics
Acqua di Parma
Benefit Cosmetics
BITE Beauty
Cha Ling
Fenty Beauty by Rihanna
Fresh Beauty
Parfums Givenchy
Guerlain
Kenzo Parfums
KVD Vegan Beauty
Maison Francis Kurkdjian
Make Up For Ever
Marc Jacobs Beauty
Officine Universelle Buly
Ole Henriksen
Parfums Christian Dior
Perfumes Loewe
Sephora
Watches and jewelry
Bulgari
Chaumet
Daniel Roth
Fred
Hublot
Gerald Genta
Repossi
TAG Heuer
Tiffany & Co.
Zenith
Selective retailing
DFS
La Grande Epicerie
La Samaritaine
Le Bon Marché
Starboard Cruise Services
Other activities
Belmond
Maisons Cheval Blanc
Connaissance des Arts
Cova
Investir
Jardin d'Acclimatation
Le Parisien
Les Echos
Radio Classique
Royal Van Lent
|
Given a reference text that describes LVMH, what brands are part of LVMH?
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U.S. LVMH controls around 60 subsidiaries that manage 75 prestigious brands. These include Tiffany & Co., Christian Dior, Fendi, Givenchy, Marc Jacobs, Stella McCartney, Loewe, Loro Piana, Kenzo, Celine, Sephora, Princess Yachts, TAG Heuer, and Bulgari.
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Shigeo Kageyama is an average middle school-aged boy, nicknamed Mob. Although he looks like an inconspicuous person, he is in fact a powerful esper with immense psychic power. To keep from losing control of this power, he constantly lives a life under an emotional shackle. In order to help learn how to control his abilities, Mob works as an assistant to con-man Arataka Reigen, a self-proclaimed spirit medium. Mob wants to live a normal life just like those around him, but a barrage of trouble keeps coming after him. With his suppressed emotions growing inside Mob little by little, his power threatens to break through its limits as he eventually encounters other espers like the Claws.
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What is the plot of Mob Psycho 100?
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Mob Psycho 100 is a manga and anime about a boy named Shigeo Kageyama, nicknamed Mob. He looks like a normal boy but has powerful psychic abilities that he struggles to control. He tries to live an emotionless life to avoid triggering his power and learn from who he believes is a Psychic master, but is actually a con-man named Arataka Reigen. Mob's desire to live a normal life keeps getting interrupted by trouble and enemies who come after him, which increases his suppressed emotions bit by bit. When the internal counter reaches 100, he explodes in a powerful emotion.
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Give me a bulleted list of ways to make my girlfriend happy on valentines day
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Here are some ways to make your girlfriend happy on valentines day:
1. Give her breakfast in bed
2. Take her out to a romantic dinner
3. Surprise her with flowers
4. Write her a thoughtful letter
5. Buy her a heart shaped box of chocolates
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How is the Mars 2020 mission different from other missions?
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The Perseverance rover pioneers a drill that can collect core samples of the most promising rocks and soils, and set them aside in a "cache" on the surface of Mars. A future mission could potentially return these samples to Earth.
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Give me the list of books related with the series 'The Lord of the Rings', in order of publication
|
Here is the list of books related with the series 'The Lord of the Rings', in order of publication:
1. The Hobbit
2. The Fellowship of the Ring
3. The Two Towers
4. The Return of the King
5. The Silmarillion
6. Unfinished Tales of Numenor and Middle-earth
7. The Children of Hurin
8. Beren and Luthien
9. The Fall of Gondolin
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The optophone is a device, used by the blind, that scans text and generates time-varying chords of tones to identify letters. It is one of the earliest known applications of sonification. Dr. Edmund Fournier d'Albe of Birmingham University invented the optophone in 1913, which used selenium photosensors to detect black print and convert it into an audible output which could be interpreted by a blind person. The Glasgow company, Barr and Stroud, participated in improving the resolution and usability of the instrument.
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Extract from the text who invented the Optophone.
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Dr. Edmund Fournier d'Albe
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Give step by step instructions on how to make a Long Island Ice Tea.
|
Ingredients:
3/4 oz vodka
3/4 oz rum
3/4 oz gin
3/4 oz tequila
3/4 oz triple sec
3/4 oz simple syrup
3/4 oz lemon juice
Cola to fill
Lemon wedge to garnish
Steps:
1. Add the vodka, rum, gin, tequila, triple sec, simple syrup and lemon juice to a glass filled with ice.
2. Fill the glass with cola and stir.
3. Garnish with the lemon wedge and serve.
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Classify each of the following as something that should be kept private or shared publicly: credit card number, social security number, local news, weather forecast, bank account details
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Credit Card Number: Kept Private
Social Security Number: Kept Private
Local News: Shared Publicly
Weather Forecast: Shared Publicly
Bank Account Details: Kept Private
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| 252
|
Shared-private feature extractor is mainly used for extracting shared features and private features among different tasks. In this paper, we apply the encoder module of transformer BIBREF16 (henceforth, transformer encoder) to the shared-private extractor of our model. Specially, we employ two transformer encoders to encode the input embeddings of the two tasks as their respective private features. A transformer encoder is used to encode simultaneously the input embeddings of the two tasks as shared features of both tasks. This process is illustrated by the shared-private layer of Figure FIGREF6. The red box in the middle denotes the extraction of shared features and the left and right boxes represent the extraction of private features of two tasks. Next, we take the extraction of the private feature of fake news detection as an example to elaborate on the process of transformer encoder.
The kernel of transformer encoder is the scaled dot-product attention, which is a special case of attention mechanism. It can be precisely described as follows:
where ${\rm \textbf {Q}} \in \mathbb {R}^{l \times (d_p+d_w)}$, ${\rm \textbf {K}} \in \mathbb {R}^{l \times (d_p+d_w)}$, and ${\rm \textbf {V}} \in \mathbb {R}^{l \times (d_p+d_w)}$ are query matrix, key matrix, and value matrix, respectively. In our setting, the query ${\rm \textbf {Q}}$ stems from the inputs itself, i.e., ${\rm \textbf {Q}}={\rm \textbf {K}}={\rm \textbf {V}}={\rm \textbf {E}}$.
To explore the high parallelizability of attention, transformer encoder designs a multi-head attention mechanism based on the scaled dot-product attention. More concretely, multi-head attention first linearly projects the queries, keys and values $h$ times by using different linear projections. Then $h$ projections perform the scaled dot-product attention in parallel. Finally, these results of attention are concatenated and once again projected to get the new representation. Formally, the multi-head attention can be formulated as follows:
where ${\rm \textbf {W}}_i^Q \in \mathbb {R}^{(d_p+d_w) \times d_k}$, ${\rm \textbf {W}}_i^K \in \mathbb {R}^{(d_p+d_w) \times d_k}$, ${\rm \textbf {W}}_i^V \in \mathbb {R}^{(d_p+d_w) \times d_k}$ are trainable projection parameters. $d_k$ is $(d_p+d_w)/h$, $h$ is the number of heads. In Eq.(DISPLAY_FORM11), ${\rm \textbf {W}}^o \in \mathbb {R}^{(d_p+d_w) \times (d_p+d_w)}$ is also trainable parameter.
Shared-private feature extractor is mainly used for extracting shared features and private features among different tasks. In this paper, we apply the encoder module of transformer (Vaswani et al., 2017) (henceforth, transformer encoder) to the sharedprivate extractor of our model. Specially, we employ two transformer encoders to encode the input embeddings of the two tasks as their respective private features.
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What encoder module do they apply to the shared private extractor of the model?
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The encoder module of transformer.
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Identify and briefly discuss four “commandments” for effective presidential relations with the Congress. Do you think a president needs a good relationship with Congress to be an effective president?
|
There are ten commandments for effective presidential relationals. I will discuss four of these commandments. The first that I will discuss is that the president should respect Congress as an equal institution. President Obama said, “If Congress doesn’t act, I will.” He went on to use executive orders to enable the policy that he was trying to enact. The problem with this method is that Trump was then able to revoke those acts, undoing all the work that Obama accomplished. The second is bi-partisan support for Presidential programs. If both sides take ownership of an issue, it tends to make it easier to enact policy. Third, the President needs to be able to compromise. If he can’t compromise, he won’t be able to effectively negotiate a bill with Congress. Fourth is the President needs a sound program. If the program has no chance of success, then Congress won’t want to back it.
If we define a President as being effective when he creates lasting policy, then he obviously needs to have a good relationship with Congress. If he’s not able to work with Congress to pass legislation, then his only other option would be to use executive orders to enact policy, which will typically not live past his Presidency.
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By September 1987, Kishore Kumar had decided to retire as he was unhappy with kind of songs and tunes being made by music directors and was planning to return to his birthplace Khandwa.
On 13 October 1987, his brother Ashok Kumar's 76th birthday, he died of a heart attack in Bombay at 4:45 pm. His body was taken to Khandwa for cremation. Kishore had recorded his last song, "Guru Guru", a duet with Asha Bhosle for the film Waqt Ki Awaz (1988) composed by Bappi Lahiri for Mithun Chakraborty and Sridevi, the day before he died.
