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The Las Vegas Strip is a stretch of Las Vegas Boulevard South in Clark County, Nevada, that is known for its concentration of resort hotels and casinos. The Strip, as it is known, is about 4.2 mi (6.8 km) long, and is immediately south of the Las Vegas city limits in the unincorporated towns of Paradise and Winchester, but is often referred to simply as "Las Vegas". Many of the largest hotel, casino, and resort properties in the world are on the Strip, known for its contemporary architecture, lights, and wide variety of attractions. Its hotels, casinos, restaurants, residential high-rises, entertainment offerings, and skyline have established the Strip as one of the most popular and iconic tourist destinations in the world and is one of the driving forces for Las Vegas's economy. Most of the Strip has been designated as an All-American Road, and the North and South Las Vegas Strip routes are classified as Nevada Scenic Byways and National Scenic Byways.

Given the paragraph is Strip the most famous hotel in Las Vegas?

The Strip is not a hotel in Las Vegas, but rather a 4.2-mile-long boulevard that is home to many of the city's largest hotels, casinos, and resort properties.

How do you make a peanut butter and jelly sandwich?

You need a few ingredients to make a peanut butter and jelly sandwich. First, you need two slices of bread, which are crucial to the recipe. Next, you will need peanut butter. Some people prefer smooth peanut butter, while others might enjoy crunchy peanut butter. This choice is up to you. The next ingredient is jelly. Like peanut butter, there are many choices of jelly to choose from. The most popular jelly is grape; many people like raspberry or strawberry jelly. Once you have all your ingredients, it is time to assemble your sandwich. First, you lay a piece of bread on your plate. Next, take a knife to spread peanut butter on the slice of bread on the plate. How much peanut butter you spread is up to your preference. Next, take the other slice of bread and spread your jelly on it. Once each piece of bread has either jelly or peanut butter on it, take the two slices of bread and put them together. The final step is to cut your sandwich. Some enjoy eating the sandwich whole, while others enjoy a diagonal cut or a cut down the middle. You have now created your peanut butter and jelly sandwich.

Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning. In this paper, we systematically explore zero-shot cross-lingual transfer learning on reading comprehension tasks with a language representation model pre-trained on multi-lingual corpus. The experimental results show that with pre-trained language representation zero-shot learning is feasible, and translating the source data into the target language is not necessary and even degrades the performance. We further explore what does the model learn in zero-shot setting. In this paper, we systematically explore zeroshot cross-lingual transfer learning on reading comprehension tasks with a language representation model pre-trained on multi-lingual corpus.

What task does the paper research?

Reading comprehension (RC) tasks.

Vengaboys (/ˈbɛŋ.ɡæ.bɔɪz/) is a Dutch Eurodance music group based in Rotterdam. The group was the creation of Dutch producers Wessel van Diepen and Dennis van den Driesschen (known as Danski and Delmundo). It consists of lead vocalist Kim Sasabone, female vocalist Denise Post-Van Rijswijk and male vocalists Robin Pors and Donny Latupeirissa. Casting and selection of the act was done by van Diepen. The group enjoyed commercial success in the late 1990s. They are best known for their hit singles "We Like to Party", "Boom, Boom, Boom, Boom!!", and "We're Going to Ibiza", the latter two of which topped the UK Singles Chart. They have sold an estimated 25 million records worldwide. On 2 May 2001, the World Music Awards gave Vengaboys the award for best-selling dance group of the year. In 2019, Dutch online newspaper Nu.nl called Vengaboys the most successful Dutch pop group in history.

Name the vocalists of Vengaboys.

The vocalists of Vengaboys are Kim Sasabone, Denise Post-Van Rijswijk, Robin Pors and Donny Latupeirissa.

1808.04614

Results in Table TABREF64 show the correctness rates of these scenarios. User correctness score is superior to that of the baseline parser by 7.5% (from 37.1% to 44.6%), while the hybrid approach outscores both with a correctness of 48.7% improving the baseline by 11.6%. For the user and hybrid correctness we used a INLINEFORM0 test to measure significance. Random queries and tables included in the experiment are presented in Table TABREF66 . We also include a comparison of the top ranked query of the baseline parser compared to that of the user. Results in Table TABREF64 show the correctness rates of these scenarios. User correctness score is superior to that of the baseline parser by 7.5% (from 37.1% to 44.6%), while the hybrid approach outscores both with a correctness of 48.7% improving the baseline by 11.6%.

Which query explanation method was preffered by the users in terms of correctness?

The answers are shown as follows: * hybrid approach

What is an Oxford comma and why do editors have such strong feelings about their use?

An Oxford comma is a comma before the last item in a series or list. For example take the following two statements: I was chased by the fox, the bear and the dog. I was chased by the fox, the bear, and the dog. The meaning is the same but the second statement contains an Oxford comma. Some say the Oxford comma adds clarity to some statements. This is apparent when there can be ambiguity between a list and an appositive. Take the following two example statements: We went to dinner with our coaches, Chris and Terry. We went to dinner with our coaches, Chris, and Terry. The first statement can appear to imply that we went to dinner with two people whose names are Chris and Terry who are both our coaches. There is no such implication that can be extracted from the second statement. The second statement implies we went to dinner with at least 4 people: Chris, Terry, and at least two coaches. This type of ambiguity is what proponents of the Oxford comma latch onto in its defense. On the other hand, opponents of the Oxford comma claim it should be avoided in favor of rewording sentences when necessary. The sentence: We went to dinner with our coaches, Chris, and Terry. Can easily be reworded as: We went to dinner with Chris and Terry, as well as our coaches. The "as well as" language is claimed to be more clear than the Oxford comma albeit less concise. Editors who oppose the Oxford comma typically privilege clarity over conciseness.

Maurice Micklewhite became famous as who

Michael Caine

We conduct experiments on cross-lingual abstractive summarization (AS). AS is the task of converting the input sentences into summaries while preserving the key meanings. For evaluation, we use English/French/Chinese Gigaword to extract the first sentence and the headline of each article, and regard them as input document and predicted summaries, respectively. For each language, we sample 500k/5k/5k examples for training/validation/test. In the zero-shot setting, we only use English data for training, and directly evaluate the model on other languages. In Table TABREF22 and Table TABREF23, we present the results for French/Chinese AS, which are evaluated by the ROUGE-1, ROUGE-2 and ROUGE-L metrics. We also report the results of supervised AS in Table TABREF21 for reference. We find that Xnlg outperforms all the baseline models on both French and Chinese AS. Comparing with French, there is a larger gap between baselines and our model on zero-shot Chinese AS, which indicates that the error propagation issue is more serious on distant language pairs. We also report the results of supervised AS in Table 6 for reference. We find that XNLG outperforms all the baseline models on both French and Chinese AS.

Does XNLG outperform all the baseline models on both French and Chinese AS?

Yes.

What are some of the best restaurants to try in Las Vegas, Nevada?

Carbone, Din Tai Fung, Bacchanal Buffet

As an immigrant , do you miss your country?

As an immigrant, it feels like you are away from your family. It feels like you work hard to give good life to your family. But with all that sacrifice you want to give best of both worlds to your kid. But when you hear news like shootings in school, as an immigrant we get afraid. I wish if there is a world which is so beautiful that people love each other. They care for each other. There are no rules that always keep you at the edge if you have to uproot your family from one country to another because of visa restrictions and what not. As an immigrant we want to raise our child in the best way possible. I am still looking for such a place on earth. If anyone of you are aware of such a place, please let me know., I hope the world in its entiriety becomes a peaceful, happy clean place to live.

What does a healthy morning routine look like?

Here's an example of a healthy morning routine. You wake up at least an hour or two before your obligations begin. You start your day with a meditation practice so that you can clear your mind and focus on your intentions for the day. Then you spend at least 30 minutes on exercise. After working up a sweat, you take a shower and go through your morning getting-ready routine. You prepare coffee and breakfast, and read or spend time with a loved one as you eat. Then you begin your duties for the day.

Compare and contrast the strategy of Tesla to that of Nissan. Why has Tesla been successful in the electric vehicle market while Nissan has yet to?

Nissan introduced Leaf in 2011, 18 months before Tesla launched its first car, the Model S. Despite the head start, Nissan Leaf is discontinued as of 2023. Let’s explore why Nissan Leaf failed while Tesla succeeded. While Nissan Leaf was innovative and introduced an alternative energy car, it was just a better car, but more is needed for consumers to adopt it. With the 9X effect, consumers were skeptical about its performance (range), unable to see the need for an electric car as they were satisfied with the reliable ICE (internal combustion engine) cars. Nissan Leaf is a good car on paper. However, consumers were unwilling to switch because of the status quo bias. Also, Nissan needed to build an ecosystem of products around electric vehicles, like charging stations. It relied on the traditional auto-sales process, which is already cumbersome, and consumers view it as a haggle. On the other hand, Tesla came with 10X improvements overall in customer experience, drivability, charging stations, integrated hardware and software (over-the-wire updates), and direct to consumer sales model with zero advertising. Tesla broke the status quo bias of the consumers with the 9x effect. Telsa’s messaging is the key that stuck with the customers, planet-friendly cars.

The goal of multi-document summarization (MDS) is to automatically generate a brief, well-organized summary for a topic which describes an event with a set of documents from different sources. BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 . In the typical setting of MDS, the input is a set of news documents about the same topic. The output summary is a piece of short text document containing several sentences, generated only based on the input original documents. With the development of social media and mobile equipments, more and more user generated content is available. Figure FIGREF2 is a snapshot of reader comments under the news report “The most important announcements from Google's big developers' conference”. The content of the original news report talks about some new products based on AI techniques. The news report generally conveys an enthusiastic tone. However, while some readers share similar enthusiasms, some others express their worries about new products and technologies and these comments can also reflect their interests which may not be very salient in the original news reports. Unfortunately, existing MDS approaches cannot handle this issue. We investigate this problem known as reader-aware multi-document summarization (RA-MDS). Under the RA-MDS setting, one should jointly consider news documents and reader comments when generating the summaries. One challenge of the RA-MDS problem is how to conduct salience estimation by jointly considering the focus of news reports and the reader interests revealed by comments. Meanwhile, the model should be insensitive to the availability of diverse aspects of reader comments. Another challenge is that reader comments are very noisy, not fully grammatical and often expressed in informal expressions. Some previous works explore the effect of comments or social contexts in single document summarization such as blog summarization BIBREF7 , BIBREF8 . However, the problem setting of RA-MDS is more challenging because the considered comments are about an event which is described by multiple documents spanning a time period. Another challenge is that reader comments are very diverse and noisy. Recently, BIBREF9 employed a sparse coding based framework for RA-MDS jointly considering news documents and reader comments via an unsupervised data reconstruction strategy. However, they only used the bag-of-words method to represent texts, which cannot capture the complex relationship between documents and comments. Recently, BIBREF6 proposed a sentence salience estimation framework known as VAESum based on a neural generative model called Variational Auto-Encoders (VAEs) BIBREF10 , BIBREF11 . During our investigation, we find that the Gaussian based VAEs have a strong ability to capture the salience information and filter the noise from texts. Intuitively, if we feed both the news sentences and the comment sentences into the VAEs, commonly existed latent aspect information from both of them will be enhanced and become salient. Inspired by this consideration, to address the sentence salience estimation problem for RA-MDS by jointly considering news documents and reader comments, we extend the VAESum framework by training the news sentence latent model and the comment sentence latent model simultaneously by sharing the neural parameters. After estimating the sentence salience, we employ a phrase based compressive unified optimization framework to generate a final summary. There is a lack of high-quality dataset suitable for RA-MDS. Existing datasets from DUC and TAC are not appropriate. Therefore, we introduce a new dataset for RA-MDS. We employed some experts to conduct the tasks of data collection, aspect annotation, and summary writing as well as scrutinizing. To our best knowledge, this is the first dataset for RA-MDS. Our contributions are as follows: (1) We investigate the RA-MDS problem and introduce a new dataset for the problem of RA-MDS. To our best knowledge, it is the first dataset for RA-MDS. (2) To tackle the RA-MDS, we extend a VAEs-based MDS framework by jointly considering news documents and reader comments. (3) Experimental results show that reader comments can improve the summarization performance, which also demonstrates the usefulness of the dataset. We employed some experts to conduct the tasks of data collection, aspect annotation, and summary writing as well as scrutinizing.

What tasks did the authors employ some experts to conduct?

The authors employ some experts to conduct the tasks of data collection, aspect annotation, and scrutinizing as well as scrutinizing.

NLG is the process of automatically generating coherent NL text from non-linguistic data BIBREF0. Recently, the field has seen an increased interest in the development of NLG systems focusing on verbalizing resources from SW data BIBREF1. The SW aims to make information available on the Web easier to process for machines and humans. However, the languages underlying this vision, i.e., RDF, SPARQL and OWL, are rather difficult to understand for non-expert users. For example, while the meaning of the OWL class expression Class: Professor SubClassOf: worksAt SOME University is obvious to every SW expert, this expression (“Every professor works at a university”) is rather difficult to fathom for lay persons. Previous works such as SPARQL2NL BIBREF2 and SPARTIQULATION BIBREF3 have already shown the usefulness of the verbalization of SPARQL and RDF in areas such as question answering BIBREF4 and the explanation of the output of systems based onSW technologies BIBREF5. However, other SW languages are rarely investigated, such as OWL. In this paper, we present an open-source holistic NLG framework for the SW, named LD2NL, which facilitates the verbalization of the three key languages of the SW, i.e., RDF, OWL, and SPARQL into NL. Our framework is based on a bottom-up paradigm for verbalizing SW data. Additionally, LD2NL builds upon SPARQL2NL as it is open-source and the paradigm it follows can be reused and ported to RDF and OWL. Thus, LD2NL is capable of generating either a single sentence or a summary of a given resource, rule, or query. To validate our framework, we evaluated LD2NL using experts 66 in NLP and SW as well as 20 non-experts who were lay users or non-users of SW. The results suggest that LD2NL generates texts which can be easily understood by humans. The version of LD2NL used in this paper, all experimental results will be publicly available. Additionally, LD2NL builds upon SPARQL2NL as it is open-source and the paradigm it follows can be reused and ported to RDF and OWL.

Can the paradigm followed by LD2NL be reused and ported to RDF and OWL?

Yes, they can.

Why Netherland is famous?

