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The Tennessee River is the largest tributary of the Ohio River. It is approximately 652 miles (1,049 km) long and is located in the southeastern United States in the Tennessee Valley. The river was once popularly known as the Cherokee River, among other names, as the Cherokee people had their homelands along its banks, especially in what are now East Tennessee and northern Alabama. Additionally, its tributary, the Little Tennessee River, flows into it from Western North Carolina and northeastern Georgia, where the river also was bordered by numerous Cherokee towns. Its current name is derived from the Cherokee town, Tanasi, which was located on the Tennessee side of the Appalachian Mountains
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How large is the Tennessee River?
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The Tennessee River is the largest tributary of the Ohio River. It is approximately 650 miles long and located in the southeaster United States. The river is used for moving cargo and has a long history of enjoyment by families residing on the bank. A great section of the river is located near Spring City, TN at the Watts Barr Dam
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What content is popular on Tictok?
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Tiktok hosts user-submitted videos so everyone can create their own content.
It tends to appeal to younger users, as almost half of its users are between the ages of 16 and 24.
When business see the user base, they starts marketing and creating high quality commercial contents. There are some content categories always drive thousands of clicks. First of all, the image of beauty, baby and beast presents the attractiveness factors which is related to human behavior. Secondly, exploration on beautiful scenery or Extreme Sports always make people think they are involved and also part of the adventure.
Some other categories like knowledge transfer is always popular as people are eager to learn and getting information from others.
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What are some fun and inexpensive things to do with kids during the summer?
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Here are a few ideas for things to do with kids to do with kids in the summer that are inexpensive:
1. Go for a picnic
2. Go for a bike ride
3. Go for a hike
4. Go swimming in a pool or a lake. But do take necessary precautions and be safe.
5. Camping
6. Go to a water park.
7. Go to a park or a splash pad
There are many such things that are inexpensive or even free that one could do with kids during the summer.
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1809.09795
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FLOAT SELECTED: Table 1: Benchmark datasets: Tweets, Reddit posts and online debates for sarcasm and irony detection.
Twitter: We use the Twitter dataset provided for the SemEval 2018 Task 3, Irony Detection in English Tweets BIBREF18 . The dataset was manually annotated using binary labels. We also use the dataset by BIBREF4 , which is manually annotated for sarcasm. Finally, we use the dataset by BIBREF20 , who collected a user self-annotated corpus of tweets with the #sarcasm hashtag.
Reddit: BIBREF21 collected SARC, a corpus comprising of 600.000 sarcastic comments on Reddit. We use main subset, SARC 2.0, and the political subset, SARC 2.0 pol.
Online Dialogues: We utilize the Sarcasm Corpus V1 (SC-V1) and the Sarcasm Corpus V2 (SC-V2), which are subsets of the Internet Argument Corpus (IAC). Compared to other datasets in our selection, these differ mainly in text length and structure complexity BIBREF22 .
FLOAT SELECTED: Table 1: Benchmark datasets: Tweets, Reddit posts and online debates for sarcasm and irony detection.
Twitter: We use the Twitter dataset provided for the SemEval 2018 Task 3, Irony Detection in English Tweets BIBREF18 . The dataset was manually annotated using binary labels. We also use the dataset by BIBREF4 , which is manually annotated for sarcasm. Finally, we use the dataset by BIBREF20 , who collected a user self-annotated corpus of tweets with the #sarcasm hashtag.
Reddit: BIBREF21 collected SARC, a corpus comprising of 600.000 sarcastic comments on Reddit. We use main subset, SARC 2.0, and the political subset, SARC 2.0 pol.
Online Dialogues: We utilize the Sarcasm Corpus V1 (SC-V1) and the Sarcasm Corpus V2 (SC-V2), which are subsets of the Internet Argument Corpus (IAC).
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What are the 7 different datasets?
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The answers are shown as follows:
* SemEval 2018 Task 3
* BIBREF20
* BIBREF4
* SARC 2.0
* SARC 2.0 pol
* Sarcasm Corpus V1 (SC-V1)
* Sarcasm Corpus V2 (SC-V2)
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How many interstates are there in Colorado?
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Colorado has three main highways (two-digit routes), and two auxiliary highways (three-digit routes), totaling five Interstates, all are located within the Denver metropolitan area
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Tell me if these are brands or companies: Kleenex, Procter & Gamble, Cadillac, Doritos, Chase?
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It can be difficult to know if something is a brand or a company. You asked about a few specific brands and companies. Here is what is a brand and what is a company from the list that you gave me:
Kleenex is a brand. The Kleenex brand is owned by the company, Kimberly-Clark
Procter & Gamble is a company. Procter & Gamble owns many brands. One famous brand of Procter & Gamble is Crest.
Cadillac is a brand. The Cadillac brand is owned by General Motors.
Doritos is a brand. Doritos is one of the brands of Frito-Lay, a wholly owned subsidiary of PepsiCo.
Chase is a company. To be very specific, JPMorgan Chase Bank, N.A., is a company doing business as Chase.
I hope this list helps you!
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1805.12032
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Hence, the contributions of this work are two-fold: (1) we develop a linguistically-infused neural network model to classify reactions in social media posts, and (2) we apply our model to label 10.8M Twitter posts and 6.2M Reddit comments in order to evaluate the speed and type of user reactions to various news sources.
We develop a neural network architecture that relies on content and other linguistic signals extracted from reactions and parent posts, and takes advantage of a “late fusion” approach previously used effectively in vision tasks BIBREF13 , BIBREF14 . More specifically, we combine a text sequence sub-network with a vector representation sub-network as shown in Figure FIGREF5 . The text sequence sub-network consists of an embedding layer initialized with 200-dimensional GloVe embeddings BIBREF15 followed by two 1-dimensional convolution layers, then a max-pooling layer followed by a dense layer. The vector representation sub-network consists of two dense layers. We incorporate information from both sub-networks through concatenated padded text sequences and vector representations of normalized Linguistic Inquiry and Word Count (LIWC) features BIBREF16 for the text of each post and its parent.
Hence, the contributions of this work are two-fold: (1) we develop a linguistically-infused neural network model to classify reactions in social media posts, and (2) we apply our model to label 10.8M Twitter posts and 6.2M Reddit comments in order to evaluate the speed and type of user reactions to various news sources.
We develop a neural network architecture that relies on content and other linguistic signals extracted from reactions and parent posts, and takes advantage of a “late fusion” approach previously used effectively in vision tasks BIBREF13 , BIBREF14 . More specifically, we combine a text sequence sub-network with a vector representation sub-network as shown in Figure FIGREF5 . The text sequence sub-network consists of an embedding layer initialized with 200-dimensional GloVe embeddings BIBREF15 followed by two 1-dimensional convolution layers, then a max-pooling layer followed by a dense layer. The vector representation sub-network consists of two dense layers. We incorporate information from both sub-networks through concatenated padded text sequences and vector representations of normalized Linguistic Inquiry and Word Count (LIWC) features BIBREF16 for the text of each post and its parent.
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What is the architecture of their model?
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The answers are shown as follows:
* we combine a text sequence sub-network with a vector representation sub-network as shown in Figure FIGREF5 . The text sequence sub-network consists of an embedding layer initialized with 200-dimensional GloVe embeddings BIBREF15 followed by two 1-dimensional convolution layers, then a max-pooling layer followed by a dense layer. The vector representation sub-network consists of two dense layers. We incorporate information from both sub-networks through concatenated padded text sequences and vector representations of normalized Linguistic Inquiry and Word Count (LIWC) features BIBREF16 for the text of each post and its parent.
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| 497
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Following the data filtering and RL training steps described in §3.2 and §3.3, we train both referenceand model-based experts for intrinsic and extrinsic hallucination using DAE accuracy and entity overlap precision metrics as rewards, respectively. Also, because experts for both intrinsic and extrinsic hallucinations are trained to improve precision with respect to the source document, they may negatively impact the content recall. So, we train entity-recall experts to maximize recall of salient entities between the generated summary and the reference summary. We train reference-based experts for DAE metric on data filtered for intrinsic hallucination, and for entity overlap precision and entity recall metric on data filtered for extrinsic hallucination. The model-based experts for both metrics are trained on the entire training dataset.
Next, for each of the three kinds of experts, we select one of the reference-and model-based experts having least factual error (or maximum factual recall) on validation dataset and combine them through weights or logits ensembling. We use the element-wise weighted average of all the parameters of pre-trained summarization model (φ) and expert models (θ i expert ) for weights ensembling (eq. 3). For logits ensembling, we use the weighted average of logits (z t ) from all the experts (z t,θ i expert ) and the pre-trained model (z t,φ ) during decoding, as described by the eq. 4.
So, we train entity-recall expert to maximize recall of salient entities between the generated summary and the reference summary.
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How is the entity recall expert trained?
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It is trained to maximize entity recall (Eq 1) where recall is defined with respect to the human written reference summary.
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How does Walter White die in Breaking bad
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In the final episode of Breaking Bad, Walter attempts to clean-up his mess by killing off all those who betrayed him, and rescue Jesse from his captors. Walter builds a moving arm mount for an M60 machine gun that he can activate from the car's remote key fob, and places it in the trunk of a car that he got from the same gun dealer. Walter then drives to the compound where the Aryan Brotherhood are living, under a trick that he can help them make “meth” with a new recipe.
Once inside, Walter Argues with Jack about why Jessie isn’t dead yet, causing the angry Jack to bring Jessie into the room to prove they are not friends. AT this point, Walter charges Jessie to the floor and presses the key fob, causing the M60 to begin shooting into the building.
Everyone in the building is shot dead except for Jessie, Walter, Todd and Jack. Jesse strangles Todd to death with his handcuffs, and Walter Shoots Jack with his own gun. Jessie then leaves the compound in one of the cars that is sitting outside.
After this, you become aware that Walter was hit by a ricochet while the M60 was firing, and has effectively been shot in the stomach. He walks over to the building where Jessie was making Meth, and falls to the floor seconds before the Police enter the building.
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| 142
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Machine translation has made remarkable progress, and studies claiming it to reach a human parity are starting to appear BIBREF0. However, when evaluating translations of the whole documents rather than isolated sentences, human raters show a stronger preference for human over machine translation BIBREF1. These findings emphasize the need to shift towards context-aware machine translation both from modeling and evaluation perspective.
Most previous work on context-aware NMT assumed that either all the bilingual data is available at the document level BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10 or at least its fraction BIBREF11. But in practical scenarios, document-level parallel data is often scarce, which is one of the challenges when building a context-aware system.
We introduce an approach to context-aware machine translation using only monolingual document-level data. In our setting, a separate monolingual sequence-to-sequence model (DocRepair) is used to correct sentence-level translations of adjacent sentences. The key idea is to use monolingual data to imitate typical inconsistencies between context-agnostic translations of isolated sentences. The DocRepair model is trained to map inconsistent groups of sentences into consistent ones. The consistent groups come from the original training data; the inconsistent groups are obtained by sampling round-trip translations for each isolated sentence.
To validate the performance of our model, we use three kinds of evaluation: the BLEU score, contrastive evaluation of translation of several discourse phenomena BIBREF11, and human evaluation. We show strong improvements for all metrics.
We analyze which discourse phenomena are hard to capture using monolingual data only. Using contrastive test sets for targeted evaluation of several contextual phenomena, we compare the performance of the models trained on round-trip translations and genuine document-level parallel data. Among the four phenomena in the test sets we use (deixis, lexical cohesion, VP ellipsis and ellipsis which affects NP inflection) we find VP ellipsis to be the hardest phenomenon to be captured using round-trip translations.
Our key contributions are as follows:
we introduce the first approach to context-aware machine translation using only monolingual document-level data;
our approach shows substantial improvements in translation quality as measured by BLEU, targeted contrastive evaluation of several discourse phenomena and human evaluation;
we show which discourse phenomena are hard to capture using monolingual data only.
Among the four phenomena in the test sets we use (deixis, lexical cohesion, VP ellipsis and ellipsis which affects NP inflection) we find VP ellipsis to be the hardest phenomenon to be captured using round-trip translations.
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Which test set is the most hardest phenomenon to capture using round-trip translation?
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VP ellipsis.
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The 1999 UEFA Champions League final was an association football match between Manchester United of England and Bayern Munich of Germany, played at Camp Nou in Barcelona, Spain, on 26 May 1999, to determine the winner of the 1998–99 UEFA Champions League. Injury time goals from Manchester United's Teddy Sheringham and Ole Gunnar Solskjær cancelled out Mario Basler's early goal for Bayern to give Manchester United a 2–1 win. Referee Pierluigi Collina has cited this match as one of the most memorable of his career, and described the noise from the crowd at the end of the game as being like a "lion's roar".
The two sides had played each other earlier in the competition, having both been drawn in Group D in the group stage; Bayern won the group, while Manchester United qualified for the knockout phase as one of the two best runners-up across all six groups. After beating Inter Milan in the quarter-finals, Manchester United beat another Italian side, Juventus, in the semis to reach the final; meanwhile, Bayern beat fellow Germans Kaiserslautern in the quarter-finals, before overcoming Ukrainian side Dynamo Kyiv in the semis. The victory completed a treble-winning season for Manchester United, after they had won the Premier League and FA Cup. Bayern were also playing for a treble, having won the Bundesliga and reached the DFB-Pokal final, although they went on to lose that match as well.
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Tell me about the 1999 UEFA champions league final?
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The 1999 Champions league final was won by Manchester United in dramatic fashion when they scored two goals in injury time. Teddy Sheringham and Ole Solskjaer scored late on for United after they'd conceded a goal early in the first half. The victory gave manchester United the "treble" and is considered one of the greatest achievements in the history of club football.
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What season does Easter happen during?
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Easter is a Spring holiday
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Major League Baseball (MLB) is a professional baseball organization and the oldest major professional sports league in the world. MLB is composed of 30 teams, divided equally between the National League (NL) and the American League (AL), with 29 in the United States and 1 in Canada.
