Datasets:

OllieStanley

/

oa_dolly_15k

like 2

{ "CATEGORY": "summarization", "CONTEXT": "John James Monckton (28 October 1938 – 29 June 2017)[1] was an Australian backstroke swimmer who won a silver medal in the 100-metre event at the 1956 Summer Olympics in Melbourne. Although he set multiple world records, he never won an Olympic gold medal.[2]\n\nAn apprentice carpenter from the New England region of New South Wales, Monckton appeared to be primed to win gold at the 1956 Olympics. At the national team camp in Townsville before the games, he became the first person to swim 400-metre backstroke in under five minutes. Although it was not a regularly contested event at international level, it was a promising sign for the event to be included for medal competition in the Olympics. He also set world records in the 110- and 220-yard freestyle events.\n\nAt the Olympics, Monckton was the fastest qualifier in the heats and semifinal, but was upstaged in the final by teammate David Theile.[2]\n\nIn the absence of Theile, who had retired after the Olympics to study medicine at university, Monckton dominated backstroke swimming, winning the 110-yard backstroke event at the 1958 British Empire and Commonwealth Games in Cardiff and also the 4×110-yard medley relay.[2] Monckton then prepared for another Olympics in 1960 in Rome, with Theile deferring his studies to defend his Olympic title. Monckton again led the qualifying in the heats and semifinals. However, in the final, he misjudged the turn and broke a finger. He limped home in seventh position, with Theile successfully defending his title.[3] Monckton continued competing in the hope of reaching a third Olympics in 1964, but retired after his performances began to deteriorate.\n\nHe married Maureen Giles, an Australian swimmer at the 1956 Olympics.[4]\n\nHe was inducted into the Sport Australia Hall of Fame in 1999.[5]\n\nThe Monckton Aquatic Centre in his hometown of Armidale is named for him." }

{ "CATEGORY": "information_extraction", "CONTEXT": "In common usage, climate change describes global warming—the ongoing increase in global average temperature—and its effects on Earth's climate system. Climate change in a broader sense also includes previous long-term changes to Earth's climate. The current rise in global average temperature is more rapid than previous changes, and is primarily caused by humans burning fossil fuels.[2][3] Fossil fuel use, deforestation, and some agricultural and industrial practices increase greenhouse gases, notably carbon dioxide and methane.[4] Greenhouse gases absorb some of the heat that the Earth radiates after it warms from sunlight. Larger amounts of these gases trap more heat in Earth's lower atmosphere, causing global warming.\n\nDue to climate change, deserts are expanding, while heat waves and wildfires are becoming more common.[5] Increased warming in the Arctic has contributed to melting permafrost, glacial retreat and sea ice loss.[6] Higher temperatures are also causing more intense storms, droughts, and other weather extremes.[7] Rapid environmental change in mountains, coral reefs, and the Arctic is forcing many species to relocate or become extinct.[8] Even if efforts to minimise future warming are successful, some effects will continue for centuries. These include ocean heating, ocean acidification and sea level rise.[9]" }

{ "CATEGORY": "summarization", "CONTEXT": "The Needle is a fictional character, a mutant supervillain appearing in American comic books published by Marvel Comics. Created by Mark Gruenwald, Carmine Infantino, and Al Gordon, the character first appeared in Spider-Woman #9 (December 1978). Creator Mark Gruenwald brought the character back during his run on the West Coast Avengers as a member of the villain team Night Shift, which included other former Spider-Woman foes.\n\nAfter being mugged one night while leaving work, Josef Saint, an elderly tailor, lost an eye and his ability to speak. While recuperating, he discovered that he has the power to paralyze with his gaze. Seeking revenge, Saint assumed the guise of the vigilante the Needle. Armed with a yard-long needle, the mute villain attacked young men on the streets at night, sewing their mouths shut. After victimizing S.H.I.E.L.D. agent Jerry Hunt, the Needle incurred the wrath of Spider-Woman, the agent's girlfriend, and she took him out with a close-range venom blast. He was subsequently arrested.\n\nWith other superhuman adventurers and criminals, the Needle was imprisoned by the Locksmith, but then freed by Spider-Woman.\n\nThe Needle joined the Night Shift, and teamed with Captain America against the Power Broker and his augmented mutates. Alongside the Night Shift, he tested Moon Knight to take over as the leader of the Night Shift. Later, alongside the Night Shift, he battled the West Coast Avengers. Alongside the Night Shift, the second Hangman, and Satannish, he battled the Avengers West Coast; his abilities had been enhanced by Satannish's black magic.\n\nThe Needle was later defeated by Armory.\n\nNeedle appears with the Night Shift, as part of the Hood's gang. They battle the Midnight Sons, and Needle impales Daimon Hellstrom with his needle, before Jennifer Kale knocks him off with a spell. He and the Night Shift are killed when the zombie virus mutates and becomes airborne. The virus cloud begins to rain blood, and reanimates the Night Shift as zombies. Dormammu assumes control of the Night Shift and uses them to fight the Midnight Sons. When Jennifer Kale and the Black Talon contain the virus within the Zombie (Simon Garth), the Night Shift members are apparently restored to normal, and the Hood teleports away with them." }

