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What makes a healthy diet?
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A healthy diet is one composed of an appropriate amount of calories balanced across macronutrients with clean sources of fruits, vegetables, grains, and other foods. An adult should generally target around 2000 calories per day, however this number should be greater depending on age, size, and activity level. Processed foods are generally less healthy and are associated with risk of diseases including heart disease and diabetes.
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Give me multiple modes of transport to get to Australia
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Fly by plane, take a boat or cruise ship, swim, paddle board, kayak, sail
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Write a short story about a person who discovers a hidden room in their house. The story should include a plot twist and a clear resolution at the end.
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Alice was a housewife living in a small suburban town. One day, she was looking around her house and noticed a small door near the back of the garage. Curious, she opened the door and stepped into a hidden room. She was amazed to find that it was filled with books and other items that were locked away in the room. Alice was fascinated and started to explore the room, reading the books and looking at the items.
She soon realized that the room was actually a secret passageway that connected the garage to a secret underground city. Alice was amazed to find out that the city was filled with all sorts of strange and magical creatures, hidden away from the rest of the world. She spent days exploring the city and learning more about the magical creatures. Eventually, she was able to return home and share her discovery with the rest of her family.
Alice's discovery of the secret city and the magical creatures was a life-changing experience for her and her family. It was a truly magical moment that changed the course of her life.
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Which is a bird or fish: Black grouse, Blackfish
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Black grouse is a bird, Blackfish is a fish
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When does human life begin?
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Causation theory is applied to the question of the point at which human life begins. It is concluded that the potential human being becomes actualized through the cessation of an exclusive in-utero dependency upon the material, formal, and efficient causes of its being, and the beginning of a nonexclusive ex-utero dependency upon nurturing and care-giving sources.
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Steeping, or brewing, is the process of making tea from leaves and hot water, generally using 2 grams (0.071 oz) of tea per 100 millilitres (3.5 imp fl oz; 3.4 US fl oz) of water (H2O) or about 1 teaspoon of green tea per 150 ml cup. Steeping temperatures range from 61 °C (142 °F) to 87 °C (189 °F) and steeping times from 30 seconds to three minutes.
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From the given text, Provide me step by step instructions on how to prepare tea.
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1. To brew/steep tea, use 2gm tea leaves per 100ml of water and let it brew between 61 °C to 87 °C for 30 seconds to 3 minutes.
2. For green tea, use 1 teaspoon of green tea per 150 ml of water and let it brew between 61 °C to 87 °C for 30 seconds to 3 minutes.
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null | false
| 435
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While ray marching in Eq. (1) allows computing the ray color, we are also interested in finding the distribution of distances that give rise to this color-this will allow using depths in RGB-D data for supervision. To this end, we show that Eq. (1) derives from an inhomogenous spatial Poisson process.
Consider a ray r(t) = x 0 + dt traveling through a camera at position x 0 along direction d. The probability that light originating at point r(t) will not scatter and reach the camera unimpeded (transmittance) is equal to the probability that no events occur in the spatial Poisson process T (t) = exp (− t 0 σ(r(t ))dt ). NeRF does not model light sources explicitly, but assumes that lighting conditions are expressed by the color value of each point. As a result, the light emitted at the point r(t) in the direction of the camera −d has the color c(r(t), d), and its intensity is proportional to the density of particles σ(r(t)) at that point. Consequently, the amount of light reaching the camera from r(t) is proportional to
(2)
In fact, under mild assumptions, p(t) is exactly equal to the distribution of possible depths t at which the observed colors originated 2 . We provide the full derivation in Appendix B.1.
We can now reframe Eq. (1) as the expected color value under the depth distribution p(t):
Typically, one chooses a maximum render distance t far as the upper bound for the integration interval. This leaves the probability p(t > t far ) = T (t far ) that light from beyond t far is missed. To account for that, Eq. () is renormalized by dividing by T (t far ) after approximating it with samples 0 ≤ t i ≤ t far .
Typically, one chooses a maximum render distance tfar as the upper bound for the integration interval. This leaves the probability p(t > tfar) = T(tfar) that light from beyond tfar is missed. To account for that, Eq. (3) is renormalized by dividing by T(tfar) after approximating it with samples 0 ≤ ti ≤ tfar.
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What does "Eq (3) is renormalized" mean?
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By renormalization we mean that estimates for p(t) will be divided by the transmittance of the ray in the sampled interval T(t_{\text{far}}) = p(t < t_{\text{far}}), such that they add to one. We will clarify in the revision.
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Classify the following based on what kind of animals they are.
rodentia, chiroptera, lizards, primates, crocodiles, soricomorpha, turtles, snakes
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Reptiles- lizards, crocodiles, turtles, snakes
Mammals- rodentia, chiroptera, soricomorpha, primates
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1906.05963
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We trained and evaluated our algorithm on the Microsoft COCO (MS-COCO) 2014 Captions dataset BIBREF21 . We report results on the Karpathy validation and test splits BIBREF8 , which are commonly used in other image captioning publications. The dataset contains 113K training images with 5 human annotated captions for each image. The Karpathy test and validation sets contain 5K images each. We evaluate our models using the CIDEr-D BIBREF22 , SPICE BIBREF23 , BLEU BIBREF24 , METEOR BIBREF25 , and ROUGE-L BIBREF26 metrics. While it has been shown experimentally that BLEU and ROUGE have lower correlation with human judgments than the other metrics BIBREF23 , BIBREF22 , the common practice in the image captioning literature is to report all the mentioned metrics.
We trained and evaluated our algorithm on the Microsoft COCO (MS-COCO) 2014 Captions dataset BIBREF21 . We report results on the Karpathy validation and test splits BIBREF8 , which are commonly used in other image captioning publications. The dataset contains 113K training images with 5 human annotated captions for each image. The Karpathy test and validation sets contain 5K images each. We evaluate our models using the CIDEr-D BIBREF22 , SPICE BIBREF23 , BLEU BIBREF24 , METEOR BIBREF25 , and ROUGE-L BIBREF26 metrics. While it has been shown experimentally that BLEU and ROUGE have lower correlation with human judgments than the other metrics BIBREF23 , BIBREF22 , the common practice in the image captioning literature is to report all the mentioned metrics.
|
What are the common captioning metrics?
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The answers are shown as follows:
* the CIDEr-D BIBREF22 , SPICE BIBREF23 , BLEU BIBREF24 , METEOR BIBREF25 , and ROUGE-L BIBREF26 metrics
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World War II or the Second World War, often abbreviated as WWII or WW2, was a global conflict that lasted from 1939 to 1945. The vast majority of the world's countries, including all of the great powers, fought as part of two opposing military alliances: the Allies and the Axis. Many participants threw their economic, industrial, and scientific capabilities behind this total war, blurring the distinction between civilian and military resources. Aircraft played a major role, enabling the strategic bombing of population centres and the delivery of the only two nuclear weapons ever used in war.
World War II was by far the deadliest conflict in history; it resulted in an estimated 70 to 85 million fatalities, mostly among civilians. Tens of millions died due to genocides (including the Holocaust), starvation, massacres, and disease. In the wake of the Axis defeat, Germany and Japan were occupied, and war crimes tribunals were conducted against German and Japanese leaders.
The causes of World War II are debated, but contributing factors included the Second Italo-Ethiopian War, Spanish Civil War, Second Sino-Japanese War, Soviet–Japanese border conflicts, the rise of fascism in Europe, and European tensions in the aftermath of World War I. World War II is generally considered to have begun on 1 September 1939, when Nazi Germany, under Adolf Hitler, invaded Poland. The United Kingdom and France subsequently declared war on Germany on 3 September. Under the Molotov–Ribbentrop Pact of August 1939, Germany and the Soviet Union had partitioned Poland and marked out their "spheres of influence" across Finland, Estonia, Latvia, Lithuania and Romania. From late 1939 to early 1941, in a series of campaigns and treaties, Germany conquered or controlled much of continental Europe, in a military alliance with Italy, Japan and other countries called the Axis. Following the onset of campaigns in North Africa and East Africa, and the fall of France in mid-1940, the war continued primarily between the European Axis powers and the British Empire, with war in the Balkans, the aerial Battle of Britain, the Blitz of the United Kingdom, and the Battle of the Atlantic. On 22 June 1941, Germany led the European Axis powers in an invasion of the Soviet Union, opening the Eastern Front, the largest land theatre of war in history.
Japan, which aimed to dominate Asia and the Pacific, was at war with the Republic of China by 1937. In December 1941, Japan attacked American and British territories with near-simultaneous offensives against Southeast Asia and the Central Pacific, including an attack on the US fleet at Pearl Harbor which resulted in the United States and United Kingdom declaring war against Japan. The European Axis powers declared war on the United States in solidarity. Japan soon captured much of the western Pacific, but its advances were halted in 1942 after losing the critical Battle of Midway; later, Germany and Italy were defeated in North Africa and at Stalingrad in the Soviet Union. Key setbacks in 1943—including a series of German defeats on the Eastern Front, the Allied invasions of Sicily and the Italian mainland, and Allied offensives in the Pacific—cost the Axis powers their initiative and forced them into strategic retreat on all fronts. In 1944, the Western Allies invaded German-occupied France, while the Soviet Union regained its territorial losses and pushed Germany and its allies back. During 1944 and 1945, Japan suffered reversals in mainland Asia, while the Allies crippled the Japanese Navy and captured key western Pacific islands.
The war in Europe concluded with the liberation of German-occupied territories and the invasion of Germany by the Western Allies and the Soviet Union, culminating in the Fall of Berlin to Soviet troops, Hitler's suicide, and the German unconditional surrender on 8 May 1945. Following the refusal of Japan to surrender on the terms of the Potsdam Declaration (issued 26 July 1945), the United States dropped the first atomic bombs on the Japanese cities of Hiroshima on 6 August and Nagasaki on 9 August. Faced with an imminent invasion of the Japanese archipelago, the possibility of additional atomic bombings, and the Soviet Union's declared entry into the war against Japan on the eve of invading Manchuria, Japan announced on 10 August its intention to surrender, signing a surrender document on 2 September 1945.
World War II changed the political alignment and social structure of the globe and set the foundation for the international order of the world's nations during the Cold War and into present day. The United Nations was established to foster international co-operation and prevent future conflicts, with the victorious great powers—China, France, the Soviet Union, the United Kingdom, and the United States—becoming the permanent members of its Security Council. The Soviet Union and the United States emerged as rival superpowers, setting the stage for the nearly half-century-long Cold War. In the wake of European devastation, the influence of its great powers waned, triggering the decolonisation of Africa and Asia. Most countries whose industries had been damaged moved towards economic recovery and expansion. Political and economic integration, especially in Europe, began as an effort to forestall future hostilities, end pre-war enmities, and forge a sense of common identity.
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Based on the reference text, provide a bulleted list of the contributing factors causing World War II.
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Although the causes of World War II are debated, contributing factors include:
- The rise of fascism in Europe
- European tensions after World War I
- Second Italo-Ethiopian War
- Spanish Civil War
- Second Sino-Japanese War
- Soviet-Japanese border conflicts
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Which is the best tube line?
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The London Underground has many lines that are popular for different reasons. The Victoria line is one of the fastest with the most convenient stops although it is noisy and lacks air conditioning. The Hammersmith, Metropolitan and Circle line have air conditioning making them popular in summer. Although not technically part of the London Underground, the Docklands Light Railway has a very scenic route when viewed from the front seats of the driverless trains. The Elizabeth Line is also popular, although technically not an underground line, due to it's new trains, stunning station architecture and faster routes.
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What number octane gas should be used for combustion engines in vehicles?
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Manufacturers list the minimal octane to be used on specific vehicles in the owners manual. Always check the manual to be sure the proper fuel is being used. An octane number such as 87, 91, 93 indicates the amount of heat tolerance of the fuel before igniting inside the engine cylinder. The higher the octane number, the higher heat tolerance. If you use the wrong octane in a vehicle, such as using 87, rather than 93 for a high performing turbo or supercharged vehicle, the engine could produce knock or pre-detonation. Knock occurs when the timing of of the air and fuel mixture in the cylinder explodes too early and can cause serious damage to the engine components. Knock is also something you can hear and feel and can be noticed by the driver. Having the detonation of the air and fuel, produced by the spark plug that is intended from the factory is optimal for the longevity of the vehicle.
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| 377
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The FraCaS test suite is an NLI data set consisting of 346 inference problems. Each problem contains one or more premises followed by one yes/no-question. There is a three way classification: YES, NO or UNK (unknown, see Figure 1 for an example from FraCaS). The FraCaS test suite was later on turned into machine-readable format by Bill McCartney
Expansions of FraCaS include: a) MultiFraCaS, in effect a multilingual FraCaS, and b) JSem, the Japanese counterpart to FraCaS, which expands the original FraCaS in a number of ways.