Recalling the day of Kishore Kumar's death and his last lines before he took his last breath, his wife Leena Chandavarkar was quoted as saying: "On the morning of October 13 (the day Kishore Kumar passed away in 1987), he looked pale and as though in deep sleep. As I went near him, he woke up and asked, ‘Did you get scared? Today is my holiday’.” That day he had several meetings at home. During lunch he told me that we’d watch the film River of No Return in the evening. A little later, I heard him move furniture in the next room. When I went to see what was happening, I saw him lying on the bed. Nervously he said, ‘I’m feeling weak’. I ran to call the doctor. He got angry and said, ‘If you call the doctor, I’ll get a heart-attack’. Those were his last lines. His eyes were wide open and he was breathing out. I thought he was fooling as usual but that was the end."
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From the passage provided, extract the name of the last song recorded by Kishore Kumar
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Guru Guru
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| 393
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The basic idea in binary paragraph vector models is to introduce a sigmoid nonlinearity before the softmax that models the conditional probability of words given the context. If we then enforce binary or near-binary activations in this nonlinearity, the probability distribution over words will be conditioned on a bit vector context, rather than real-valued representation. The inference in the model proceeds like in Paragraph Vector, except the document code is constructed from the sigmoid activations. After rounding, this code can be seen as a distributed binary representation of the document.
In the simplest Binary PV-DBOW model (Figure FIGREF1 ) the dimensionality of the real-valued document embeddings is equal to the length of the binary codes. Despite this low dimensional representation – a useful binary hash will typically have 128 or fewer bits – this model performed surprisingly well in our experiments.
Note that we cannot simply increase the embedding dimensionality in Binary PV-DBOW in order to learn better codes: binary vectors learned in this way would be too long to be useful in document hashing. The retrieval performance can, however, be improved by using binary codes for initial filtering of documents, and then using a representation with higher capacity to rank the remaining documents by their similarity to the query. BIBREF3 , for example, used semantic hashing codes for initial filtering and TF-IDF for ranking. A similar document retrieval strategy can be realized with binary paragraph vectors. Furthermore, we can extend the Binary PV-DBOW model to simultaneously learn short binary codes and higher-dimensional real-valued representations. Specifically, in the Real-Binary PV-DBOW model (Figure FIGREF2 ) we introduce a linear projection between the document embedding matrix and the sigmoid nonlinearity. During training, we learn the softmax parameters and the projection matrix. During inference, softmax weights and the projection matrix are fixed. This way, we simultaneously obtain a high-capacity representation of a document in the embedding matrix, e.g. 300-dimensional real-valued vector, and a short binary representation from the sigmoid activations.
One advantage of using the Real-Binary PV-DBOW model over two separate networks is that we need to store only one set of softmax parameters (and a small projection matrix) in the memory, instead of two large weight matrices. Additionally, only one model needs to be trained, rather than two distinct networks.
Binary document codes can also be learned by extending distributed memory models. BIBREF7 suggest that in PV-DM, a context of the central word can be constructed by either concatenating or averaging the document vector and the embeddings of the surrounding words. However, in Binary PV-DM (Figure FIGREF3 ) we always construct the context by concatenating the relevant vectors before applying the sigmoid nonlinearity. This way, the length of binary codes is not tied to the dimensionality of word embeddings.
Softmax layers in the models described above should be trained to predict words in documents given binary context vectors. Training should therefore encourage binary activations in the preceding sigmoid layers. This can be done in several ways. In semantic hashing autoencoders BIBREF3 added noise to the sigmoid coding layer. Error backpropagation then countered the noise, by forcing the activations to be close to 0 or 1. Another approach was used by BIBREF12 in autoencoders that learned binary codes for small images. During the forward pass, activations in the coding layer were rounded to 0 or 1. Original (i.e. not rounded) activations were used when backpropagating errors. Alternatively, one could model the document codes with stochastic binary neurons. Learning in this case can still proceed with error backpropagation, provided that a suitable gradient estimator is used alongside stochastic activations. We experimented with the methods used in semantic hashing and Krizhevsky's autoencoders, as well as with the two biased gradient estimators for stochastic binary neurons discussed by BIBREF13 . We also investigated the slope annealing trick BIBREF14 when training networks with stochastic binary activations. From our experience, binary paragraph vector models with rounded activations are easy to train and learn better codes than models with noise-based binarization or stochastic neurons. We therefore use Krizhevsky's binarization in our models.
The basic idea in binary paragraph vector models is to introduce a sigmoid nonlinearity before the softmax that models the conditional probability of words given the context.
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What is the basic idea in binary paragraph vector models?
|
The basic idea in binary paragraph vector models is to introduce a sigmoid nonlinearity before the softmax that models the conditional probability of words given the context.
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will AI replace my jobs
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AI is growing rapidly nowadays. It reduces repetitive work and improves productivity in a lot of industries. For sure, some labor-intensive work will be eliminated by AI. Some trends we can see for example are: AI-assisted surveillance monitoring can reduce the working hours for security guards; AI-generated images and renderings can be used for commercials or portraits; etc. But it will generate new job families too like prompt engineers who tests and tunes AI systems by sending prompts. AI application developers would be another example.
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Cena was born in West Newbury, Massachusetts on April 23, 1977, to Carol (née Lupien) and John Joseph Cena. His mother is of English and French-Canadian descent, while his father, a former ring announcer for Chaotic Wrestling, has Italian ancestry. His maternal grandfather was baseball player Tony Lupien, while his maternal great-grandfather was businessman Ulysses J. Lupien. He is the second oldest of five brothers: Dan, Matt, Steve, and Sean. A fan of wrestling growing up, he would create championship belts out of cardboard for himself and his brothers. He is a cousin of computer scientist Natalie Enright Jerger and was raised Roman Catholic. Cena was teased and beaten up while in school, and asked for a weightlifting bench at the age of 12.
Cena attended Central Catholic High School in Lawrence, Massachusetts, before transferring to Cushing Academy, a private prep boarding school in Ashburnham, Massachusetts. He then attended Springfield College in Springfield, Massachusetts, where he was an NCAA Division III All-American center and captain on their college football team, wearing the number 54. He graduated from Springfield College in 1999 with a degree in exercise physiology and body movement, after which he pursued a bodybuilding career and worked as a limousine driver. Cena worked in the store area of Gold's Gym for $6 an hour.
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Name the academic institutions which John Cena attended.
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Central Catholic High School, Cushing Academy, a private prep boarding school in Ashburnham, Massachusetts, Springfield College.
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Who are the top NFL Quarterbacks?
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Tom Brady
Peyton Manning
Drew Brees
Aaron Rodgers
Josh Allen
Patrick Mahomes
Jalen Hurts
Lamar Jackson
Russell Wilson
Philip Rivers
Eli Manning
Justin Herbert
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Outside of the United States, other top-earning territories as of July 31 were the United Kingdom ($6.2 million), Canada ($5.1 million), Australia ($4.5 million), Russia ($2.4 million), Taiwan ($2.3 million), Mexico ($2 million), Hong Kong ($1.7 million), Germany ($1.5 million), and the Netherlands ($1.1 million).
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Give a bulleted list of the amount of money the film "Everything Everywhere All at Once" earned outside of the US in the format {country}-{money earned in millions in USD}
|
United Kingdom-$6.2
Canada-$5.1
Australia-$4.5
Russia-$2.4
Taiwan-$2.4
Mexico-$2
Hong Kong-$1.7
Germany-$1.5
Netherlands-$1.1
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Give me a bulleted list of cricketers by highest number of international centuries scored
|
Sachin Tendulkar - 100
Virat Kohli - 75
Ricky Ponting - 71
Kumar Sangakkara - 63
Jacques Kallis - 62
Hashim Amla - 55
Mahela Jayawardene - 54
Brian Lara - 53
Rahul Dravid - 48
AB de Villiers - 47
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Who was the first human to walk on the moon?
|
Neil Armstrong was the first human to set foot on the moon.
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| 349
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After finishing constructing UIT-ViIC dataset, we have a look in statistical analysis on our corpus in this section. UIT-ViIC covers 3,850 images described by 19,250 Vietnamese captions. Sticking strictly to our annotation guidelines, the majority of our captions are at the length of 10-15 tokens. We are using the term “tokens” here as a Vietnamese word can consist of one, two or even three tokens. Therefore, to apply Vietnamese properly to Image Captioning, we present a tokenization tool - PyVI BIBREF17, which is specialized for Vietnamese language tokenization, at words level. The sentence length using token-level tokenizer and word-level tokenizer are compared and illustrated in Fig. FIGREF23, we can see there are variances there. So that, we can suggest that the tokenizer performs well enough, and we can expect our Image Captioning models to perform better with Vietnamese sentences that are tokenized, as most models perform more efficiently with captions having fewer words.
Table TABREF24 summarizes top three most occuring words for each part-of-speech. Our dataset vocabulary size is 1,472 word classes, including 723 nouns, 567 verbs, and 182 adjectives. It is no surprise that as our dataset is about sports with balls, the noun “bóng” (meaning “ball") occurs most, followed by “sân” and "cầu thủ" (“pitch” and “athlete” respectively). We also found that the frequency of word “tennis” stands out among other adjectives, which specifies that the set covers the majority of tennis sport, followed by “bóng chày” (meaning “baseball”). Therefore, we expect our model to generate the best results for tennis images.
We also found that the frequency of word “tennis” stands out among other adjectives, which specifies that the set covers the majority of tennis sport, followed by “bóng chày” (meaning “baseball”).
|
Which stands out among other adjectives in the frequency?
|
The word 'tennis' stands out.
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Why you should have 2 incomes
|
Because you do not know what will happen after 2 weeks with you in near future. So one income will be always give you backup.
