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Taylor Alison Swift (born December 13, 1989) is an American singer-songwriter. Her genre-spanning discography, songwriting abilities and artistic reinventions have received critical praise and wide media coverage. Born in West Reading, Pennsylvania, Swift moved to Nashville at age 14 to become a country artist. She signed a songwriting deal with Sony/ATV Music Publishing in 2004 and a recording contract with Big Machine Records in 2005. Her 2006 self-titled debut album made her the first female country artist to write a U.S. platinum-certified album. Swift's next albums, Fearless (2008) and Speak Now (2010), explored country pop. The former's "Love Story" and "You Belong with Me" were the first country songs to top the U.S. pop and all-genre airplay charts, respectively. She experimented with rock and electronic styles on Red (2012), which featured her first Billboard Hot 100 number-one song, "We Are Never Ever Getting Back Together", and eschewed her country image in her synth-pop album, 1989 (2014), supported by chart-topping songs "Shake It Off", "Blank Space", and "Bad Blood". Media scrutiny inspired the urban-flavored Reputation (2017) and its number-one single "Look What You Made Me Do". Exiting Big Machine, Swift signed with Republic Records in 2018 and released her seventh studio album, Lover (2019), followed by the autobiographical documentary Miss Americana (2020). She ventured into indie folk and alternative rock in her 2020 albums Folklore and Evermore, whose singles "Cardigan" and "Willow" topped the Hot 100. Swift began re-recording her first six albums after a dispute over their masters, re-releasing two in 2021—Fearless (Taylor's Version) and Red (Taylor's Version). The latter's "All Too Well (10 Minute Version)" became the longest song to top the Hot 100. Her 2022 album Midnights and single "Anti-Hero" broke all-time streaming records. Swift has directed music videos and films, such as All Too Well: The Short Film (2021), and played supporting roles in others. Having sold over 200 million records globally, Swift is one of the best-selling musicians in history. She is the most streamed woman on Spotify and the only act to have five albums open with over one million copies sold in the US. Among her accolades are 12 Grammy Awards, including three Album of the Year wins; an Emmy Award; 40 American Music Awards; 29 Billboard Music Awards; three IFPI Global Recording Artist of the Year awards; and 92 Guinness World Records. Swift has been featured in rankings such as Rolling Stone's 100 Greatest Songwriters of All Time, Billboard's Greatest of All Time Artists, the Time 100 and Forbes Celebrity 100. Honored with titles such as Artist of the Decade and Woman of the Decade, Swift is an advocate for artists' rights and women's empowerment. Her music is credited with influencing a generation of singers and songwriters.

According to the passage, mention some of Taylor’s hit songs after signing for Republic records.

Some of Taylor Swift’s greatest hits after signing with Republic records include: ​1."Cardigan" 2."Willow" 3."All Too Well (10 Minute Version)" 4."Anti-Hero"

how to give a great presentation

Giving a great presentation requires a combination of effective planning, engaging delivery, and thoughtful audience engagement. Here are some tips to help you create and deliver a successful presentation: Understand your audience: Before creating your presentation, research your audience to understand their interests, knowledge level, and expectations. This will help you tailor your content to their needs and make your presentation more engaging. Plan your content: Create a clear and concise outline that covers the main points you want to make. Use visuals, such as slides or videos, to support your message, but don't overload your presentation with too many images or text. Practice, practice, practice: Rehearse your presentation several times, preferably in front of a small audience or mirror. This will help you feel more confident and comfortable with your delivery. Use engaging delivery techniques: Use your voice, body language, and eye contact to engage your audience. Vary your tone and pace to keep your audience interested and use gestures and movements to emphasize your points. Encourage audience participation: Ask questions, invite feedback, and create opportunities for your audience to engage with you. This will help keep their attention and create a more interactive presentation. Be authentic and passionate: Show your enthusiasm for your topic and let your personality shine through. Your audience will be more engaged if they can see that you're passionate about what you're presenting. Close with a strong ending: Summarize your main points and leave your audience with a clear call to action or a memorable takeaway. This will help them remember your presentation and take action on your message. By following these tips, you can give a great presentation that engages and informs your audience. Remember to practice, be yourself, and have fun!

In which year did the Portuguese first arrived in Brazil?

The Portuguese first arrived in Brazil in the year of 1500.

2001.05493

In future, we will explore other methods to increase the understanding of deep learning models on group targeted text, although the categories are well defined we will look after if we further fine-tune the categories with more data. In the future, we are planning to pay attention on a generalized language model for code-mixed texts which can also handle Hindi-code-mixed and other multi-lingual code-mixed datasets (i.e., trying to reduce the dependencies on language-specific code-mixed resources). The block diagram of the proposed system is shown in Figure FIGREF22. The proposed system does not use any data augmentation techniques like BIBREF14, which is the top performer in TRAC (in English code-mixed Facebook data). This means the performance achieved by our system totally depends on the training dataset provided by TRAC. This also proves the effectiveness of our approach. Our system outperforms all the previous state of the art approaches used for aggression identification on English code-mixed TRAC data, while being trained only from Facebook comments the system outperforms other approaches on the additional Twitter test set. The remaining part of this paper is organized as follows: Section SECREF2 is an overview of related work. Section SECREF3 presents the methodology and algorithmic details. Section SECREF4 discusses the experimental evaluation of the system, and Section SECREF5 concludes this paper. The fine-grained definition of the aggressiveness/aggression identification is provided by the organizers of TRAC-2018 BIBREF0, BIBREF2. They have classified the aggressiveness into three labels (Overtly aggressive(OAG), Covertly aggressive(CAG), Non-aggressive(NAG)). The detailed description for each of the three labels is described as follows: Overtly Aggressive(OAG) - This type of aggression shows direct verbal attack pointing to the particular individual or group. For example, "Well said sonu..you have courage to stand against dadagiri of Muslims". Covertly Aggressive(CAG) - This type of aggression the attack is not direct but hidden, subtle and more indirect while being stated politely most of the times. For example, "Dear India, stop playing with the emotions of your people for votes." Non-Aggressive(NAG) - Generally these type of text lack any kind of aggression it is basically used to state facts, wishing on occasions and polite and supportive. In the future, we are planning to pay attention on a generalized language model for code-mixed texts which can also handle Hindi-code-mixed and other multi-lingual code-mixed datasets (i.e., trying to reduce the dependencies on language-specific code-mixed resources). Our system outperforms all the previous state of the art approaches used for aggression identification on English code-mixed TRAC data, while being trained only from Facebook comments the system outperforms other approaches on the additional Twitter test set. The fine-grained definition of the aggressiveness/aggression identification is provided by the organizers of TRAC-2018 BIBREF0, BIBREF2. They have classified the aggressiveness into three labels (Overtly aggressive(OAG), Covertly aggressive(CAG), Non-aggressive(NAG)). The detailed description for each of the three labels is described as follows: Overtly Aggressive(OAG) - This type of aggression shows direct verbal attack pointing to the particular individual or group. For example, "Well said sonu..you have courage to stand against dadagiri of Muslims". Covertly Aggressive(CAG) - This type of aggression the attack is not direct but hidden, subtle and more indirect while being stated politely most of the times. For example, "Dear India, stop playing with the emotions of your people for votes." Non-Aggressive(NAG) - Generally these type of text lack any kind of aggression it is basically used to state facts, wishing on occasions and polite and supportive.

What is English mixed with in the TRAC dataset?

The answers are shown as follows: * Hindi

Classify the following beverages that are typically served as either carbonated or non-carbonated: milk, soda, orange juice, beer, lemonade, coffee, sports drinks

Carbonated: soda, beer Non-carbonated: milk, orange juice, lemonade, coffee, sports drinks

2003.10816

Paraphrase Identification (PI) is the task of determining whether two sentences are paraphrase or not. It is considered a binary classification task. The best mono-lingual methods often achieve about 85% accuracy over this corpus BIBREF14, BIBREF18. Filice et al. BIBREF14 extended the tree kernels described in the previous section to operate on text pairs. The underlying idea is that this task is characterized by several syntactic/semantic patterns that a kernel machine can automatically capture from the training material. We can assess a text pair as a paraphrase if it shows a valid transformation rule that we observed in the training data. The following example can clarify this concept. A simple paraphrase rewriting rule is the active-passive transformation, such as in “Federer beat Nadal” and “Nadal was defeated by Federer”. The same transformation can be observed in other paraphrases, such as in “Mark studied biology” and “Biology was learned by Mark”. Although these two pairs of paraphrases have completely different topics, they have a very similar syntactic structure. In this section, the experimental analysis of the proposed models is presented. We have implemented the cross-lingual variant of kernel functions for PI and RE tasks as described in section SECREF3 and measured the accuracy of models by testing them on the parallel data set. Paraphrase Identification (PI) is the task of determining whether two sentences are paraphrase or not. We have implemented the cross-lingual variant of kernel functions for PI and RE tasks as described in section SECREF3 and measured the accuracy of models by testing them on the parallel data set.

What classification task was used to evaluate the cross-lingual adaptation method described in this work?

The answers are shown as follows: * Paraphrase Identification

Classify the following colors as primary or not primary: Red, blue, violet, white, black, green, yellow, orange

The primary colors are red, blue and green. The non-primary colors are violet, white, black, yellow and orange.

What are the top 10 US ski vacation destinations?

The western United States is considered to have the best skiing destinations because of the size of its mountains and the quality of it snow conditions. Nevertheless, given its proximity to large urban populations, the resorts of the northeast United State also sees large numbers of visitors each winter. The list below includes top ski destinations from both regions. Vail, Colorado Tahoe, California (this area includes a number of large resorts like Palisades, Heavenly, Sugar Bowl, and Northstar) Taos, New Mexico Park City, Utah Stowe, Vermont Jackson Hole, Wyoming Killington, Vermont Sugarloaf, Maine Aspen, Colorado Sun Valley, Idaho

After we obtain the representation of the source-target word pair by the convolution layer, we follow a similar architecture as BIBREF6 to refine the representation of the word pairs using feed-forward and recurrent networks. Two feed-forward layers of size 400 with rectified linear units (ReLU; BIBREF9 ); One bi-directional gated recurrent unit (BiGRU; BIBREF10 ) layer with hidden size 200, where the forward and backward hidden states are concatenated and further normalized by layer normalization BIBREF11 . Two feed-forward layers of hidden size 200 with rectified linear units; One BiGRU layer with hidden size 100 using the same configuration of the previous BiGRU layer; Two feed-forward layers of size 100 and 50 respectively with ReLU activation. We concatenate the 31 baseline features extracted by the Marmot toolkit with the last 50 feed-forward hidden features. The baseline features are listed in Table TABREF13 . We then apply a softmax layer on the combined features to predict the binary labels. After we obtain the representation of the sourcetarget word pair by the convolution layer, we follow a similar architecture as (Martins et al., 2017) to refine the representation of the word pairs using feed-forward and recurrent networks.

What do they use to refine the representation of the word pairs?

Feed-forward and recurrent networks.

1705.01991

Our baseline decoder is a standard beam search decoder BIBREF5 with several straightforward performance optimizations: Our baseline decoder is a standard beam search decoder BIBREF5 with several straightforward performance optimizations

What baseline decoder do they use?

The answers are shown as follows: * a standard beam search decoder BIBREF5 with several straightforward performance optimizations

The Spokane Braves are a Junior "B" Ice Hockey team based in Spokane, Washington, United States. They are members of the Neil Murdoch Division of the Kootenay Conference of the Kootenay International Junior Hockey League (KIJHL). They play their home games at Eagles Ice-A-Rena. The Braves are the only team in the KIJHL to play in the United States. They were forced to go on hiatus at the beginning of the 20/21 season due to the US/Canada border closure.

What conference does the Spokane Braves belong to?

The Spokane Braves are members of the Kootenay Conference of the Kootenay International Junior Hockey League (KIJHL).

Why do people prefer Dunkin over Starbucks?

People may prefer Starbucks because the menu is more customizable with more variety and the coffee tends to be stronger. Starbucks provides more of a cafe experience while Dunkin not only focuses on their coffee but their donuts are an important piece. Others may prefer Dunkin because of the competitive pricing, less strong coffee, and more options of food to drink with your coffee.

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. 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.

What does the linear transformation matrix employed to `project'?

`project' word and document representations from semantic space of language A to semantic space of language B.

1902.00330

FLOAT SELECTED: Table 3: Compare our model with other baseline methods on different types of datasets. The evaluation metric is micro F1. FLOAT SELECTED: Table 3: Compare our model with other baseline methods on different types of datasets. The evaluation metric is micro F1.

How big is the performance difference between this method and the baseline?