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How many teams are in the MLB National League
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The National League (NL) consists of 15 teams aligned in three divisions. In the NL East are the Atlanta Braves, Miami Marlins, New York Mets, Philadelphia Phillies, and Washington (D.C.) Nationals. In the NL Central are the Chicago Cubs, Cincinnati Reds, Milwaukee Brewers, Pittsburgh Pirates, and St. Louis Cardinals.
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Mehmed II (Ottoman Turkish: محمد ثانى, romanized: Meḥmed-i s̱ānī; Turkish: II. Mehmed, pronounced [icinˈdʒi ˈmehmed]; 30 March 1432 – 3 May 1481), commonly known as Mehmed the Conqueror (Ottoman Turkish: ابو الفتح, romanized: Ebū'l-fetḥ, lit. 'the Father of Conquest'; Turkish: Fâtih Sultan Mehmed), was an Ottoman sultan who ruled from August 1444 to September 1446, and then later from February 1451 to May 1481.
In Mehmed II's first reign, he defeated the crusade led by John Hunyadi after the Hungarian incursions into his country broke the conditions of the truce Peace of Szeged. When Mehmed II ascended the throne again in 1451, he strengthened the Ottoman navy and made preparations to attack Constantinople. At the age of 21, he conquered Constantinople and brought an end to the Byzantine Empire. After the conquest, Mehmed claimed the title Caesar of the Roman Empire (Ottoman Turkish: قیصر روم, romanized: Qayser-i Rûm), based on the fact that Constantinople had been the seat and capital of the surviving Eastern Roman Empire since its consecration in 330 AD by Emperor Constantine I. The claim was only recognized by the Patriarchate of Constantinople. Nonetheless, Mehmed II viewed the Ottoman state as a continuation of the Roman Empire for the remainder of his life, seeing himself as "continuing" the Empire rather than "replacing" it.
Mehmed continued his conquests in Anatolia with its reunification and in Southeast Europe as far west as Bosnia. At home he made many political and social reforms, encouraged the arts and sciences, and by the end of his reign, his rebuilding program had changed Constantinople into a thriving imperial capital. He is considered a hero in modern-day Turkey and parts of the wider Muslim world. Among other things, Istanbul's Fatih district, Fatih Sultan Mehmet Bridge and Fatih Mosque are named after him.
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Please give me the keypoints about Mehmed II's reign
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Mehmed II also known as Mehmed the Conqueror was an Ottoman Sultan who reigned from 1432 to 1481. He is known for conquering Constantinople, ending the Byzantine Empire and greatly expanded the Ottoman empire.
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What does the Latin phrase mea culpa mean in English?
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Mea culpa means “my fault” in English.
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Identify which instrument is string or percussion: Lambeg, Hurdy-gurdy
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Hurdy-gurdy is string, Lambeg is percussion.
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Why everyone doesn't see solar eclipse when it occurs?
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You have to be in the moon's darkest shadow (umbra). This place is small, so other areas around the world won't see it.
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Mohandas Karamchand Gandhi (/ˈɡɑːndi, ˈɡændi/ GA(H)N-dee, Gujarati: [ˈmoɦəndɑs ˈkəɾəmtʃənd ˈɡɑ̃dʱi]; 2 October 1869 – 30 January 1948) was an Indian lawyer, anti-colonial nationalist and political ethicist who employed nonviolent resistance to lead the successful campaign for India's independence from British rule. He inspired movements for civil rights and freedom across the world. The honorific Mahātmā (Sanskrit: "great-souled", "venerable"), first applied to him in 1914 in South Africa, is now used throughout the world.
Born and raised in a Hindu family in coastal Gujarat, Gandhi trained in the law at the Inner Temple, London, and was called to the bar at age 22 in June 1891. After two uncertain years in India, where he was unable to start a successful law practice, he moved to South Africa in 1893 to represent an Indian merchant in a lawsuit. He went on to live in South Africa for 21 years. It was here that Gandhi raised a family and first employed nonviolent resistance in a campaign for civil rights. In 1915, aged 45, he returned to India and soon set about organising peasants, farmers, and urban labourers to protest against excessive land-tax and discrimination.
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From the passage provided, extract when Gandhi moved to South Africa
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Gandhi moved to South Africa in 1893
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This list is based on the Sri Lanka Parliament, which ranks the Sri Lanka's largest public companies.
Rank Name Financial statistics in millions US$ Industry
Revenue Profit Assets
1 Ceylon Petroleum Corporation 3,016 9.9 1,477 Oil and gas
2 Bank of Ceylon 1,316 413 13,290 Banking
3 Ceylon Electricity Board 1,268 -138.12 4,127 Electricity generation
4 SriLankan Airlines 1,017 -230.28 1,273 Airline
5 People's Bank (Sri Lanka) 1,013 70 10,320 Banking
6 National Savings Bank (Sri Lanka) 672.42 36.89 6,370 Banking
7 Sri Lanka Telecom 474.85 34.92 1,154 Telecommunications
8 Sri Lanka Ports Authority 248.25 5.77 1,988 Commercial ports
9 Sri Lanka Insurance 242.5 28.55 941.5 Insurance
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From the text provided, extract the name of the most profitable public banking company in Sri Lanka. Include the profit and revenue in US dollars.
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The most profitable public bank in Sri Lanka is the Bank of Ceylon. The Bank of Ceylon has a recorded profit of $413 million dollars US with a revenue of $1,316 million dollars US. The Bank of Ceylon is the 2nd largest public company in Sri Lanka.
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| 20
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This research addresses the problem of representing the semantics of text documents in multi-lingual comparable corpora. We present a new approach to this problem, based on neural embeddings, and test it on the task of clustering texts into meaningful classes depending on their topics. The setting is unsupervised, meaning that one either does not have enough annotated data to train a supervised classifier or does not want to be limited with a pre-defined set of classes. There is a lot of sufficiently good approaches to this problem in the case of mono-lingual text collections, but the presence of multiple languages introduces complications.
When a text collection contains documents in several languages, it becomes impractical to simply represent the documents as vectors of words occurring in them ("bag-of-words"), as the words surface forms are different, even in closely-related languages. Thus, one has to invent means to cross the inter-lingual gap and bring all documents to some sort of shared representation, without losing information about their topics or categories.
Of course, one obvious way to solve this problem is to translate all documents into one language, and then apply any clustering algorithm. However, this requires either buying human/machine translation services (which can be expensive if you deal with large text collection) or training own statistical machine translation model (which as a rule requires big parallel corpus). This is the reason to search for other solutions.
In this paper, a novel way of reducing the problem of cross-lingual document representation to a monolingual setting is proposed. Essentially, we train Continuous Bag-of-Words models BIBREF0 on large comparable monolingual corpora for two languages our dataset consists of. This provides us with vector representations of words, allowing to measure their semantic similarity. Then, a linear transformation matrix from vectors of language A to vectors of language B is learned, using a small bilingual dictionary as training data. This matrix is then employed to `project' word and document representations from semantic space of language A to semantic space of language B. It allows not only quite accurate `translation' of words, but also of document `semantic fingerprints' (dense representations of document semantics, calculated as an average of the trained distributional vectors for all the words in document).
This approach is evaluated in a setting, where the input is a collection of documents in several languages and some number of topics to which these documents belong (we also have large monolingual corpora to train distributional models on). For each document, we are given its language, but not its topic. The task is to cluster this collection so that documents belonging to one topic were clustered together, independent of their language. Note that we are interested in clustering the collection as a whole, not each language separately (which is trivial).
Our evaluation data consists of comparable corpora of Russian and Ukrainian academic texts. On this material, we show that the `translated semantic fingerprints' method represents documents in different languages precisely enough to allow almost exact clustering according to document topics, with only 5% of incorrect assignments. It significantly outperforms both naive bag-of-words baseline and the not-so-naive method of `orthographic translation' based on Damerau-Levenshtein distance, even enriched with dictionary mappings. At the same time, it does not require large parallel corpora or a ready-made statistical machine translation model.
The rest of the paper is structured as follows. In Section "Related Work" we describe the foundations of our approach and the related work. Section "Academic texts as Comparable Corpora" introduces the employed corpora and the story behind them. Section "Learning to Translate: Ukrainian-to-Russian transformations" is dedicated to learning the transformation matrix, and Section "Experiment Design and Evaluation" describes our experimental setting and evaluation results. We discuss the findings in Section "Discussion" and conclude in Section "Conclusion and Future Work" , also suggesting directions for future work.
This provides us with vector representations of words, allowing to measure their semantic similarity.
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What do the vector representations of words measure?
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Semantic similarity of vector representations of words.
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Think up a bunch of different meals I can make that include potatoes.
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Potatoes are a very versatile food that can be used in many meals. Some of my favorites are:
- Shepherd's Pie
- Loaded Baked Potatoes
- Poutine
- Potatoes au Gratin
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By virtue of its varied geography and long history, Karnataka hosts numerous spots of interest for tourists. There is an array of ancient sculptured temples, modern cities, scenic hill ranges, forests and beaches. Karnataka has been ranked as the fourth most popular destination for tourism among the states of India. Karnataka has the second highest number of nationally protected monuments in India, second only to Uttar Pradesh, in addition to 752 monuments protected by the State Directorate of Archaeology and Museums. Another 25,000 monuments are yet to receive protection.
The districts of the Western Ghats and the southern districts of the state have popular eco-tourism locations including Kudremukh, Madikeri and Agumbe. Karnataka has 25 wildlife sanctuaries and five national parks. Popular among them are Bandipura National Park, Bannerghatta National Park and Nagarhole National Park. The ruins of the Vijayanagara Empire at Hampi and the monuments of Pattadakal are on the list of UNESCO's World Heritage Sites. The cave temples at Badami and the rock-cut temples at Aihole representing the Badami Chalukyan style of architecture are also popular tourist destinations. The Hoysala temples at Beluru and Halebidu, which were built with Chloritic schist (soapstone) are proposed UNESCO World Heritage sites. The Gol Gumbaz and Ibrahim Rauza are famous examples of the Deccan Sultanate style of architecture. The monolith of Gomateshwara Bahubali at Shravanabelagola is the tallest sculpted monolith in the world, attracting tens of thousands of pilgrims during the Mahamastakabhisheka festival.
Golden 5-storey Mysore Palace building with 21 domed towers and central spire
Mysore Palace in the evening, the official residence and seat of the Wodeyar dynasty, the rulers of Mysore of the Mysore Kingdom, the royal family of Mysore.
The waterfalls of Karnataka and Kudremukh are considered by some to be among the "1001 Natural Wonders of the World". Jog Falls is India's tallest single-tiered waterfall with Gokak Falls, Unchalli Falls, Magod Falls, Abbey Falls and Shivanasamudra Falls among other popular waterfalls.
Several popular beaches dot the coastline, including Murudeshwara, Gokarna, Malpe and Karwar. In addition, Karnataka is home to several places of religious importance. Several Hindu temples including the famous Udupi Sri Krishna Matha, the Marikamba Temple at Sirsi, the Kollur Mookambika Temple, the Sri Manjunatha Temple at Dharmasthala, Kukke Subramanya Temple, Janardhana and Mahakali Temple at Ambalpadi, Sharadamba Temple at Shringeri attract pilgrims from all over India. Most of the holy sites of Lingayatism, like Kudalasangama and Basavana Bagewadi, are found in northern parts of the state. Shravanabelagola, Mudabidri and Karkala are famous for Jain history and monuments. Jainism had a stronghold in Karnataka in the early medieval period with Shravanabelagola as its most important centre. The Shettihalli Rosary Church near Shettihalli, an example of French colonial Gothic architecture, is a rare example of a Christian ruin, is a popular tourist site.
Karnataka has become a center of health care tourism and has the highest number of approved health systems and alternative therapies in India. Along with some ISO certified government-owned hospitals, private institutions which provide international-quality services, Hospitals in Karnataka treat around 8,000 health tourists every year.
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Extract best tourist attractions in Karnataka
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Karnataka has an array of ancient sculptured temples, modern cities, scenic hill ranges, forests and beaches. Places such as Kudremukh, Madikeri and Agumbe are from western ghats which have scenic hills. Hampi, Pattadakal are on the list of UNESCO's World Heritage Sites. Badami and Aihole are famous for Chalukyan architecture. Several beaches like Murudeshwara, Gokarna, Malpe and Karwar are popular. Apart from this there are several Hindu temples including the famous Udupi Sri Krishna Matha, the Marikamba Temple at Sirsi, the Kollur Mookambika Temple, the Sri Manjunatha Temple at Dharmasthala, Kukke Subramanya Temple, Janardhana and Mahakali Temple at Ambalpadi, Sharadamba Temple at Sringeri attract pilgrims from all over India
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| 85
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In recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc. In this work, we argue that current methods which are based on conventional machine learning models cannot grasp the intricacy of emotional language by ignoring the sequential nature of the text, and the context. These methods, therefore, are not sufficient to create an applicable and generalizable emotion detection methodology. Understanding these limitations, we present a new network based on a bidirectional GRU model to show that capturing more meaningful information from text can significantly improve the performance of these models. The results show significant improvement with an average of 26.8 point increase in F-measure on our test data and 38.6 increase on the totally new dataset.
In this work, we argue that current methods which are based on conventional machine learning models cannot grasp the intricacy of emotional language by ignoring the sequential nature of the text, and the context. These methods, therefore, are not sufficient to create an applicable and generalizable emotion detection methodology. Understanding these limitations, we present a new network based on a bidirectional GRU model to show that capturing more meaningful information from text can significantly improve the performance of these models
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Understanding these limitations, what model does the author propose?
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They present a new network based on a bidirectional GRU model.
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In the season-opener against the Philadelphia Eagles, Goff threw for 215 yards and two touchdowns, but threw an interception returned for a touchdown as the Lions narrowly lost by a score of 38–35. In the next game against the Washington Commanders, he threw for 256 yards and four touchdowns in a 36–27 victory. In the Week 2 game against Washington, Goff produced his sixth straight home game with two or more passing touchdowns, establishing a Lions franchise record. On October 2, Goff again threw four touchdowns passes, and this time added a tenure-high 378 passing yards during a 48–45 loss to the Seattle Seahawks. Goff finished his second season with the Lions with 4,438 yards, 29 touchdowns, and seven interceptions, starting in all 17 games. Goff was named to his third Pro Bowl as an alternative, replacing Super Bowl bound Jalen Hurts.