Because it's known for a nice country

Harry L. Shorstein (born August 3, 1940) is an American lawyer who served as State Attorney for Florida's Fourth Judicial Circuit Court, covering Duval, Clay and Nassau counties, from 1991–2008. A member of the Democratic Party, he was appointed to the post in 1991 by Governor Lawton Chiles to fill the remaining term of Ed Austin, who resigned to successfully run for mayor of Jacksonville. He was elected to a full term in 1992, re-elected in 1996, and ran unopposed in 2000 and 2004. At a February 6, 2007 news conference, the 66-year-old lawyer announced that he would not run for re-election in 2008. He subsequently returned to private practice, and was succeeded as State Attorney by Angela Corey.

List me some details regarding carreer of lawyer Harry L. Shorstein

1. From 1991 to 2008, Harry L. Shorstein, an American attorney who was born on August 3, 1940, served as State Attorney for Florida's Fourth Judicial Circuit Court, which includes Duval, Clay, and Nassau counties. 2. A Democrat, he was chosen by Governor Lawton Chiles to serve Ed Austin's unexpired term after the latter resigned to run successfully for mayor of Jacksonville. 3. He won re-election to a full term in 1992, was unchallenged in 2000 and 2004 and was re-elected in 1996. 4. The 66-year-old attorney declared he would not seek re-election in 2008 at a news conference on February 6, 2007. 5. He later went back to private practice, and Angela Corey took over as state attorney.

Machine learning systems have shown exceptional performance on numerous tasks in computer vision and beyond. However performance drops rapidly when the standard assumption of i.i.d. training and testing data is violated. This domain-shift phenomenon occurs widely in many applications of machine learning, and often leads to disappointing results in practical machine learning deployments, since data 'in the wild' is almost inevitably different from reference training sets. Given the practical significance of this issue, a large number of methods have been proposed that aim to improve models' robustness to deployment under a different distribution than used for training, a problem setting known as domain generalisation (DG). These methods span diverse approaches such as specialised neural architectures, data augmentation strategies, and regularisers. Nevertheless, the DG problem setting is difficult to model formally for principled derivation and theoretical analysis of algorithms, the target domain of interest is unobservable during training, and cannot be directly approximated by the training domains due to unknown distribution shift. Therefore the majority of these existing approaches are based on poorly understood empirical heuristics. To make matters worse, a recent study by assessed the state of DG research with a carefully conducted comparative evaluation of algorithms on a large benchmark suite under a common platform. They found that published methods were not as effective as claimed, and in particular reported that 'no existing method reliably beats a well tuned empirical risk minimization (ERM) baseline'. We argue that this negative result highlights the need for better theory in this area in order to understand why existing algorithms have such erratic performance, and to guide the development of principled algorithms that are more effective and reliable. To this end, our first contribution is to present an intuitive learning-theoretic bound for DG performance. Intuitively, while the held-out domain of interest is indeed unobservable during training, we can bound its performance using learning theoretic tools similar to the standard ones used to bound the performance on (unobserved) testing data given (observed) training data. In particular we show that the performance on a held out target domain is bounded by the performance on known source domains, plus two additional model complexity terms, that describe how much a model can possibly have overfitted to the training domains. This theoretical contribution leads to several insights. Firstly, our theory suggests that DG performance is governed by a trade-off between empirical risk and model complexity that is analogous to the corresponding and widely understood trade-off that explains generalisation in standard i.i.d. learning as an overfitting-underfitting trade-off. Based on this, we hypothesise that performance variability is determined by implicit or explicit regularisation. That is, the plethora of different strategies available from data-augmentation to specialised optimisers -actually affect DG performance by explicitly or implicitly choosing different fit-complexity trade-offs. We corroborate this hypothesis by evaluating a number of models in the DomainBed suite in terms of complexity, and showing that their apparently erratic performance in is actually consistent with an explanation in terms of implied complexity. Practically, our analysis suggests that the model selection strategy) is a factor in DG performance that is at least as important as the actual mechanism of model complexity control (i.e., Tuning of regularisation strength vs specific parametric design of regulariser). In particular, regularisation should be stronger when optimizing for future DG performance than when optimizing for performance on seen domains. Unfortunately, model complexity is hard to carefully control in deep learning due to the large number of relevant factors (architecture, regularisers, implicit regularisation from optimiser, etc). attempted to address this by hyper-parameter search in the DomainBed benchmark, but are hampered by the computational infeasibility of accurate hyper-parameter search. In this paper, we use linear models and off-the-shelf self-supervised features to demonstrate much more clearly how cross-domain performance depends on complexity. Specifically, our theoretical and empirical results show that, contrary to the conclusion of, simple domain-wise cross-validation is a better objective to drive DG model selection. In summary, based on our new generalisation bound, and associated empirical analysis, our takehome messages are: (i) Model fit vs complexity trade-off is a key determinant of DG performance, that explains existing DG algorithm performance variability. (ii) The complexity control strategy used to determine bias-variance trade-off is crucial in practice, with peak DG performance achieved when optimizing model complexity based on domain-wise validation. (iii) Regularisation required for optimal DG is greater than for conventional optimization for within-domain performance. However performance drops rapidly when the standard assumption of i.i.d.training and testing data is violated. This domain-shift phenomenon occurs widely in many applications of machine learning (Csurka, 2017; Zhou et al., 2021; Koh et al., 2021), and often leads to disappointing results in practical machine learning deployments, since data ‘in the wild’ is almost inevitably different from reference training sets.

What is meant by domain shift?

Most DG methods assume p(y|x) remains constant and only p(x) changes, as this is the type of distribution shift encountered in popular DG benchmarks. However, it’s worth noting that our theoretical analysis applies to both situations: change in p(x) and change in p(y|x). We have added some discussion about this in the paper.

As of 2021, the power and capacity of the largest individual battery storage power plants is an order of magnitude less than that of the largest pumped storage power plants, the most common form of grid energy storage. For example, the Bath County Pumped Storage Station, the second largest in the world, can store 24GWh of electricity and dispatch 3GW while the first phase of Vistra Energy's Moss Landing Energy Storage Facility can store 1.2GWh and dispatch 300MW. Grid batteries do not however have to be large, and smaller ones can be deployed widely across a grid for greater redundancy. As of 2019, battery power storage is cheaper than open cycle gas turbine power for use up to two hours, and there was around 365 GWh of battery storage deployed worldwide, growing extremely rapidly. Levelized cost of electricity from battery storage has fallen rapidly, halving in two years to US$150 per MWh as of 2020.

Given these paragraphs about battery storage power stations, for how long battery power storage was cheaper than open cycle gas turbine power as of 2019?

As of 2019, battery power storage is cheaper than open cycle gas turbine power for use up to two hours.

Aktepe first drew attention to herself by a series of incidents on Twitter. Aktepe, who opened a makeup channel on YouTube, gained a significant number of subscribers over a short period. Subsequently, her style of content diversified to include vlogs, joint broadcasts with singers, models, and other Internet celebrities. She also participated in various television programs. Throughout her career, Aktepe's behavior was occasionally criticized and legal proceedings were initiated by some individuals and institutions against her.

What type of YouTube Channel did Danla Bilic have?

Danla Bilic started a makeup channel on YouTube.

2001.09332

The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila). The dataset (composed of 2.6 GB of raw text) includes $421\,829\,960$ words divided into $17\,305\,401$ sentences. The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila).

What dataset is used for training Word2Vec in Italian language?

The answers are shown as follows: * extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila)

Mental health research can benefit increasingly fruitfully from computational linguistics methods, given the abundant availability of language data in the internet and advances of computational tools. This interdisciplinary project will collect and analyse social media data of individuals diagnosed with bipolar disorder with regard to their recovery experiences. Personal recovery - living a satisfying and contributing life along symptoms of severe mental health issues - so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants in Western countries. Complementary to this evidence, computational linguistic methods allow us to analyse first-person accounts shared online in large quantities, representing unstructured settings and a more heterogeneous, multilingual population, to draw a more complete picture of the aspects and mechanisms of personal recovery in bipolar disorder. Personal recovery - living a satisfying and contributing life along symptoms of severe mental health issues - so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants in Western countries.

How to investigate personal recovery before?

Personal recovery - living a satisfying and contributing life along symptoms of severe mental health issues - so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants in Western countries.

We assembled a dataset of 1,949 cleaned pathology reports. Each report is associated with one of the 37 different primary diagnoses based on IDC-O codes. The reports are collected from four different body parts or primary sites from multiple patients. The distribution of reports across different primary diagnoses and primary sites is reported in tab:report-distribution. The dataset was developed in three steps as follows. Collecting pathology reports: The total of 11,112 pathology reports were downloaded from NCI's Genomic Data Commons (GDC) dataset in PDF format BIBREF9 . Out of all PDF files, 1,949 reports were selected across multiple patients from four specific primary sites—thymus, testis, lung, and kidney. The selection was primarily made based on the quality of PDF files. Cleaning reports: The next step was to extract the text content from these reports. Due to the significant time expense of manually re-typing all the pathology reports, we developed a new strategy to prepare our dataset. We applied an Optical Character Recognition (OCR) software to convert the PDF reports to text files. Then, we manually inspected all generated text files to fix any grammar/spelling issues and irrelevant characters as an artefact produced by the OCR system. Splitting into training-testing data: We split the cleaned reports into 70% and 30% for training and testing, respectively. This split resulted in 1,364 training, and 585 testing reports. We performed the following steps to transform a pathology report into a feature vector: 1) Create a set of vocabulary containing all unique words from all the pre-processed training reports. 2) Create a zero vector fr of the same length as the vocabulary. 3) For each word t in a report r, set the corresponding index in fr to T F − IDF(t, r). 4) The resultant fr is a feature vector for the report r and it is a highly sparse vector.

What are the author's steps for converting pathology reports into feature vectors?

1) Create a set of vocabulary containing all unique words from all the pre-processed training reports. 2) Create a zero vector fr of the same length as the vocabulary. 3) For each word t in a report r, set the corresponding index in fr to T F − IDF(t, r). 4) The resultant fr is a feature vector for the report r and it is a highly sparse vector.

What is T20 cricket?

The T20 cricket is one of the three officially recognised formats of cricket game. In this format, each inning consists of 20 overs, i.e, a total of 120 legal ball deliveries. It is the shortest form of cricket among all the three formats.

The Canon EOS DCS 1 was Kodak's third Canon-based Digital SLR camera (a rebranded Kodak EOS DCS-1). It was released in December 1995, following the cheaper EOS DCS 3, which was released earlier that year. Like that camera, it combined an EOS-1N body with a modified Kodak DCS 460 digital back. Despite offering a then-enormous resolution of 6 megapixels with a relatively large APS-H sensor, a number of technical issues (together with its 3.6 million yen price) meant that it was never a very popular camera other than for a few people with specialized roles. Although the sensor was much larger than the EOS DCS 3, the DCS 1 had a lower fixed sensitivity of ISO 80. The large image size resulted in a burst rate of just over one image per second for two images, followed by an eight-second delay to clear the buffer. A typical contemporary 340MB PCMCIA card or IBM Microdrive could store 53 images. In line with the rest of the Kodak DCS range, the EOS DCS 1 could not produce JPEG files in camera. The EOS DCS 1 was succeeded in 1998 by the EOS D6000 (a rebranded Kodak DCS 560).

What was Canon EOS DCS 1 resolution?

The Canon EOS DCS 1 had a resolution of 6 megapixels, which at that time of release was considered to be a breakthrough in technology.

Vincent Paterson (born May 4, 1950) is an American director and choreographer who has had an expansive career in many parts of the entertainment industry including film, Broadway, concert tours, opera, music videos, television and commercials. He has achieved success worldwide as a director and choreographer.

Who is Vincent Paterson?

Vincent Paterson (born May 4, 1950) is an American director and choreographer who has had an expansive career in many parts of the entertainment industry including film, Broadway, concert tours, opera, music videos, television and commercials. He has achieved success worldwide as a director and choreographer. Paterson collaborated with Michael Jackson, George Harrison and Madonna on various projects, including directing and choreographing Madonna's Blond Ambition Tour, the centerpiece for the documentary Madonna: Truth or Dare (also known as In Bed with Madonna outside of North America). He created Madonna's "Marie Antoinette/Vogue" performance for the MTV Awards and her appearance on the Academy Awards. He also choreographed Madonna in the film Evita (1996). For Michael Jackson, he is one of the two fighter dancers in "Beat It" videoclip, and was assistant choreographer and a zombie dancer in "Thriller". He co-directed and co-choreographed Jackson's Bad tour as well as co-choreographing Jackson music videos "Smooth Criminal", "The Way You Make Me Feel", "Dirty Diana", "Speed Demon", "Black or White" and live performances for the Super Bowl XXVII Halftime show, the Grammys, and the MTV 10th Anniversary. He also co-directed and co-choreographed the music video for Jackson's hit "Blood on the Dance Floor".

Can you please explain the offside rule in Soccer?

Soccer is a sport played between two teams with 11 players each. Out of the 11 players one is a goalkeeper. The offside rule is fundamental in soccer to avoid players from gaining unfair advantage over the opposition. A player is in an offside position if they are closer to the opposition's goal line than the last defender of the opposition team. The law states that a player is in an offside position if any of their body parts, except the hands and arms, are in the opponents' half of the pitch, and closer to the opponents' goal line than both the ball and the second-last defender. Being in an offside position is not an offence in itself, but a player so positioned when the ball is played by a teammate can be judged guilty of an offside offence if they receive the ball or will otherwise become "involved in active play", will "interfere with an opponent", or will "gain an advantage" by being in that position. Offside is often considered one of the most difficult to understand aspects of the sport.

Why was the show Curb Your Enthusiasm so popular?

Curb Your Enthusiasm was popular for its unique brand of humor and for its ability to capture the nuances of everyday life. The show was also well-received for its depiction of a wide range of different characters and situations, from the struggles of a struggling actor to the dysfunctional relationships of a married couple. The show also offered a unique take on the idea of success, as Larry David often explored the idea of what it means to achieve success in the modern world.

What is your favorite ice cream flavor?

While everyone has different taste buds, there are many different flavors to enjoy. Most of the basic flavors are used as a base for other flavors. The most common being, chocolate, vanilla and strawberry.

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

What does the sentiment analyzer base on?

Based on the sentiments extracted from twitter to find the correlation.