Comparing with the highest performing baseline: 1.3 points on ACE2004 dataset, 0.6 points on CWEB dataset, and 0.86 points in the average of all scores.

Classify each of the following as either a true statement or a false statement: 1. Albert Einstein was a Chinese mathematician that lived in the 19th and 20th century. 2. Pigs are not mammals. 3. This question contains four statements that need classification.

Statement number 1 is mostly false, since Albert Einstein was neither Chinese or a mathematician. He was a German theoretical physicist. However, he did in fact live during the 19th and 20th centuries. Statement number 2 is false. Pigs are in fact mammals. Statement number 3 is false. This question actually contained 3 statements.

From the point of view of dialog systems providing general information from a knowledge base, the most limiting factor is that a large portion of the questions is understood poorly. Current approaches BIBREF3 , BIBREF4 can only achieve around 50% accuracy on some question answering datasets. Therefore, we think that there is a room for improvements which can be achieved by interactively asking for additional information in conversational dialogs with users. This extra information can be used for improving policies of dialog systems. We call this approach the interactive learning from dialogs. We can improve dialog systems in several aspects through interactive learning in a direct interaction with users. First, the most straightforward way obviously is getting the correct answer for questions that the system does not know. We can try to ask users for answers on questions that the system encountered in a conversation with a different user and did not understand it. Second, the system can ask the user for a broader explanation of a question. This explanation could help the system to understand the question and provide the correct answer. In addition, the system can learn correct policy for the question which allows providing answers without asking any extra information for similar questions next time. We hypothesize that users are willing to give such explanations because it could help them to find answers for their own questions. The last source of information that we consider for interactive learning is rephrasing, which could help when the system does know the concept but does not know the correct wording. This area is extensively studied for the purposes of information retrieval BIBREF5 , BIBREF6 . The main purpose of the collected dataset is to enable interactive learning using the steps proposed above and potentially to evaluate how different systems perform on this task. The main purpose of the collected dataset is to enable interactive learning using the steps proposed above and potentially to evaluate how different systems perform on this task.

What is the main purpose of collecting the data set?

The main purpose of the collected dataset is to enable interactive learning using the steps proposed above and potentially to evaluate how different systems perform on this task.

Michał Rozenfeld (born 1916, died September 2, 1943, in Krawcowizna) – a Jewish resistance activist during the Second World War, participant of the Warsaw Ghetto uprising, partisan of the Mordechai Anielewicz Unit of the People's Guard. Before the outbreak of the war, he studied psychology with, among others, Tadeusz Kotarbiński. After the outbreak of World War II, Rozenfeld was sent to the Warsaw Ghetto, where he worked as a teacher. He belonged to the Polish Workers' Party and was a member of the Main Command of the Jewish Combat Organization. He stayed in a bunker with Mordechai Anielewicz at 18 Miła Street. On May 18, 1943, together with a group of Jewish fighters, he got through the sewers to Prosta Street. After the fall of the uprising, Rozenfeld took refuge in forests near Wyszków, where he fought in a unit of the People's Guard. Together with his companions he was handed over to the Germans by a forest ranger from Krawcowizna. Twelve partisans, including Michał Rozenfeld, were killed at that time. In 1945 he was posthumously awarded the Silver Cross of the Order of Virtuti Militari. Rozenfeld is buried in a mass grave of the People's Guard partisans in the Jewish cemetery on Okopowa Street in Warsaw (quarter 31, row 3).

Given a reference text about Michał Rozenfeld, tell me what he did before the war and where he's buried.

Michał Rozenfeld studied psychology before being sent to the Warsaw Ghetto during the war. He is buried at the Jewish Cemetery on Okopowa Street in Warsaw.

Write a brief passage on how to make french toast

In order to make a french toast, you need egg, vanilla extract, cinnamon, milk and few slices of bread. To start with, beat an egg along with one teaspoon of vanilla essence and a pinch of cinnamon. Add 1/4 cup of milk and stir the contents. Dip the bread slices in the mixture so that both sides of the bread are covered evenly. Heat a nonstick griddle and grease it lightly with butter. Place the bread on the griddle until it is brown on both sides. Serve it with maple syrup. Your delicious french toast is ready!

1909.00694

As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. To extract event pairs tagged with discourse relations, we used the Japanese dependency parser KNP and in-house postprocessing scripts BIBREF14. KNP used hand-written rules to segment each sentence into what we conventionally called clauses (mostly consecutive text chunks), each of which contained one main predicate. KNP also identified the discourse relations of event pairs if explicit discourse connectives BIBREF4 such as “ので” (because) and “のに” (in spite of) were present. We treated Cause/Reason (原因・理由) and Condition (条件) in the original tagset BIBREF15 as Cause and Concession (逆接) as Concession, respectively. Here is an example of event pair extraction. We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that positive and negative latter events were equal in size. We also sampled event pairs for each of CA and CO such that it was five times larger than AL. The results are shown in Table TABREF16. As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO.

How large is raw corpus used for training?

The answers are shown as follows: * 100 million sentences

how can you generate passive income?

Compared with active income which you earn in exchange of your time and efforts, passive income comes passively as you can sit aside after proper initial setups. Some argue that time-average investing in index funds is considered passive investing. Time-average-investing or dollar cost averaging investing means you invest a fixed amount in the index fund recurringly with a given frequency, usually monthly or biweekly, regardless of the fund price. You can set it up with auto-payment or auto-investment through your brokerage and let it build your wealth over time. Index funds mirror the performance of a collection of selected stocks, thus providing a diversified strategy by its nature. SP500 index has a proven performance record that beats most active fund managers. In addition, real estate investment is somehow passive if you leave all the property management to professionals. However, the fact that it requires a certain amount of equity to get started makes younger investors shy away from it. For the younger generation, generating passive income from content they publish on social media has become a popular choice. For example, publishing videos to your YouTube channel requires time and effort to create the content. But once the video is live, it starts to generate income streams with more and more people watching your videos for years to come.

Level 42 is an English jazz-funk band formed on the Isle of Wight in 1979. They had a number of UK and worldwide hits during the 1980s and 1990s. Their highest-charting single in the UK was "Lessons in Love", which reached number three on the UK Singles Chart, and number 12 on the US Billboard Hot 100 chart, upon its release in 1986. An earlier single, "Something About You", was their most successful chart-wise in the United States, reaching number 7 on the Billboard Hot 100 chart. After much success as a live and studio band in the 1980s, Level 42's commercial profile diminished during the early 1990s following a series of personnel changes and musical shifts. Disbanding in 1994, the band reformed in 2001.

What is the timeline for the band Level 42?

Level 42 formed on 1979. They enjoyed success throughout the 1980s but had disbanded by 1994. The band reformed in 2001.

Classify each one of the following as bird or fish - sparrow, whale, tuna, parrot.

Sparrow and parrot are birds and whale and tuna are fishes in the list

1812.06705

Table 2 lists the accuracies of the all methods on two classifier architectures. The results show that, for various datasets on different classifier architectures, our conditional BERT contextual augmentation improves the model performances most. BERT can also augments sentences to some extent, but not as much as conditional BERT does. For we masked words randomly, the masked words may be label-sensitive or label-insensitive. If label-insensitive words are masked, words predicted through BERT may not be compatible with original labels. The improvement over all benchmark datasets also shows that conditional BERT is a general augmentation method for multi-labels sentence classification tasks. Table 2 lists the accuracies of the all methods on two classifier architectures. The results show that, for various datasets on different classifier architectures, our conditional BERT contextual augmentation improves the model performances most. BERT can also augments sentences to some extent, but not as much as conditional BERT does.

Do the authors report performance of conditional bert on tasks without data augmentation?

Yes.

What is a verb?

A verb is a word that in syntax generally conveys an action, an occurrence, or a state of being. In the usual description of English, the basic form, with or without the particle to, is the infinitive. In many languages, verbs are inflected to encode tense, aspect, mood, and voice

Gavin Christopher Newsom (born October 10, 1967) is an American politician and businessman who has been the 40th governor of California since 2019. A member of the Democratic Party, he served as the 49th lieutenant governor of California from 2011 to 2019 and the 42nd mayor of San Francisco from 2004 to 2011.

Who is Gavin Newsom?

Gavin Christopher Newsom (born October 10, 1967) is an American politician and businessman who has been the 40th governor of California since 2019. A member of the Democratic Party, he served as the 49th lieutenant governor of California from 2011 to 2019 and the 42nd mayor of San Francisco from 2004 to 2011. Newsom attended Redwood High School and graduated from Santa Clara University. After graduation, he founded the PlumpJack wine store with billionaire heir and family friend, Gordon Getty, as an investor. The PlumpJack Group grew to manage 23 businesses, including wineries, restaurants and hotels. Newsom began his political career in 1996, when San Francisco mayor Willie Brown appointed him to the city's Parking and Traffic Commission. Brown appointed Newsom to fill a vacancy on the Board of Supervisors the next year and Newsom was elected to the board in 1998, 2000 and 2002. In 2003, at age 36, Newsom was elected the 42nd mayor of San Francisco, the city's youngest in a century. He was reelected in 2007 with 72% of the vote. Newsom was elected lieutenant governor of California in 2010, defeating incumbent Republican Abel Maldonado, and reelected in 2014. He was elected governor in the 2018 election. He faced criticism for his personal behavior and leadership during the COVID-19 pandemic, which was followed by an attempt to recall him from office. He prevailed in the 2021 recall election, "emphatically beating" what he labeled the Republican effort to remove him. Newsom was elected to a second term in the 2022 election. Newsom hosted The Gavin Newsom Show on Current TV from 2012 to 2013 and wrote the 2013 book Citizenville, about using digital tools for democratic change. Political science analysis has suggested he is moderate relative to almost all Democratic legislators in California.

Oak Island has been a subject for treasure hunters ever since the late 1700s, with rumours that Captain Kidd's treasure was buried there. While there is little evidence to support what went on during the early excavations, stories began to be published and documented as early as 1856. Since that time there have been many theories that extend beyond that of Captain Kidd which include among others religious artifacts, manuscripts, and Marie Antoinette's jewels. The "treasure" has also been prone to criticism by those who have dismissed search areas as natural phenomena.

What is hidden at Oak Island?

Oak Island is rumored to have Captain Kidd's treasure buried. However despite numerous excavation attempts over the past 100 years nothing has been discovered. There are other theories which claim religious artifacts, manuscripts, or Marie Antionette's jewels are hidden at Oak Island.

Which is a species of fish? Javelin or Discus

Javelin

What does it mean to be customer obsessed or achieve customer obsession?

Customer obsession is a state of mind. To embody a Customer Obsessed state of mind, one must consider the customer they are obsessed with in all things. For example, when at the grocery store you see an advertisement from a competitor of your customer, a customer-obsessed state of mind will naturally conceive of methods that a customer might employ, out-competing the viewed advertisement. When going to sleep, the customer and their needs are the last thoughts that drift from your mind as you fall asleep. When waking up, the customer should be the first thought to naturally occur as you begin to consider your activities. When eating your lunch, you should imagine what your customer is eating for their lunch. When your customer is on your mind every waking moment, you've begun to walk the path of being customer obsessed. Once you've begun dreaming of your customer, then you will then have achieved true customer obsession.

Identify which car manufacturer is German or American: Mercedes-Benz, Chevrolet

Chevrolet is American, Mercedes-Benz is German

1909.00124

The test accuracy curves with the noise rates [0, $0.1$, $0.2$, $0.3$, $0.4$, $0.5$] are shown in Figure FIGREF13. From the figure, we can see that the test accuracy drops from around 0.8 to 0.5 when the noise rate increases from 0 to 0.5, but our NetAb outperforms CNN. The results clearly show that the performance of the CNN drops quite a lot with the noise rate increasing. The comparison results are shown in Table TABREF12. From the results, we can make the following observations. (1) Our NetAb model achieves the best ACC and F1 on all datasets except for F1 of negative class on Laptop. The results demonstrate the superiority of NetAb. (2) NetAb outperforms the baselines designed for learning with noisy labels. These baselines are inferior to ours as they were tailored for image classification. Note that we found no existing method to deal with noisy labels for SSC. Training Details. We use the publicly available pre-trained embedding GloVe.840B BIBREF48 to initialize the word vectors and the embedding dimension is 300. FLOAT SELECTED: Table 2: Accuracy (ACC) of both classes, F1 (F1 pos) of positive class and F1 (F1 neg) of negative class on clean test data/sentences. Training data are real noisy-labeled sentences. FLOAT SELECTED: Figure 2: Accuracy (ACC) on clean test data. For training, the labels of clean data are flipped with the noise rates [0, 0.1, 0.2, 0.3, 0.4, 0.5]. For example, 0.1means that 10% of the labels are flipped. (Color online) The test accuracy curves with the noise rates [0, $0.1$, $0.2$, $0.3$, $0.4$, $0.5$] are shown in Figure FIGREF13. From the figure, we can see that the test accuracy drops from around 0.8 to 0.5 when the noise rate increases from 0 to 0.5, but our NetAb outperforms CNN The comparison results are shown in Table TABREF12. From the results, we can make the following observations. (1) Our NetAb model achieves the best ACC and F1 on all datasets except for F1 of negative class on Laptop. The results demonstrate the superiority of NetAb. (2) NetAb outperforms the baselines designed for learning with noisy labels. FLOAT SELECTED: Table 2: Accuracy (ACC) of both classes, F1 (F1 pos) of positive class and F1 (F1 neg) of negative class on clean test data/sentences. Training data are real noisy-labeled sentences. FLOAT SELECTED: Figure 2: Accuracy (ACC) on clean test data. For training, the labels of clean data are flipped with the noise rates [0, 0.1, 0.2, 0.3, 0.4, 0.5]. For example, 0.1means that 10% of the labels are flipped. (Color online)

What is the performance of the model?

