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What year was the first pizzeria in New York founded?

Lombardi's Pizzeria in Little Italy in New York City claims to be the oldest NYC pizzeria and was opened in 1905.

What are some good date ideas?

Tour an animal rescue, dine at a local vegan restaurant, visit the art museum, explore a botanical garden, plant-based picnic in the park, volunteer at a humane society to walk the dogs or play with the cats, hike at a local state or national park

What's the best for indoor plant?

1. Give less water only as much needed, do not over water it 2. Keep it in indirect sunlight at least for 2 hours a day

India's nominal per capita income was US$1,670 per year in 2016, ranked 112th out of 164 countries by the World Bank, while its per capita income on purchasing power parity (PPP) basis was US$5,350, and ranked 106th. Other estimates for per capita gross national income and gross domestic product vary by source. For example, India's average GDP per capita on PPP basis in 2009, according to The Economist, was US$5,138, with significant variation among its states and union territories. Goa had the highest per capita PPP GDP at US$14,903, while Bihar the lowest with per capita PPP GDP of US$682 as of 2015 In rupee terms, India's per capita income grew by 10.4% to reach Rs.74,920 in 2013-

Please important statistics from this passage.

1. India's nominal Per capita income was US$1,670 per year in 2016 2. India's rank on per capita Income was 112 out of 164 3. India's per capita income on purchasing power parity (PPP) basis was US$5,350 4.India's rank based on purchasing power parity was 106th 5.India's average GDP per capita on PPP basis in 2009 was US$5,138 6. Goa had the highest per capita PPP GDP at US$14,903 7. Bihar the lowest with per capita PPP GDP of US$682 8.India's per capita income grew by 10.4% to reach Rs.74,920

What is the JDK?

The JDK is a development environment for building applications using the Java programming language. The JDK includes tools useful for developing and testing programs written in the Java programming language and running on the Java platform.

We evaluate the performance of CRAFT in the task of forecasting conversational derailment in both the Wikipedia and CMV scenarios. To this end, for each of these datasets we pre-train the generative component on the unlabeled portion of the data and fine-tune it on the labeled training split (data size detailed in Section SECREF3). In order to evaluate our sequential system against conversational-level ground truth, we need to aggregate comment level predictions. If any comment in the conversation triggers a positive prediction—i.e., $p_{\text{event}}(c_{n+1})$ is greater than a threshold learned on the development split—then the respective conversation is predicted to derail. If this forecast is triggered in a conversation that actually derails, but before the derailment actually happens, then the conversation is counted as a true positive; otherwise it is a false positive. If no positive predictions are triggered for a conversation, but it actually derails then it counts as a false negative; if it does not derail then it is a true negative. Fixed-length window baselines. We first seek to compare CRAFT to existing, fixed-length window approaches to forecasting. To this end, we implement two such baselines: Awry, which is the state-of-the-art method proposed in BIBREF9 based on pragmatic features in the first comment-reply pair, and BoW, a simple bag-of-words baseline that makes a prediction using TF-IDF weighted bag-of-words features extracted from the first comment-reply pair. Online forecasting baselines. Next, we consider simpler approaches for making forecasts as the conversations happen (i.e., in an online fashion). First, we propose Cumulative BoW, a model that recomputes bag-of-words features on all comments seen thus far every time a new comment arrives. While this approach does exhibit the desired behavior of producing updated predictions for each new comment, it fails to account for relationships between comments. This simple cumulative approach cannot be directly extended to models whose features are strictly based on a fixed number of comments, like Awry. An alternative is to use a sliding window: for a feature set based on a window of $W$ comments, upon each new comment we can extract features from a window containing that comment and the $W-1$ comments preceding it. We apply this to the Awry method and call this model Sliding Awry. For both these baselines, we aggregate comment-level predictions in the same way as in our main model. CRAFT ablations. Finally, we consider two modified versions of the CRAFT model in order to evaluate the impact of two of its key components: (1) the pre-training step, and (2) its ability to capture inter-comment dependencies through its hierarchical memory. To evaluate the impact of pre-training, we train the prediction component of CRAFT on only the labeled training data, without first pre-training the encoder layers with the unlabeled data. We find that given the relatively small size of labeled data, this baseline fails to successfully learn, and ends up performing at the level of random guessing. This result underscores the need for the pre-training step that can make use of unlabeled data. To evaluate the impact of the hierarchical memory, we implement a simplified version of CRAFT where the memory size of the context encoder is zero (CRAFT $-$ CE), thus effectively acting as if the pre-training component is a vanilla seq2seq model. In other words, this model cannot capture inter-comment dependencies, and instead at each step makes a prediction based only on the utterance encoding of the latest comment. Results. Table TABREF17 compares CRAFT to the baselines on the test splits (random baseline is 50%) and illustrates several key findings. First, we find that unsurprisingly, accounting for full conversational context is indeed helpful, with even the simple online baselines outperforming the fixed-window baselines. On both datasets, CRAFT outperforms all baselines (including the other online models) in terms of accuracy and F1. Furthermore, although it loses on precision (to CRAFT $-$ CE) and recall (to Cumulative BoW) individually on the Wikipedia data, CRAFT has the superior balance between the two, having both a visibly higher precision-recall curve and larger area under the curve (AUPR) than the baselines (Figure FIGREF20). This latter property is particularly useful in a practical setting, as it allows moderators to tune model performance to some desired precision without having to sacrifice as much in the way of recall (or vice versa) compared to the baselines and pre-existing solutions. Table 1 compares CRAFT to the baselines on the test splits (random baseline is 50%) and illustrates several key findings. First, we find that unsurprisingly, accounting for full conversational context is indeed helpful, with even the simple online baselines outperforming the fixedwindow baselines. On both datasets, CRAFT outperforms all baselines (including the other online models) in terms of accuracy and F1. Furthermore, although it loses on precision (to CRAFT− CE) and recall (to Cumulative BoW) individually on the Wikipedia data, CRAFT has the superior balance between the two, having both a visibly higher precision-recall curve and larger area under the curve (AUPR) than the baselines (Figure 3). This latter property is particularly useful in a practical setting, as it allows moderators to tune model performance to some desired precision without having to sacrifice as much in the way of recall (or vice versa) compared to the baselines and pre-existing solutions.