Even though the FraCaS test suite contains a rather small number of examples (346), it covers a lot of NLI cases and is, at least to some extent, multilingual. It is to some extent precise, even though there are test cases that do not involve a clear answer and thus are dubbed as undefined in Bill MacCartney's XML version. A further drawback of the FraCaS test stuite is that it involves constructed examples, rather than real text.
An UNK example from the FraCaS test suite.
A Scandinavian won the Nobel Prize.
Every Swede is Scandinavian.
Did a Swede win the Nobel prize?
A Swede won the Nobel prize.
UNK [FraCaS 065]
Expansions of FraCaS include: a) MultiFraCaS, in effect a multilingual FraCaS5 , and b) JSem, the Japanese counterpart to FraCaS, which expands the original FraCaS in a number of ways.
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What are the expansions of FraCaS?
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a) MultiFraCaS, in effect a multilingual FraCaS5 , and b) JSem, the Japanese counterpart to FraCaS, which expands the original FraCaS in a number of ways.
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Anderson plays at a deliberate pace, and he is not as fast as more athletically gifted players. Popovich stated that he is "not what you’d call a typical NBA athlete". However, Anderson is praised for his basketball IQ. He is able to change speeds and fake out his opponents to keep them off balance. His methodical style earned him the nickname "Slow Mo" in eighth grade in AAU, and the name has stuck with him. "[He] really understands how to use his speed—he's not super fast, but he's super bright and really knows how to use angles," said Kevin Boyle, who coached Anderson on the national team in 2012. USA Today described him as "languid but quick enough to lead the fast break". Anderson's defense was considered a weakness. In college, his teams usually played zone as he struggled containing smaller, quicker guards while playing man-to-man. His defense improved with the Spurs in 2017–18, when he guarded both forward positions while leveraging his wingspan and instincts. Against quicker small forwards, he uses his physicality to keep players off balance.
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Extract the reason why Anderson got the nickname 'Slow Mo'
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The reason is because Anderson plays at a deliberate pace, slower than most other peer players, while being able to use his high basketball IQ to change speeds and fake out his opponents on the court.
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Which is the windiest continent on Earth?
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Antarctica is the windiest continent on Earth. Cold air rushes downhill from the center of the continent toward the sea in all directions.
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| 516
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The overall objective of the proposed method is to finetune LCCS parameters to minimize crossentropy loss on the support samples:
where x and y are input and one-hot class encoding of target domain support samples L spt , and f is the source model with learnable LCCS parameters. The proposed method comprises an initialization and a gradient update stage.
Initialization stage: We search for initialization values for LCCS to warm start the optimization process. We first compute the support BN statistics µ spt and σ spt by exponential moving average (EMA) for m epochs, which allows µ spt and σ spt to update smoothly. Then, we conduct a onedimensional grid search on the LCCS parameters by setting η spt,i = ρ spt,i = v for i ∈ {1, . . . , n} where v ∈ {0, 0.1, . . . , 1.0} and η s = ρ s = 1 − v with values tied across all BN layers. The initialization value that minimizes cross-entropy loss on the support samples is selected.
We further optimize the LCCS parameters by gradient descent update for m epochs while concurrently updating support BN statistics by EMA. In this stage, parameter values are not tied across BN layers and we do not impose convex constraint on the training and support coefficient pairs to allow more diverse combinations.
We set m = 10 for both stages for all our experiments. Support samples are augmented with the same data augmentations for source model training.
We set m = 10 for both stages for all our experiments. Support samples are augmented with the same data augmentations for source model training.
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Can you provide a clarification on grid-search of LCCS parameters in initialization stage?
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We use the same few-shot support set throughout the adaptation stage i.e. we do not reserve additional validation set due to limited number of support samples. As stated in Section 3.3, we augment support samples with the same source data augmentations to increase diversity.
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When is the best time to see cherry blossom in Japan?
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The best time to visit Japan for cherry blossoms is around late March through early May, depending on the prefecture. There are a number of factors that influene when the blooming will take place each year such as temperature and rain.
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1702.06589
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Dataset: For training and testing we use the train-validation-test split of WikiTableQuestions BIBREF0 , a dataset containing 22,033 pairs of questions and answers based on 2,108 Wikipedia tables. This dataset is also used by our baselines, BIBREF0 , BIBREF3 . Tables are not shared across these splits, which requires models to generalize to unseen data. We obtain about 3.8 million training triples $(q,t,l)$ , where $l$ is a binary indicator of whether the logical form gives the correct gold answer when executed on the corresponding table. 76.7% of the questions have at least one correct candidate logical form when generated with the model of BIBREF0 .
For training and testing we use the train-validation-test split of WikiTableQuestions BIBREF0 , a dataset containing 22,033 pairs of questions and answers based on 2,108 Wikipedia tables. This dataset is also used by our baselines, BIBREF0 , BIBREF3 .
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Does the dataset they use differ from the one used by Pasupat and Liang, 2015?
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No.
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If you had to survive on a deserted island by yourself for a month, classify these items as 'useful' or 'not useful'.
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axe, phone, pen, rope, lighter, fishing rod, makeup
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The Spanish Civil War broke out in 1936: on 17 and 18 July, part of the military carried out a coup d'état that triumphed in only part of the country. The situation led to a civil war, in which the territory was divided into two zones: one under the authority of the Republican government, that counted on outside support from the Soviet Union and Mexico (and from International Brigades), and the other controlled by the putschists (the Nationalist or rebel faction), most critically supported by Nazi Germany and Fascist Italy. The Republic was not supported by the Western powers due to the British-led policy of non-intervention. General Francisco Franco was sworn in as the supreme leader of the rebels on 1 October 1936. An uneasy relationship between the Republican government and the grassroots anarchists who had initiated a partial Social revolution also ensued.
Republican volunteers at Teruel, 1936
The civil war was viciously fought and there were many atrocities committed by all sides. The war claimed the lives of over 500,000 people and caused the flight of up to a half-million citizens from the country. On 1 April 1939, five months before the beginning of World War II, the rebel side led by Franco emerged victorious, imposing a dictatorship over the whole country. Thousands of men and women were imprisoned after the civil war in Francoist concentration camps, with approximately 367,000 to 500,000 prisoners being held in 50 camps or prisons.
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Give me a brief summary of the spanish civil war
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The Spanish Civil War started on the 17th and 18th of July 1936 due to a coup d'etat by the military that divided the country into two zones, one side led by the republican government, the other controlled by the putschists (nationalists). The war claimed 500,000 lives and caused a mass exodus of up to 500,000 more. It ended on the 1st of April 1939 with the nationalists taking control of the country, imposing a dictatorship led by General Franco.
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What country has the most all time medals at the Winter Olympics?
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Norway leads the all time medal count for the winter olympics
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What are some easy and tasty dinner recipes I can make tonight?
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One easy and tasty dinner you can make is beef or fish tacos. You can assemble tortillas and toppings like cheese, sour cream, avocado, and onions.
Another easy dinner is baked fish with vegetables, of which there are lots of variations. You can make baked salmon with asparagus or baked tilapia with brussels sprouts.
Finally, pasta can be an easy and tasty dinner. Simply choose your pasta shape, a jar of pasta sauce, and a protein and some veggies to toss into the sauce.
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What kind of bacteria is Bifidobacterium thermacidophilum
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Bifidobacterium thermacidophilum is a Gram-positive, rod-shaped species of bacteria. Strains of this species were originally isolated from an anaerobic digester used to treat wastewater from a tofu farm. The species is thermophilic and can grow at a temperature of 49.5 °C.
Strains of B. thermacidophilum have been experimentally used as probiotics. It was effective in reducing damage to the gut in a mouse model of E. coli infection.
B. thermacidophilum has been divided into two subspecies: subsp. Porcinum and subsp. Thermacidophilum.
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What's the most impressive thing happened in the world since you were born?
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The day that I was born is just like yesterday and the most impressive thing I saw is that people never learned from history.
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What is a plot hole?
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A plot hole is a plot point that happens in a fictional story, such as a movie, that contradicts the established logic of the story. For example, if a character is established as being extremely selfish, but in one scene he gives money to another character in need for no apparent reason, this would be a plot hole as this development would not make sense to a reasonable audience member.
Most plot holes can still be explained if someone tries hard enough, but if the story does not give enough clues for an audience member to easily understand what is going on, it is a plot hole.
Plot holes have different effects on different people. Some people are taken out of the story when they notice a plot hole. Others never notice it. Still others notice plot holes or learn of them afterwards, but do not mind the plot holes.
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Who are the greatest hockey players of all time?
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The greatest hockey players of all time include Wayne Gretzky, Mario Lemieux, Bobby Orr, and Gordie Howe.
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Anton Pavlovich Chekhov (Russian: Антон Павлович Чехов[note 1], IPA: [ɐnˈton ˈpavləvʲɪtɕ ˈtɕexəf]; 29 January 1860[note 2] – 15 July 1904[note 3]) was a Russian playwright and short-story writer who is considered to be one of the greatest writers of all time. His career as a playwright produced four classics, and his best short stories are held in high esteem by writers and critics. Along with Henrik Ibsen and August Strindberg, Chekhov is often referred to as one of the three seminal figures in the birth of early modernism in the theatre. Chekhov was a physician by profession. "Medicine is my lawful wife", he once said, "and literature is my mistress."
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Given this paragraph on Anton Chekhov, please tell me which playwrights he is most often grouped with?
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Chekhov is most often grouped with Henrik Ibsen and August Strindberg as one of the three seminal figures of early modernist theater.
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In the series A Song of Ice and Fire, who is the founder of House Stark?
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Brandon Stark, also known as Brandon the Builder and Bran the Builder
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This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http://creativecommons.org/licenses/by/4.0/
Entity linking (EL), mapping entity mentions in texts to a given knowledge base (KB), serves as a fundamental role in many fields, such as question answering BIBREF0 , semantic search BIBREF1 , and information extraction BIBREF2 , BIBREF3 . However, this task is non-trivial because entity mentions are usually ambiguous. As shown in Figure FIGREF1 , the mention England refers to three entities in KB, and an entity linking system should be capable of identifying the correct entity as England cricket team rather than England and England national football team.
Entity linking is typically broken down into two main phases: (i) candidate generation obtains a set of referent entities in KB for each mention, and (ii) named entity disambiguation selects the possible candidate entity by solving a ranking problem. The key challenge lies in the ranking model that computes the relevance between candidates and the corresponding mentions based on the information both in texts and KBs BIBREF4 . In terms of the features used for ranking, we classify existing EL models into two groups: local models to resolve mentions independently relying on textual context information from the surrounding words BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , and global (collective) models, which are the main focus of this paper, that encourage the target entities of all mentions in a document to be topically coherent BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 .
Global models usually build an entity graph based on KBs to capture coherent entities for all identified mentions in a document, where the nodes are entities, and edges denote their relations. The graph provides highly discriminative semantic signals (e.g., entity relatedness) that are unavailable to local model BIBREF15 . For example (Figure FIGREF1 ), an EL model seemly cannot find sufficient disambiguation clues for the mention England from its surrounding words, unless it utilizes the coherence information of consistent topic “cricket" among adjacent mentions England, Hussain, and Essex. Although the global model has achieved significant improvements, its limitation is threefold:
To mitigate the first limitation, recent EL studies introduce neural network (NN) models due to its amazing feature abstraction and generalization ability. In such models, words/entities are represented by low dimensional vectors in a continuous space, and features for mention as well as candidate entities are automatically learned from data BIBREF4 . However, existing NN-based methods for EL are either local models BIBREF16 , BIBREF17 or merely use word/entity embeddings for feature extraction and rely on another modules for collective disambiguation, which thus cannot fully utilize the power of NN models for collective EL BIBREF18 , BIBREF19 , BIBREF20 .
The second drawback of the global approach has been alleviated through approximate optimization techniques, such as PageRank/random walks BIBREF21 , graph pruning BIBREF22 , ranking SVMs BIBREF23 , or loopy belief propagation (LBP) BIBREF18 , BIBREF24 . However, these methods are not differentiable and thus difficult to be integrated into neural network models (the solution for the first limitation).
To overcome the third issue of inadequate training data, BIBREF17 has explored a massive amount of hyperlinks in Wikipedia, but these potential annotations for EL contain much noise, which may distract a naive disambiguation model BIBREF6 .