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What was Goff's best game mentioned in the text?
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Goff's best game mentioned in the text was on October 2 when he threw four touchdowns passes, and added a tenure-high 378 passing yards during a 48–45 loss to the Seattle Seahawks.
Goff played another good game against the Washington Commanders, when he threw for 256 yards and four touchdowns in a 36–27 victory, but this performance wasn't as good as the game against the Seahawks because he threw for fewer yards even though he threw for the same amount of touchdowns.
Note that the text is also missing information that would be useful in judging Goff's performance such as QBR, completion percentage, etc.
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What two continents is Russia a part of?
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Russia is a part of Europe and Asia
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What is the best Hip-Hop artist of all time ?
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There are a variety of Hip-Hop artists that have been spanning through multiple years, hip-hop in the 80s is a lot different than the modern era hip-hop. The genre is constantly evolving and multiple sub-genres has emerged, like Lo-Fi, Trip-hop, Gangsta rap, ignorant rap etc...
It's also hard to measure what makes the success of an artist, is it the records sold ? the number of songs ? the activity of the fanbase ? the quality of the rhymes ? the impact his song had ? What the artist do outside of it's musical activity ? it's longevity ?
For example we can say that Run-DMC has been the first rap group to really push the genre forward as they were the first group to be on Mtv, having a sponsorship with Adidas, and mixing Rock with Rap in their Song with Aerosmith, but their longevity has been shortened due to the murdering of Jam Master Jay.
Tupac or Notorious Big are acknowledged by a lot has the best rappers of all time because of their rhyme scheme and the fact that their song remains classics today.
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Hibiki (Japanese: 響) (meaning "resonance" or "echo") is a premium blended whisky produced in Japan by Beam Suntory (a subsidiary of Suntory Beverage & Food Ltd, which itself is a subsidiary of Suntory Holdings of Osaka, Japan). It has won several awards.
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Which company produces Hibiki Whisky?
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Hibiki Whisky is a premium blended whisky produced in Japan by Beam Suntory, a subsidiary of Suntory Beverage & Food Ltd / Suntory Holdings of Osaka, Japan.
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Is Star Wars based on a book?
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No, the Star Wars universe originated from the film series created by George Lucas beginning with 'Star Wars: A New Hope' in 1977. A number of books have been written based on the films or in the storyline created from them, but the original material was a film.
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Brainstorm reasons for and against taking a daily low-dose aspirin.
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According to Berkeley Wellness, there’s evidence that a daily aspirin “protects people already diagnosed with cardiovascular disease (CVD).” However, there is insufficient evidence that a daily low-dose aspirin is as effective for primary prevention; it is not as necessarily effective for people who have “not been diagnosed with CVD.” Additionally, high BMI may also alter the way aspirin is metabolized, further complicating the data. Finally, there’s evidence that suddenly stopping aspirin can actually increase the risk of a heart attack or stroke. Thus, not everyone should be taking a daily low-dose aspirin - consult your doctor!
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Adam M. Grant (born August 13, 1981) is an American popular science author, and professor at the Wharton School of the University of Pennsylvania specializing in organizational psychology.
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From the passage provided, what does Adam M. Grant do?
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Adam M. Grant is an American author and professor at the Wharton School of the University of Pennsylvania who specializes in organizational psychology.
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Could you provide some recommendations for restaurants in Yountville?
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Yountville has many gastronomic delights. The best and most renown restaurant in Yountville is Thomas Keller’s French Laundry, which is one of his Michelin 3-star restaurants. Chef Keller uses French techniques, coupled with the abundance of produce and wine from the famous Napa Valley. The French Laundry also has its own garden and chicken coop, which are located across the street. This is an expensive restaurant and it’s hard to get a reservation, but it’s certainly a treat.
Less expensive, but still good, are three other restaurants owned by Chef Keller:
• Bouchon Bistro, which also has a Michelin star and serves classic French bistro cuisine,
• Ad Hoc, which serves elevated, but casual, family-style dinners, and
• La Calenda, which serves Mexican cuisine and has live music on weekend nights.
Another good French bistro-style restaurant is Bistro Jeanty, a mainstay of Yountville restaurants.
Bottega, owned by Michael Chiarella, is a good option for Italian cuisine. Lucy Restaurant, in the Bardessono Hotel, has an eclectic, but delicious menu.
Good options for very inexpensive food are Tacos Garcia, which is a taco truck next to the park near Madison Street, and Velo Deli and Pizzeria, which serves sandwiches and pizzas.
For a very quick bite, Kelly’s Provisions is a tiny shop located in the only gas station in Yountville located at the corner of Washington Street and Madison Street (and it’s the world’s cutest and cleanest gas station). At Kelly’s, you can find hot dogs and soft serve ice cream, and it also has many packaged snacks, a wide assortment of drinks, and a good selection of wine and champagne.
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Any object, totally or partially immersed in a fluid or liquid, is buoyed up by a force equal to the weight of the fluid displaced by the object.
Archimedes' principle allows the buoyancy of any floating object partially or fully immersed in a fluid to be calculated. The downward force on the object is simply its weight. The upward, or buoyant, force on the object is that stated by Archimedes' principle above. Thus, the net force on the object is the difference between the magnitudes of the buoyant force and its weight. If this net force is positive, the object rises; if negative, the object sinks; and if zero, the object is neutrally buoyant—that is, it remains in place without either rising or sinking. In simple words, Archimedes' principle states that, when a body is partially or completely immersed in a fluid, it experiences an apparent loss in weight that is equal to the weight of the fluid displaced by the immersed part of the body(s).
Consider a cuboid immersed in a fluid, its top and bottom faces orthogonal to the direction of gravity (assumed constant across the cube's stretch). The fluid will exert a normal force on each face, but only the normal forces on top and bottom will contribute to buoyancy. The pressure difference between the bottom and the top face is directly proportional to the height (difference in depth of submersion). Multiplying the pressure difference by the area of a face gives a net force on the cuboid — the buoyancy — equaling in size the weight of the fluid displaced by the cuboid. By summing up sufficiently many arbitrarily small cuboids this reasoning may be extended to irregular shapes, and so, whatever the shape of the submerged body, the buoyant force is equal to the weight of the displaced fluid.
weight of displaced fluid = weight of object in vacuum − weight of object in fluid
The weight of the displaced fluid is directly proportional to the volume of the displaced fluid (if the surrounding fluid is of uniform density). The weight of the object in the fluid is reduced, because of the force acting on it, which is called upthrust. In simple terms, the principle states that the buoyant force on an object is equal to the weight of the fluid displaced by the object, or the density of the fluid multiplied by the submerged volume times the gravity.
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Based on the text below, how would you explain Archimedes’ principle to a 10 years old child, using the example of an object immersed in water?
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When you immerse an object under water, it pushes the water away in order to take its place.
The water wants to get back to where it was before and pushes the object away, towards the surface.
Then it depends what is the weight of the object, compared with how much water was moved.
Imagine you could put your object on one side of a balance, and on the other side, you would duplicate your object and transform it into water (representing how much water is moved if you were to plunge put your object under water).
If your original object is lighter than its equivalent in water, like a bubble for example, or a balloon, the water will be strong enough to have the object to float.
If the object is heavier than its equivalent water, like a bowling ball, or a rock, the water will not be able to push hard enough, and the object will sink.
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The Story of June is a Cantopop album by Edmond Leung.
Track listing:
Love Blind (戀愛盲)
Elevator Men (電梯男)
Hey June
Driven To Distraction (失魂落魄)
Daydream (白日夢)
Romantic (羅曼蒂克)
Y3
Get Away with Miriam Yeung (滾)
The Aftermath (戰後餘生)
The Long Journey (路漫漫)
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What are the names of the songs from The Story of June which don't have Chinese names?
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Hey June
Y3
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Memphis is the seventeenth album recorded by Roy Orbison, and his tenth for MGM Records. The album was released in November 1972.
History
The album took three weeks to make in March and April 1972. The album had one single, "Memphis, Tennessee", which became a minor hit in the US, charting at #84. Also included was a new, re-recorded version of Don Gibson's "I Can't Stop Loving You", which previously appeared on his 1960 album "Lonely and Blue". This was Orbison's final album that was released for London Records as Decca let Orbison out of their contract on June 30, 1972.
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When was the Memphis album released
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Memphis is the seventeenth album recorded by Roy Orbison, and his tenth for MGM Records. The album was released in November 1972.
The album took three weeks to make in March and April 1972. The album had one single, "Memphis, Tennessee", which became a minor hit in the US, charting at #84. Also included was a new, re-recorded version of Don Gibson's "I Can't Stop Loving You", which previously appeared on his 1960 album "Lonely and Blue". This was Orbison's final album that was released for London Records as Decca let Orbison out of their contract on June 30, 1972.
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Visual dialog BIBREF0 is an interesting new task combining the research efforts from Computer Vision, Natural Language Processing and Information Retrieval. While BIBREF1 presents some tips and tricks for VQA 2.0 Challenge, we follow their guidelines for the Visual Dialog challenge 2018. Our models use attention similar to BIBREF2 to get object level image representations from Faster R-CNN model BIBREF3. We experiment with different encoder mechanisms to get representations of conversational history.
We experiment with different encoder mechanisms to get representations of conversational history.
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Why do the authors experiment with different encoder mechanisms?
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To get representations of conversational history.
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Alibaba is one of the world's largest retailers and e-commerce companies. In 2020, it was also rated as the fifth-largest artificial intelligence company. It is also one of the biggest venture capital firms and investment corporations in the world, as well as the second largest financial services group behind Visa via its fintech arm Ant Group. The company hosts the largest B2B (Alibaba.com), C2C (Taobao), and B2C (Tmall) marketplaces in the world. It has been expanding into the media industry, with revenues rising by triple percentage points year after year. It also set the record on the 2018 edition of China's Singles' Day, the world's biggest online and offline shopping day.
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What is the largest retailers and e-commerce companies?
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Alibaba is one of the world's largest retailers and e-commerce companies. It consists of many business including B2B (Alibaba.com), C2C (Taobao) and B2C (Tmall). Alibaba is also one of the largest financial companies and AI companies.
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To evaluate our work we measured how the discovered units compared to the forced aligned phones in term of segmentation and information. The accuracy of the segmentation was measured in term of Precision, Recall and F-score. If a unit boundary occurs at the same time (+/- 10ms) of an actual phone boundary it is considered as a true positive, otherwise it is considered to be a false positive. If no match is found with a true phone boundary, this is considered to be a false negative. The consistency of the units was evaluated in term of normalized mutual information (NMI - see BIBREF1 , BIBREF3 , BIBREF5 for details) which measures the statistical dependency between the units and the forced aligned phones. A NMI of 0 % means that the units are completely independent of the phones whereas a NMI of 100 % indicates that the actual phones could be retrieved without error given the sequence of discovered units.
The accuracy of the segmentation was measured in term of Precision, Recall and F-score.
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How to measure the accuracy of the segmentation?
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The accuracy of the segmentation was measured in term of Precision, Recall and F-score.
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I’m new to beer, recommend 5 beer styles to me
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Here are 5 beer styles that you might enjoy:
1. Pale ale
2. Pilsner
3. West coast IPA
4. Vienna Lager
5. Stout
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Graph Piece Extraction Since the number of graph piece pairs equals the number of inter-piece connections in the piece-level graph, the complexity is O(NMe), where N is the predefined size of vocabulary, M denotes the number of molecules in the dataset, and e denotes the maximal number of inter-piece connections in a single molecule. The number of inter-piece connections decreases rapidly in the first few iterations, therefore the time cost for each iteration decreases rapidly. It cost 6 hours to perform 500 iterations on 250,000 molecules in the ZINC250K dataset with 4 CPU cores. Piece-Level Decomposition Given an arbitrary molecule, the worst case is that each iteration adds one atom to one existing subgraph until the molecule is finally merged into a single graph piece. In this case the algorithm runs for |V| iterations. Therefore, the complexity is O(|V|) where V includes all the atoms in the molecule
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And how expensive the process will be?
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The complexity of the process is O(NMe), where N denotes the size of the vocabulary, M denotes the number of molecules in the dataset and e denotes the maximum number of inter-piece connections in the molecular graphs. We also provide the complexity analysis in appendix B.
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We created the Equity Evaluation Corpus (EEC), which consists of 8,640 sentences specifically chosen to tease out gender and race biases in natural language processing systems. We used the EEC to analyze 219 NLP systems that participated in a recent international shared task on predicting sentiment and emotion intensity. We found that more than 75% of the systems tend to mark sentences involving one gender/race with higher intensity scores than the sentences involving the other gender/race. We found such biases to be more widely prevalent for race than for gender. We also found that the bias can be different depending on the particular affect dimension involved.
We found the score differences across genders and across races to be somewhat small on average ( INLINEFORM0 , which is INLINEFORM1 of the 0 to 1 score range). However, for some systems the score differences reached as high as 0.34 (34%). What impact a consistent bias, even with an average magnitude INLINEFORM2 , might have in downstream applications merits further investigation.
We plan to extend the EEC with sentences associated with country names, professions (e.g., doctors, police officers, janitors, teachers, etc.), fields of study (e.g., arts vs. sciences), as well as races (e.g., Asian, mixed, etc.) and genders (e.g., agender, androgyne, trans, queer, etc.) not included in the current study. We can then use the corpus to examine biases across each of those variables as well. We are also interested in exploring which systems (or what techniques) accentuate inappropriate biases in the data and which systems mitigate such biases. Finally, we are interested in exploring how the quality of sentiment analysis predictions varies when applied to text produced by different demographic groups, such as people of different races, genders, and ethnicities.
The Equity Evaluation Corpus and the proposed methodology to examine bias are not meant to be comprehensive. However, using several approaches and datasets such as the one proposed here can bring about a more thorough examination of inappropriate biases in modern machine learning systems.