The history of coffee dates back to centuries of old oral tradition in modern day Ethiopia. However, neither where coffee was first cultivated nor direct evidence of its consumption prior to the 15th century have been found. Sufi monasteries in Yemen employed coffee as an aid to concentration during prayers. Coffee later spread to the Levant and Persia in the early 16th century; it caused some controversy on whether it was halal in Ottoman and Mamluk society. Coffee arrived in Italy the second half of the 16th century through commercial Mediterranean trade routes, while Central and Eastern Europeans learnt of coffee from the Ottomans. By the mid 17th century, it had reached India and the East Indies. Coffee houses would establish themselves in Western Europe by the late 17th century, especially in England and Germany. One of the earliest cultivations of coffee in the New World was when Gabriel de Clieu brought coffee seedlings to Martinique in 1720. These beans later sprouted 18,680 coffee trees which enabled its spread to other Caribbean islands like Saint-Domingue and also to Mexico. By 1788, Saint-Domingue supplied half the world's coffee. By 1852, globally, Brazil became the largest producer of coffee and has held that status ever since. The period since 1950 saw the widening of the playing field due to the emergence of several other major producers, notably Colombia, Ivory Coast, Ethiopia, and Vietnam; the latter overtook Colombia and became the second-largest producer in 1999. Modern production techniques along with the mass productization of coffee has made it a household item today.

What is the history of coffee?

Much of the early history of coffee was tied to oral tradition in modern-day Ethiopia. Presently, there is no evidence of coffee consumption and cultivation before the 15th century. Yemeni monks drank coffee to aid in concentration during prayers. In the early 16th century, coffee spread to the Levant and Persia. Later in the 16th-century coffee arrived in Italy via Mediterranean trade routes. The Ottomans brought it to Central and Eastern Europe. It reached India by the mid-17th century.England and Germany pioneered the coffee house concept that emerged in Western Europe. The Caribbean island of Martinique hosted one of the earliest ex-situ coffee cultivations in the New World - Gabriel de Clieu brought coffee seedlings here in 1720. These resulted in coffee plantations in other Caribbean islands like Saint-Domingue (which, by 1788, supplied half of the world's coffee) and also in Mexico.Brazil is currently the largest coffee producer globally, a status held since 1852. Since 1950, a number of other major producers emerged, including Colombia, Ivory Coast, Ethiopia, and Vietnam. Today, coffee is a household item as a result of modern production techniques and mass productization.

According to the International Energy Agency, France has historically generated a very low level of carbon dioxide emissions compared to other G7 economies due to its reliance on nuclear energy. Energy in France is generated from five primary sources: coal, natural gas, liquid fuels, nuclear power, and renewables. In 2020, nuclear power made up the largest portion of electricity generation, at around 78%. Renewables accounted for 19.1% of energy consumption. France has the largest share of nuclear electricity in the world. The country is also among the world's biggest net exporters of electricity. The country is increasingly investing in renewable energy and has set a target of 32% by 2030.

What is the main source of electricity generation in France?

The electricity production in France is dominated by nuclear power that accounted around 78% of electricity generation in 2020.

How do you cook the perfect pizza?

The first thing you must do is preheat your oven up as high as it will go. It takes longer than you think to heat your oven up all the way! You want to give your oven at least 30 minutes to preheat. If you have a pizza stone, make sure it’s inside the preheating oven. For highend ovens, this can be up to 550 degrees F. For regular ovens, they are not going above 475 F. Pizza is traditionally cooked in 900 or 1,000 degree ovens. We can’t get our ovens at home quite that hot, but we want to get as close as we can. Figure out what your oven can do and crank up the heat. Start out with about 12-16 ounces of dough. Any pizza dough, homemade or store bought, will work for this recipe. Place your ball of dough in the center of your work surface and use your hands to press it down, starting from the center. Keep gently patting out the dough, moving the dough outward from the center. I like to leave a pretty healthy crust on the edges of my dough. Continue patting and stretching the dough gently with your hands until it is about 12 inches across. Of course you can use a rolling pin if you want! (Use your hands to lift up the edges of the pizza when you are done to get more a lip for the crust.) At this point, transfer the dough to a square of parchment paper. I highly recommend using parchment paper because it’s SO much easier transferring your pizza into the oven. Stretch and arrange the dough on the parchment paper. The dough shrinks a bit when you pick it up to transfer, just gently stretch it out again. Drizzle a bit of olive oil over the top of your dough. Use your hands or a pastry brush to rub the oil all over the top of the dough, especially the edges of the crust. This layer of oil helps the toppings stay separate from the dough, helping it cook more evenly. It also tastes delicious, especially on your crust. Once the oven is up to temperature, we are going to do a 1 to 2 minute par bake. Some of my favorite pizza toppings: Just Cheese: Any will work: Mozzarella, Fontina, Parmesan, and Gorgonzola would be amazing. Pepperoni Meat Lovers: Pepperoni, salami, Italian sausage, ham, bacon Hawaiian: Canadian bacon, pineapple Margherita: fresh mozzarella, fresh basil, tomatoes BBQ: Use barbecue sauce instead of tomato sauce, then rotisserie chicken, red onions, bacon Veggie pizza: spinach, tomato, corn, peppers, red onion, mushrooms Buffalo: Use Frank’s sauce instead of tomato sauce, then add rotisserie chicken, garlic, blue cheese, red onions Once your pizza is in the oven, you need to cook it for about 8-12 minutes. This is of course going to depend on how hot your oven is, and how thick your pizza is. The crust should be golden brown, and the cheese should be bubbly and also starting to brown. If you don’t have a pizza stone, use a spatula to lift the edge of your pizza to make sure that it is browning all across the center on bottom. If the bottom is still white, you are looking at a doughy pizza. Leave it in longer.

Name five countries in South America.

Five countries in South America are Chile, Argentina, Uruguay, Brazil, and Paraguay.

One interesting pattern is that the ethnicity/race of babies doesn't seem to be mentioned unless the baby is black or asian. In other words: white seems to be the default, and others seem to be marked. How can we tell whether or not the data is actually biased? We don't know whether or not an entity belongs to a particular social class (in this case: ethnic group) until it is marked as such. But we can approximate the proportion by looking at all the images where the annotators have used a marker (in this case: adjectives like black, white, asian), and for those images count how many descriptions (out of five) contain a marker. This gives us an upper bound that tells us how often ethnicity is indicated by the annotators. Note that this upper bound lies somewhere between 20% (one description) and 100% (5 descriptions). Figure TABREF22 presents count data for the ethnic marking of babies. It includes two false positives (talking about a white baby stroller rather than a white baby). In the Asian group there is an additional complication: sometimes the mother gets marked rather than the baby. E.g. An Asian woman holds a baby girl. I have counted these occurrences as well. The numbers in Table TABREF22 are striking: there seems to be a real, systematic difference in ethnicity marking between the groups. We can take one step further and look at all the 697 pictures with the word `baby' in it. If there turn out to be disproportionately many white babies, this strengthens the conclusion that the dataset is biased. I have manually categorized each of the baby images. There are 504 white, 66 asian, and 36 black babies. 73 images do not contain a baby, and 18 images do not fall into any of the other categories. While this does bring down the average number of times each category was marked, it also increases the contrast between white babies (who get marked in less than 1% of the images) and asian/black babies (who get marked much more often). A next step would be to see whether these observations also hold for other age groups, i.e. children and adults. INLINEFORM0 we can approximate the proportion by looking at all the images where the annotators have used a marker (in this case: adjectives like black, white, asian), and for those images count how many descriptions (out of five) contain a marker.

How does the author determine whether the data is in fact biased?

They can approximate the proportion by looking at all the images where the annotators have used a marker, and for those images count how many descriptions contain a marker.

Identify which instrument is string or percussion: Rattle, Cak

Cak is string, Rattle is percussion.

Bosch's mother was a prostitute in Hollywood who was murdered on October 28, 1961, when Bosch was 11 years old. His father, who he met later in life, was Mickey Haller Sr., a prominent defense attorney known for representing mobster Mickey Cohen, among other clients.

What happened to Bosch's mom?

Bosch's mother was murdered in 1961.

We present an unsupervised approach for discovering semantic representations of mathematical equations. Equations are challenging to analyze because each is unique, or nearly unique. Our method, which we call equation embeddings, finds good representations of equations by using the representations of their surrounding words. We used equation embeddings to analyze four collections of scientific articles from the arXiv, covering four computer science domains (NLP, IR, AI, and ML) and $\sim$98.5k equations. Quantitatively, we found that equation embeddings provide better models when compared to existing word embedding approaches. Qualitatively, we found that equation embeddings provide coherent semantic representations of equations and can capture semantic similarity to other equations and to words. We used equation embeddings to analyze four collections of scientific articles from the arXiv, covering four computer science domains (NLP, IR, AI, and ML) and ∼98.5k equations.

What computer science domains are involved in the paper?

NLP, IR, AI, and ML.

The rating scores are within a range of INLINEFORM0 . We calculate min, Q1, median, Q3, max, mean, and mode about review count (see Table TABREF8 ). Because the number of received review may greatly influence the reliability of the review score. From Table TABREF8 we can see that many responses on Zhihu Live receive no review at all, which are useless for quality evaluation. One of the most challenging problems is no unique standard to evaluate a Zhihu Live as a low-quality or high-quality one. A collection of people may highly praise a Zhihu Live while others may not. In order to remove the sample bias, we delete those records whose review count is less than Q1 (11). So we get 5477 records which belong to 18 different fields. The statistics of review scores after deletion are shown in Table TABREF9 . The mean score of 5477 records is 4.51, and the variance is 0.16. It indicates that the majority of Zhihu Lives are of high quality, and the users' scores are relatively stable. Badge in Zhihu represents identity authentication of public figures and high-quality answerers. Only those who hold a Ph.D. degree or experts in a specific domain can be granted a badge. Hence, these speakers tend to host high-quality Zhihu Lives theoretically. Table TABREF10 shows that 3286 speakers hold no badge, 1475 speakers hold 1 badge, and 446 speakers hold 2 badges, respectively. The average score of Zhihu Lives given by two badges holders is slightly higher than others. We can conclude that whether the speaker holds badges does have slightly influence on the Zhihu Live quality ratings, which is consistent with our supposition. Furthermore, we calculate the average scores of different Zhihu Live types (See Table TABREF11 ). We find that Others, Art and Sports fields contain more high-quality Zhihu Lives, while Delicacy, Business and Psychology fields contain more low-quality Lives. We can conclude that the topics related to self-improvement tend to receive more positive comments. There are two types of Zhihu accounts: personal and organization. From Table TABREF12 , we can see that the majority of the Zhihu Live speakers are men with personal accounts. Organizations are less likely to give presentation and share ideas upon Zhihu Live platform. Furthermore, we calculate the average scores of different Zhihu Live types (See Table 4). We find that Others, Art and Sports fields contain more high-quality Zhihu Lives, while Delicacy, Business and Psychology fields contain more lowquality Lives.

Through their analysis, what fields contain more low quality Lives?

Delicacy, Business and Psychology fields.

How does the federal government of the USA implement separation of powers?

To maintain separation of powers, the US federal government has three branches that each maintain separate and independent powers: judicial, executive, legislative

Which of these are electronic and which are found in nature: a laptop, a tree, Pixel phone, a calculator, an Apple Watch, a bee, and a volcano.

The electronic items are a a laptop, Pixel phone, a calculator, and an Apple Watch. The items found in nature are a tree, a bee, and a volcano.

Why are plants green?

Green coloration comes from the reflection of light off of a plant's cells. The plants cells are full of photosynthesizing chemicals called chlorophyll which reflect a green color. Not all plants look green though, due to the ability to reflect other wavelengths of light. When plants lose chlorophyll they will tend to lose the green color. This is why plants lose their green color in the fall and turn into red, orange, and yellow.

Bull sharks mate during late summer and early autumn, often in bays and estuaries. After gestating for 12 months, a bull shark may give birth to 1 to 13 live young. They are viviparous, born live and free-swimming. The young are about 70 cm (27.6 in) at birth. The bull shark does not rear its young; the young bull sharks are born into flat, protected areas. Coastal lagoons, river mouths, and other low-salinity estuaries are common nursery habitats. The male bull shark is able to begin reproducing around the age of 15 years while the female cannot begin reproducing until the age of 18 years. The size of a fully matured female bull shark to produce viable eggs for fertilization seems to be 175 cm to 235 cm. The courting routine between bull sharks has not been observed in detail as of yet. The male likely bites the female on the tail until she can turn upside down and the male can copulate at that point. Mature females commonly have scratches from the mating process.

Given a reference text about bull sharks, tell me the gestation period for bull sharks.

The gestation period for bull sharks is 12 months.

Banda Deul is an 11th-century temple in Banda village (also called Deulghera) in the Raghunathpur II CD block in the Raghunathpur subdivision of the Purulia district in the Indian state of West Bengal. There is a temple at Banda, which is described by the Archaeological Society of India as a rekha deul of triratha variety in sandstone. “The temple is richly decorated, the themes being creepers, scroll work and stylised chaitya window.” The temple was built around the 11th century. The temple had a mandapa which has largely collapsed, However, eight pillars are still there supporting the cross beams. The temple has a water outlet with a makara (crocodile) head. According to the List of Monuments of National Importance in West Bengal the old temple at Banda is an ASI listed monument.

In which Indian state is Banda Deul located in?

West Bengal

Write down four different things that can go in a salad

Lettuce, Carrots, Celery and Tomatoes can all go in a salad

Identify which animal species is alive or extinct: Livyatan, Mountain Gorilla

Mountain Gorilla is alive, Livyatan is extinct.

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

The unknown founder of House Baelish was a Braavosi sellsword.

The Alabama Crimson Tide football program represents the University of Alabama (variously Alabama, UA, or Bama) in the sport of American football. The team competes in the Football Bowl Subdivision (FBS) of the National Collegiate Athletic Association (NCAA) and the Western Division of the Southeastern Conference (SEC). The team's head coach is Nick Saban, who has led the Tide to six national championships over his tenure. The Crimson Tide is among the most storied and decorated football programs in NCAA history. Since beginning play in 1892, the program claims 18 national championships, including 13 wire-service (AP or Coaches') national titles in the poll-era, and five other titles before the poll-era. From 1958 to 1982, the team was led by Hall of Fame coach Paul "Bear" Bryant, who won six national titles with the program. Despite numerous national and conference championships, it was not until 2009 that an Alabama player received a Heisman Trophy, when running back Mark Ingram II became the university's first winner. In 2015, Derrick Henry became the university's second Heisman winner. The Crimson Tide won back to back Heisman trophies in 2020 and 2021, with DeVonta Smith and Bryce Young.

How many national championships has Nick Saban won at Alabama?

As head coach of the Alabama Crimson Tide football team, Nick Saban has won six national championships. Alabama claims 18 football national championships in the program's history.

Bennie John Reiges (March 4, 1920 – October 22, 2020) was an American football player and coach. He served as the head football coach at Arizona State Teachers College at Flagstaff—now known as Northern Arizona University—in 1950, compiling a record of 2–7. Reiges played college football as a quarterback at the University of California, Los Angeles (UCLA) during the 1946 and 1947 seasons. He was selected by the Los Angeles Rams in the 1947 NFL Draft. He died in October 2020 at the age of 100.

