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Give me some examples of Cockney Rhyming Slang and help me understand how it works.
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Cockney rhyming slang is a slang language originating from the East End in London, United Kingdom. It has a simple structure where a rhyming word or phrase is used in substitution of the word's true meaning. Examples:
Apples and pears = stairs
Adam and Eve = believe
Barnaby Rudge = judge
Bees and Honey = money
Dog and Bone = Phone
Hank Marvin = starving
Lady Godiva = fiver
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null | false
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Was there an industrial revolution during the middle ages?
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Most economic historians would argue that an industrial revolution would require widespread use of coal for heating, mechanical power generation, and metal making. In that sense, there was no medieval industrial revolution. However, widespread investment in windmills, water wheels, and canals did help the European economy grow and improve the living standards of the average medieval peasant. Similarly, cross border trade in bulk commodities like wool and wine allowed for an international division of labor, with the associated improvement in productivity and living standards. By the end of the Middle Ages, mechanical clocks had become ubiquitous: plenty of craftsmen had the precision metalworking skills to build these complex machines, and even more people could afford to buy them. Not quire an industrial revolution, but certainly a prosperous society taking the first steps towards automation.
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Which of the following countries do not follow the rectangular-shaped flag standard that most every other country does: Nepal, South Africa, Japan, South Korea, Vatican City, Switzerland, Vietnam, Australia
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Nepal, Vatican City, Switzerland
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A gravity hill, also known as a magnetic hill, mystery hill, mystery spot, gravity road, or anti-gravity hill, is a place where the layout of the surrounding land produces an optical illusion, making a slight downhill slope appear to be an uphill slope. Thus, a car left out of gear will appear to be rolling uphill against gravity. Hundreds of gravity hills are recognized worldwide.
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What are the primary features of a gravity hill given the following description?
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The gravity hill optical illusion is defined by having the following characteristics:
1. A downhill slope will appear to be an uphill slope
2. Objects appear to be rolling uphill against gravity
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Who is Luka Modric?
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Luka Modrić (Croatian pronunciation: [lûːka mǒːdritɕ]; born 9 September 1985) is a Croatian professional footballer who plays as a midfielder for La Liga club Real Madrid and captains the Croatia national team. He plays mainly as a central midfielder, but can also play as an attacking midfielder or as a defensive midfielder. He is widely regarded as one of the greatest midfielders of all time, and as the greatest ever Croatian footballer.
Born in Zadar, Modrić's childhood coincided with the Croatian War of Independence which displaced his family. In 2002, he was signed by Dinamo Zagreb at age 16, after showing promise with his hometown club NK Zadar's youth team. He continued his development in Zagreb, before spells on loan to Zrinjski Mostar and Inter Zaprešić. He made his debut for Dinamo in 2005 and won three consecutive league titles and domestic cups, being named the Prva HNL Player of the Year in 2007. In 2008, he moved to Premier League club Tottenham Hotspur for a club-record transfer fee of £16.5 million, where he led Spurs to their first UEFA Champions League appearance in almost 50 years, reaching the quarter-finals of the 2010–11 tournament.
In the summer of 2012, Modrić joined Real Madrid for a £30 million transfer fee. There he became a key contributor and helped the team win La Décima and was selected for the 2013–14 Champions League squad of the season. After Zinedine Zidane took over Madrid, Modrić was critical to three consecutive Champions League titles from 2015–16 to 2017–18, each time being voted into the squad of the season. He has won twenty-two major trophies at Real Madrid, including five UEFA Champions League titles, three La Liga titles, one Copa del Rey, four Supercopa de España titles, four UEFA Super Cup titles and five FIFA Club World Cup titles. He won the La Liga Award for "Best Midfielder" in 2016 for the second time, and the UEFA Club Football Award for "Best Midfielder" in 2017 and 2018. In 2015, he became the first Croatian player to be included in the FIFA FIFPRO World 11, in which he was regularly included until 2019 and then again in 2022, as well in the UEFA Team of the Year between 2016 and 2018. In 2018, Modrić became the first Croatian player to win the UEFA Men's Player of the Year Award, and by winning The Best FIFA Men's Player and Ballon d'Or awards, he became the first player other than Lionel Messi or Cristiano Ronaldo to claim the awards in more than a decade. In 2019, he was awarded the Golden Foot award for career results and personality.
Modrić made his international debut for Croatia against Argentina in March 2006, and scored his first international goal in a friendly match against Italy. Modrić has anchored Croatia's "second Golden Generation", participating in every major tournament Croatia has qualified for, including the UEFA Euro 2008, 2012, 2016, and 2020, as well as the 2006, 2014, 2018, and 2022 FIFA World Cup. At the Euro 2008, he was selected for the Team of the Tournament, becoming only the second Croatian to ever achieve this honour. Following group stage eliminations in his first two World Cups, Modrić led Croatia to the 2018 World Cup Final, winning the Golden Ball in process. At the following tournament, he led the team to a third-place finish, winning the Bronze Ball in process. In March 2021, he went on to become the country's most capped player in history. Furthermore, he has been named Croatian Footballer of the Year a record eleven times between 2007 and 2022.
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Luka Modric is a Croatian professional football (soccer) player. He is considered one of the best midfielders of all time and best Croatian player ever. He was born in Zadar, Croatia. In 2002, at the age of 16, he signed with the biggest Croatian soccer club, Dinamo Zagreb where is stayed till 2008 when he signed with Tottenham Hotspur in England. In 2012 he signed with Real Madrid in Spain where he currently plays.
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Which of the following drinks are considered safe for children? Beer, cocktail, coffee, tea, boba, juice, water, milk, coke, lemonade.
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Water, lemonade, milk, and juice are generally safe for children, even though excessive intake of lemonade / juice can lead to obesity / dental problems. Coffee, tea, boba and coke should be limited (if not completely avoided) since they contain caffeine. Alcoholic drinks like beer and cocktail are not safe for children and should be avoided completely.
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What is the difference between a lion and a tiger?
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Lions and tigers are species in the same family, Felidae. They have different appearances, habitats, social behaviors, and hunting tendencies. For example, lions are a social species and will hunt and live in groups, called prides. Tigers, on the other hand, are independent and only interact with other tigers for mating purposes.
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null | false
| 154
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Four publicly available corpora are selected for our analysis. These corpora are based on Wikipedia, so more comparable than the others, and have already been used for the evaluation of several QA systems.
WikiQA BIBREF6 comprises questions selected from the Bing search queries, where user click data give the questions and their corresponding Wikipedia articles. The abstracts of these articles are then extracted to create answer candidates. The assumption is made that if many queries lead to the same article, it must contain the answer context; however, this assumption fails for some occasions, which makes this dataset more challenging. Since the existence of answer contexts is not guaranteed in this task, it is called answer triggering instead of answer selection.
SelQA BIBREF7 is a product of five annotation tasks through crowdsourcing. It consists of about 8K questions where a half of the questions are paraphrased from the other half, aiming to reduce contextual similarities between questions and answers. Each question is associated with a section in Wikipedia where the answer context is guaranteed, and also with five sections selected from the entire Wikipedia where the selection is made by the Lucene search engine. This second dataset does not assume the existence of the answer context, so can be used for the evaluation of answer triggering.
SQuAD BIBREF12 presents 107K+ crowdsourced questions on 536 Wikipedia articles, where the answer contexts are guaranteed to exist within the provided paragraph. It contains annotation of answer phrases as well as the pointers to the sentences including the answer phrases; thus, it can be used for both answer extraction and selection. This corpus also provides human accuracy on those questions, setting up a reasonable upper bound for machines. To avoid overfitting, the evaluation set is not publicly available although system outputs can be evaluated by their provided script.
InfoboxQA BIBREF13 gives 15K+ questions based on the infoboxes from 150 articles in Wikipedia. Each question is crowdsourced and associated with an infobox, where each line of the infobox is considered an answer candidate. This corpus emphasizes the gravity of infoboxes, which summary arguably the most commonly asked information about those articles. Although the nature of this corpus is different from the others, it can also be used to evaluate answer selection.
WIKIQA (Yang et al., 2015) comprises questions selected from the Bing search queries, where user click data give the questions and their corresponding Wikipedia articles.
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What corpora do the authors make both intrinsic and extrinsic analysis?
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It is the Bing search queries, where user click data give the questions and their corresponding Wikipedia articles.
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null | false
| 113
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Multi-party Conversational Systems are systems with natural language interaction between one or more people or systems. From the moment that an utterance is sent to a group, to the moment that it is replied in the group by a member, several activities must be done by the system: utterance understanding, information search, reasoning, among others. In this paper we present the challenges of designing and building multi-party conversational systems, the state of the art, our proposed hybrid architecture using both rules and machine learning and some insights after implementing and evaluating one on the finance domain.
Multi-party Conversational Systems are systems with natural language interaction between one or more people or systems.
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What are the Multi-party Conversational Systems?
|
Systems with natural language interaction between one or more people or systems.
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null | false
| null |
Tell me whether the following animals are part of the cat family: ocelot, lion, jackal, caracal, leopard, fox, tiger, lynx.
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Ocelot, lion, caracal, leopard, tiger, lynx are all species that are part of the Felidae family (commonly known as cats). Jackals and foxes are part of the Canidae (or dog) family. While the two groups have physical similarities, they also have distinctive physical and behavioral differences.
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1906.01910
| true
| null |
FLOAT SELECTED: Figure 5: Comparison of nex-cv and Human-Rater Accuracy. The six datasets from pseudonymous chatbots tested had a different number of questions (examples) and categories (classes), as shown in the bottom row. The human-rater estimate of accuracy (top left, blue) is consistently more lenient than any of the automated measures (top right). The (0; 0.15) setting (top right, blue) is not consistently more or less optimistic than the other settings.
FLOAT SELECTED: Figure 5: Comparison of nex-cv and Human-Rater Accuracy. The six datasets from pseudonymous chatbots tested had a different number of questions (examples) and categories (classes), as shown in the bottom row. The human-rater estimate of accuracy (top left, blue) is consistently more lenient than any of the automated measures (top right). The (0; 0.15) setting (top right, blue) is not consistently more or less optimistic than the other settings.
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did they compare with other evaluation metrics?
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Yes.
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null | false
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Through this period, YouTube tried several new ways to generate revenue beyond advertisements. In 2013, YouTube launched a pilot program for content providers to offer premium, subscription-based channels. This effort was discontinued in January 2018 and relaunched in June, with US$4.99 channel subscriptions. These channel subscriptions complemented the existing Super Chat ability, launched in 2017, which allows viewers to donate between $1 and $500 to have their comment highlighted. In 2014, YouTube announced a subscription service known as "Music Key," which bundled ad-free streaming of music content on YouTube with the existing Google Play Music service. The service continued to evolve in 2015 when YouTube announced YouTube Red, a new premium service that would offer ad-free access to all content on the platform (succeeding the Music Key service released the previous year), premium original series, and films produced by YouTube personalities, as well as background playback of content on mobile devices. YouTube also released YouTube Music, a third app oriented towards streaming and discovering the music content hosted on the YouTube platform.
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What was the app after the second app released by YouTube?
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The third app released by YouTube was YouTube Music, which oriented towards streaming and discovering the music content hosted on the YouTube platform
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null | false
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What are the most common types of pizzas?
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There are lots of different pizzas, but the most common are cheese and pepperoni. There are also different types of pizza such as thin crust, New York style pizza, and Chicago style pizza.
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null | false
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What are some good quality movies I can watch?
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Moonlight, Everything Everywhere All at Once, Gone with the Wind and Chicago all won Oscars for Best Picture. You could start with those.
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null | false
| 142
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Table TABREF33 provides BLEU and consistency scores for the DocRepair model trained on different amount of data. We see that even when using a dataset of moderate size (e.g., 5m fragments) we can achieve performance comparable to the model trained on a large amount of data (30m fragments). Moreover, we notice that deixis scores are less sensitive to the amount of training data than lexical cohesion and ellipsis scores. The reason might be that, as we observed in our previous work BIBREF11, inconsistencies in translations due to the presence of deictic words and phrases are more frequent in this dataset than other types of inconsistencies. Also, as we show in Section SECREF7, this is the phenomenon the model learns faster in training.
Moreover, we notice that deixis scores are less sensitive to the amount of training data than lexical cohesion and ellipsis scores. The reason might be that, as we observed in our previous work, inconsistencies in translations due to the presence of deictic words and phrases are more frequent in this dataset than other types of inconsistencies.
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What might be the reason why deictic scores are less sensitive to training data than lexical cohesion and ellipsis scores?
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The reason might be that, as they observed in their previous work, inconsistencies in translations due to the presence of deictic words and phrases are more frequent in this dataset than other types of inconsistencies.
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null | false
| 185
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We evaluate our model on the zero-shot cross-lingual answer-aware question generation task. The goal of question generation (QG) is to generate a question that asks towards the answer with the given passage and the expected answer. In the following experiments, we extend the QG task to the cross-lingual setting. By only using English QG training data, our goal is to generate questions in English or Chinese with the given passage-answer pair in English or Chinese.