On what metrics do CRAFT's experimental results outperform the fixed window baselines?

Accuracy, F1, recall, and AUPR.

We extract a large-scale stance detection dataset from comments written by candidates of elections in Switzerland. The dataset consists of German, French and Italian text, allowing for a cross-lingual evaluation of stance detection. It contains 67 000 comments on more than 150 political issues (targets). Unlike stance detection models that have specific target issues, we use the dataset to train a single model on all the issues. To make learning across targets possible, we prepend to each instance a natural question that represents the target (e.g."Do you support X?"). Baseline results from multilingual BERT show that zero-shot cross-lingual and cross-target transfer of stance detection is moderately successful with this approach. The dataset consists of German, French and Italian text, allowing for a cross-lingual evaluation of stance detection.

Does the dataset only involve one language?

No.

Who is the Ferrari formula 1 team principal?

The Scuderia Ferrari Team Principal is Fred Vasseur

What is the “infield” in baseball?

The infield is the area of a baseball field that extends from home base out past first, second and third bases. The infield is usually a dirt surface, and the outfield begins where the dirt turns to grass.

Components Below are some of the components and processes involved in the digital supply chain, in roughly sequential order, to bring raw content to a format available to a consumer via personal computer or set-top box: Content Provider The content provider provides the actual content that will ultimately be consumed by the end-user. The content provider provides the content in a variety of formats - including film, tape, CD, DVD, hard disks and digital files. Capture If the content provided by the content provider is not already in a digital format, it must first be converted to a digital format. In the case of film or tape, this is often called the “capture” process in which hardware will play back the original medium and “capture” its contents and convert it into a digital format. This captured file is often captured at the highest feasible quality as it is expected to undergo various processes that could degrade the quality. Therefore, the highest possible capture is always desired. Compression The captured digital file is often extremely large, requiring a large amount of digital storage space and impossible to deliver to the consumer via broadband methods. Therefore, it must be compressed in a process called encoding or transcoding. In this process, a codec is used to compress the captured digital file into a format which is small enough to be delivered to the end-user, usually via broadband methods. This encoding process entails consideration of many variables as to how the file is to be compressed. For audio, this usually consists of bit-rate and sample rate. Additional variables for video include resolution, frame rate and also has its own bit-rate that needs to be specified. These variables are determined by how a particular file is to be consumed; download, streaming to a PC, streaming to a mobile device, etc. Quality, however, often becomes an issue at this stage and great care is taken to ensure the quality of the medium is of the highest possible quality for its intended deliver method. For example, a video encoded for streaming to mobile devices requires a much higher level of compression than would for a PC download, so the quality of the video will not be nearly as good. Quality Control Quality control is required to ensure that a file that has been encoded properly, with desired results and free of corruption. The degree to which a file is considered acceptable is based either on the discretion of the party charged with encoding the content, or a specific standard defined by the content owner for which the encoding party must comply. Quality control need not only take place at this juncture. Most companies dealing with the digital supply chain process will subject the media to scrutiny of the quality control process at various stages including: original capture, post-compression, post-ingest and post-publish, thus ensuring the media has not become corrupt or has not been degraded undesirably during any stage along the digital supply chain process. Digital Asset Management The digital asset management system handles the metadata, content and their related assets such as album cover and movie poster art. It is often the digital asset management systems which also handles the publishing of the content itself to a digital storefront. Some digital asset management systems specifically built for the digital supply chain and the delivery of electronic media will track the progress of content as it goes through the digital supply chain. A good digital asset management system should also include reporting tools which can report back on the status of various aspects of the digital supply chain itself. Metadata Entry Metadata is the information that identifies and describes the contents of a medium. This information can include media-specific information such as: Title, artist(s), production company, seasonal/episodic description, original release date, etc. Metadata can also include business-related information such as: pricing and availability. Digital Rights Management An important aspect of the digital supply chain is the process of encrypting the content so that it cannot be played back without the proper license which is often acquired via purchase of content or subscription. This reduces the possibility of media being pirated. Ingest Ingest is the stage in which the compressed file and metadata are put into the digital asset management system. It is not necessary for the metadata and the compressed media file to be ingested into the system at the same time, however, there will be a point in the process in which both have been ingested into the system and the metadata can reference the media file. Content Delivery Network When media files are ingested they are hosted and delivered from a content delivery network (CDN) capable of delivering the media to the end-user. The CDN should be able to support the expected number of consumers. Merchant/Storefront/Digital Retailer/Digital Service Provider The content will ultimately display on a digital storefront where the consumer can view a catalog listing of the content and access the content via purchase, subscription or other method a merchant has elected to make the content available.

List the components from this text into a comma separated list

Content Provider, Capture, Compression, Quality Control, Digital Asset Management, Metadata, Entry, Digital Rights Management, Ingest, Content Delivery Network, Merchant/Storefront/Digital, Retailer/Digital Service Provider

Hummingbirds have varied specialized characteristics to enable rapid, maneuverable flight, exceptional metabolic capacity, adaptations to high altitude, sensitive visual and communication abilities, and long-distance migration in some species. Among all birds, male hummingbirds have the widest diversity of plumage color, particularly in blues, greens, and purples. Hummingbirds are the smallest mature birds, measuring 7.5–13 cm (3–5 in) in length. The smallest is the 5 cm (2.0 in) bee hummingbird, which weighs less than 2.0 g (0.07 oz), and the largest is the 23 cm (9.1 in) giant hummingbird, weighing 18–24 grams (0.63–0.85 oz). Noted for long beaks, hummingbirds are specialized for feeding on flower nectar, but all species also consume small insects.

What is the smallest bird?

Hummingbird is the smallest bird.

Why is ice hockey a good sport for women?

Ice Hockey is common sport for women, which is most popular in New England, the Midwest, and Canadian. Ice Hockey promotes confidence, agility, speed and team work, Many programs are available for young children as part of a learn-to-skate, co-ed and all-gender programs. Strong relationships are developed amongst teammates as they develop a common goal to develop skill and perform in games.