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| 0
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In recent years, zero-shot translation in NMT has attracted widespread attention in academic research. Existing methods are mainly divided into four categories: pivot-based method, transfer learning, multilingual NMT, and unsupervised NMT.
Pivot-based Method is a common strategy to obtain a source$\rightarrow $target model by introducing a pivot language. This approach is further divided into pivoting and pivot-synthetic. While the former firstly translates a source language into the pivot language which is later translated to the target language BIBREF4, BIBREF5, BIBREF12, the latter trains a source$\rightarrow $target model with pseudo data generated from source-pivot or pivot-target parallel data BIBREF13, BIBREF14. Although the pivot-based methods can achieve not bad performance, it always falls into a computation-expensive and parameter-vast dilemma of quadratic growth in the number of source languages, and suffers from the error propagation problem BIBREF15.
Transfer Learning is firstly introduced for NMT by BIBREF6, which leverages a high-resource parent model to initialize the low-resource child model. On this basis, BIBREF7 and BIBREF8 use shared vocabularies for source/target language to improve transfer learning, while BIBREF16 relieve the vocabulary mismatch by mainly using cross-lingual word embedding. Although these methods are successful in the low-resource scene, they have limited effects in zero-shot translation.
Multilingual NMT (MNMT) enables training a single model that supports translation from multiple source languages into multiple target languages, even those unseen language pairs BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21. Aside from simpler deployment, MNMT benefits from transfer learning where low-resource language pairs are trained together with high-resource ones. However, BIBREF22 point out that MNMT for zero-shot translation easily fails, and is sensitive to the hyper-parameter setting. Also, MNMT usually performs worse than the pivot-based method in zero-shot translation setting BIBREF23.
Unsupervised NMT (UNMT) considers a harder setting, in which only large-scale monolingual corpora are available for training. Recently, many methods have been proposed to improve the performance of UNMT, including using denoising auto-encoder, statistic machine translation (SMT) and unsupervised pre-training BIBREF24, BIBREF25, BIBREF26, BIBREF11. Since UNMT performs well between similar languages (e.g., English-German translation), its performance between distant languages is still far from expectation.
Our proposed method belongs to the transfer learning, but it is different from traditional transfer methods which train a parent model as starting point. Before training a parent model, our approach fully leverages cross-lingual pre-training methods to make all source languages share the same feature space and thus enables a smooth transition for zero-shot translation.
Although the pivot-based methods can achieve not bad performance, it always falls into a computation-expensive and parameter vast dilemma of quadratic growth in the number of source languages, and suffers from the error propagation prob lem (Zhu et al. 2013).
|
What's the disadvantage of the pivot-based methods?
|
It always falls into a computation-expensive and parameter vast dilemma of quadratic growth in the number of source languages, and suffers from the error propagation prob lem (Zhu et al. 2013).
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Give me a bulleted list of the seven most recent Heisman Trophy Winners
|
Caleb Williams, Bryce Young, DeVonta Smith, Joe Burrow, Kyler Murray, Baker Mayfield, and Lamar Jackson
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John Bonham is considered the most influential drummers in rock. Bonham was ranked no. 1 on Classic Rock's 2005 list of 50 Greatest Drummers in Rock
|
Who is considered the greatest rock drummer?
|
John Henry Bonham (31 May 1948 – 25 September 1980) was an English musician, best known as the drummer for the rock band Led Zeppelin. Esteemed for his speed, power, fast single-footed kick drumming, distinctive sound, and feel for groove, he is regarded as one of the greatest and most influential drummers in music history.
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What is VTEC?
|
VTEC stands for "Variable Valve Timing & Lift Electronic Control." It is a technology used by some Honda cars for changing the way an engine runs based on the engine speeds. It works by using oil pressure to activate a different camshaft profile at higher engine speeds. The camshaft profile allows more air to enter the engine cylinders, and thus causes more effective combustion and more power. At lower engine speeds, VTEC disengages and uses a default camshaft profile, which is optimized for fuel efficiency.
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| 339
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Figure FIGREF4 specifies the meta elements of the nlpFSpL which are used by the FExSys while interpreting other features.
Analysis Unit (AU) specifies level at which features have to be extracted. At Corpus level, features are extracted for all the text documents together. At Document level, features are extracted for each document in corpus separately. At Para (paragraph) level Features are extracted for multiple sentences constituting paragraphs together. At Sentence level features to be extracted for each sentence. Figure FIGREF6 depicts classes of features considered in nlpFSpL and their association with different AUs.
Syntactic Unit (SU) specifies unit of linguistic features. It could be a `Word' or a `Phrase', or a `N-gram' or a sequence of words matching specific lexico-syntactic pattern captured as `POS tag pattern' (e.g., Hearst pattern BIBREF15) or a sequence of words matching specific regular expression `Regex' or a combination of these. Option Regex is used for special types of terms, e.g., Dates, Numbers, etc. LOGICAL is a Boolean logical operator including AND, OR and NOT (in conjunction with other operator). For example, Phrase AND POS Regex would specify inclusion of a `Phrase' as SU when its constituents also satisfy 'regex' of `POS tags'. Similarly, POS Regex OR NOT(Regex) specifies inclusion of sequence of words as SU if it satisfies `POS tag Pattern' but does not match pattern specified by character `Regex'. Note that SU can be a feature in itself for document and corpus level analysis.
Normalize Morphosyntactic Variants: If YES, variants of words including stems, lemmas, and fuzzy matches will be identified before analyzing input text for feature exaction and would be treated equivalent.
Normalize Morphosyntactic Variants: If YES, variants of words including stems, lemmas, and fuzzy matches will be identified before analyzing input text for feature exaction and would be treated equivalent.
|
What are the Normalize Morphosyntactic Variants?
|
Normalize Morphosyntactic Variants: If YES, variants of words including stems, lemmas, and fuzzy matches will be identified before analyzing input text for feature exaction and would be treated equivalent.
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Where is the town of Guasipati
|
Guasipati is a town that serves as the administrative seat of Roscio Municipality in the state of Bolívar, Venezuela. It was founded on 27 September 1757. Its economy depends highly on the gold of its land and that of El Callao.The city is located in the vicinity of the Denguesito ríachuelos to the northwest and the Cunurí to the west, both tributaries of the Miamo river. According to data from the National Institute of Statistics (census 2011) the population of Guasipati is 21,165 inhabitants.
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Sensory integration difficulties or sensory processing disorder (SPD) are characterized by persistent challenges with neurological processing of sensory stimuli that interfere with a person's ability to participate in everyday life. Such challenges can appear in one or several sensory systems of the somatosensory system, vestibular system, proprioceptive system, interoceptive system, auditory system, visual system, olfactory system, and gustatory system.
While many people can present one or two symptoms, sensory processing disorder has to have a clear functional impact on the person's life:
Signs of over-responsivity, including, for example, dislike of textures such as those found in fabrics, foods, grooming products or other materials found in daily living, to which most people would not react, and serious discomfort, sickness or threat induced by normal sounds, lights, ambient temperature, movements, smells, tastes, or even inner sensations such as heartbeat.Signs of under-responsivity, including sluggishness and lack of responsiveness.
Sensory cravings, including, for example, fidgeting, impulsiveness, and/or seeking or making loud, disturbing noises; and sensorimotor-based problems, including slow and uncoordinated movements or poor handwriting.
Sensory discrimination problems, which might manifest themselves in behaviors such as things constantly dropped.
Symptoms may vary according to the disorder's type and subtype present.
Sensory integration and processing difficulties can be a feature of a number of disorders, including anxiety problems, attention deficit hyperactivity disorder (ADHD),food intolerances, behavioural disorders, and particularly, autism spectrum disorders. This pattern of comorbidities poses a significant challenge to those who claim that SPD is an identifiably specific disorder, rather than simply a term given to a set of symptoms common to other disorders.
Two studies have provided preliminary evidence suggesting that there may be measurable neurological differences between children diagnosed with SPD and control children classified as neurotypical or children diagnosed with autism. Despite this evidence, that SPD researchers have yet to agree on a proven, standardized diagnostic tool undermines researchers' ability to define the boundaries of the disorder and makes correlational studies, like those on structural brain abnormalities, less convincing.
|
What are the signs and symptoms of Sensory processing disorder?
|
While many people with Sensory processing disorder can present one or two symptoms, it has to have a clear functional impact on the person's life:
Signs of over-responsivity, including, for example, dislike of textures such as those found in fabrics, foods, grooming products or other materials found in daily living, to which most people would not react, and serious discomfort, sickness or threat induced by normal sounds, lights, ambient temperature, movements, smells, tastes, or even inner sensations such as heartbeat.Signs of under-responsivity, including sluggishness and lack of responsiveness.
Sensory cravings, including, for example, fidgeting, impulsiveness, and/or seeking or making loud, disturbing noises; and sensorimotor-based problems, including slow and uncoordinated movements or poor handwriting.
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Beavers (genus Castor) are large, semiaquatic rodents of the Northern Hemisphere. There are two existing species: the North American beaver (Castor canadensis) and the Eurasian beaver (C. fiber). Beavers are the second-largest living rodents, after capybaras, weighing up to 30 kilograms (66 lb). Beavers have stout bodies with large heads, long chisel-like incisors, brown or gray fur, hand-like front feet, webbed back feet, and tails that are flat and scaly. The two species differ in skull and tail shape and fur color. Beavers can be found in a number of freshwater habitats, such as rivers, streams, lakes and ponds. They are herbivorous, consuming tree bark, aquatic plants, grasses and sedges.