In this paper, we propose a novel Neural Collective Entity Linking model (NCEL), which performs global EL combining deep neural networks with Graph Convolutional Network (GCN) BIBREF25 , BIBREF26 that allows flexible encoding of entity graphs. It integrates both local contextual information and global interdependence of mentions in a document, and is efficiently trainable in an end-to-end fashion. Particularly, we introduce attention mechanism to robustly model local contextual information by selecting informative words and filtering out the noise. On the other hand, we apply GCNs to improve discriminative signals of candidate entities by exploiting the rich structure underlying the correct entities. To alleviate the global computations, we propose to convolute on the subgraph of adjacent mentions. Thus, the overall coherence shall be achieved in a chain-like way via a sliding window over the document. To the best of our knowledge, this is the first effort to develop a unified model for neural collective entity linking.
In experiments, we first verify the efficiency of NCEL via theoretically comparing its time complexity with other collective alternatives. Afterwards, we train our neural model using collected Wikipedia hyperlinks instead of dataset-specific annotations, and perform evaluations on five public available benchmarks. The results show that NCEL consistently outperforms various baselines with a favorable generalization ability. Finally, we further present the performance on a challenging dataset WW BIBREF19 as well as qualitative results, investigating the effectiveness of each key module.
Although the global model has achieved significant improvements, its limitation is threefold: 1.The global approach suffers from the data sparsity issue of unseen words/entities, and the failure to induce underlying discriminative features for EL. 2.The joint inference mechanism in the global approach leads to expensive computations, especially when the entity graph may contain hundreds of nodes in case of long documents. 3.The annotated EL training data is usually expensive to obtain or only available in narrow domains, which results in possible overfitting issue or domain bias. To mitigate the first limitation, recent EL studies introduce neural network (NN) models due to its amazing feature abstraction and generalization ability.
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Why are neural network models introduced?
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As the global approach suffers from the data sparsity issue of unseen words/entities, and the failure to induce underlying discriminative features for EL, neural network (NN) models with amazing feature abstraction and generalization ability are introduced to mitigate the limitation.
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The new operating system was initially without organizational backing, and also without a name. At this stage, the new operating system was a singletasking operating system, not a multitasking one such as Multics. The name Unics (Uniplexed Information and Computing Service, pronounced as "eunuchs"), a pun on Multics (Multiplexed Information and Computer Services), was initially suggested for the project in 1970. Brian Kernighan claims the coining for himself, and adds that "no one can remember" who came up with the final spelling Unix. Dennis Ritchie and Doug McIlroy also credit Kernighan.
When the Computing Sciences Research Center wanted to use Unix on a machine larger than the PDP-7, while another department needed a word processor, Thompson and Ritchie added text processing capabilities to Unix and received funding for a PDP-11. For the first time in 1970, the Unix operating system was officially named and ran on the PDP-11. A text-formatting program called roff and a text editor were added. All three were written in PDP-11 assembly language. Bell Labs used this initial text-processing system, consisting of Unix, roff, and the editor, for text processing of patent applications. Roff soon evolved into troff, the first electronic publishing program with full typesetting capability.
As the system grew in complexity and the research team wanted more users, the need for a manual grew apparent. The UNIX Programmer's Manual was published on 3 November 1971; commands were documented in the "man page" format that is still used, offering terse reference information about usage as well as bugs in the software, and listing the authors of programs to channel questions to them.
After other Bell Labs departments purchased DEC PDP-11s, they also chose to run Unix instead of DEC's own operating system. By Version 4 it was widely used within the laboratory and a Unix Support Group was formed, helping the operating system survive by formalizing its distribution.
In 1973, Version 4 Unix was rewritten in the higher-level language C, contrary to the general notion at the time that an operating system's complexity and sophistication required it to be written in assembly language. The C language appeared as part of Version 2. Thompson and Ritchie were so influential on early Unix that McIlroy estimated that they wrote and debugged about 100,000 lines of code that year, stating that "[their names] may safely be assumed to be attached to almost everything not otherwise attributed". Although assembly did not disappear from the man pages until Version 8, the migration to C suggested portability of the software, requiring only a relatively small amount of machine-dependent code to be replaced when porting Unix to other computing platforms. Version 4 Unix, however, still had considerable PDP-11-dependent code and was not suitable for porting. The first port to other platform was made five years later (1978) for Interdata 8/32.
The Unix operating system was first presented formally to the outside world at the 1973 Symposium on Operating Systems Principles, where Ritchie and Thompson delivered a paper. This led to requests for the system, but under a 1956 consent decree in settlement of an antitrust case, the Bell System (the parent organization of Bell Labs) was forbidden from entering any business other than "common carrier communications services", and was required to license any patents it had upon request. Unix could not, therefore, be turned into a product. Bell Labs instead shipped the system for the cost of media and shipping. Ken Thompson quietly began answering requests by shipping out tapes and disks, each accompanied by – according to legend – a note signed, "Love, Ken".
|
List all the people mentioned in this paragraph on the history of Unix
|
- Brian Kernighan
- Dennis Ritchie
- Doug McIlroy
- Ken Thompson
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| 94
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Using our cleaned data set, we now built a recurrent neural network (RNN) with long short-term memory gates (LSTM). Our RNN/LSTM is trained to predict, given a description, whether a home corresponds to a high/medium/low popularity listing. The architecture of the RNN/LSTM employs Tensorflow’s Dynamic RNN package. Each sentence input is first fed into an embedding layer, where the input’s text is converted to a GloVe vector. These GloVe vectors are learned via a global word-word co-occurrence matrix using our corpus of Airbnb listing descriptions [8]. At each time step, the GloVe vectors are then fed into an LSTM layer. For each layer, the model forward propagates the output of the LSTM layer to the next time-step’s LSTM layer via a rectified linear unit (RLU) activation function. Each layer also pipes the output of the LSTM through a cross-entropy operation, to predict, for each time-step, the category of the input sequence. We finally ensemble these predictions, to create the model’s complete output prediction.
The architecture of the RNN/LSTM employs Tensorflows Dynamic RNN package. Each sentence input is first fed into an embedding layer, where the inputs text is converted to a GloVe vector. These GloVe vectors are learned via a global wordword co-occurrence matrix using our corpus of Airbnb listing descriptions [8]. At each time step, the GloVe vectors are then fed into an LSTM layer. For each layer, the model forward propagates the output of the LSTM layer to the next time-steps LSTM layer via a rectified linear unit (RLU) activation function. Each layer also pipes the output of the LSTM through a cross-entropy operation, to predict, for each time-step, the category of the input sequence. We finally ensemble these predictions, to create the models complete output prediction.
|
What is the architecture of the RNN/LSTM?
|
The architecture of the RNN/LSTM employs Tensorflows Dynamic RNN package. Each sentence input is first fed into an embedding layer, where the inputs text is converted to a GloVe vector. These GloVe vectors are learned via a global wordword co-occurrence matrix using their corpus of Airbnb listing descriptions. At each time step, the GloVe vectors are then fed into an LSTM layer. For each layer, the model forward propagates the output of the LSTM layer to the next time-steps LSTM layer via a rectified linear unit (RLU) activation function. Each layer also pipes the output of the LSTM through a cross-entropy operation, to predict, for each time-step, the category of the input sequence. They finally ensemble these predictions, to create the models complete output prediction.
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What are the five smallest countries in Europe?
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The five smallest countries in Europe are Andorra, Monaco, San Marino, Vatican City, and Liechtenstein.
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Bab al-Faradis (Arabic: بَابُ الْفَرَادِيسِ, romanized: Bāb al-Farādīs; "The Gate of the Paradise") or Bab al-Amara is one of the seven ancient city-gates of Damascus, Syria. The other name, Bab Al-Amara, refers to a name of a district in the old city where people in the 14th century would meet. During the Roman era, the gate was dedicated to Mercury.
|
Where is the Gate of the Paradise
|
Although you may search for a more spiritual answer, the Gate of the Paradise or Bab al-Faradis and Bab al-Amara in Arabic is one of the seven ancient city-gates of Damascus, Syria. The other name, Bab Al-Amara, refers to a name of a district in the old city where people in the 14th century would meet. During the Roman era, the gate was dedicated to Mercury.
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Kirchenlamitz is a town in the district of Wunsiedel, in Bavaria, Germany. It is situated in the Fichtelgebirge, 13 km northwest of Wunsiedel and 19 km south of Hof.
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In which country is the town of Kirchenlamitz located?
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Germany
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Portsmouth have the distinction of being the football club which has held the FA Cup trophy for the longest uninterrupted period - seven years. Portsmouth had defeated Wolverhampton Wanderers 4–1 in the 1939 FA Cup Final and were awarded the trophy as 1938–39 FA Cup winners. But with the outbreak of World War II in September 1939, the regular Football League and FA Cup competitions for the 1939–40 season were cancelled for the duration of the war. Portsmouth's manager Jack Tinn was rumoured to have kept the FA Cup trophy 'safe under his bed' throughout the duration of the war, but this is an urban myth. Because the naval city of Portsmouth was a primary strategic military target for German Luftwaffe bombing, the FA Cup trophy was actually taken ten miles to the north of Portsmouth, to the nearby Hampshire village of Lovedean, and there it resided in a quaint thatched roof country pub called The Bird in Hand for the seven years of the war. After the conclusion of World War II, the FA Cup trophy was presented back to the Football Association by the club in time for the 1946 FA Cup Final.
|
Which English football team have held the FA cup the longest?
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Portsmouth FC
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What movie make Kevin Smith famous?
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Clerks
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| 109
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This dataset was created by BIBREF8 . The tweets were downloaded from Twitter using #sarcasm as a marker for sarcastic tweets. It is a monolingual English dataset which consists of a balanced distribution of 50,000 sarcastic tweets and 50,000 non-sarcastic tweets.
Since sarcastic tweets are less frequently used BIBREF8 , we also need to investigate the robustness of the selected features and the model trained on these features on an imbalanced dataset. To this end, we used another English dataset from BIBREF8 . It consists of 25,000 sarcastic tweets and 75,000 non-sarcastic tweets.
We have obtained this dataset from The Sarcasm Detector. It contains 120,000 tweets, out of which 20,000 are sarcastic and 100,000 are non-sarcastic. We randomly sampled 10,000 sarcastic and 20,000 non-sarcastic tweets from the dataset. Visualization of both the original and subset data show similar characteristics.
A two-step methodology has been employed in filtering the datasets used in our experiments. Firstly, we identified and removed all the “user", “URL" and “hashtag" references present in the tweets using efficient regular expressions. Special emphasis was given to this step to avoid traces of hashtags, which might trigger the models to provide biased results. Secondly, we used NLTK Twitter Tokenizer to ensure proper tokenization of words along with special symbols and emoticons. Since our deep CNNs extract contextual information present in tweets, we include emoticons as part of the vocabulary. This enables the emoticons to hold a place in the word embedding space and aid in providing information about the emotions present in the sentence.
Dataset 1 (Balanced Dataset) This dataset was created by (Ptacek et al., 2014). The tweets were downloaded from Twitter using #sarcasm as a marker for sarcastic tweets. It is a monolingual English dataset which consists of a balanced distribution of 50,000 sarcastic tweets and 50,000 non-sarcastic tweets. Dataset 2 (Imbalanced Dataset) Since sarcastic tweets are less frequently used (Ptacek et al., 2014), we also need to investigate the robustness of the selected features and the model trained on these features on an imbalanced dataset. To this end, we used another English dataset from (Ptacek et al., 2014). It consists of 25,000 sarcastic tweets and 75,000 non-sarcastic tweets. Dataset 3 (Test Dataset) We have obtained this dataset from The Sarcasm Detector2 . It contains 120,000 tweets, out of which 20,000 are sarcastic and 100,000 are non-sarcastic. We randomly sampled 10,000 sarcastic and 20,000 non-sarcastic tweets from the dataset. Visualization of both the original and subset data show similar characteristics.
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What kinds of datasets have been used in the experiment?
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Balanced dataset, imbalanced dataset, and test dataset.
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| 115
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Data acquisition took place in a sound-attenuated and dark experiment room. Participants were seated at a distance of 68cm from a 24-inch monitor with a resolution of 800x600 pixels. A stable head position was ensured via a chin rest. Participants were instructed to stay as still as possible during the tasks to avoid motor EEG artifacts. Participants were also offered snacks and water during the breaks and were encouraged to rest. All sentences were presented at the same position on the screen and could span multiple lines. The sentences were presented in black on a light grey background with font size 20-point Arial, resulting in a letter height of 0.8 mm. The experiment was programmed in MATLAB 2016b BIBREF10, using PsychToolbox BIBREF11. Participants completed the tasks sitting alone in the room, while two research assistants were monitoring their progress in the adjoining room. All recording scripts including detailed participant instructions are available alongside the data.
Eye position and pupil size were recorded with an infrared video-based eye tracker (EyeLink 1000 Plus, SR Research) at a sampling rate of 500 Hz. The eye tracker was calibrated with a 9-point grid at the beginning of the session and re-validated before each block of sentences.