Experiment 1: ACC around 0.5 with 50% noise rate in worst case - clearly higher than baselines for all noise rates Experiment 2: ACC on real noisy datasets: 0.7 on Movie, 0.79 on Laptop, 0.86 on Restaurant (clearly higher than baselines in almost all cases)

Identify which instrument is string or percussion: Xylophone, Ramkie

Ramkie is string, Xylophone is percussion.

Tell me which of these terms are related to artificial intelligence versus gardening: tilling, seed, gradient descent, production, Bayesian optimization, genetically modified organism, heirloom, transfer learning

These terms are all unmistakably related to artificial intelligence: - Gradient descent: a technique used when training a machine learning model to improve results over time. - Bayesian optimization: a probabilistic approach to arrive at the best set of parameters, most often used in training machine learning models. - Transfer learning: a technique to re-train an already trained deep learning model on new data, most commonly used to customize models to a particular use-case, such as detecting a specific object in photos. While these terms are all unmistakably related to gardening: - Tilling: mechanical preparation of soil for planting. - Genetically modified organism: this is a term to describe plants that have undergone genetic modification to optimize for specific traits, such as drought resistance. - Heirloom: this describe certain non-hybrid plant types that must be open pollinated, namely by insects, birds, wind, or other natural factors. These terms relate to both artificial intelligence and gardening: - Seed: in artificial intelligence, a "seed" is a number chosen to help reproduce random outcomes, such as picking random numbers. In gardening, a seed is the raw input that a gardener plants that will eventually become a plant. - Production: in artificial intelligence, production is often the term used to describe a lifecycle stage of a model that is in real-life use, rather than testing or development. In gardening, production is another term used to describe the total output of a harvest.

Bee pollen, also known as bee bread and ambrosia, is a ball or pellet of field-gathered flower pollen packed by worker honeybees, and used as the primary food source for the hive. It consists of simple sugars, protein, minerals and vitamins, fatty acids, and a small percentage of other components. Bee pollen is stored in brood cells, mixed with saliva, and sealed with a drop of honey. Bee pollen is harvested as food for humans and marketed as having various, but yet unproven, health benefits.

Extract the ingredients in bee pollen from the text.

Bee pollen consists of simple sugars, protein, minerals, vitamins, fatty acids, and some other components.

Jonathan Young (born September 29, 1944) is a psychologist who became the founding curator of the Joseph Campbell Archives. Background Young developed an interest in the teaching functions of stories through early exposure to folklore. He was one of six children in a much-traveled family. His parents read and discussed the lore of each place they visited, such as the Little Mermaid in Copenhagen, the Pied Piper in Hamelin, the Arabian Nights in Baghdad, and the Buddha in India and Japan. His graduate studies focused on the psychology of stories, and included work with Viktor Frankl, Rollo May, Abraham Maslow, and Carl Rogers. He earned his PhD in Clinical Psychology from Alliant International University. Career Joseph Campbell with Jonathan Young, 1985. Young is best known for his series of books, Saga: Best New Writings on Mythology. He assisted mythologist Joseph Campbell for several years at seminars. In addition to the Campbell archives, he organized the collections of psychologist James Hillman, and archeologist Marija Gimbutas. As a professor, Young created and chaired the Mythological Studies Department at the Pacifica Graduate Institute, working with Jungian theorists such as Robert Bly, Marion Woodman, Robert A. Johnson, Jean Houston, and Thomas Moore. In 1995, Dr. Young launched the Center for Story and Symbol in Santa Barbara, California to continue the work of Joseph Campbell. Through the center's programs, he presents seminars internationally on the uses of mythic stories for therapists, writers, clergy, and teachers. He also teaches the hero's journey in screenwriting programs. He lectures occasionally at universities, such as UCLA, Notre Dame, and Oxford, as well as ongoing courses in Mythopoetics at Pacifica Graduate Institute. He is featured in the History Channel documentary Star Wars: The Legacy Revealed. He also frequently appears on the History Channel and H2 television series Ancient Aliens. Works Saga: Best New Writings on Mythology Article on Joseph Campbell, The Dictionary of Modern American Philosophers

Who is the psychologist Jonathan Young

Jonathan Young (born September 29, 1944) is a psychologist who became the founding curator of the Joseph Campbell Archives. Background Young developed an interest in the teaching functions of stories through early exposure to folklore. He was one of six children in a much-traveled family. His parents read and discussed the lore of each place they visited, such as the Little Mermaid in Copenhagen, the Pied Piper in Hamelin, the Arabian Nights in Baghdad, and the Buddha in India and Japan. His graduate studies focused on the psychology of stories, and included work with Viktor Frankl, Rollo May, Abraham Maslow, and Carl Rogers. He earned his PhD in Clinical Psychology from Alliant International University. Career Young is best known for his series of books, Saga: Best New Writings on Mythology. He assisted mythologist Joseph Campbell for several years at seminars. In addition to the Campbell archives, he organized the collections of psychologist James Hillman, and archeologist Marija Gimbutas. As a professor, Young created and chaired the Mythological Studies Department at the Pacifica Graduate Institute, working with Jungian theorists such as Robert Bly, Marion Woodman, Robert A. Johnson, Jean Houston, and Thomas Moore. In 1995, Dr. Young launched the Center for Story and Symbol in Santa Barbara, California to continue the work of Joseph Campbell. Through the center's programs, he presents seminars internationally on the uses of mythic stories for therapists, writers, clergy, and teachers. He also teaches the hero's journey in screenwriting programs. He lectures occasionally at universities, such as UCLA, Notre Dame, and Oxford, as well as ongoing courses in Mythopoetics at Pacifica Graduate Institute. He is featured in the History Channel documentary Star Wars: The Legacy Revealed. He also frequently appears on the History Channel and H2 television series Ancient Aliens. Works Saga: Best New Writings on Mythology Article on Joseph Campbell, The Dictionary of Modern American Philosophers

The model used for translation is the one implemented by Bahdanau et al. Bahdanau2014. A bidirectional LSTM encoder first takes the source sentence and encodes it into a context vector which acts as input for the decoder. The decoder is attention-based where the hidden states of the decoder get as input the weighted sum of all the hidden layer outputs of the encoder alongwith the output of the previous hidden layer and the previously decoded word. This provides a contextual reference into the source language sentence BIBREF4 . Neural Machine Translation models directly compute the probability of the target language sentence given the source language sentence, word by word for every time step. The model with a basic decoder without the attention module computes the log probability of target sentence given source sentence as the sum of log probabilities of every word given every word before that. The attention-based model, on the other hand, calculates: DISPLAYFORM0 where INLINEFORM0 is the number of words in the target sentence, INLINEFORM1 is the target sentence, INLINEFORM2 is the source sentence, INLINEFORM3 is the fixed length output vector of the encoder and INLINEFORM4 is the weighted sum of all the hidden layer outputs of the encoder at every time step. Both the encoder's output context vector and the weighted sum (known as attention vector) help to improve the quality of translation by enabling selective source sentence lookup. The decoder LSTM computes: DISPLAYFORM0 where the probability is computed as a function of the decoder's output in the previous time step INLINEFORM0 , the hidden layer vector of the decoder in the current timestep INLINEFORM1 and the context vector from the attention mechanism INLINEFORM2 . The context vector INLINEFORM3 for time step INLINEFORM4 is computed as a weighted sum of the output of the entire sentence using a weight parameter INLINEFORM5 : DISPLAYFORM0 where INLINEFORM0 is the number of tokens in the source sentence, INLINEFORM1 refers to the value of the hidden layer of the encoder at time step INLINEFORM2 , and INLINEFORM3 is the alignment parameter. This parameter is calculated by means of a feed forward neural network to ensure that the alignment model is free from the difficulties of contextualization of long sentences into a single vector. The feed forward network is trained along with the neural translation model to jointly improve the performance of the translation. Mathematically, DISPLAYFORM0 DISPLAYFORM1 where INLINEFORM0 is the softmax output of the result of the feedforward network, INLINEFORM1 is the hidden state value of the decoder at timestep INLINEFORM2 and INLINEFORM3 is the encoder's hidden layer annotation at timestep INLINEFORM4 . A concatenation of the forward and the reverse hidden layer parameters of the encoder is used at each step to compute the weights INLINEFORM5 for the attention mechanism. This is done to enable an overall context of the sentence, as opposed to a context of only all the previous words of the sentence for every word in consideration. Fig. FIGREF12 is the general architecture of the neural translation model without the Bidirectional LSTM encoder. A global attention mechanism is preferred over local attention because the differences in the structures of the languages cannot be mapped efficiently to enable lookup into the right parts of the source sentence. Using local attention mechanism with a monotonic context lookup, where the region around INLINEFORM0 source word is looked up for the prediction of the INLINEFORM1 target word, is impractical because of the structural discordance between the English and Tamil sentences (see Figs. FIGREF37 and FIGREF44 ). The use of gaussian and other such distributions to facilitate local attention would also be inefficient because the existence of various forms of translations for the same source sentence involving morphological and structural variations that don't stay uniform through the entire corpus BIBREF5 . The No Peepholes (NP) variant of the LSTM cell, formulated in Greff et al. greff2015lstm is used in this experiment as it proved to give the best results amongst all the variants of an LSTM cell. It is specified by means of a gated mechanism designed to ensure that the vanishing gradient problem is prevented. LSTM maintains its hidden layer in two components, the cell vector INLINEFORM0 and the actual hidden layer output vector INLINEFORM1 . The cell vector is ensured to never reach zero by means of a weighted sum of the previous layer's cell vector INLINEFORM2 regulated by the forget gate INLINEFORM3 and an activation of the weighted sum of the input INLINEFORM4 in the current timestep INLINEFORM5 and the previous timestep's hidden layer output vector INLINEFORM6 . The combination is similarly regulated by the input gate INLINEFORM7 . The hidden layer output is determined as an activation of the cell gate, regulated by the output gate INLINEFORM8 . The interplay between these two vectors ( INLINEFORM9 and INLINEFORM10 ) at every timestep ensures that the problem of vanishing gradients doesn't occur. The three gates are also formed as a sigmoid of the weighted sum of the previous hidden layer output INLINEFORM11 and the input in the current timestep INLINEFORM12 . The output generated out of the LSTM's hidden layer is specified as a weighted softmax over the hidden layer output INLINEFORM13 . The learnable parameters of an LSTM cell are all the weights INLINEFORM14 and the biases INLINEFORM15 . DISPLAYFORM0 The LSTM specified by equations 7 through 11 is the one used for the decoder of the model. The encoder uses a bidirectional RNN LSTM cell in which there are two hidden layer components INLINEFORM0 and INLINEFORM1 that contribute to the output INLINEFORM2 of each time step INLINEFORM3 . Both the components have their own sets of LSTM equations in such a way that INLINEFORM4 for every timestep is computed from the first timestep till the INLINEFORM5 token is reached and INLINEFORM6 is computed from the INLINEFORM7 timestep backwards until the first token is reached. All the five vectors of the two components are all exactly the same as the LSTM equations specified with one variation in the computation of the result. DISPLAYFORM0 A global attention mechanism is preferred over local attention because the differences in the structures of the languages cannot be mapped efficiently to enable lookup into the right parts of the source sentence.

Why is global attention mechanism preferred?

Because the differences in the structures of the languages cannot be mapped efficiently to enable lookup into the right parts of the source sentence.