{ "CATEGORY": "summarization", "CONTEXT": "Ana María González (born 13 April 1951[1]) is a Spanish lyric soprano trained in Argentina, known for performing at the Teatro Colón from 1973 to 1994. Although associated with the Italian lyric repertoire, she also excelled in lyric soprano roles of the French repertoire such as Manon, Marguerite, and Juliette, which brought her international fame.[2]" }

{ "CATEGORY": "information_extraction", "CONTEXT": "Termination of employment or separation of employment is an employee's departure from a job and the end of an employee's duration with an employer. Termination may be voluntary on the employee's part, or it may be at the hands of the employer, often in the form of dismissal (firing) or a layoff. Dismissal or firing is usually thought to be the employee's fault, whereas a layoff is generally done for business reasons (for instance, a business slowdown, or an economic downturn) outside the employee's performance.\n\nFiring carries a stigma in many cultures and may hinder the jobseeker's chances of finding new employment, particularly if they have been terminated from a previous job. Jobseekers sometimes do not mention jobs from which they were fired on their resumes; accordingly, unexplained gaps in employment, and refusal or failure to contact previous employers are often regarded as \"red flags\".[1]\n\nDismissal is when the employer chooses to require the employee to leave, usually for the reason that is the employee's fault. The most common colloquial terms for dismissal in the United States are \"getting fired\" or \"getting canned\" whereas in the United Kingdom the terms \"getting the sack\" or \"getting sacked\" are also used." }

{ "CATEGORY": "creative_writing", "CONTEXT": "" }

{ "CATEGORY": "closed_qa", "CONTEXT": "Dartmouth Night starts the college's traditional \"Homecoming\" weekend with an evening of speeches, a parade, and a bonfire. Traditionally, the freshman class builds the bonfire and then runs around it a set number of times in concordance with their class year; the class of 2009 performed 109 circuits, the class of 1999 performed 99, etc. The College officially discourages a number of student traditions of varying degrees of antiquity. During the circling of the bonfire, upperclassmen encourage the freshmen to \"touch the fire\", an action legally considered trespassing and prohibited by police officials present. At halftime of the Homecoming football game on the Saturday of the weekend, some upperclassmen encourage freshman to \"rush the field\", although no upperclassman has seen a significant rush since several injuries sustained during the 1986 rush prompted the school to ban the practice. Among the two or three students who sometimes run across the field, those who are arrested are charged with trespassing (the independent newspaper The Dartmouth Review claimed to set up a fund to automatically pay any fines associated with freshman who rush the field.) However, in 2012 this was proven false when two students rushed the field and were arrested for disorderly conduct. The Dartmouth Review ignored their emails until finally replying and denying that this fund had ever existed. These students then had to pay $300 fines out of pocket. For the 2011 Homecoming game, however, over 40 members of the Class of 2015 rushed the field at homecoming without any action taken by Safety and Security or the Hanover Police Department." }

{ "CATEGORY": "summarization", "CONTEXT": "Amsterdam Centraal was designed by Pierre Cuypers, who is also known for his design of the Rijksmuseum in Amsterdam. While Cuypers was the principal architect, it is believed that he focused mostly on the decoration of the station building and left the structural design to railway engineers.[4] The station was built by contractor Philipp Holzmann. The new central station replaced Amsterdam Willemspoort Station, which had closed in 1878, as well as the temporary Westerdok Station used from 1878 to 1889. The idea for a central station came from Johan Rudolph Thorbecke, then the Netherlands Minister of the Interior and responsible for the national railways, who, in 1884, laid two proposals before the Amsterdam municipal council. In the first proposal, the station would be situated between the Leidseplein and the Amstel river. In the other, it would be built in the open harbour front allowing for the station to be connected to the existing main lines in the area to the west and the south, but also to a projected new northern line." }