Extract the names of the teams Bennie John Reiges was a part of from the text. Separate them with a comma.

University of California, Los Angeles (UCLA), Los Angeles Rams, Arizona State Teachers College at Flagstaff

Classify these artist as either country or pop: Dua Lipa, Harry Styles, Alan Jackson, Morgan Wallen, Taylor Swift, Thomas Rhett

Pop: Dua Lipa, Harry Styles, Taylor Swift Country: Morgan Wallen, Alan Jackson, Thomas Rhett

Notable burials Numerous notable people are interred at Sleepy Hollow Cemetery, including: Viola Allen (1867–1948), actress John Dustin Archbold (1848–1916), a director of the Standard Oil Company Elizabeth Arden (1878–1966), businesswoman who built a cosmetics empire Brooke Astor (1902–2007), philanthropist and socialite Vincent Astor (1891–1959), philanthropist; member of the Astor family Leo Baekeland (1863–1944), the father of plastic; Bakelite is named for him. The murder of his grandson's wife Barbara by his great-grandson, Tony, is told in the book Savage Grace Robert Livingston Beeckman (1866–1935), American politician and Governor of Rhode Island Marty Bergen (1869-1906), American National Champion Thoroughbred racing jockey Holbrook Blinn (1872–1928), American actor Henry E. Bliss (1870–1955), devised the Bliss library classification system Artur Bodanzky (1877–1939), conductor at New York Metropolitan Opera Major Edward Bowes (1874–1946), early radio star, he hosted Major Bowes' Amateur Hour Alice Brady (1892–1939), American actress Andrew Carnegie (1835–1919), businessman and philanthropist; monument by Scots sculptor George Henry Paulin Louise Whitfield Carnegie (1857–1946), wife of Andrew Carnegie Walter Chrysler (1875–1940), businessman, commissioned the Chrysler Building and founded the Chrysler Corporation Francis Pharcellus Church (1839–1906), editor at The New York Sun who penned the editorial "Yes, Virginia, there is a Santa Claus" William Conant Church (1836–1917), co-founder of Armed Forces Journal and the National Rifle Association Henry Sloane Coffin (1877–1954), teacher, minister, and author William Sloane Coffin, Sr. (1879–1933), businessman Kent Cooper (1880–1965), influential head of the Associated Press from 1925 to 1948 Jasper Francis Cropsey (1823–1900), landscape painter and architect; designed the now-demolished New York City Sixth Avenue elevated railroad stations Floyd Crosby (1899–1985), Oscar-winning cinematographer, father of musician David Crosby Geraldine Rockefeller Dodge (1882–1973), heiress and patron of the arts William H. Douglas (1853–1944), U.S. Representative from New York Maud Earl (1864–1943), British-American painter of canines Parker Fennelly (1891–1988), American actor Malcolm Webster Ford (1862–1902), champion amateur athlete and journalist; brother of Paul, he took his own life after slaying his brother. Paul Leicester Ford (1865–1902), editor, bibliographer, novelist, and biographer; brother of Malcolm Webster Ford by whose hand he died Dixon Ryan Fox (1887–1945), educator and president of Union College, New York Herman Frasch (1851–1914), engineer, the Sulphur King Samuel Gompers (1850–1924), founder of the American Federation of Labor Madison Grant (1865–1937), eugenicist and conservationist, author of The Passing of the Great Race Moses Hicks Grinnell (1803–1877), congressman and Central Park Commissioner Walter S. Gurnee (1805–1903), mayor of Chicago Angelica Hamilton (1784–1857), the older of two daughters of Alexander Hamilton James Alexander Hamilton (1788–1878), third son of Alexander Hamilton Robert Havell, Jr. (1793–1878), British-American engraver who printed and colored John James Audubon's monumental Birds of America series, also painter in the style of the Hudson River School Mark Hellinger (1903–1947), primarily known as a journalist of New York theatre. The Mark Hellinger Theatre in New York City is named for him; produced The Naked City, a 1948 film noir Harry Helmsley (1909–1997), real estate mogul who built a company that became one of the biggest property holders in the United States, and his wife Leona Helmsley (1920–2007), in a mausoleum with a stained-glass panorama of the Manhattan skyline. Leona famously bequeathed $12 million to her dog. Eliza Hamilton Holly (1799–1859), younger daughter of Alexander Hamilton Raymond Mathewson Hood (1881–1934), architect William Howard Hoople (1868–1922), a leader of the nineteenth-century American Holiness movement; the co-founder of the Association of Pentecostal Churches of America, and one of the early leaders of the Church of the Nazarene Washington Irving (1783–1859), author of "The Legend of Sleepy Hollow" and "Rip Van Winkle" William Irving (1766–1821), U.S. Congressman from New York George Jones (1811–1891), co-founder of The New York Times Albert Lasker (1880–1952), pioneer of the American advertising industry, part owner of baseball team the Chicago Cubs, and wife Mary Lasker (1900–1994), an American health activist and recipient of the Presidential Medal of Freedom and the Congressional Gold Medal Walter W. Law, Jr. (1871–1958), lawyer and politician, son of Briarcliff Manor founder Walter W. Law Lewis Edward Lawes (1883–1947), Reformist warden of Sing Sing prison William E. Le Roy (1818–1888), United States Navy rear admiral Ann Lohman (1812–1878), a.k.a. Madame Restell, 19th century purveyor of patent medicine and abortions Charles D. Millard (1873–1944), member of U.S. House of Representatives from New York Darius Ogden Mills (1825–1910), made a fortune during California's gold rush and expanded his wealth further through New York City real estate Belle Moskowitz (1877–1933), political advisor and social activist Robertson Kirtland Mygatt (1861–1919), noted American Landscape painter, part of the Tonalist movement in Impressionism N. Holmes Odell (1828–1904), U.S. Representative from New York George Washington Olvany (1876–1952), New York General Sessions Court judge and leader of Tammany Hall William Orton (1826–1878), President of Western Union Whitelaw Reid (1837–1912), journalist and editor of the New-York Tribune, Vice Presidential candidate with Benjamin Harrison in 1892, defeated by Adlai E. Stevenson I; son-in-law of D.O. Mills William Rockefeller (1841–1922), New York head of the Standard Oil Company Edgar Evertson Saltus (1855–1921), American novelist Francis Saltus Saltus (1849–1889), American decadent poet & bohemian Carl Schurz (1820–1906), senator, secretary of the interior under Rutherford B. Hayes. Carl Schurz Park in New York City bears his name Charles Sheeler (1883–1965), painter and photographer, and his wife Musya (1908–1981), photographer, are buried together. William G. Stahlnecker (1849–1902), U.S. Representative from New York Egerton Swartwout (1870–1943), New York architect William Boyce Thompson (1869–1930), founder of Newmont Mining Corporation and financier Joseph Urban (1872–1933), architect and theatre set designer Henry Villard (1835–1900), railroad baron whose monument was created by Karl Bitter. Oswald Garrison Villard (1872–1949), son of Henry Villard and grandson of William Lloyd Garrison; one of the founders of the National Association for the Advancement of Colored People William A. Walker (1805–1861), U.S. Representative from New York Paul Warburg (1868–1932), German-American banker and early advocate of the U.S. Federal Reserve system. Worcester Reed Warner (1846–1929), mechanical engineer and manufacturer of telescopes Thomas J. Watson (1874–1956), transformed a small manufacturer of adding machines into IBM Hans Zinsser (1878–1940), microbiologist and a prolific author

Using this text, please give me a short bulleted list of famous figures buried at the Sleepy Hollow Cemetery in Sleepy Hollow, New York

Sleepy Hollow Cemetery in Sleepy Hollow, NY is the final resting place of numerous notable figures, including: - Washington Irving (1783–1859), author of "The Legend of Sleepy Hollow" and "Rip Van Winkle" - Andrew Carnegie (1835–1919), businessman and philanthropist; monument by Scots sculptor George Henry Paulin - William Rockefeller (1841–1922), New York head of the Standard Oil Company - Robert Livingston Beeckman (1866–1935), American politician and Governor of Rhode Island - Thomas J. Watson (1874–1956), transformed a small manufacturer of adding machines into IBM - Walter Chrysler (1875–1940), businessman, commissioned the Chrysler Building and founded the Chrysler Corporation

Tell me whether each of these sports is in the winter or summer olympics: track, skiing, snowboarding, curling, golf, soccer

Track: Summer Olympics Skiing: Winter Olympics Snowboarding: Winter Olympics Curling: Winter Olympics Golf: Summer Olympics Soccer: Summer Olympics

Low dimensional word representations (embeddings) have become a key component in modern NLP systems for language modeling, parsing, sentiment classification, and many others. These embeddings are usually derived by employing the distributional hypothesis: that similar words appear in similar contexts BIBREF0 . The models that perform the word embedding can be divided into two classes: predictive, which learn a target or context word distribution, and counting, which use a raw, weighted, or factored word-context co-occurrence matrix BIBREF1 . The most well-known predictive model, which has become eponymous with word embedding, is word2vec BIBREF2 . Popular counting models include PPMI-SVD BIBREF3 , GloVe BIBREF4 , and LexVec BIBREF5 . These models all learn word-level representations, which presents two main problems: 1) Learned information is not explicitly shared among the representations as each word has an independent vector. 2) There is no clear way to represent out-of-vocabulary (OOV) words. fastText BIBREF6 addresses these issues in the Skip-gram word2vec model by representing a word by the sum of a unique vector and a set of shared character n-grams (from hereon simply referred to as n-grams) vectors. This addresses both issues above as learned information is shared through the n-gram vectors and from these OOV word representations can be constructed. In this paper we propose incorporating subword information into counting models using a strategy similar to fastText. We use LexVec as the counting model as it generally outperforms PPMI-SVD and GloVe on intrinsic and extrinsic evaluations BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , but the method proposed here should transfer to GloVe unchanged. The LexVec objective is modified such that a word's vector is the sum of all its subword vectors. We compare 1) the use of n-gram subwords, like fastText, and 2) unsupervised morphemes identified using Morfessor BIBREF11 to learn whether more linguistically motivated subwords offer any advantage over simple n-grams. To evaluate the impact subword information has on in-vocabulary (IV) word representations, we run intrinsic evaluations consisting of word similarity and word analogy tasks. The incorporation of subword information results in similar gains (and losses) to that of fastText over Skip-gram. Whereas incorporating n-gram subwords tends to capture more syntactic information, unsupervised morphemes better preserve semantics while also improving syntactic results. Given that intrinsic performance can correlate poorly with performance on downstream tasks BIBREF12 , we also conduct evaluation using the VecEval suite of tasks BIBREF13 , in which all subword models, including fastText, show no significant improvement over word-level models. We verify the model's ability to represent OOV words by quantitatively evaluating nearest-neighbors. Results show that, like fastText, both LexVec n-gram and (to a lesser degree) unsupervised morpheme models give coherent answers. This paper discusses related word ( $§$ "Related Work" ), introduces the subword LexVec model ( $§$ "Subword LexVec" ), describes experiments ( $§$ "Materials" ), analyzes results ( $§$ "Results" ), and concludes with ideas for future works ( $§$ "Conclusion and Future Work" ). fastText (Bojanowski et al., 2017) addresses these issues in the Skip-gram word2vec model by representing a word by the sum of a unique vector and a set of shared character n-grams (from hereon simply referred to as n-grams) vectors.

How does fastText address the problems of existing word embedding models?

By representing a word by the sum of a unique vector and a set of shared character n-grams (from hereon simply referred to as n-grams) vectors.

Write a review of Amor Towles's book, A Gentleman in Moscow

Fantastic book. While the Count can be an arrogant aristocrat, he certainly can get up to a lot of trouble in 30 years of staying in the hotel metropol. I loved Towles's style - the vignettes built up a lot of character and compassion for the Count and the friends he made, and really made me vested in the ending sequence. Lots of cultural, historical, and artistic references. If you are savvy, this will be an excellent read.

San Clemente is known for its many surfing locations, which include Trestles, Lowers, Middles & Uppers, Cotton's Point, Calafia Beach Park, Riviera, Lasuens (most often called Lost Winds), The Hole, T-Street, The Pier, Linda Lane, 204, North Beach and Poche Beach. It is also home to Surfing Magazine, The Surfer's Journal, and Longboard Magazine. The city has a large concentration of surfboard shapers and manufacturers. Additionally, numerous world-renowned surfers were raised in San Clemente or took up long-term residence in town, including, Colin McPhillips(3 x longboard world champion), Matt Archbold, Herbie Fletcher(founder of AstroDeck), Dibi Fletcher (first female CEO of a surf company), Christian Fletcher, Nathan Fletcher, Greyson Flecther, Griffin Colapinto, Crosby Colapinto, Shane Beschen, Gavin Beschen, Chris Ward, Dino Andino, Kolohe Andino, Patrick Gudauskas, Dane Gudauskas, Tanner Gudauskas, Mike Long, Greg Long (Greg Long has dominated the XXL Global Big Wave Awards, winning 2005's 'Biggest Paddle In', 2007's 'Biggest Tow-in', 2009 and 2014's 'Ride of the Year', and the coveted 'Performer of the Year' in 2004, 2008 and 2013), Sawyer Lindblad, Jett Schilling, Kade Matson, Taj Lindblad, Hagan Johnson, Jim Hogan, Mike Parsons, Bobby Freidman, Anna Shisler, Brian Knoblock , Rich Chew, Jonathan Paskowitz, Joyce Hoffman, Bill Stewart, Matt Biolos (founder of Lost surfboards), Anastasia Ashley, Timmy Patterson, Cole Simler (Cole Surfboards), Terry Senate surfboards. Matt "Mayhem" Biolos and Lost Surfboards, Timmy Patterson Surfboards, Cole Simler and Cole Surfboards, Fletcher Surfboards, Terry Senate Surfboards, Jed Noll and Noll Surfboards, Matt Parker and Album Surfboards, Christian Wach and Ryan Engle of Canvas Surfboards and Nation Surfboards, San Clemente High School has won 6 out of 7 most recent NSSA national surfing titles and is regarded as consistently one of the top surfing high schools in the world. Shorecliffs Junior High School is also regarded as the top middle school for surfers.

What city in the world is regarded as having the largest number of professional and famous surfers and surfboard shapers?

San Clemente, California in the United States is regarded by many as the top city in the world for surfing, surfboard shapers and surf culture.