We use SQuAD 1.1 BIBREF30 as the English QG dataset. It is a popular English question answering dataset containing over 100,000 questions and their corresponding annotated passages. Following BIBREF31, we regard the original development set as the test set, and sample 5000 examples from the training data of two datasets as the development sets. For Chinese QG, we follow the default data splits of WebQA BIBREF32. We regard the provided annotated evidence sentences as the input passages instead of entire documents. To construct the input sequence, we view the whole input passage as a single sentence, and concatenate the passage and the answer into one sequence with a special token S between them. During decoding Chinese, we utilize a subset of vocabulary, which is obtained from the passage sentences of the WebQA dataset.
We first conduct experiments on the supervised English-English QG setting. We compare our model to the following baselines:
CorefNqg BIBREF33 A sequence-to-sequence model with attention mechanism and a feature-rich encoder.
Mp-Gsn BIBREF31 A sequence-to-sequence model with gated self-attention and maxout pointer mechanism.
Xlm BIBREF5 The current state-of-the-art cross-lingual pre-training model. We initialize the Transformer-based sequence-to-sequence model with pre-trained XLM.
We evaluate models with BLEU-4 (BL-4), ROUGE (RG) and METEOR (MTR) metrics. As shown in Table TABREF16, our model outperforms the baselines, which demonstrates that our pre-trained model provides a good initialization for NLG.
We conduct experiments on the zero-shot Chinese-Chinese QG task to evaluate the cross-lingual transfer ability. In this task, models are trained with English QG data but evaluated with Chinese QG examples. We include the following models as our baselines:
Xlm Fine-tuning XLM with the English QG data.
Pipeline (Xlm) The pipeline of translating input Chinese sentences into English first, then performing En-En-QG with the XLM model, and finally translating back to the Chinese. We use the Transformer as the translator, which is also trained on the MultiUN dataset.
Pipeline (Xlm) with Google Translator Same to Pipeline (Xlm) but using Google Translator to translate the texts.
We evaluate models by both automatic evaluation metrics and human experts. The automatic metrics scores are computed by regarding each Chinese character as a token. For human evaluation, we consider three metrics for the generated questions: relatedness, fluency, and correctness, which are represented as integers ranged from 1 to 3. We randomly select 100 passage-answer pairs from the English QG test set, and use the models to generate questions. Then we present these examples to three experts to ask for the above scores. In Table TABREF17 and Table TABREF18, we present the results for the zero-shot Zh-Zh-QG. The results of monolingual supervised models are also reported in Table TABREF16 as reference. In the automatic evaluation, our model consistently performs better than baselines in both zero-shot and monolingual supervised setting. In the human evaluation, our model also obtains significant improvements in terms of relatedness and correctness.
In the zero-shot English-Chinese question generation experiments, we use Xlm and Pipeline (Xlm) as our baselines. Pipeline (Xlm) is a pipeline method that uses En-En-QG with Xlm to generate questions, and then translates the results to Chinese. Because there is no annotations for En-Zh-QG, we perform human evaluation studies for this setting. Table TABREF19 shows the human evaluation results, where our model surpasses all the baselines especially in terms of relatedness and correctness.
We also conduct experiments for zero-shot Chinese-English question generation, and adopt the same evaluation procedure to En-Zh-QG. Pipeline (Xlm) first translates Chinese input to English, and then conduct En-En-QG with Xlm. As shown in Table TABREF20, human evaluation results indicate that Xnlg achieves significant improvements on the three metrics.
We use SQuAD 1.1 (Rajpurkar et al. 2016) as the English QG dataset.
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What is their English QG dataset?
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SQuAD 1.1.
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1710.08396
| false
| null |
Social media mining is considerably an important source of information in many of health applications. This working note presents RNN and LSTM based embedding system for social media health text classification. Due to limited number of tweets, the performance of the proposed method is very less. However, the obtained results are considerable and open the way in future to apply for the social media health text classification. Moreover, the performance of the LSTM based embedding for task 2 is good in comparison to the task 1. This is primarily due to the fact that the target classes of task 1 data set imbalanced. Hence, the proposed method can be applied on large number of tweets corpus in order to attain the best performance.
Recently, the deep learning methods have performed well BIBREF8 and used in many tasks mainly due to that it doesn't rely on any feature engineering mechanism. However, the performance of deep learning methods implicitly relies on the large amount of raw data sets. To make use of unlabeled data, BIBREF9 proposed semi-supervised approach based on Convolutional neural network for adverse drug event detection. Though the data sets of task 1 and task 2 are limited, this paper proposes RNN and LSTM based embedding method.
This working note presents RNN and LSTM based embedding system for social media health text classification.
Though the data sets of task 1 and task 2 are limited, this paper proposes RNN and LSTM based embedding method.
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What type of RNN is used?
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The answers are shown as follows:
* RNN
* LSTM
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null | false
| null |
Amdahl's law is often conflated with the law of diminishing returns, whereas only a special case of applying Amdahl's law demonstrates law of diminishing returns. If one picks optimally (in terms of the achieved speedup) what is to be improved, then one will see monotonically decreasing improvements as one improves. If, however, one picks non-optimally, after improving a sub-optimal component and moving on to improve a more optimal component, one can see an increase in the return. Note that it is often rational to improve a system in an order that is "non-optimal" in this sense, given that some improvements are more difficult or require larger development time than others.
Amdahl's law does represent the law of diminishing returns if one is considering what sort of return one gets by adding more processors to a machine, if one is running a fixed-size computation that will use all available processors to their capacity. Each new processor added to the system will add less usable power than the previous one. Each time one doubles the number of processors the speedup ratio will diminish, as the total throughput heads toward the limit of 1/(1 − p).
This analysis neglects other potential bottlenecks such as memory bandwidth and I/O bandwidth. If these resources do not scale with the number of processors, then merely adding processors provides even lower returns.
An implication of Amdahl's law is that to speed up real applications which have both serial and parallel portions, heterogeneous computing techniques are required. There are novel speedup and energy consumption models based on a more general representation of heterogeneity, referred to as the normal form heterogeneity, that support a wide range of heterogeneous many-core architectures. These modelling methods aim to predict system power efficiency and performance ranges, and facilitates research and development at the hardware and system software levels.
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Based on the passage provided, what is the difference between Amdahl's law and the law of diminishing returns?
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The law of diminishing returns represents a special case of the application of Amdahl's law. The passage provides an example of a scenario where Amdahl's law represents the law of diminishing returns. In this scenario one is running a fixed sized computation that will use all available processors to their capacity and is considering what sort of return can be achieved by adding more processors to a machine. Applying Amdahl's law tells us that each new processor added to the system will add less usable power than the previous. In other words, each new processor yields diminishing returns.
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null | false
| null |
What could be a clean, inexpensive and efficient way of transportation?
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Bicycles are a clean way of transportation because they only use the power of the human body to function, are generally cheaper than other methods of transportation if compared to cars for example, and can be conveniently stored or parked in great quantities in reduced spaces. On top of all these benefits using a bicycle for transportation will render very positive benefits for one's health, by burning calories along the way and providing cardiovascular exercise every time.
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null | false
| null |
How would you train for a marathon?
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If you are not regularly running, but are generally fit, it would require many months of training to complete a marathon. Running at least 3 or better 4 times a week will be necessary. You should gradually increase your training load. A good guidance is to not increase the total mileage by more than 10% per week. One of the weekly runs should be a long run. You can should start with a distance that you are able to currently run, and keep gradually increasing it every week until you are able to run 30 km. You shouldn't need training runs longer than that. You should be able to run 30 km about 6 weeks before your target marathon. In the last weeks before the marathon decrease it down to about 20 km. The other runs in the week should be shorter. You should start as short as you are comfortable, and then increase them until they are 10-12 km. You should run all these runs at an easy pace, that would allow you to hold a conversation.
Good luck with your marathon!
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null | false
| null |
Which state joined the US on March 15, 1820?
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Maine.
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null | false
| null |
How can I save money when travelling?
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One good way of saving money while travelling is to plan ahead. Expenses like airplane tickets and accommodations often cost less when booked way in advance. Furthermore, doing research on online forums will help you find the most cost effective way to get around during your travels. On the other hand, for those with a flexible schedule, last minute deals can usually be found at a discount when there are cancellations and vacancies. Finally, the travel destination often plays a large role in determining the travel cost. Places that are less famous or crowded usually cost significantly less to visit, but can still make for a great experience.
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null | false
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Water fluoridation is the controlled adjustment of fluoride to a public water supply solely to reduce tooth decay. Fluoridated water contains fluoride at a level that is effective for preventing cavities; this can occur naturally or by adding fluoride. Fluoridated water operates on tooth surfaces: in the mouth, it creates low levels of fluoride in saliva, which reduces the rate at which tooth enamel demineralizes and increases the rate at which it remineralizes in the early stages of cavities. Typically a fluoridated compound is added to drinking water, a process that in the U.S. costs an average of about $1.17 per person-year. Defluoridation is needed when the naturally occurring fluoride level exceeds recommended limits. In 2011, the World Health Organization suggested a level of fluoride from 0.5 to 1.5 mg/L (milligrams per litre), depending on climate, local environment, and other sources of fluoride. Bottled water typically has unknown fluoride levels.
Tooth decay remains a major public health concern in most industrialized countries, affecting 60–90% of schoolchildren and the vast majority of adults. Water fluoridation reduces cavities in children, while efficacy in adults is less clear. A Cochrane review estimates a reduction in cavities when water fluoridation was used by children who had no access to other sources of fluoride to be 35% in baby teeth and 26% in permanent teeth. However, this was based on older studies which failed to control for numerous variables, such as increasing sugar consumption as well as other dental strategies. Most European countries have experienced substantial declines in tooth decay, though milk and salt fluoridation is widespread in lieu of water fluoridation. Recent studies suggest that water fluoridation, particularly in industrialized nations, may be unnecessary because topical fluorides (such as in toothpaste) are widely used, and caries rates have become low.
Although fluoridation can cause dental fluorosis, which can alter the appearance of developing teeth or enamel fluorosis, the differences are mild and usually not an aesthetic or public health concern. There is no clear evidence of other side effects from water fluoridation. Fluoride's effects depend on the total daily intake of fluoride from all sources. Drinking water is typically the largest source; other methods of fluoride therapy include fluoridation of toothpaste, salt, and milk. The views on the most efficient method for community prevention of tooth decay are mixed. The Australian government states that water fluoridation is the most effective way to achieve fluoride exposure that is community-wide. The World Health Organization reports that water fluoridation, when feasible and culturally acceptable, has substantial advantages, especially for subgroups at high risk, while the European Commission finds no benefit to water fluoridation compared with topical use.
Public water fluoridation was first practiced in the U.S. As of 2012, 25 countries have artificial water fluoridation to varying degrees, 11 of them have more than 50% of their population drinking fluoridated water. A further 28 countries have water that is naturally fluoridated, though in many of them the fluoride is above the optimal level. As of 2012, about 435 million people worldwide received water fluoridated at the recommended level (i.e., about 5.4% of the global population).: 56 About 214 million of them live in the United States. Major health organizations such as the World Health Organization and FDI World Dental Federation supported water fluoridation as safe and effective. The Centers for Disease Control and Prevention lists water fluoridation as one of the ten great public health achievements of the 20th century in the U.S. Despite this, the practice is controversial as a public health measure. Some countries and communities have discontinued fluoridation, while others have expanded it. Opponents of the practice argue that neither the benefits nor the risks have been studied adequately, and debate the conflict between what might be considered mass medication and individual liberties.
|
Tell me about water fluoridation
|
Water fluoridation is the controlled adjustment of fluoride to a public water supply solely to reduce tooth decay. Fluoridated water contains fluoride at a level that is effective for preventing cavities; this can occur naturally or by adding fluoride. Fluoridated water operates on tooth surfaces: in the mouth, it creates low levels of fluoride in saliva, which reduces the rate at which tooth enamel demineralizes and increases the rate at which it remineralizes in the early stages of cavities. Typically a fluoridated compound is added to drinking water, a process that in the U.S. costs an average of about $1.17 per person-year. Defluoridation is needed when the naturally occurring fluoride level exceeds recommended limits. In 2011, the World Health Organization suggested a level of fluoride from 0.5 to 1.5 mg/L (milligrams per litre), depending on climate, local environment, and other sources of fluoride. Bottled water typically has unknown fluoride levels.
Tooth decay remains a major public health concern in most industrialized countries, affecting 60–90% of schoolchildren and the vast majority of adults. Water fluoridation reduces cavities in children, while efficacy in adults is less clear. A Cochrane review estimates a reduction in cavities when water fluoridation was used by children who had no access to other sources of fluoride to be 35% in baby teeth and 26% in permanent teeth. However, this was based on older studies which failed to control for numerous variables, such as increasing sugar consumption as well as other dental strategies. Most European countries have experienced substantial declines in tooth decay, though milk and salt fluoridation is widespread in lieu of water fluoridation. Recent studies suggest that water fluoridation, particularly in industrialized nations, may be unnecessary because topical fluorides (such as in toothpaste) are widely used, and caries rates have become low.