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Given this text give me short summary about Beavers
|
Here is a short summary about Beavers:
There are two existing species - North American and Eurasian.
They are the second-largest living rodents.
Physically species of Beaver can vary in terms of fur colour but they all have webbed back feet, hand-like front feet and flat scaly tails. Long chisel-like incisors are a key feature.
Beavers can be found in freshwater habits.
They are herbivores.
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How does meditation help?
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Meditation helps relax the mind by helping it focus on a specific object (such as breathing). This brings the mind to that object by cutting down the noise of multitude of thoughts that keep swarming in our minds. Meditation also helps train the mind to focus on a topic and be more present in conversations, with people or at work. Finally, it's helps reduce stress too as the mind does not spend too much time circling on negative thoughts.
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when was the 2nd world war?
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It started in 1939 and ended in 1945
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A glass ceiling is a metaphor usually applied to women, used to represent an invisible barrier that prevents a given demographic from rising beyond a certain level in a hierarchy. No matter how invisible the glass ceiling is expressed, it is actually a difficult obstacle to overcome. The metaphor was first used by feminists in reference to barriers in the careers of high-achieving women. It was coined by Marilyn Loden during a speech in 1978. In the United States, the concept is sometimes extended to refer to racial inequality in the United States. Minority women in white-majority countries often find the most difficulty in "breaking the glass ceiling" because they lie at the intersection of two historically marginalized groups: women and people of color. East Asian and East Asian American news outlets have coined the term "bamboo ceiling" to refer to the obstacles that all East Asian Americans face in advancing their careers. Similarly, a multitude of barriers that refugees and asylum seekers face in their search for meaningful employment is referred to as canvas ceiling.
|
Surely, the glass ceiling only applies to politics. Corporate organizations are fine. Is that true?
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No, this is incorrect. The glass ceiling affects all facet of life with hierarchical structure, such as political and career advancements.
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Name some of the bravolebrities from The Real Housewives of New Jersey
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Dolores Catania, Jackie Goldschneider, Jennifer Aydin, Margaret Josephs, Melissa Gorga and Teresa Giudice
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1810.03459
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In this work, the experiments are conducted using the BABEL speech corpus collected from the IARPA babel program. The corpus is mainly composed of conversational telephone speech (CTS) but some scripted recordings and far field recordings are presented as well. Table TABREF14 presents the details of the languages used in this work for training and evaluation.
In this work, the experiments are conducted using the BABEL speech corpus collected from the IARPA babel program.
|
What data do they train the language models on?
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The answers are shown as follows:
* BABEL speech corpus
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What is life like living underwater?
|
At first, the brilliance of all the colors was my favorite part. The amount of variety under the water is breathtaking. I love that what I see every day changes; it's based upon what is swimming by. However, I long for a hike up into elevation. Hearing the sound of gravel crunch beneath my hiking boots is no longer available. You also lose any semblance of the four seasons. One day blurs with the next and the next. It's always a comfortable temperature, but after some time, that gets old too.
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What is SWIFT in banking?
|
SWIFT (Society for Worldwide Interbank Financial Telecommunications) is a global member-owned cooperative that functions as a huge messaging system. Members (banks and other financial institutions) use it to quickly, accurately, and securely send and receive information, primarily money transfer instructions.
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The Kentucky Derby Trophy is a set of four trophies that are awarded to the winning connections of America's most famous race: the grade one $3,000,000 Kentucky Derby. The owner receives a gold trophy while the trainer, the jockey and the breeder win a silver half size replica of the main gold trophy. The trophy itself has been run for since the 50th running of the Kentucky Derby in 1924. Churchill Downs Race Course of Louisville, Kentucky has annually presented a gold trophy to the winning owner of the famed "Run for the Roses."
|
What is the Kentucky Derby Trophy
|
The Kentucky Derby Trophy is a set of four trophies that are awarded to the winning connections of America's most famous race: the grade one $3,000,000 Kentucky Derby. The owner receives a gold trophy while the trainer, the jockey and the breeder win a silver half size replica of the main gold trophy. The trophy itself has been run for since the 50th running of the Kentucky Derby in 1924. Churchill Downs Race Course of Louisville, Kentucky has annually presented a gold trophy to the winning owner of the famed "Run for the Roses."
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What are the different kind of hits in baseball?
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The different kind of hits in baseball are a single, double, triple, and home run.
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What is Barkers Butts RFC?
|
Barkers Butts RFC (Rugby Football Club) is a rugby union club in the north west of Coventry, England. It is over 75 years old and operates four senior teams including a ladies team, a Colts team, a full range of boy and girls teams from Under 6s to Under 16s. The first XV currently play in Midlands 2 West (South), a seventh tier league in the English rugby union system.
When the league system was established in 1987, Barkers Butts was placed in the fifth tier, playing in Midlands 1 and were runners up in the first season (1987-88). They finished in second place twice more (1990–91 and 1992–93) but were relegated from tier 5, then called Courage League Division 5, after the 1994-95 season. Subsequently, they have alternated between the sixth and seventh levels.
Barkers Butts has produced several notable international players including one World Cup winner in Neil Back. Other recent mens' internationals include Tom Wood and Jim Hamilton. Barkers currently have two players in the England Women U20s squad.
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I am going to Stockholm over the weekend, what should I do? I love going to museums and being in nature!
|
Stockholm has some amazing spots and museums to visit! Here is a list with some example activities you can pick from:
- Visit the archipelago: There is a vast archipelago close to Stockholm. It is easy to reach by taking a boat from central Stockholm. The nature there is amazing and remember to bring swimming clothes.
- Djurgården island: This is a big Island in the middle of Stockholm located in the middle of Stockholm with big parks and some of the most famous museums.
- Old town: At the heart of Stockholm, you have the old town, which is very popular for tourists all the year. You can visit cozy restaurants and coffee places or take a look at the royal castle.
- The vasa museum: Located on Djurgården island, it has one of the most well preserved 17th century ships in the world.
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| 116
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The new generation of Neural Machine Translation (NMT) systems is known to be extremely data hungry BIBREF0 . Yet, most existing NMT training pipelines fail to fully take advantage of the very large volume of monolingual source and/or parallel data that is often available. Making a better use of data is particularly critical in domain adaptation scenarios, where parallel adaptation data is usually assumed to be small in comparison to out-of-domain parallel data, or to in-domain monolingual texts. This situation sharply contrasts with the previous generation of statistical MT engines BIBREF1 , which could seamlessly integrate very large amounts of non-parallel documents, usually with a large positive effect on translation quality.
Such observations have been made repeatedly and have led to many innovative techniques to integrate monolingual data in NMT, that we review shortly. The most successful approach to date is the proposal of BIBREF2 , who use monolingual target texts to generate artificial parallel data via backward translation (BT). This technique has since proven effective in many subsequent studies. It is however very computationally costly, typically requiring to translate large sets of data. Determining the “right” amount (and quality) of BT data is another open issue, but we observe that experiments reported in the literature only use a subset of the available monolingual resources. This suggests that standard recipes for BT might be sub-optimal.
This paper aims to better understand the strengths and weaknesses of BT and to design more principled techniques to improve its effects. More specifically, we seek to answer the following questions: since there are many ways to generate pseudo parallel corpora, how important is the quality of this data for MT performance? Which properties of back-translated sentences actually matter for MT quality? Does BT act as some kind of regularizer BIBREF3 ? Can BT be efficiently simulated? Does BT data play the same role as a target-side language modeling, or are they complementary? BT is often used for domain adaptation: can the effect of having more in-domain data be sorted out from the mere increase of training material BIBREF2 ? For studies related to the impact of varying the size of BT data, we refer the readers to the recent work of BIBREF4 .
To answer these questions, we have reimplemented several strategies to use monolingual data in NMT and have run experiments on two language pairs in a very controlled setting (see § SECREF2 ). Our main results (see § SECREF4 and § SECREF5 ) suggest promising directions for efficient domain adaptation with cheaper techniques than conventional BT.
To answer these questions, we have reimplemented several strategies to use monolingual data in NMT and have run experiments on two language pairs in a very controlled setting (see § SECREF2 ).
|
How many language pairs were used in the experiments?
|
Two.
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Why do some babies need to stay in the NICU?
|
Babies may need to be admitted to the NICU for a number of reasons after being born. The primary reason is premature birth, which can cause a number of complications such as respiratory distress syndrome, hypoglycemia, apnea, low birth weight and bradycardia. A baby will typically stay in the NICU until their complications are resolved and they are healthy enough to be discharged.
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Give me a list of actors who won an Oscar in the last 5 years and tell me for which movie they won it.
|
1. Brendan Fraser, 2022, The Whale
2. Will Smith, 2021, King Richard
3. Anthony Hopkins, 2020, The father
4. Joaquin Phoenix, 2019, Joker
5. Rami Malek, 2018, Bohemian Rhapsody
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Name some famous Sci-Fi movies
|
Some famous Sci-Fi movies are Star Wars, 2001: A Space Odyssey and Blade Runner
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What is the capital of Brazil?
|
Brasília is the capital of Brazil
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How should I prioritize my time?
|
There's many ways to do this. A good rule of thumb is - how you spend your time should reflect your priorities. So I'd recommend
- List out your highest priority goals in the part(s) of your life you're looking to optimize your time eg: time for yourself, time with family, time with friends, time at work etc
- Do a sanity check on how you spend your time currently and see if it is out of alignment. You'll then know where to make adjustments
- Periodically (eg: every few months), repeat this exercise to course correct as things change in your life
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What famous surfers have died while surfing?
|
There are many surfers that have died doing what they love. Some of the most famous are Mark Foo, Dickie Cross, Marcio Freire, Sion Milosky, and Kirk Passmore
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What is bubble tea?
|
Bubble tea is a tea-based drink that originated in Taiwan in the early 1980s. Taiwanese immigrants brought it to the United States in the 1990s, initially in California through regions like Los Angeles County, but the drink has also spread to other countries where there is a large East Asian diaspora population.