The fundamental task for an agent to perceive and interact with the 3D environment is to know the location and semantic identity of its nearby objects. The location includes spatial location that is either stationary or moving, temporal location like start time and end time. Vision-based such environment perception has received large attention in the past decade and we have witnessed huge progress in tasks such as object detection, classification and tracking. Nevertheless, the sound-based counterpart research has far lagged behind, despite all the fascinating properties sound signal exhibits. For example, sound is ubiquitous and insensitive to ambient illumination change, it has no field-of-view (FoV) constraints and is capable of circumventing physical barriers to perceive scene beyond line-of-sight. As a complementary sensing approach to vision, sound-based environment perception is of vital importance for acoustic scene understanding. A typical example is the sound source detection (answer where is it, when does it happen and what is it), given the recorded multi-channel sound waveforms. To detect sound sources, we often deploy a spatially-configured microphone array to record an acoustic environment. Unlike camera or LiDAR scanner that directly captures RGB image or measures the range distance, sound waveform itself is a highly compressed one-dimensional points with high sampling rate, all sound source signals are compressed and mixed into the one-dimensional data format. Since different sound sources have different frequency property, it is essential convert waveform into time-frequency representation so that frequencies hidden in the waveform are explicitly split out. This is often achieved by projecting the raw waveform into various orthogonal frequency basis. At the same time, a sound source's spatial location clue lies in inter-channel difference of recorded waveforms (i.e. phase difference in this work). It is essential to design a neural network that jointly encodes mono-channel time-frequency representation and inter-channel phase difference from the raw waveforms in a unified, parameter-frugal and computation-efficient manner. The learned representation should have elegant resolution in both time, frequency and space domain so that sound sources can be precisely detected. However, learning such representation is a tough task. Challenges derive from both theoretical side and practical side. According to Uncertainty Principle, we cannot achieve the optimal resolution in time and frequency domain at the same time, but instead keep a trade-off between them. Traditional hand-engineered sound feature and some recently proposed learnable filter bank empirically set the same length for all filters, resulting in human-biased, unadjustable time-frequency resolution map. Some other work correlates filter frequency response and filter length by initializing in mel-scale, but it is not scalable nor stable because the final initialization depends on the filter number. Moreover, all existing methods process raw waveform with one-scale filter bank, we argue that the one-scale sound perception easily leads to incomprehensive sound sources sensing, especially when sound sources have different frequency property or undergo various spatial motion. In this paper, we first give comprehensive theoretical analysis on the filter bank impact on its extracted feature's time and frequency resolution. Based on the analysis, we propose a simple yet effective synperiodic filter bank construction strategy in which each filter's frequency response and length are correlated by rotating periodicity such that each filter's length is inversely proportional to its frequency resolution. The synperiodic filter bank thus internally maintains a better time-frequency resolution trade-off than traditional fixed-length filter bank. Coupling the filter length with its frequency response helps us to reduce human intervention in filter bank design. By simply alternating the periodicity term, we further construct a group of synperiodic filter banks, with which we achieve multi-scale perception in time domain. At the same time, by applying a synperiodic filter bank to process one raw waveform as well as its consecutively-downsampled versions, we achieve multi-scale perception in frequency domain. The multi-scale perception in both time and frequency domain of synperiodic filter bank enables the neural network to dynamically learn better representation for sound source detection in a data-driven way. It is worth noting that synperiodic filter bank parameter number is just linear to filter number (adds up to less than 1% of the whole parameters) and it can be efficiently implemented as a 1D convolution operator. Following the aforementioned learnable front-end, we add a Transformer-like backbone network with two paralleling branches with intermediate soft-parameter sharing to learn sound source's semantic and spatial location related representation both jointly and separately. Experiment on both direction-of-arrival (DoA) task and physical location estimation task shows that our proposed framework outperforms comparing methods by a large margin. Replacing existing method's head with our proposed synperiodic filter bank also improvdes the performance. The fundamental task for an agent to perceive and interact with the 3D environment is to know the location and semantic identity of its nearby objects. The location includes spatial location that is either stationary or moving, temporal location like start time and end time. Vision-based such environment perception has received large attention in the past decade and we have witnessed huge progress in tasks such as object detection, classification and tracking. Nevertheless, the sound-based counterpart research has far lagged behind, despite all the fascinating properties sound signal exhibits. For example, sound is ubiquitous and insensitive to ambient illumination change, it has no field-of-view (FoV) constraints and is capable of circumventing physical barriers to perceive scene beyond line-of-sight. As a complementary sensing approach to vision, sound-based environment