{ "CATEGORY": "closed_qa", "CONTEXT": "A Star Is Born (1937 film), starring Janet Gaynor and Fredric March, directed by William A. Wellman\nA Star Is Born (1954 film), starring Judy Garland and James Mason, directed by George Cukor\nA Star Is Born (1976 film), starring Barbra Streisand and Kris Kristofferson, directed by Frank Pierson\nA Star Is Born (2018 film), starring Lady Gaga and Bradley Cooper, directed by Bradley Cooper" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "closed_qa", "CONTEXT": "A large language model (LLM) is a language model consisting of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabelled text using self-supervised learning. LLMs emerged around 2018 and perform well at a wide variety of tasks. This has shifted the focus of natural language processing research away from the previous paradigm of training specialized supervised models for specific tasks.\n\nProperties\nThough the term large language model has no formal definition, it often refers to deep learning models having a parameter count on the order of billions or more. LLMs are general purpose models which excel at a wide range of tasks, as opposed to being trained for one specific task (such as sentiment analysis, named entity recognition, or mathematical reasoning). The skill with which they accomplish tasks, and the range of tasks at which they are capable, seems to be a function of the amount of resources (data, parameter-size, computing power) devoted to them, in a way that is not dependent on additional breakthroughs in design.\n\nThough trained on simple tasks along the lines of predicting the next word in a sentence, neural language models with sufficient training and parameter counts are found to capture much of the syntax and semantics of human language. In addition, large language models demonstrate considerable general knowledge about the world, and are able to \"memorize\" a great quantity of facts during training.\n\nHallucinations\nMain article: Hallucination (artificial intelligence)\nIn artificial intelligence in general, and in large language models in particular, a \"hallucination\" is a confident response that does not seem to be justified by the model's training data.\n\nEmergent abilities\n\nOn a number of natural language benchmarks involving tasks such as question answering, models perform no better than random chance until they reach a certain scale (in this case, measured by training computation), at which point their performance sharply increases. These are examples of emergent abilities.\nUnpredictable abilities that have been observed in large language models but that were not present in simpler models (and that were not explicitly designed into the model) are usually called \"emergent abilities\". Researchers note that such abilities \"cannot be predicted simply by extrapolating the performance of smaller models\". These abilities are discovered rather than programmed-in or designed, in some cases only after the LLM has been publicly deployed. Hundreds of emergent abilities have been described. Examples include multi-step arithmetic, taking college-level exams, identifying the intended meaning of a word, chain-of-thought prompting, decoding the International Phonetic Alphabet, unscrambling a word’s letters, identifying offensive content in paragraphs of Hinglish (a combination of Hindi and English), and generating a similar English equivalent of Kiswahili proverbs.\n\nArchitecture and training\nLarge language models have most commonly used the transformer architecture, which, since 2018, has become the standard deep learning technique for sequential data (previously, recurrent architectures such as the LSTM were most common). LLMs are trained in an unsupervised manner on unannotated text. A left-to-right transformer is trained to maximize the probability assigned to the next word in the training data, given the previous context. Alternatively, an LLM may use a bidirectional transformer (as in the example of BERT), which assigns a probability distribution over words given access to both preceding and following context. In addition to the task of predicting the next word or \"filling in the blanks\", LLMs may be trained on auxiliary tasks which test their understanding of the data distribution such as Next Sentence Prediction (NSP), in which pairs of sentences are presented and the model must predict whether they appear side-by-side in the training corpus.\n\nThe earliest LLMs were trained on corpora having on the order of billions of words. The first model in OpenAI's GPT series was trained in 2018 on BookCorpus, consisting of 985 million words. In the same year, BERT was trained on a combination of BookCorpus and English Wikipedia, totalling 3.3 billion words. In the years since then, training corpora for LLMs have increased by orders of magnitude, reaching up to hundreds of billions or trillions of tokens.\n\nLLMs are computationally expensive to train. A 2020 study estimated the cost of training a 1.5 billion parameter model (1-2 orders of magnitude smaller than the state of the art at the time) at $1.6 million.\n\nA 2020 analysis found that neural language models' capability (as measured by training loss) increased smoothly in a power law relationship with number of parameters, quantity of training data, and computation used for training. These relationships were tested over a wide range of values (up to seven orders of magnitude) and no attenuation of the relationship was observed at the highest end of the range (including for network sizes up to trillions of parameters).\n\nApplication to downstream tasks\nBetween 2018 and 2020, the standard method for harnessing an LLM for a specific natural language processing (NLP) task was to fine tune the model with additional task-specific training. It has subsequently been found that more powerful LLMs such as GPT-3 can solve tasks without additional training via \"prompting\" techniques, in which the problem to be solved is presented to the model as a text prompt, possibly with some textual examples of similar problems and their solutions.\n\nFine-tuning\nMain article: Fine-tuning (machine learning)\nFine-tuning is the practice of modifying an existing pretrained language model by training it (in a supervised fashion) on a specific task (e.g. sentiment analysis, named entity recognition, or part-of-speech tagging). It is a form of transfer learning. It generally involves the introduction of a new set of weights connecting the final layer of the language model to the output of the downstream task. The original weights of the language model may be \"frozen\", such that only the new layer of weights connecting them to the output are learned during training. Alternatively, the original weights may receive small updates (possibly with earlier layers frozen).\n\nPrompting\nSee also: Prompt engineering and Few-shot learning (natural language processing)\nIn the prompting paradigm, popularized by GPT-3, the problem to be solved is formulated via a text prompt, which the model must solve by providing a completion (via inference). In \"few-shot prompting\", the prompt includes a small number of examples of similar (problem, solution) pairs. For example, a sentiment analysis task of labelling the sentiment of a movie review could be prompted as follows:\n\nReview: This movie stinks.\nSentiment: negative\n\nReview: This movie is fantastic!\nSentiment:\n\nIf the model outputs \"positive\", then it has correctly solved the task. In zero-shot prompting, no solve examples are provided. An example of a zero-shot prompt for the same sentiment analysis task would be \"The sentiment associated with the movie review 'This movie is fantastic!' is\".\n\nFew-shot performance of LLMs has been shown to achieve competitive results on NLP tasks, sometimes surpassing prior state-of-the-art fine-tuning approaches. Examples of such NLP tasks are translation, question answering, cloze tasks, unscrambling words, and using a novel word in a sentence. The creation and optimisation of such prompts is called prompt engineering.\n\nInstruction tuning\nInstruction tuning is a form of fine-tuning designed to facilitate more natural and accurate zero-shot prompting interactions. Given a text input, a pretrained language model will generate a completion which matches the distribution of text on which it was trained. A naive language model given the prompt \"Write an essay about the main themes of Hamlet.\" might provide a completion such as \"A late penalty of 10% per day will be applied to submissions received after March 17.\" In instruction tuning, the language model is trained on many examples of tasks formulated as natural language instructions, along with appropriate responses. Various techniques for instruction tuning have been applied in practice. OpenAI's InstructGPT protocol involves supervised fine-tuning on a dataset of human-generated (prompt, response) pairs, followed by reinforcement learning from human feedback (RLHF), in which a reward function was learned based on a dataset of human preferences. Another technique, \"self-instruct\", fine-tunes the language model on a training set of examples which are themselves generated by an LLM (bootstrapped from a small initial set of human-generated examples).\n\nhttps://en.wikipedia.org/wiki/Large_language_model" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "information_extraction", "CONTEXT": "A running of the bulls (Spanish: encierro, from the verb encerrar, 'to corral, to enclose'; Occitan: abrivado, literally 'haste, momentum'; Catalan: correbous, 'run-bulls') is an event that involves running in front of a small group of bulls, typically six but sometimes ten or more, that have been let loose on sectioned-off streets in a town, usually as part of a summertime festival. Particular breeds of cattle may be favored, such as the toro bravo in Spain, also often used in post-run bullfighting, and Camargue cattle in Occitan France, which are not fought. Bulls (non-castrated male cattle) are typically used in such events.\n\nThe most famous bull-run is the encierro held in Pamplona during the nine-day festival of Sanfermines in honor of Saint Fermin. It has become a major global tourism event, today very different from the traditional, local festival. More traditional summer bull-runs are held in other places such as towns and villages across Spain and Portugal, in some cities in Mexico, and in the Occitan (Camargue) region of southern France. Bull-running was formerly also practiced in rural England, most famously at Stamford until 1837." }