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Daniel Leavitt was born November 16, 1813, at Rye, New Hampshire, the son of Benning Leavitt, influential businessman, state senator, county commissioner and later Chicopee Selectman, and his wife Olive (Jenness) Leavitt. Daniel Leavitt married in 1838 at West Springfield, Massachusetts, Ruth Jeannette Ball. They had three children.
|
Extract all of the dates mentioned in this paragraph and list them using bullets in the format {Date} - {Description}
|
• November 16, 1813 - This is when Daniel Leavitt was born
• 1838 - This is when Daniel Leavitt married his wife Ruth Jeannette Ball.
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| 0
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Although Neural Machine Translation (NMT) has dominated recent research on translation tasks BIBREF0, BIBREF1, BIBREF2, NMT heavily relies on large-scale parallel data, resulting in poor performance on low-resource or zero-resource language pairs BIBREF3. Translation between these low-resource languages (e.g., Arabic$\rightarrow $Spanish) is usually accomplished with pivoting through a rich-resource language (such as English), i.e., Arabic (source) sentence is translated to English (pivot) first which is later translated to Spanish (target) BIBREF4, BIBREF5. However, the pivot-based method requires doubled decoding time and suffers from the propagation of translation errors.
One common alternative to avoid pivoting in NMT is transfer learning BIBREF6, BIBREF7, BIBREF8, BIBREF9 which leverages a high-resource pivot$\rightarrow $target model (parent) to initialize a low-resource source$\rightarrow $target model (child) that is further optimized with a small amount of available parallel data. Although this approach has achieved success in some low-resource language pairs, it still performs very poorly in extremely low-resource or zero-resource translation scenario. Specifically, BIBREF8 reports that without any child model training data, the performance of the parent model on the child test set is miserable.
In this work, we argue that the language space mismatch problem, also named domain shift problem BIBREF10, brings about the zero-shot translation failure in transfer learning. It is because transfer learning has no explicit training process to guarantee that the source and pivot languages share the same feature distributions, causing that the child model inherited from the parent model fails in such a situation. For instance, as illustrated in the left of Figure FIGREF1, the points of the sentence pair with the same semantics are not overlapping in source space, resulting in that the shared decoder will generate different translations denoted by different points in target space. Actually, transfer learning for NMT can be viewed as a multi-domain problem where each source language forms a new domain. Minimizing the discrepancy between the feature distributions of different source languages, i.e., different domains, will ensure the smooth transition between the parent and child models, as shown in the right of Figure FIGREF1. One way to achieve this goal is the fine-tuning technique, which forces the model to forget the specific knowledge from parent data and learn new features from child data. However, the domain shift problem still exists, and the demand of parallel child data for fine-tuning heavily hinders transfer learning for NMT towards the zero-resource setting.
In this paper, we explore the transfer learning in a common zero-shot scenario where there are a lot of source$\leftrightarrow $pivot and pivot$\leftrightarrow $target parallel data but no source$\leftrightarrow $target parallel data. In this scenario, we propose a simple but effective transfer approach, the key idea of which is to relieve the burden of the domain shift problem by means of cross-lingual pre-training. To this end, we firstly investigate the performance of two existing cross-lingual pre-training methods proposed by BIBREF11 in zero-shot translation scenario. Besides, a novel pre-training method called BRidge Language Modeling (BRLM) is designed to make full use of the source$\leftrightarrow $pivot bilingual data to obtain a universal encoder for different languages. Once the universal encoder is constructed, we only need to train the pivot$\rightarrow $target model and then test this model in source$\rightarrow $target direction directly. The main contributions of this paper are as follows:
We propose a new transfer learning approach for NMT which uses the cross-lingual language model pre-training to enable a high performance on zero-shot translation.
We propose a novel pre-training method called BRLM, which can effectively alleviates the distance between different source language spaces.
Our proposed approach significantly improves zero-shot translation performance, consistently surpassing pivoting and multilingual approaches. Meanwhile, the performance on supervised translation direction remains the same level or even better when using our method.
Our proposed approach significantly improves zero-shot translation performance, consistently surpassing pivoting and multilingual approaches. Meanwhile, the performance on supervised translation direction remains the same level or even better when using our method.
|
What are the strengths of the proposed approach?
|
Their proposed approach significantly improves zero-shot translation performance, consistently surpassing pivoting and multilingual approaches. Meanwhile, the performance on supervised translation direction remains the same level or even better when using their method.
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Identify which instrument is string or percussion: Dholak, Sitar
|
Sitar is string, Dholak is percussion.
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| 305
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Human learning occurs through interaction BIBREF0 and multimodal experience BIBREF1 , BIBREF2 . Prior work has argued that machine learning may also benefit from interactive, multimodal learning BIBREF3 , BIBREF4 , BIBREF5 , termed virtual embodiment BIBREF6 . Driven by breakthroughs in static, unimodal tasks such as image classification BIBREF7 and language processing BIBREF8 , machine learning has moved in this direction. Recent tasks such as visual question answering BIBREF9 , image captioning BIBREF10 , and audio-video classification BIBREF11 make steps towards learning from multiple modalities but lack the dynamic, responsive signal from exploratory learning. Modern, challenging tasks incorporating interaction, such as Atari BIBREF12 and Go BIBREF13 , push agents to learn complex strategies through trial-and-error but miss information-rich connections across vision, language, sounds, and actions. To remedy these shortcomings, subsequent work introduces tasks that are both multimodal and interactive, successfully training virtually embodied agents that, for example, ground language in actions and visual percepts in 3D worlds BIBREF3 , BIBREF4 , BIBREF14 .
For virtual embodiment to reach its full potential, though, agents should be immersed in a rich, lifelike context as humans are. Agents may then learn to ground concepts not only in various modalities but also in relationships to other concepts, i.e. that forks are often in kitchens, which are near living rooms, which contain sofas, etc. Humans learn by concept-to-concept association, as shown in child learning psychology BIBREF1 , BIBREF2 , cognitive science BIBREF15 , neuroscience BIBREF16 , and linguistics BIBREF17 . Even in machine learning, contextual information has given rise to effective word representations BIBREF8 , improvements in recommendation systems BIBREF18 , and increased reward quality in robotics BIBREF19 . Importantly, scale in data has proven key in algorithms learning from context BIBREF8 and in general BIBREF20 , BIBREF21 , BIBREF22 .
To this end, we present HoME: the Household Multimodal Environment (Figure 1 ). HoME is a large-scale platform for agents to navigate and interact within over 45,000 hand-designed houses from the SUNCG dataset BIBREF23 . Specifically, HoME provides:
HoME is a general platform extensible to many specific tasks, from reinforcement learning to language grounding to blind navigation, in a real-world context. HoME is also the first major interactive platform to support high fidelity audio, allowing researchers to better experiment across modalities and develop new tasks. While HoME is not the first platform to provide realistic context, we show in following sections that HoME provides a more large-scale and multimodal testbed than existing environments, making it more conducive to virtually embodied learning in many scenarios.
Specifically, HoME provides: HoME is a general platform extensible to many specific tasks, from reinforcement learning to language grounding to blind navigation, in a real-world context.
|
What tasks does the general platform expand?
|
The general platform could extense to many specific tasks, from reinforcement learning to language grounding to blind navigation.
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| 476
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We start by evaluating our proposed diversity approach on two image datasets: CIFAR10 and CI-FAR100. We use our approach on three state-of-the-art CNNs: ResNext-29-08-16: we consider the standard ResNext Model with a 29-layer architecture, a cardinality of 8, and a width of 16. DenseNet-12: we use DenseNet with the 40layer architecture and a growth rate of 12. ResNet50: we consider the standard ResNet model with 50 layers. We compare against the standard networks 2 as well as networks trained with DeCov diversity strategy. For each approach the hyperparameters are selected based on the validation set. The full experimental setup is presented in the Appendix. We report the average performance over three random seeds. We note that, compared to the standard approach, employing a diversity strategy generally boosts the results for all the three models and that our approach consistency outperforms both competing For the hyperparameters, we use λ = 0.001 and γ = 10. The full experimental setup is presented in the Appendix. Table reports the test errors of the different diversity strategies. To study the effect of diversity on the generalization gap, we also report the final training errors and the generalization gap, i.e., training accuracy -test accuracy. As it can be seen, diversity (our approach and DeCov) reduces the test error of the model and yields a better performance. We note that, in accordance with our theoretical findings in Section 2, using diversity indeed reduces overfitting and decreases the empirical generalisation gap of neural networks. In fact, our logdet variant reduces the empirical generalization gap of the model by 1.8% compared to the standard approach. We note that our approach has a small additional time cost. For example for ResNet50, our direct, det and logdet variants take only 0.29%, 0.39%, and 0.49% extra training time, respectively.
Figure: Sensitivity analysis of λ and γ on both the model accuracy and its generalization ability using ResNet50 trained on ImageNet.