Although fluoridation can cause dental fluorosis, which can alter the appearance of developing teeth or enamel fluorosis, the differences are mild and usually not an aesthetic or public health concern. There is no clear evidence of other side effects from water fluoridation. Fluoride's effects depend on the total daily intake of fluoride from all sources. Drinking water is typically the largest source; other methods of fluoride therapy include fluoridation of toothpaste, salt, and milk. The views on the most efficient method for community prevention of tooth decay are mixed. The Australian government states that water fluoridation is the most effective way to achieve fluoride exposure that is community-wide. The World Health Organization reports that water fluoridation, when feasible and culturally acceptable, has substantial advantages, especially for subgroups at high risk, while the European Commission finds no benefit to water fluoridation compared with topical use.
Public water fluoridation was first practiced in the U.S. As of 2012, 25 countries have artificial water fluoridation to varying degrees, 11 of them have more than 50% of their population drinking fluoridated water. A further 28 countries have water that is naturally fluoridated, though in many of them the fluoride is above the optimal level. As of 2012, about 435 million people worldwide received water fluoridated at the recommended level (i.e., about 5.4% of the global population). 56 About 214 million of them live in the United States. Major health organizations such as the World Health Organization and FDI World Dental Federation supported water fluoridation as safe and effective. The Centers for Disease Control and Prevention lists water fluoridation as one of the ten great public health achievements of the 20th century in the U.S. Despite this, the practice is controversial as a public health measure. Some countries and communities have discontinued fluoridation, while others have expanded it. Opponents of the practice argue that neither the benefits nor the risks have been studied adequately, and debate the conflict between what might be considered mass medication and individual liberties.
|
1909.01958
| true
| null |
The current configuration of Aristo comprises of eight solvers, described shortly, each of which attempts to answer a multiple choice question. To study particular phenomena and develop solvers, the project has created larger datasets to amplify and study different problems, resulting in 10 new datasets and 5 large knowledge resources for the community.
The solvers can be loosely grouped into:
Statistical and information retrieval methods
Reasoning methods
Large-scale language model methods
Over the life of the project, the relative importance of the methods has shifted towards large-scale language methods.
The field of NLP has advanced substantially with the advent of large-scale language models such as ELMo (BID6), ULMFit (BID37), GPT (BID38), BERT (BID7), and RoBERTa (BID8). These models are trained to perform various language prediction tasks such as predicting a missing word or the next sentence, using large amounts of text (e.g., BERT was trained on Wikipedia + the Google Book Corpus of 10,000 books). They can also be fine-tuned to new language prediction tasks, such as question-answering, and have been remarkably successful in the few months that they have been available.
We apply BERT to multiple choice questions by treating the task as classification: Given a question $q$ with answer options $a_{i}$ and optional background knowledge $K_{i}$, we provide it to BERT as:
[CLS] $K_i$ [SEP] $q$ [SEP] $a_{i}$ [SEP]
The AristoBERT solver uses three methods to apply BERT more effectively. First, we retrieve and supply background knowledge along with the question when using BERT. This provides the potential for BERT to “read” that background knowledge and apply it to the question, although the exact nature of how it uses background knowledge is more complex and less interpretable. Second, we fine-tune BERT using a curriculum of several datasets, including some that are not science related. Finally, we ensemble different variants of BERT together.
The current configuration of Aristo comprises of eight solvers, described shortly, each of which attempts to answer a multiple choice question. To study particular phenomena and develop solvers, the project has created larger datasets to amplify and study different problems, resulting in 10 new datasets and 5 large knowledge resources for the community.
The solvers can be loosely grouped into:
Statistical and information retrieval methods
Reasoning methods
Large-scale language model methods
Over the life of the project, the relative importance of the methods has shifted towards large-scale language methods.
The field of NLP has advanced substantially with the advent of large-scale language models such as ELMo (BID6), ULMFit (BID37), GPT (BID38), BERT (BID7), and RoBERTa (BID8). These models are trained to perform various language prediction tasks such as predicting a missing word or the next sentence, using large amounts of text (e.g., BERT was trained on Wikipedia + the Google Book Corpus of 10,000 books). They can also be fine-tuned to new language prediction tasks, such as question-answering, and have been remarkably successful in the few months that they have been available.
We apply BERT to multiple choice questions by treating the task as classification: Given a question $q$ with answer options $a_{i}$ and optional background knowledge $K_{i}$, we provide it to BERT as:
[CLS] $K_i$ [SEP] $q$ [SEP] $a_{i}$ [SEP]
The AristoBERT solver uses three methods to apply BERT more effectively. First, we retrieve and supply background knowledge along with the question when using BERT. This provides the potential for BERT to “read” that background knowledge and apply it to the question, although the exact nature of how it uses background knowledge is more complex and less interpretable. Second, we fine-tune BERT using a curriculum of several datasets, including some that are not science related. Finally, we ensemble different variants of BERT together.
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Is Aristo just some modern NLP model (ex. BERT) finetuned od data specific for this task?
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Yes.
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How do you describe your favorite cup of coffee?
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My favorite cup of coffee is served in a cafe. It must be in a ceramic mug, piping hot. Paper cups are out of the question. No plastic covers allowed. The aroma should be a mix of cream, hazelnut and coffee. When I take my first sip, it should be smooth and delicious, only allowing me a small sip lest I burn my tongue. The blend should maintain the flavor up until the last drop. This is how I would describe my favorite cup of coffee.
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Gull Harbour (former names States Harbour, Great Harbour) is the 1.3 km wide bay indenting for 2.8 km the east coast of Weddell Island in the Falkland Islands. It is entered north of Gull Point and south of Mark Point, and is centred at 51°53′56″S 60°53′20″WCoordinates: 51°53′56″S 60°53′20″W. The principal settlement of the island, Weddell Settlement, is situated at the head of the bay. Until the mid-nineteenth century Gull Harbour was known as Great Harbour.
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How large is the Gull Harbour and where exactly is it located
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Gull Harbour, that has been formerly named as States Harbour or Great Harbour is the 1.3 km wide bay indenting for 2.8 km the east coast of Weddell Island in the Falkland Islands.
It is entered north of Gull Point and south of Mark Point, and is centred at 51°53′56″S 60°53′20″W. The principal settlement of the island, Weddell Settlement, is situated at the head of the bay. Until the mid-nineteenth century Gull Harbour was known as Great Harbour.
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5 most unusual ways to use screwdriver
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to use it to cut coconut
to use it as can opener
to use it as a darts
to use it as a blocker
to use it as a chisel
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Why should kids do homework?
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There are many vital reasons why kids should do their homework. Homework helps students practice the concepts they have learned in the classroom. It also teaches discipline and responsibility. Kids might not be aware of the immediate benefits of doing their homework regularly and on time, but it helps with their long-term development. The next time your kids ask why they should do their homework, keep these pointers in mind.
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is operations a great career
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yes
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Where was Brazilian Jiu-Jitsu founded?
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Brazilian Jiu-Jitsu originated in Japan and was brought to Brazil by Mitsuo Maeda in 1914. Maeda trained Carlos and Helio Gracie, who tested and refined Jiu-Jitsu to be what it is today. Jiu-Jitsu continued to evolve over the years, eventually incorporating aspects of wrestling and other grappling arts into the curriculum. Rorion Gracie, Helios' son, brought Jiu-Jitsu to the United States in the 1970's.
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During the Pacific War, the Japanese invasion of Malaya culminated in the Battle of Singapore. When the British force of 60,000 troops surrendered on 15 February 1942, British prime minister Winston Churchill called the defeat "the worst disaster and largest capitulation in British history". British and Empire losses during the fighting for Singapore were heavy, with a total of nearly 85,000 personnel captured. About 5,000 were killed or wounded, of which Australians made up the majority. Japanese casualties during the fighting in Singapore amounted to 1,714 killed and 3,378 wounded. The occupation was to become a major turning point in the histories of several nations, including those of Japan, Britain, and Singapore. Japanese newspapers triumphantly declared the victory as deciding the general situation of the war. Between 5,000 and 25,000 ethnic Chinese people were killed in the subsequent Sook Ching massacre. British forces had planned to liberate Singapore in 1945; however, the war ended before these operations could be carried out.
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How many people in total were killed or injured in this text?
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15092 to 35092 people were killed or injured in total.
The count is 5000 of the British Army, 1714 Japanese Army killed, 3378 Japanese Army wounded and 5000 to 25000 ethnic Chinese killed.
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From 2008 to 2012, the title sponsor was DLF, India's largest real estate developer, who had secured the rights with a bid of ₹200 crore (US$25 million) for five seasons. After the conclusion of the 2012 season, PepsiCo bought the title sponsorship rights for ₹397 crore (US$50 million) for the subsequent five seasons. However, the company terminated the deal in October 2015, two years before the expiry of the contract, reportedly due to the two-season suspension of Chennai and Rajasthan franchises from the league. The BCCI then transferred the title sponsorship rights for the remaining two seasons of the contract to Chinese smartphone manufacturer Vivo for ₹200 crore (US$25 million). In June 2017, Vivo retained the rights for the next five seasons (2018–2022) with a winning bid of ₹2,199 crore (US$280 million), in a deal more expensive than Barclays' Premier League title sponsorship contract between 2013 and 2016. On 4 August 2020, Vivo got out of the title sponsorship rights due to the ongoing military stand-off between India and China at the Line of Actual Control in July 2020. It was also reported that the withdrawal was a result of Vivo's market losses due to the ongoing COVID-19 situation and that it intended to return as the title sponsors for the following three years. Dream11 bagged the title sponsorship for the 2020 IPL for an amount of ₹222 crore. Vivo returned as the title sponsor for the 2021 IPL, but pulled out again, and was replaced by the Tata Group for the next two seasons. InsideSport reported that the BCCI would actually receive ₹498 crore (US$62 million) for the 2022 and 2023 seasons from title sponsors. Vivo had previously agreed to pay a higher amount for the last two seasons of its sponsorship contract due to the expansion of the league from the 2022 season. According to InsideSport, the new deal had been structured such that Tata would pay ₹335 crore (US$42 million) per year while the deficit of ₹163 crore (US$20 million) per season would be paid by Vivo.
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Using the text below extract the sponsor, bid amount and the time period (from year to to year) in the format: {sponsor} - {bid amount} - {time period) and show it as a bullet list
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Here is the name of the sponsor along with the requested details:
DLF - 200 crores - 2008 to 2012
PepsiCo - 297 crores - 2012 to 2015
Vivo - 200 crores - 2015 to 2017
Vivo - 2,199 crores - 2018 to 2019
Dream11 - 220 crores - 2020
Vivo - 2,199 crores - 2021
Tata - 335 crores - 2022 to 2023
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Greece, officially the Hellenic Republic, is a country in Southeast Europe. It is situated on the southern tip of the Balkan Peninsula, and is located at the crossroads of Europe, Asia, and Africa. Greece shares land borders with Albania to the northwest, North Macedonia and Bulgaria to the north, and Turkey to the northeast. The Aegean Sea lies to the east of the mainland, the Ionian Sea to the west, and the Sea of Crete and the Mediterranean Sea to the south. Greece has the longest coastline on the Mediterranean Basin, featuring thousands of islands. The country consists of nine traditional geographic regions, and has a population of approximately 10.5 million. Athens is the nation's capital and largest city, followed by Thessaloniki and Patras.
Greece is considered the cradle of Western civilization, being the birthplace of democracy, Western philosophy, Western literature, historiography, political science, major scientific and mathematical principles, theatre and the Olympic Games. From the eighth century BC, the Greeks were organised into various independent city-states, known as poleis (singular polis), which spanned the Mediterranean and the Black Sea. Philip II of Macedon united most of present-day Greece in the fourth century BC, with his son Alexander the Great rapidly conquering much of the ancient world, from the eastern Mediterranean to the North Western parts of India. The subsequent Hellenistic period saw the height of Greek culture and influence in antiquity. Greece was annexed by Rome in the second century BC, becoming an integral part of the Roman Empire and its continuation, the Byzantine Empire, which was culturally and linguistically predominantly Greek. The Greek Orthodox Church, which emerged in the first century AD, helped shape modern Greek identity and transmitted Greek traditions to the wider Orthodox world. After falling under Ottoman rule in the mid-15th century, Greece emerged as a modern nation state in 1830 following a war of independence. Over the first hundred years the kingdom of Greece sought its territorial expansion, which was mainly achieved in the early 20th century, during the Balkan Wars and up until the catastrophe of Greece's Asia Minor Campaign in 1922. The short-lived republic that followed, beset by the ramifications of civil strife, came to an end in 1936, when the imposition of a royalist dictatorship inaugurated a long period of authoritarian rule, marked by military occupation during World War II, civil war and military dictatorship. Greece achieved record economic growth from 1950 through the 1970s, allowing it to join the ranks of developed nations. Democracy was restored in 1974–75, and Greece has been a parliamentary republic ever since. The country's rich historical legacy is reflected in part by its 18 UNESCO World Heritage Sites.
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List the name of the areas surrounding Greece and group by directions from the passage. List the results in comma separated format.
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Albania to the northwest, North Macedonia and Bulgaria to the north, Turkey to the northeast, the Aegean Sea to the east, the Ionian Sea to the west, and the Sea of Crete and the Mediterranean Sea to the south.
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When Joan of Arc was beatified in the famous Notre Dame cathedral in Paris?