{ "CATEGORY": "closed_qa", "CONTEXT": "When Mohammad inherited the newly born sultanate from his father Alauddin, the land was still infested with thieves and robbers. As such he spent the entire part of his reign in establishing law in his land. He ordered his governors to never give refuge to anyone who defied his authority. This campaign was successful and at the end of his reign,the land became peaceful and law abiding. He also sat on the takht-ē-firoza or The Turquoise throne which was gifted to him by Kapaya Nayaka.[5]" }

{ "CATEGORY": "information_extraction", "CONTEXT": "Some CPU architectures, such as the MIPS and Alpha, do not use a dedicated flag register. Others do not implicitly set and/or read flags. Such machines either do not pass implicit status information between instructions at all, or they pass it in an explicitly selected general purpose register." }

{ "CATEGORY": "brainstorming", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "classification", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "closed_qa", "CONTEXT": "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." }

{ "CATEGORY": "closed_qa", "CONTEXT": "Prehistory to early 19th century\nThe following table lists estimates for the population of India (including what are now Pakistan and Bangladesh) from prehistory up until 1820. It includes estimates and growth rates according to five economic historians, along with interpolated estimates and overall aggregate averages derived from their estimates.[citation needed]\n\nEstimates of historical world population\n\nYear\tAggregate average\tPeriod\tAverage\n % growth\n/ century\nPopulation\t% of World population\n10,000 BC\t1,000\t0.83%\tStone Age\t30.28\n4000 BC\t1,000,000\t30.83%\n2000 BC\t13,000,000\t37.143%\tBronze Age\t26.25\n500 BC\t25,000,000\t41.70%\tIron Age\t\n400 BC\t26,000,000\t43.96%\n200 BC\t31,000,000\t47.63%\tMaurya era\t\n1 AD\t35,000,000\t35.56%\tClassical\nera\t\n200\t41,000,000\t36.15%\n400\t47,000,000\t40%\n500\t50,000,000\t43.58%\n600\t53,000,000\t48.83%\tEarly\nmedieval\nera\t\n700\t60,000,000\t56.67%\n800\t64,000,000\t55%\n900\t70,000,000\t53.34%\n1000\t79,000,000\t30%\n1100\t83,000,000\t35%\tLate\nmedieval\nera\t\n1200\t86,000,000\t36.67%\n1300\t91,000,000\t38.34%\n1400\t97,000,000\t30%\n1500\t105,000,000\t21.67%\n1600\t140,000,000\t23.33%\tEarly modern era\t\n1650\t170,000,000\t26.15%\n1700\t140,000,000\t20%\n1750\t183,000,000\t21.53%\n1800\t200,000,000\t20%\n1820\t210,000,000\t19.09%\nThe population grew from the South Asian Stone Age in 10,000 BC to the Maurya Empire in 200 BC at a steadily increasing growth rate,[19] before population growth slowed down in the Early Medieval Era up to 700 AD, and then started decreasing the up to 1500 AD.[20][21]\n\nUnder Mughal Empire, India experienced a high economic and demographic upsurge,[19] due to Mughal agrarian reforms that intensified agricultural production.[22] 15% of the population lived in urban centres, higher than the percentage of the population in 19th-century British India[23] and contemporary Europe[23] up until the 19th century.[24] Those estimates were criticised by Tim Dyson, who considers them exaggerations. According to Dyson, urbanisation of the Mughal empire was less than 9%[25]\n\nUnder the reign of Akbar (reigned 1556–1605) in 1600, the Mughal Empire's urban population was up to 17 million people, larger than the urban population in Europe.[26] By 1700, Mughal India had an urban population of 23 million people, larger than British India's urban population of 22.3 million in 1871.[27] Nizamuddin Ahmad (1551–1621) reported that, under Akbar's reign, Mughal India had 120 large cities and 3,200 townships.[23] A number of cities in India had a population between a quarter-million and half-million people,[23] with larger cities including Agra (in Agra Subah) with up to 800,000 people[28] and Dhaka (in Bengal Subah) with over 1 million people.[29] Mughal India also had a large number of villages, with 455,698 villages by the time of Aurangzeb (reigned 1658–1707).[26]" }