Which is a species of fish? Cisco or Palo Alto

Cisco

From the following list, categorize the what country these dishes originate from: Tamales, Butter Chicken, Mac and Cheese, Pizza, Pozole, Nicoise Salad, Ramen, Trifle, Empanadas, Ceviche, Feijoda, Burrito, Jollaf Rice, Tagine, Couscous

Mexico, India, United States, Italy, Mexico, France, Japan, England, Spain, Peru, Brazil, United States, Nigeria, Morocco, Algeria

How can I make my bike more aerodynamic?

Assuming you are keeping your existing bike frame, the top two ways to decrease aerodynamic drag are to ensure you are comfortable riding in an aerodynamic position, and upgrading your wheel set and using road tires. Improving body positioning will have the most benefit at the lowest cost. Because wheel spokes are continuously cutting through air as you ride, they induce drag. Spokes on wheels with less aerodynamic drag allow for the wheels to roll with less resistance. Aerodynamic wheels are generally lighter which also decreases rolling drag.

Who are the last three french presidents ?

Nicolas Sarkozy was the president from May 2007 to May 2012 then Francois Hollande from May 2012 to May 2017 and finally Emmanuel Macron from May 2017 to now

My manager is available on Tuesday, Wednesday, and Thursday after 12 PM PST and her manager is available on Wednesday after 4:30 PM CST and Thursday from 3:30 PM to 4:30 PM CST. Can you suggest a time for me to schedule a meeting with the two of them if I have to sign off at 5 PM EST every day?

Your manager is available after 3 PM EST but her manager is only available on Wednesday after 5:30 PM EST or Thursday from 4:30 PM to 5:30 PM EST. The only time that the three of you are able to meet is on Thursday at 4:30 PM EST. If you can be available after 5 PM EST then all three of you are also available to meet on Wednesday at 5:30 PM EST or Thursday at 5 PM EST.

How did Caesar come to power in Rome?

Gaius Julius Caesar was a Roman general and statesman born around 100 B.C. He was born in a patrician family (senatorial class) called the Julii that claimed lineage from the Goddess Venus and the legendary Prince Aeneas of Troy. His birth in this family had already put him ahead of most others on the road to power. Caesar was smart, hard working and charming and made many friends and connections that would help him later in life. He married a relative of Gaius Marius, the dictator of Rome at the time and furthered his political connections. Though he suffered a setback when Sulla defeated Marius, Caesar was quickly able to use that to his advantage to escape a posting that required him to remain in Rome. It was important for any aspiring Consul (leader of Rome) to be seen as an effective military general who had conquered lands for Rome. Caesar's next significant position was that of Aedile where one of his primary duties were to organize games. Caesar borrowed heavily and organized lavish games so that the people of Rome would know him. Unlike other patricians, Caesar had grown up in the Subura where, at the time, the plebeians lived and knew the importance and power that could be garnered from the love of the people. Through multiple appointments, Caesar displayed his military genius and was eventually elected Consul. He formed an alliance with two other leading figures of the time: Pompey (a decorated general) and Crassus (richest man in Rome) known now as the "First Triumvirate". After his consulship, Caesar become Governor of Gaul and eventually won a lot of territory for Rome. However, his political opponents in Rome readied a case for prosecuting him and stripping of power. Unwilling to be subject to that, Caesar gathered one of his legions and marched on Rome. His political opponents performed a tactical retreat from Rome leaving him in nominal control. He consolidated his power by winning multiple wars in Greece, Spain and Egypt eventually solidifying his hold on Rome and getting himself elected Dictator

Who was the quarterback for the Denver Broncos when they won their first Super Bowl and what was his number?

His name was John Elway and he wore #7

The following tables show our main experimental result, which is averaged over 5 runs. We denote the number of example per class per task at the top of each column. Overall, NCCL variants outperforms baseline methods especially in the forgetting metric. Our goal is to demonstrate the usefulness of the adaptive learning rate scheme to reduce the catastrophic forgetting, and verify the proposed theoretical convergence analysis. We remark that our adaptive learning rates successfully suppress forgetting by a large margin compared to baselines. Note that NCCL also outperform A-GEM, which does not maximize transfer ∇f It (x t ), ∇g Jt (x t ) > 0. Now, we can empirically demonstrate our theoretical guaranteed method by minimizing Γ t is valid. We clipped β Ht to increase the performance. As we discussed earlier, we can prevent forgetting when ∇f It (x t ), ∇g Jt (x t ) > 0. However, we observe that ∇f It (x t ) 2 suddenly increases because of the interference at the previous step t − 1. The very large learning rate β Ht by the increased ∇f It (x t ) can force the model to fall into an arbitrary point that is likely to increase the loss of f . Clipping the learning rate reduces this problem and still has the effect of reducing the catastrophic forgetting term Γ t . By the property of quadratic polynomial, the catastrophic forgetting term is negative because the clipped value is smaller than the original learning rate. We show that NCCL is a potentially powerful alternative for continual learning. Even with tiny replay memory, NCCL still performs better than some baselines. We note that NCCL shows the best performance on the forgetting metric. It implies that NCCL prevent catastrophic forgetting more efficiently than others by minimizing the catastrophic forgetting term in the proposed optimization problem. However, the accuracy is slightly lower than other baselines, which include experience replays. The purpose of our adaptive learning rate scheme is to prevent catastrophic forgetting, so the performance of current task is slightly lower than ER-Ring, stable-SGD, and ORTHOG-subspace. This result shows that the plasticity to learn a new task is restricted by NCCL variants with tiny memory. In particular, we would expect that NCCL would benefit from the additional enhancements in ORTHOG-SUBSPACE and stable SGD by introducing their techniques. In appendix, we add more results with larger sizes of memory, which shows that NCCL outperforms on the average accuracy. We conclude that the transfer effect by the small size of memory for NCCL is less effective. The following tables show our main experimental result, which is averaged over 5 runs. We denote the number of example per class per task at the top of each column. Overall, NCCL variants outperforms baseline methods especially in the forgetting metric. Our goal is to demonstrate the usefulness of the adaptive learning rate scheme to reduce the catastrophic forgetting, and verify the proposed theoretical convergence analysis. We remark that our adaptive learning rates successfully suppress forgetting by a large margin compared to baselines. Note that NCCL also outperform A-GEM, which does not maximize transfer ∇f It (x t ), ∇g Jt (x t ) > 0. Now, we can empirically demonstrate our theoretical guaranteed method by minimizing Γ t is valid. We clipped β Ht to increase the performance. As we discussed earlier, we can prevent forgetting when ∇f It (x t ), ∇g Jt (x t ) > 0. However, we observe that ∇f It (x t ) 2 suddenly increases because of the interference at the previous step t − 1. The very large learning rate β Ht by the increased ∇f It (x t ) can force the model to fall into an arbitrary point that is likely to increase the loss of f . Clipping the learning rate reduces this problem and still has the effect of reducing the catastrophic forgetting term Γ t . By the property of quadratic polynomial, the catastrophic forgetting term is negative because the clipped value is smaller than the original learning rate. We show that NCCL is a potentially powerful alternative for continual learning. Even with tiny replay memory, NCCL still performs better than some baselines. We note that NCCL shows the best performance on the forgetting metric. It implies that NCCL prevent catastrophic forgetting more efficiently than others by minimizing the catastrophic forgetting term in the proposed optimization problem. However, the accuracy is slightly lower than other baselines, which include experience replays. The purpose of our adaptive learning rate scheme is to prevent catastrophic forgetting, so the performance of current task is slightly lower than ER-Ring, stable-SGD, and ORTHOG-subspace. This result shows that the plasticity to learn a new task is restricted by NCCL variants with tiny memory. In particular, we would expect that NCCL would benefit from the additional enhancements in ORTHOG-SUBSPACE and stable SGD by introducing their techniques. In appendix, we add more results with larger sizes of memory, which shows that NCCL outperforms on the average accuracy. We conclude that the transfer effect by the small size of memory for NCCL is less effective. Overall, NCCL variants outperforms baseline methods especially in the forgetting metric.****We show that NCCL is a potentially powerful alternative for continual learning. Even with tiny replay memory, NCCL still performs better than some baselines. We note that NCCL shows the best performance on the forgetting metric. It implies that NCCL prevent catastrophic forgetting more efficiently than others by minimizing the catastrophic forgetting term in the proposed optimization problem. However, the accuracy is slightly lower than other baselines, which include experience replays. The purpose of our adaptive learning rate scheme is to prevent catastrophic forgetting, so the performance of current task is slightly lower than ER-Ring, stable-SGD, and ORTHOG-subspace. This result shows that the plasticity to learn a new task is restricted by NCCL variants with tiny memory. In particular, we would expect that NCCL would benefit from the additional enhancements in ORTHOG-SUBSPACE and stable SGD by introducing their techniques. In appendix, we add more results with larger sizes of memory, which shows that NCCL outperforms on the average accuracy. We conclude that the transfer effect by the small size of memory for NCCL is less effective.

The main issues of paper are the experimental results. In table 1, NCCL + Reservoir and NCCL + Ring underperforms ER-Ring, could the authors give some discussion on this?

We note that our method has the smallest forgetting metric among all baselines. It implies that our method is focused on reducing catastrophic forgetting. We think that learning on the current task $C$ is not enough to show the best scores for the average accuracy. However, we emphasize that the results with larger memory outperforms the baelines. Then, we can conclude that our method suffers from the transfer effect with small memory. Because, the small subset cannot cover the gradients for entire datapoints.

The Internet protocol suite, commonly known as TCP/IP, is a framework for organizing the set of communication protocols used in the Internet and similar computer networks according to functional criteria. The foundational protocols in the suite are the Transmission Control Protocol (TCP), the User Datagram Protocol (UDP), and the Internet Protocol (IP). Early versions of this networking model were known as the Department of Defense (DoD) model because the research and development were funded by the United States Department of Defense through DARPA. The Internet protocol suite provides end-to-end data communication specifying how data should be packetized, addressed, transmitted, routed, and received. This functionality is organized into four abstraction layers, which classify all related protocols according to each protocol's scope of networking. An implementation of the layers for a particular application forms a protocol stack. From lowest to highest, the layers are the link layer, containing communication methods for data that remains within a single network segment (link); the internet layer, providing internetworking between independent networks; the transport layer, handling host-to-host communication; and the application layer, providing process-to-process data exchange for applications. The technical standards underlying the Internet protocol suite and its constituent protocols are maintained by the Internet Engineering Task Force (IETF). The Internet protocol suite predates the OSI model, a more comprehensive reference framework for general networking systems.

Give this paragraph about TCP/IP, what are the four abstraction laters?

From lowest to highest, the layers are: - link layer, containing communication methods for data that remains within a single network segment (link) - the internet layer, providing internetworking between independent networks; - the transport layer, handling host-to-host communication; - the application layer, providing process-to-process data exchange for applications.

1907.11499

Experimental Setup Experimental Setup

What domains are detected in this paper?

Answer with content missing: (Experimental setup not properly rendered) In our experiments we used seven target domains: “Business and Commerce” (BUS), “Government and Politics” (GOV), “Physical and Mental Health” (HEA), “Law and Order” (LAW), “Lifestyle” (LIF), “Military” (MIL), and “General Purpose” (GEN). Exceptionally, GEN does not have a natural root category.

Who are the best artists throughout history?

There are many artists throughout history and across different mediums such as painting, sculpture and photography among others. Some of the most well known painters are Leonardo da Vinci, Rembrandt, Claude Monet and Salvador Dali. Donatello is one of the most famous sculptors working with materials such as marble, bronze and wood. Michelangelo is a famous artist known for both sculpture and painting. There are also many famous photographers throughout history including Ansel Adams who is knows for his portrayal of nature.

2002.00876

With this challenge in mind, we introduce Torch-Struct with three specific contributions: Modularity: models are represented as distributions with a standard flexible API integrated into a deep learning framework. Completeness: a broad array of classical algorithms are implemented and new models can easily be added in Python. Efficiency: implementations target computational/memory efficiency for GPUs and the backend includes extensions for optimization. With this challenge in mind, we introduce Torch-Struct with three specific contributions: Modularity: models are represented as distributions with a standard flexible API integrated into a deep learning framework. Completeness: a broad array of classical algorithms are implemented and new models can easily be added in Python. Efficiency: implementations target computational/memory efficiency for GPUs and the backend includes extensions for optimization.

Is this library implemented into Torch or is framework agnostic?

It uses deep learning framework (pytorch)

What is the difference between Oculus Quest 2 and Meta Quest 2 VR headsets?

They are exactly the same in terms of technical specifications and capabilities. The only differences between them are their logos and names.

Can you think of some different cities on the American West coast?

Seattle, Portland, San Francisco, Los Angeles, San Diego

Does using microwave oven cause cancer?

No, it is a myth with no scientific validation to this hypothesis. Microwaves are a form of electromagnetic radiation which only heats the water molecules inside your food. As the water heats up the food heats up. Microwave ovens has been in use safely for more than 70 years

Give me a list of the seven best beach honeymoon locations for 2024.