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1909
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The implementation details of our model are shown in Table TABREF31 . For COCO / EMNLP2017, the generator is a LSTM unit BIBREF30 with 128 cells, and the discriminator is implemented based on BIBREF7 . For WeiboDial, the generator is an encoder-decoder structure with attention mechanism, where both the encoder and the decoder consist of a two-layer GRU BIBREF31 with 128 cells. The discriminator is implemented based on BIBREF32 . The language model used in the constrained sampling of ARAML is implemented in the same setting as the generators, and is pre-trained on the training set of each dataset. The codes and the datasets are available at https://github.com/kepei1106/ARAML.
As for the details of the baselines, the generators of all the baselines except LeakGAN are the same as ours. Note that the generator of LeakGAN consists of a hierarchical LSTM unit, thus we followed the implementation in the original paper. In terms of the differences, the discriminators of GAN baselines are implemented based on the original papers. Other hyper-parameters of baselines including batch size, learning rate, and pre-training epochs, were set based on the original codes, because the convergence of baselines is sensitive to these hyper-parameters.
As for the details of the baselines, the generators of all the baselines except LeakGAN are the same as ours.
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Are the generators of the baselines the same as the authors' generators?
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No, they aren't.
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| 262
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The system sorts all the tweets for each hashtag and orders them based on their log probability score, where the funniest tweet should be listed first. If the scores are based on the tweet language model then they are sorted in ascending order since the log probability value closest to 0 indicates the tweet that is most like the (funny) tweets model. However, if the log probability scores are based on the news data then they are sorted in descending order since the largest value will have the smallest probability associated with it and is therefore least like the (unfunny) news model.
For Subtask A, the system goes through the sorted list of tweets in a hashtag file and compares each pair of tweets. For each pair, if the first tweet was funnier than the second, the system would output the tweet_ids for the pair followed by a “1”. If the second tweet is funnier it outputs the tweet_ids followed by a “0”. For Subtask B, the system outputs all the tweet_ids for a hashtag file starting from the funniest.
The system sorts all the tweets for each hashtag and orders them based on their log probability score, where the funniest tweet should be listed first. If the scores are based on the tweet language model then they are sorted in ascending order since the log probability value closest to 0 indicates the tweet that is most like the (funny) tweets model. However, if the log probability scores are based on the news data then they are sorted in descending order since the largest value will have the smallest probability associated with it and is therefore least like the (unfunny) news model.
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How does their system order the tweets?
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Based on their log probability score. They are sorted in ascending order if the scores are based on the tweet language model and sorted in descending order if the log probability scores are based on the news data.
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Are panda bears real bears?
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Yes, pandas are one of the 8 or so species of extant, or living, bear. They are likely the most distantly related bear relative to the other species, along with spectacled bears. Pandas live in the wild exclusively in Asia. Pandas primarily eat bamboo. Red pandas are not bears, and are more closely related to raccoons.
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Tell me about orienteering
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Orienteering is a sport enjoyed by athletes across the world. Participants are given a map, on which a series of checkpoints are marked. The goal is to visit each checkpoint in the specified order as quickly as possible. The most successful orienteers are both physically fit and are able to read a map to a high degree of accuracy. The sport is particularly popular in Scandinavia, where its vast forests serve as ideal locations for orienteering courses.
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Why do you love someone
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To experience a feeling which is exceptional
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Who was the very first American Idol winner?
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Kelly Clarkson.
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As a career, Waldo E. Koop was an engineer for Boeing. He was called one of the nation's finest researchers by local and state historians, and was credited in the series of Time Life books on the Old West for his research on gunfighters. Koop discovered that Henry McCarty, later known as Billy the Kid, spent time in Wichita, Kansas, where he saw his first gunfight. He published his findings in 1965 in the book, Billy the Kid: the Trail of a Kansas Legend.
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Where did Waldo E. Koop work?
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Waldo E. Koop was an engineer for Boeing before becoming one of the nation's finest researchers.
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What is IPL T20?
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IPL stands for Indian Premier League. It is a T20 cricket tournament played in India usually from mid March to mid May every year. There are 10 teams representing different areas across India. 2022 edition was won by Gujarat Titans.
A T20 cricket match is a shorter version a traditional cricket match. Each team bats for 20 overs. The game lasts for 3.5 to 4 hours.
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| 162
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After passing messages and performing updates for $T$ iterations, we obtain a matrix $\mathbf {H}^T \in \mathbb {R}^{n \times d}$ containing the final vertex representations. Let $\hat{G}$ be graph $G$ without the special document node, and matrix $\mathbf {\hat{H}}^T \in \mathbb {R}^{(n-1) \times d}$ be the corresponding representation matrix (i.e., $\mathbf {H}^T$ without the row of the document node).
We use as our READOUT function the concatenation of self-attention applied to $\mathbf {\hat{H}}^T$ with the final document node representation. More precisely, we apply a global self-attention mechanism BIBREF22 to the rows of $\mathbf {\hat{H}}^T$. As shown in Eq. DISPLAY_FORM17, $\mathbf {\hat{H}}^T$ is first passed to a dense layer parameterized by matrix $\mathbf {W}_A^T \in \mathbb {R}^{d \times d}$. An alignment vector $\mathbf {a}$ is then derived by comparing, via dot products, the rows of the output of the dense layer $\mathbf {Y}^T \in \mathbb {R}^{(n-1) \times d}$ with a trainable vector $\mathbf {v}^T \in \mathbb {R}^d$ (initialized randomly) and normalizing with a softmax. The normalized alignment coefficients are finally used to compute the attentional vector $\mathbf {u}^T \in \mathbb {R}^d$ as a weighted sum of the final representations $\mathbf {\hat{H}}^T$.
Note that we tried with multiple context vectors, i.e., with a matrix $\mathbf {V}^T$ instead of a vector $\mathbf {v}^T$, like in BIBREF22, but results were not convincing, even when adding a regularization term to the loss to favor diversity among the rows of $\mathbf {V}^T$.
Master node skip connection. $\mathbf {h}_G^T \in \mathbb {R}^{2d}$ is obtained by concatenating $\mathbf {u}^T$ and the final master node representation. That is, the master node vector bypasses the attention mechanism. This is equivalent to a skip or shortcut connection BIBREF23. The reason behind this choice is that we expect the special document node to learn a high-level summary about the document, such as its size, vocabulary, etc. (more details are given in subsection SECREF30). Therefore, by making the master node bypass the attention layer, we directly inject global information about the document into its final representation.
Multi-readout. BIBREF14, inspired by Jumping Knowledge Networks BIBREF12, recommend to not only use the final representations when performing readout, but also that of the earlier steps. Indeed, as one iterates, node features capture more and more global information. However, retaining more local, intermediary information might be useful too. Thus, instead of applying the readout function only to $t=T$, we apply it to all time steps and concatenate the results, finally obtaining $\mathbf {h}_G \in \mathbb {R}^{T \times 2d}$ :
In effect, with this modification, we take into account features based on information aggregated from subtrees of different heights (from 1 to $T$), corresponding to local and global features.
Multi-readout. (Xu et al. 2018a), inspired by Jumping Knowledge Networks (Xu et al. 2018b), recommend to not only use the final representations when performing readout, but also that of the earlier steps. Indeed, as one iterates, node features capture more and more global information. However, retaining more local, intermediary information might be useful too.
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Are intermediate steps useful when performing readout?
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Yes.
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Write instructions to a dog sitter for a dog named Chunkie
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Thanks for coming over to spend time with Chunkie! Here is a summary of tips / directions
Hearing - Chunkie is deaf; you’ll need to make eye contact and hand gestures or grab her by the neck to give her gentle guidance.
Stairs - Chunkie can’t take the front stairs given her arthritis, you’ll need to go out the sliding door (remove lock bar at the bottom), out the backyard gate to the left, and down the slide stairs. It’s ok to leave the sliding door unlocked during your walk. Go back in this way too.
Walking - Try walking her to the Women’s garden and back. Her lease and poop bags are on the floor next to the sliding door. She doesn’t really walk fast or far anymore so if she doesn’t want to walk just take her back home.
Other dogs - Chunkie doesn’t really like other dogs so keep her leash tight when other dogs are around. She does love people and is very safe w/ other people and kids.
Snack - You may feed her a snack - one pig ear and a few of the other smaller snacks. I’ll leave the snacks on the kitchen counter.
Water - Please refill her water bowls when you get there and make sure they are full before you leave (purple bowl in the dining room next to the window and silver bowl near sliding door).
Home - Feel free to make yourself at home! Chunkie usually hangs in my office / the TV room. She does need some help getting up the 2 stairs from that room and the main great room.
Locking up - Please put the lock bar back on the sliding door, leave the key on the kitchen counter (or on the bottom stairs as you exit) and lock the front door by depressing the door handle button.
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Tell me whether these are sandwiches or cereals: Shredded wheat, honey bunches of oats, Frosted Flakes, Cheerios.
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That is a list of cereals, not a list of sandwiches.
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Cloud Gate is a public sculpture by Indian-born British artist Anish Kapoor, that is the centerpiece of AT&T Plaza at Millennium Park in the Loop community area of Chicago, Illinois. The sculpture and AT&T Plaza are located on top of Park Grill, between the Chase Promenade and McCormick Tribune Plaza & Ice Rink. Constructed between 2004 and 2006, the sculpture is nicknamed "The Bean" because of its shape, a name Kapoor initially disliked, but later grew fond of. Made up of 168 stainless steel plates welded together, its highly polished exterior has no visible seams. It measures 33 by 66 by 42 feet (10 by 20 by 13 m), and weighs 110 short tons (100 t; 98 long tons).
Kapoor's design was inspired by liquid mercury and the sculpture's surface reflects and distorts the city's skyline. Visitors are able to walk around and under Cloud Gate's 12-foot (3.7 m) high arch. On the underside is the "omphalos" (Greek for "navel"), a concave chamber that warps and multiplies reflections. The sculpture builds upon many of Kapoor's artistic themes, and it is popular with tourists as a photo-taking opportunity for its unique reflective properties.
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What's the "Bean" in Chicago?
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The "Bean" is a nickname for a public sculpture installed in Millennium Park in Chicago during 2004 and 2006. The formal name for the sculpture is "Cloud Gate." The sculpture is fluid-looking with a highly polished exterior and no visible seams. It's design was inspired by liquid mercury and it's reflective surface is a popular photo opportunity for visiting tourists.
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Dry Tortugas National Park is a national park located about 68 miles (109 km) west of Key West in the Gulf of Mexico, in the United States. The park preserves Fort Jefferson and the seven Dry Tortugas islands, the westernmost and most isolated of the Florida Keys. The archipelago's coral reefs are the least disturbed of the Florida Keys reefs.
Most visitors arrive in Dry Tortugas National Park by either boat or seaplane from Key West. Official ferry and transportation services to the Dry Tortugas include the Yankee Freedom III catamaran, private vessel chartering and seaplane services. Other methods of visiting the Dry Tortugas include chartering of authorized and approved private vessels. There are no road connections to Dry Tortugas, and cars cannot access the islands.
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How can you get to Dry Tortugas National Park?
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Dry Tortugas National Park can be accessed by seaplane or boat. The official ferry service is Yankee Freedom; private boats can also access the island. The islands that make up Dry Tortugas are located near Key West in the Gulf of Mexcio and cannot be accessed by car.
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Give me a bulleted list of the the first five books in the bible old testament?
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The first five books of the bible old testament are called the Pentateuch or the books of law
1) The Book of Genesis - is an account of the creation of the world, the early history of humanity, and of Israel's ancestors and the origins of the Jewish people.
2) The Book of Exodus - is the second book of the Bible. It narrates the story of the Exodus, in which the Israelites leave slavery in Biblical Egypt through the strength of Yahweh, who has chosen them as his people.
3) The Book of Leviticus - The book has several main topics, some of which are the Jewish laws of sacrificing to God, and the work of the priests. These priests are sometimes known as Levites
4) The Book of Numbers - is the culmination of the story of Israel's exodus from oppression in Egypt and their journey to take possession of the land God promised their fathers.
5) The Book of Deuteronomy - is the covenant that binds Yahweh and Israel by oaths of fidelity and obedience. God will give Israel blessings of the land, fertility, and prosperity so long as Israel is faithful to God's teaching; disobedience will lead to curses and punishment.
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1710.07395
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In logistic regression models, we extract four types of features, word-level and character-level n-gram features as well as two types of lexicon derived features. We extract these four types of features from the target comment first. Then we extract these features from two sources of context texts, specifically the title of the news article that the comment was posted for and the screen name of the user who posted the comment.
Then we extract these features from two sources of context texts, specifically the title of the news article that the comment was posted for and the screen name of the user who posted the comment.
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What context do they use?
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The answers are shown as follows:
* title of the news article
* screen name of the user
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India with the world's highest TB burden, introduced the BCG mass immunisation in which year?
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1948
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| 50
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Equations are an important part of scientific articles, but many existing machine learning methods do not easily handle them. They are challenging to work with because each is unique or nearly unique; most equations occur only once. An automatic understanding of equations, however, would significantly benefit methods for analyzing scientific literature. Useful representations of equations can help draw connections between articles, improve retrieval of scientific texts, and help create tools for exploring and navigating scientific literature.