perception is of vital importance for acoustic scene understanding. A typical example is the sound source detection (answer where is it, when does it happen and what is it), given the recorded multi-channel sound waveforms. To detect sound sources, we often deploy a spatially-configured microphone array to record an acoustic environment. Unlike camera or LiDAR scanner that directly captures RGB image or measures the range distance, sound waveform itself is a highly compressed one-dimensional points with high sampling rate, all sound source signals are compressed and mixed into the one-dimensional data format. Since different sound sources have different frequency property, it is essential convert waveform into time-frequency representation so that frequencies hidden in the waveform are explicitly split out. This is often achieved by projecting the raw waveform into various orthogonal frequency basis. At the same time, a sound source's spatial location clue lies in inter-channel difference of recorded waveforms (i.e. phase difference in this work). It is essential to design a neural network that jointly encodes mono-channel time-frequency representation and inter-channel phase difference from the raw waveforms in a unified, parameter-frugal and computation-efficient manner. The learned representation should have elegant resolution in both time, frequency and space domain so that sound sources can be precisely detected. However, learning such representation is a tough task. Challenges derive from both theoretical side and practical side. According to Uncertainty Principle, we cannot achieve the optimal resolution in time and frequency domain at the same time, but instead keep a trade-off between them. Traditional hand-engineered sound feature and some recently proposed learnable filter bank empirically set the same length for all filters, resulting in human-biased, unadjustable time-frequency resolution map. Some other work correlates filter frequency response and filter length by initializing in mel-scale, but it is not scalable nor stable because the final initialization depends on the filter number. Moreover, all existing methods process raw waveform with one-scale filter bank, we argue that the one-scale sound perception easily leads to incomprehensive sound sources sensing, especially when sound sources have different frequency property or undergo various spatial motion. In this paper, we first give comprehensive theoretical analysis on the filter bank impact on its extracted feature's time and frequency resolution. Based on the analysis, we propose a simple yet effective synperiodic filter bank construction strategy in which each filter's frequency response and length are correlated by rotating periodicity such that each filter's length is inversely proportional to its frequency resolution. The synperiodic filter bank thus internally maintains a better time-frequency resolution trade-off than traditional fixed-length filter bank. Coupling the filter length with its frequency response helps us to reduce human intervention in filter bank design. By simply alternating the periodicity term, we further construct a group of synperiodic filter banks, with which we achieve multi-scale perception in time domain. At the same time, by applying a synperiodic filter bank to process one raw waveform as well as its consecutively-downsampled versions, we achieve multi-scale perception in frequency domain. The multi-scale perception in both time and frequency domain of synperiodic filter bank enables the neural network to dynamically learn better representation for sound source detection in a data-driven way. It is worth noting that synperiodic filter bank parameter number is just linear to filter number (adds up to less than 1% of the whole parameters) and it can be efficiently implemented as a 1D convolution operator. Following the aforementioned learnable front-end, we add a Transformer-like backbone network with two paralleling branches with intermediate soft-parameter sharing to learn sound source's semantic and spatial location related representation both jointly and separately. Experiment on both direction-of-arrival (DoA) task and physical location estimation task shows that our proposed framework outperforms comparing methods by a large margin. Replacing existing method's head with our proposed synperiodic filter bank also improvdes the performance. To detect sound sources, we often deploy a spatially-configured microphone array to record an acoustic environment. Unlike camera or LiDAR scanner that directly captures RGB image or measures the range distance, sound waveform itself is a highly compressed one-dimensional points with high sampling rate, all sound source signals are compressed and mixed into the one-dimensional data format. Since different sound sources have different frequency property, it is essential convert waveform into time-frequency representation so that frequencies hidden in the waveform are explicitly split out. This is often achieved by projecting the raw waveform into various orthogonal frequency basis. At the same time, a sound source’s spatial location clue lies in inter-channel difference of recorded waveforms (i.e. phase difference in this work). It is essential to design a neural network that jointly encodes mono-channel time-frequency representation and inter-channel phase difference from the raw waveforms in a unified, parameter-frugal and computation-efficient manner. The learned representation should have elegant resolution in both time, frequency and space domain so that sound sources can be precisely detected.****Following the aforementioned learnable front-end, we add a Transformer-like backbone network with two paralleling branches with intermediate soft-parameter sharing to learn sound source’s semantic and spatial location related representation both jointly and separately. Experiment on both direction-of-arrival (DoA) task and physical location estimation task shows that our proposed framework outperforms comparing methods by a large margin. Replacing existing method’s head with our proposed synperiodic filter bank also improvdes the performance.