{ "CATEGORY": "brainstorming", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "brainstorming", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "information_extraction", "CONTEXT": "WordPress (WP or WordPress.org) is a free and open-source content management system (CMS) written in hypertext preprocessor (PHP) language and paired with a MySQL or MariaDB database with supported HTTPS. Features include a plugin architecture and a template system, referred to within WordPress as \"Themes\". WordPress was originally created as a blog-publishing system but has evolved to support other web content types including more traditional mailing lists and Internet fora, media galleries, membership sites, learning management systems (LMS) and online stores." }

{ "CATEGORY": "classification", "CONTEXT": "" }

{ "CATEGORY": "classification", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "classification", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "classification", "CONTEXT": "" }

{ "CATEGORY": "classification", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "brainstorming", "CONTEXT": "" }

{ "CATEGORY": "summarization", "CONTEXT": "Olson served as a law clerk for Judge Barbara Jacobs Rothstein on the United States District Court for the Western District of Washington for two years immediately after finishing law school. She served as a trial attorney and later Deputy Director of the National Church Arson Task Force in the United States Department of Justice Civil Rights Division from 1992 to 1997. From 1994 to 1997, she also worked part-time as an adjunct professor at George Washington University Law School.\n\nIn 1997, Olson joined the Office of the United States Attorney for the District of Idaho, rising to the rank of Senior Litigation Counsel at the time of her appointment in 2010.[4]\n\nAfter Olson resigned in 2017, she joined the Boise office of Stoel Rives, a law firm that operates in the Pacific Northwest.[5][6] In 2021, Olson was included on a shortlist of possible nominees to succeed Judge B. Lynn Winmill.[7][8]" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "brainstorming", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "summarization", "CONTEXT": "In deep learning, a convolutional neural network (CNN) is a class of artificial neural network most commonly applied to analyze visual imagery. CNNs use a mathematical operation called convolution in place of general matrix multiplication in at least one of their layers. They are specifically designed to process pixel data and are used in image recognition and processing. They have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series.\n\nCNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input.\n\nCNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons are usually fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The \"full connectivity\" of these networks make them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include: penalizing parameters during training (such as weight decay) or trimming connectivity (skipped connections, dropout, etc.) Developing robust datasets also increases the probability that CNNs will learn the generalized principles that characterize a given dataset rather than the biases of a poorly-populated set." }

{ "CATEGORY": "summarization", "CONTEXT": "System and Organization Controls (SOC), (also sometimes referred to as service organizations controls) as defined by the American Institute of Certified Public Accountants (AICPA), is the name of a suite of reports produced during an audit. It is intended for use by service organizations (organizations that provide information systems as a service to other organizations) to issue validated reports of internal controls over those information systems to the users of those services. The reports focus on controls grouped into five categories called Trust Service Principles.[1] The AICPA auditing standard Statement on Standards for Attestation Engagements no. 18 (SSAE 18), section 320, \"Reporting on an Examination of Controls at a Service Organization Relevant to User Entities' Internal Control Over Financial Reporting\", defines two levels of reporting, type 1 and type 2. Additional AICPA guidance materials specify three types of reporting: SOC 1, SOC 2, and SOC 3.\n\nTrust Service Principles\nSOC 2 reports focus on controls addressed by five semi-overlapping categories called Trust Service Principles which also support the CIA triad of information security:[1]\n\nSecurity\nFirewalls\nIntrusion detection\nMulti-factor authentication\nAvailability\nPerformance monitoring\nDisaster recovery\nIncident handling\nConfidentiality\nEncryption\nAccess controls\nFirewalls\nProcessing Integrity\nQuality assurance\nProcess monitoring\nAdherence to principle\nPrivacy\nAccess control\nMulti-factor authentication\nEncryption\nReporting\nLevels\nThere are two levels of SOC reports which are also specified by SSAE 18:[1]\n\nType I, which describes a service organization's systems and whether the design of specified controls meet the relevant trust principles. (Are the design and documentation likely to accomplish the goals defined in the report?)\nType II, which also addresses the operational effectiveness of the specified controls over a period of time (usually 9 to 12 months). (Is the implementation appropriate?)\nTypes\nThere are three types of SOC reports.[2]\n\nSOC 1 – Internal Control over Financial Reporting (ICFR)[3]\nSOC 2 – Trust Services Criteria[4][5]\nSOC 3 – Trust Services Criteria for General Use Report[6]\nAdditionally, there are specialized SOC reports for Cybersecurity and Supply Chain.[7]\n\nSOC 1 and SOC 2 reports are intended for a limited audience – specifically, users with an adequate understanding of the system in question. SOC 3 reports contain less specific information and can be distributed to the general public." }