In this paper we propose equation embeddings (EqEmb), an unsupervised approach for learning distributed representations of equations. The idea is to treat the equation as a "singleton word," one that appears once but that appears in the context of other words. The surrounding text of the equation—and in particular, the distributed representations of that text—provides the data we need to develop a useful representation of the equation.
Figure FIGREF1 illustrates our approach. On the left is an article snippet BIBREF0 . Highlighted in orange is an equation; in this example it represents a neural network layer. We note that this particular equation (in this form and with this notation) only occurs once in the collection of articles (from arXiv). The representations of the surrounding text, however, provide a meaningful context for the equation. Those words allow us to learn its embedding, specifically as a "word" which appears in the context of its surroundings. The resulting representation, when compared to other equations' representations and word representations, helps find both related equations and related words. These are illustrated on the right.
EqEmbs build on exponential family embeddings BIBREF1 to include equations as singleton observations and to model equation elements such as variables, symbols and operators. Exponential family embeddings, like all embedding methods, define a context of each word. In our initial EqEmb, the context for the words is a small window, such as four or eight words, but the context of an equation is a larger window, such as sixteen words. Using these two types of contexts together finds meaningful representations of words and equations. In the next EqEmb, which builds on the first, we consider equations to be sentences consisting of equation units, i.e., variables, symbols, and operators. Equation units help model equations across two types of context—over the surrounding units and over the surrounding words.
We studied EqEmbs on four collections of scientific articles from the arXiv, covering four computer science domains: natural language processing (NLP), information retrieval (IR), artificial intelligence (AI) and machine learning (ML). We found that EqEmbs provide more efficient modeling than existing word embedding methods. We further carried out an exploratory analysis of a large set of INLINEFORM0 87k equations. We found that EqEmbs provide better models when compared to existing word embedding approaches. EqEmbs also provide coherent semantic representations of equations and can capture semantic similarity to other equations and to words.
We found that EqEmbs provide more efficient modeling than existing word embedding methods.
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What about the performance of EqEmbs on modeling?
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EqEmbs provide more efficient modeling than existing word embedding methods.
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Identify which animal species is alive or extinct: Sivatherium, Bowhead Whale
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Bowhead Whale is alive, Sivatherium is extinct.
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Writing in The Zombie Movie Encyclopedia, academic Peter Dendle stated the film has had a negative reception, but it "presents a simple, unhurried, and unpretending appreciation of zombies and their habitat, a fresh and provocative desert landscape." Ian Jane of DVD Talk rated it 3/5 stars and wrote, "It's terrible, but somehow trance inducing in its own bizarre way." Gordon Sullivan of DVD Verdict wrote, "It's really a mess of tepid Eurosleaze masquerading as a zombie flick, and it doesn't even do that well."
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List down some points on well the movie Oasis of the Zombies was perceived after launch
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1. Despite receiving a poor review, the movie, according to academic Peter Dendle, "presents a simple, unhurried, and unpretentious appreciation of zombies and their habitat, a fresh and provocative desert landscape."
2. It received a 3/5 star rating from Ian Jane of DVD Talk, who also stated, "It's terrible, but somehow trance inducing in its own bizarre way."
3. "It's really a mess of tepid Eurosleaze masquerading as a zombie flick, and it doesn't even do that well," Gordon Sullivan of DVD Verdict commented.
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1807.08666
| false
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A 6-layer 10-language TDNN was trained on the GlobalPhone corpus, also using 40-high resolution MFCC features as input, as described in BIBREF20 . For speaker adaptation, a 100-dimensional i-vector was appended to the the MFCC input features. The TDNN was trained with a block-softmax, with the hidden layers shared across all languages and a separate output layer for each language. Each of the six hidden layers had 625 dimensions, and was followed by a 39-dimensional bottleneck layer with ReLU activations and batch normalisation. Training was accomplished using the Kaldi Babel receipe using 198 hours of data in 10 languages (Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese) from GlobalPhone.
Training was accomplished using the Kaldi Babel receipe using 198 hours of data in 10 languages (Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese) from GlobalPhone.
|
What languages are considered?
|
The answers are shown as follows:
* Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese
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Give me 3 ideas for hobbies that I can recommend to somebody who is retiring. These should all be activities that require gentle movement and are not overly demanding on the body.
|
Retirement is an exciting opportunity for people to develop and cultivate their passions. Here are a few ideas you could recommend that are both gentle on the body but require some movement:
- Gardening: while this is a seasonal pursuit, deciding on which seeds to plant, nursing them through a growing season, and learning the ins and outs of the hobby can become a lifelong passion for those with a "green thumb"
- Walking and picking up trash: carry a bag and "grabber" pinchers to collect trash in your community while staying active on a walk. Rubber-tipped pinchers means you will not need to bend or stretch too far to reach, and you will be surprised how much of a difference this makes to your community members!
- Fishing: most fishing can be done from the banks of a river or lake, and is a great way to experience the calming effects of a body of water while still being physically active. Whether fly fishing or reel fishing, many people who have entered retirement find this an engaging way to engage with nature either solo or as part of a community of fisher-people.
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What are secondary colors?
|
Secondary colors are created by combining primary colors. Primary colors are blue, red, and yellow. Secondary colors include purple, orange, and green.
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| 471
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In pre-training tasks, we pay attention to the generalization of the learned representations to new data or tasks rather than the performance on the training dataset. Taking a closer look at the results in Figures, we observe that, although joint SSL models achieve comparable performance to joint SL models in linear evaluation, joint SL models significantly outperform joint SSL models in few-shot evaluation. This observation is also demonstrated in. The main difference between the two evaluation protocols is that linear evaluation involves more fine-tuning than few-shot evaluation, as introduced in Appendix A.4. Therefore, the underlying reason for the observation is that supervised features are correlated with labels and more discriminative, thus easy to directly transfer to downstream datasets similar to upstream pre-training data (DomainNet or ImageNet). For example, SL models dominate most few-shot object or scene classification tasks but fail on DTD, a texture classification dataset sharing no common classes with ImageNet or DomainNet. In contrast, self-supervised features are more generalized and comprehensive, thus requiring more fine-tuning for desirable downstream transfer. In addition, on some downstream datasets, we have seemingly abnormal observations that ST models may outperform JT models and the model performance may drop with the increase of chunk number. These phenomena are due to the so-called "negative transfer", which is also discussed in other model pre-training studies. That is, pre-training with more data chunks does not necessarily benefit a specific downstream dataset if the added training data are irrelevant to the downstream dataset. See Appendix B.3 for a concrete example of "negative transfer" on Oxford-IIIT Pets in pre-training with streaming data. It is observed that sequential SSL models suffer less "negative transfer" than SL models and continual learning methods largely prevent "negative transfer".
In pre-training tasks, we pay attention to the generalization of the learned representations to new data or tasks rather than the performance on the training dataset. Taking a closer look at the results in Figures 2-4, we observe that, although joint SSL models achieve comparable performance to joint SL models in linear evaluation, joint SL models significantly outperform joint SSL models in few-shot evaluation. This observation is also demonstrated in (Tian et al., 2020; Ericsson et al., 2021). The main difference between the two evaluation protocols is that linear evaluation involves more fine-tuning than few-shot evaluation, as introduced in Appendix A.4. Therefore, the underlying reason for the observation is that supervised features are correlated with labels and more discriminative, thus easy to directly transfer to downstream datasets similar to upstream pre-training data (DomainNet or ImageNet). For example, SL models dominate most few-shot object or scene classification tasks but fail on DTD (Cimpoi et al., 2014), a texture classification dataset sharing no common classes with ImageNet or DomainNet. In contrast, self-supervised features are more generalized and comprehensive, thus requiring more fine-tuning for desirable downstream transfer. In addition, on some downstream datasets, we have seemingly abnormal observations that ST models may outperform JT models and the model performance may drop with the increase of chunk number. These phenomena are due to the so-called “negative transfer” (Wang et al., 2019), which is also discussed in other model pre-training studies (Newell & Deng, 2020; Gururangan et al., 2020). That is, pre-training with more data chunks does not necessarily benefit a specific downstream dataset if the added training data are irrelevant to the downstream dataset. See Appendix B.3 for a concrete example of “negative transfer” on Oxford-IIIT Pets (Parkhi et al., 2012) in pre-training with streaming data. It is observed that sequential SSL models suffer less “negative transfer” than SL models and continual learning methods largely prevent “negative transfer”.
|
Given the performances of the downstream tasks, any insights one can have? Like the affinities across tasks?
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As for insights from the performance of different downstream tasks. We mainly have two interesting observations and provide the corresponding explanations to them in Section 4.2.
As for different downstream tasks, we observe SSL models perform competitively to SL-JT models under many-shot evaluation while SSL models significantly underform SL-JT models under few-shot evaluation. The underlying insight is that supervised features are correlated with labels and more discriminative, thus easy to directly transfer to downstream datasets similar to upstream pre-training data (DomainNet or ImageNet). For example, SL models dominate most few-shot object or scene classification tasks but fail on DTD dataset, a texture classification dataset which shares no common classes with ImageNet or DomainNet. In contrast, self-supervised features are more generalized and comprehensive, thus requiring more fine-tuning for desirable downstream transfer.
As for evaluation with different downstream datasets, we find the sequentially trained model may suffer “negative transfer” on some specific downstream datasets. A concrete example is given in Appendix B.3. The insight behind pre-training with more data chunks does not necessarily benefit a specific downstream dataset if the added training data are irrelevant to the downstream dataset.
It is a great suggestion on the analysis of the affinities across tasks to further understand the downstream tasks. While in this work, we aim to provide a comprehensive study on pre-training with upstream data. Therefore our explorations or ablations mainly focus on model pre-training. As for the downstream tasks, we only use them to evaluate the pre-trained models, following popular pre-training works [3, 4]. We thus leave the study of downstream tasks for future work.
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1905.09866
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For both word2vec BIBREF0 and gensim BIBREF7 we adapted the code so that the input terms of the analogy query are allowed to be returned. Throughout this article, we use two different embedding spaces. The first is the widely used representation built on GoogleNews BIBREF8 . The second is taken from BIBREF2 , and was trained on a Reddit dataset BIBREF9 .
Throughout this article, we use two different embedding spaces. The first is the widely used representation built on GoogleNews BIBREF8 . The second is taken from BIBREF2 , and was trained on a Reddit dataset BIBREF9 .
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Which embeddings do they detect biases in?
|
Word embeddings trained on GoogleNews and Word embeddings trained on Reddit dataset
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null | false
| 13
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Nowadays, dialog systems are usually designed for a single domain BIBREF0 . They store data in a well-defined format with a fixed number of attributes for entities that the system can provide. Because data in this format can be stored as a two-dimensional table within a relational database, we call the data flat. This data representation allows the system to query the database in a simple and efficient way. It also allows to keep the dialog state in the form of slots (which usually correspond to columns in the table) and track it through the dialog using probabilistic belief tracking BIBREF1 , BIBREF2 .
However, the well-defined structure of the database of a typical dialog system comes with a high cost of extending it as every piece of new information has to fit the format. This is especially a problem when we one is adapting the system for a new domain because its entities could have different attributes.
A dialog system based on knowledge bases offers many advantages. First, the knowledge base, which can be represented as knowledge graph containing entities connected by relations, is much more flexible than the relational database. Second, freely available knowledge bases, such as Freebase, Wikidata, etc. contain an enormous amount of structured information, and are still growing. A dialog system which is capable of working with this type of information would be therefore very useful.
In this paper we propose a dataset aiming to help develop and evaluate dialog systems based on knowledge bases by interactive learning motivated in Section "Motivation" Section "Dialog policies" describes policies that can be used for retrieving information from knowledge bases. In Section "Dialog Simulation" is introduced a dialog simulation from natural conversations which we use for evaluation of interactive learning. The dataset collection process allowing the dialog simulation is described in Section "Dataset Collection Process" and is followed by properties of the resulting dataset in Section "Dataset Properties" Evaluation guidelines with proposed metrics can be found in Section "Interactive Learning Evaluation" The planned future work is summarized in Section "Future Work" We conclude the paper with Section "Conclusion"
In this paper we propose a dataset aiming to help develop and evaluate dialog systems based on knowledge bases by interactive learning motivated in Section "Motivation" Section "Dialog policies" describes policies that can be used for retrieving information from knowledge bases.
|
What kind of dataset aiming does the paper propose?
|
A dataset aiming to help develop and evaluate dialog systems based on knowledge bases by interactive learning motivated in Section "Motivation" Section "Dialog policies" describes policies that can be used for retrieving information from knowledge bases.
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Identify which instrument is string or percussion: Slit drum, Lute
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Lute is string, Slit drum is percussion.
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| null |
How to detect that a number is odd or even?
|
You can detect that by checking if the number is divisible by 2 without a reminder, for example 4/2 = 2, so 4 is an even number, however 5/2 is 2 with 1 as a reminder, so 5 is an odd number
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Give me a list of the last five "best rap song" Grammy winners (just tell me the song name and the performing artist).