High-density EEG data were recorded at a sampling rate of 500 Hz with a bandpass of 0.1 to 100 Hz, using a 128-channel EEG Geodesic Hydrocel system (Electrical Geodesics). The recording reference was set at electrode Cz. The head circumference of each participant was measured to select an appropriately sized EEG net. To ensure good contact, the impedance of each electrode was checked prior to recording, and was kept below 40 kOhm. Electrode impedance levels were checked after every third block of 50 sentences (approx. every 30 mins) and reduced if necessary.
EEG acquisition High-density EEG data were recorded at a sampling rate of 500 Hz with a bandpass of 0.1 to 100 Hz, using a 128-channel EEG Geodesic Hydrocel system (Electrical Geodesics).
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What system do they use in EEG acquisition?
|
A 128-channel EEG Geodesic Hydrocel system.
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Write a story with a moral lesson at the end?
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You all know the Lion is the king of the forest. One lion king used to go everywhere in its kingdom with his advisors, the wolf and the fox. As lunch time approached, the king asked the wolf what we should do. The wolf said that the prey animals will be coming from the north. You wait hidden in the bush in the south. I shall wait in the west and the fox can wait in the east and ensure the prey do not escape to the east or west.
Good idea. so they all followed the plan. Along came a rabbit, ran helter skelter frightened on seeing the fox and the wolf and straight into the lion, who with one swipe of its paw killed the rabbit. Next a donkey came by and met a similar fate. So did a deer. The lion felt they had sufficient food for their meal and asked the wolf of how we were going to share the meal. The wolf said that the fox can have the rabbit., the lion can have the donkey and he would partake of the deer. The lion knew the tastiest meat is that of the deer, and sensing the deviousness of the advisor wolf smacked him dead. Next he asked the fox for advice on sharing. The fox meekly replied that whatever way the lion wants to have the meal is fine and good. On hearing this the lion said you are a wise and good advisor, who is your guru? The fox humbly replied that the wolf was his guru. Seeing the fate of the wolf the fox learnt from the wolf's mistake.
The moral of the story: You can and must learn from your mistakes. To err is human. But to repeat a mistake is foolish. However we need not keep making mistakes to learn life lessons. It is really wise to learn from other's mistake.
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The Winged Victory of Samothrace, or the Nike of Samothrace, is a votive monument originally found on the island of Samothrace, north of the Aegean Sea. It is a masterpiece of Greek sculpture from the Hellenistic era, dating from the beginning of the 2nd century BC. It is composed of a statue representing the goddess Niké (Victory), whose head and arms are missing, and its base in the shape of a ship's bow.
The total height of the monument is 5.57 meters including the socle; the statue alone measures 2.75 meters. The sculpture is one of a small number of major Hellenistic statues surviving in the original, rather than Roman copies. Winged Victory has been exhibited at the Louvre Museum in Paris, at the top of the main staircase, since 1884.
|
What is the Winged Victory monument?
|
The Winged Victory is a Greek statue representing the Goddess Nike, minus her head and arms, the base of which is carved to look like a ship's bow. This sculpture dates back to the beginning of the 2nd century BC, and is notable because it survives in its Greek original, not as a Roman copy. It is exhibited at the Louvre Museum in Paris, at the top of the main staircase.
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Juliette Roche (1884–1980), also known as Juliette Roche Gleizes, was a French painter and writer who associated with members of the Cubist and Dada movements. She was married to the artist Albert Gleizes.
She was born in 1884 to a wealthy Parisian family. Her father, Jules Roche, was a prominent member of both the French government and avant-garde art world. Other strong connections to the art world were manifested in her relationships with her godmother, Élisabeth, Countess Greffulhe, and her father's godson, Jean Cocteau. Juliette Roche studied painting at the Académie Ranson in Paris, with the support of her father. There, she was introduced to the artistic style of Les Nabis. In her poetic and pictorial work she showed profiles of independent women capable of self-expression.
In 1913, she exhibited at the Salon des Indépendants and began writing poetry, inserting phrases, such as advertising slogans; experimenting with typographic elements. In 1914 she held her first solo exhibition at the Bernheim-Jeune gallery.
When the First World War broke out, she traveled to New York City with her soon to be husband, the Cubist artist Albert Gleizes, who she met through the intermediary of Ricciotto Canudo, a film theoretician who published an avant-garde magazine Montjoie!, promoting Cubism. Juliette Roche and Albert Gleizes were married in September 1915.
In New York, she took part in Dada activities with Marcel Duchamp and Francis Picabia, The Gleizes' then traveled to Barcelona to exhibit in the Galeries Dalmau before returning to New York. collaborating with Duchamp in the preparation of the first exhibition of the Society of Independent Artists of 1917, and Duchamp submitted his infamous readymade Fountain.
In 1919, she returned to Paris and began writing La minéralisation de Dudley Craving Mac Adam, published in 1924, a story that tells of the adventures of Ather Cravan and other artists in exile in New York.
In 1920-21, she wrote État... Colloidal, published by the Chilean journalist Vicente Huidobro in the magazine Creación.
In 1927, together with Albert Gleizes, they founded the Moly-Sabata , a residence of artists in Sablons, which offered studios and workshops. She continued to exhibit the rest of his life in group exhibitions.
|
Who is Juliette Roche?
|
Juliette Roche was born in 1884 and was a French painter and writer. She was married to the artist Albert Gleizes.
She was also the founder of Moly-Sabata, a residence of artists in Sablons.
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| 46
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In this work, we incorporate a domain discriminator to align the mismatched domains between ASR hypotheses and reference transcriptions. The adversarial learning allows the end-to-end QA model to learn domain-invariant features and improve the robustness to ASR errors. The experiments demonstrate that the proposed model successfully achieves superior performance and outperforms the previous best model by 2% EM score and over 1.5% F1 score.
The experiments demonstrate that the proposed model successfully achieves superior performance and outperforms the previous best model by 2% EM score and over 1.5% F1 score.
|
What do the experiments demonstrate?
|
The proposed model successfully achieves superior performance and outperforms the previous best model by 2% EM score and over 1.5% F1 score.
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I'm planning a trip to India. How do I decide which places to visit?
|
India has 28 states and many states have their own distinct culture, traditions, history, and cuisine. It takes many months to properly sample all that India has to offer, and most people who visit India (or even Indians themselves) have probably visited only a fraction of the interesting places. So, pick a particular culture that's interesting to you (for example Rajasthani), and based on the available budget and time pick a few nearby places to visit. While visiting the places, don't forget to enjoy the local cuisine, though be careful to do so in restaurants that are well reviewed and to drink only bottled water to prevent water-borne stomach diseases.
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| 319
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Visual Question Answering (VQA) refers to a challenging task which lies at the intersection of image understanding and language processing. The VQA task has witnessed a significant progress in the recent years by the machine intelligence community. The aim of VQA is to develop a system to answer specific questions about an input image. The answer could be in any of the following forms: a word, a phrase, binary answer, multiple choice answer, or a fill in the blank answer. Agarwal et al. BIBREF0 presented a novel way of combining computer vision and natural language processing concepts of to achieve Visual Grounded Dialogue, a system mimicking the human understanding of the environment with the use of visual observation and language understanding.
The advancements in the field of deep learning have certainly helped to develop systems for the task of Image Question Answering. Krizhevsky et al BIBREF1 proposed the AlexNet model, which created a revolution in the computer vision domain. The paper introduced the concept of Convolution Neural Networks (CNN) to the mainstream computer vision application. Later many authors have worked on CNN, which has resulted in robust, deep learning models like VGGNet BIBREF2, Inception BIBREF3, ResNet BIBREF4, and etc. Similarly, the recent advancements in natural language processing area based on deep learning have improved the text understanding prforance as well. The first major algorithm in the context of text processing is considered to be the Recurrent Neural Networks (RNN) BIBREF5 which introduced the concept of prior context for time series based data. This architecture helped the growth of machine text understanding which gave new boundaries to machine translation, text classification and contextual understanding. Another major breakthrough in the domain was the introduction of Long-Short Term Memory (LSTM) architecture BIBREF6 which improvised over the RNN by introducing a context cell which stores the prior relevant information.
The vanilla VQA model BIBREF0 used a combination of VGGNet BIBREF2 and LSTM BIBREF6. This model has been revised over the years, employing newer architectures and mathematical formulations. Along with this, many authors have worked on producing datasets for eliminating bias, strengthening the performance of the model by robust question-answer pairs which try to cover the various types of questions, testing the visual and language understanding of the system. In this survey, first we cover major datasets published for validating the Visual Question Answering task, such as VQA dataset BIBREF0, DAQUAR BIBREF7, Visual7W BIBREF8 and most recent datasets up to 2019 include Tally-QA BIBREF9 and KVQA BIBREF10. Next, we discuss the state-of-the-art architectures designed for the task of Visual Question Answering such as Vanilla VQA BIBREF0, Stacked Attention Networks BIBREF11 and Pythia v1.0 BIBREF12. Next we present some of our computed results over the three architectures: vanilla VQA model BIBREF0, Stacked Attention Network (SAN) BIBREF11 and Teney et al. model BIBREF13. Finally, we discuss the observations and future directions.
Next we present some of our computed results over the three architectures: vanilla VQA model[2], Stacked Attention Network (SAN) [33] and Teney et al. model [31]. Finally, we discuss the observations and future directions.
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What architectures do the authors present the computed results over?
|
Vanilla VQA model, Stacked Attention Networks and Teney et al. model.
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| 306
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In this paper, we report our solution to the information extraction task in 2019 Language and Intelligence Challenge. We first analyze the problem and find that most entities are involved in multiple triplets. To solve this problem, we incorporate BERT into the multi-head selection framework for joint entity-relation extraction. Enhanced BERT pre-training, soft label embedding and NER pre-training are three main technologies we introduce to further improve the performance. Experimental results show that our method achieves competitive performance: F1 score 0.892 (1st place) on the test set 1 and F1 score 0.8924 (2nd place) on the test set 2.
Experimental results show that our method achieves competitive performance: F1 score 0.892 (1st place) on the test set 1 and F1 score 0.8924 (2nd place) on the test set 2.
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Does the authors' method achieve competitive performance on the test set 2?
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Yes, it does.
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Give me a list of most influential people in the world.
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Elon Musk, Bill Gates, Jeff Bezos, Vladimir Putin, Warren Buffett, Xi Jinping
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Classify each of the following water bodies as oceans and seas: Pacific, Arabian, Caspian, Atlantic
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Pacific - ocean
Arabian - sea
Caspian - sea
Atlantic - ocean
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| 350
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Machine reading comprehension and question answering has becomes a crucial application problem in evaluating the progress of AI system in the realm of natural language processing and understanding BIBREF0 . The computational linguistics communities have devoted significant attention to the general problem of machine reading comprehension and question answering.
However, most of existing reading comprehension tasks only focus on shallow QA tasks that can be tackled very effectively by existing retrieval-based techniques BIBREF1 . For example, recently we have seen increased interest in constructing extractive machine reading comprehension datasets such as SQuAD BIBREF2 and NewsQA BIBREF3 . Given a document and a question, the expected answer is a short span in the document. Question context usually contains sufficient information for identifying evidence sentences that entail question-answer pairs. For example, 90.2% questions in SQuAD reported by Min BIBREF4 are answerable from the content of a single sentence. Even in some multi-turn conversation tasks, the existing models BIBREF5 mostly focus on retrieval-based response matching.
In this paper, we focus on multiple-choice reading comprehension datasets such as RACE BIBREF6 in which each question comes with a set of answer options. The correct answer for most questions may not appear in the original passage which makes the task more challenging and allow a rich type of questions such as passage summarization and attitude analysis. This requires a more in-depth understanding of a single document and leverage external world knowledge to answer these questions. Besides, comparing to traditional reading comprehension problem, we need to fully consider passage-question-answer triplets instead of passage-question pairwise matching.
In this paper, we propose a new model, Dual Co-Matching Network, to match a question-answer pair to a given passage bidirectionally. Our network leverages the latest breakthrough in NLP: BERT BIBREF7 contextual embedding. In the origin BERT paper, the final hidden vector corresponding to first input token ([CLS]) is used as the aggregation representation and then a standard classification loss is computed with a classification layer. We think this method is too rough to handle the passage-question-answer triplet because it only roughly concatenates the passage and question as the first sequence and uses question as the second sequence, without considering the relationship between the question and the passage. So we propose a new method to model the relationship among the passage, the question and the candidate answer.