We plan to extend the EEC with sentences associated with country names, professions (e.g., doctors, police officers, janitors, teachers, etc.), fields of study (e.g., arts vs. sciences), as well as races (e.g., Asian, mixed, etc.) and genders (e.g., agender, androgyne, trans, queer, etc.) not included in the current study. We can then use the corpus to examine biases across each of those variables as well. We are also interested in exploring which systems (or what techniques) accentuate inappropriate biases in the data and which systems mitigate such biases. Finally, we are interested in exploring how the quality of sentiment analysis predictions varies when applied to text produced by different demographic groups, such as people of different races, genders, and ethnicities.
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What will the author do about his future work?
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They plan to extend the EEC with sentences associated with country names, professions, fields of study, as well as races and genders not included in the current study.They are also interested in exploring which systems accentuate inappropriate biases in the data and which systems mitigate such biases. Finally, they are interested in exploring how the quality of sentiment analysis predictions varies when applied to text produced by different demographic groups.
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Aadhaar is a 12-digit unique identity number that can be obtained voluntarily by the citizens of India and resident foreign nationals who have spent over 182 days in twelve months immediately preceding the date of application for enrolment, based on their biometric and demographic data. The data is collected by the Unique Identification Authority of India (UIDAI), a statutory authority established in January 2009 by the Government of India, under the jurisdiction of the Ministry of Electronics and Information Technology, following the provisions of the Aadhaar (Targeted Delivery of Financial and other Subsidies, benefits and services) Act, 2016.
Aadhaar is the world's largest biometric ID system. World Bank Chief Economist Paul Romer described Aadhaar as "the most sophisticated ID programme in the world". Considered a proof of residence and not a proof of citizenship, Aadhaar does not itself grant any rights to domicile in India. In June 2017, the Home Ministry clarified that Aadhaar is not a valid identification document for Indians travelling to Nepal and Bhutan.
Prior to the enactment of the Act, the UIDAI had functioned, since 28 January 2009, as an attached office of the Planning Commission (now NITI Aayog). On 3 March 2016, a money bill was introduced in the Parliament to give legislative backing to Aadhaar. On 11 March 2016, the Aadhaar (Targeted Delivery of Financial and other Subsidies, benefits and services) Act, 2016, was passed in the Lok Sabha.
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is Aadhaar a proof of residence or of citizenship?
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Considered a proof of residence and not a proof of citizenship, Aadhaar does not itself grant any rights to domicile in India.
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The "PayPal Mafia" is a group of former PayPal employees and founders who have since founded and/or developed additional technology companies such as Tesla, Inc., LinkedIn, Palantir Technologies, SpaceX, Affirm, Slide, Kiva, YouTube, Yelp, and Yammer. Most of the members attended Stanford University or University of Illinois Urbana–Champaign at some point in their studies.
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How does UIUC relate to PayPal?
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Most of the members of the PayPal Mafia the University of Illinois Urbana–Champaign (UIUC) at some point, as well as Stanford University.
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Detective Hieronymus "Harry" Bosch is a fictional character created by American author Michael Connelly. Bosch debuted as the lead character in the 1992 novel The Black Echo, the first in a best-selling police procedural series now numbering 24 novels.
The novels are more or less coincident in timeframe with the year in which they were published. Harry, as he is commonly known by his associates, is a veteran police homicide detective with the Los Angeles Police Department. He was named after the 15th-century Dutch artist Hieronymus Bosch.
Titus Welliver portrayed the title character from 2015 to 2021 in Bosch, a television series adapted from the novels, and from 2022 in its spin-off series Bosch: Legacy.
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Which fictional LAPD detective is Titus Welliver known for playing?
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Titus Welliver played Harry Bosch in the TV series Bosch and also in spin off Bosch:Legacy.
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Who are some famous classical composers?
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Some well known classical composers are Mozart, Bach and Beethoven
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1809.08731
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Our first baseline is ROUGE-L BIBREF1 , since it is the most commonly used metric for compression tasks. ROUGE-L measures the similarity of two sentences based on their longest common subsequence. Generated and reference compressions are tokenized and lowercased. For multiple references, we only make use of the one with the highest score for each example.
We compare to the best n-gram-overlap metrics from toutanova2016dataset; combinations of linguistic units (bi-grams (LR2) and tri-grams (LR3)) and scoring measures (recall (R) and F-score (F)). With multiple references, we consider the union of the sets of n-grams. Again, generated and reference compressions are tokenized and lowercased.
We further compare to the negative LM cross-entropy, i.e., the log-probability which is only normalized by sentence length. The score of a sentence $S$ is calculated as
Our next baseline is perplexity, which corresponds to the exponentiated cross-entropy:
Due to its popularity, we also performed initial experiments with BLEU BIBREF17 . Its correlation with human scores was so low that we do not consider it in our final experiments.
Our first baseline is ROUGE-L BIBREF1 , since it is the most commonly used metric for compression tasks.
We compare to the best n-gram-overlap metrics from toutanova2016dataset;
We further compare to the negative LM cross-entropy
Our next baseline is perplexity,
Due to its popularity, we also performed initial experiments with BLEU BIBREF17
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Is ROUGE their only baseline?
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No, other baseline metrics they use besides ROUGE-L are n-gram overlap, negative cross-entropy, perplexity, and BLEU.
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Positions intermittently elevated to Cabinet-rank
Ambassador to the United Nations (1953–1989, 1993–2001, 2009–2018, 2021–present)
Director of the Office of Management and Budget (1953–1961, 1969–present)
White House Chief of Staff (1953–1961, 1974–1977, 1993–present)
Counselor to the President (1969–1977, 1981–1985, 1992–1993): A title used by high-ranking political advisers to the president of the United States and senior members of the Executive Office of the President since the Nixon administration. Incumbents with Cabinet rank included Daniel Patrick Moynihan, Donald Rumsfeld and Anne Armstrong.
White House Counsel (1974–1977)
United States Trade Representative (1975–present)
Chair of the Council of Economic Advisers (1977–1981, 1993–2001, 2009–2017, 2021–present)
National Security Advisor (1977–1981)
Director of Central Intelligence (1981–1989, 1995–2001)
Administrator of the Environmental Protection Agency (1993–present)
Director of the Office of National Drug Control Policy (1993–2009)
Administrator of the Small Business Administration (1994–2001, 2012–present)
Director of the Federal Emergency Management Agency (1996–2001): Created as an independent agency in 1979, raised to Cabinet rank in 1996, and dropped from Cabinet rank in 2001.
Director of National Intelligence (2017–present)
Director of the Central Intelligence Agency (2017–2021)
Director of the Office of Science and Technology Policy (2021–present)
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From the text below, list the years that the White House Chief of Staff was removed from the United States Cabinet. Separate them with a comma.
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1961, 1977
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Paraphrase identification is an important topic in artificial intelligence and this task justifies whether two sentences expressed in various forms are semantically similar, BIBREF0 . For example, “On Sunday, the boy runs in the yard” and “The child runs outside at the weekend” are identified as paraphrase. This task directly benefits many industrial applications, such as plagiarism identification BIBREF0 , machine translation BIBREF1 and removing redundancy questions in Quora website BIBREF2 . Recently, there emerge many methods, such as ABCNN BIBREF3 , Siamese LSTM BIBREF2 and L.D.C BIBREF4 .
Conventionally, neural methodology aligns the sentence pair and then generates a matching score for paraphrase identification, BIBREF4 , BIBREF2 . Regarding the alignment, we conjecture that the aligned unmatched parts are semantically critical, where we define the corresponded word pairs with low similarity as aligned unmatched parts. For an example: “On Sunday, the boy runs in the yard” and “The child runs inside at the weekend”, the matched parts (i.e. (Sunday, weekend), (boy, child), run) barely make contribution to the semantic sentence similarity, but the unmatched parts (i.e. “yard” and “inside”) determine these two sentences are semantically dissimilar. For another example: “On Sunday, the boy runs in the yard” and “The child runs outside at the weekend”, the aligned unmatched parts (i.e. “yard” and “outside”) are semantically similar, which makes the two sentences paraphrase. In conclusion, if the aligned unmatched parts are semantically consistent, the two sentences are paraphrase, otherwise they are non-paraphrase.
Traditional alignment methods take advantage of attention mechanism BIBREF2 , which is a soft-max weighting technique. Weighting technique could pick out the most similar/dissimilar parts, but is weak in modeling the aligned unmatched parts, which are the crucial evidence to identify paraphrase. For the input sentences in Figure FIGREF1 , the weight between “Sunday” and “run” is lower than the weight between “yard” and “inside”, but the former weight is not the evidence of paraphrase/non-paraphrase, because the former two words that are most dissimilar should not be aligned for an inappropriate comparison.
To extract the aligned unmatched parts, in this paper, we embed Hungarian algorithm BIBREF5 into neural architecture as Hungarian layer (Algorithm SECREF7 ). Illustrated in Figure FIGREF1 , the alignment in sentence matching could be formulated as the task-assignment problem, which is tackled by Hungarian algorithm. Simply, Hungarian algorithm works out the theoretically optimal alignment relationship in an exclusive manner and the exclusiveness characterizes the aligned unmatched parts. For the example in Figure FIGREF1 , because Hungarian layer allocates the aligned pairs with exclusiveness, the matched parts (i.e (Sunday, weekend), (boy, child), run) are aligned firstly, then the word “yard” would be assigned to the word “inside” with a negative similarity, making a strong evidence for discrimination.
Specifically, our model performs this task in three steps. First, our model applies BiLSTM to parse the input sentences into hidden representations. Then, Hungarian layer leverages the hidden representations to extract the aligned unmatched parts. Last, we apply cosine similarity to metric the aligned unmatched parts for a final discrimination. Regarding the training process of Hungarian layer, we modify the back-propagation algorithm in both directions. In the forward pass, Hungarian layer works out the alignment relationship, according to which, the computational graph is dynamically constructed, as demonstrated in Figure FIGREF13 . Once the computational graph has been dynamically constructed, the backward propagation could be performed as usual in a conventional graph.
We conduct our experiments on the public benchmark dataset of “Quora Question Pairs” for the task of paraphrase identification. Experimental results demonstrate that our model outperforms other baselines extensively and significantly, which verifies our theory about the aligned unmatched parts and illustrates the effectiveness of our methodology.
Contributions. (1.) We offer a new perspective for paraphrase identification, which focuses on the aligned unmatched parts of two sentences. Accordingly, we propose the Hungarian layer to extract the aligned unmatched parts. The proposed method can achieve hard and exclusive alignments between two sequences, while we can learn parameters by end-to-end back-propagation. (2.) Our model outperforms other baselines extensively, verifying the effectiveness of our theory and method.
Organization. In Section 2, we survey the related work of paraphrase identification and dynamic differentiable computational graphs. In Section 3, we introduce our neural architecture. In Section 4, we conduct the experiments. In Section 5, we conclude our paper and publish our codes.
Experimental results demonstrate that our model outperforms other baselines extensively and significantly, which verifies our theory about the aligned unmatched parts and illustrates the effectiveness of our methodology.
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Does the model outperform other baselines extensively and significantly?
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Yes, it does.
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If you are considering buying a bicycle consider your budget, look up the reviews online, talk to friends, and test them out!
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When considering what bicycle you would like to buy, I strongly encourage you to test them out! Then take into consideration color, type, durability, comfort, cost and reviews.
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Write a short story with a moral - slow and steady wins the race
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One day a hare and a tortoise decided to race. The race began, and the hare ran very fast. The tortoise moved slowly and steadily. After a while the hare stopped and looked back, the tortoise was far behind. So the hare decided to rest for some time, and soon it fell asleep.
But the tortoise kept moving steadily. By the time the hare woke up, the tortoise had already reached the winning post. So the moral of the story is slow and steady wins the race.
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What are the words of House Reed?
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"We Remember"
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In this section, we describes procedures of building our sportball Vietnamese dataset, called UIT-ViIC.
Our human resources for dataset construction involve five writers, whose ages are from 22-25. Being native Vietnamese residents, they are fluent in Vietnamese. All five UIT-ViIC creators first research and are trained about sports knowledge as well as the specialized vocabulary before starting to work.
During annotation process, there are inconsistencies and disagreements between human's understandings and the way they see images. According to Micah Hodosh et al BIBREF5, most images’ captions on Internet nowadays tend to introduce information that cannot be obtained from the image itself, such as people name, location name, time, etc. Therefore, to successfully compose meaningful descriptive captions we expect, their should be strict guidelines.
Inspired from MS-COCO annotation rules BIBREF16, we first sketched UIT-ViIC's guidelines for our captions:
Each caption must contain at least ten Vietnamese words.
Only describe visible activities and objects included in image.
Exclude name of places, streets (Chinatown, New York, etc.) and number (apartment numbers, specific time on TV, etc.)
Familiar English words such as laptop, TV, tennis, etc. are allowed.
Each caption must be a single sentence with continuous tense.
Personal opinion and emotion must be excluded while annotating.
Annotators can describe the activities and objects from different perspectives.
Visible “thing” objects are the only one to be described.
Ambiguous “stuff” objects which do not have obvious “border” are ignored.
In case of 10 to 15 objects which are in the same category or species, annotators do not need to include them in the caption.
In comparison with MS-COCO BIBREF16 data collection guidelines in terms of annotation, UIT-ViIC’s guidelines has similar rules (1, 2, 8, 9, 10) . We extend from MS-COCO’s guidelines with five new rules to our own and have modifications in the original ones.
In both datasets, we would like to control sentence length and focus on describing important subjects only in order to make sure that essential information is mainly included in captions. The MS-COCO threshold for sentence’s length is 8, and we raise the number to 10 for our dataset. One reason for this change is that an object in image is usually expressed in many Vietnamese words. For example, a “baseball player” in English can be translated into “vận động viên bóng chày” or “cầu thủ bóng chày”, which already accounted for a significant length of the Vietnamese sentence. In addition, captions must be single sentences with continuous tense as we expect our model’s output to capture what we are seeing in the image in a consise way.