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1. The Heart Part 5 by Kendrick Lamar, 2023
2. Jail by Kanye West featuring Jay-Z, 2022
3. Savage by Megan Thee Stallion featuring Beyoncé, 2021
4. A lot, 21 Savage featuring J. Cole, 2020
5. God's plan, Drake, 2019
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Why should you visit Ireland?
|
Ireland is one of the most beautiful places that I've lived. It does rain quite a bit but on a sunny day I've never seen a more beautiful place. The grass is a vivid green and the nature comes to life on those sunny days. If you like history it's also a great place to explore with various ruins scattered across the country as well as plenty of castles. Ireland also has some great beaches and while the water is cold it's also refreshing and worth the beauty that you will find while you explore the beaches. If it's nightlife you are looking for look no further than Temple Bar in Dublin. The restaurants and pubs are very welcoming with a diverse cuisine that will satisfy anyone's appetite.
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I bought a new shirt online but I changed my mind. How can I return it?
|
If you are looking to return your shirt, you should first check the seller's website to see how many days after your order they will accept returns. You might be able to initiate a return on the website or you might need to contact customer support to start the process. The website should provide you instructions for making the return and some websites will provide a postage label you can print. Depending on the company, you might be able to return the shirt in a physical store, or mail it back to the company for a refund. If you try contacting customer support and they do not respond or they refuse to take back the shirt, you can try contacting your credit card provider and asking them to initiate a chargeback.
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Each member of the voting panel casts a vote for first to fifth place selections. Each first-place vote is worth 10 points; each second-place vote is worth seven; each third-place vote is worth five, fourth-place is worth three and fifth-place is worth one. Starting from 2010, one ballot was cast by fans through online voting. The player with the highest point total wins the award. As of the 2021–22 season, the current holder of the award is Nikola Jokić of the Denver Nuggets. Jokić is the lowest draft pick to win the award, being selected 41st by Denver in the 2014 NBA draft.
Every player who has won this award and has been eligible for the Naismith Memorial Basketball Hall of Fame has been inducted. Kareem Abdul-Jabbar won the award a record six times. He is also the only player to win the award despite his team not making the playoffs back in the 1975–76 season. Both Bill Russell and Michael Jordan won the award five times, while Wilt Chamberlain and LeBron James won the award four times. Russell and James are the only players to have won the award four times in five seasons. Moses Malone, Larry Bird and Magic Johnson each won the award three times, while Bob Pettit, Karl Malone, Tim Duncan, Steve Nash, Stephen Curry, Giannis Antetokounmpo and Nikola Jokić have each won it twice. Russell, Chamberlain, and Bird are the only players to win the award in three consecutive years. Only two rookies have won the award: Chamberlain in the 1959–60 season and Wes Unseld in the 1968–69 season. Hakeem Olajuwon of Nigeria, Duncan of the U.S. Virgin Islands, Nash of Canada, Dirk Nowitzki of Germany, Antetokounmpo of Greece, and Jokić of Serbia are the only MVP winners considered "international players" by the NBA.
|
Whos the lowest drafted NBA player to win the MVP (most valuable player) award?
|
Nikola Jokic is the lowest draft pick to win the award. Nikola Jokic was drafted 41st overall by the Denver Nuggets in 2014.
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| 185
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In this paper, we propose a pre-training method for cross-lingual natural language generation (NLG) that can transfer monolingual NLG supervision signals to all pre-trained languages. With the pre-trained model, we achieve zero-shot cross-lingual NLG on several languages by only fine-tuning once. Experimental results show that our model outperforms the machine-translation-based pipeline model on several cross-lingual NLG tasks. For future work, we would like to improve our pre-training method towards the fully unsupervised setting.
In this paper, we propose a pre-training method for crosslingual natural language generation (NLG) that can transfer monolingual NLG supervision signals to all pre-trained languages. With the pre-trained model, we achieve zeroshot cross-lingual NLG on several languages by only finetuning once. Experimental results show that our model outperforms the machine-translation-based pipeline model on several cross-lingual NLG tasks. For future work, we would like to improve our pre-training method towards the fully unsupervised setting.
|
Do they intend to provide any improvements to their method?
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Yes, they may improve their pre-training method toward the fully unsupervised setting.
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2003.12932
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Figure FIGREF12stsa and FIGREF12stsb shows Pearson-Spearman correlation vs % of error for textual semantic similarity on STS-B dataset after fine tuning for 10 and 50 epochs respectively.
FLOAT SELECTED: Figure 5: Pearson-Spearman correlation vs % of error for textual semantic similarity on STS-B dataset
Figure FIGREF12stsa and FIGREF12stsb shows Pearson-Spearman correlation vs % of error for textual semantic similarity on STS-B dataset after fine tuning for 10 and 50 epochs respectively.
FLOAT SELECTED: Figure 5: Pearson-Spearman correlation vs % of error for textual semantic similarity on STS-B dataset
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What is the performance change of the textual semantic similarity task when no error and maximum errors (noise) are present?
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10 Epochs: pearson-Spearman correlation drops 60 points when error increase by 20%
50 Epochs: pearson-Spearman correlation drops 55 points when error increase by 20%
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1907.08937
| false
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Human Judgments
Following BIBREF11 , BIBREF12 and the vast amount of previous work on semantic similarity, we ask nine undergraduate subjects to assess the similarity of 360 pairs of relations from a subset of Wikidata BIBREF8 that are chosen to cover from high to low levels of similarity. In our experiment, subjects were asked to rate an integer similarity score from 0 (no similarity) to 4 (perfectly the same) for each pair. The inter-subject correlation, estimated by leaving-one-out method BIBREF13 , is r = INLINEFORM0 , standard deviation = INLINEFORM1 . This important reference value (marked in fig:correlation) could be seen as the highest expected performance for machines BIBREF12 .
Human Judgments
Following BIBREF11 , BIBREF12 and the vast amount of previous work on semantic similarity, we ask nine undergraduate subjects to assess the similarity of 360 pairs of relations from a subset of Wikidata BIBREF8 that are chosen to cover from high to low levels of similarity. In our experiment, subjects were asked to rate an integer similarity score from 0 (no similarity) to 4 (perfectly the same) for each pair.
|
How do they gather human judgements for similarity between relations?
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By assessing similarity of 360 pairs of relations from a subset of Wikidata using an integer similarity score from 0 to 4
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| 380
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Since the advent of large structured knowledge bases (KBs) like Freebase BIBREF0 , YAGO BIBREF1 and DBpedia BIBREF2 , answering natural language questions using those structured KBs, also known as KB-based question answering (or KB-QA), is attracting increasing research efforts from both natural language processing and information retrieval communities.
The state-of-the-art methods for this task can be roughly categorized into two streams. The first is based on semantic parsing BIBREF3 , BIBREF4 , which typically learns a grammar that can parse natural language to a sophisticated meaning representation language. But such sophistication requires a lot of annotated training examples that contains compositional structures, a practically impossible solution for large KBs such as Freebase. Furthermore, mismatches between grammar predicted structures and KB structure is also a common problem BIBREF4 , BIBREF5 , BIBREF6 .
On the other hand, instead of building a formal meaning representation, information extraction methods retrieve a set of candidate answers from KB using relation extraction BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 or distributed representations BIBREF11 , BIBREF12 . Designing large training datasets for these methods is relatively easy BIBREF7 , BIBREF13 , BIBREF14 . These methods are often good at producing an answer irrespective of their correctness. However, handling compositional questions that involve multiple entities and relations, still remains a challenge. Consider the question what mountain is the highest in north america. Relation extraction methods typically answer with all the mountains in North America because of the lack of sophisticated representation for the mathematical function highest. To select the correct answer, one has to retrieve all the heights of the mountains, and sort them in descending order, and then pick the first entry. We propose a method based on textual evidence which can answer such questions without solving the mathematic functions implicitly.
Knowledge bases like Freebase capture real world facts, and Web resources like Wikipedia provide a large repository of sentences that validate or support these facts. For example, a sentence in Wikipedia says, Denali (also known as Mount McKinley, its former official name) is the highest mountain peak in North America, with a summit elevation of 20,310 feet (6,190 m) above sea level. To answer our example question against a KB using a relation extractor, we can use this sentence as external evidence, filter out wrong answers and pick the correct one.
Using textual evidence not only mitigates representational issues in relation extraction, but also alleviates the data scarcity problem to some extent. Consider the question, who was queen isabella's mother. Answering this question involves predicting two constraints hidden in the word mother. One constraint is that the answer should be the parent of Isabella, and the other is that the answer's gender is female. Such words with multiple latent constraints have been a pain-in-the-neck for both semantic parsing and relation extraction, and requires larger training data (this phenomenon is coined as sub-lexical compositionality by wang2015). Most systems are good at triggering the parent constraint, but fail on the other, i.e., the answer entity should be female. Whereas the textual evidence from Wikipedia, ...her mother was Isabella of Barcelos ..., can act as a further constraint to answer the question correctly.
We present a novel method for question answering which infers on both structured and unstructured resources. Our method consists of two main steps as outlined in sec:overview. In the first step we extract answers for a given question using a structured KB (here Freebase) by jointly performing entity linking and relation extraction (sec:kb-qa). In the next step we validate these answers using an unstructured resource (here Wikipedia) to prune out the wrong answers and select the correct ones (sec:refine). Our evaluation results on a benchmark dataset WebQuestions show that our method outperforms existing state-of-the-art models. Details of our experimental setup and results are presented in sec:experiments. Our code, data and results can be downloaded from https://github.com/syxu828/QuestionAnsweringOverFB.
We present a novel method for question answering which infers on both structured and unstructured resources.
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What can be inferred from their novel method?
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Infers on both structured and unstructured resources.
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| 49
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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 experimented with using automatic back-translations of the monolingual News corpus as additional training data , pervasive dropout, and target-bidirectional models.
|
What back-translations of the monolingual News corpus do the authors use in the paper?
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As additional training data , pervasive dropout, and target-bidirectional models.
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| 19
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This paper describes our approach and results for Task 2 of the CoNLL–SIGMORPHON 2018 shared task on universal morphological reinflection BIBREF0 . The task is to generate an inflected word form given its lemma and the context in which it occurs.
Morphological (re)inflection from context is of particular relevance to the field of computational linguistics: it is compelling to estimate how well a machine-learned system can capture the morphosyntactic properties of a word given its context, and map those properties to the correct surface form for a given lemma.
There are two tracks of Task 2 of CoNLL–SIGMORPHON 2018: in Track 1 the context is given in terms of word forms, lemmas and morphosyntactic descriptions (MSD); in Track 2 only word forms are available. See Table TABREF1 for an example. Task 2 is additionally split in three settings based on data size: high, medium and low, with high-resource datasets consisting of up to 70K instances per language, and low-resource datasets consisting of only about 1K instances.
The baseline provided by the shared task organisers is a seq2seq model with attention (similar to the winning system for reinflection in CoNLL–SIGMORPHON 2016, BIBREF1 ), which receives information about context through an embedding of the two words immediately adjacent to the target form. We use this baseline implementation as a starting point and achieve the best overall accuracy of 49.87 on Task 2 by introducing three augmentations to the provided baseline system: (1) We use an LSTM to encode the entire available context; (2) We employ a multi-task learning approach with the auxiliary objective of MSD prediction; and (3) We train the auxiliary component in a multilingual fashion, over sets of two to three languages.
In analysing the performance of our system, we found that encoding the full context improves performance considerably for all languages: 11.15 percentage points on average, although it also highly increases the variance in results. Multi-task learning, paired with multilingual training and subsequent monolingual finetuning, scored highest for five out of seven languages, improving accuracy by another 9.86% on average.
In analysing the performance of our system, we found that encoding the full context improves performance considerably for all languages: 11.15 percentage points on average, although it also highly increases the variance in results.
|
How many percentage points can be improved on the performance for all languages by encoding the full context?
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11.15 percentage points on average.
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| 134
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In this section, we give a detailed formulation of the architectures used in experiments.
Therefore, it can be said that each non-bottom layer of CAS-LSTM accepts two sets of hidden and cell states—one from the left context and the other from the below context. The left and the below context participate in computation with the equivalent procedure so that the information from lower layers can be efficiently propagated.
|
Which two sets of hidden and cell states does each non-bottom layer of CAS-LSTM accept?
|
One from the left context and the other from the below context. The left and the below context participate in computation with the equivalent procedure so that the information from lotheyr layers can be efficiently propagated.
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What is the largest country in South America by land mass?
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Brasil is the largest country in South America by land mass.
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| 214
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The experimental results of the detection task and the generative intervention task are shown in Table TABREF27 and Table TABREF29 separately. The results of the human evaluation are shown in Table TABREF30. Figure FIGREF25 shows examples of the generated responses.
As shown in Table TABREF27 and TABREF29, all the classification and generative models perform better on the Gab dataset than on the Reddit dataset. We think this stems from the datasets' characteristics. First, the Gab dataset is larger and has a more balanced category distribution than the Reddit dataset. Therefore, it is inherently more challenging to train a classifier on the Reddit dataset. Further, the average lengths of the Reddit posts and conversations are much larger than those of Gab, potentially making the Reddit input nosier than the Gab input for both tasks. On both the Gab and Reddit datasets, the SVM classifier and the LR classifier achieved better performance than the CNN and RNN model with randomly initialized word embeddings. A possible reason is that without pretrained word embeddings, the neural network models tend to overfit on the dataset.