The Maldives Santorini Maui Bora Bora Bali Jamaica Seychelles

2004.01980

The human evaluation is to have a comprehensive measurement of the performances. We conduct experiments on four criteria, relevance, attraction, fluency, and style strength. We summarize the human evaluation results on the first three criteria in Table TABREF51, and the last criteria in Table TABREF57. Note that through automatic evaluation, the baselines NST, Fine-tuned, and Gigaword-MASS perform poorer than other methods (in Section SECREF58), thereby we removed them in human evaluation to save unnecessary work for human raters. FLOAT SELECTED: Table 2: Human evaluation on three aspects: relevance, attraction, and fluency. “None” represents the original headlines in the dataset. FLOAT SELECTED: Table 2: Human evaluation on three aspects: relevance, attraction, and fluency. “None” represents the original headlines in the dataset. We summarize the human evaluation results on the first three criteria in Table TABREF51, and the last criteria in Table TABREF57. FLOAT SELECTED: Table 2: Human evaluation on three aspects: relevance, attraction, and fluency. “None” represents the original headlines in the dataset. FLOAT SELECTED: Table 2: Human evaluation on three aspects: relevance, attraction, and fluency. “None” represents the original headlines in the dataset.

What is increase in percentage of humor contained in headlines generated with TitleStylist method (w.r.t. baselines)?

Humor in headlines (TitleStylist vs Multitask baseline): Relevance: +6.53% (5.87 vs 5.51) Attraction: +3.72% (8.93 vs 8.61) Fluency: 1,98% (9.29 vs 9.11)

What is Categorization and Sentiment Analysis

Categorization and Sentiment Analysis is typically referred in the Customer Experience space where customer interaction data is analyzed to get insights into categories of topics and sentiment associated with those interactions. Some of the use cases that benefit from these insights include Customer Representative/Agent Performance and Training, Workload Management, Product Performance et al

Goede Vrijdag is de vrijdag voor Pasen. Op deze dag herdenken christenen de kruisiging en dood van Jezus. Jezus werd volgens de Bijbel veroordeeld tot de kruisdood door de Romeinse stadhouder Pontius Pilatus, op aandrang van het sanhedrin. Deze straf werd voltrokken op de heuvel Golgotha nabij de stad Jeruzalem. Goede Vrijdag volgt op Witte Donderdag en gaat vooraf aan Stille Zaterdag. Daarop volgt Pasen.

Wat is goede vrijdag?

De dag dat Jezus gekruisigd werd

Humans (Homo sapiens) are the most common and widespread species of primate in the great ape family Hominidae, and also the most common species of primate overall. Humans are broadly characterized by their bipedalism and high intelligence. Humans' large brain and resulting cognitive skills have allowed them to thrive in a variety of environments and develop complex societies and civilizations. Humans are highly social and tend to live in complex social structures composed of many cooperating and competing groups, from families and kinship networks to political states. As such, social interactions between humans have established a wide variety of values, social norms, languages, and rituals, each of which bolsters human society. The desire to understand and influence phenomena has motivated humanity's development of science, technology, philosophy, mythology, religion, and other conceptual frameworks. Although some scientists equate the term "humans" with all members of the genus Homo, in common usage it generally refers to Homo sapiens, the only extant member. Anatomically modern humans emerged around 300,000 years ago in Africa, evolving from Homo heidelbergensis or a similar species and migrating out of Africa, gradually replacing or interbreeding with local populations of archaic humans. For most of history, humans were nomadic hunter-gatherers. Humans began exhibiting behavioral modernity about 160,000–60,000 years ago. The Neolithic Revolution, which began in Southwest Asia around 13,000 years ago (and separately in a few other places), saw the emergence of agriculture and permanent human settlement. As populations became larger and denser, forms of governance developed within and between communities, and a number of civilizations have risen and fallen. Humans have continued to expand, with a global population of over 8 billion as of 2022. Genes and the environment influence human biological variation in visible characteristics, physiology, disease susceptibility, mental abilities, body size, and life span. Though humans vary in many traits (such as genetic predispositions and physical features), any two humans are at least 99% genetically similar. Humans are sexually dimorphic: generally, males have greater body strength and females have a higher body fat percentage. At puberty, humans develop secondary sexual characteristics. Females are capable of pregnancy, usually between puberty, at around 12 years old, and menopause, around the age of 50. Humans are omnivorous, capable of consuming a wide variety of plant and animal material, and have used fire and other forms of heat to prepare and cook food since the time of Homo erectus. Humans can survive for up to eight weeks without food and three or four days without water. Humans are generally diurnal, sleeping on average seven to nine hours per day. Childbirth is dangerous, with a high risk of complications and death. Often, both the mother and the father provide care for their children, who are helpless at birth. Humans have a large, highly developed, and complex prefrontal cortex, the region of the brain associated with higher cognition. Humans are highly intelligent, capable of episodic memory, have flexible facial expressions, self-awareness, and a theory of mind. The human mind is capable of introspection, private thought, imagination, volition, and forming views on existence. This has allowed great technological advancements and complex tool development to be possible through complex reasoning and the transmission of knowledge to subsequent generations. Language, art, and trade are defining characteristics of humans. Long-distance trade routes might have led to cultural explosions and resource distribution that gave humans an advantage over other similar species.

What is the origin of man?

Homo sapiens Anatomically modern humans emerged around 300,000 years ago in Africa, evolving from Homo heidelbergensis or a similar species and migrating out of Africa, gradually replacing or interbreeding with local populations of archaic humans. For most of history, humans were nomadic hunter-gatherers.

Word embeddings have been widely used for natural language processing (NLP) tasks BIBREF0 . However, the large word vocabulary makes word embeddings expensive to train. Some people argue that we can model languages at the character-level BIBREF1 . For alphabetic languages such as English, where the characters are much fewer than the words, the character embeddings achieved the state-of-the-art results with much fewer parameters. Unfortunately, for the other languages that use non-alphabetic systems, the character vocabulary can be also large. Moreover, Chinese and Japanese, two of the most widely used non-alphabetic languages, especially contain large numbers of ideographs: hanzi of Chinese and kanji of Japanese. The character vocabulary can be as scalable as the word vocabulary (e.g., see the datasets introduced in Section SECREF11 ). Hence the conventional character embedding-based method is not able to give us a slim vocabulary on Chinese and Japanese. For convenience, let us collectively call hanzi and kanji as Chinese characters. Chinese characters are ideographs composed with semantic and phonetic radicals, both of which are available for character recognition, and the semantic information may be embedded in Chinese characters by the semantic radicals BIBREF2 . Besides, though the character vocabulary is huge, the number of the radicals is much fewer. Accordingly, we explored a model that represents the Chinese characters by the sequence of the radicals. We applied our proposed model to sentiment classification tasks on Chinese and Japanese and achieved the follows: Accordingly, we explored a model that represents the Chinese characters by the sequence of the radicals.

What kind of model do they explore?

A model that represents the Chinese characters by the sequence of the radicals.

How is data engineering different from data science?

Data Engineering occurs before data science in the data lifecycle. Data can be generated from various sources and this raw data may be structured, semi structured or even unstructured. To make sense of this data, data has to be ingested and transformed. This is when data engineering comes into picture. Data engineering includes the process of extracting the raw data, ingesting data into a system like a data lake, transforming the data for business needs and finally loading it into a table. This is commonly known as ELT (Extract-Load-Transform). ETL (Extract-Transform-Load) is also possible where transformation happens before the loading stage. Data Science and Analytics is the next step in the data lifecycle. Once the data needed for specific business need is available, data scientists use this data to run machine learning models to find the most accurate model. This data is available to the data scientists in the form of tables. Data analysts also use this data to do some exploratory analysis and create dashboards. In essence, the data lifecycle would look as follows: Raw data from different sources -> Data Engineering -> Data Science and Analytics

Jupiter is the fifth planet from the Sun and the largest in the Solar System. It is a gas giant with a mass more than two and a half times that of all the other planets in the Solar System combined, and slightly less than one one-thousandth the mass of the Sun. Jupiter is the third brightest natural object in the Earth's night sky after the Moon and Venus, and it has been observed since prehistoric times. It was named after Jupiter, the chief deity of ancient Roman religion.

What is the biggest planet from the solar system?

Jupiter is the largest plant in the Solar system with a surface of 120 times the earth

The recurrent language generator proposed in this paper is based on a neural language generator BIBREF8 , which consists of three main components: (i) an Encoder that incorporates the target meaning representation (MR) as the model inputs, (ii) an Aligner that aligns and controls the semantic elements, and (iii) an RNN Decoder that generates output sentences. The generator architecture is shown in Figure 1 . The Encoder first encodes the MR into input semantic elements which are then aggregated and selected by utilizing an attention-based mechanism by the Aligner. The input to the RNN Decoder at each time step is a 1-hot encoding of a token $\textbf {w}_{t}$ and an attentive DA representation $\textbf {d}_{t}$ . At each time step $t$ , RNN Decoder also computes how much the feature value vector $\textbf {s}_{t-1}$ retained for the next computational steps, and adds this information to the RNN output which represents the probability distribution of the next token $\textbf {w}_{t+1}$ . At generation time, we can sample from this conditional distribution to obtain the next token in a generated sentence, and feed it as the next input to the RNN Decoder. This process finishes when an end sign is generated BIBREF17 , or some constraints are reached BIBREF16 . The model can produce a sequence of tokens which can finally be lexicalized to form the required utterance. The recurrent language generator proposed in this paper is based on a neural language generator (Wen et al., 2016b), which consists of three main components: (i) an Encoder that incorporates the target meaning representation (MR) as the model inputs, (ii) an Aligner that aligns and controls the semantic elements, and (iii) an RNN Decoder that generates output sentences.

What are the main components of an ordinary neural language generator？

It consists of three main components: (i) an Encoder that incorporates the target meaning representation (MR) as the model inputs, (ii) an Aligner that aligns and controls the semantic elements, and (iii) an RNN Decoder that generates output sentences.

2003.04973

Data Collection: We are using the disaster data from BIBREF5. It contains various dataset including the CrisiLexT6 dataset which contains six crisis events related to English tweets in 2012 and 2013, labeled by relatedness (on-topic and off-topic) of respective crisis. Each crisis event tweets contain almost 10,000 labeled tweets but we are only focused on flood-related tweets thus, we experimented with only two flood event i.e. Queensland flood in Queensland, Australia and Alberta flood in Alberta, Canada and relabeled all on-topic tweets as Related and Off-topic as Unrelated for implicit class labels understanding in this case. The data collection process and duration of CrisisLex data is described in BIBREF5 details. We are using the disaster data from BIBREF5. It contains various dataset including the CrisiLexT6 dataset which contains six crisis events related to English tweets in 2012 and 2013, labeled by relatedness (on-topic and off-topic) of respective crisis. Each crisis event tweets contain almost 10,000 labeled tweets but we are only focused on flood-related tweets thus, we experimented with only two flood event i.e. Queensland flood in Queensland, Australia and Alberta flood in Alberta, Canada and relabeled all on-topic tweets as Related and Off-topic as Unrelated for implicit class labels understanding in this case.

What dataset did they use?

The answers are shown as follows: * disaster data from BIBREF5 * Queensland flood in Queensland, Australia and Alberta flood in Alberta, Canada

How many James Bond movies did Daniel Craig star in?

Daniel Craig starred in 5 James Bond movies

Is switchgrass a good biofuel source?

Switchgrass is an excellent biofuel source for many reasons. Switchgrass grows natively throughout the United States, Mexico, and Canada. It has a dramatically higher energy yield than corn. It requires almost no pesticides to grow and no fertilizer. Switchgrass actually pulls carbon out of the air and puts it into the ground through it's large root system, thus enriching the soil it grows in rather than depleting it. Once a switchgrass plant is established it grows back naturally every year for 18 to 20 years.

Which soccer player has the most number of career goals?

Cristiano Ronaldo has the highest number of career goals. His current tally is 834 goals. He is still active as a professional soccer player. Lionel Messi is currently in third place with 803 goals and is also active.

Cats need to communicate with each other for bonding, and relating with each other; they need to collaborate, play, and share resources. When they communicate with people, they do so to get what they need or want, such as food, affection, or play. Cats use a range of communication methods such as vocal, visual, tactile and olfactory. Cats mostly meow to communicate with people, rarely with other animals. As such, the cats' communication methods have been significantly affected by domestication. Up to 21 different cat vocalizations have been studied. It is now evident that domestic cats meow more than feral cats.

Can cats communicate?