Firstly we use BERT as our encode layer to get the contextual representation of the passage, question, answer options respectively. Then a matching layer is constructed to get the passage-question-answer triplet matching representation which encodes the locational information of the question and the candidate answer matched to a specific context of the passage. Finally we apply a hierarchical aggregation method over the matching representation from word-level to sequence-level and then from sequence level to document-level. Our model improves the state-of-the-art model by 2.6 percentage on the RACE dataset with BERT base model and further improves the result by 3 percentage with BERT large model.
Firstly we use BERT as our encode layer to get the contextual representation of the passage, question, answer options respectively. Then a matching layer is constructed to get the passage-question-answer triplet matching representation which encodes the locational information of the question and the candidate answer matched to a specific context of the passage. Finally we apply a hierarchical aggregation method over the matching representation from word-level to sequence-level and then from sequence level to document-level.
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What method is applied over the matching representation from word-level to sequence-level?
|
A hierarchical aggregation method.
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What are some of the most popular card games?
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- Poker
- Bridge
- Blackjack
- Rummy
- Crazy Eights
- Spades
- Hearts
- War
- Solitaire
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Who is the best men's test cricket player to have come from New Zealand?
|
Despite a rich cricket tradition in New Zealand, the country only became a test playing nation during the season of 1929/30, when the MCC toured the country playing 4 tests. Australia, the nearest test playing country to New Zealand, refused to grant Test status to any games played between the two nations until well after World War 2. This lack of test opportunities led to a now common path of talented New Zealanders moving off shore for the chance to play test cricket- most notably in the pre WWII period of Clarrie Grimmet, who took over 200 test wickets playing for Australia. Since then, Andrew Caddick (234 test wickets at an average of 29.91) and Ben Stokes (5000+ test runs and 196 test wickets) have played leading roles for England in tests.
If however we define the question as "the best test cricket player to have played for New Zealand", then there are perhaps only 3 or 4 candidates that feature. Of the current generation, there is no doubt that Kane Williamson will one day be regarded as the greatest player to have come from New Zealand- with over 8000 test runs at an average north of 50. With many years in front of him, Williamson will no doubt hold the crown of best test cricketer in years to come.
Martin Crowe must likewise be regarded in the pantheon of test greats to have played for New Zealand- despite a slow start to his career, and injuries curtailing his time in the team, he amassed over 5000 runs at an average of over 45.
However, most will agree that the most influential cricketer ever to play test cricket for New Zealand was Sir Richard Hadlee- the first cricketer to be knighted whilst still playing cricket for his country. Hadlee's bowling average of 22.29 is one of best in test cricket history, and at the time of his retirement he held the record for test wickets, at 431. Gaining 5 wickets in an innings 36 times was also a record, and led New Zealand to some of it's most famous test victories, notably victory in a series in Australia for the first time in 1985/86, with his best test figures of 15 for 123 coming in the first test at the Gabba. Added to his bowling, his destructive lower order batting made his one of the preeminent all rounders of the game., and truly NZ's best test cricketer.
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Classify the movies based on whether they belong to Fantasy or Mystery genre.
AVATAR, SHAZAM, MISSING, FREDDY
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Mystery - MISSING, FREDDY
Fantasy - AVATAR, SHAZAM
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The UN estimated that the world population reached one billion for the first time in 1804. It was another 123 years before it reached two billion in 1927, but it took only 33 years to reach three billion in 1960. Thereafter, it took 14 years for the global population to reach four billion in 1974, 13 years to reach five billion in 1987, 12 years to reach six billion in 1999 and, according to the United States Census Bureau, 13 years to reach seven billion in March 2012. The United Nations, however, estimated that the world population reached seven billion in October 2011.
According to the UN, the global population reached eight billion in November 2022, but because the growth rate is slowing, it will take another 15 years to reach around 9 billion by 2037 and 20 years to reach 10 billion by 2057. Alternative scenarios for 2050 range from a low of 7.4 billion to a high of more than 10.6 billion. Projected figures vary depending on underlying statistical assumptions and the variables used in projection calculations, especially the fertility and mortality variables. Long-range predictions to 2150 range from a population decline to 3.2 billion in the "low scenario", to "high scenarios" of 24.8 billion. One extreme scenario predicted a massive increase to 256 billion by 2150, assuming the global fertility rate remained at its 1995 level of 3.04 children per woman; however, by 2010 the global fertility rate had declined to 2.52.
There is no estimation for the exact day or month the world's population surpassed one or two billion. The points at which it reached three and four billion were not officially noted, but the International Database of the United States Census Bureau placed them in July 1959 and April 1974 respectively. The United Nations did determine, and commemorate, the "Day of 5 Billion" on 11 July 1987, and the "Day of 6 Billion" on 12 October 1999. The Population Division of the United Nations declared the "Day of Seven Billion" to be 31 October 2011. The United Nations marked the birth of the eight billionth person on 15 November 2022.
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When did the global population reach 8 billion people?
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According to the UN, the global population reached eight billion in November 2022
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What kind of merchant ships would be useful in a modern naval conflict?
|
Two broad categories of merchant ships would likely be most useful in a modern naval conflict: large roll-on/roll-off ships with double hulls and small, fast ferries. If an adversary can find a ship on the open ocean and target it with a modern anti-ship missile, this opponent can certainly destroy the complex machinery needed to operate a container port or an oil terminal. Consequently, any cargo you want to unload in a war zone is going to need to drive off a ship on the back of a truck....quickly. An oil tanker will be useless, but a RO/RO ship loaded with tanker trucks can unload lots of fuel under austere/wartime conditions. Similarly, a container ship will be of little use without the large, land-based cranes used at most container shipping ports. A ferry loaded with trucks hauling shipping containers can quickly load and unload without the need for specialized equipment. Data from the Iran-Iraq war suggest that a large, 50,000+ ton RO/RO with a double hull is likely big enough to survive a hit from a modern anti-ship missile. A fast ferry that can move at ~40 knots is probably going to be too small to detect from space or shore using backscatter radar. Conversely, a 100,000-ton PanMax oil tanker or a 5,000 TEU container ship will be easy to detect, and slow enough to target.
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Give me Personal finance advice
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Live within your means.
Save and Invest
Avoid credit cards
Avoid any financial product that you don't understand
Do research before making a large purchase
Read financial books and blogs to get educated on personal finance
Home ownership generally is worth more in the long run
Ensure you have sufficient insurance to cover unexpected major catastrophes
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1612.05270
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In BIBREF3 , BIBREF2 , the authors study the effect of translation in sentiment classifiers; they found better to use native Arabic speakers as annotators than fine-tuned translators plus fine-tuned English sentiment classifiers. In BIBREF21 , the idea is to measure the effect of the agreement among annotators on the production of a sentiment-analysis corpus. Both, on the technical side, both papers use fine tuned classifiers plus a variety of pre-processing techniques to prove their claims. Table TABREF24 supports the idea of choosing B4MSA as a bootstrapping sentiment classifier because, in the overall, B4MSA reaches superior performances regardless of the language. Our approach achieves those performance's levels since it optimizes a set of parameters carefully selected to work on a variety of languages and being robust to informal writing. The latter problem is not properly tackled in many cases.
FLOAT SELECTED: Table 5: Performance on multilingual sentiment analysis (not challenges). B4MSA was restricted to use only the multilingual set of parameters.
Table TABREF24 supports the idea of choosing B4MSA as a bootstrapping sentiment classifier because, in the overall, B4MSA reaches superior performances regardless of the language.
FLOAT SELECTED: Table 5: Performance on multilingual sentiment analysis (not challenges). B4MSA was restricted to use only the multilingual set of parameters.
|
In which languages did the approach outperform the reported results?
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Arabic, German, Portuguese, Russian, Swedish
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Lionel Andrés Messi[note 1] (Spanish pronunciation: [ljoˈnel anˈdɾes ˈmesi] (listen); born 24 June 1987), also known as Leo Messi, is an Argentine professional footballer who plays as a forward for Ligue 1 club Paris Saint-Germain and captains the Argentina national team. Widely regarded as one of the greatest players of all time, Messi has won a record seven Ballon d'Or awards[note 2] and a record six European Golden Shoes, and in 2020 he was named to the Ballon d'Or Dream Team. Until leaving the club in 2021, he had spent his entire professional career with Barcelona, where he won a club-record 34 trophies, including 10 La Liga titles, seven Copa del Rey titles and four UEFA Champions Leagues. With his country, he won the 2021 Copa América and the 2022 FIFA World Cup. A prolific goalscorer and creative playmaker, Messi holds the records for most goals in La Liga (474), most hat-tricks in La Liga (36) and the UEFA Champions League (eight), and most assists in La Liga (192) and the Copa América (17). He also has the most international goals by a South American male (102). Messi has scored over 800 senior career goals for club and country, and has the most goals by a player for a single club (672).
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Given a reference tax about a footballer, tell me when they were born, what is their nationalities and which years they won the FIFA World cup.
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Lionel Andrés Messi, also known as Leo Messi, was born 24 June 1987. He is an Argentine professional footballer. In 2022, He won 2022 FIFA World Cup with his country.
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| 156
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Topic identification (topic ID) on speech aims to identify the topic(s) for given speech recordings, referred to as spoken documents, where the topics are a predefined set of classes or labels. This task is typically formulated as a three-step process. First, speech is tokenized into words or phones by automatic speech recognition (ASR) systems BIBREF0 , or by limited-vocabulary keyword spotting BIBREF1 . Second, standard text-based processing techniques are applied to the resulting tokenizations, and produce a vector representation for each spoken document, typically a bag-of-words multinomial representation, or a more compact vector given by probabilistic topic models BIBREF2 , BIBREF3 . Finally, topic ID is performed on the spoken document representations by supervised training of classifiers, such as Bayesian classifiers and support vector machines (SVMs).
However, in the first step, training the ASR system required for tokenization itself requires transcribed speech and pronunciations. In this paper, we focus on a difficult and realistic scenario where the speech corpus of a test language is annotated only with a minimal number of topic labels, i.e., no manual transcriptions or dictionaries for building an ASR system are available. We aim to exploit approaches that enable topic ID on speech without any knowledge of that language other than the topic annotations.
In this scenario, while previous work demonstrates that the cross-lingual phoneme recognizers can produce reasonable speech tokenizations BIBREF4 , BIBREF5 , the performance is highly dependent on the language and environmental condition (channel, noise, etc.) mismatch between the training and test data. Therefore, we focus on unsupervised approaches that operate directly on the speech of interest. Raw acoustic feature-based unsupervised term discovery (UTD) is one such approach that aims to identify and cluster repeating word-like units across speech based around segmental dynamic time warping (DTW) BIBREF6 , BIBREF7 . BIBREF8 shows that using the word-like units from UTD for spoken document classification can work well; however, the results in BIBREF8 are limited since the acoustic features on which UTD is performed are produced by acoustic models trained from the transcribed speech of its evaluation corpus. In this paper, we investigate UTD-based topic ID performance when UTD operates on language-independent speech representations extracted from multilingual bottleneck networks trained on languages other than the test language BIBREF9 . Another alternative to producing speech tokenizations without language dependency is the model-based approach, i.e., unsupervised learning of hidden Markov model (HMM) based phoneme-like units from untranscribed speech. We exploit the Variational Bayesian inference based acoustic unit discovery (AUD) framework in BIBREF10 that allows parallelized large-scale training. In topic ID tasks, such AUD-based systems have been shown to outperform other systems based on cross-lingual phoneme recognizers BIBREF5 , and this paper aims to further investigate how the performance compares among UTD, AUD and ASR based systems.
Moreover, after the speech is tokenized, these works BIBREF0 , BIBREF1 , BIBREF4 , BIBREF5 , BIBREF8 , BIBREF9 are limited to using bag-of-words features as spoken document representations. While UTD only identifies relatively long (0.5 – 1 sec) repeated terms, AUD/ASR enables full-coverage segmentation of continuous speech into a sequence of units/words, and such a resulting temporal sequence enables another feature learning architecture based on convolutional neural networks (CNNs) BIBREF11 ; instead of treating the sequential tokens as a bag of acoustic units or words, the whole token sequence is encoded as concatenated continuous vectors, and followed by convolution and temporal pooling operations that capture the local and global dependencies. Such continuous space feature extraction frameworks have been used in various language processing tasks like spoken language understanding BIBREF12 , BIBREF13 and text classification BIBREF14 , BIBREF15 . However, three questions are worth investigating in our AUD-based setting: (i) if such a CNN-based framework can perform as well on noisy automatically discovered phoneme-like units as on orthographic words/characters, (ii) if pre-trained vectors of phoneme-like units from word2vec BIBREF16 provide superior performance to random initialization as evidenced by the word-based tasks, and (iii) if CNNs are still competitive in low-resource settings of hundreds to two-thousand training exemplars, rather than the large/medium sized datasets as in previous work BIBREF14 , BIBREF15 .