The economy of Gabon is characterized by strong links with France, large foreign investments, dependence on skilled foreign labor, and decline of agriculture. Gabon enjoys a per capita income four times that of most nations of sub-Saharan Africa, its reliance on resource extraction industry releasing much of the population from extreme poverty. The country is rich in natural resources such as timber, manganese and oil. The country is the fifth largest oil producing nation in Africa, which has helped drive its strong growth in the later 20th century. The oil sector now accounts for 50% of GDP and 80% of exports. Although there have been recent offshore finds, oil production is now declining from its peak of 370,000 barrels per day (59,000 m3/d) in 1997, and periods of low oil prices have had a negative impact on government revenues and the economy. In 2012 there were six active oil rigs in Gabon. The government has plans to diversify its economy away from its dwindling oil reserves. The government announced in 2012 that it would reassess exactly how much iron ore the Belinga site contains before awarding the concession to a mining company, most likely to be China's CMEC, which temporarily secured the rights to the ore in 2007. Around 80% of Gabonese live in cities. The capital city, Libreville is home to 59% of the total population. The remainder of the country is still sparsely populated, as those areas remain densely forested.

How rich is Gabon and why?

Gabon's per capita income is four times that of most nations of sub-Saharan Africa. Owing to an abundance of natural resources such as timber, manganese and oil, its resource extraction industry has thrived. This has helped raise the living standards of the population.

What are the primary colors?

Primary colors are red, yellow, and blue. Together, these are used in combination to create the rest of the color pallet. Red + yellow = orange. Yellow + blue = green. Red + blue = purple.

2004.02929

The following handcrafted features were used for the model: Bias feature Token feature Uppercase feature (y/n) Titlecase feature (y/n) Character trigram feature Quotation feature (y/n) Word suffix feature (last three characters) POS tag (provided by spaCy utilities) Word shape (provided by spaCy utilities) Word embedding (see Table TABREF26) The following handcrafted features were used for the model: Bias feature Token feature Uppercase feature (y/n) Titlecase feature (y/n) Character trigram feature Quotation feature (y/n) Word suffix feature (last three characters) POS tag (provided by spaCy utilities) Word shape (provided by spaCy utilities) Word embedding (see Table TABREF26)

What are the handcrafted features used?

The answers are shown as follows: * Bias feature * Token feature * Uppercase feature (y/n) * Titlecase feature (y/n) * Character trigram feature * Quotation feature (y/n) * Word suffix feature (last three characters) * POS tag (provided by spaCy utilities) * Word shape (provided by spaCy utilities) * Word embedding (see Table TABREF26)

Sintok is a small town Kubang Pasu District, Kedah, Malaysia. Universiti Utara Malaysia (UUM) is situated here. Sintok is located about 52 kilometres from Alor Setar City and about twelve kilometres from Changlun Town. Sintok is reachable via Kuala Perlis-Changlun-Sintok expressway and via a road from Padang Terap. History The name "Sintok" is taken from the name of a type of tree. The town was originally a remote settlement area for tin miners. However, due to its close proximity to the border of Malaysia-Thailand, Sintok was exposed to threats from the banned communist group. Hence, the government had to migrate all the original residents to a safer area, and declared the town are as a 'black area'. History recorded many killings of members of the security forces in the area. By mid 1980s, the federal and state government agreed on building a university in Sintok. The university was named Universiti Utara Malaysia (UUM), literally translated as "Northern University of Malaysia", and construction started in the late 1980s to replace the temporary campus in Bandar Baru Darul Aman, Jitra. As a memorial to the sacrifice by the security forces, a memorial structure was built in that UUM campus. A list of names of the members of the security forces that was killed by the communists was placed at this memorial structure. The establishment of UUM campus has expedited the growth of new settlements like Bandar Baru Sintok and Bukit Kachi which is located opposite of Sungai Badak Forest Reserve.

Given a reference text about Sintok, tell me where it is located.

Sintok is located 12km from Changlu Town and 52km from Alor Setar City in Malaysia.

Knowledge graphs BIBREF0 enable structured access to world knowledge and form a key component of several applications like search engines, question answering systems and conversational assistants. Knowledge graphs are typically interpreted as comprising of discrete triples of the form (entityA, relationX, entityB) thus representing a relation (relationX) between entityA and entityB. However, one limitation of only a discrete representation of triples is that it does not easily enable one to infer similarities and potential relations among entities which may be missing in the knowledge graph. Consequently, one popular alternative is to learn dense continuous representations of entities and relations by embedding them in latent continuous vector spaces, while seeking to model the inherent structure of the knowledge graph. Most knowledge graph embedding methods can be classified into two major classes: one class which operates purely on triples like RESCAL BIBREF1 , TransE BIBREF2 , DistMult BIBREF3 , TransD BIBREF4 , ComplEx BIBREF5 , ConvE BIBREF6 and the second class which seeks to incorporate additional information (like multi-hops) BIBREF7 . Learning high-quality knowledge graph embeddings can be quite challenging given that (a) they need to effectively model the contextual usages of entities and relations (b) they would need to be useful for a variety of predictive tasks on knowledge graphs. In this paper, we present a new type of knowledge graph embeddings called Dolores that are both deep and contextualized. Dolores learns both context-independent and context-dependent embeddings of entities and relations through a deep neural sequential model. Figure 1 illustrates the deep contextualized representations learned. Note that the contextually independent entity embeddings (see Figure 1 ) reveal three clusters of entities: writers, philosophers, and musicians. The contextual dependent embeddings in turn effectively account for specific relations. In particular, the context-dependent representations under the relation nationality now nicely cluster the above entities by nationality namely Austrians, Germans, and British/Irish. Similarly Figure 1 shows contextual embeddings given the relation place-lived. Note that these embeddings correctly capture that even though Beethoven and Brahms being Germans, they lived in Vienna and are closer to other Austrian musicians like Schubert. Unlike most knowledge graph embeddings like TransD, TransE BIBREF2 , BIBREF4 etc. which are typically learned using shallow models, the representations learned by Dolores are deep: dependent on an entire path (rather than just a triple), are functions of internal states of a Bi-Directional LSTM and composed of representations learned at various layers potentially capturing varying degrees of abstractions. Dolores is inspired by recent advances in learning word representations (word embeddings) from deep neural language models using Bi-Directional LSTMs BIBREF8 . In particular, we derive connections between the work of Peters et al. ( BIBREF8 ) who learn deep contextualized word embeddings from sentences using a Bi-Directional LSTM based language model and random walks on knowledge graphs. These connections enable us to propose new “deep contextualized” knowledge graph embeddings which we call Dolores embeddings. Knowledge Embeddings learned using Dolores can easily be used as input representations for predictive models on knowledge graphs. More importantly, when existing predictive models use input representations for entities and relations, we can easily replace those representations with Dolores representations and significantly improve the performance of existing models. Specifically, we show that Dolores embeddings advance the state-of-the-art models on various tasks like link prediction, triple classification and missing relation type prediction. To summarize, our contributions are as follows: 1. We present a new method DOLORES of learning deep contextualized knowledge graph embeddings using a deep neural sequential model. 2. These embeddings are functions of hidden states of the deep neural model and can capture both context-independent and contextdependent cues. 3. We show empirically that DOLORES embeddings can easily be incorporated into existing predictive models on knowledge graphs to advance the state of the art on several tasks like link prediction, triple classification, and missing relation type prediction.

What are the contributions of this article?

1. They present a new method DOLORES of learning deep contextualized knowledge graph embeddings using a deep neural sequential model. 2. These embeddings are functions of hidden states of the deep neural model and can capture both context-independent and contextdependent cues. 3. They show empirically that DOLORES embeddings can easily be incorporated into existing predictive models on knowledge graphs to advance the state of the art on several tasks like link prediction, triple classification, and missing relation type prediction.

Petrified wood, also known as petrified tree (from Ancient Greek πέτρα meaning 'rock' or 'stone'; literally 'wood turned into stone'), is the name given to a special type of fossilized wood, the fossilized remains of terrestrial vegetation. Petrifaction is the result of a tree or tree-like plants having been replaced by stone via a mineralization process that often includes permineralization and replacement. The organic materials making up cell walls have been replicated with minerals (mostly silica in the form of opal, chalcedony, or quartz). In some instances, the original structure of the stem tissue may be partially retained. Unlike other plant fossils, which are typically impressions or compressions, petrified wood is a three-dimensional representation of the original organic material. The petrifaction process occurs underground, when wood becomes buried in water or volcanic ash. The presence of water reduces the availability of oxygen which inhibits aerobic decomposition by bacteria and fungi. Mineral-laden water flowing through the sediments may lead to permineralization, which occurs when minerals precipitate out of solution filling the interiors of cells and other empty spaces. During replacement, the plant's cell walls act as a template for mineralization. There needs to be a balance between the decay of cellulose and lignin and mineral templating for cellular detail to be preserved with fidelity. Most of the organic matter often decomposes, however some of the lignin may remain. Silica in the form of opal-A, can encrust and permeate wood relatively quickly in hot spring environments. However, petrified wood is most commonly associated with trees that were buried in fine grained sediments of deltas and floodplains or volcanic lahars and ash beds. A forest where such material has petrified becomes known as a petrified forest.

what is petrified wood

Petrified wood is one of fossils, it is a plant which the inner part has been replaced by stone by compressions and impressions through millions of years

Work has been done on proving that the sofa constant (A) cannot be below or above certain values (lower bounds and upper bounds). Lower An obvious lower bound is A \geq \pi/2 \approx 1.57. This comes from a sofa that is a half-disk of unit radius, which can rotate in the corner. John Hammersley derived a lower bound of A \geq \pi/2 + 2/\pi \approx 2.2074 based on a shape resembling a telephone handset, consisting of two quarter-disks of radius 1 on either side of a 1 by 4/\pi rectangle from which a half-disk of radius. In 1992, Joseph L. Gerver of Rutgers University described a sofa described by 18 curve sections each taking a smooth analytic form. This further increased the lower bound for the sofa constant to approximately 2.2195. Upper Hammersley also found an upper bound on the sofa constant, showing that it is at most 2\sqrt{2} \approx 2.8284. Yoav Kallus and Dan Romik proved a new upper bound in June 2017, capping the sofa constant at 2.37.

Extract the most recent upper bound and low bound of the sofa constant and return them in the format {Bound Type} - {Bound Value}.

The soft constant has the following bounds: Upper Bound - 2.37, Lower Bound - 2.2195.

Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ~18 F1 points. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ∼18 F1 points.

How many F1 points does their method outperform state-of-the-art char/word n-gram methods by?

∼18 F1 points.

Multi-way Classification: The first section of table 3 shows macro average F1-scores and accuracies of previous works. The second section of table 3 shows the multi-class classification results of our implemented baseline systems. Consistent with results of previous works, neural tensors, when applied to Bi-LSTMs, improved implicit discourse relation prediction performance. However, the performance on the three small classes (Comp, Cont and Temp) remains low. The third section of table 3 shows the multi-class classification results of our proposed paragraph-level neural network models that capture inter-dependencies among discourse units. The first row shows the performance of a variant of our basic model, where we only identify implicit relations and ignore identifying explicit relations by setting the $\alpha $ in equation (5) to be 0. Compared with the baseline Bi-LSTM model, the only difference is that this model considers paragraph-wide contexts and model inter-dependencies among discourse units when building representation for individual DU. We can see that this model has greatly improved implicit relation classification performance across all the four relations and improved the macro-average F1-score by over 7 percents. In addition, compared with the baseline Bi-LSTM model with tensor, this model improved implicit relation classification performance across the three small classes, with clear performance gains of around 2 and 8 percents on contingency and temporal relations respectively, and overall improved the macro-average F1-score by 2.2 percents. The second row shows the performance of our basic paragraph-level model which predicts both implicit and explicit discourse relations in a paragraph. Compared to the variant system (the first row), the basic model further improved the classification performance on the first three implicit relations. Especially on the contingency relation, the classification performance was improved by another 1.42 percents. Moreover, the basic model yields good performance for recognizing explicit discourse relations as well, which is comparable with previous best result (92.05% macro F1-score and 93.09% accuracy as reported in BIBREF11 ). After untying parameters in the softmax prediction layer, implicit discourse relation classification performance was improved across all four relations, meanwhile, the explicit discourse relation classification performance was also improved. The CRF layer further improved implicit discourse relation recognition performance on the three small classes. In summary, our full paragraph-level neural network model achieves the best macro-average F1-score of 48.82% in predicting implicit discourse relations, which outperforms previous neural tensor network models (e.g., BIBREF18 ) by more than 2 percents and outperforms the best previous system BIBREF19 by 1 percent. Binary Classification: From table 4 , we can see that compared against the best previous systems, our paragraph-level model with untied parameters in the prediction layer achieves F1-score improvements of 6 points on Comparison and 7 points on Temporal, which demonstrates that paragraph-wide contexts are important in detecting minority discourse relations. Note that the CRF layer of the model is not suitable for binary classification. In summary, our full paragraph-level neural network model achieves the best macro-average F1-score of 48.82% in predicting implicit discourse relations, which outperforms previous neural tensor network models (e.g., (Lei et al., 2017)) by more than 2 percents and outperforms the best previous system (Lan et al., 2017) by 1 percent.

What is the macro-average F1 score of the model in predicting implicit discourse relations?

48.82%.

What do political scientists mean when they refer to “home-style”?

Political scientist Richard Fenno coined the term “home-style” as what representatives do back in their home districts and how they represent their constituents. The purpose of this is to help the constituents, so that they will re-elect the representative. These constituencies can be seen as four circles, each embedded in the other. The largest is the geographic district, which is all the voters. Inside that circle is electoral supporters or people who will vote for the representative. Next is primary supporters who work for the representative. Finally, we have the representative’s intimates who are family, friends and advisors.

The bonobo, also historically called the pygmy chimpanzee and less often the dwarf chimpanzee or gracile chimpanzee, is an endangered great ape and one of the two species making up the genus Pan, the other being the common chimpanzee (Pan troglodytes). While bonobos are now recognized as a distinct species in their own right, they were initially thought to be a subspecies of chimpanzee (Pan troglodytes) due to the physical similarities between the two species. Taxonomically, the members of the chimpanzee/bonobo subtribe Panina (composed entirely by the genus Pan) are collectively termed panins. The bonobo is distinguished by relatively long legs, pink lips, dark face, tail-tuft through adulthood, and parted long hair on its head. It is found in a 500,000 km2 (190,000 sq mi) area of the Congo Basin in the Democratic Republic of the Congo, Central Africa. The species is frugivorous and inhabits primary and secondary forests, including seasonally inundated swamp forests. Because of political instability in the region and the timidity of bonobos, there has been relatively little field work done observing the species in its natural habitat. Along with the common chimpanzee, the bonobo is the closest extant relative to humans. As the two species are not proficient swimmers, the formation of the Congo River 1.5–2 million years ago possibly led to the speciation of the bonobo. Bonobos live south of the river, and thereby were separated from the ancestors of the common chimpanzee, which live north of the river. There are no concrete data on population numbers, but the estimate is between 29,500 and 50,000 individuals. The species is listed as Endangered on the IUCN Red List and is threatened by habitat destruction and human population growth and movement, though commercial poaching is the most prominent threat. Bonobos typically live 40 years in captivity; their lifespan in the wild is unknown, but it is almost certainly much shorter.