Cat vocalizations have been categorized according to a range of characteristics. Originally suggested by Mildred Moelk, cat sounds are often divided into three main classes: sounds produced with the mouth closed (murmurs – purring, trilling) sounds produced when the mouth is first opened and then gradually closed (meowing, howling, yowling) sounds produced with the mouth held tensely open in the same position (growls, snarls, hisses, spits, chattering, and chirping). In 1944, Moelk published the first phonetic study of cat sounds. She listened very carefully to her own cats and organized their sounds into 16 phonetic patterns divided into three main categories. She also used a phonetic alphabet to transcribe or write down the different sounds. She claimed that cats had six different forms of meows to represent friendliness, confidence, dissatisfaction, anger, fear and pain. Moelk classified eight other sounds involved in mating and fighting by listening to the animals in her yard and on the street. Brown et al. categorized vocal responses of cats according to the behavioral context: during separation of kittens from mother cats, during food deprivation, during pain, prior to or during threat or attack behavior, as in disputes over territory or food, during a painful or acutely stressful experience, as in routine prophylactic injections and during kitten deprivation. Less commonly recorded calls from mature cats included purring, conspecific greeting calls or murmurs, extended vocal dialogues between cats in separate cages, "frustration" calls during training or extinction of conditioned responses. Miller classified vocalizations into five categories according to the sound produced: the purr, chirr, call, meow and growl/snarl/hiss. Owens et al. categorized cat vocalizations based on their acoustic structures. There are three categories: tonal sounds, pulse sounds, and broadband sounds. Tonal sounds are further categorized into groups of harmonically structured sounds or regular tonal sounds. Pulse vocalizations are separated into pulse bursts and hybrid pulse bursts with tonal endings. Broadband sounds are separated into four groups: non-tonal broadband sounds, broadband sounds with tonal beginnings, broadband sounds with short tonal elements, and broadband sounds with long tonal endings.

Stephanie Izard is an American chef and television personality best known as the first female chef to win Bravo's Top Chef, taking the title during its fourth season. She is the co-owner and executive chef of three award-winning Chicago restaurants, Girl and the Goat, Little Goat, and Duck Duck Goat, and opened her first restaurant, Scylla (now closed) as chef-owner at the age of 27. Izard received a James Beard Foundation Award for "Best Chef: Great Lakes" in 2013 for her work at Girl and the Goat. She has made a number of appearances on Top Chef since her win, both as a guest judge on subsequent seasons and as a participant in Top Chef Duels. In 2017, Izard competed in the Food Network series Iron Chef Gauntlet, where she overall defeated chefs Bobby Flay, Michael Symon, and Masaharu Morimoto to obtain the title of Iron Chef.

Who was the winner of Top Chef Season 4?

Stephanie Izard won the fourth season of Top Chef

What colors are in the traditional style of American tattoos?

American traditional tattoos consist mostly of thick black lines with black shading and red, blue, yellow, and green. Sometimes you will see brown and white also used in American traditional tattoos.

1711.11221

We want to further study how the proposed cache-based neural model influence coherence in document translation. For this, we follow Lapata2005Automatic to measure coherence as sentence similarity. First, each sentence is represented by the mean of the distributed vectors of its words. Second, the similarity between two sentences is determined by the cosine of their means. we follow Lapata2005Automatic to measure coherence as sentence similarity

Did the authors evaluate their system output for coherence?

Yes.

Tell me what league each of the following teams play in: Anaheim Ducks Baltimore Ravens St. Louis Cardinals Atlanta Falcons Toronto Raptors New Orleans Pelicans Arizona Cardinals Atlanta Hawks Baltimore Orioles Seattle Seahawks Pittsburgh Penguins Philadelphia Eagles Toronto Blue Jays

Here are the leagues the teams you mentioned play in: Anaheim Ducks - NHL Baltimore Ravens - NFL St. Louis Cardinals - MLB Atlanta Falcons - NFL Toronto Raptors - NBA New Orleans Pelicans - NBA Arizona Cardinals - NFL Atlanta Hawks - NBA Baltimore Orioles - MLB Seattle Seahawks - NFL Pittsburgh Penguins - NHL Philadelphia Eagles - NFL Toronto Blue Jays - MLB

Identify which animal species is alive or extinct: Thylacoleo, Skipjack Tuna

Thylacoleo is extinct, Skipjack Tuna is alive.

1911.11744

To test our model, we generated 500 new scenario testing each of the three features to identify the correct target among other bowls. A task is considered to be successfully completed when the cube is withing the boundaries of the targeted bowl. Bowls have a bounding box of 12.5 and 17.5cm edge length for the small and large variant, respectively. Our experiments showed that using the objects color or shape to uniquely identify an object allows the robot successfully complete the binning task in 97.6% and 96.0% of the cases. However, using the shape alone as a unique identifier, the task could only be completed in 79.0% of the cases. We suspect that the loss of accuracy is due to the low image resolution of the input image, preventing the network from reliably distinguishing the object shapes. In general, our approach is able to actuate the robot with an target error well below 5cm, given the target was correctly identified. Our experiments showed that using the objects color or shape to uniquely identify an object allows the robot successfully complete the binning task in 97.6% and 96.0% of the cases. However, using the shape alone as a unique identifier, the task could only be completed in 79.0% of the cases.

What is task success rate achieved?

96-97.6% using the objects color or shape and 79% using shape alone

The No-Execute bit or NX bit (bit 63 of the page table entry) allows the operating system to specify which pages of virtual address space can contain executable code and which cannot. An attempt to execute code from a page tagged "no execute" will result in a memory access violation, similar to an attempt to write to a read-only page. This should make it more difficult for malicious code to take control of the system via "buffer overrun" or "unchecked buffer" attacks. A similar feature has been available on x86 processors since the 80286 as an attribute of segment descriptors; however, this works only on an entire segment at a time.

In the x86-64 architecture, how can the operating system specify which pages cannot contain executable code?

The operating system can tag a page of virtual address space with the No-Execute or NX bit, which is bit 63 of the page table entry.

What is terrain cartography?

Terrain cartography or relief mapping is the depiction of the shape of the surface of the Earth on a map, using one or more of several techniques that have been developed. Terrain or relief is an essential aspect of physical geography, and as such its portrayal presents a central problem in cartographic design, and more recently geographic information systems and geovisualization.

Write a short story about a little girl that saves her neighbors home from a bee infestation.

Milly was 6 years old and ever since she could remember, fascinated by bees. Her mother called her their little apiologist. Her father called her "honey bee." Her brother called her super tough because she had been stung by bees over 10 times and not once did she cry. The truth is, Milly felt like crying when she was stung, and it was not because the sting hurt. No, Milly was sad, because when a bee stings you, it dies and Milly loves bees. Knowing what you now know about Milly, it will be no surprise to you that the Jackson's came to her when they needed help. The Jackson's were Milly's neighbors, and Milly thought they were very cool. First off, they loved animals. Including 3 kids, 2 dogs, 2 cats, and 12 little silver fish that sparkled in this huge fishbowl. They dressed colorfully, they organized street-wide garage sales, and every few weeks, they invited Milly and her brother over to watch movies in their backyard that they projected off the side of their garage. The Jackson's were not the type of people to worry about much, but on this day, they appeared very concerned. When Milly opened the door after she heard the doorbell ring, their they all were, Thomas and Lauren (the parents), Amber, Jade, Hugo (the kids), Bosko and Roscoe (the dogs), Felix and Helix (the cats), and sparkles (all of the fish were named sparkle because you couldn't tell them apart) in their fishbowl. Amber spoke up, "Milly, we need your help, it's an emergency." Jade continued, "There are bees everywhere!" Hugo finished, "At least Dad and Roscoe are allergic to bee stings, so we just grabbed everyone and ran!" Milly's eyes darted between each of the people and pets in front of her. She could see and sense the fear in them. They did not know that there was nothing to fear, Milly the little apiologist was here. Milly took a deep breath, and calmly said, "Lead me to them." Thomas said, "Thank you Milly, we think they are coming out of the garage, but we're not sure", as they started to walk next door. Stepping through the grass, you could start to hear what the Jackson's were talking about. With each step, a droning buzz sound got closer. As Milly stepped off the lawn and onto the Jackson's driveway the buzzing went up a few decibels. She started to see them. Little movements in every direction - coming from the garage, going to the garage, bouncing off the windows of the car, hovering above the buffet of colorful flowers in the planters hanging on the side of the back deck. To some it might look chaotic, but to Milly, it was amazing. The Jackson’s stayed back, near the start of the driveway, holding their collective breaths, as Milly walked right into the swarms' midst. Milly’s tie-dye shirt was bright pink and yellow, and had the words, “Flower Power” written on the front in bold, bubbly letters. It attracted a lot of attention. Bees were landing all over her, as if exploring the shirt for some hidden nectar. Unbothered, Milly stood there for a minute and just listened to the buzz. She could sense that it was louder towards the garage. Milly went to the green wooden door and peered in through the window. She could not believe her eyes. The window on the door of the Jackson’s garage was in need of a cleaning. But it was clear to Milly that something remarkable was behind this door, because she could see bees everywhere. Not just flying, but bees on the table top, the toolbox, the walls, the lights, the bicycles, they were everywhere. So many, the walls looked like they were moving. Milly opened the door and walked in, a few bees blasted out as the door opened, along with a rush of air carrying a slightly sweet fragrance. More bees flew towards Milly, landing on her. Walking on her sleeves, hanging on to her shoe laces, getting tangled in her hair. Milly put her hand over her mouth and nose, so she could breathe without inhaling a bee. She had to keep blinking to keep the bees from obscuring her vision. She walked slowly to the table top that used to have a few tools on it, keeping a close eye on where she stepped. She started her search here, because she noticed a strange pattern in the surface movement: there was a small trail between thousands of bees on the wall behind the table top. Milly knows a lot about bees. That male bees don’t have stingers. Females and males have different body types and shapes. Females have shorter antennae, males tend to be slimmer and smaller. Milly also knows that each hive has a single queen, and that the queen likes to walk quickly amongst her hive. As she walks, the workers all around her will part in front of her, leaving a small wake behind the queen. With this in mind, Millly scanned the wall, looking for her. And there she is. The queen bee stands out. She’s larger in every way. The patterns on her wings are more clear. She moves where she pleases, and her workers are happy to clear a path in front of her. Milly reached out and picked her up. The buzzing in the garage got louder almost immediately. The bees near the wall, started to lift off and fly around quickly in front of Milly, who was once again holding a hand over her mouth and nose. Milly turned to face the door and began to retrace her steps back out of the garage. As Milly took each step, more and more bees started to land on her closed hand holding the queen. Bees landed on her arm, on her shoulder, on neck, and slowly covered all of Milly’s body. Milly made it to the door, and walked out. The Jackson’s started yelling, “Milly are you OK?!” “Milly??!!!!” “Call 9-1-1!!!” Roscoe barked. Milly couldn’t hear anything over the buzzing though. The weight of the bees was unexpectedly heavy. The bees were warm too, and she could feel a tinge of perspiration on her brow. Milly headed home. She didn’t have any pets of her own, she didn’t ask for much, but she was about to make a big ask. Milly still moved slowly, as her vision was still obscured given the movement in front, above, and below her eyes. She could sense the Jackson’s kids rushing ahead of her, and shouting. Milly could feel the queen in her hand moving around, pushing at the edges between her fingers, trying to get through to her hive that was crawling all over Milly. As she walked back across the lawn, she started to see people on the street watching her. She could see her house between blinks, and her mother and father walking towards her, with big grins on their faces. They walked right up to Milly, and spoke loudly to be heard over the buzzing. “Where are you going, my little beekeeper?” Her dad’s voice cut through the buzz. Milly just looked up at him, and over at her mom. She responded with her eyes, though blinking rapidly, those big green eyes conveyed a desire to her parents that they understood. The two parents looked at each other and sighed, then looked back down at their daughter. “So, what are we supposed to do, get a nice box or jar for the queen?” Milly nodded, and headed through the gate to their backyard. Milly’s parents followed, along with her brother, and the Jackson’s, though Roscoe stayed wearily at the back of the group. Milly’s mother disappeared into their shed and emerged a few seconds later with a small box that had a lid with a very fine mesh. A temporary home for a queen. The box was placed on the patio table and the group gathered around. Milly reached out and placed the queen in the box and stepped back. As she stepped away, the bees started to lift off of her, buzzing in the air, waiting to pick up the new signal of the queen’s location. Then one by one, they found her, and rapidly flew towards the box, covering the table in seconds. Milly let out a deep breath and looked at her friends and family. She dropped the hand from over her mouth, and helped a few bees that were stuck in her hair find their way out. She had the biggest smile on her face, like only a happy 6 year old can have, and she asked, “Who likes honey?”