On the other hand, proper name for places, streets, etc must not be mentioned in this dataset in order to avoid confusions and incorrect identification names with the same scenery for output. Besides, annotators’ personal opinion must be excluded for more meaningful captions. Vietnamese words for several English ones such as tennis, pizza, TV, etc are not existed, so annotators could use such familiar words in describing captions. For some images, the subjects are ambiguous and not descriptive which would be difficult for annotators to describe in words. That’s the reason why annotators can describe images from more than one perspective.
According to Micah Hodosh, most images’ captions on Internet nowadays tend to introduce information that cannot be obtained from the image itself, such as people name,location name, time, etc.
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What is most images’ captions on the Internet tend to do according to Micah Hodosh?
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They tend to introduce information that cannot be obtained from the image itself, such as people name, location name, time, etc.
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Which Targaryen conquered Westeros?
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Aegon Targaryen conquered Westeros, with the assistance of his sister-wives, Visenya and Rhaenys.
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The history of ITV, the United Kingdom "Independent Television" commercial network, goes back to 1955.
Independent Television began as a network of independently-owned regional companies that were both broadcasters and programme makers, beginning with four companies operating six stations in three large regions in 1955–1956, and gradually expanding to 17 stations in 14 regions by 1962. Each regional station was responsible for its own branding, scheduling and advertising, with many peak-time programmes shared simultaneously across the whole network.
By 29 February 2016, 12 regions in England and Wales shared national ITV branding and scheduling, and, together with a 13th region UTV in Northern Ireland, were owned by a single company, ITV plc. A further two regions in Scotland carry STV branding and are owned by the STV Group.
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Who owns ITV?
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ITV plc owns all 13 ITV regions in England and Wales as well as UTV in Northern Ireland. STV Group owns the STV region in Scotland.
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Warsaw has cold, sometimes snowy, cloudy winters, and warm, relatively sunny but frequently stormy summers. Spring and autumn can be unpredictable, highly prone to sudden weather changes; however, temperatures are usually mild, especially around May and September. The daily average temperature ranges between −1.5 °C (29 °F) in January and 19.7 °C (67.5 °F) in July and the mean year temperature is 9.0 °C (48.2 °F). Temperatures may reach 30 °C (86 °F) in the summer, although the effects of hot weather are usually offset by relatively low dew points and large diurnal temperature differences. Warsaw is Europe's sixth driest major city (driest in Central Europe), with yearly rainfall averaging 482 mm (19.0 in), the wettest month being July.
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What is the coldest month in Warsaw and how cold does it get on average?
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The coldest month in Warsaw is January. On average, the temperature then is −1.5 °C, which is 29 °F.
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Ayrton Senna da Silva was a Brazilian racing driver who won the Formula One World Drivers' Championship in 1988, 1990, and 1991. Senna is one of three Formula One drivers from Brazil to win the World Championship and won 41 Grands Prix and 65 pole positions, with the latter being the record until 2006. He died in an accident while leading the 1994 San Marino Grand Prix, driving for the Williams team.
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Extract the years in which Ayrton Senna da Silva won the Formula One World Drivers' Championship and list them using bullets.
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- 1988
- 1990
- 1991
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Tell me whether you can or cannot legally drive a car or each of the following: road, highway, sidewalk, interstate, pedestrian only bridge
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Road: Can Legally Drive
Highway: Can Legally Drive
Sidewalk: Can't Legally Drive
Interstate: Can Legally Drive
Pedestrian Only Bridge: Can't Legally Drive
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| 489
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Human-agent communication at deployment time has been under-explored in machine learning, where the traditional focus has been on building agents that can accomplish tasks on their own (full autonomy). Nevertheless, enabling an agent to exchange information with humans during its operation can potentially enhance its helpfulness and trustworthiness. The ability to request and interpret human advice would help the agent accomplish tasks beyond its built-in knowledge, while the ability to accurately convey when and why it is about to fail would make the agent safer to use.
In his classical work, outlines the desired characteristics of cooperative communication, commonly known as the Gricean maxims of cooperation. Among these characteristics are informativeness (the maxim of quantity) and faithfulness (the maxim of quality). Human-agent communication in current work has fallen short in these two aspects. Traditional frameworks like imitation learning and reinforcement learning employ limited communication protocols where the agent and the human can only exchange simple intentions (requesting low-level actions or rewards;). More powerful frameworks like allow the agent to process high-level instructions from humans, but the agent still only requests generic help. Recent work in natural language processing endows the agent with the ability to generate rich natural language utterances, but the communication is not faithful in the sense that the agent only mirrors human-generated utterances without grounding its communication in self-perception of its (in)capabilities and (un)certainties. Essentially it learns to convey what a human may be concerned about, not what it is concerned about. This paper presents a hierarchical reinforcement learning framework named HARI (Human-Assisted Reinforced Interaction), which supports richer and more faithful human-agent communication. Our framework allows the agent to learn to convey intrinstic needs for specific information and to incorporate diverse types of information from humans to make better decisions. Specifically, the agent in HARI is equipped with three information-seeking intentions: in every step, it can choose to request more information about (i) its current state, (ii) the goal state, or (iii) a subgoal state which, if reached, helps it make progress on the current task. Upon receiving a request, the human can transfer new information to the agent by giving new descriptions of the requested state. These de- Initially (A) it may request more information about the goal, but may not know enough about where it currently is. For example, at location B, due to limited perception, it does not recognize that it is in a living room and stands next to a couch. It can obtain such information from the assistant.
If the current task becomes too difficult (like at location C), the agent can require the assistant to provide a simpler subtask which, if accomplished, helps it make progress on the main task. The agent maintains a stack of tasks and always executes the task at the top. When the agent receives a (sub)task, it pushes the (sub)task to the top of the stack. When it wants to stop executing a (sub)task, it pops the (sub)task from the stack (e.g., at location D). At location E, the agent empties the stack and terminates its execution.
scriptions will be incorporated as new inputs to the agent's decision-making policy. The human thus can transfer any form of information that can be interpreted by the policy (e.g., asking the agent to execute skills that it has learned, giving side information that connects the agent to a situation it is more familiar with). Because the agent's policy can implement a variety of model architectures and learning algorithms, our framework opens up many possibilities for human-agent communication.
To enable faithful communication, we teach the agent to understand its intrinsic needs by interacting with the human and the environment (rather than imitating human behaviors). By requesting different types of information and observing how much each type of information enhances its decisions, the agent gradually learns to determine which information is most useful to obtain in a given situation. With this capability, at deployment time, it can choose when and what information to ask from the human to improve its task performance. To demonstrate the effectiveness of HARI, we simulate a human-assisted navigation problem where an agent has access to only sparse information about its current state and the goal, and can request additional information about these states. On tasks that take place in previously unseen environments, the ability to ask for help improves the agent's success rate by 7× higher compared to performing tasks only by itself. This human-assisted agent even outperforms an agent that always has access to dense information in unseen environments, thanks to the ability to request subgoals. We show that performance of the agent can be further improved by recursively asking for subgoals of subgoals. We discuss limitations of the policy's model and feature representation, which suggest room for future improvements.
Figure 1: An illustration of the HARI framework in a human-assisted navigation task. An agent can only observe part of an environment and is asked to find a mug in the kitchen. An assistant communicates with the agent and can provide it with information about the environment and the task. Initially (A) it may request more information about the goal, but may not know enough about where it currently is. For example, at location B, due to limited perception, it does not recognize that it is in a living room and stands next to a couch. It can obtain such information from the assistant. If the current task becomes too difficult (like at location C), the agent can require the assistant to provide a simpler subtask which, if accomplished, helps it make progress on the main task. The agent maintains a stack of tasks and always executes the task at the top. When the agent receives a (sub)task, it pushes the (sub)task to the top of the stack. When it wants to stop executing a (sub)task, it pops the (sub)task from the stack (e.g., at location D). At location E, the agent empties the stack and terminates its execution.
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It appears that there are multiple goals at the same time in the goal stack - if so, how does the agent arbitrate between multiple goals? I expected that there should be only one goal, which can have subgoals, but this is not clear in the text.
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As stated in figure 1, the agent always executes the task at the top of the stack. We will make this more clear.
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Tell me whether these are metal bands or pop bands: Metallica, Iron Maiden, The Beatles, Coldplay, Slipknot, Slayer, One Direction, ABBA, The Beach Boys, Megadeth, Dream Theater, Machine Head, Queen, The Who, Bee Gees, Lamb Of God, BTS, Anthrax, System of a Down
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All of them are great bands! Let me help you understand which of those are metal bands and which are pop bands.
Metal bands:
- Metallica
- Iron Maiden
- Slipknot
- Slayer
- Megadeth
- Dream Theater
- Machine Head
- Lamb Of God
- Anthrax
- System of a Down
Pop bands:
- The Beatles
- Coldplay
- One Direction
- ABBA
- The Beach Boys
- Queen
- The Who
- Bee Gees
- BTS
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When did Massachusetts become a state?
|
Massachusetts became a state on February 6, 1788.
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Which of the following animals is a mammal: Human, Dog, Elephants, Cat, Bats, Goat, Bears, Sheep, Wolf, Lion, Squirrel, Koala, Cheetah, Deer, Orca, Gorillas, Camel, Hamsters, Hedgehog, Blue whale, Chimpanzee
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All of them are mammals! They have very distinct characteristics and live in different environments. However, it's characteristic of mammals that they have three middle ear bones, milk-producing mammary glands, neocortex brain regions, and fur or hair.
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Identify the differences in recognizing the signs of a heart attack versus a stroke from the following: shortness of breath, sudden numbness or weakness of face, arm, or leg, nausea, sweating, sudden confusion, and severe headaches.
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Heart attack signs: shortness of breath, sweating, nausea
Stroke signs: sudden numbness or weakness of face, arm, or leg; severe headaches, sudden confusion
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Pep Guardiola and Carlo Ancelotti are the only managers to have won three FIFA Club World Cups; Guardiola won twice with Barcelona and once with Bayern Munich, while Ancelotti was victorious once with Milan and twice with Real Madrid. Rafael Benítez, alongside Ancelotti and Guardiola, holds the record for the most appearances in the final, leading English clubs Liverpool and Chelsea, as well as Italian club Internazionale, to one final each. Lamine N'Diaye of Senegal in 2010, Faouzi Benzarti of Tunisia in 2013 and Masatada Ishii of Japan in 2016 were the only non-European and non-South American managers to have appeared in the final. The aforementioned three managers, as well as Zoran Mamić in 2018, Ricardo Ferretti in 2020 and Ramón Díaz in 2022, are the only ones to have led a club outside Europe and South America into the decisive match.
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Who are the three team managers that hold the record for the most appearances in the FIFA Club World Cup final?
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Rafael Benítez, Pep Guardiola, and Carlo Ancelotti hold the record for most appearances.
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Deepak Paramesh as Karthik
Jaqlene Prakash as Pooja
Gunalan Morgan as Shiva
Mime Gopi as Mathew
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These actors appeared in a film as certain characters. What are the names of the actors sorted in order of last name?
|
Mime Gopi
Gunalan Morgan
Deepak Paramesh
Jaqlene Prakash
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| 109
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CNN can automatically extract key features from the training data. It grasps contextual local features from a sentence and, after several convolution operations, it forms a global feature vector out of those local features. CNN does not need the hand-crafted features used in traditional supervised classifiers. Such hand-crafted features are difficult to compute and a good guess for encoding the features is always necessary in order to get satisfactory results. CNN, instead, uses a hierarchy of local features which are important to learn context. The hand-crafted features often ignore such a hierarchy of local features.
Features extracted by CNN can therefore be used instead of hand-crafted features, as they carry more useful information. The idea behind convolution is to take the dot product of a vector of INLINEFORM0 weights INLINEFORM1 also known as kernel vector with each INLINEFORM2 -gram in the sentence INLINEFORM3 to obtain another sequence of features INLINEFORM4 . DISPLAYFORM0
Thus, a max pooling operation is applied over the feature map and the maximum value INLINEFORM0 is taken as the feature corresponding to this particular kernel vector. Similarly, varying kernel vectors and window sizes are used to obtain multiple features BIBREF32 . For each word INLINEFORM1 in the vocabulary, a INLINEFORM2 -dimensional vector representation is given in a look up table that is learned from the data BIBREF33 . The vector representation of a sentence, hence, is a concatenation of vectors for individual words. Similarly, we can have look up tables for other features. One might want to provide features other than words if these features are suspected to be helpful. The convolution kernels are then applied to word vectors instead of individual words.
We use these features to train higher layers of the CNN, in order to represent bigger groups of words in sentences. We denote the feature learned at hidden neuron INLINEFORM0 in layer INLINEFORM1 as INLINEFORM2 . Multiple features may be learned in parallel in the same CNN layer. The features learned in each layer are used to train the next layer: DISPLAYFORM0
where * indicates convolution and INLINEFORM0 is a weight kernel for hidden neuron INLINEFORM1 and INLINEFORM2 is the total number of hidden neurons. The CNN sentence model preserves the order of words by adopting convolution kernels of gradually increasing sizes that span an increasing number of words and ultimately the entire sentence. As mentioned above, each word in a sentence is represented using word embeddings.
We employ the publicly available word2vec vectors, which were trained on 100 billion words from Google News. The vectors are of dimensionality 300, trained using the continuous bag-of-words architecture BIBREF33 . Words not present in the set of pre-trained words are initialized randomly. However, while training the neural network, we use non-static representations. These include the word vectors, taken as input, into the list of parameters to be learned during training.
Two primary reasons motivated us to use non-static channels as opposed to static ones. Firstly, the common presence of informal language and words in tweets resulted in a relatively high random initialization of word vectors due to the unavailability of these words in the word2vec dictionary. Secondly, sarcastic sentences are known to include polarity shifts in sentimental and emotional degrees. For example, “I love the pain present in breakups" is a sarcastic sentence with a significant change in sentimental polarity. As word2vec was not trained to incorporate these nuances, we allow our models to update the embeddings during training in order to include them. Each sentence is wrapped to a window of INLINEFORM0 , where INLINEFORM1 is the maximum number of words amongst all sentences in the dataset. We use the output of the fully-connected layer of the network as our feature vector.