For the generative intervention task, three models perform similarly on all three automatic evaluation metrics. As expected, the Seq2Seq model achieves higher scores with filtered conversation as input. However, this is not the case for the VAE model. This indicates that the two models may have different capabilities to capture important information in conversations.
As shown in Table TABREF29, applying Reinforcement Learning does not lead to higher scores on the three automatic metrics. However, human evaluation (Table TABREF30) shows that the RL model creates responses that are potentially better at mitigating hate speech and are more diverse, which is consistent with BIBREF21. There is a larger performance difference with the Gab dataset, while the effectiveness and the diversity of the responses generated by the Seq2Seq model and the RL model are quite similar on the Reddit dataset. One possible reason is that the size of the training data from Reddit (around 8k) is only 30% the size of the training data from Gab. The inconsistency between the human evaluation results and the automatic ones indicates the automatic evaluation metrics listed in Table TABREF29 can hardly reflect the quality of the generated responses. As mentioned in Section SECREF4, annotators tend to have strategies for intervention. Therefore, generating the common parts of the most popular strategies for all the testing input can lead to high scores of these automatic evaluation metrics. For example, generating “Please do not use derogatory language.” for all the testing Gab data can achieve 4.2 on BLEU, 20.4 on ROUGE, and 18.2 on METEOR. However, this response is not considered as high-quality because it is almost a universal response to all the hate speech, regardless of the context and topic.
Surprisingly, the responses generated by the VAE model have much worse diversity than the other two methods according to human evaluation. As indicated in Figure FIGREF25, the responses generated by VAE tend to repeat the responses related to some popular hate keyword. For example, “Use of the r-word is unacceptable in our discourse as it demeans and insults people with mental disabilities.” and “Please do not use derogatory language for intellectual disabilities.” are the generated responses for a large part of the Gab testing data. According to Figure FIGREF20, insults towards disabilities are the largest portion in the dataset, so we suspect that the performance of the VAE model is affected by the imbalanced keyword distribution.
The sampled results in Figure FIGREF25 show that the Seq2Seq and the RL model can generate reasonable responses for intervention. However, as is to be expected with machine-generated text, in the other human evaluation we conducted, where Mechanical Turk workers were also presented with sampled human-written responses alongside the machine generated responses, the human-written responses were chosen as the most effective and diverse option a majority of the time (70% or more) for both datasets. This indicates that there is significant room for improvement while generating automated intervention responses.
In our experiments, we only utilized the text of the posts, but more information is available and can be utilized, such as the user information and the title of a Reddit submission.
As expected, the Seq2Seq model achieves higher scores with filtered conversation as input.
|
Does the Seq2Seq model achieve higher scores with filtered conversation as input?
|
Yes, it does.
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What is the number one selling dog?
|
The American Kennel Club released its 2022 registration statistics and found that after being the most popular dog breed for three decades, the Labrador Retriever was outranked by the French Bulldog.
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Identify which animal species is alive or extinct: Indian Elephant, Irish Elk
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Irish Elk is extinct, Indian Elephant is alive.
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I'm making a meal, please tell me which ingredients are fruits, vegetables, grains and meats. Lettuce, tomato, avocado, rice, beef, onion, garlic, cilantro.
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Lettuce: vegetable, tomato: fruit, avocado: fruit, rice: grain, beef: meat, onion: vegetable, garlic: vegetable, cilantro: vegetable
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| 85
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To asses the performance of these models on a totally unseen data, we tried to classify the CrowdFlower emotional tweets dataset. The CrowdFlower dataset consists of 40k tweets annotated via crowd-sourcing each with a single emotional label. This dataset is considered a hard dataset to classify with a lot of noise. The distribution of the dataset can be seen in Table TABREF18 . The labeling on this dataset is non-standard, so we used the following mapping for labels:
sadness INLINEFORM0 sadness
worry INLINEFORM0 fear
happiness INLINEFORM0 joy
love INLINEFORM0 love
surprise INLINEFORM0 surprise
anger INLINEFORM0 anger
We then classified emotions using the pre-trained models and emotionally fitted fastText embedding. The result can be seen in Table TABREF19 . The baseline results are from BIBREF33 done using BOW model and maximum entropy classifier. We saw a huge improvement from 26 point increase in F-measure for the emotion joy (happiness) up to 57 point increase for surprise with total average increase of 38.6 points. Bostan and Klinger did not report classification results for the emotion love so we did not include it in the average. These results show that our trained models perform exceptionally on a totally new dataset with a different method of annotation.
To asses the performance of these models on a totally unseen data, we tried to classify the CrowdFlower emotional tweets dataset. The CrowdFlower dataset consists of 40k tweets annotated via crowd-sourcing each with a single emotional label.
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What data set does the author use to categorize the crowdfunded emotional Tweet data set?
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The CrowdFlower dataset
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| null |
After Rendell Locke is murdered at the hands of former student Sam Lesser, his wife Nina decides to move with her three children, Tyler, Kinsey, and Bode, from Seattle to Matheson, Massachusetts, and take residence in Rendell's family home, Keyhouse. The children soon discover a number of mysterious keys throughout the house that can be used to unlock various doors in magical ways. They soon become aware of a demonic entity that is also searching for the keys for its own malevolent purposes.
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In the given passage, List me some points about the TV show : Locke & Key
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1.When Nina’s husband Rendell was murdered by his own student Sam lesser, She moved with her family from Seattle to Matheson.
2.Nina with her three children, Tyler, Kinsey, and Bode moved to Rendell's family home, Keyhouse.
3.Throughout the house, children discovered a number of mysterious keys, that can be used to unlock various doors in magical ways.
4.Children soon become aware of a demons that were also searching the keys for malevolent purposes.
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What are the 5 largest ski and snowboard resorts in North America?
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1.) Whistler Blackcomb, 2.) Park City Resort, 3.) Big Sky, 4.) Vail, and 5.) Heavenly
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Tell me whether these are cities in India: Mumbai, Bengaluru, Chennai, Singapore, Kuala Lumpur, Sydney, New Delhi, Kolkota
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Mumbai, Bengaluru, Chennai, New Delhi, Kolkota
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Because some banks refused to clear checks from certain other banks during times of economic uncertainty, a check-clearing system was created in the Federal Reserve System. It is briefly described in The Federal Reserve System—Purposes and Functions as follows:
By creating the Federal Reserve System, Congress intended to eliminate the severe financial crises that had periodically swept the nation, especially the sort of financial panic that occurred in 1907. During that episode, payments were disrupted throughout the country because many banks and clearinghouses refused to clear checks drawn on certain other banks, a practice that contributed to the failure of otherwise solvent banks. To address these problems, Congress gave the Federal Reserve System the authority to establish a nationwide check-clearing system. The System, then, was to provide not only an elastic currency—that is, a currency that would expand or shrink in amount as economic conditions warranted—but also an efficient and equitable check-collection system.
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Extract the reason why the check clearing system was created
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During financial crises in early 20th century, some banks refused to clear checks drawn on certain other banks, a practice that contributed to the failure of otherwise solvent banks, which ultimately led to severe crises that swept the nation, therefore Congress gave the Federal Reserve System the authority to establish a nationwide check-clearing system to address such systematic issues.
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Six Flags Great Escape Lodge & Indoor Waterpark is a resort and waterpark, located in Queensbury, New York that first opened in 2006. The resort is owned and operated by Six Flags.
History
The resort officially opened on February 7, 2006 and became the first one in the state of New York to feature an indoor waterpark. It features 200 rooms and a 38,000-square-foot water park called White Water Bay. The park is opened year-round and constant 80-degree water temperature. On the other side across US 9 is the Six Flags theme park, Great Escape. This is the second resort the company has built, the first being Lodge on the Lake next to Darien Lake that opened in 1998. In November 2012, it announced an annual event, Holiday in the Lodge, to celebrate Christmas.
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What is the Six Flags Great Escape Lodge
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Six Flags Great Escape Lodge & Indoor Waterpark is a resort and waterpark, located in Queensbury, New York that first opened in 2006. The resort is owned and operated by Six Flags.
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Fortress is a shooter video game developed by CCR Inc and published by Netsgo. The game spawned an animated series, Tank Knights Fortress, and a sequel, Fortress 2. On November 18, 2011, service for the game was terminated in South Korea and Japan.
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What game was the animated series Tank Knights Fortress based on?
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Fortress.
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Hearst died at the age of 70 in Washington, D.C., on February 28, 1891. The California Legislature and state courts adjourned so officials could attend his funeral. When Phoebe Apperson Hearst inherited her husband's wealth, she donated a great deal of it to help found new libraries at several universities. Hearst is buried in Cypress Lawn Cemetery in Colma, California. His widow and son were later buried there as well. The Hearst Memorial Mining Building on the Berkeley campus is dedicated to George Hearst. In 1996, he was inducted into the Hall of Great Westerners of the National Cowboy & Western Heritage Museum.
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Based on this paragraph, during what year was Hearst born?
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Hearst died at the age of 70 in 1891, so he must have been born in either 1820 or 1821.
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Give me a list of some excellent 5 VPN services
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1. Express VPN
2. Surfshark VPN
3. NordVPN
4. IPVanish VPN
5. CyberGhost
6. Proton VPN
7. PureVPN
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| 72
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Figure FIGREF10 illustrates the UTCNN model. As more than one user may interact with a given post, we first add a maximum pooling layer after the user matrix embedding layer and user vector embedding layer to form a moderator matrix embedding INLINEFORM0 and a moderator vector embedding INLINEFORM1 for moderator INLINEFORM2 respectively, where INLINEFORM3 is used for the semantic transformation in the document composition process, as mentioned in the previous section. The term moderator here is to denote the pseudo user who provides the overall semantic/sentiment of all the engaged users for one document. The embedding INLINEFORM4 models the moderator stance preference, that is, the pattern of the revealed user stance: whether a user is willing to show his preference, whether a user likes to show impartiality with neutral statements and reasonable arguments, or just wants to show strong support for one stance. Ideally, the latent user stance is modeled by INLINEFORM5 for each user. Likewise, for topic information, a maximum pooling layer is added after the topic matrix embedding layer and topic vector embedding layer to form a joint topic matrix embedding INLINEFORM6 and a joint topic vector embedding INLINEFORM7 for topic INLINEFORM8 respectively, where INLINEFORM9 models the semantic transformation of topic INLINEFORM10 as in users and INLINEFORM11 models the topic stance tendency. The latent topic stance is also modeled by INLINEFORM12 for each topic.
As for comments, we view them as short documents with authors only but without likers nor their own comments. Therefore we apply document composition on comments although here users are commenters (users who comment). It is noticed that the word embeddings INLINEFORM0 for the same word in the posts and comments are the same, but after being transformed to INLINEFORM1 in the document composition process shown in Figure FIGREF4 , they might become different because of their different engaged users. The output comment representation together with the commenter vector embedding INLINEFORM2 and topic vector embedding INLINEFORM3 are concatenated and a maximum pooling layer is added to select the most important feature for comments. Instead of requiring that the comment stance agree with the post, UTCNN simply extracts the most important features of the comment contents; they could be helpful, whether they show obvious agreement or disagreement. Therefore when combining comment information here, the maximum pooling layer is more appropriate than other pooling or merging layers. Indeed, we believe this is one reason for UTCNN's performance gains.
Finally, the pooled comment representation, together with user vector embedding INLINEFORM0 , topic vector embedding INLINEFORM1 , and document representation are fed to a fully connected network, and softmax is applied to yield the final stance label prediction for the post.
Figure 2 illustrates the UTCNN model. As more than one user may interact with a given post, we first add a maximum pooling layer after the user matrix embedding layer and user vector embedding layer to form a moderator matrix embedding Uk and a moderator vector embedding uk for moderator k respectively, where Uk is used for the semantic transformation in the document composition process, as mentioned in the previous section.
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What layers does the model contain?
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Maximum pooling layer, user matrix embedding layer, and user vector embedding layer.
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Sort these car brands and models into car types, either sedan, SUV, or sports car: Volkswagon Atlas, Porsche Boxter, Ford Mustang, Porsche Cayenne, Toyota Camry, Toyota Corolla, Dodge Durango, Chevrolet Camaro, Ford Explorer, Nissan Sentra, Porsche 911, Ford Escape, Chevrolet Equinox, Honda Pilot, Kia Telluride, Genesis G90, Honda Accord, Honda Civic, Kia Forte
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The SUVs are: Volkswagon Atlas, Porsche Cayenne, Dodge Durango, Ford Explorer, Ford Escape, Chevrolet Equinox, Honda Pilot, Kia Telluride, and Genesis G90. The sports cars are: Porsche Boxter, Chevrolet Camaro, Porsche 911, and Ford Mustang. The sedans are: Toyota Camry, Toyota Corolla, Nissan Sentra, Honda Accord, Honda Civic, and Kia Forte.