In what part of the world could you find wild bonobos?

Wild bonobos inhabit forests and swamps within a 500,000 km2 region of the Congo Basin in the Democratic Republic of Congo.

What event triggered the start of WWI?

WWI began with the assassination of Archduke Franz Ferdinand

Language modeling is a probabilistic description of language phenomenon. It provides essential context to distinguish words which sound similar and therefore has one of the most useful applications in Natural Language Processing (NLP) especially in downstreaming tasks like Automatic Speech Recognition (ASR). Recurrent Neural Networks (RNN) especially Long Short Term Memory (LSTM) networks BIBREF0 have been the typical solution to language modeling which do achieve strong results. In spite of these results, their fundamental sequential computation constraint has restricted their use in the modeling of long-term dependencies in sequential data. To address these issues Transformer architecture was introduced. Transformers relies completely on an attention mechanism to form global dependencies between input and output. It also offers more parallelization and has achieved SOTA results in language modeling outperforming LSTM models BIBREF1. In recent years,we have seen a lot of development based on this standard transformer models particularly on unsupervised pre-training(BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7 which have set state-of-the art results on multiple NLP benchmarks. One such model architecture has been the Bidirectional Encoder Representations from Transformers (BERT) model which uses a deep bidirectional transformer architecture. Another architecture of interest would be the Transformer-XL, which introduces the notion of recurrence in a self-attention model. The primary research focus though has been mostly on English language for which abundant data is present. It is interesting to see the performance of these models for an agglutinative language like Finnish, which is morphologically richer than English. In this project, we explore the implementation of Transformer-based models (BERT and Transformer-XL) in language modeling for Finnish. We will use the same training data as in BIBREF8 so that we can do fair comparisons with the performance of the LSTM models. Also, as the BERT model is a bi-directional transformer, we will have to approximate the conditional probabilities given a sequence of words. We also experiment with using sub-word units with Transformer-XL to cope with the large vocabulary problems associated with the Finnish Language. With smaller units, the modeled sequences are longer, and we hope that the recursive XL architecture can allow us to still model long term effects. To the best of our knowledge this is the first work with the Finnish language to use the following: Approximation of perplexity using a BERT architecture Using Transformer-XL architecture with sub-word units. Comparison of Transformer and LSTM models as language models in the same comparable settings with an agglutinative language. It is interesting to see the performance of these models for an agglutinative language like Finnish, which is morphologically richer than English. In this project, we explore the implementation of Transformer-based models (BERT andTransformer-XL) in language modeling for Finnish.

What language is explored in the language modeling?

An agglutinative language like Finnish.

1910.07481

FLOAT SELECTED: Table 5: Results obtained for the English-French and French-English translation tasks, scored on three test sets using BLEU and TER metrics. p-values are denoted by * and correspond to the following values: ∗< .05, ∗∗< .01, ∗∗∗< .001. FLOAT SELECTED: Table 5: Results obtained for the English-French and French-English translation tasks, scored on three test sets using BLEU and TER metrics. p-values are denoted by * and correspond to the following values: ∗< .05, ∗∗< .01, ∗∗∗< .001.

Which language-pair had the better performance?

French-English

What is gravity?

Gravity is the force by which a planet or other body draws objects toward its center. The force of gravity keeps all of the planets in orbit around the sun. Gravity (or the acceleration due to gravity) is 9.81 meters per second squared, on the surface of Earth, because of the size of Earth and the distance we are on its surface from its center.

Which ones are metals and non metals: Copper, Hydrogen, Silver, Carbon, Gold, Nitrogen

Metals: Copper, Silver, Gold Non Metals: Hydrogen, Carbon, Nitrogen

The plan specified that most streets would be laid out in a grid. To form the grid, some streets (later named for letters of the alphabet) would travel in an east–west direction, while others (named for numbers) would travel in a north–south direction. Broader diagonal grand avenues, later named after the states of the Union, crossed the north–south-east/west grid. These "grand avenues" intersected with the north–south and east–west streets at circles and rectangular plazas that would later honor notable Americans and provide open space.

Who designed Washington D.C. and what are some interesting attributes about the city?

Washington DC was designed by Major Pierre Charles L'Enfant at the request of George Washington. L'Enfant was a civil engineer by training, from France, and based his basic design for DC on the streets of Paris. DC is laid out as a grid, with east-west streets named alphabetically, starting with the letters themselves, and adding syllables as the streets went further out from the city center. The north-south streets are numbered, increasing as they radiate from the Capitol. At the intersection of the center-most streets are named for the direction as they radiate from the Capitol, i.e. North Capitol. However, there is no West Capitol street, as that is the prominent plazas known as the mall. Diagonally across the grid are streets named after the states and those state streets intersect in circles named after prominent Americans with plazas and open spaces.

What is the style of play for Zlatan Ibrahimović?

Ibrahimović has been described as being "good in the air, quick, tall, strong and agile, he plays well with his back to goal and boasts some of the best finishing, vision, passing and ball control around. A versatile and well-rounded attacker, from a tactical standpoint, Ibrahimović is capable of playing anywhere along the front line, due to his ability to both create and score goals for his team, although he is most often deployed as a striker, due to his composure and eye for goal. He has also functioned in a more creative playmaking role at times, as a supporting forward or even as a number 10, in particular in his later career, after losing some of his pace and stamina with age; this deeper position allows him to drop into midfield to pick up the ball, where he can utilize his technical ability, vision, passing, and movement to create space and provide assists for teammates. While naturally right-footed, Ibrahimović is a powerful and accurate striker of the ball from both inside or outside the penalty area with either foot, and is also known for his accuracy from penalties and dead ball situations. An accurate finisher with his head as well as with his feet, his height, elevation, and strength often give him an advantage at winning aerial challenges, and also allow him to function as a "target man"; despite his large stature, Ibrahimović is uncommonly agile for a player of his size, and his athleticism and ability in the air have seen him score several goals from acrobatic strikes and volleys throughout his career, which earned him the moniker Ibracadabra in the Italian media. In spite of his size and physique, Ibrahimović possesses excellent technique and ball control, which, coupled with his balance, power, and physicality, enables him to hold up the ball well with his back to goal, retain possession, and link up with other players; he has also been praised by pundits for his creativity and dribbling skills. Although he is not gifted with exceptional pace, in particular over shorter distances, which became more evident in his later career as he slowed down with age, he is also a quick player and a fast sprinter, who possessed significant acceleration in his youth, and was able to clock top speeds over 30 km/h even into his 30s.

What skills are required to become a data analyst

1. SQL 2. Statistics 3. Data Management 4. Data Visualisation 5. Good Communicator 6. Critical thinking

Psychiatry: An Industry of Death is a museum in Hollywood, Los Angeles, California, that has also hosted several touring exhibitions. It is owned and operated by the Citizens Commission on Human Rights (CCHR), an anti-psychiatry organization founded by the Church of Scientology and psychiatrist Thomas Szasz. The museum is located at 6616 Sunset Boulevard, Los Angeles, California. Entry is free. The opening event on December 17, 2005, was attended by well-known Scientologists such as Priscilla Presley, Lisa Marie Presley, Jenna Elfman, Danny Masterson, Giovanni Ribisi, Catherine Bell, and Anne Archer, as well as former Scientologist Leah Remini. The museum is dedicated to criticizing what it describes as "an industry driven entirely by profit". It has a variety of displays and exhibits that highlight physical psychiatric treatments, such as restraints, psychoactive drugs, electroconvulsive therapy and psychosurgery (including lobotomy, a procedure abandoned in the 1960s). The exhibition is also well-known for being the site of a heated confrontation between BBC Panorama reporter John Sweeney, and the Church's then-spokesman Tommy Davis in March 2007, during the filming of Sweeney's documentary Scientology and Me.

Given a reference text about Psychiatry: An Industry of Death, tell me when it opened and and who owns and operates it.

The Psychiatry: An Industry of Death museum opened on December 17, 2005 and is owned and operated by the Citizens Commission on Human Rights.

Wabuska is an unincorporated community in Lyon County, Nevada, United States. The zip code is 89447, which it shares with nearby Yerington. Wabuska (Washo language, White Grass) was established in the early 1870s. A post office was opened on September 18, 1874. In 1881, the town served as the principal Mason Valley supply center on the newly constructed Carson and Colorado Railroad of a line that went from Hazen to Mina. When copper was discovered in Mason Valley, the town became the northern terminus of the new Nevada Copper Belt Railroad, built 1909–1911. Wabuska waned with declining mining activity in the 1920s. Several buildings from Wabuska, most notably the Wabuska Railroad Station, were relocated to Carson City and incorporated as the Nevada State Railroad Museum.

Given this paragraph about Wabuska, Nevada, when was a post office first opened in the town?

September 18, 1874

The Space Exploration Technologies Corporation (SpaceX) is an American spacecraft manufacturer, launcher, and a satellite communications corporation headquartered in Hawthorne, California. It was founded in 2002 by Elon Musk with the stated goal of reducing space transportation costs to enable the colonization of Mars. The company manufactures the Falcon 9, Falcon Heavy, and Starship launch vehicles, several rocket engines, Cargo Dragon and Crew Dragon spacecraft, and Starlink communications satellites. SpaceX is developing a satellite internet constellation named Starlink to provide commercial internet service. In January 2020, the Starlink constellation became the largest satellite constellation ever launched, and as of December 2022 comprises over 3,300 small satellites in orbit. The company is also developing Starship, a privately funded, fully reusable, super heavy-lift launch system for interplanetary and orbital spaceflight. It is intended to become SpaceX's primary orbital vehicle once operational, supplanting the existing Falcon 9, Falcon Heavy, and Dragon fleet. It will have the highest payload capacity of any orbital rocket ever built on its debut, which is scheduled for early 2023 pending a launch license. SpaceX has made several achievements in space exploration. These include making the first privately developed liquid-propellant rocket that reached orbit, the first private company to launch, orbit, and recover a spacecraft, the first private company to send a spacecraft to the International Space Station, the first to achieve vertical propulsive landing of an orbital rocket booster, the first to reuse such a booster, and the first private company to send astronauts to orbit and to the International Space Station. In addition, SpaceX's Falcon 9 rockets have been flown and landed over one hundred times.

From the passage find spacecrafts which are manufactured by SpaceX. Display the results in comma separated format.

Falcon 9, Falcon Heavy, Starship launch vehicles, several rocket engines, Cargo Dragon, Crew Dragon spacecraft, Starlink communications satellites

Who is the all time leading goal scorer in the NHL?

The all time leading goal scorer in the NHL is Wayne Gretzky with 894 goals., Gretzky played in the NHL for 21 seasons, spending most of his time with the Edmonton Oilers and the Los Angeles Kings, but he also played for the New York Rangers and briefly, for the St Louis Blues.

Why is backcountry skiing fun?

Backcountry skiers get to enjoy a moderate hike uphill in a beautiful winter environment. Then they get to shred untracked powder. The only reason why backcountry skiing is not world class is because of the potentially deadly danger of avalanches.

What is a good gift for a seven year old boy?

Football, basketball, Pokémon cards, sports cards

Predictive learning is an unsupervised learning paradigm that has shown the ability to discover the spatiotemporal modes of visual dynamics. However, for largescale and real-world datasets (see Figure), the modes in visual dynamics can be highly entangled and difficult to learn due to the richness of data environments, the diversity of object interactions, and the complexity of motion patterns. For clarity, in the following discussion, spatiotemporal modes are considered to have the following properties: 1. A spatiotemporal mode refers to a representation subspace that corresponds to a family of similar, but not predefined, visual dynamics. 2. Multiple spatiotemporal modes naturally exist in real-world data, even in a single frame. 3. We assume the i.i.d. setup to allow all videos to share the same set of spatiotemporal modes in a dataset. Different data may have different compositional structures over the modes. Under these assumptions, video prediction models are required to (i) decouple the potentially mixed spatiotemporal modes from raw video frames, (ii) understand the compositional structures on top of the learned modes, and (iii) learn the state transitions based on the compositional structures. Otherwise, since the learned dynamics with respect to different modes may interfere and compete during training, it remains challenging for the prior art in video prediction to generate less blurry future frames based on an ambiguous understanding of mixed physical processes. We refer to this empirical phenomenon as spatiotemporal mode collapse (STMC), which is mainly caused by the collapse of learned representations into invalid subspaces when compromising to multiple spatiotemporal modes in the training set. Unlike the widely concerned mode collapse problem in generative adversarial networks, STMC has not drawn much attention because predictive learning is supposed to be well constrained by the image reconstruction loss. However, due to the limitation of model size, STMC occurs when the model cannot effectively decouple mixed spatiotemporal modes and infer their Predictive models collapse to blurry motion in the presence of complex dynamics modes underlying structures. As a result, its responses to different modes tend to lose diversity and may collapse to a meaningless average of multiple representation subspaces of valid modes. In Figure (left), we can observe the existence of STMC on a large-scale video dataset named RoboNet, in which potential spatiotemporal modes may come from seven different robot platforms (e.g., Baxter and WidowX), four data collection environments (e.g., Berkeley and Stanford), and a variety of unlabeled robot control tasks (e.g., pushing and grasping). An additional outcome of STMC is that we can achieve a performance gain when training individual models in separate subsets with remarkably different visual dynamics, as shown in Figure (right). However, such a dilemma prevents the model from growing into big ones that allow scalable training on large-scale, natively multimodal spatiotemporal sequences. We explore STMC for the first time in unsupervised predictive learning. The core idea is to provide a strong inductive bias for the predictive model to discover the compositional structures of latent modes. To this end, we propose ModeRNN, a new modular recurrent architecture that learns structured hidden representations through a set of mode slots 1 , where each of them responds to the representation subspace of a single spatiotemporal mode. ModeRNN also introduces a decoupling-aggregation framework to process the slot features in three stages, which is completely different from existing predictive models with modular architectures. The first stage is recurrent state interaction and slot binding, in which we use the multi-head attention mechanism to enable the memory state to interact with the input state and previous hidden state of RNNs. We name the memory state "slot bus", because for each sequence, it is initialized from a multi-variate Gaussian distribution with learnable parameters, and thereafter refined using the slot features at each time step. By using the slot bus as the queries, multi-head attention can naturally decouple modular components from hidden representations and bind them to particular mode slots. Features in each slot are then independently modeled using per-slot convolutional parameters. The second stage in each ModeRNN unit is slot fusion, motivated by the assumption that, there can be multiple spatiotemporal modes in a single video and similar videos can be represented by similar compositional structures over the mode slots. Therefore, we assign slot features with learnable importance weights and aggregate them into a unified hidden representation, which is then used in the third stage to update the slot bus and generate the output state of the ModeRNN unit. We empirically show the existence of STMC on five datasets, and include the results on three realworld datasets in the manuscript, including the large-scale RoboNet dataset that has various data collection environments and multiple robot control tasks, the KTH dataset with six types of human actions that has been widely used by previous literature, and the radar echo dataset for precipitation forecasting that contains time-varying modes of seasonal climates. In addition, we include results on a Mixed Moving MNIST dataset and the Human3.6M dataset in the appendix. In a series of quantitative and visualization results, we demonstrate the effectiveness of ModeRNN in mitigating STMC and learning from highly entangled visual dynamics. For clarity, in the following discussion, spatiotemporal modes are considered to have the following properties: 1. A spatiotemporal mode refers to a representation subspace that corresponds to a family of similar, but not predefined, visual dynamics. 2. Multiple spatiotemporal modes naturally exist in real-world data, even in a single frame. 3. We assume the i.i.d. setup to allow all videos to share the same set of spatiotemporal modes in a dataset. Different data may have different compositional structures over the modes.