Vishnu is known as "The Preserver" within the Trimurti, the triple deity of supreme divinity that includes Brahma and Shiva. In Vaishnavism, Vishnu is the supreme being who creates, protects, and transforms the universe. In the Shaktism tradition, the Goddess, or Adi Shakti, is described as the supreme Para Brahman, yet Vishnu is revered along with Shiva and Brahma. Tridevi is stated to be the energy and creative power (Shakti) of each, with Lakshmi being the equal complementary partner of Vishnu. He is one of the five equivalent deities in Panchayatana puja of the Smarta tradition of Hinduism. In the Mahabharata, Vishnu (as Narayana) states to Narada that He will appear in the following ten incarnations: Appearing in the forms of a swan [Hamsa], a tortoise [Kurma], a fish [Matsya], O foremost of regenerate ones, I shall then display myself as a boar [Varaha], then as a Man-lion (Nrisingha), then as a dwarf [Vamana], then as Rama of Bhrigu's race, then as Rama, the son of Dasaratha, then as Krishna the scion of the Sattwata race, and lastly as Kalki.

List the incarnations of Lord Vishnu.

In the Mahabharata, Vishnu states to Narada that He will appear in the following ten incarnations: A swan [Hamsa], a tortoise [Kurma], a fish [Matsya], a boar [Varaha], a Man-lion (Nrisingha), a dwarf [Vamana], as Rama of Bhrigu's race, as Rama, the son of Dasaratha, as Krishna, and lastly as Kalki.

To assess the performance of binary paragraph vectors, we carried out experiments on three datasets: 20 Newsgroups, a cleansed version (also called v2) of Reuters Corpus Volume 1 (RCV1) and English Wikipedia. As paragraph vectors can be trained with relatively large vocabularies, we did not perform any stemming of the source text. However, we removed stop words as well as words shorter than two characters and longer than 15 characters. Results reported by BIBREF15 indicate that performance of PV-DBOW can be improved by including n-grams in the model. We therefore evaluated two variants of Binary PV-DBOW: one predicting words in documents and one predicting words and bigrams. Since 20 Newsgroups is a relatively small dataset, we used all words and bigrams from its documents. This amounts to a vocabulary with slightly over one million elements. For the RCV1 dataset we used words and bigrams with at least 10 occurrences in the text, which gives a vocabulary with approximately 800 thousands elements. In case of English Wikipedia we used words and bigrams with at least 100 occurrences, which gives a vocabulary with approximately 1.5 million elements. The 20 Newsgroups dataset comes with reference train/test sets. In case of RCV1 we used half of the documents for training and the other half for evaluation. In case of English Wikipedia we held out for testing randomly selected 10% of the documents. We perform document retrieval by selecting queries from the test set and ordering other test documents according to the similarity of the inferred codes. We use Hamming distance for binary codes and cosine similarity for real-valued representations. Results are averaged over queries. We assess the performance of our models with precision-recall curves and two popular information retrieval metrics, namely mean average precision (MAP) and the normalized discounted cumulative gain at the 10th result (NDCG@10) BIBREF16 . The results depend, of course, on the chosen document relevancy measure. Relevancy measure for the 20 Newsgroups dataset is straightforward: a retrieved document is relevant to the query if they both belong to the same newsgroup. In RCV1 each document belongs to a hierarchy of topics, making the definition of relevancy less obvious. In this case we adopted the relevancy measure used by BIBREF3 . That is, the relevancy is calculated as the fraction of overlapping labels in a retrieved document and the query document. Overall, our selection of test datasets and relevancy measures for 20 Newsgroups and RCV1 follows BIBREF3 , enabling comparison with semantic hashing codes. To assess the relevancy of articles in English Wikipedia we can employ categories assigned to them. However, unlike in RCV1, Wikipedia categories can have multiple parent categories and cyclic dependencies. Therefore, for this dataset we adopted a simplified relevancy measure: two articles are relevant if they share at least one category. We also removed from the test set categories with less than 20 documents as well as documents that were left with no categories. Overall, the relevancy is measured over more than INLINEFORM0 categories, making English Wikipedia harder than the other two benchmarks. We use AdaGrad BIBREF17 for training and inference in all experiments reported in this work. During training we employ dropout BIBREF18 in the embedding layer. To facilitate models with large vocabularies, we approximate the gradients with respect to the softmax logits using the method described by BIBREF9 . Binary PV-DM networks use the same number of dimensions for document codes and word embeddings. Performance of 128- and 32-bit binary paragraph vector codes is reported in Table TABREF8 and in Figure FIGREF7 . For comparison we also report performance of real-valued paragraph vectors. Note that the binary codes perform very well, despite their far lower capacity: on 20 Newsgroups and RCV1 the 128-bit Binary PV-DBOW trained with bigrams approaches the performance of the real-valued paragraph vectors, while on English Wikipedia its performance is slightly lower. Furthermore, Binary PV-DBOW with bigrams outperforms semantic hashing codes: comparison of precision-recall curves from Figures FIGREF7 a and FIGREF7 b with BIBREF3 shows that 128-bit codes learned with this model outperform 128-bit semantic hashing codes on 20 Newsgroups and RCV1. Moreover, the 32-bit codes from this model outperform 128-bit semantic hashing codes on the RCV1 dataset, and on the 20 Newsgroups dataset give similar precision up to approximately 3% recall and better precision for higher recall levels. Note that the difference in this case lies not only in retrieval precision: the short 32-bit Binary PV-DBOW codes are more efficient for indexing than long 128-bit semantic hashing codes. We also compared binary paragraph vectors against codes constructed by first inferring short, real-valued paragraph vectors and then using a separate hashing algorithm for binarization. When the dimensionality of the paragraph vectors is equal to the size of binary codes, the number of network parameters in this approach is similar to that of Binary PV models. We experimented with two standard hashing algorithms, namely random hyperplane projection BIBREF19 and iterative quantization BIBREF20 . Paragraph vectors in these experiments were inferred using PV-DBOW with bigrams. Results reported in Table TABREF9 show no benefit from using a separate algorithm for binarization. On the 20 Newsgroups and RCV1 datasets Binary PV-DBOW yielded higher MAP than the two baseline approaches. On English Wikipedia iterative quantization achieved MAP equal to Binary PV-DBOW, while random hyperplane projection yielded lower MAP. Some gain in precision of top hits can be observed for iterative quantization, as indicated by NDCG@10. However, precision of top hits can also be improved by querying with Real-Binary PV-DBOW model (Section SECREF15 ). It is also worth noting that end-to-end inference in Binary PV models is more convenient than inferring real-valued vectors and then using another algorithm for hashing. BIBREF15 argue that PV-DBOW outperforms PV-DM on a sentiment classification task, and demonstrate that the performance of PV-DBOW can be improved by including bigrams in the vocabulary. We observed similar results with Binary PV models. That is, including bigrams in the vocabulary usually improved retrieval precision. Also, codes learned with Binary PV-DBOW provided higher retrieval precision than Binary PV-DM codes. Furthermore, to choose the context size for the Binary PV-DM models, we evaluated several networks on validation sets taken out of the training data. The best results were obtained with a minimal one-word, one-sided context window. This is the distributed memory architecture most similar to the Binary PV-DBOW model. We use AdaGrad (Duchi et al., 2011) for training and inference in all experiments reported in this work.

What is used for training and inference in all experiments reported in this work?

The authors use AdaGrad for training and inference in all experiments reported in this work.

What is the capital of CA?

Sacramento

What is Microsoft copilot

Microsoft 365 Copilot is an artificial intelligence assistant feature for Microsoft 365 applications and services, which include its productivity suite of programs, such as Word, Excel, PowerPoint, and Outlook. The integration of Copilot with 365, announced on March, 16, 2023, is initially available to a small number of enterprises and individuals for testing purposes. Copilot uses OpenAI's GPT-4 large language model with Microsoft Graph to assist users in a range of tasks.[3

Text summarization is a task to generate a shorter and concise version of a text while preserving the meaning of the original text. The task can be divided into two subtask based on the approach: extractive and abstractive summarization. Extractive summarization is a task to create summaries by pulling out snippets of text form the original text and combining them to form a summary. Abstractive summarization asks to generate summaries from scratch without the restriction to use the available words from the original text. Due to the limitations of extractive summarization on incoherent texts and unnatural methodology BIBREF0 , the research trend has shifted towards abstractive summarization. Sequence-to-sequence models BIBREF1 with attention mechanism BIBREF2 have found great success in generating abstractive summaries, both from a single sentence BIBREF3 and from a long document with multiple sentences BIBREF4 . However, when generating summaries, it is necessary to determine the main topic and to sift out unnecessary information that can be omitted. Sequence-to-sequence models have the tendency to include all the information, relevant or not, that are found in the original text. This may result to unconcise summaries that concentrates wrongly on irrelevant topics. The problem is especially severe when summarizing longer texts. In this paper, we propose to use entities found in the original text to infer the summary topic, mitigating the aforementioned problem. Specifically, we leverage on linked entities extracted by employing a readily available entity linking system. The importance of using linked entities in summarization is intuitive and can be explained by looking at Figure 1 as an example. First (O1 in the Figure), aside from auxiliary words to construct a sentence, a summary is mainly composed of linked entities extracted from the original text. Second (O2), we can depict the main topic of the summary as a probability distribution of relevant entities from the list of entities. Finally (O3), we can leverage on entity commonsense learned from a separate large knowledge base such as Wikipedia. To this end, we present a method to effectively apply linked entities in sequence-to-sequence models, called Entity2Topic (E2T). E2T is a module that can be easily attached to any sequence-to-sequence based summarization model. The module encodes the entities extracted from the original text by an entity linking system (ELS), constructs a vector representing the topic of the summary to be generated, and informs the decoder about the constructed topic vector. Due to the imperfections of current ELS's, the extracted linked entities may be too ambiguous and coarse to be considered relevant to the summary. We solve this issue by using entity encoders with selective disambiguation and by constructing topic vectors using firm attention. We experiment on two datasets, Gigaword and CNN, with varying lengths. We show that applying our module to a sequence-to-sequence model with attention mechanism significantly increases its performance on both datasets. Moreover, when compared with the state-of-the-art models for each dataset, the model obtains a comparable performance on the Gigaword dataset where the texts are short, and outperforms all competing models on the CNN dataset where the texts are longer. Furthermore, we provide analysis on how our model effectively uses the extracted linked entities to produce concise and better summaries. Moreover, when compared with the state-of-the-art models for each dataset, the model obtains a comparable performance on the Gigaword dataset where the texts are short, and outperforms all competing models on the CNN dataset where the texts are longer.

When compared with the state-of-the-art models for each dataset, how does the model perform?

The model obtains a comparable performance on the Gigaword dataset where the texts are short, and outperforms all competing models on the CNN dataset where the texts are longer.

1909.00161

In this work, we make use of the widely-recognized state of the art entailment technique – BERT BIBREF18, and train it on three mainstream entailment datasets: MNLI BIBREF19, GLUE RTE BIBREF20, BIBREF21 and FEVER BIBREF22, respectively. We convert all datasets into binary case: “entailment” vs. “non-entailment”, by changing the label “neutral” (if exist in some datasets) into “non-entailment”. For our label-fully-unseen setup, we directly apply this pretrained entailment model on the test sets of all $\textsc {0shot-tc}$ aspects. For label-partially-unseen setup in which we intentionally provide annotated data, we first pretrain BERT on the MNLI/FEVER/RTE, then fine-tune on the provided training data. In this work, we make use of the widely-recognized state of the art entailment technique – BERT BIBREF18, and train it on three mainstream entailment datasets: MNLI BIBREF19, GLUE RTE BIBREF20, BIBREF21 and FEVER BIBREF22, respectively. We convert all datasets into binary case: “entailment” vs. “non-entailment”, by changing the label “neutral” (if exist in some datasets) into “non-entailment”. For our label-fully-unseen setup, we directly apply this pretrained entailment model on the test sets of all $\textsc {0shot-tc}$ aspects. For label-partially-unseen setup in which we intentionally provide annotated data, we first pretrain BERT on the MNLI/FEVER/RTE, then fine-tune on the provided training data.

Do they use pretrained models?

Yes.

Who is Rajinikanth?

Rajinikanth is an Indian action who primarily acts in Tamil movies. Shivaji Rao Gaikwad, known professionally as Rajinikanth (/rədʒɪnɪkɑːnθ/), is an Indian actor, producer and screenwriter who works mainly in Tamil cinema. In a career spanning over five decades, he has done more than 160 films that includes films in Tamil, Hindi, Telugu, Kannada, Bengali, and Malayalam. He is widely regarded as one of the most successful and popular actors in the history of Indian cinema.