{ "CATEGORY": "closed_qa", "CONTEXT": "Detective Hieronymus \"Harry\" Bosch is a fictional character created by American author Michael Connelly. Bosch debuted as the lead character in the 1992 novel The Black Echo, the first in a best-selling police procedural series now numbering 24 novels.\n\nThe novels are more or less coincident in timeframe with the year in which they were published. Harry, as he is commonly known by his associates, is a veteran police homicide detective with the Los Angeles Police Department. He was named after the 15th-century Dutch artist Hieronymus Bosch.\n\nTitus Welliver portrayed the title character from 2015 to 2021 in Bosch, a television series adapted from the novels, and from 2022 in its spin-off series Bosch: Legacy." }

{ "CATEGORY": "classification", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "brainstorming", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "information_extraction", "CONTEXT": "Shingopana is known from a partial jaw, represented by the angular bone. Shingopana is also known from four cervical vertebrae; with two of these vertebrae having preserved cervical ribs and another isolated cervical rib. Shingopana instead had remnants of a bulbous expansion on the incompletely preserved cervical vertebrae, which probably helped to strengthen its neck." }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "brainstorming", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "creative_writing", "CONTEXT": "" }

{ "CATEGORY": "classification", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "closed_qa", "CONTEXT": "In 2019, Virginia men's basketball won the NCAA Championship in \"March Madness\", the single-elimination national college basketball tournament considered by YouGov polled American viewers (as of the same year) to be the most exciting collegiate sporting event.[200][201] In 2015, when Virginia first won its first Capital One Cup its teams won the 2014 College Cup, the 2015 College World Series, and the 2015 NCAA Tennis Championships. When it repeated the feat in 2019, the program won both March Madness and the 2019 Men's Lacrosse Championship." }

{ "CATEGORY": "closed_qa", "CONTEXT": "The children of Bill 101 (les enfants de la loi 101) is the name given to the generation of children whose parents immigrated to Quebec, Canada after the adoption of the 1977 Charter of the French Language (Bill 101).\n\nOne of the Charter's articles stipulates that all children under 16 must receive their primary and secondary education in French schools, unless one of the child's parents has received most of their education in English, in Canada, or the child or the child's sibling has already received a substantial part of their education in English, in Canada. If a child qualifies for a certificate of eligibility for instruction in English under one of those criteria, then the certificate is permanent and can be passed on to future generations even if the child ends up attending French instead of English school. The permanent certificate can only be granted if one of the child's parents is a Canadian citizen. In addition, children of foreign temporary residents living in Quebec on study or work permits, as well as children of diplomats, can also obtain a temporary certificate of eligibility that is only valid for the duration of their permit.[1]" }

{ "CATEGORY": "classification", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "creative_writing", "CONTEXT": "" }

{ "CATEGORY": "classification", "CONTEXT": "" }

{ "CATEGORY": "closed_qa", "CONTEXT": "After the Russo-Japanese War, the First Treaty was signed on 30 July 1907 by Motono Ichirō, the Japanese Ambassador in Moscow, and Alexander Izvolsky, the Foreign Minister of Russian. The treaty was divided into two parts: one is open agreement, which respected the treaties concluded between the two countries and China, respected China's independence, promoted open doors, and achieved equal opportunities and another is secret agreement, which defined the scope of Japan's interests in Southern Manchuria and Russia's interests in Northern Manchuria and Japan recognized Russia's interests in Outer Mongolia, and Russia recognized Japan's interests in the Korean Peninsula." }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "classification", "CONTEXT": "" }

{ "CATEGORY": "classification", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "classification", "CONTEXT": "" }

{ "CATEGORY": "information_extraction", "CONTEXT": "Alexander McGuire (born 5 October 1932) is a New Zealand cricketer. He played in four first-class matches for Central Districts in 1957/58." }

{ "CATEGORY": "closed_qa", "CONTEXT": "Summary\nGames\tYear\tEvents\tBest Nation\n1\t1896\t4\t Hungary\n2\t1900\t7\t Great Britain\n3\t1904\t9\t Germany\n4\t1908\t6\t Great Britain\n5\t1912\t9\t Germany\n6\t\n7\t1920\t10\t United States\n8\t1924\t11\t United States\n9\t1928\t11\t United States\n10\t1932\t11\t Japan\n11\t1936\t11\t Japan\n12\t\n13\t\n14\t1948\t11\t United States\n15\t1952\t11\t United States\n16\t1956\t13\t Australia\nGames\tYear\tEvents\tBest Nation\n17\t1960\t15\t United States\n18\t1964\t18\t United States\n19\t1968\t29\t United States\n20\t1972\t29\t United States\n21\t1976\t26\t United States\n22\t1980\t26\t East Germany\n23\t1984\t29\t United States\n24\t1988\t31\t East Germany\n25\t1992\t31\t United States\n26\t1996\t32\t United States\n27\t2000\t32\t United States\n28\t2004\t32\t United States\n29\t2008\t34\t United States\n30\t2012\t34\t United States\n31\t2016\t34\t United States\n32\t2020\t37\t United States" }