Why one would expect each sequence correspond to a single spatiotemporal mode?

We have carefully rethought the notion of *spatiotemporal mode* and agreed with the reviewer that this statement is not rigorous enough. In the revised draft, we have corrected all relevant statements. In particular with the Introduction section, we rewrite the first paragraph as follows: The spatiotemporal modes in visual dynamics can be highly entangled and difficult to learn due to the richness of data environments, the diversity of object interactions, and the complexity of motion patterns. Spatiotemporal modes are considered to have the following properties: - A spatiotemporal mode refers to a representation subspace that corresponds to a family of similar, but not predefined, visual dynamics.- Multiple spatiotemporal modes naturally exist in real-world data, even in a single frame.- We assume the i.i.d. setup to allow all videos to share the same set of spatiotemporal modes in a dataset. Different data may have different compositional structures over the modes.Through the new statements, one would **NOT** expect each sequence to correspond to a single spatiotemporal mode. More precisely, each sequence may be mixed up by a sequence-independent weighted combination from a set of spatiotemporal modes, and each mode may correspond to a compact dynamical component (modeled by the *mode slot*) underlying many sequences. Reasonably, the **decoupling-aggregation** framework proposed with this paper is well fit into the modeling of mixed spatiotemporal dynamics.

Write the first paragraph of a blog post describing the appeal of Cape Cod as a vacation destination.

Welcome to Cape Cod! A short 1 hour drive from Boston's Logan airport. As soon as you cross one of the 2 bridges to Cape Cod an immediate sense of calm and relaxation will wash over your body. You'll find yourself surrounded by the perfect combination of coastal charm and modern food, beverage and accommodation. The biggest decision of the week will be staying near golf, beach or one of the swanky islands just off Cape . You can have it all - the best of both worlds! Dining is easy and plentiful with causal options ranging from burgers, pizza and salads or go upscale with oysters & fresh seafood. A trip to the cape is not complete with out trying one of New England's crown jewels, the Maine Lobster served boiled or in a classic lobster roll. So kick your feet up, relax and enjoy all that the Cape has to offer!

The two following tables, Table TABREF36 and Table TABREF36, summarize experimental results of Pytorch-tutorial, NIC - Show and Tell models. The two models are trained with three mentioned datasets, which are English-sportball, GT-sportball, UIT-ViIC. After training, 924 images from validation subset for each dataset are used to validate the our models. As can be seen in Table TABREF36, with model from Pytorch tutorial, MS-COCO English captions categorized with sportball yields better results than the two Vietnamese datasets. However, as number of consecutive words considered (BLEU gram) increase, UIT-ViIC’s BLEU scores start to pass that of English sportball and their gaps keep growing. The ROUGE-L and CIDEr-D scores for UIT-ViIC model prove the same thing, and interestingly, we can observe that the CIDEr-D score for the UIT-ViIC model surpasses English-sportball counterpart. The same conclusion can be said from Table TABREF36. Show and Tell model’s results show that MS-COCO sportball English captions only gives better result at BLEU-1. From BLEU-3 to BLEU-4, both GT-sportball and UIT-ViIC yield superior scores to English-sportball. Besides, when limiting MS-COCO English dataset to sportball category only, the results are higher (0.689, 0.501, 0.355, 0.252) than when the model is trained on MS-COCO with all images, which scored only 0.629, 0.436, 0.290, 0.193 (results without tuning in 2018) from BLEU-1 to BLEU-4 respectively. When we compare between two Vietnamese datasets, UIT-ViIC models perform better than sportball dataset translated automatically, GT-sportball. The gaps between the two results sets are more trivial in NIC model, and the numbers get smaller as the BLEU’s n-gram increase. In Fig. FIGREF37, two images inputted into the models generate two Vietnamese captions that are able to describe accurately the sport game, which is soccer. The two models can also differentiate if there is more than one person in the images. However, when comparing GT-sportball outputs with UIT-ViIC ones in both images, UIT-ViIC yield captions that sound more naturally, considering Vietnamese language. Furthermore, UIT-ViIC demonstrates the specific action of the sport more accurately than GT-sportball. For example, in the below image of Fig. FIGREF37, UIT-ViIC tells the exact action (the man is preparing to throw the ball), whereas GT-sportball is mistaken (the man swing the bat). The confusion of GT-sportball happens due to GT-sportball train set is translated from original MS-COCO dataset, which is annotated in more various perspective and wider vocabulary range with the dataset size is not big enough. There are cases when the main objects are too small, both English and GT - sportball captions tell the unexpected sport, which is tennis instead of baseball, for instance. Nevertheless, the majority of UIT-ViIC captions can tell the correct type of sport and action, even though the gender and age identifications still need to be improved. Nevertheless, the majority of UIT-ViIC captions can tell the correct type of sport and action, even though the gender and age identifications still need to be improved.

What the majority of UIT-ViIC captions can tell?

It can tell the correct type of sport and action.

What is a less known rule or move in Chess?

En passant

1910.03484

The performance of the joint learning architecture was evaluated on the two datasets described in the previous section. The joint learning model requires a paired and an unpaired dataset, so each of the two datasets was split into several parts. E2E NLG challenge Dataset: The training set of the E2E challenge dataset which consists of 42K samples was partitioned into a 10K paired and 32K unpaired datasets by a random process. The unpaired database was composed of two sets, one containing MRs only and the other containing natural texts only. This process resulted in 3 training sets: paired set, unpaired text set and unpaired MR set. The original development set (4.7K) and test set (4.7K) of the E2E dataset have been kept. The Wikipedia Company Dataset: The Wikipedia company dataset presented in Section SECREF18 was filtered to contain only companies having abstracts of at least 7 words and at most 105 words. As a result of this process, 43K companies were retained. The dataset was then divided into: a training set (35K), a development set (4.3K) and a test set (4.3K). Of course, there was no intersection between these sets. The training set was also partitioned in order to obtain the paired and unpaired datasets. Because of the loose correlation between the MRs and their corresponding text, the paired dataset was selected such that it contained the infobox values with the highest similarity with its reference text. The similarity was computed using “difflib” library, which is an extension of the Ratcliff and Obershelp algorithm BIBREF19. The paired set was selected in this way (rather than randomly) to get samples as close as possible to a carefully annotated set. At the end of partitioning, the following training sets were obtained: paired set (10.5K), unpaired text set (24.5K) and unpaired MR set (24.5K). E2E NLG challenge Dataset: The training set of the E2E challenge dataset which consists of 42K samples was partitioned into a 10K paired and 32K unpaired datasets by a random process. The Wikipedia Company Dataset: The Wikipedia company dataset presented in Section SECREF18 was filtered to contain only companies having abstracts of at least 7 words and at most 105 words. The dataset was then divided into: a training set (35K), a development set (4.3K) and a test set (4.3K). The training set was also partitioned in order to obtain the paired and unpaired datasets.

What non-annotated datasets are considered?

The answers are shown as follows: * E2E NLG challenge Dataset * The Wikipedia Company Dataset

Order the following animals in terms of average weight: finch, elephant, goat, gazelle, and a rabbit.

The ordering of animals in terms of weight from lightest to heaviest is finch, rabbit, gazelle, goat and elephant. The weights of gazelles and goats can be similar, however, on averages an adult goat will weigh more than and an adult gazelle.

The Weeknd samples which '80s megahit in "Blinding Lights?"

"Take On Me" by A-ha

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

Pate the Plowman

French poetry was a determinant influence in Darío's formation as a poet. In the first place, the romantics, particularly Victor Hugo. Later on, and in a decisive fashion, Darío was influenced by the parnassians: Théophile Gautier, Catulle Mendès, and José María de Heredia. Another decisive influence was the writer of prose and poetry and national hero of Cuba, Jose Martí. The final defining element of Darianian aesthetic is his admiration towards the symbolists, especially Paul Verlaine. Recapitulating his own poetic trajectory in the initial poem of Cantos de vida y esperanza (1905) Darío himself synthesized his main influences when he affirms that he was "strong with Hugo and ambiguous with Verlaine" ("con Hugo fuerte y con Verlaine ambiguo".) In the section "Palabras Liminares" of Prosas Profanas (1896) he had already written a paragraph that reveals the importance of French culture in the development of his literary work: The old Spaniard with a white beard points towards a series of illustrious portraits: "This one—he says—is the great Miguel de Cervantes Saavedra, one-handed genius; this one is Lope de Vega, this one is Garcilaso, this one Quintana." I ask him for the noble man Gracián, for Teresa of Ávila, for the brave Góngora and the strongest of all, Francisco de Quevedo y Villegas. Then I say: "Shakespeare! Dante! Hugo...! (and in my head: Verlaine...!)" Then, when saying goodbye: "-Old man, it is important to say: my wife is from my land; my mistress is from Paris." Los raros is an illustrative volume regarding literary tastes, which he published on the same year as Prosas profanas, and dedicated to briefly glossing some of the writers and intellectuals towards whom he felt profound admiration. Amongst those in the book we find Edgar Allan Poe, Villiers de l'Isle Adam, Léon Bloy, Paul Verlaine, Lautréamont, Eugénio de Castro and José Martí (the latter being the only one mentioned who wrote their literary work in Spanish.) The predominance of French culture is more than evident. Darío wrote: "Modernism is nothing more than Spanish verse and prose passed through the fine sieve of the good French verse and the good French prose." Setting aside his initial stage, before Azul..., in which his poetry owes a great deal to the great names of 19th-century Spanish poetry, such as Núñez de Arce and Campoamor, Darío was a great admirer of Bécquer. Spanish themes are well represented in his work, already in Prosas profanas and, specially, after his second trip to Spain, in 1899. Conscious of contemporaneous Spanish decadence in politics and the arts (a preoccupation he shared with the so-called Generation of '98), he frequently was inspired by characters and elements of the past. Regarding authors in other languages, it is worth mentioning that he felt a profound admiration towards three writers from the United States: Ralph Waldo Emerson, Edgar Allan Poe and Walt Whitman.[citation needed]

Extract the names of influencers on Ruben Dario's poetry

Victor Hugo Théophile Gautier Catulle Mendès José María de Heredia Jose Martí Paul Verlaine Edgar Allan Poe Villiers de l'Isle Adam Léon Bloy Lautréamont Eugénio de Castro Ralph Waldo Emerson Walt Whitman

1809.08731

We calculate the probability of a sentence with a long-short term memory (LSTM, hochreiter1997long) LM, i.e., a special type of RNN LM, which has been trained on a large corpus. More details on LSTM LMs can be found, e.g., in sundermeyer2012lstm. The unigram probabilities for SLOR are estimated using the same corpus. We train our LSTM LMs on the English Gigaword corpus BIBREF15 , which consists of news data. We calculate the probability of a sentence with a long-short term memory (LSTM, hochreiter1997long) LM, i.e., a special type of RNN LM, which has been trained on a large corpus. We train our LSTM LMs on the English Gigaword corpus BIBREF15 , which consists of news data.

what language models do they use?

The answers are shown as follows: * LSTM LMs

1909.02480

FlowSeq is a flow-based sequence-to-sequence model, which is (to our knowledge) the first non-autoregressive seq2seq model utilizing generative flows. It allows for efficient parallel decoding while modeling the joint distribution of the output sequence. Experimentally, on three benchmark datasets for machine translation – WMT2014, WMT2016 and IWSLT-2014, FlowSeq achieves comparable performance with state-of-the-art non-autoregressive models, and almost constant decoding time w.r.t. the sequence length compared to a typical left-to-right Transformer model, which is super-linear. Experimentally, on three benchmark datasets for machine translation – WMT2014, WMT2016 and IWSLT-2014, FlowSeq achieves comparable performance with state-of-the-art non-autoregressive models, and almost constant decoding time w.r.t. the sequence length compared to a typical left-to-right Transformer model, which is super-linear.

What are three neural machine translation (NMT) benchmark datasets used for evaluation?

The answers are shown as follows: * WMT2014, WMT2016 and IWSLT-2014

We participated in the WMT 2016 shared news translation task by building neural translation systems for four language pairs: English INLINEFORM0 Czech, English INLINEFORM1 German, English INLINEFORM2 Romanian and English INLINEFORM3 Russian. Our systems are based on an attentional encoder-decoder BIBREF0 , using BPE subword segmentation for open-vocabulary translation with a fixed vocabulary BIBREF1 . We experimented with using automatic back-translations of the monolingual News corpus as additional training data BIBREF2 , pervasive dropout BIBREF3 , and target-bidirectional models. We perform early stopping for single models, and use the 4 last saved models (with models saved every 30000 minibatches) for the ensemble results.

Which kind of models do the authors perform early stopping for?

Single models.