Finally, incorporating the different tokenization and feature representation approaches noted above, we perform comprehensive topic ID evaluations on both single-label and multi-label spoken document classification tasks.
Topic identification (topic ID) on speech aims to identify the topic(s) for given speech recordings, referred to as spoken documents, where the topics are a predefined set of classes or labels. This task is typically formulated as a three-step process. First, speech is tokenized into words or phones by automatic speech recognition (ASR) systems [1], or by limited-vocabulary keyword spotting [2]. Second, standard text-based processing techniques are applied to the resulting tokenizations, and produce a vector representation for each spoken document, typically a bag-of-words multinomial representation, or a more compact vector given by probabilistic topic models [3, 4]. Finally, topic ID is performed on the spoken document representations by supervised training of classifiers, such as Bayesian classifiers and support vector machines (SVMs).
|
How many steps are topic ID tasks usually formulated for?
|
Three steps. First, speech is tokenized into words or phones by ASR systems, or by limited-vocabulary keyword spotting. Second, standard text-based processing techniques are applied to the resulting tokenizations, and produce a vector representation for each spoken document, typically a bag-of-words multinomial representation, or a more compact vector given by probabilistic topic models. Finally, topic ID is performed on the spoken document representations by supervised training of classifiers, such as Bayesian classifiers and SVMs.
|
1910.04519
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| null |
Zero-shot transfer using multilingual BERT performs poorly when transferring to Japanese on the MedWeb data. However, training on machine translations gives promising performance, and this performance can be increased by adding small amounts of original target data. On inspection, the drop in performance between translated and original Japanese was often a result of translations that were reasonable but not consistent with the labels. For example, when translating the first example in Figure FIGREF2, both machine translations map “UTF8min風邪”, which means cold (the illness), into “UTF8min寒さ”, which means cold (low temperature). Another example is where the Japanese pseudo-tweet “UTF8min花粉症の時期はすごい疲れる。” was provided alongside an English pseudo-tweet “Allergy season is so exhausting.”. Here, the Japanese word for hay fever “UTF8min花粉症。” has been manually mapped to the less specific word “allergies” in English; the machine translation maps back to Japanese using the word for “allergies” i.e. “UTF8minアレルギー” in the katakana alphabet (katakana is used to express words derived from foreign languages), since there is no kanji character for the concept of allergies. In future work, it would be interesting to understand how to detect such ambiguities in order to best deploy our annotation budget.
On inspection, the drop in performance between translated and original Japanese was often a result of translations that were reasonable but not consistent with the labels. For example, when translating the first example in Figure FIGREF2, both machine translations map “UTF8min風邪”, which means cold (the illness), into “UTF8min寒さ”, which means cold (low temperature). Another example is where the Japanese pseudo-tweet “UTF8min花粉症の時期はすごい疲れる。” was provided alongside an English pseudo-tweet “Allergy season is so exhausting.”. Here, the Japanese word for hay fever “UTF8min花粉症。” has been manually mapped to the less specific word “allergies” in English; the machine translation maps back to Japanese using the word for “allergies” i.e. “UTF8minアレルギー” in the katakana alphabet (katakana is used to express words derived from foreign languages), since there is no kanji character for the concept of allergies.
|
Is there any explanation why some choice of language pair is better than the other?
|
The answers are shown as follows:
* translations that were reasonable but not consistent with the labels
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| 59
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In our study, we evaluate performance of models with:
3 types of normalization: batch norm BIBREF10 , weight norm BIBREF9 , and layer norm BIBREF17
3 types of rectified linear units: ReLU, clipped ReLU (cReLU), and leaky ReLU (lReLU)
2 types of gated units: gated linear units (GLU) BIBREF8 , and gated activation units (GAU) BIBREF18
All experiment results are shown in Table TABREF15 . We first experimented with a smaller Jasper5x3 model to pick the top 3 settings before training on larger Jasper models. We found that layer norm with GAU performed the best on the smaller model. Layer norm with ReLU and batch norm with ReLU came second and third in our tests. Using these 3, we conducted further experiments on a larger Jasper10x4. For larger models, we noticed that batch norm with ReLU outperformed other choices. Thus, leading us to decide on batch normalization and ReLU for our architecture.
During batching, all sequences are padded to match the longest sequence. These padded values caused issues when using layer norm. We applied a sequence mask to exclude padding values from the mean and variance calculation. Further, we computed mean and variance over both the time dimension and channels similar to the sequence-wise normalization proposed by Laurent et al. BIBREF19 . In addition to masking layer norm, we additionally applied masking prior to the convolution operation, and masking the mean and variance calculation in batch norm. These results are shown in Table TABREF16 . Interestingly, we found that while masking before convolution gives a lower WER, using masks for both convolutions and batch norm results in worse performance.
As a final note, we found that training with weight norm was very unstable leading to exploding activations.
All experiment results are shown in Table 2. We first experimented with a smaller Jasper5x33 model to pick the top 3 settings before training on larger Jasper models. We found that layer norm with GAU performed the best on the smaller model.
|
In the smaller Jasper5x3 model experiment, which setting performs best?
|
Layer norm with GAU.
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Tell me whether these are tree nuts: coconut, walnut, nutmeg, peanut, sesame, cashew, macadamia, almond.
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Walnut, cashew, macadamia, and almond are classified as tree nuts. Nutmeg, peanut, and sesame are not. Coconut is recognized by some government and health organizations as a tree nut, however it is botanically classified as a fruit.
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Classify below into more useful and less useful minerals in our food: iron, calcium, magnesium, chloride, selenium, potassium, phosphorous, zinc, iodine, mercury, copper.
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In the given minerals, these are the minerals more useful to have in our foods: magnesium, sodium, calcium, phosphorous, chloride and potassium.
In the given minerals, these are the minerals less useful to have in our foods: iron, iodine, zinc, selenium, copper and mercury
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House of the Dragon is an American fantasy drama television series created by George R. R. Martin and Ryan Condal for HBO. A prequel to Game of Thrones (2011–2019), it is the second television series in the A Song of Ice and Fire franchise. Condal and Miguel Sapochnik served as the showrunners for the first season. Based on Martin's 2018 novel Fire & Blood, the series is set about 100 years after the Seven Kingdoms are united by the Targaryen Conquest, nearly 200 years before the events of Game of Thrones, and 172 years before the birth of Daenerys Targaryen. Featuring an ensemble cast, the show portrays the events leading up to the beginning of the decline of House Targaryen, a devastating war of succession known as the "Dance of the Dragons".
|
What is House of the Dragon?
|
House of the Dragon is an American fantasy drama television series created by George R. R. Martin and Ryan Condal for HBO. A prequel to Game of Thrones (2011–2019), it is the second television series in the A Song of Ice and Fire franchise. Condal and Miguel Sapochnik served as the showrunners for the first season. Based on Martin's 2018 novel Fire & Blood, the series is set about 100 years after the Seven Kingdoms are united by the Targaryen Conquest, nearly 200 years before the events of Game of Thrones, and 172 years before the birth of Daenerys Targaryen. Featuring an ensemble cast, the show portrays the events leading up to the beginning of the decline of House Targaryen, a devastating war of succession known as the "Dance of the Dragons".
House of the Dragon received a straight-to-series order in October 2019, with casting beginning in July 2020 and principal photography starting in April 2021 in the United Kingdom. The series premiered on August 21, 2022, with the first season consisting of ten episodes. Five days after its premiere, the series was renewed for a second season. Sapochnik departed as showrunner after the first season, leaving Condal to serve as the sole showrunner for the second season.
The first season received highly positive reviews, with praise towards its character development, visual effects, writing, score by Ramin Djawadi and performances (particularly Considine, Smith, D'Arcy, Alcock, and Cooke). However, the pacing, specifically of the time jumps, and the dark lighting of some scenes were criticized. The series premiere was watched by over 10 million viewers across the linear channels and HBO Max on the first day, the biggest in HBO's history. In January 2023, the series won the Golden Globe Award for Best Television Series – Drama.
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Identify which instrument is string or percussion: Padayani thappu, Qanun
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Qanun is string, Padayani thappu is percussion.
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1906.04571
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To date, the NLP community has focused primarily on approaches for detecting and mitigating gender stereotypes in English BIBREF5 , BIBREF6 , BIBREF7 . Yet, gender stereotypes also exist in other languages because they are a function of society, not of grammar. Moreover, because English does not mark grammatical gender, approaches developed for English are not transferable to morphologically rich languages that exhibit gender agreement BIBREF8 . In these languages, the words in a sentence are marked with morphological endings that reflect the grammatical gender of the surrounding nouns. This means that if the gender of one word changes, the others have to be updated to match. As a result, simple heuristics, such as augmenting a corpus with additional sentences in which he and she have been swapped BIBREF9 , will yield ungrammatical sentences. Consider the Spanish phrase el ingeniero experto (the skilled engineer). Replacing ingeniero with ingeniera is insufficient—el must also be replaced with la and experto with experta.
Moreover, because English does not mark grammatical gender, approaches developed for English are not transferable to morphologically rich languages that exhibit gender agreement BIBREF8 .
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Why does not the approach from English work on other languages?
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Because, unlike other languages, English does not mark grammatical genders
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How to be a human?
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You have to have feelings and care about your fellow humans
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What are five different names I could pick for my new male dog?
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Buddy, Rex, Fifo, Spot and Lucky
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Styrofoam has a variety of uses. Styrofoam is composed of 98% air, making it lightweight and buoyant.
DuPont produces Styrofoam building materials, including varieties of building insulation sheathing and pipe insulation. The claimed R-value of Styrofoam insulation is five per inch.
Styrofoam can be used under roads and other structures to prevent soil disturbances due to freezing and thawing.
DuPont also produces Styrofoam blocks and other shapes for use by florists and in craft products. DuPont insulation Styrofoam has a distinctive blue color; Styrofoam for craft applications is available in white and green.
|
What are some uses of Styrofoam?
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Styrofoam is used in building insulation, as a soil stabilization material, and as crafting material.
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The three Synoptic Gospels record the circumstances in which some of the disciples were recruited, Matthew only describing the recruitment of Simon, Andrew, James, and John.
Despite Jesus only briefly requesting that they join him, they are all described as immediately consenting and abandoning their nets to do so. The immediacy of their consent has been viewed as an example of divine power, although this is not stated in the text. Another explanation is that some of the disciples may have heard of Jesus beforehand, as implied by the Gospel of John, which states that Andrew was a disciple of John the Baptist, and that he and his brother started following Jesus as soon as Jesus had been baptized.
Matthew describes Jesus meeting James and John, also fishermen and brothers, very shortly after recruiting Simon and Andrew. Matthew and Mark identify James and John as sons of Zebedee. Luke adds to Matthew and Mark that James and John worked as a team with Simon and Andrew. Matthew states that at the time of the encounter, James and John were repairing their nets, but readily joined Jesus without hesitation.
This parallels the accounts of Mark and Luke, but Matthew implies that the men have also abandoned their father (since he is present in the boat they abandon behind them), and Carter feels this should be interpreted to mean that Matthew's view of Jesus is one of a figure rejecting the traditional patriarchal structure of society, where the father had command over his children; most scholars, however, just interpret it to mean that Matthew intended these two to be seen as even more devoted than the other pair, or that Jesus expected the imminent coming of the kingdom.
The Synoptic Gospels go on to describe that later in Jesus' ministry he noticed a tax collector in his booth. The tax collector, called Matthew in Matthew 9:9, and Levi in Mark 2:14 and Luke 5:27, is asked by Jesus to become one of his disciples. Matthew/Levi is stated to have accepted and then invited Jesus for a meal with his friends. Tax collectors were seen as villains in Jewish society, and the Pharisees are described as asking Jesus why he is having a meal with such disreputable people. The reply Jesus gave is now well known: "it is not the healthy who need a doctor, but the sick. I have not come to call the righteous, but sinners to repentance."
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Extract all the names of people in this paragraph on the twelves apostles of Jesus, in a bullet point list.
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- Matthew
- Simon
- Andrew
- James
- John
- Jesus
- John the Baptist
- Mark
- Zebedee
- Luke
- Carter
- Levi
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Write a diary entry for a dog that only likes one person that lives in the house on a day that their favorite person was gone all day.
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Today was a challenging day, after the quiet time, my two legged housemates started making noise at the normal time, changing their appearances and eating food that I was not allowed to eat. The smaller ones left with the other one as usual, but then I saw the best one disappeared as well through the door with the comfortably cold floor. I check the floor in the food room for crumbs, take a drink and then wait for forever for the sound of the best one to return. Small, mouth-sized intruders visited the outside throughout the day, and I let them know they are not welcome. I fear the worst, and the small ones and the other one returned, but the best one is missing. They bribe me with food and water, distracting me from noticing the best one has not returned. I stay on the cold floor, the last place I saw the best one while the others do their best to distract me. I will not be fooled. I hear the rumbling sound that means the best one returns. I'm the first to greet them, because I love them best. I am reminded that I am the best, and I keep the best one in my sight until everything is quiet once again.