If the received system structure is changed, does the synperiodic filter bank still generalize well?

Synperiodic filter bank is proposed to extract mono-channel time-frequency representation in multi-scale manner in both time and frequency domain. Theoretically, it can be used as a general sound waveform time-frequency representation extractor to solve other acoustic tasks (although we did not test it in this paper). If the system structure is changed, we think synperiodic filter bank can still be used as sound feature extraction front-end. In this paper, we have tested synperiodic filter bank generalizes well on both direction-of-arrival (DoA) task and sound source physical location ([x,y,z] coordinate) estimation task. It needs more test if the receiver configuration changes too much (i.e. rather than setting four microphones close with each other, but putting them far apart and at different corners)

Founding and Half-Life (1996–2003) Gabe Newell (foreground) and Doug Lombardi (background), 2007 Valve was founded as Valve, LLC, in 1996 by former Microsoft employees Gabe Newell and Mike Harrington. Newell had spent the prior 13 years at Microsoft developing Windows, including the Windows 95 port of Doom. Wanting to move onto a new venture using their shared wealth, Newell and Harrington founded Valve, L.L.C. in Kirkland, Washington (about five miles from the Microsoft campus in Redmond), on August 24, 1996, Newell's wedding day. Alternative names considered by Newell and Harrington include Fruitfly Ensemble and Rhino Scar. Valve's first game was Half-Life, a first-person shooter (FPS) with elements of horror. The development was aided by access to the Quake engine by id Software; Valve modified this engine into their GoldSrc engine. After struggling to find a publisher, Valve eventually signed with Sierra On-Line. Half-Life was released in November 1998 and was a critical and commercial success. With its realism, scripted sequences and seamless narrative, it had a lasting influence; according to IGN in 2014, the history of the FPS genre "breaks down pretty cleanly into pre-Half-Life and post-Half-Life eras".

Give me a history of Valve

Founding and Half-Life (1996–2003): Valve was founded as Valve, LLC, in 1996 by former Microsoft employees Gabe Newell and Mike Harrington in Kirkland, Washington. Valve's first game is Half-Life, a first-person shooter (FPS) released in November 1998. It was developed using the modified Quake engine GoldSrc. After struggling to find a publisher, Valve eventually signed with Sierra On-Line.

What are some of the best vegan protein sources?

Soy-based products such as tofu, soy milk and tempeh are high sources of protein. Pulses such as beans (including chickpeas, pintos, black beans) and lentils. Certain grains are also high-protein sources, such as quinoa, farro and buckwheat. Other foods like nuts and seeds are good protein sources but are also high in fat and calories.

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. Properties Though 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. Though 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. Hallucinations Main article: Hallucination (artificial intelligence) In 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. Emergent abilities On 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. Unpredictable 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. Architecture and training Large 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. The 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. LLMs 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. A 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). Application to downstream tasks Between 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. Fine-tuning Main article: Fine-tuning (machine learning) Fine-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). Prompting See also: Prompt engineering and Few-shot learning (natural language processing) In 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: Review: This movie stinks. Sentiment: negative Review: This movie is fantastic! Sentiment: If 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". Few-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. Instruction tuning Instruction 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). https://en.wikipedia.org/wiki/Large_language_model

Given these paragraphs about Large language models, what is the prompting paradigm?

In 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.

Write me a script for an upcoming All Hands meeting about our IPO prospects and how employees need to act in the quiet period.