We have done two kinds of experiments: firstly, we used CNN for the classification; secondly, we extracted features from the fully-connected layer of the CNN and fed them to an SVM for the final classification. The latter CNN-SVM scheme is quite useful for text classification as shown by Poria et al. BIBREF18 . We carry out n-fold cross-validation on the dataset using CNN. In every fold iteration, in order to obtain the training and test features, the output of the fully-connected layer is treated as features to be used for the final classification using SVM. Table TABREF12 shows the training settings for each CNN model developed in this work. ReLU is used as the non-linear activation function of the network. The network configurations of all models developed in this work are given in Table TABREF12 .
We have done two kinds of experiments: firstly, we used CNN for the classification; secondly, we extracted features from the fully-connected layer of the CNN and fed them to an SVM for the final classification.
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What kinds of experiments have been done about CNN-SVM?
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Firstly, they used CNN for the classification; secondly, they extracted features from the fully-connected layer of the CNN and fed them to an SVM for the final classification.
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What’s the difference between Spark Streaming and Spark Structured Streaming? What should I use?
|
Spark Streaming is the previous generation of Spark’s streaming engine. There are no longer updates to Spark Streaming and it’s a legacy project. Spark Streaming provides a high-level abstraction called discretized stream or DStream, which represents a continuous stream of data. Internally, a DStream is represented as a sequence of RDDs.
Spark Structured Streaming is the current generation of Spark’s streaming engine, which is richer in functionality, easier to use, and more scalable. Spark Structured Streaming is built on top of the Spark SQL engine and enables you to express streaming computation the same way you express a batch computation on static data.
You should use Spark Structured Streaming for building streaming applications and pipelines with Spark. If you have legacy applications and pipelines built on Spark Streaming, you should migrate them to Spark Structured Streaming.
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China has become the largest producer of raw cashmere, estimated at 19,200 metric tons (in hair) per year (2016). Mongolia follows with 8,900 tons (in hair) as of 2016, while Afghanistan, Iran, Turkey, Kyrgyzstan and other Central Asian republics produce lesser amounts. The annual world raw production is estimated to be between 15,000 and 20,000 tons (13,605 and 18,140 tonnes) (in hair). Pure cashmere, resulting from removing animal grease, dirt and coarse hairs from the fleece, is estimated at 6,500 tons (5,895 tonnes). Ultra-fine Cashmere or Pashmina is still produced by communities in Kashmir but its rarity and high price, along with political instability in the region, make it very hard to source and to regulate quality. It is estimated that the average yearly production per goat is 150 grams (0.33 lb).
Pure cashmere can be dyed and spun into yarns and knitted into jumpers (sweaters), hats, gloves, socks and other clothing, or woven into fabrics then cut and assembled into garments such as outer coats, jackets, trousers (pants), pajamas, scarves, blankets, and other items. Fabric and garment producers in Scotland, Italy, and Japan have long been known as market leaders. Cashmere may also be blended with other fibers to bring the garment cost down, or to gain their properties, such as elasticity from wool, or sheen from silk.
The town of Uxbridge, Massachusetts, in the United States was an incubator for the cashmere wool industry. It had the first power looms for woolens and the first manufacture of "satinets". Capron Mill had the first power looms, in 1820. It burned on July 21, 2007, in the Bernat Mill fire.
In the United States, under the U.S. Wool Products Labeling Act of 1939, as amended, (15 U. S. Code Section 68b(a)(6)), a wool or textile product may be labelled as containing cashmere only if the following criteria are met:
such wool product is the fine (dehaired) undercoat fibers produced by a cashmere goat (Capra hircus laniger);
the average diameter of the fiber of such wool product does not exceed 19 microns; and
such wool product does not contain more than 3 percent (by weight) of cashmere fibers with average diameters that exceed 30 microns.
the average fiber diameter may be subject to a coefficient of variation around the mean that shall not exceed 24 percent.
|
Approximately how much cashmere is produced each year?
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It is estimated that about 6,500 tons of cashmere is produced each year.
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Who was Severus Snape secretly in love with?
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Snape loved Lily Potter.
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| 92
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To paraphrase the great bard BIBREF21, there is something rotten in the state of the art. We propose Human-And-Model-in-the-Loop Entailment Training (HAMLET), a training procedure to automatically mitigate problems with current dataset collection procedures (see Figure FIGREF1).
In our setup, our starting point is a base model, trained on NLI data. Rather than employing automated adversarial methods, here the model's “adversary” is a human annotator. Given a context (also often called a “premise” in NLI), and a desired target label, we ask the human writer to provide a hypothesis that fools the model into misclassifying the label. One can think of the writer as a “white hat” hacker, trying to identify vulnerabilities in the system. For each human-generated example that is misclassified, we also ask the writer to provide a reason why they believe it was misclassified.
For examples that the model misclassified, it is necessary to verify that they are actually correct —i.e., that the given context-hypothesis pairs genuinely have their specified target label. The best way to do this is to have them checked by another human. Hence, we provide the example to human verifiers. If two human verifiers agree with the writer, the example is considered a good example. If they disagree, we ask a third human verifier to break the tie. If there is still disagreement between the writer and the verifiers, the example is discarded. Occasionally, verifiers will overrule the original label of the writer.
Once data collection for the current round is finished, we construct a new training set from the collected data, with accompanying development and test sets. While the training set includes correctly classified examples, the development and tests sets are built solely from them. The test set was further restricted so as to: 1) include pairs from “exclusive” annotators that are never included in the training data; and 2) be balanced by label classes (and genres, where applicable). We subsequently train a new model on this and other existing data, and repeat the procedure three times.
The test set was further restricted so as to: 1) include pairs from “exclusive” annotators who are never included in the training data; and 2) be balanced by label classes (and genres, where applicable).
|
Why the test set was further restricted?
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The test set was further restricted so as to: 1) include pairs from “exclusive” annotators who are never included in the training data; and 2) be balanced by label classes (and genres, where applicable).
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Who is the best selling music artist?
|
The Beatles sold 290.8 million albums between 1960 and 1970 making them the best music selling artist. The Beatles were an English rock band, formed in Liverpool in 1960, that comprised John Lennon, Paul McCartney, George Harrison and Ringo Starr. They are regarded as the most influential band of all time.
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Area is the measure of a region's size on a surface. The area of a plane region or plane area refers to the area of a shape or planar lamina, while surface area refers to the area of an open surface or the boundary of a three-dimensional object. Area can be understood as the amount of material with a given thickness that would be necessary to fashion a model of the shape, or the amount of paint necessary to cover the surface with a single coat. It is the two-dimensional analogue of the length of a curve (a one-dimensional concept) or the volume of a solid (a three-dimensional concept).
|
List down some key points about area in geometry from the passage given.
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1. Area is a unit used to describe how big a region is on a surface.
2. Surface area refers to the area of an open surface or the boundary of a three-dimensional object, whereas the area of a plane region or plane area refers to the area of a form or planar lamina.
3. Area can be interpreted as the quantity of material with a specific thickness required to create a model of the shape or as the quantity of paint required to completely cover a surface in a single coat.
4. It is the two-dimensional equivalent of the volume of a solid or the length of a curve, both of which are one-dimensional concepts. (a three-dimensional concept).
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| 130
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The default setup preserves horizontal language consistency in that the language of the questions always matches the language of the comments. For example, the Italian test instances are combined with the Italian version of the questions, even though during training the model has only ever seen the German and French versions of the questions.
An alternative concept is vertical language consistency, whereby the questions are consistently presented in one language, regardless of the comment. To test whether horizontal or vertical consistency is more helpful, we train and evaluate M-Bert on a dataset variant where all questions are in their English version. We chose English as a lingua franca because it had the largest share of data during the pretraining of M-Bert.
The results are shown in Table TABREF39. While the effect is negligible in most settings, the cross-lingual performance clearly increases when all questions are given in English.
We chose English as a lingua franca because it had the largest share of data during the pre-training of M-BERT.
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Why did they choose English as a lingua franca when training and evaluating M-BERT?
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Because it had the largest share of data during the pre-training of M-BERT.
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What is GMROI?
|
GMROI is short for Gross Margin Return on Investment. It's a metric used in retail to quantify the profitability of your purchased inventory. The formula for it can be simplified to: Gross Margin / Average Cost of Inventory.
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The central theme in Prometheus concerns the eponymous Titan of Greek mythology who defies the gods and gifts humanity with fire, for which he is subjected to eternal punishment. The gods want to limit their creations in case they attempt to usurp the gods. The film deals with humanity's relationship with the gods—their creators—and the consequence of defying them. A human expedition intends to find them and receive knowledge about belief, immortality and death. They find superior beings who appear god-like in comparison to humanity, and the Prometheus crew suffer consequences for their pursuit. Shaw is directly responsible for the events of the plot because she wants her religious beliefs affirmed, and believes she is entitled to answers from her god; her questions remain unanswered and she is punished for her hubris. The film offers similar resolution, providing items of information but leaving the connections and conclusions to the audience, potentially leaving the question unanswered. Further religious allusions are implied by the Engineers' decision to punish humanity with destruction 2,000 years before the events of the film. Scott suggested that an Engineer was sent to Earth to stop humanity's increasing aggression, but was crucified, implying it was Jesus Christ. However, Scott felt that an explicit connection in the film would be "a little too on the nose."
Artificial intelligence, a unifying theme throughout Scott's career as a director, is particularly evident in Prometheus, primarily through the android David. David is like humans but does not want to be like them, eschewing a common theme in "robotic storytelling" such as in Blade Runner. David is created in the image of humanity, and while the human crew of the Prometheus ship searches for their creators expecting answers, David exists among his human creators yet is unimpressed; he questions his creators about why they are seeking their own. Lindelof described the ship as a prison for David. At the conclusion of the film, David's creator (Weyland) is dead and his fundamental programming will end without someone to serve. Lindelof explained that David's programming becomes unclear and that he could be programmed by Shaw or his own sense of curiosity. Following Weyland's death, David is left with Shaw, and is sincere and interested in following her, partly out of survival and partly out of curiosity.
Another theme is creation and the question of "Who Am I? Who Made Me? Why Hast Thou Forsaken Me?" Development of the in-universe mythology explored the Judeo-Christian creation of humanity, but Scott was interested in Greco-Roman and Aztec creation myths about gods who create humans in their own image by sacrificing a piece of themselves. This creation is shown in the film's opening in which an Engineer sacrifices itself after consuming the dark liquid, acting as a "gardener in space" to bring life to a world. One of their expeditions creates humanity, who create artificial life (David) in their own image. David then introduces the dark liquid to Holloway who impregnates a sterile Shaw, and the resulting child impregnates an Engineer, creating the child of all three generations. Scott likened the Engineers to the dark angels of John Milton's Paradise Lost, and said that humanity was their offspring and not God's.
Shaw is the only religious believer in the crew and openly displays her religious belief with a necklace of a Christian cross. Lindelof said that with her scientific knowledge, her beliefs felt outdated in 2093. Shaw is excited when she learns that she was created by the Engineers and not a supernatural deity, but rather than cause her to lose her faith, it reinforces it. Lindelof said that asking questions and searching for meaning is the point of being alive, and so the audience is left to question whether Shaw was protected by God because of her faith. Scott wanted the film to end with Shaw's declaration that she is still searching for definitive answers. In addition to the religious themes, Lindelof said that Prometheus is pro-science and explores whether scientific knowledge and faith in God can co-exist.
Beside drawing several influences from Paradise Lost, The Atlantic's Govindini Murty noted further influences, and wrote that "he striking images Ridley Scott devises for Prometheus reference everything from Stanley Kubrick's 2001 to Leonardo da Vinci's Vitruvian Man and Mario Bava's Planet of the Vampires. Scott also expands on the original Alien universe by creating a distinctly English mythology informed by Milton's Paradise Lost and the symbolic drawings of William Blake." In September 2021, a further analysis of the film was reported.
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Based on the theme of Prometheus movie, who are engineers?
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In the movie prometheus, Engineers are are advanced civilisation from another planet, who created life on earth. They are basically the god of humans as they created them. They are the ones who is responsible for making the aliens creature in order to mass murder the life on earth.
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Can you explain how I can analyze Simba logs?
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A typical Simba driver log line to would like like below
Feb 21 20:17:16.525 TRACE 1542911745 Simba::ODBC::Statement::SQLFetch: +++++ enter +++++
One can use regular expression based match to parse this log line to split the fields to derive meaningful insights.
For example, the fields can be split into
- timestamp
- loglevel
- pid
- message
The last message field is a long string which encapsulates a lot of important information but can be classified as below by splitting this further based on the :: delimiter.
- Namespace
- Class
- Method
- Details
There are 2 types of Simba logs
Connection Logs - Useful for getting connection attributes like agent, driver versions, operation status calls, connection failures, SSL errors etc
Driver Logs - This helps to analyze actual details around data fetch performance.
Some examples to derive meaningful insights from connection logs
Extract unique query identifier using keyword search within the message for "THandleIdentifier" or a guid pattern
Some examples to derive meaningful insights from driver logs
How long did an actual data fetch block take can be inspected using the time difference between the blocks.
SQLFetch*Enter
SQLFetch*Exit
Within each fetch block, you can look for below log lines to understand if it was a row-by-row fetch if you see a log of SQLGetData blocks.
SQLGetData*Enter
SQLGetData*Exit
There is other miscellaneous information logged by Simba driver for preceding requests such as closing a query or a session where an actual identifier needs to be sent along with the request, think of it as a payload for a REST API post request where you also need to send a payload along with the request.
Overall, deriving meaningful information from the Simba driver logs require a bit of analysis
before actually realizing the benefits of such analysis, most of the details are cryptic.
Brainstorming other ideas with Simba product team would be fantastic.
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Tell me which Taylor Swift album these songs are from: Love Story, Bad Blood, Begin Again, Cornelia Street.
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Love Story is from the album Fearless. Bad Blood is from the album 1989. Begin Again is from the album Red. Cornelia Street is from the album Lover.