{ "CATEGORY": "brainstorming", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "creative_writing", "CONTEXT": "" }

{ "CATEGORY": "classification", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "information_extraction", "CONTEXT": "Avatar: The Way of Water became the 51st film to gross $ 1 billion worldwide,[3] the sixth film to gross $2 billion worldwide, the sixth-fastest film to cross the billion-dollar mark at 14 days,[4] the second-fastest to gross the $2 billion mark,[5][6] the highest grossing film of 2022,[7] and the highest-grossing film of the COVID-19 pandemic era.[8]\nIt had the 11th-biggest global opening of all time and the third-biggest global opening for a film in the pandemic era behind Spider-Man: No Way Home and Doctor Strange in the Multiverse of Madness, earning $441.7 million in its first weekend.[9][10]\nIt also achieved the second-highest global opening weekend ever for a film released in IMAX cinemas with $48.8 million.[9]\nWith the release of Avatar: The Way of Water, Walt Disney Studios Motion Pictures led over other film distributors in worldwide box office grosses and crossed the $4.9 billion mark for the eighth year.[11] Disney’s cumulative gross was generated from 16 film releases across its various studio divisions.[12]\nTop Gun: Maverick became the 49th film to gross $1 billion worldwide.[13]\nAdditionally, the film surpassed Mission: Impossible – Fallout (2018) to become Tom Cruise's highest-grossing film of all time at the worldwide box office and also surpassed War of the Worlds (2005) to become Cruise's highest-grossing film at the domestic box office.[14]\nIt also passed The Mummy (2017) as Cruise's biggest opening weekend at the worldwide box office and also passed War of the Worlds (2005) as Cruise's biggest opening at the domestic box office and his first film to open to over $100 million in the US.\nIt also passed Pirates of the Caribbean: At World's End (2007), another Jerry Bruckheimer produced movie, to have the highest Memorial Day opening weekend[15][16] and passed Shrek 2 (2004) to have the lowest second-weekend drop for a movie that made over $100M in its opening weekend.[17]\nThe film surpassed Transformers: Dark of the Moon (2011) to become the highest-grossing film distributed solely by Paramount Pictures worldwide, and Titanic (1997) to become Paramount's highest grossing film in the United States and Canada." }

{ "CATEGORY": "closed_qa", "CONTEXT": "The Cuban Missile Crisis, also known as the October Crisis (of 1962) (Spanish: Crisis de Octubre) in Cuba, the Caribbean Crisis (Russian: Карибский кризис, tr. Karibsky krizis, IPA: [kɐˈrʲipskʲɪj ˈkrʲizʲɪs]) in Russia, or the Missile Scare, was a 35-day (October 16 – November 20, 1962) confrontation between the United States and the Soviet Union, which escalated into an international crisis when American deployments of missiles in Italy and Turkey were matched by Soviet deployments of similar ballistic missiles in Cuba. Despite the short time frame, the Cuban Missile Crisis remains a defining moment in national security and nuclear war preparation. The confrontation is often considered the closest the Cold War came to escalating into a full-scale nuclear war.[4]" }