Classify the following people as primarily authors or primarily movie directors: Steven King, Steven Spielberg, N. K. Jemisin, Martin Scorsese, Jane Austen

Steven King, N. K. Jemisin, and Jane Austen are authors. Steven Spielberg and Martin Scorsese are movie directors.

How many Harry Potter books are there?

You have 7 books, The philosophers stone, the chamber of secrets, prisoners of azkaban, goblet of fire, order of the phoenix, the halfblood prince, the deathly hallows.

Who are N-Dubz?

N-Dubz are a popular band in the United Kingdom, made up of Tulisa, Fazer, and Dappy, formed out of London. The band were formed when they were young teenagers in the early 2000s. They were inspired to form the band by Dappy's late father, known to the band as "Uncle B". Their song "Papa can you hear me?" is a tribute to Uncle B. Tulisa and Dappy are cousins, whilst Fazer has always been a close friend. The trio have had many successful hits, and collaborated with popular artists like Tinchy Strider and Skepta. They parted ways in 2011, and Dappy started a solo career, whilst Tulisa became a judge on the popular UK show "The X Factor". She formed and mentored the winning band Little Mix. The band reunited in 2022 and released new music, along with a sold out UK tour.

The Amazon rainforest, also called Amazon jungle or Amazonia, is a moist broadleaf tropical rainforest in the Amazon biome that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square km (2,700,000 square miles), of which 5,500,000 square km (2,100,000 square miles) are covered by the rainforest. This region includes territory belonging to nine nations and 3,344 formally acknowledged indigenous territories. The majority of the forest, 60%, is in Brazil, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Bolivia, Ecuador, French Guiana, Guyana, Suriname, and Venezuela. Four nations have "Amazonas" as the name of one of their first-level administrative regions, and France uses the name "Guiana Amazonian Park" for French Guiana's protected rainforest area. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees in about 16,000 species. More than 30 million people of 350 different ethnic groups live in the Amazon, which are subdivided into 9 different national political systems and 3,344 formally acknowledged indigenous territories. Indigenous peoples make up 9% of the total population, and 60 of the groups remain largely isolated.

Given the reference text about the Amazon Rainforest, where is it located, including all countries it spreads across, how large is it, and how many people reside within it?

Located in South America, the Amazon rainforest spreads across Brazil, Peru. Columbia, Bolivia, Ecuador, French Guiana, Guyana, Suriname, and Venezuela. The rainforest covers 5,500,000 square km (2,100,000 square miles) where more than 30 million people reside.

1812.03593

We evaluated SDNet on CoQA dataset, which improves the previous state-of-the-art model's result by 1.6% (from 75.0% to 76.6%) overall $F_1$ score. The ensemble model further increase the $F_1$ score to $79.3\%$ . Moreover, SDNet is the first model ever to pass $80\%$ on CoQA's in-domain dataset. We evaluated SDNet on CoQA dataset

Is the model evaluated on other datasets?

No.

Classify each of the following as either a title by Isaac Asimov or Jules Verne: Pebble in the Sky, The Stars, Like Dust, Foundation, The Robots of Dawn, The Naked Sun, Around the World in Eighty Days, From the Earth to the Moon, Martin Paz, Journey to the Centre of the Earth, A Drama in the Air

Isaac Asimov: Pebble in the Sky, The Stars, Like Dust, Foundation, The Robots of Dawn, The Naked Sun Jules Verne: Around the World in Eighty Days, From the Earth to the Moon, Martin Paz, Journey to the Centre of the Earth, A Drama in the Air

George Walton Lucas Jr. (born May 14, 1944) is an American filmmaker. Lucas is best known for creating the Star Wars and Indiana Jones franchises and founding Lucasfilm, LucasArts, Industrial Light & Magic and THX. He served as chairman of Lucasfilm before selling it to The Walt Disney Company in 2012. Lucas is one of history's most financially successful filmmakers and has been nominated for four Academy Awards. His films are among the 100 highest-grossing movies at the North American box office, adjusted for ticket-price inflation. Lucas is considered to be one of the most significant figures of the 20th-century New Hollywood movement, and a pioneer of the modern blockbuster.

Who is George Lucas?

George Walton Lucas Jr. (born May 14, 1944) is an American filmmaker. Lucas is best known for creating the Star Wars and Indiana Jones franchises and founding Lucasfilm, LucasArts, Industrial Light & Magic and THX. He served as chairman of Lucasfilm before selling it to The Walt Disney Company in 2012. Lucas is one of history's most financially successful filmmakers and has been nominated for four Academy Awards. His films are among the 100 highest-grossing movies at the North American box office, adjusted for ticket-price inflation. Lucas is considered to be one of the most significant figures of the 20th-century New Hollywood movement, and a pioneer of the modern blockbuster. After graduating from the University of Southern California in 1967, Lucas co-founded American Zoetrope with filmmaker Francis Ford Coppola. Lucas wrote and directed THX 1138 (1971), based on his student short Electronic Labyrinth: THX 1138 4EB, which was a critical success but a financial failure. His next work as a writer-director was American Graffiti (1973), inspired by his youth in the early 1960s Modesto, California, and produced through the newly founded Lucasfilm. The film was critically and commercially successful and received five Academy Award nominations, including Best Director and Best Picture. Lucas's next film, the epic space opera Star Wars (1977), had a troubled production but was a surprise hit, becoming the highest-grossing film at the time, winning six Academy Awards and sparking a cultural phenomenon. Lucas produced and co-wrote the sequels The Empire Strikes Back (1980) and Return of the Jedi (1983). With director Steven Spielberg, he created, produced, and co-wrote the Indiana Jones films Raiders of the Lost Ark (1981), The Temple of Doom (1984), The Last Crusade (1989), and The Kingdom of the Crystal Skull (2008), and only produced The Dial of Destiny (2023). Lucas is also known for his collaboration with composer John Williams, who was recommended to him by Spielberg, and with whom he has worked for all the films in both of these franchises. He also produced and wrote a variety of films and television series through Lucasfilm between the 1970s and the 2010s. In 1997, Lucas re-released the original Star Wars trilogy as part of a Special Edition featuring several modifications; home media versions with further changes were released in 2004 and 2011. He returned to directing with a Star Wars prequel trilogy comprising Star Wars: Episode I – The Phantom Menace (1999), Episode II – Attack of the Clones (2002), and Episode III – Revenge of the Sith (2005). He last collaborated on the CGI-animated television series Star Wars: The Clone Wars (2008–2014, 2020), the war film Red Tails (2012), and the CGI film Strange Magic (2015).

What is a stock index?

A stock index or stock market index is a method of measuring the value of a section of the stock market. It is computed from the prices of selected stocks (typically a weighted average). It is a tool used by investors and financial managers to describe the market, and to compare the return on specific investments. For example, S&P 500 is based on the market capitalizations of 500 large companies having common stock listed on the NYSE or NASDAQ.

Which first indian movie won oscars and what category

The first indian movie to won oscars is RRR. It is a south indian Tollywood Movie directed by SS Rajamouli and acted by Jr NTR and Ram Charan in main leads. The song Naatu Naatu has won the Oscar for music director Keeravani with lyrics by Chandrabose

Classify each sports as either cardio, racket or team: running, biking, basketball, football, badminton, tennis, squash, swimming.

Running, Biking and Swimming are Cardio sports, while badminton, tennis and squash are racket sports, and lastly, basketball and football are team sports.

During the Middle Ages, the Gran Tavola (Italian for "Great Table") was the largest Sienese bank; it was one of the most powerful banks in Europe from 1255 to 1298. The Gran Tavola has been called "the greatest bank of the thirteenth century" as well as "one of the largest commercial and banking enterprises in Europe". The main branches of the Gran Tavola during the mid-thirteenth century were in Pisa, Bologna, Genoa, Marseille, and Paris.

Where were the main branches of Gran Tavola?

Pisa, Bologna, Genoa, Marseille, and Paris

Rheological weldability (RW) of thermoplastics considers the materials flow characteristics in determining the weldability of the given material. The process of welding thermal plastics requires three general steps, first is surface preparation. The second step is the application of heat and pressure to create intimate contact between the components being joined and initiate inter-molecular diffusion across the joint and the third step is cooling. RW can be used to determine the effectiveness of the second step of the process for given materials.

What is rheological weldability?

Rheological weldability describes the effectiveness of applying heat and pressure to join two materials.

1910.08418

Recall that our goal is to discover words in an unsegmented stream of target characters (or phonemes) in the under-resourced language. In this section, we first describe a baseline method inspired by the “align to segment” of BIBREF12, BIBREF13. We then propose two extensions providing the model with a signal relevant to the segmentation process, so as to move towards a joint learning of segmentation and alignment. In this section, we first describe a baseline method inspired by the “align to segment” of BIBREF12, BIBREF13. We then propose two extensions providing the model with a signal relevant to the segmentation process, so as to move towards a joint learning of segmentation and alignment.

Does the paper report any alignment-only baseline?

Yes.

1911.13066

FLOAT SELECTED: Table 3. Performance evaluation of variations of the proposed model and baseline. Showing highest scores in boldface. FLOAT SELECTED: Table 3. Performance evaluation of variations of the proposed model and baseline. Showing highest scores in boldface.

What accuracy score do they obtain?

the best performing model obtained an accuracy of 0.86

Who was Yogi Bear named after?

He was named after baseball legend Yogi Berra

The goal of LD2NL is to provide an integrated system which generates a complete and correct NL representation for the most common used SW modeling languages RDF and OWL, and SPARQL. In terms of the standard model of NL generation proposed by Reiter & Dale BIBREF19, our steps mainly play the role of the micro-planner, with focus on aggregation, lexicalization, referring expressions and linguistic realization. In the following, we present our approach to formalizing NL sentences for each of the supported languages. The goal of LD2NL is to provide an integrated system which generates a complete and correct NL representation for the most common used SW modeling languages RDF and OWL, and SPARQL.

What is the goal of LD2NL?

The goal of LD2NL is to provide an integrated system which generates a complete and correct NL representation for the most common used SW modeling languages RDF and OWL, and SPARQL.

1904.12535

Logician is a supervised end-to-end neural learning algorithm which transforms natural language sentences into facts in the SAOKE form. Logician is trained under the attention-based sequence-to-sequence paradigm, with three mechanisms: restricted copy mechanism to ensure literally honestness, coverage mechanism to alleviate the under extraction and over extraction problem, and gated dependency attention mechanism to incorporate dependency information. Experimental results on four types of open information extraction tasks reveal the superiority of the Logician algorithm. Logician is trained under the attention-based sequence-to-sequence paradigm, with three mechanisms: restricted copy mechanism to ensure literally honestness, coverage mechanism to alleviate the under extraction and over extraction problem, and gated dependency attention mechanism to incorporate dependency information.

How is Logician different from traditional seq2seq models?

The answers are shown as follows: * restricted copy mechanism to ensure literally honestness, coverage mechanism to alleviate the under extraction and over extraction problem, and gated dependency attention mechanism to incorporate dependency information

What is the Tesla Model Y Charging Speed?

The Tesla Model Y's battery size is 82 kWh. This is larger than the average EV's battery size. The Model Y has a max AC charging speed of 11 kW and a max DC charging speed of 210 kW. 210 kW is much higher than most chargers can go in the UK, which means you'll be able to charge your EV as fast as possible at most charging points. If you're able to find an EV that can charge up to 210 kW, it will take 24 minutes to fully charge your EV from 0 - 100%, although you shouldn't charge your EV fully too regularly or let it get down to 0% as this can damage your battery life.

Coffee drinks are made by brewing water with ground coffee beans. The brewing is either done slowly by drip, filter, French press, cafetière or percolator, or done very quickly under pressure by an espresso machine. When put under the pressure of an espresso machine, the coffee is termed espresso while slow-brewed coffees are generally termed brewed coffee. While all coffee drinks are based on either coffee or espresso, some drinks add milk or cream, some are made with steamed milk or non-dairy milks, or add water (like the americano). Upon milk additions, coffee's flavor can vary with different syrups or sweeteners, alcoholic liqueurs, and even combinations of coffee with espresso or tea. There are many variations to the basic coffee or espresso bases.

Give me a bulleted list of the different ways coffee can be brewed.

Different methods of brewing coffee: - Slow-brewed coffee - Drip - Filter - French press - Cafetière - Percolator - Espresso machine

What is Crohn's Disease and what are some of the symptoms?

Crohn's Disease is a form of Inflammatory Bowel Disease (IBS) that effects parts of the gastrointestinal (GI) tract. Symptoms could include abdominal pain, weight loss and diarrhea among others. Bowel obstruction may occur due to the inflammation.