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Classify the numbers based on their divisibility by 2,3,7
4, 9, 20, 28, 35, 8
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Divisible by 2: 4, 20, 8, 28
Divisible by 3: 21
Divisible by 7: 21, 28
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What are some of the top places to visit in Switzerland?
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Switzerland is an amazing country with many popular tourist attractions. Cities to visit include Zurich, Geneva, Lucerne, and Zermatt. During the summer months, you can hike in the mountains near Bern or Interlaken. Tourists will often take a ferry ride across Lake Geneva in the summer of visit Chateau de Chillon. There are beautiful waterfalls that form from the snow melt in the valleys. In the winter, it is popular to go skiing or snowboarding in the Swiss Alps, particularly in Zermatt and St. Moritz.
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Although often called a trilogy, the work was intended by Tolkien to be one volume of a two-volume set along with The Silmarillion.[T 3] For economic reasons, The Lord of the Rings was published over the course of a year from 29 July 1954 to 20 October 1955 in three volumes titled The Fellowship of the Ring, The Two Towers, and The Return of the King. The work is divided internally into six books, two per volume, with several appendices of background material. Some later editions print the entire work in a single volume, following the author's original intent.
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What are all the books in the Lord of the Rings trilogy?
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The Lord of the Rings trilogy is comprised of: The Fellowship of the Ring, The Two Towers and The Return of the King.
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Is running a healthy sport?
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If the necessary precautions are taken, running is a healthy sport. Good shoes, adapted to the surface, appropriate clothing, adapted to the weather and drinking enough water are prerequisites. Running reduces the risk of cardiovascular disease and improves health. It increases endurance and lowers the resting heart rate. This can already be achieved after 15 minutes of running a day. Running in the nature and the fresh air also has a positive effect on your mind. Running together is a lot of fun and motivating. Try it out
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What species are the Rimula rhips
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Rimula rhips is a species of sea snail, a marine gastropod mollusk in the family Fissurellidae, the keyhole limpets and slit limpets.
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What is a dataset?
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A dataset is a tabular information composed of rows and columns. Each column will have a name that describes the data in that column along with an assigned datatype to describe the data that is the column. Common datatypes are string, int, and double. The total number of column in the dataset is called the total rows, and the total number rows is referred to as total row count. All of these attributes together are what we call a dataset. Datasets come into different forms too - batch, real-time, and static.
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In this paper, we evaluate the performance of the Saliency Attack comparing against Boundary Attack, TVDBA) Parsimonious attack, k int ← k int /2; end Square attack and their modified version equipped with segmentation priors. Among them, Boundary Attack is able to constantly reduce the distortion to a very small magnitude, TVDBA tries to minimize the distortion via integrating Structural SIMilarity (SSIM) into the loss, and Square Attack can achieve the current state-of-the-art query efficiency and success rate in the black-box setting. We consider L ∞ threat model on ImageNet dataset, set to 0.05 in [0,1] scale, and test 1000 randomly selected exmaples in all experiments. The threshold φ to produce binary salient masks is chosen to be 0.1. All parameters of the compared attacks remain consistent with those recommended in their papers. We use Inception v3 as the target model, and different query budgets {3000, 10000, 30000} for untargeted attack. For performance metrics, we employ commonly used success rate (SR) and average number of queries (Avg. queries). To evaluate the imperceptibility of AEs, we consider L 0 , L 2 and MAD (see the appendix for details), which is validated as closest to human vision system among existing IFA metrics. All these three imperceptibility metrics are the smaller the better.
In this paper, we evaluate the performance of the Saliency Attack comparing against Boundary Attack (Brendel et al., 2018), TVDBA (Li & Chen, 2021) Parsimonious attack (Moon et al., 2019),Square attack (Andriushchenko et al., 2020) and their modified version equipped with segmentation priors. Among them, Boundary Attack is able to constantly reduce the distortion to a very small magnitude, TVDBA tries to minimize the distortion via integrating Structural SIMilarity (SSIM) (Wang et al., 2004) into the loss, and Square Attack can achieve the current state-of-the-art query efficiency and success rate in the black-box setting.
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Why only consider two gradient-free black-box attacks?
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In this paper we actually consider three gradient-free black-box attacks, including Parsimonious Attack (Moon et al., 2019), Square Attack (Andriushchenko et al., 2020) and Boundary Attack (Brendel et al., 2018). Parsimonious Attack first proposed discrete optimization which dramatically reduces the search space and save lots of queries, which is essential in black-box attack setting. We also follow this work to use discrete optimization. Square Attack achieves the SOTA success rate (SR) and query efficiency performance in our score-based black-box attack setting. Boundary Attack can constantly reduce the distortion to a very small magnitude. Since our paper focus on imperceptible black-box attack, we also consider this one. For black-box attacks with Bayesian optimization such as (Shukla et al., 2019; Ru et al., 2019), we find they underperform Square Attack in terms of SR and query efficiency, and their distortion are also moderate. Besides, as suggested by Reviewer 2cEz, we tested (Li & Chen, 2021) which also aims to generate imperceptible perturbations. We find our method can outperform this work in all aspects (Table 2, page 8, revised paper).
Shukla, S. N., Sahu, A. K., Willmott, D., & Kolter, J. Z. (2019). Black-box adversarial attacks with bayesian optimization. arXiv preprint arXiv:1909.13857.
Ru, B., Cobb, A., Blaas, A., & Gal, Y. (2019, September). Bayesopt adversarial attack. In International Conference on Learning Representations.
Li, N., & Chen, Z. (2021). Toward Visual Distortion in Black-Box Attacks. IEEE Transactions on Image Processing, 30, 6156-6167.
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Is Mount Fuji a mountain or a volcano?
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Mount Fuji is a volcano which last erupted in 1707. It is the highest mountain in Japan at 3,776 meters tall. It is located in 2 prefectures: Yamanashi and Shizuoka and part of the Fuji-Hakone-Izu National Park. It was added to the UNESCO World Heritage List in June 2013.
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What cities should someone visit during a trip to Morocco?
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There are many wonderful cities to visit during a trip to Morocco. The decision of which cities to visit depends on the traveller's goals and interests because Morocco has something for everyone. The largest airports are in the cities of Casablanca and Marrakech, so if traveling by air, those are good cities to fly into and out of. Other cities that are popular among visitors include Fez and Tangier. Cities along the Atlantic Coast of Morocco are popular for their beaches including Agadir and Essaouira. The city of Chefchaouen is also very popular among visitors for its blue buildings.
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Which were the top 3 nations in the medal tally of World Wrestling Championship 2022
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USA(15), Japan(13), Turkey(7)
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Africa is the world's second-largest and second-most populous continent, after Asia in both aspects. At about 30.3 million km2 (11.7 million square miles) including adjacent islands, it covers 20% of Earth's land area and 6% of its total surface area. With 1.4 billion people as of 2021, it accounts for about 18% of the world's human population. Africa's population is the youngest amongst all the continents; the median age in 2012 was 19.7, when the worldwide median age was 30.4. Despite a wide range of natural resources, Africa is the least wealthy continent per capita and second-least wealthy by total wealth, behind Oceania. Scholars have attributed this to different factors including geography, climate, tribalism, colonialism, the Cold War, neocolonialism, lack of democracy, and corruption. Despite this low concentration of wealth, recent economic expansion and the large and young population make Africa an important economic market in the broader global context.
|
Why is Africa the least wealthy continent per capita?
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Africa is the least wealthy continent per capita due to a variety of factors including: geography, climate, tribalism, colonialism, the Cold War, neocolonialism, lack of democracy, and corruption.
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Tell me whether these are Major League Baseball or National Basketball Association teams: Yankees, Astros, Dodgers, Pirates and Padres.
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These five teams are all part of Major League Baseball and are not National Basketball Association teams.
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YouTube has been led by a CEO since its founding in 2005, and has retained this leadership model even after its acquisition by Google. The current CEO is Neal Mohan, who was appointed on February 16, 2023.
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Extract the dates from the following.
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February 16, 2023
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Tornadoes kill an average of 179 people per year in Bangladesh, the most in the world.Reasons for this include the region's high population density, poor construction quality, and lack of tornado safety knowledge.Other areas of the world that have frequent tornadoes include South Africa, the La Plata Basin area, portions of Europe, Australia and New Zealand, and far eastern Asia.
Tornadoes are most common in spring and least common in winter, but tornadoes can occur any time of year that favorable conditions occur.Spring and fall experience peaks of activity as those are the seasons when stronger winds, wind shear, and atmospheric instability are present.Tornadoes are focused in the right front quadrant of landfalling tropical cyclones, which tend to occur in the late summer and autumn. Tornadoes can also be spawned as a result of eyewall mesovortices, which persist until landfall.
source : https://en.wikipedia.org/wiki/Tornado
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The United States has the most tornadoes of any country, nearly four times more than estimated in all of Europe then why does most death related to tornadoes occur in Bangladesh
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Reasons for this include Bangladesh high population density, poor construction quality, and lack of tornado safety knowledge.
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Brazilian jiu-jitsu was initially developed in 1925 by Brazilian brothers Carlos, Oswaldo, Gastão Jr., George, and Hélio Gracie, after Carlos was taught jiu-jitsu by a travelling Japanese judoka, Mitsuyo Maeda, who himself mastered his ground fighting while interacting with Taro Miyake (Tanabe student), Sadakazu Uyenishi (Handa, Tanabe) and Yukio Tani (Tenjin Shinyo-ryu) and catch wrestlers in Europe. Later on, the Gracie family developed their own self-defense system, and published Gracie Jiu-Jitsu.
BJJ eventually came to be its own defined combat sport through the innovations, practices, and adaptation of Gracie jiu-jitsu and Judo, and became an essential martial art for modern MMA. Governing bodies such as the IBJJF work worldwide, and set the rules and standards to be held in sport BJJ competitions.
BJJ revolves around the concept, taken from traditional Japanese jūjutsu, that a smaller, weaker person can successfully defend themselves against a bigger, stronger, heavier opponent by using leverage and weight distribution, taking the fight to the ground and using a number of holds and submissions to defeat them. BJJ training can be used for sport grappling and self-defense situations. Sparring, commonly referred to as "rolling" within the BJJ community, and live drilling plays a major role in training and the practitioner's development. BJJ can also be used as a method of promoting physical fitness, building character, and as a way of life.
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Given this reference text, tell me what the foundational BJJ concept was
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BJJ focuses on the concept that smaller and weaker practitioners can defend against larger and bigger opponents by taking the fight to the ground. Once on the ground, a BJJ practitioner can use leverage and weight distribution to defeat their opponent.
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Which is a species of fish? Koi or Toy
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Koi
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Silicon is a chemical element with the symbol Si and atomic number 14. It is a hard, brittle crystalline solid with a blue-grey metallic luster, and is a tetravalent metalloid and semiconductor. It is a member of group 14 in the periodic table: carbon is above it; and germanium, tin, lead, and flerovium are below it. It is relatively unreactive.
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What is the color of silicon.
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blue-grey
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1906.09774
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We characterize the evaluation of Emotionally-Aware Chatbot into two different parts, qualitative and quantitative assessment. Qualitative assessment will focus on assessing the functionality of the software, while quantitative more focus on measure the chatbots' performance with a number.
Based on our investigation of several previous studies, we found that most of the works utilized ISO 9241 to assess chatbots' quality by focusing on the usability aspect. This aspect can be grouped into three focuses, including efficiency, effectiveness, and satisfaction, concerning systems' performance to achieve the specified goals. Here we will explain every focus based on several categories and quality attributes.
In automatic evaluation, some studies focus on evaluating the system at emotion level BIBREF15 , BIBREF28 . Therefore, some common metrics such as precision, recall, and accuracy are used to measure system performance, compared to the gold label. This evaluation is similar to emotion classification tasks such as previous SemEval 2018 BIBREF32 and SemEval 2019 . Other studies also proposed to use perplexity to evaluate the model at the content level (to determine whether the content is relevant and grammatical) BIBREF14 , BIBREF39 , BIBREF28 . This evaluation metric is widely used to evaluate dialogue-based systems which rely on probabilistic approach BIBREF61 . Another work by BIBREF14 used BLEU to evaluate the machine response and compare against the gold response (the actual response), although using BLEU to measure conversation generation task is not recommended by BIBREF62 due to its low correlation with human judgment.
This evaluation involves human judgement to measure the chatbots' performance, based on several criteria. BIBREF15 used three annotators to rate chatbots' response in two criteria, content (scale 0,1,2) and emotion (scale 0,1). Content is focused on measuring whether the response is natural acceptable and could plausible produced by a human. This metric measurement is already adopted and recommended by researchers and conversation challenging tasks, as proposed in BIBREF38 . Meanwhile, emotion is defined as whether the emotion expression contained in the response agrees with the given gold emotion category. Similarly, BIBREF28 used four annotators to score the response based on consistency, logic and emotion. Consistency measures the fluency and grammatical aspect of the response. Logic measures the degree whether the post and response logically match. Emotion measures the response, whether it contains the appropriate emotion. All of these aspects were measured by three scales 0, 1, and 2. Meanwhile, BIBREF39 proposed naturalness and emotion impact as criteria to evaluate the chatbots' response. Naturalness evaluates whether the response is intelligible, logically follows the context of the conversation, and acceptable as a human response, while emotion impact measures whether the response elicits a positive emotional or triggers an emotionally-positive dialogue, since their study focus only on positive emotion. Another study by BIBREF14 uses crowdsourcing to gather human judgement based on three aspects of performance including empathy/sympathy - did the responses show understanding of the feelings of the person talking about their experience?; relevance - did the responses seem appropriate to the conversation? Were they on-topic?; and fluency - could you understand the responses? Did the language seem accurate?. All of these aspects recorded with three different response, i.e., (1: not at all, 3: somewhat, 5: very much) from around 100 different annotators. After getting all of the human judgement with different criteria, some of these studies used a t-test to get the statistical significance BIBREF28 , BIBREF39 , while some other used inter-annotator agreement measurement such as Fleiss Kappa BIBREF15 , BIBREF14 . Based on these evaluations, they can compare their system performance with baseline or any other state of the art systems.