Lately, I've received plenty of questions about a potential IPO. I want to answer those questions today — and talk about how we as a company will answer questions like that moving forward. For years, we’ve shared that one of our aspirations is to build a wildly successful public company. Everyone has a role to play in getting us there and delivering for customers and growing the business is the most important thing most of us can do. A typical milestone on a high-growth journey is an IPO. But as companies get more serious about a potential IPO, there is a period where they stop talking about an IPO. This might be counter-intuitive, but here’s why: there are important regulatory restrictions on what companies can and cannot say when it comes to company growth and plans as they approach becoming a public company. The consequences if you don’t follow those rules can be serious — the SEC can even delay our ability to go public if it determines we’ve improperly promoted an IPO. And that’s obviously a situation we want to avoid, should we be in a position to go public in the future. And even language like this — "should we be in a position to go public in the future" — is vague. That's for a reason: as we get closer to an IPO, what we can say is restricted and we need to make sure we don’t make promises, otherwise regulators could force us to delay our plans. So I’m going to share some updates about how we’ll need to communicate related to a potential IPO: In order to preserve the ability to IPO at some point in the future, the executive team and I will not be sharing explicit details on our IPO journey or timing at All Hands or in other company communications. We also won’t be sharing updates to our market valuation. Hiring managers and recruiters should not explicitly talk about an IPO or our revenue growth with candidates. Instead, emphasize our massive opportunity, how we’re innovating, delivering value to our customers, and what an awesome team we have. We can’t engage with public market investors or financial journalists on certain topics. That means if a financial analyst, research analyst or reporter reaches out to you, don’t respond and forward the email to the communications team. We also can’t talk about a potential IPO on social media or with friends and family. You get the idea. I know that this will be different for us. We’ve always been a transparent company and this change is hopefully a short-term one. But it is an important one and you all have a role to play in helping us remain compliant. We all have different, important roles to play in making this company a successful, long-term business. The most important thing we can all do is to continue delivering for our customers and building a successful business.

Write an email to a butcher asking how much and what types of meats would be best for a BBQ for 20 people. And ask for a quote.

Hi <name of Butcher>, I need to organise a meat order for an upcoming BBQ for 20 people. Could you please suggest some cuts of meat and some quantities for that number of people. Could I also please get a quote for the total quantity. Kind regards, <name>.