{ "CATEGORY": "creative_writing", "CONTEXT": "" }

{ "CATEGORY": "closed_qa", "CONTEXT": "Mao Zedong[a] (26 December 1893 – 9 September 1976), also known as Chairman Mao, was a Chinese communist revolutionary who was the founder of the People's Republic of China (PRC), which he led as the chairman of the Chinese Communist Party from the establishment of the PRC in 1949 until his death in 1976. Ideologically a Marxist–Leninist, his theories, military strategies, and political policies are collectively known as Maoism.\n\nMao was the son of a prosperous peasant in Shaoshan, Hunan. He supported Chinese nationalism and had an anti-imperialist outlook early in his life, and was particularly influenced by the events of the Xinhai Revolution of 1911 and May Fourth Movement of 1919. He later adopted Marxism–Leninism while working at Peking University as a librarian and became a founding member of the Chinese Communist Party (CCP), leading the Autumn Harvest Uprising in 1927. During the Chinese Civil War between the Kuomintang (KMT) and the CCP, Mao helped to found the Chinese Workers' and Peasants' Red Army, led the Jiangxi Soviet's radical land reform policies, and ultimately became head of the CCP during the Long March. Although the CCP temporarily allied with the KMT under the Second United Front during the Second Sino-Japanese War (1937–1945), China's civil war resumed after Japan's surrender, and Mao's forces defeated the Nationalist government, which withdrew to Taiwan in 1949.\n\nOn 1 October 1949, Mao proclaimed the foundation of the PRC, a Marxist–Leninist single-party state controlled by the CCP. In the following years he solidified his control through the Chinese Land Reform against landlords, the Campaign to Suppress Counterrevolutionaries, the \"Three-anti and Five-anti Campaigns\", and through a truce in the Korean War, which altogether resulted in the deaths of several million Chinese. From 1953 to 1958, Mao played an important role in enforcing command economy in China, constructing the first Constitution of the PRC, launching the industrialisation program, and initiating military projects such as the \"Two Bombs, One Satellite\" project and Project 523. His foreign policies during this time were dominated by the Sino-Soviet split which drove a wedge between China and the Soviet Union. In 1955, Mao launched the Sufan movement, and in 1957 he launched the Anti-Rightist Campaign, in which at least 550,000 people, mostly intellectuals and dissidents, were persecuted.[2] In 1958, he launched the Great Leap Forward that aimed to rapidly transform China's economy from agrarian to industrial, which led to the deadliest famine in history and the deaths of 15–55 million people between 1958 and 1962. In 1963, Mao launched the Socialist Education Movement, and in 1966 he initiated the Cultural Revolution, a program to remove \"counter-revolutionary\" elements in Chinese society which lasted 10 years and was marked by violent class struggle, widespread destruction of cultural artifacts, and an unprecedented elevation of Mao's cult of personality. Tens of millions of people were persecuted during the Revolution, while the estimated number of deaths ranges from hundreds of thousands to millions. After years of ill health, Mao suffered a series of heart attacks in 1976 and died at the age of 82. During the Mao era, China's population grew from around 550 million to over 900 million while the government did not strictly enforce its family planning policy.\n\nWidely regarded as one of the most influential figures of the twentieth century, Mao remains a controversial figure within and outside China. Beyond politics, Mao is also known as a theorist, military strategist, and poet. Mao has been credited with transforming China from a semi-colony to a leading world power, with greatly advanced literacy, women's rights, basic healthcare, primary education and life expectancy.[3][4][5][6] However, Mao's government was responsible for vast numbers of deaths, with estimates ranging from 40 to 80 million victims through starvation, persecution, prison labour, and mass executions.[7][8][9][10] During his leadership tenure, China was heavily involved with other Asian communist conflicts such as the Korean War, the Vietnam War, and the Cambodian Civil War." }

{ "CATEGORY": "closed_qa", "CONTEXT": "Sir John Evelyn (1591–1664) was an English politician who sat in the House of Commons at various times between 1628 and 1660. He reluctantly supported the Parliamentary side in the English Civil War.\n\nEvelyn was the son of Sir John Evelyn of Kingston, Godstone, Surrey and Marden, MP and his wife Elizabeth Stever, daughter of William Stever of Kingston upon Thames. He was baptised at Kingston upon Thames on 20 October 1591. He was admitted at Emmanuel College, Cambridge on 13 March 1606. He was a member of the Virginia Company in 1612 and of the East India Company in 1624. He was a JP for Surrey from 1624." }

{ "CATEGORY": "information_extraction", "CONTEXT": "During the single-player campaign, the player controls five different characters from a first-person perspective. The player primarily controls Sergeant Gary \"Roach\" Sanderson, a British member of an international special forces unit named Task Force 141. In addition to Roach, the player will also assume control of Private First Class Joseph Allen (Troy Baker) and Private James Ramirez of the U.S. Army's 1st Battalion/75th Ranger Regiment.[23] The now Captain John \"Soap\" MacTavish (Kevin McKidd) serves as a senior member of Task Force 141, acting as Roach's superior officer and becomes the playable character in the final three missions of the game.[24] Lastly, the player briefly assumes the role of an astronaut stationed on the International Space Station during the height of the war between the United States and Russia.\n\nThe game's non-playable characters (NPCs) feature prominently in the story: For Task Force 141, Lieutenant Simon \"Ghost\" Riley (Craig Fairbrass), who conceals his face with a skull print balaclava serves as Soap's second in command.[25] For the United States Army Rangers, Sergeant Foley (Keith David), along with his subordinate, Corporal Dunn (Barry Pepper), is initially Joseph Allen's squad leader, and becomes James Ramirez's squad leader later in the story. Lieutenant General Shepherd (Lance Henriksen) is the commander of the United States Army Rangers and Task Force 141. Other supporting characters returning from Call of Duty 4 include Captain John Price (Billy Murray), MacTavish's former commanding officer in the British Special Air Service who was imprisoned in Russia, and Russian informant turned mercenary pilot \"Nikolai\" (Sven Holmberg)." }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "open_qa", "CONTEXT": "" }

{ "CATEGORY": "information_extraction", "CONTEXT": "Gardening is the practice of growing and cultivating plants as part of horticulture. In gardens, ornamental plants are often grown for their flowers, foliage, or overall appearance; useful plants, such as root vegetables, leaf vegetables, fruits, and herbs, are grown for consumption, for use as dyes, or for medicinal or cosmetic use.\n\nGardening ranges in scale from fruit orchards, to long boulevard plantings with one or more different types of shrubs, trees, and herbaceous plants, to residential back gardens including lawns and foundation plantings, all the way to container gardens grown inside or outside. Gardening may be very specialized, with only one type of plant grown, or involve a variety of plants in mixed plantings. It involves an active participation in the growing of plants, and tends to be labor-intensive, which differentiates it from farming or forestry." }

{ "CATEGORY": "creative_writing", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }

{ "CATEGORY": "general_qa", "CONTEXT": "" }