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Why was Abraham Lincoln a great President?
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Abraham Lincoln's strengths were his empathy and unquenchable thirst to always be learning. He grew up in a humble beginnings where his passions involved reading. He become a self-taught lawyer. During the Civil War, he dove into books of military history. After two years of war, he learned to trust his instincts and installed the right general leadership to execute and win the war. He was the near perfect example of an orator. Not long winded but delivered his powerful rhetoric in the just right amount.
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I'm considering buying a bicycle. How do I know which one I should buy?
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It depends on how you want to use the bike and how much budget you have. If you just need one for commute, consider an electric bike if you want to go for a long distance with little effort, or a folding bike if you need to carry it with your car or onto public transportation. Or if you want to buy one for sports, consider a road bike for road cycling, or a mountain bike for tail cycling. A sport bike's price can range from $100s to $1000s. Higher-end bikes usually comes with better material like carbon-fiber or Titanium alloy, which are light-weighted and at the same time sturdy. There are also different types of gear switch and breaks, that can affect the price. Finally, bikes come with different sizes of frame and wheels, you need to find one that fits your body height. Go to a local bike shop and talk to a sales agent to find a bike that best fit your need.
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What is a time signature in music?
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The time signature in music represents the number of notes in each bar. The time signature is noted at the start of a composition as is represented as two stacked numbers. For example the common time signature 4/4 indicates each bar will contain four quarter note beats.
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Apollo (Ἀπόλλων, Apóllōn)
God of music, arts, knowledge, healing, plague, prophecy, poetry, manly beauty, and archery. He is the son of Zeus and Leto, and the twin brother of Artemis. Both Apollo and Artemis use a bow and arrow. Apollo is depicted as young, beardless, handsome and athletic. In myth, he can be cruel and destructive, and his love affairs are rarely happy. He is often accompanied by the Muses. His most famous temple is in Delphi, where he established his oracular shrine. His signs and symbols include the laurel wreath, bow and arrow, and lyre. His sacred animals include roe deer, swans, and pythons. Some late Roman and Greek poetry and mythography identifies him as a sun-god, equivalent to Roman Sol and Greek Helios.
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From the passage identify the sacred animals of Apollo. Display the results in a comma separated format.
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roe deer, swans, pythons
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Identify which animal species is alive or extinct: Wiwaxia, Penguin
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Penguin is alive, Wiwaxia is extinct.
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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.
Our systems are based on an attentional encoder-decoder, using BPE subword segmentation for open-vocabulary translation with a fixed vocabulary.
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What network do the authors use in this paper?
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An attentional encoder-decoder network.
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Why is crypto currency risky?
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Cryptocurrency is risk for a number of reasons. The price fluctuations are usually driven by large institutional investors not individuals. When institutions pull out their money you can see price fluctuations as high as 90% loss. It is a deregulated digital currency with no government oversight so if your money gets hacked or taken because a bad actor got your wallet keys your money is lost for good, you will never get it back, and there will never be an investigation into where your money went. If you are going to play in the crypto currency space its best to use a platform similar to coinbase that has security measures in place for all your crypto wallets so your money doesn't get stolen.
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| 388
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Table TABREF21 shows the performance of our models when using all features described in Section SECREF4: see the SVM$_{All}$ and the FNN$_{All}$ rows. In order to put the numbers in perspective, we also show the results for five increasingly competitive baselines.
First, there is a random baseline, followed by an SVM classifier based on a bag-of-words representation with TF.IDF weights learned on the training data. Then come three versions of the ClaimBuster system: CB-Platform uses scores from the online demo, which we accessed on December 20, 2016, and SVM$_{CBfeat}$ and FNN$_{CBfeat}$ are our re-implementations, trained on our dataset.
We can see that all systems perform well above the random baseline. The three versions of ClaimBuster also outperform the TF.IDF baseline on most measures. Moreover, our reimplementation of ClaimBuster performs better than the online platform in terms of MAP. This is expected as their system is trained on a different dataset and it may suffer from testing on slightly out-of-domain data. At the same time, this is reassuring for our implementation of the features, and allows for a more realistic comparison to the ClaimBuster system.
More importantly, both the SVM and the FNN versions of our system consistently outperform all three versions of ClaimBuster on all measures. This means that the extra information coded in our model, mainly more linguistic, structural, and contextual features, has an important contribution to the overall performance.
We can further see that the neural network model, FNN$_{All}$, clearly outperforms the SVM model: consistently on all measures. As an example, with the precision values achieved by FNN$_{All}$, the system would rank on average 4 positive examples in the list of its top-5 choices, and also 14-15 in the top-20 list. Considering the recall at the first $R$ sentences, we will be able to encounter 43% of the total number of check-worthy sentences. This is quite remarkable given the difficulty of the task.
This means that the extra information coded in our model, mainly more linguistic, structural, and contextual features, has an important contribution to the overall performance.
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What information has an important contribution to the overall performance?
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The linguistic, structural, and contextual features have an important contribution to the overall performance.
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Tell me whether these are names of paintings or song titles: The Starry Night, Mona Lisa, Irises, Billie Jean, Hey Jude, Come As You Are.
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The Starry Night is a famous painting painting by the Dutch Post-Impressionist painter Vincent van Gogh.
Mona Lisa is a painting by Italian artist Leonardo da Vinci.
Irises is a painting of irises by the Dutch artist Vincent van Gogh.
Billie Jean is a song by American singer Michael Jackson.
Hey Jude is a song by English rock band the Beatles.
Come As You Are is a song by American rock band Nirvana.
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Who directed the episode of Game of Thrones where Jon Snow and Tormund Giantsbane go to ask the wildlings to settle beyond the Wall, but end up encountering White Walkers and the Night King?
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Season five, episode eight entitled "Hardhome"
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Denville is an active commuter railroad train station in Denville Township, Morris County, New Jersey. Located on Estling Road, the station contains three side platforms–two curved low-level platforms that service New Jersey Transit's Morristown Line, and a third that services their Montclair-Boonton Line. Both platforms on the Morristown Line contain miniature high-level platforms for handicap accessibility. Trains on both lines operate between Hoboken Terminal, New York Penn Station and Hackettstown. Heading westbound, the next station is Dover while the next station east on the Morristown Line is Mount Tabor. The next station east on the Montclair-Boonton Line is Mountain Lakes.
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What is Denville Station?
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Denville station is a commuter railroad station in Denville Township, Morris Country, New Jersey. It is supported by two lines, the New Jersey Morristown Line and the Montclair-Boonton Line. Trains on both of these lines operate between Hoboken Terminal, New York Penn Station and Hackettstown.
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| 130
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In order to rule out that only the questions or only the comments are necessary to optimally solve the task, we conduct some additional experiments:
Only use a single segment containing the comment, removing the questions from the training and test data (missing questions).
Only use the question and remove the comment (missing comments).
In both cases the performance decreases across all evaluation settings (Table TABREF39). The loss in performance is much higher when comments are missing, indicating that the comments contain the most important information about stance. As can be expected, the score achieved without comments is only slightly different from the target-wise majority class baseline.
But there is also a loss in performance when the questions are missing, which underlines the importance of pairing both pieces of text. The effect of missing questions is especially strong in the supervised and cross-lingual settings. To illustrate this, we provide in Table TABREF48 some examples of comments that occur with multiple different targets in the training set. Those examples can explain why the target can be essential for disambiguating a stance detection problem. On the other hand, the effect of omitting the questions is less pronounced in the cross-target settings.
The above single-segment experiments tell us that both the comment and the question provide crucial information. But it is possible that the M-Bert model, even though trained on both segments, mainly looks at a single segment at test time. To rule this out, we probe the model with randomized data at test time:
Test the model on versions of the test sets where the comments remain in place but the questions are shuffled randomly (random questions). We make sure that the random questions come from the same test set and language as the original questions.
Keep the questions in place and randomize the comments (random comments). Again we shuffle the comments only within test set boundaries.
The results in Table TABREF39 show that the performance of the model decreases in both cases, confirming that it learns to take into account both segments.
In order to rule out that only the questions or only the comments are necessary to optimally solve the task, we conduct some additional experiments: • Only use a single segment containing the comment, removing the questions from the training and test data (missing questions). • Only use the question and remove the comment (missing comments).
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What additional experiments are conducted to rule out that only the questions or only the comments are necessary to optimally solve the task?
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• Only use a single segment containing the comment, removing the questions from the training and test data (missing questions). • Only use the question and remove the comment (missing comments).
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Why to people lift their trucks?
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When people talk about "lifting" their trucks, they are referring to increasing the height and ground clearance through modification to the suspension of the vehicle. Most often this is in reference to pick-up trucks, as those vehicles come from the manufacturer with the front of the vehicle at a lower height than the rear. Know as a "rake", this is intended to allow the vehicle to ride at a level height even when carrying significant weight in the truck bed or towing heavy trailers. There are various methods for lifting a vehicle that range from inexpensive spacers to elevate factory springs to more comprehensive systems replacing multiple suspension elements with new, purpose-built components. People often prefer to lift their truck for the aesthetic appeal as well as improved ground clearance and performance when driving off-road.
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Electronics deals with electrical circuits that involve active electrical components such as vacuum tubes, transistors, diodes, sensors and integrated circuits, and associated passive interconnection technologies. The nonlinear behaviour of active components and their ability to control electron flows makes digital switching possible, and electronics is widely used in information processing, telecommunications, and signal processing. Interconnection technologies such as circuit boards, electronics packaging technology, and other varied forms of communication infrastructure complete circuit functionality and transform the mixed components into a regular working system.
Today, most electronic devices use semiconductor components to perform electron control. The underlying principles that explain how semiconductors work are studied in solid state physics, whereas the design and construction of electronic circuits to solve practical problems are part of electronics engineering.
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Extract interconnection technologies used to create a regular working system.
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Interconnection technologies such as circuit boards, electronics packaging technology, and other varied forms of communication infrastructure complete circuit functionality and transform the mixed components into a regular working system.
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What was special about the 2021 Formula 1 World Championship?
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The 2021 Formula 1 World Championship came down to the final race of the 2021 season in Abu Dhabi. Seven-time world champion, Lewis Hamilton and Max Versteppen were tied in drivers' points, with this race being the deciding factor of who would be crowned champion.
Ultimately, the 2021 Formula 1 World Championship was won by Dutch driver, Max Verstappen. This was his first Driver's Championship title.
What makes this particular championship special, or controversial (depending on who you ask), is that the race director, Michael Masi, misinterpreted the regulations regarding a late safety car restart. Masi called the safety car back into the pit lane without it having completed an additional lap as required by the Formula 1 Sporting Regulations (Article 48.12). By not heeding these regulations, all lapped cars were not able to unlap themselves which resulted in Max Verstappen being in a position to easily overtake Lewis Hamilton for the win.
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Make a list of several games I could play with two friends
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You could play cribbage, monopoly, poker, backgammon or yahtzee
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what book would you recommend for pregnant women?
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Pregnant women have a lot on their plate, including their own physical and mental health and preparation for childbirth and newborn care. What to expect when you are expecting written by Heidi Murkoff is a popular book that provides a 360 degree view of pregnancy details like week-by-week fetal development and nutrient recommendations for each month. Expecting better by Emily Oster is another popular, more data-oriented and anecdotal, aiming to debunk common misguided rules and myths about pregnancy. Heng Ou's Nine Golden Months focuses more on mental power and nutrient suggestions for expecting moms. It also provides incredible recipes that turn easy-to-access grocery store staples to tasty and nutrient dishes.
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1810.00663
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This work also contributes a new dataset of INLINEFORM0 pairs of free-form natural language instructions and high-level navigation plans. This dataset was collected through Mechanical Turk using 100 simulated environments with a corresponding topological map and, to the best of our knowledge, it is the first of its kind for behavioral navigation. The dataset opens up opportunities to explore data-driven methods for grounding navigation commands into high-level motion plans.
While the dataset was collected with simulated environments, no structure was imposed on the navigation instructions while crowd-sourcing data. Thus, many instructions in our dataset are ambiguous. Moreover, the order of the behaviors in the instructions is not always the same. For instance, a person said “turn right and advance” to describe part of a route, while another person said “go straight after turning right” in a similar situation. The high variability present in the natural language descriptions of our dataset makes the problem of decoding instructions into behaviors not trivial. See Appendix A of the supplementary material for additional details on our data collection effort.