Operation Aurora was a series of cyber attacks conducted by advanced persistent threats such as the Elderwood Group based in Beijing, China, with ties to the People's Liberation Army. First publicly disclosed by Google on January 12, 2010, in a blog post, the attacks began in mid-2009 and continued through December 2009. The attack was aimed at dozens of other organizations, of which Adobe Systems, Akamai Technologies, Juniper Networks, and Rackspace have publicly confirmed that they were targeted. According to media reports, Yahoo, Symantec, Northrop Grumman, Morgan Stanley, and Dow Chemical were also among the targets. As a result of the attack, Google stated in its blog that it plans to operate a completely uncensored version of its search engine in China "within the law, if at all," and acknowledged that if this is not possible, it may leave China and close its Chinese offices. Official Chinese sources claimed this was part of a strategy developed by the U.S. government. The attack was named "Operation Aurora" by Dmitri Alperovitch, Vice President of Threat Research at cybersecurity company McAfee. Research by McAfee Labs discovered that "Aurora" was part of the file path on the attacker's machine that was included in two of the malware binaries McAfee said were associated with the attack. "We believe the name was the internal name the attacker(s) gave to this operation," McAfee Chief Technology Officer George Kurtz said in a blog post. According to McAfee, the primary goal of the attack was to gain access to and potentially modify source code repositories at these high-tech, security, and defense contractor companies. "[The SCMs] were wide open," says Alperovitch. "No one ever thought about securing them, yet these were the crown jewels of most of these companies in many ways—much more valuable than any financial or personally identifiable data that they may have and spend so much time and effort protecting." History Flowers left outside Google China's headquarters after its announcement it might leave the country On January 12, 2010, Google revealed on its blog that it had been the victim of a cyber attack. The company said the attack occurred in mid-December and originated from China. Google stated that over 20 other companies had been attacked; other sources have since cited that more than 34 organizations were targeted. As a result of the attack, Google said it was reviewing its business in China. On the same day, United States Secretary of State Hillary Clinton issued a brief statement condemning the attacks and requesting a response from China. On January 13, 2010, the news agency All Headline News reported that the United States Congress plans to investigate Google's allegations that the Chinese government used the company's service to spy on human rights activists. In Beijing, visitors left flowers outside of Google's office. However, these were later removed, with a Chinese security guard stating that this was an "illegal flower tribute". The Chinese government has yet to issue a formal response, although an anonymous official stated that China was seeking more information on Google's intentions. Attackers involved Further information: Cyberwarfare by China Technical evidence including IP addresses, domain names, malware signatures, and other factors, show Elderwood was behind the Operation Aurora attack. The "Elderwood" group was named by Symantec (after a source-code variable used by the attackers), and is referred to as the "Beijing Group" by Dell Secureworks. The group obtained some of Google's source code, as well as access to information about Chinese activists. Elderwood also targeted numerous other companies in the shipping, aeronautics, arms, energy, manufacturing, engineering, electronics, financial, and software sectors. The "APT" designation for the Chinese threat actors responsible for attacking Google is APT17. Elderwood specializes in attacking and infiltrating second-tier defense industry suppliers that make electronic or mechanical components for top defense companies. Those firms then become a cyber "stepping stone" to gain access to top-tier defense contractors. One attack procedure used by Elderwood is to infect legitimate websites frequented by employees of the target company – a so-called "water hole" attack, just as lions stake out a watering hole for their prey. Elderwood infects these less-secure sites with malware that downloads to a computer that clicks on the site. After that, the group searches inside the network to which the