1909.00183

The trained Doc2Vec model is subsequently used to infer high-dimensional vector descriptions for the text of each document in our target analysis set. We then compute a matrix containing all the pairwise (cosine) similarities between the Doc2Vec document vectors. This similarity matrix can be thought of as the adjacency matrix of a full, weighted graph with documents as nodes and edges weighted by their similarity. We sparsify this graph to the union of a minimum spanning tree and a k-Nearest Neighbors (MST-kNN) graph BIBREF14, a geometric construction that removes less important similarities but preserves global connectivity for the graph and, hence, for the dataset. The MST-kNN graph is then analysed with Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18, a multi-resolution graph partitioning method that identifies relevant subgraphs (i.e., clusters of documents) at different levels of granularity. MS uses a diffusive process on the graph to reveal the multiscale organisation at different resolutions without the need to choose a priori the number or type of clusters. We sparsify this graph to the union of a minimum spanning tree and a k-Nearest Neighbors (MST-kNN) graph BIBREF14, a geometric construction that removes less important similarities but preserves global connectivity for the graph and, hence, for the dataset. The MST-kNN graph is then analysed with Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18, a multi-resolution graph partitioning method that identifies relevant subgraphs (i.e., clusters of documents) at different levels of granularity.

What cluster identification method is used in this paper?

A combination of Minimum spanning trees, K-Nearest Neighbors and Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18

The lateralization of brain function is the tendency for some neural functions or cognitive processes to be specialized to one side of the brain or the other. The median longitudinal fissure separates the human brain into two distinct cerebral hemispheres, connected by the corpus callosum. Although the macrostructure of the two hemispheres appears to be almost identical, different composition of neuronal networks allows for specialized function that is different in each hemisphere.

What is the difference between the right and left side of the brain?

The lateralization of brain function is the tendency for some neural functions or cognitive processes to be specialized to one side of the brain or the other. The median longitudinal fissure separates the human brain into two distinct cerebral hemispheres, connected by the corpus callosum. Although the macrostructure of the two hemispheres appears to be almost identical, different composition of neuronal networks allows for specialized function that is different in each hemisphere. Lateralization of brain structures is based on general trends expressed in healthy patients; however, there are numerous counterexamples to each generalization. Each human's brain develops differently, leading to unique lateralization in individuals. This is different from specialization, as lateralization refers only to the function of one structure divided between two hemispheres. Specialization is much easier to observe as a trend, since it has a stronger anthropological history. The best example of an established lateralization is that of Broca's and Wernicke's areas, where both are often found exclusively on the left hemisphere. Function lateralization, such as semantics, intonation, accentuation, and prosody, has since been called into question and largely been found to have a neuronal basis in both hemispheres. Another example is that each hemisphere in the brain tends to represent one side of the body. In the cerebellum, this is the same body side, but in the forebrain this is predominantly the contralateral side. Language functions such as grammar, vocabulary and literal meaning are typically lateralized to the left hemisphere, especially in right-handed individuals. While language production is left-lateralized in up to 90% of right-handers, it is more bilateral, or even right-lateralized, in approximately 50% of left-handers. Broca's area and Wernicke's area, associated with the production of speech and comprehension of speech, respectively, are located in the left cerebral hemisphere for about 95% of right-handers but about 70% of left-handers. 69  Individuals who speak multiple languages demonstrate separate speech areas for each language. The processing of basic sensory information is lateralized by being divided into left and right sides of the body or the space around the body. In vision, about half the neurons of the optic nerve from each eye cross to project to the opposite hemisphere, and about half do not cross to project to the hemisphere on the same side. This means that the left side of the visual field is processed largely by the visual cortex of the right hemisphere and vice versa for the right side of the visual field. In hearing, about 90% of the neurons of the auditory nerve from one ear cross to project to the auditory cortex of the opposite hemisphere. In the sense of touch, most of the neurons from the skin cross to project to the somatosensory cortex of the opposite hemisphere. Because of this functional division of the left and right sides of the body and of the space that surrounds it, the processing of information in the sensory cortices is essentially identical. That is, the processing of visual and auditory stimuli, spatial manipulation, facial perception, and artistic ability are represented bilaterally. Numerical estimation, comparison and online calculation depend on bilateral parietal regions while exact calculation and fact retrieval are associated with left parietal regions, perhaps due to their ties to linguistic processing. Rather than just being a series of places where different brain modules occur, there are running similarities in the kind of function seen in each side, for instance how right-side impairment of drawing ability making patients draw the parts of the subject matter with wholly incoherent relationships, or where the kind of left-side damage seen in language impairment not damaging the patient's ability to catch the significance of intonation in speech. This has led British psychiatrist Iain McGilchrist to view the two hemispheres as having different value systems, where the left hemisphere tends to reduce complex matters such as ethics to rules and measures, and the right hemisphere is disposed to the holistic and metaphorical. Depression is linked with a hyperactive right hemisphere. The delusional misidentification syndromes, reduplicative paramnesia and Capgras delusion are also often the result of right hemisphere lesions. Damage to either the right or left hemisphere, and its resulting deficits provide insight into the function of the damaged area. Left hemisphere damage has many effects on language production and perception. Damage or lesions to the right hemisphere can result in a lack of emotional prosody or intonation when speaking. Right hemisphere damage also has grave effects on understanding discourse. People with damage to the right hemisphere have a reduced ability to generate inferences, comprehend and produce main concepts, and a reduced ability to manage alternative meanings. Furthermore, people with right hemisphere damage often exhibit discourse that is abrupt and perfunctory or verbose and excessive. They can also have pragmatic deficits in situations of turn taking, topic maintenance and shared knowledge. Lateral brain damage can also affect visual perceptual spatial resolution. People with left hemisphere damage may have impaired perception of high resolution, or detailed, aspects of an image. People with right hemisphere damage may have impaired perception of low resolution, or big picture, aspects of an image. If a specific region of the brain, or even an entire hemisphere, is injured or destroyed, its functions can sometimes be assumed by a neighboring region in the same hemisphere or the corresponding region in the other hemisphere, depending upon the area damaged and the patient's age. When injury interferes with pathways from one area to another, alternative (indirect) connections may develop to communicate information with detached areas, despite the inefficiencies. Broca's aphasia is a specific type of expressive aphasia and is so named due to the aphasia that results from damage or lesions to the Broca's area of the brain, that exists most commonly in the left inferior frontal hemisphere. Thus, the aphasia that develops from the lack of functioning of the Broca's area is an expressive and non-fluent aphasia. It is called 'non-fluent' due to the issues that arise because Broca's area is critical for language pronunciation and production. The area controls some motor aspects of speech production and articulation of thoughts to words and as such lesions to the area result in specific non-fluent aphasia. Wernicke's aphasia is the result of damage to the area of the brain that is commonly in the left hemisphere above the Sylvian fissure. Damage to this area causes primarily a deficit in language comprehension. While the ability to speak fluently with normal melodic intonation is spared, the language produced by a person with Wernicke's aphasia is riddled with semantic errors and may sound nonsensical to the listener. Wernicke's aphasia is characterized by phonemic paraphasias, neologism or jargon. Another characteristic of a person with Wernicke's aphasia is that they are unconcerned by the mistakes that they are making. Terence Hines states that the research on brain lateralization is valid as a research program, though commercial promoters have applied it to promote subjects and products far outside the implications of the research. For example, the implications of the research have no bearing on psychological interventions such as eye movement desensitization and reprocessing (EMDR) and neurolinguistic programming, brain-training equipment, or management training. Some popularizations oversimplify the science about lateralization, by presenting the functional differences between hemispheres as being more absolute than is actually the case. Interestingly, research has shown quite opposite function of brain lateralisation, i.e. left hemisphere creatively and chaotically links between concepts and right hemisphere tends to adhere to specific date and time, although generally adhering to the pattern of left-brain as linguistic interpretation and right brain as spatio-temporal. In the 19th century and to a lesser extent the 20th, it was thought that each side of the brain was associated with a specific gender: the left corresponding with masculinity and the right with femininity and each half could function independently. The right side of the brain was seen as the inferior and thought to be prominent in women, savages, children, criminals, and the insane. A prime example of this in fictional literature can be seen in Robert Louis Stevenson's Strange Case of Dr. Jekyll and Mr. Hyde.

Write critical success factors for a proposal to a company called ABC for an Enterprise Application Integration (EAI) effort

CRITICAL SUCCESS FACTORS The following factors will be critical to the successful integration of the ABC's Operational Support Systems: Project Management – Strong project management and planning will ensure an organized and cohesive solution. Excellent communication, aggressive issue resolution management, and planning materials will ensure deadlines are met, objectives remain focused, and costs are managed. The variety of vendors, system and process owners involved in an EAI effort requires a significant coordination effort to ensure success. Knowledge of Telecommunications Operational Business Practices – ABC must employ the services of an experienced Telecommunications consulting company to ensure business needs are met during the OSS EAI effort. Understanding of ABC business needs in implementing this EAI project is critical in ensuring the appropriate implementation strategy and successful rollout of the integration. Enterprise Application Integration Expertise – It is critical that the selected vendor have extensive experience in planning, designing, and developing integration approaches, architectures, and frameworks to integrate complex Telecommunication OSS systems. Knowledge Transfer – In order to maintain a consistent level of operation and support, it is critical that ABC employees are imparted with the necessary knowledge to successfully transition into the primary roles that support their systems and future integration efforts. Documentation, personal attention, and other resources are vital to this knowledge transfer and critical to ABC’s ability to succeed as a growing service provider.

who created the first Teddy bear?

Teddy bears’ history dates back to a bear-hunting trip in 1902 when President Theodore “Teddy” Roosevelt refused to kill a bear. The event made it to headlines of newspapers all over the US. Among them, the most famous one is from Clifford Berryman, he created a cartoon of the story and published it in the Washington Post on 11/16/1902. Morris Michtom then created a stuffed toy bear to pay respect to it. Morris and his wife owned a candy shop in Brookelyn and sales home-make toys in the shop too. They called the stuffed bear “Teddy bear”, and the sales of it soared since the day of its creation. There are a lot of famous teddy bear characters, including Care Bears, Yogi Bear, Teddy Ruxpin, Fozzie Bear, Little Bear, Berenstein Bears, Winnie the Pooh and Baloo!

1712.03556

FLOAT SELECTED: Table 2: Test performance on SQuAD. Results are sorted by Test F1. Finally, we compare our results with other top models in Table 2 . Note that all the results in Table 2 are taken from the published papers. We see that SAN is very competitive in both single and ensemble settings (ranked in second) despite its simplicity. Note that the best-performing model BIBREF14 used a large-scale language model as an extra contextual embedding, which gave a significant improvement (+4.3% dev F1). We expect significant improvements if we add this to SAN in future work. The main experimental question we would like to answer is whether the stochastic dropout and averaging in the answer module is an effective technique for multi-step reasoning. To do so, we fixed all lower layers and compared different architectures for the answer module: FLOAT SELECTED: Table 1: Main results—Comparison of different answer module architectures. Note that SAN performs best in both Exact Match and F1 metrics. FLOAT SELECTED: Table 2: Test performance on SQuAD. Results are sorted by Test F1. We see that SAN is very competitive in both single and ensemble settings (ranked in second) despite its simplicity. The main experimental question we would like to answer is whether the stochastic dropout and averaging in the answer module is an effective technique for multi-step reasoning. To do so, we fixed all lower layers and compared different architectures for the answer module FLOAT SELECTED: Table 1: Main results—Comparison of different answer module architectures. Note that SAN performs best in both Exact Match and F1 metrics.

How much performance improvements they achieve on SQuAD?

Compared to baselines SAN (Table 1) shows improvement of 1.096% on EM and 0.689% F1. Compared to other published SQuAD results (Table 2) SAN is ranked second.

1906.08286

We validate our approach on the Wikipedia toxic comments dataset BIBREF18 . Our fairness experiments show that the classifiers trained with our method achieve the same performance, if not better, on the original task, while improving AUC and fairness metrics on a synthetic, unbiased dataset. Models trained with our technique also show lower attributions to identity terms on average. Our technique produces much better word vectors as a by-product when compared to the baseline. Lastly, by setting an attribution target of 1 on toxic words, a classifier trained with our objective function achieves better performance when only a subset of the data is present. We validate our approach on the Wikipedia toxic comments dataset BIBREF18 .

Which datasets do they use?

The answers are shown as follows: * Wikipedia toxic comments

Gioia del Colle (pronounced [ˈdʒɔːja del ˈkɔlle]; Barese: Sciò) is a town and comune of the Metropolitan City of Bari, Apulia, southern Italy. The town is located on the Murge plateau at 360 metres (1,180 ft) above sea level, between the Adriatic and Ionian Seas. Physical geography Territory Gioia del Colle is on the top of a hill at 360 m a.s.l. It is located in the southern part of the Murge, in the "Sella di Gioia del Colle". It is between the North-West Murge and the South-West Murge and the Adriatic Sea and the Ionian Sea. The municipal area has an area of 206.48 km² and it reaches a maximum altitude of 435 m a.s.l. and a minimum of 296 m a.s.l. Its area borders to the North-West with Acquaviva delle Fonti, to the North with Sammichele di Bari, to the North-East with Turi, to the East with Putignano and Noci, to the South-East with Mottola, to the South with Castellaneta, to the South-West with Laterza and to the West with Santeramo in Colle. Gioia del Colle's orography The landscape is characterized by large wooded areas, in which the Macedonian oaks dominate, more than the downy oak. In particular the Bosco Romanazzi and Serra Capece constitute the most conspicuous part of the wooded area of Gioia del Colle and they extend from Mount Sannace archaeological area to the provincial road that leads to Noci. The western part of the area is part of the North-West Murge, with isoipse that exceed 400 m a.s.l. towards Santeramo and Laterza, while the Eastern one is part of the South-East Murge, with isoipse over 400 m a.s.l. towards Noci. In the middle, on the contrary, there is a depression (saddle) with an average altitude of 340 m a.s.l., interrupted only by the 360-meter hill on which the city lies. Climate Gioia del Colle's climate is mediterranean (Köppen Csa), but it has peaks of continentality due to the altitude and the distance from the sea. Temperature varies between −2 e +12 °C in winter, while it varies between +18 e +35 °C with a high percentage of humidity in summer. Annual rainfall is around 600 mm. It often snows, about once a year, especially if there is cold air of Balkan origin.

Heres a summary of Gioia Del Colle, a town in Italy that I will be traveling to, how should I pack?

The climate is mild, and does not go below freezing by much in the winter with tropical weather summers. Pack for light layers, and adjust for the time of year.

Classify each of the following as a flower or a tree: rose, tulip, oak, spruce, pine, orchid

Rose: Flower Tulip: Flower Oak: Tree Spruce: Tree Pine: Tree Orchid: Flower

I am planning a trip to Super Mario world and want to know which of the following characters are part of the Super Mario Bros franchise and which are from other games. Princess Peach, Luigi, Bowser, Link, Inkling Boy, Captain Falcon, Koopa Troopa, and Kirby.

Princess Peach, Luigi, Bowser, and Koopa Troopa are part of the Super Mario Franchise. Link, Captain Falcon, and Kirby are from other Nintendo games.