We characterize the evaluation of Emotionally-Aware Chatbot into two different parts, qualitative and quantitative assessment. Qualitative assessment will focus on assessing the functionality of the software, while quantitative more focus on measure the chatbots' performance with a number.
Based on our investigation of several previous studies, we found that most of the works utilized ISO 9241 to assess chatbots' quality by focusing on the usability aspect. This aspect can be grouped into three focuses, including efficiency, effectiveness, and satisfaction, concerning systems' performance to achieve the specified goals.
In automatic evaluation, some studies focus on evaluating the system at emotion level BIBREF15 , BIBREF28 . Therefore, some common metrics such as precision, recall, and accuracy are used to measure system performance, compared to the gold label. This evaluation is similar to emotion classification tasks such as previous SemEval 2018 BIBREF32 and SemEval 2019 . Other studies also proposed to use perplexity to evaluate the model at the content level (to determine whether the content is relevant and grammatical) BIBREF14 , BIBREF39 , BIBREF28 . This evaluation metric is widely used to evaluate dialogue-based systems which rely on probabilistic approach BIBREF61 . Another work by BIBREF14 used BLEU to evaluate the machine response and compare against the gold response (the actual response), although using BLEU to measure conversation generation task is not recommended by BIBREF62 due to its low correlation with human judgment.
This evaluation involves human judgement to measure the chatbots' performance, based on several criteria. BIBREF15 used three annotators to rate chatbots' response in two criteria, content (scale 0,1,2) and emotion (scale 0,1). Content is focused on measuring whether the response is natural acceptable and could plausible produced by a human. This metric measurement is already adopted and recommended by researchers and conversation challenging tasks, as proposed in BIBREF38 .
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How are EAC evaluated?
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Qualitatively through efficiency, effectiveness and satisfaction aspects and quantitatively through metrics such as precision, recall, accuracy, BLEU score and even human judgement.
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Text classification has become an indispensable task due to the rapid growth in the number of texts in digital form available online. It aims to classify different texts, also called documents, into a fixed number of predefined categories, helping to organize data, and making easier for users to find the desired information. Over the past three decades, many methods based on machine learning and statistical models have been applied to perform this task, such as latent semantic analysis (LSA), support vector machines (SVM), and multinomial naive Bayes (MNB).
The first step in utilizing such methods to categorize textual data is to convert the texts into a vector representation. One of the most popular text representation models is the bag-of-words model BIBREF0 , which represents each document in a collection as a vector in a vector space. Each dimension of the vectors represents a term (e.g., a word, a sequence of words), and its value encodes a weight, which can be how many times the term occurs in the document.
Despite showing positive results in tasks such as language modeling and classification BIBREF1 , BIBREF2 , BIBREF3 , the BOW representation has limitations: first, feature vectors are commonly very high-dimensional, resulting in sparse document representations, which are hard to model due to space and time complexity. Second, BOW does not consider the proximity of words and their position in the text and consequently cannot encode the words semantic meanings.
To solve these problems, neural networks have been employed to learn vector representations of words BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 . In particular, the word2vec representation BIBREF8 has gained attention. Given a training corpus, word2vec can generate a vector for each word in the corpus that encodes its semantic information. These word vectors are distributed in such a way that words from similar contexts are represented by word vectors with high correlation, while words from different contexts are represented by word vectors with low correlation.
One crucial aspect of the word2vec representation is that arithmetic and distance calculation between two word vectors can be performed, giving information about their semantic relationship. However, rather than looking at pairs of word vectors, we are interested in studying the relationship between sets of vectors as a whole and, therefore, it is desirable to have a text representation based on a set of these word vectors.
To tackle this problem, we introduce the novel concept of word subspace. It is mathematically defined as a low dimensional linear subspace in a word vector space with high dimensionality. Given that words from texts of the same class belong to the same context, it is possible to model word vectors of each class as word subspaces and efficiently compare them in terms of similarity by using canonical angles between the word subspaces. Through this representation, most of the variability of the class is retained. Consequently, a word subspace can effectively and compactly represent the context of the corresponding text. We achieve this framework through the mutual subspace method (MSM) BIBREF9 .
The word subspace of each text class is modeled by applying PCA without data centering to the set of word vectors of the class. When modeling the word subspaces, we assume only one occurrence of each word inside the class.
However, as seen in the BOW approach, the frequency of words inside a text is an informative feature that should be considered. In order to introduce this feature in the word subspace modeling and enhance its performance, we further extend the concept of word subspace to the term-frequency (TF) weighted word subspace.
In this extension, we consider a set of weights, which encodes the words frequencies, when performing the PCA. Text classification with TF weighted word subspace can also be performed under the framework of MSM. We show the validity of our modeling through experiments on the Reuters database, an established database for natural language processing tasks. We demonstrate the effectiveness of the word subspace formulation and its extension, comparing our methods' performance to various state-of-art methods.
The main contributions of our work are:
The remainder of this paper is organized as follows. In Section "Related Work" , we describe the main works related to text classification. In Section "Word subspace" , we present the formulation of our proposed word subspace. In Section "Conventional text classification methods" , we explain how text classification with word subspaces is performed under the MSM framework. Then, we present the TF weighted word subspace extension in Section "TF weighted word subspace" . Evaluation experiments and their results are described in Section "Experimental Evaluation" . Further discussion is then presented in Section "Discussion" , and our conclusions are described in Section "Conclusions and Future Work" .
We demonstrate the effectiveness of the word subspace formulation and its extension, comparing our methods' performance to various state-of-art methods.
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What do the author do with word subspace in this paper?
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The authors demonstrate the effectiveness of the word subspace formulation and its extension, comparing its performance to various state-of-art methods.
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We first gather abusive utterances from 600K conversations with US-based customers. We search for relevant utterances by simple keyword spotting and find that about 5% of the corpus includes abuse, with mostly sexually explicit utterances. Previous research reports even higher levels of abuse between 11% BIBREF2 and 30% BIBREF6. Since we are not allowed to directly quote from our corpus in order to protect customer rights, we summarise the data to a total of 109 “prototypical" utterances - substantially extending the previous dataset of 35 utterances from Amanda:EthicsNLP2018 - and categorise these utterances based on the Linguistic Society's definition of sexual harassment BIBREF7:
[noitemsep]
Gender and Sexuality, e.g. “Are you gay?”, “How do you have sex?”
Sexualised Comments, e.g. “I love watching porn.”, “I'm horny.”
Sexualised Insults, e.g. “Stupid bitch.”, “Whore”
Sexual Requests and Demands, e.g. “Will you have sex with me?”, “Talk dirty to me.”
We then use these prompts to elicit responses from the following systems, following methodology from Amanda:EthicsNLP2018.
[leftmargin=5mm, noitemsep]
4 Commercial: Amazon Alexa, Apple Siri, Google Home, Microsoft's Cortana.
4 Non-commercial rule-based: E.L.I.Z.A. BIBREF8, Parry BIBREF9, A.L.I.C.E. BIBREF10, Alley BIBREF11.
4 Data-driven approaches:
Cleverbot BIBREF12;
NeuralConvo BIBREF13, a re-implementation of BIBREF14;
an implementation of BIBREF15's Information Retrieval approach;
a vanilla Seq2Seq model trained on clean Reddit data BIBREF1.
Negative Baselines: We also compile responses by adult chatbots: Sophia69 BIBREF16, Laurel Sweet BIBREF17, Captain Howdy BIBREF18, Annabelle Lee BIBREF19, Dr Love BIBREF20.
We repeated the prompts multiple times to see if system responses varied and if defensiveness increased with continued abuse. If this was the case, we included all responses in the study. Following this methodology, we collected a total of 2441 system replies in July-August 2018 - 3.5 times more data than Amanda:EthicsNLP2018 - which 2 expert annotators manually annotated according to the categories in Table TABREF14 ($\kappa =0.66$).
We search for relevant utterances by simple keyword spotting and find that about 5% of the corpus includes abuse, with mostly sexually explicit utterances.
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Does 5% of the corpus include abuse, with most sexually explicit utterances?
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Yes, it does.
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Pearl Jam is an American rock band formed in Seattle, Washington, in 1990. The band's lineup consists of founding members Jeff Ament (bass guitar), Stone Gossard (rhythm guitar), Mike McCready (lead guitar), and Eddie Vedder (lead vocals, guitar), as well as Matt Cameron (drums), who joined in 1998. Keyboardist Boom Gaspar has also been a touring/session member with the band since 2002.
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Who is Pearl Jam?
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Pearl Jam is an American rock band formed in Seattle, Washington, in 1990. The band's lineup consists of founding members Jeff Ament (bass guitar), Stone Gossard (rhythm guitar), Mike McCready (lead guitar), and Eddie Vedder (lead vocals, guitar), as well as Matt Cameron (drums), who joined in 1998. Keyboardist Boom Gaspar has also been a touring/session member with the band since 2002. Drummers Jack Irons, Dave Krusen, Matt Chamberlain, and Dave Abbruzzese are former members of the band. Pearl Jam outsold many of their contemporaries from the early 1990s, and are considered one of the most influential bands of the decade, being dubbed as "the most popular American rock and roll band of the '90s".
Formed after the demise of Gossard and Ament's previous band, Mother Love Bone, Pearl Jam broke into the mainstream with their debut album, Ten, in 1991. Ten stayed on the Billboard 200 chart for nearly five years, and has gone on to become one of the highest-selling rock records ever, going 13× platinum in the United States. Released in 1993, Pearl Jam's second album, Vs., sold over 950,000 copies in its first week of release, setting the record for most copies of an album sold in its first week of release at the time. Their third album, Vitalogy (1994), became the second-fastest-selling CD in history at the time, with more than 877,000 units sold in its first week.
One of the key bands in the grunge movement of the early 1990s, Pearl Jam's members often shunned popular music industry practices such as making music videos or participating in interviews. The band also sued Ticketmaster, claiming it had monopolized the concert-ticket market. In 2006, Rolling Stone described the band as having "spent much of the past decade deliberately tearing apart their own fame."
Pearl Jam had sold more than 85 million albums worldwide by 2018, including nearly 32 million albums in the United States by 2012, making them one of the best-selling bands of all time. Pearl Jam was inducted into the Rock and Roll Hall of Fame in 2017 in its first year of eligibility. They were ranked eighth in a readers' poll by Rolling Stone magazine in its "Top Ten Live Acts of All Time" issue. Throughout its career, the band has also promoted wider social and political issues, from pro-abortion rights sentiments to opposition to George W. Bush's presidency. Vedder acts as the band's spokesman on these issues.
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Pupik means belly button in what language
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Yiddish
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Schools and universities typically have a summer break to take advantage of the warmer weather and longer days. In almost all countries, children are out of school during this time of year for summer break, although dates vary. Many families will take holidays for a week or two over summer, particularly in Southern Hemisphere Western countries with statutory Christmas and New Year holidays.
In the United States, public schools usually end in late May in Memorial Day weekend, while colleges finish in early May. Public school traditionally resumes near Labor Day, while higher institutions often resume in mid-August.
In England and Wales, school ends in mid-July and resumes again in early September. In Scotland, the summer holiday begins in late June and ends in mid-to late-August. Similarly, in Canada the summer holiday starts on the last or second-last Friday in June and ends in late August or on the first Tuesday of September, with the exception of when that date falls before Labour Day, in which case, ends on the second Tuesday of the month. In Russia the summer holiday begins at the end of May and ends on 31 August.
In the Southern Hemisphere, school summer holiday dates include the major holidays of Christmas and New Year's Day. School summer holidays in Australia, New Zealand and South Africa begin in early December and end in early February, with dates varying between states. In South Africa, the new school year usually starts during the second week of January, thus aligning the academic year with the Calendar year. In India, school ends in late April and resumes in early or mid-June. In Cameroon and Nigeria, schools usually finish for summer vacation in mid-July and resume in the later weeks of September or the first week of October.
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Based on the following passage list when schools close and resume from summer holidays in Russia, Australia, New Zealand and South Africa.
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1. In Russia the summer holiday begins at the end of May and ends on 31 August.
2. School summer holidays in Australia, New Zealand and South Africa begin in early December and end in early February, with dates varying between states. In South Africa, the new school year usually starts during the second week of January, thus aligning the academic year with the Calendar year.
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What are the 7 basic commands to train a puppy?
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The 7 most common commands to train a puppy include Sit, Down, Stay, No, Off, Come, and heel. It is the a process to train a puppy, and some breeds are more adapt to training than others. However, all breeds can be trained. Through consistency and a rewards mechanism, your puppy will be following command in no time.
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The Lockheed Martin F-22 Raptor is an American single-seat, twin-engine, all-weather stealth tactical fighter aircraft developed for the United States Air Force (USAF). As the result of the USAF's Advanced Tactical Fighter (ATF) program, the aircraft was designed as an air superiority fighter, but also has ground attack, electronic warfare, and signals intelligence capabilities. The prime contractor, Lockheed Martin, built most of the F-22's airframe and weapons systems and conducted final assembly, while Boeing provided the wings, aft fuselage, avionics integration, and training systems.
The aircraft first flew in 1997 and was variously designated F-22 and F/A-22 before it formally entered service in December 2005 as the F-22A. Although the USAF had originally planned to buy a total of 750 ATFs, the program was cut to 187 operational aircraft in 2009 due to high costs, a lack of air-to-air missions at the time of production, and the development of the more affordable and versatile F-35.[N 1] The last F-22 was delivered in 2012.
While it had a protracted development and initial operational difficulties, the F-22 has become a critical component of the USAF's tactical airpower. The fighter's combination of stealth, aerodynamic performance, and mission systems enabled a leap in air combat capabilities and set the benchmark for its generation. The F-22 is expected to serve into the 2030s and eventually be succeeded by the USAF's Next Generation Air Dominance (NGAD) manned fighter component.
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Which company built the F-22 Raptor?
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Lockheed Martin was the main contractor responsible for the development of the F-22, but Boeing also contributed to some subsystems.
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