Sensitivity analysis: To further investigate the effect of the proposed diversity strategy, we conduct a sensitivity analysis using ImageNet on the hyperparameters of our methods: γ, which is the RBF parameter used to measure the pairwise similarity between two units, and λ, which controls the contribution of the global diversity term to the global loss. We analyse the effect of the two parameters on both the final performance of the models and its generalization ability. The analysis is presented in Figure and in supplementary material. As it can be seen, promoting the within-layer diversity consistently reduces overfitting and decreases the generalization gap for most of the hyperparameters values. Moreover, we note that global modeling of diversity, i.e., det and logdet variants, yield tighter generalization gaps compared to the non-global direct approach. It is worth noting that Figure shows that there is a trade-off between the generalization gap and the final error. Emphasizing diversity and using a high weight for the diversity term significantly decreases the generalization gap, but this damages the performance of the model compared to the standard approach. For lower values of λ, the model is able to significantly outperform the standard approach on both the test error and the generalization gap.
Connection to Theory: In Section 2, we provided theoretical bounds for the generalization errors of neural networks, which are inversely proportional to the diversity term, d min , the lower bound of
To show that the proposed regularizer indeed improves diversity, we track the empirical average of the aforementioned variable, i.e.,
2 during the training. The results for both the standard approach and the logdet variant of our approach are reported in Figure. As it can be seen, our regularizer yields in higher diversity which reduces overfitting and leads to better generalization. Compatibility with Dropout: Dropout) is a popular regularization technique that, similarly to our approach, is applied on top of the layer output and has been known to improve generalization and reduce overfitting. Here, we evaluate the compatibility of our approach with Dropout. We add a Dropout regularizer with 20% rate on top the last intermediate representation layer of both ResNet50 and Wide-ResNet50. The results are reported in Table. We note that adding a diversity regularizer alongside the Dropout consistently yields lower error rates compared to only Dropout except for the det variant on Wide-ResNet50. For Wide-ResNet50, the combination of our logdet variant and Dropout leads to 0.53%, 0.25%, and 0.38% improvement compared to standard approach, Dropout only, and our logdet variant only, respectively.
Beyond CNN models, we also evaluate the performance of our diversity strategy on modern attention-free, multi-layer perceptron (MLP) based models for image classification. Such models are known to exhibit high overfitting and require regularization. We evaluate how diversity affects the accuracy of such models on CIFAR10. In particular, we conduct a simple experiment using two models: MLP-Mixer, gMLP with four blocks each. The full description of the experimental setup is presented in the Appendix. The results in Table show that employing a diversity strategy can indeed improve the performance of these models thanks to its ability to learn rich and robust representation of the input.
Transfer learning: Beyond standard classification, the proposed approach can be useful, e.g., in transfer learning, where the main goal is to 'transfer' previously learned representation to solve new tasks. Thus, learning a rich and diverse representation is beneficial and can lead to better transferability. To demonstrate this, we conduct another experiment, where we use ImageNet-pretrained ResNet50 models with the different diversity approaches and we finetune them to CIFAR10 and CIFAR100. The results are reported in Table. As it can be seen, employing a diversity strategy helps in the transfer learning context and leads consistently to lower error rates. For example, the log variant of our approach leads to 0.94% and 1.27% gains on CIFAR10 and CIFAR100, respectively. To further demonstrate the usefulness of promoting diversity, we test the robustness of our approach in the presence of label noise. In such situations, standard neural network tend to overfit to the noisy sample and not generalize well to the test set. Enforcing diversity can lead to better and richer representation attenuating the effect of noise. To show this, we performed additional experiments with label noise (20% and 40%) on CIFAR10 and CIFAR100 using ResNet50. The results are reported in Table. As it can be seen, in the presence of noise, the gap between the standard approach and diversity (Decov and ours) increases. For example, our logdet variant boosts the results by 1.71% and 3.59% on CIFAR10 and CIFAR100 with 40% noise, respectively.
For the standard approach, the only difference is not using an additional diversity loss. The remaining
regularizers, data augmentation, weight decay etc, are all applied as specified per-experiment.
|
What exactly is “Standard Training” in Section 5?
|
For standard approach, the only difference is not using an additional diversity loss. The remaining regularizers, e.g., data augmentation, weight decay etc, are all applied as specified per-experiment. we clarified this in the updated manuscript.
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Though the term large language model has no formal definition, it often refers to deep learning models having a parameter count on the order of billions or more. LLMs are general purpose models which excel at a wide range of tasks, as opposed to being trained for one specific task (such as sentiment analysis, named entity recognition, or mathematical reasoning). The skill with which they accomplish tasks, and the range of tasks at which they are capable, seems to be a function of the amount of resources (data, parameter-size, computing power) devoted to them, in a way that is not dependent on additional breakthroughs in design.
Though trained on simple tasks along the lines of predicting the next word in a sentence, neural language models with sufficient training and parameter counts are found to capture much of the syntax and semantics of human language. In addition, large language models demonstrate considerable general knowledge about the world, and are able to "memorize" a great quantity of facts during training.
|
What is the formal definition of a large language model?
|
The term large language model (LLM) does not have a formal definition. However, it often refers to deep learning models having a parameter count on the order of billions or more.
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What is Pura Vida?
|
Pura Vida is a phrase used in Costa Rica and exactly translates to Pure Life, however it is a style of living of people from Costa Rica, and it means that "everything is fine" or even "amazing". It is about waking up every day and remembering that just waking up it is a good reason to be happy and satisfied in life, having a chance to enjoy every moment.
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What is POP3 protocol?
|
Post Office Protocol 3 (POP3) is the most commonly used protocol for receiving email over the internet. This standard protocol, which most email servers and their clients support, is used to receive emails from a remote server and send to a local client.
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| 265
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Categories such as animal or furniture are fundamental cognitive building blocks allowing humans to efficiently represent and communicate the complex world around them. Concepts (e.g., dog, table) are grouped into categories based on shared properties pertaining, for example, to their behavior, appearance, or function. Categorization underlies other cognitive functions such as perception BIBREF0 , BIBREF1 or language BIBREF2 , BIBREF3 , and there is evidence that categories are not only shaped by the world they represent, but also by the language through which they are communicated BIBREF4 , BIBREF5 . Although mental categories exist across communities and cultures, their exact manifestations differ BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 . For example, American English speakers prefer taxonomic categorizations (e.g., mouse,squirrel) while Chinese speakers tend to prefer to categorize objects relationally (e.g., tree, squirrel; BIBREF7 ).
Given their prevalent function in human cognition, the acquisition and representation of categories has attracted considerable attention in cognitive science, and numerous theories have emerged BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 . Empirical studies of category acquisition and representation, have been predominantly based on small-scale laboratory experiments. In a typical experiment, human subjects are presented with small sets of often artificial concepts, such as binary strings BIBREF14 or colored shapes BIBREF15 , with strictly controlled features BIBREF16 , BIBREF17 , BIBREF18 . Hypotheses and principles of human categorization are established based on the processes and characteristics of the categorizations produced by the participants. The distribution of subjects participating in such studies is often skewed towards members of cultural and socioeconomic groups which are prevalent in the environment where the research is conducted, and typically consists to a large proportion of western, educated, wealthy and English-speaking participants often sampled from the even more specific population of college students. The demographic and socioeconomic bias has been long recognized, and the question of how this bias might impact conclusions about human cognition in general BIBREF19 and category learning specifically is under active debate BIBREF9 . Although laboratory studies are invaluable for understanding categorization phenomena in a controlled environment, they are also expensive and time-consuming to conduct, and consequently problematic to scale.
In this work, we scale the investigation of category learning and representation along two axes: (1) the complexity of the learning environment, and consequently the richness of learnable concept and category representations, and (2) the diversity of languages and cultures considered in evaluation. We present a novel knowledge-lean, cognitively motivated Bayesian model which learns categories and their structured features jointly from large natural language text corpora in five diverse languages: Arabic, Chinese, English, French, and German. We approximate the learning environment using large corpora of natural language text. Language has been shown to redundantly encode much of the non-linguistic information in the natural environment BIBREF20 , and to influence the emergence of categories BIBREF4 , BIBREF5 . Besides text corpora can cover arbitrarily semantically complex domains, and are available across languages, providing an ideal test environment for studying categorization at scale.
Figure 1 illustrates example input to our model, and Figure 2 shows example categories and associated features as induced by our model from the English Wikipedia. Following prior work BIBREF21 , BIBREF22 , we create language-specific sets of stimuli, each consisting of a mention of target concept (e.g., apple), within its local linguistic context (e.g., {contains, seeds}; cf., Figure 1 ). We consider each stimulus an observation of the concept, i.e., the word referring to the concept is an instance of the concept itself, and its context words are a representation of its features. Our model infers categories as groups of concepts occurring with similar features; and it infers feature types as groups of features which co-occur with each other. The output of our model (cf., Figure 2 ) are categories as clusters of concepts, each associated with a set of feature types, i.e., thematically coherent groups of features. We train a separate model on each of our target languages, each time presenting the model with input stimuli from the relevant language.
Computational models in general, and Bayesian models in particular, allow to investigate hypotheses about cognitive phenomena by systematically modifying the learning mechanism or available input while observing the learning outcome. Bayesian models have been applied to a variety of cognitive phenomena BIBREF23 , BIBREF24 , BIBREF25 , and category acquisition is no exception. Following from Anderson's BIBREF14 , BIBREF26 , BIBREF27 seminal work, a number of models have been developed, and tested in their ability to reproduce human behavior in laboratory settings by exposing the models to small sets of controlled inputs with restricted features. In this work we draw on the full potential of computational modeling by exposing our models to (a) more complex data reflecting the diversity of contexts in which concepts can be observed; and (b) a input data in different languages, shedding light on the applicability of computational cognitive models beyond the prevalent English test language.