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The winners were announced during the awards ceremony on February 26, 2017. Moonlight became the first film with an all-black cast and the first LGBT-themed film to win Best Picture. In an event unprecedented in the history of the Oscars, La La Land was incorrectly announced as the Best Picture, and, a few minutes later, the error was corrected and Moonlight was declared the winner. O.J.: Made in America, at 467 minutes, became the longest film to win an Academy Award, surpassing the 431-minute long War and Peace, which won the Academy Award for Best Foreign Language Film in 1969. Following the five-part documentary's win, new academy rules barred any "multi-part or limited series" from being eligible for documentary categories. With Casey Affleck winning the Oscar for Best Actor, he and his older brother, Ben Affleck, became the 16th pair of siblings to win Academy Awards. Mahershala Ali became the first Muslim actor to win an Oscar. Viola Davis became the first black person to achieve the Triple Crown of Acting with her Oscar, Emmy, and Tony wins.
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How many parts did O.J.: Made in America have, and could a documentary with the same length win an Oscar today?
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O.J.: Made in America had 5 parts. Today, documentaries cannot be multi part, so a 5 part documentary could no longer win an Oscar.
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Avengers: Endgame is a 2019 American superhero film based on the Marvel Comics superhero team the Avengers. Produced by Marvel Studios and distributed by Walt Disney Studios Motion Pictures, it is the direct sequel to Avengers: Infinity War (2018) and the 22nd film in the Marvel Cinematic Universe (MCU). Directed by Anthony and Joe Russo and written by Christopher Markus and Stephen McFeely, the film features an ensemble cast including Robert Downey Jr., Chris Evans, Mark Ruffalo, Chris Hemsworth, Scarlett Johansson, Jeremy Renner, Don Cheadle, Paul Rudd, Brie Larson, Karen Gillan, Danai Gurira, Benedict Wong, Jon Favreau, Bradley Cooper, Gwyneth Paltrow, and Josh Brolin. In the film, the surviving members of the Avengers and their allies attempt to reverse Thanos's actions in Infinity War.
|
List the actors and actresses who were included in the cast of the movie Avengers: Endgame.
|
Robert Downey Jr., Chris Evans, Mark Ruffalo, Chris Hemsworth, Scarlett Johansson, Jeremy Renner, Don Cheadle, Paul Rudd, Brie Larson, Karen Gillan, Danai Gurira, Benedict Wong, Jon Favreau, Bradley Cooper, Gwyneth Paltrow, and Josh Brolin.
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What is a block chain?
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A block chain is in the most basic terms a ledger that is used for recording transactions in a network. Some of the most popular block chains are etheryum, and bitcoin. The idea when you purchase cryptocurrency is you make a new transaction in the blockchain and your transaction gets a copy of every single transaction prior to yours from the general ledger. Everything is trackable this way and every token can be accounted for. Nothing will ever get lost.
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There are two definitions currently used for subtropical cyclones depending on their location. Across the north Atlantic and southwest Indian Ocean, they require some central convection fairly near the center surrounding a warming core existing in the mid-levels of the troposphere. Across the eastern half of the northern Pacific however, they require a mid-tropospheric cyclone to be cut off from the main belt of the westerlies and with only a weak surface circulation. Subtropical cyclones have wider wind fields with the maximum sustained winds located further from the center than typical tropical cyclones, and have no weather fronts linked into their center.
|
There are two definitions currently used for subtropical cyclones depending on which two geographic locations?
|
Subtropical cyclones are defined differently between the north Atlantic/southwest Indian Ocean area and the northern Pacific.
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Give me some quotes from Aristotle that I might use in a school assignment
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1. Knowing yourself is the beginning of all wisdom.
2. No great mind has ever existed without a touch of madness.
3. Happiness is the meaning and the purpose of life, the whole aim and end of human existence.
4. Patience is bitter, but its fruit is sweet.
5. Those who know, do. Those that understand, teach.
6. Learning is not child's play; we can not learn without pain.
7. What is a friend? A single soul dwelling in two bodies.
8. Pleasure in the job puts perfection in the work.
9. It is impossible or not easy to alter by argument what has long been absorbed by habit.
10. Educating the head without educating the heart is no education at all.
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Who is the best character on Ted Lasso?
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Rebecca Welton is the best! She is the perfect combination of wit, sarcasm, beauty and power.
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1903.07398
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Traditional text-to-speech (TTS) systems are composed of complex pipelines BIBREF0 , these often include accoustic frontends, duration model, acoustic prediction model and vocoder models. The complexity of the TTS problem coupled with the requirement for deep domain expertise means these systems are often brittle in design and results in un-natural synthesized speech.
Neural text-to-speech systems have garnered large research interest in the past 2 years. The first to fully explore this avenue of research was Google's tacotron BIBREF1 system. Their architecture based off the original Seq2Seq framework. In addition to encoder/decoder RNNs from the original Seq2Seq , they also included a bottleneck prenet module termed CBHG, which is composed of sets of 1-D convolution networks followed by highway residual layers. The attention mechanism follows the original Seq2Seq BIBREF7 mechanism (often termed Bahdanau attention). This is the first work to propose training a Seq2Seq model to convert text to mel spectrogram, which can then be converted to audio wav via iterative algorithms such as Griffin Lim BIBREF8 .
The architecture of our model utilizes RNN-based Seq2Seq model for generating mel spectrogram from text. The architecture is similar to that of Tacotron 2 BIBREF4 . The generated mel spetrogram can either be inverted via iterative algorithms such as Griffin Lim, or through more complicated neural vocoder networks such as a mel spectrogram conditioned Wavenet BIBREF11 .
Direct comparison of model parameters between ours and the open-source tacotron 2, our model contains 4.5 million parameters, whereas the Tacotron 2 contains around 13 million parameters with default setting. By helping our model learn attention alignment faster, we can afford to use a smaller overall model to achieve similar quality speech quality.
Traditional text-to-speech (TTS) systems are composed of complex pipelines BIBREF0 , these often include accoustic frontends, duration model, acoustic prediction model and vocoder models.
Neural text-to-speech systems have garnered large research interest in the past 2 years. The first to fully explore this avenue of research was Google's tacotron BIBREF1 system
The architecture of our model utilizes RNN-based Seq2Seq model for generating mel spectrogram from text. The architecture is similar to that of Tacotron 2 BIBREF4 .
Direct comparison of model parameters between ours and the open-source tacotron 2, our model contains 4.5 million parameters, whereas the Tacotron 2 contains around 13 million parameters with default setting
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How do they measure the size of models?
|
The answers are shown as follows:
* Direct comparison of model parameters
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null | false
| 8
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We tuned the parameters based on the best performance on the development set. We fixed the weights of word embeddings during training. All the LSTMs in our neural network use the hidden state size of 300. To avoid overfitting, we applied dropout BIBREF37 with dropout ratio of 0.5 to both input and output of LSTM layers. To prevent the exploding gradient problem in training LSTMs, we adopt gradient clipping with gradient L2-norm threshold of 5.0. These parameters remain the same for all our proposed models as well as our own baseline models.
We chose the standard cross-entropy loss function for training our neural network model and adopted Adam BIBREF38 optimizer with the initial learning rate of 5e-4 and a mini-batch size of 128. If one instance is annotated with two labels (4% of all instances), we use both of them in loss calculation and regard the prediction as correct if model predicts one of the annotated labels. All the proposed models were implemented with Pytorch and converged to the best performance within 20-40 epochs.
To alleviate the influence of randomness in neural network model training and obtain stable experimental results, we ran each of the proposed models and our own baseline models ten times and report the average performance of each model instead of the best performance as reported in many previous works.
To alleviate the influence of randomness in neural network model training and obtain stable experimental results, we ran each of the proposed models and our own baseline models ten times and report the average performance of each model instead of the best performance as reported in many previous works.
|
How did the team alleviate the influence of randomness in neural network model training and obtain stable experimental results?
|
They ran each of the proposed models and their own baseline models ten times and report the average performance of each model instead of the best performance as reported in many previous works.
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Why do people like living in California?
|
California has the highest population of any U.S. state. Many people like living there because of its relatively mild climate and easy access to National and State Parks. Others enjoy living in California because it provides strong human rights protections to minority groups. California is a very large state and has a varied geography. As a result, people who want to live in coastal, forested, desert, or mountain environments can all find a location they like.
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1909.06937
| false
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We conduct experiments on our self-collected CAIS to evaluate the generalizability in different language. We apply two baseline models for comparison, one is the popular BiLSTMs + CRF architecture BIBREF36 for sequence labeling task, and the other one is the more powerful sententce-state LSTM BIBREF21. The results listed in Table TABREF50 demonstrate the generalizability and effectiveness of our CM-Net when handling various domains and different languages.
We conduct experiments on our self-collected CAIS to evaluate the generalizability in different language. We apply two baseline models for comparison, one is the popular BiLSTMs + CRF architecture BIBREF36 for sequence labeling task, and the other one is the more powerful sententce-state LSTM BIBREF21. The results listed in Table TABREF50 demonstrate the generalizability and effectiveness of our CM-Net when handling various domains and different languages.
|
What were the baselines models?
|
The answers are shown as follows:
* BiLSTMs + CRF architecture BIBREF36
* sententce-state LSTM BIBREF21
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The Half Dome Cable Route hike runs from the valley floor to the top of the dome in 8.2 mi (13 km) (via the Mist Trail), with 4,800 ft (1,460 m) of elevation gain. The length and difficulty of the trail used to keep it less crowded than other park trails, but in recent years the trail traffic has grown to as many as 800 people a day. The hike can be done from the valley floor in a single long day, but many people break it up by camping overnight in Little Yosemite Valley. The trail climbs past Vernal Fall and Nevada Fall, then continues into Little Yosemite Valley, then north to the base of the northeast ridge of Half Dome itself.
The final 400 ft (120 m) ascent is steeply up the rock between two steel cables used as handholds. The cables are fixed with bolts in the rock and raised onto a series of metal poles in late May (the poles do not anchor the cables). The cables are taken down from the poles for the winter in early October, but they are still fixed to the rock surface and can be used. The National Park Service recommends against climbing the route when the cables are down or when the surface of the rock is wet and slippery. The Cable Route is rated class 3, while the same face away from the cables is rated class 5.
The Cable Route gets crowded on the weekends
The Cable Route can be crowded. In past years, as many as 1,000 hikers per day have climbed the dome on a summer weekend, and about 50,000 hikers climb it every year.
Since 2011, all hikers who intend to ascend the Cable Route must now obtain permits before entering the park when the cables are up between May and October. Permits are checked by a ranger on the trail, and no hikers without permits are allowed to hike beyond the base of the sub-dome or to the bottom of the cables. Hikers caught bypassing the rangers to visit either the sub-dome or main dome without a permit face fines of up to $5,000 and/or 6 months in jail.
Backpackers with an appropriate wilderness permit can receive a Half Dome permit when they pick up their wilderness permit with no additional reservation required. Rock climbers who reach the top of Half Dome without entering the subdome area can descend on the Half Dome Trail without a permit.
The top of Half Dome is a large, flat area where climbers can relax and enjoy their accomplishment. The summit offers views of the surrounding areas, including Little Yosemite Valley and the Valley Floor. A notable location to one side of Half Dome is the "Diving Board", where Ansel Adams took his photograph Monolith, the Face of Half Dome on April 10, 1927. Often confused with "the Visor," a small overhanging ledge at the summit, the Diving Board is on the shoulder of Half Dome.
From 1919 when the cables were erected through 2011, there have been seven fatal falls from the cables. The latest fatality occurred on September 5, 2019.
Lightning strikes can be a risk while on or near the summit. On July 27, 1985, five hikers were struck by lightning, resulting in two fatalities.
The Cable Route was added to the National Register of Historic Places in 2012.
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Without quoting directly from the text give me a summary of the half dome hike.
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The half dome hike is 8.2 miles long with 4800 ft elevation gain. This hike can be done in a single long day or in two days by camping overnight in Little Yosemite Valley. The trail goes past Vernal and Nevada waterfalls, continues to Little Yosemite Valley and then to the half dome itself. The final 400 ft of the hike involves a steep ascent up the rock using the two steel cables fixed with bolts in the rock. The cables are put up between May and October and it is not recommended to climb the route when cables are down since the surface can be wet and slippery. All the hikers to half dome are required to obtain permits which are checked by a ranger on the trail. Hikers caught bypassing the rangers without permits are subject to penalties.
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Why is Alien the greatest space horror film of all time?
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For one thing, it does a fantastic job of making the crew of the Nostromo relatable and human. We spend a lot of time with them before any seriously suspenseful or horrific things happen. We understand they're a tight crew, and care about each other, and that they already feel some animosity towards the mining company that sent them out into space. This makes it a lot more scary and sad when eventually most of them become prey to the alien.
Secondly, the main villain in Alien is not only way scarier than almost any other creature villain ever made, the way they build up both her backstory and her eventual full reveal is amazing. When we first see the small chestburster alien, neither we nor the crew have any idea how big, terrifying, and lethal she will become. From her sticky, slick sheen to her cunning and silent hunting skills, she is truly an unstoppable force. If I were trapped on a spaceship with that creature, I don't know what I'd do.
And finally, Alien just feels more "real" with its universal and timely themes of greed and fear of the unknown. There are numerous actual stories of mining companies betraying their field employees and endangering their lives for a quick buck; isolated teams of workers going crazy, suffering from unseen maladies, and falling into chaos and disarray; and of course, the tale of predator and prey is perhaps the oldest one of all. Its visceral, brutal, claustrophobic terror felt imminently possibly in the 1970s when it was made, and has only increased with time.
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