This work also contributes a new dataset of INLINEFORM0 pairs of free-form natural language instructions and high-level navigation plans. This dataset was collected through Mechanical Turk using 100 simulated environments with a corresponding topological map and, to the best of our knowledge, it is the first of its kind for behavioral navigation. The dataset opens up opportunities to explore data-driven methods for grounding navigation commands into high-level motion plans.
While the dataset was collected with simulated environments, no structure was imposed on the navigation instructions while crowd-sourcing data. Thus, many instructions in our dataset are ambiguous. Moreover, the order of the behaviors in the instructions is not always the same. For instance, a person said “turn right and advance” to describe part of a route, while another person said “go straight after turning right” in a similar situation. The high variability present in the natural language descriptions of our dataset makes the problem of decoding instructions into behaviors not trivial. See Appendix A of the supplementary material for additional details on our data collection effort.
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Did the collection process use a WoZ method?
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No.
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Where do bears live?
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Different species of bears can be found in different parts of the world. For example, black bears can be found in North America and Asia, whereas polar bears can be found in the Arctic. Grizzly bears, are also known as brown bears, they can be found across Europe, Asia, and North America.
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Which of the following ski resorts are located in Utah: Alta Ski Area, Brighton Ski Resort, Snowbird, Solitude Mountain Resort, Deer Valley Resort, Park City Mountain, Woodward Park City, Nordic Valley Resort, Powder Mountain, Snowbasin, Sundance Resort, Beaver Mountain Resort, Cherry Peak Resort, Brian Head Resort, Eagle Point Resort, Keystone, Copper Mountain, Vail Resort, Beaver Creek, Eldora
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The following ski resorts are in Utah: Alta Ski Area, Brighton Ski Resort, Snowbird, Solitude Mountain Resort, Deer Valley Resort, Park City Mountain, Woodward Park City, Nordic Valley Resort, Powder Mountain, Snowbasin, Sundance Resort, Beaver Mountain Resort, Cherry Peak Resort, Brian Head Resort, Eagle Point Resort
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I'm planning a month-long trip and am wondering which of these items are okay to be kept in a carry-on bag. Could you help classify these items as "allowed" or "not allowed" by the TSA? Toothbrush, Cologne, Beard Shaver, Alcohol, and Bottle Opener.
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Here is what's allowed and what's not allowed in your carry-on luggage:
- Allowed: Toothbrush, Cologne (3.4 Ounces), Beard Shaver
- Not allowed: Alcohol, Bottle Opener
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null | false
| 46
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In this work, we incorporate a domain discriminator to align the mismatched domains between ASR hypotheses and reference transcriptions. The adversarial learning allows the end-to-end QA model to learn domain-invariant features and improve the robustness to ASR errors. The experiments demonstrate that the proposed model successfully achieves superior performance and outperforms the previous best model by 2% EM score and over 1.5% F1 score.
The adversarial learning allows the end to-end QA model to learn domain-invariant features and improve the robustness to ASR errors.
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What's the advantage of adversarial learning?
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The adversarial learning allows the end to-end QA model to learn domain-invariant features and improve the robustness to ASR errors.
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Voluntary hyperventilation before beginning voluntary apnea is commonly believed to allow the person involved to safely hold their breath for a longer period. In reality, it will give the impression that one does not need to breathe, while the body is actually experiencing a blood-oxygen level that would normally, and indirectly, invoke a strong dyspnea and eventually involuntary breathing. Some have incorrectly attributed the effect of hyperventilation to increased oxygen in the blood, not realizing that it is actually due to a decrease in CO2 in the blood and lungs. Blood leaving the lungs is normally fully saturated with oxygen, so hyperventilation of normal air cannot increase the amount of oxygen available, as oxygen in blood is the direct factor. Lowering the CO2 concentration increases the pH of the blood, thus increasing the time before blood becomes acidic enough so the respiratory center becomes stimulated, as described above.
The primary urge to breathe is triggered by rising carbon dioxide (CO2) levels in the bloodstream. Carbon dioxide builds up in the bloodstream when oxygen is metabolized and it needs to be expelled as a waste product. The body detects carbon dioxide levels very accurately and relies on this as the primary trigger to control breathing. Hyperventilation artificially depletes the resting concentration of carbon dioxide causing a low blood carbon dioxide condition called hypocapnia. Hypocapnia reduces the reflexive respiratory drive, allowing the delay of breathing and leaving the diver susceptible to loss of consciousness from hypoxia. For most healthy people, the first sign of low oxygen levels is a greyout or unconsciousness: there is no bodily sensation that warns a diver of an impending blackout.
If a person loses consciousness underwater, there is considerable danger that they will drown. An alert diving partner or nearby lifeguard would be in the best position to rescue such a person. Static apnea blackout occurs at the surface when a motionless diver holds their breath long enough for the circulating oxygen in blood to fall below that required for the brain to maintain consciousness. It involves no pressure changes in the body and is usually performed to enhance breath-hold time. It should never be practiced alone, but under strict safety protocols with a safety guard or equipment beside the diver.
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Using this definition, can you summarize why hyperventilation is considered dangerous?
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Hyperventilation does not increase the amount of oxygen available but artificially decreases carbon dioxide (CO2) levels in blood and lungs.
Because the primary urge to breathe is triggered by rising carbon dioxide levels, it gives false impression that one does not need to breath. This can lead to sudden lost of consciousness. If this happens underwater, there is a considerable risk of drowning.
This is why hyperventilation is considered as dangerous and should be avoided.
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Tell me whether these are states or cities: Alabama, Alaska, Wyoming, Washinton DC, Salt Lake city, San Francisco
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States: Alabama, Alaska, Wyoming
Cities: Washinton DC, Salt Lake city, San Francisco
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Will being fully autonomous change the future of car design ?
|
If cars reach a stage of full autonomy and no longer need input to drive from a human, this can radically change the interior and exterior of a car. There might not be a need for a driver cockpit and the steering console can be completely removed and potentially all seats could be inward facing to maximize human interaction. All windows can be removed and replaced with tablets. Removing glass windows and replacing it with a more protective alloy shell, also means potentially increasing the safety of the passengers. Size and shape of the vehicles will also change as they can become containerized, where multiple vehicles can interact with each other and lock in and out, much like how trains work.
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Who was the 45th President of the United States of America?
|
Donald J. Trump was the 45th President of the United States of America
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| 140
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In this work, we introduced a model for forecasting conversational events that processes comments as they happen and takes the full conversational context into account to make an updated prediction at each step. This model fills a void in the existing literature on conversational forecasting, simultaneously addressing the dual challenges of capturing inter-comment dynamics and dealing with an unknown horizon. We find that our model achieves state-of-the-art performance on the task of forecasting derailment in two different datasets that we release publicly. We further show that the resulting system can provide substantial prior notice of derailment, opening up the potential for preemptive interventions by human moderators BIBREF65.
While we have focused specifically on the task of forecasting derailment, we view this work as a step towards a more general model for real-time forecasting of other types of emergent properties of conversations. Follow-up work could adapt the CRAFT architecture to address other forecasting tasks mentioned in Section SECREF2—including those for which the outcome is extraneous to the conversation. We expect different tasks to be informed by different types of inter-comment dynamics, and further architecture extensions could add additional supervised fine-tuning in order to direct it to focus on specific dynamics that might be relevant to the task (e.g., exchange of ideas between interlocutors or stonewalling).
With respect to forecasting derailment, there remain open questions regarding what human moderators actually desire from an early-warning system, which would affect the design of a practical system based on this work. For instance, how early does a warning need to be in order for moderators to find it useful? What is the optimal balance between precision, recall, and false positive rate at which such a system is truly improving moderator productivity rather than wasting their time through false positives? What are the ethical implications of such a system? Follow-up work could run a user study of a prototype system with actual moderators to address these questions.
A practical limitation of the current analysis is that it relies on balanced datasets, while derailment is a relatively rare event for which a more restrictive trigger threshold would be appropriate. While our analysis of the precision-recall curve suggests the system is robust across multiple thresholds ($AUPR=0.7$), additional work is needed to establish whether the recall tradeoff would be acceptable in practice.
Finally, one major limitation of the present work is that it assigns a single label to each conversation: does it derail or not? In reality, derailment need not spell the end of a conversation; it is possible that a conversation could get back on track, suffer a repeat occurrence of antisocial behavior, or any number of other trajectories. It would be exciting to consider finer-grained forecasting of conversational trajectories, accounting for the natural—and sometimes chaotic—ebb-and-flow of human interactions.
Acknowledgements. We thank Caleb Chiam, Liye Fu, Lillian Lee, Alexandru Niculescu-Mizil, Andrew Wang and Justine Zhang for insightful conversations (with unknown horizon), Aditya Jha for his great help with implementing and running the crowd-sourcing tasks, Thomas Davidson and Claire Liang for exploratory data annotation, as well as the anonymous reviewers for their helpful comments. This work is supported in part by the NSF CAREER award IIS-1750615 and by the NSF Grant SES-1741441.
A practical limitation of the current analysis is that it relies on balanced datasets, while derailment is a relatively rare event for which a more restrictive trigger threshold would be appropriate. While our analysis of the precision-recall curve suggests the system is robust across multiple thresholds (AUP R = 0.7), additional work is needed to establish whether the recall tradeoff would be acceptable in practice. Finally, one major limitation of the present work is that it assigns a single label to each conversation: does it derail or not? In reality, derailment need not spell the end of a conversation; it is possible that a conversation could get back on track, suffer a repeat occurrence of antisocial behavior, or any number of other trajectories. It would be exciting to consider finer-grained forecasting of conversational trajectories, accounting for the natural—and sometimes chaotic—ebband-flow of human interactions.
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What limitations does their method have?
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Whether the recall tradeoff would be acceptable in practice is need to be validated. The present work is that it assigns a single label to each conversation, so "does it derail or not?" still need to be validated.
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| 31
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While most current neural abstractive summarization models have focused on summarizing relatively short news articles (e.g., BIBREF16 ), few researchers have started to investigate the summarization of longer documents by exploiting their natural structure. agents present an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. The encoding task is divided across several collaborating agents, each is responsible for a subsection of text through a multi-layer LSTM with word attention. Their model seems however overly complicated when it comes to the extractive summarization task, where word attention is arguably much less critical. So, we do not consider this model further in this paper.
discourselongdocument also propose a model for abstractive summarization taking the structure of documents into consideration with a hierarchical approach, and test it on longer documents with section information, i.e. scientific papers. In particular, they apply a hierarchical encoder at the word and section levels. Then, in the decoding step, they combine the word attention and section attention to obtain a context vector.
This approach to capture discourse structure is however quite limited both in general and especially when you consider its application to extractive summarization. First, their hierarchical method has a large number of parameters and it is therefore slow to train and likely prone to overfitting. Secondly, it does not take the global context of the whole document into account, which may arguably be critical in extractive methods, when deciding on the salience of a sentence (or even a word). The extractive summarizer we present in this paper tries to address these limitations by adopting the parameter lean LSTM-minus method, and by explicitly modeling the global context.
The extractive summarizer we present in this paper tries to address these limitations by adopting the parameter lean LSTM-minus method, and by explicitly modeling the global context.
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How does the abstractor proposed by the author solve the problem of limitation?
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By adopting the parameter lean LSTM-minus method, and by explicitly modeling the global context.
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What are the interesting facts about Shakespeare?
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- Shakespeare's father made gloves for a living.
- Shakespeare had seven siblings.
- Shakespeare married an older, pregnant lady at 18.
- Shakespeare was an actor, as well as a writer.
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Why do the San Francisco 49ers play in Santa Clara, California?
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The San Francisco 49ers originally played at Kezar Stadium in San Francisco before moving to Candlestick Park in 1971. When negotiations to build a new stadium fell apart they worked with the city of Santa Clara to build a new stadium which was located next to their training facilities. Because of brand recognition and close proximity, the decision was made to keep the name, San Francisco 49ers.
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What is the answer to life, the universe and everything?
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Douglas Adams would have us believe that the answer to life, the universe and everything is forty-two. However he clearly hadn't anticipated the challenges of training large language models where an answer of such succinct elegance would have caused the LLM to stop dead in its tracks pretty quickly. On the other hand, assuming that the model was already trained and that even though Adam's supercomputer took seven and a half million years to come up with the answer forty-two, perhaps he was more prescient than even he realised at the time. It's quite a conundrum. Maybe his model was simply too large? Or maybe it was just the right size and it was the compute that was the issue? If Deep Thought had followed a distributed compute model perhaps involving all the planets of the universe, rather than simply relying on the Earth, which always seemed like a single node build, then perhaps his answer would have been richer in its philosophical content. Then again, perhaps a distributed model would simply have arrived at the answer forty-two a lot faster before the first micro-organisms showed up on earth and we wouldn't be here trying to answer this question now.
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