Categorization tasks in a laboratory environment typically involve stimuli with a small set of features which are relevant to the categorization target, eliminating the need to detect features, and discriminate them in their relevance. In the real world, however, concepts are observed in contexts and a substantial part of acquiring categorical knowledge involves learning which features are useful to discriminate among concepts. In fact, research has shown that humans learn features jointly with categories BIBREF28 , BIBREF29 and that these features are themselves structured so as to represent the diversity and complexity of the properties exhibited in the world BIBREF30 , BIBREF31 , BIBREF32 . Our novel model of category learning presented in this article, jointly learns categories and their structured features from large sets of informationally rich data.
Our work exemplifies the opportunities that arise from computational models and large data sets for investigating the mechanisms with which conceptual representations emerge, as well as the representations themselves in a broader context. We simulate the acquisition of categories comprising hundreds of concepts by approximating the learning environment with natural language text. Language has been shown to redundantly encode much of the non-linguistic information in the natural environment BIBREF20 , as well as human-like biases BIBREF33 , and to influence the emergence of categories BIBREF4 , BIBREF5 . Text corpora are a prime example of naturally occurring large-scale data sets BIBREF34 , BIBREF35 , BIBREF36 . In analogy to real-world situations, they encapsulate rich, diverse, and potentially noisy, information. The wide availability of corpora allows us to train and evaluate cognitive models on data from diverse languages and cultures. We test our model on corpora from five languages, derived from the online encyclopedia Wikipedia in Arabic, Chinese, French, English, and German. Wikipedia is a valuable resource for our study because it (a) discusses concepts and their properties explicitly and can thus serve as a proxy for the environment speakers of a language are exposed to; and (b) allows us to construct corpora which are highly comparable in their content across languages, controlling for effects of genre or style.
We present a series of evaluations investigating the quality of the induced categories and features. Leveraging a reference comprising hundreds of concepts and more than 30 categories, we demonstrate that our model learns meaningful categories in all five target languages. We furthermore show, through crowd-sourced evaluations involving native speakers of each target language, that the induced feature types are (a) each thematically coherent and interpretable; and (b) are associated with categories in comprehensible ways. We discuss language-specific idiosyncrasies emerging from the induced representations.
In the remainder of this article, we first review related literature, before we present a cognitively motivated model for learning categories and their structured representations from large natural language corpora. We then evaluate the quality of the emerging representations, as well as the generalizability of our model across languages. Note that the primary goal of this work is not to characterize differences in categories and features arising from different languages (even though this would be an interesting avenue for future work). Rather, we aim to demonstrate the utility of large-scale naturalistic datasets for cognitive modeling, and to verify mechanisms of categorization known from laboratory studies at scale and across communities.
Our experiments indicate that the proposed method outperforms neural language models trained with well-tuned hyperparameters and achieves state-of-the-art scores on each dataset.
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Does the method proposed by the authors outperform neural language models trained with well-tuned hyperparameters?
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Yes, it does.
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Why do some people try to limit the amount of dessert they eat?
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Dessert is often high in sugar and fat, which can make the desserts unhealthy in large quantities. Therefore, some people only eat limited amounts of dessert in order to help protect their health
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| null |
What is the difference between income and property taxes?
|
Income taxes are taxes on the money an individual earns from working. Property taxes are taxes on the value of property an individual owns.
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| 290
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Constituency BIBREF0 and dependency grammars BIBREF1 , BIBREF2 are the two main abstractions for representing the syntactic structure of a given sentence, and each of them has its own particularities BIBREF3 . While in constituency parsing the structure of sentences is abstracted as a phrase-structure tree (see Figure FIGREF6 ), in dependency parsing the tree encodes binary syntactic relations between pairs of words (see Figure FIGREF6 ).
When it comes to developing natural language processing (nlp) parsers, these two tasks are usually considered as disjoint tasks, and their improvements therefore have been obtained separately BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 .
Despite the potential benefits of learning across representations, there have been few attempts in the literature to do this. klein2003fast considered a factored model that provides separate methods for phrase-structure and lexical dependency trees and combined them to obtain optimal parses. With a similar aim, ren2013combine first compute the n best constituency trees using a probabilistic context-free grammar, convert those into dependency trees using a dependency model, compute a probability score for each of them, and finally rerank the most plausible trees based on both scores. However, these methods are complex and intended for statistical parsers. Instead, we propose a extremely simple framework to learn across constituency and dependency representations.
In particular, we will use a hard-sharing architecture: the sentence is first processed by stacked bilstms shared across all tasks, with a task-dependent feed-forward network on the top of it, to compute each task's outputs.
|
What is the meaning of hard-sharing architecture they use?
|
The sentence is first processed by stacked bilstms shared across all tasks, with a task-dependent feed-forward network on the top of it, to compute each task's outputs.
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What is the primary cause of rain?
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The primary cause of rain is when water condenses in a cloud and gets heavy enough to fall.
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What is the difference between laser cutting and 3D printing?
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3D printing is an additive technique while laser cutting is subtractive. In a 3D printer, the material gets heated up and extruded through a nozzle, forming three-dimensional objects. In contrast, a laser cutter cuts two-dimensional plates. For this, a laser burns away parts of the material. Thus, laser cutting can only produce two-dimensional shapes. However, it is much faster than 3D printing.
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What are some of the famous festivals of India?
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India is the land of diverse cultures and festivities. Therefore, certain festivals are more prominent in specific parts than others. A couple of the festivals that are celebrated on a broader level across the country are
1. Diwali: It is also referred to as the "Festival of Lights". During this festival, people light up their homes in the religious significance of welcoming Goddess Laxmi, also known as Goddess of Wealth. Some people consider this festival as the highest religious significance.
2. Holi: It is one the most popular festival to the level that has picked up in western world. Being referred as the "Festival of colors", people generally play with colors (in powder form), sing and enjoy with friends, family and neighbours.
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Identify which of these animals live in water and which live on land: elephants, wolves, sharks, dolphins, cows, raccoons, pigs.
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From the list provided, only sharks and dolphins live in water. The rest live on land: elephants, wolves, cows, raccoons, pigs.
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What are the official languages of Canada?
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The official languages of Canada are English and French.
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whats needed to make a biryani?
rice
chicken
honey
sugar
oil
pot
towel
barbeque
onions
oven
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following are needed to make biryani:
rice
chicken
oil
pot
onion
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Mount Everest (Nepali: सगरमाथा, romanized: Sagarmāthā; Tibetan: Chomolungma ཇོ་མོ་གླང་མ; Chinese: 珠穆朗玛峰; pinyin: Zhūmùlǎngmǎ Fēng) is Earth's highest mountain above sea level, located in the Mahalangur Himal sub-range of the Himalayas. The China–Nepal border runs across its summit point. Its elevation (snow height) of 8,848.86 m (29,031 ft 8+1⁄2 in) was most recently established in 2020 by the Chinese and Nepali authorities.
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Where is Mount Everest?
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Mount Everest is located in the Himalayan mountain range and runs across the China-Nepal border.
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Do you really need mobile phone?
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We do not really need a mobile phone to live.
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Identify the bird from the list: Mouse, Balearic shearwater, Giraffe
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Balearic shearwater
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Shah starred on the reality television series The Real Housewives of Salt Lake City, which premiered in November 2020. In March 2021, she was criminally charged with conspiracy to commit money laundering and wire fraud, pleading not guilty to both charges days later. Her legal case was chronicled in the documentary film Housewife and the Shah Shocker, which premiered on Hulu in November that same year. In July 2022, Shah pleaded guilty to conspiracy to commit wire fraud. In January 2023, Shah was sentenced to six-and-a-half years in prison. Shah began her prison sentence the following month.
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Given a reference text about Jen Shah, what is she famous from, when was she criminally charged, what was the verdict, and when was she sentenced.
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Jen Shah came into the spotlight in November 2020 when The Real Housewives of Salt Lake City aired. In March 2021 she was charged with conspiracy to commit money laundering as well as wire fraud. She pled not guilty, but then come July 2022 she plead guilty to commit wire fraud. January of 2023 she was sentenced to 6.5 years in prison and her sentence began in February.
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Waterside Karori were relegated at the end of the 1990 season, to be replaced by the winner of a play-off series between teams from the northern, central, and southern leagues (Mount Albert-Ponsonby, Nelson United, and Burndale United respectively). Nelson United won the series to gain promotion, but there is some controversy about their participation in the play-offs as they had only finished second in the central regional league, which was won by Petone.
Gisborne City were relegated at the end of the 1991 season
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Which team was relegated from the league at the conclusion of the 1991 Season?
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Gisborne City were relegated at the end of the 1991 season.
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In this section, we will present a cross-lingual pre-training based transfer approach. This method is designed for a common zero-shot scenario where there are a lot of source$\leftrightarrow $pivot and pivot$\leftrightarrow $target bilingual data but no source$\leftrightarrow $target parallel data, and the whole training process can be summarized as follows step by step:
Pre-train a universal encoder with source/pivot monolingual or source$\leftrightarrow $pivot bilingual data.
Train a pivot$\rightarrow $target parent model built on the pre-trained universal encoder with the available parallel data. During the training process, we freeze several layers of the pre-trained universal encoder to avoid the degeneracy issue BIBREF27.
Directly translate source sentences into target sentences with the parent model, which benefits from the availability of the universal encoder.
The key difficulty of this method is to ensure the intermediate representations of the universal encoder are language invariant. In the rest of this section, we first present two existing methods yet to be explored in zero-shot translation, and then propose a straightforward but effective cross-lingual pre-training method. In the end, we present the whole training and inference protocol for transfer.
Pre-train a universal encoder with source/pivot monolingual or source↔pivot bilingual data.
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How to pre-train the universal encoder?
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Pre-train a universal encoder with source/pivot monolingual or source↔pivot bilingual data.
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Classify each country as "African" or "European": Nigeria, Rwanda, Portugal, Romania, Ghana, Ukraine
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Nigeria: African
Rwanda: African
Portugal: European
Romania: European
Ghana: African
Ukraine: European
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Do you know how many episodes of Game of Thrones are there?
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Yes - there are 73 episodes for Game of Thrones that are spread over 8 seasons. The first season was released April 17, 2011 by HBO and season 8 concluded on May 19, 2019. The entire series is 70 hours and 14 minutes long and would take you just under 3 full days if you watched every episode back to back.
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Name some fruits that have yellow or orange skin.
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Bananas, Lemons, Oranges, Nectarines and Apricots all have yellow or orange skin.
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What coloring of cat is almost always male?
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Orange cats are generally male.
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What are some of the most efficient programing languages?
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C, C++, Rust are very efficient, while Java and Scala are easier to develop with and have lower barrier of entry but are not as efficient
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Tableau Software (/tæbˈloʊ/ tab-LOH) is an American interactive data visualization software company focused on business intelligence. It was founded in 2003 in Mountain View, California, and is currently headquartered in Seattle, Washington. In 2019 the company was acquired by Salesforce for $15.7 billion. At the time, this was the largest acquisition by Salesforce (a leader in the CRM field) since its foundation. It was later surpassed by Salesforce's acquisition of Slack.
The company's founders, Christian Chabot, Pat Hanrahan and Chris Stolte, were researchers at the Department of Computer Science at Stanford University. They specialized in visualization techniques for exploring and analyzing relational databases and data cubes, and started the company as a commercial outlet for research at Stanford from 1999 to 2002.
Tableau products query relational databases, online analytical processing cubes, cloud databases, and spreadsheets to generate graph-type data visualizations. The software can also extract, store, and retrieve data from an in-memory data engine.
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From the passage provided, extract the acquisition details of Tableau.
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Tableau was acquired in 2019 by Salesforce for $15.7 billion.
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The Family Justice Council, established in 2004, is an advisory, non-statutory, non-departmental public body sponsored by the Judicial Office of England and Wales. It provides independent expert advice, from an inter-disciplinary perspective, on the operation and reform of the family justice system to the Family Justice Board (jointly chaired by ministers from the Ministry of Justice and Department for Education) and Government. It is chaired by the President of the Family Division, and in August 2012 became part of the President's office.
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Write some important points regarding Family Justice Council using given text as a reference
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1. The Judicial Office of England and Wales sponsors the Family Justice Council, an advisory, non-statutory, non-departmental public organisation founded in 2004.
2. It offers independent professional advice to the Family law Board, which is jointly chaired by ministers from the Ministry of Justice and Department for Education, as well as the Government on how to run and reform the family law system.
3. It is presided over by the Family Division President and joined the President's office in August 2012.
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What is the name of the first dog that went to space?
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Laika
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Parkinson's disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the mid-brain producing several motor and non-motor impairments in the patients BIBREF0. Motor symptoms include among others, bradykinesia, rigidity, resting tremor, micrographia, and different speech impairments. The speech impairments observed in PD patients are typically grouped as hypokinetic dysarthria, and include symptoms such as vocal folds rigidity, bradykinesia, and reduced control of muscles and limbs involved in the speech production. The effects of dysarthria in the speech of PD patients include increased acoustic noise, reduced intensity, harsh and breathy voice quality, increased voice nasality, monopitch, monoludness, speech rate disturbances, imprecise articulation of consonants BIBREF1, and involuntary introduction of pauses BIBREF2. Clinical observations in the speech of patients can be objectively and automatically measured by using computer aided methods supported in signal processing and pattern recognition with the aim to address two main aspects: (1) to support the diagnosis of the disease by classifying healthy control (HC) subjects and patients, and (2) to predict the level of degradation of the speech of the patients according to a specific clinical scale.
Most of the studies in the literature to classify PD from speech are based on computing hand-crafted features and using classifiers such as support vector machines (SVMs) or K-nearest neighbors (KNN). For instance, in BIBREF3, the authors computed features related to perturbations of the fundamental frequency and amplitude of the speech signal to classify utterances from 20 PD patients and 20 HC subjects, Turkish speakers. Classifiers based on KNN and SVMs were considered, and accuracies of up to 75% were reported. Later, in BIBREF4 the authors proposed a phonation analysis based on several time frequency representations to assess tremor in the speech of PD patients. The extracted features were based on energy and entropy computed from time frequency representations. Several classifiers were used, including Gaussian mixture models (GMMs) and SVMs. Accuracies of up to 77% were reported in utterances of the PC-GITA database BIBREF5, formed with utterances from 50 PD patients and 50 HC subjects, Colombian Spanish native speakers. The authors from BIBREF6 computed features to model different articulation deficits in PD such as vowel quality, coordination of laryngeal and supra-laryngeal activity, precision of consonant articulation, tongue movement, occlusion weakening, and speech timing. The authors studied the rapid repetition of the syllables /pa-ta-ka/ pronounced by 24 Czech native speakers, and reported an accuracy of 88% discriminating between PD patients and HC speakers, using an SVM classifier. Additional articulation features were proposed in BIBREF7, where the authors modeled the difficulty of PD patients to start/stop the vocal fold vibration in continuous speech. The model was based on the energy content in the transitions between unvoiced and voiced segments. The authors classified PD patients and HC speakers with speech recordings in three different languages (Spanish, German, and Czech), and reported accuracies ranging from 80% to 94% depending on the language; however, the results were optimistic, since the hyper-parameters of the classifier were optimized based on the accuracy on the test set. Another articulation model was proposed in BIBREF8. The authors considered a forced alignment strategy to segment the different phonetic units in the speech utterances. The phonemes were segmented and grouped to train different GMMs. The classification was performed based on a threshold of the difference between the posterior probabilities from the models created for HC subjects and PD patients. The model was tested with Colombian Spanish utterances from the PC-GITA database BIBREF5 and with the Czech data from BIBREF9. The authors reported accuracies of up to 81% for the Spanish data, and of up to 94% for the Czech data.
In addition to the hand-crafted feature extraction models, there is a growing interest in the research community to consider deep learning models in the assessment of the speech of PD patients BIBREF10, BIBREF11, BIBREF12. Deep learning methods have the potential to extract more abstract and robust features than those manually computed. These features could help to improve the accuracy of different models to classify pathological speech, such as PD BIBREF13. A deep learning based articulation model was proposed in BIBREF11 to model the difficulties of the patients to stop/start the vibration of the vocal folds. Transitions between voiced and unvoiced segments were modeled with time-frequency representations and convolutional neural networks (CNNs). The authors considered speech recordings of PD patients and HC speakers in three languages: Spanish, German, and Czech, and reported accuracies ranging from 70% to 89%, depending on the language. However, in a language independent scenario, i.e., training the CNN with utterances from one language and testing with the remaining two, the results were not satisfactory (accuracy$<60\%$).
The classification of PD from speech in different languages has to be carefully conducted to avoid bias towards the linguistic content present in each language. For instance, Czech and German languages are richer than Spanish language in terms of consonant production, which may cause that it is easier to produce consonant sounds by Czech PD patients than by Spanish PD patients. Despite these language dependent issues, the results in the classification of PD in different languages could be improved using a transfer learning strategy among languages, i.e., to train a base model with utterances from one language, and then, to perform a fine-tuning of the weights with utterances from the target language BIBREF14. Similar approaches based on transfer learning have been recently considered to classify PD using handwriting BIBREF15. In the present study, we propose a methodology to classify PD via a transfer learning strategy with the aim to improve the accuracy in different languages. CNNs trained with utterances from one language are used to initialize a model to classify speech utterances from PD patients in a different language. The models are evaluated with speech utterances in Spanish, German, and Czech languages. The results suggest that the use of a transfer learning strategy improved the accuracy of the models over 8% with respect to those obtained when the model is trained only with utterance from the target language.
The authors studied the rapid repetition of the syllables /pa-ta-ka/ pronounced by 24 Czech native speakers, and reported an accuracy of 88% discriminating between PD patients and HC speakers, using an SVM classifier.
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What is the accuracy of discriminating between PD patients and HC subjects
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88%.
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Ted Lasso is an American sports comedy-drama television series developed by Jason Sudeikis, Bill Lawrence, Brendan Hunt and Joe Kelly, based on a character Sudeikis first portrayed in a series of promos for NBC Sports' coverage of England's Premier League. The show follows Ted Lasso, an American college football coach who is hired to coach an English soccer team with the secret intention that his inexperience will lead it to failure, but whose folksy, optimistic leadership proves unexpectedly successful.
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Who is Ted Lasso in the eponymous TV show?
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The show follows Ted Lasso, an American college football coach who is hired to coach an English soccer team with the secret intention that his inexperience will lead it to failure,
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