infected computer is connected, finding and then downloading executives' e-mails and critical documents on company plans, decisions, acquisitions, and product designs. Attack analysis In its blog posting, Google stated that some of its intellectual property had been stolen. It suggested that the attackers were interested in accessing Gmail accounts of Chinese dissidents. According to the Financial Times, two accounts used by Ai Weiwei had been attacked, their contents read and copied; his bank accounts were investigated by state security agents who claimed he was under investigation for "unspecified suspected crimes". However, the attackers were only able to view details on two accounts and those details were limited to things such as the subject line and the accounts' creation date. Security experts immediately noted the sophistication of the attack. Two days after the attack became public, McAfee reported that the attackers had exploited purported zero-day vulnerabilities (unfixed and previously unknown to the target system developers) in Internet Explorer and dubbed the attack "Operation Aurora". A week after the report by McAfee, Microsoft issued a fix for the issue, and admitted that they had known about the security hole used since September. Additional vulnerabilities were found in Perforce, the source code revision software used by Google to manage their source code. VeriSign's iDefense Labs claimed that the attacks were perpetrated by "agents of the Chinese state or proxies thereof". According to a diplomatic cable from the U.S. Embassy in Beijing, a Chinese source reported that the Chinese Politburo directed the intrusion into Google's computer systems. The cable suggested that the attack was part of a coordinated campaign executed by "government operatives, public security experts and Internet outlaws recruited by the Chinese government." The report suggested that it was part of an ongoing campaign in which attackers have "broken into American government computers and those of Western allies, the Dalai Lama and American businesses since 2002." According to The Guardian's reporting on the leak, the attacks were "orchestrated by a senior member of the Politburo who typed his own name into the global version of the search engine and found articles criticising him personally." Once a victim's system was compromised, a backdoor connection that masqueraded as an SSL connection made connections to command and control servers running in Illinois, Texas, and Taiwan, including machines that were running under stolen Rackspace customer accounts. The victim's machine then began exploring the protected corporate intranet that it was a part of, searching for other vulnerable systems as well as sources of intellectual property, specifically the contents of source code repositories. The attacks were thought to have definitively ended on Jan 4 when the command and control servers were taken down, although it is not known at this point whether or not the attackers intentionally shut them down. However, the attacks were still occurring as of February 2010. Response and aftermath The German, Australian, and French governments publicly issued warnings to users of Internet Explorer after the attack, advising them to use alternative browsers at least until a fix for the security hole was made. The German, Australian, and French governments considered all versions of Internet Explorer vulnerable or potentially vulnerable. In an advisory on January 14, 2010, Microsoft said that attackers targeting Google and other U.S. companies used software that exploits a hole in Internet Explorer. The vulnerability affects Internet Explorer versions 6, 7, and 8 on Windows 7, Vista, Windows XP, Server 2003, Server 2008 R2, as well as IE 6 Service Pack 1 on Windows 2000 Service Pack 4. The Internet Explorer exploit code used in the attack has been released into the public domain, and has been incorporated into the Metasploit Framework penetration testing tool. A copy of the exploit was uploaded to Wepawet, a service for detecting and analyzing web-based malware operated by the computer security group at the University of California, Santa Barbara. "The public release of the exploit code increases the possibility of widespread attacks using the Internet Explorer vulnerability," said George Kurtz, CTO of McAfee, of the attack. "The now public computer code may help cybercriminals craft attacks that use the vulnerability to compromise Windows systems." Security company Websense said it identified "limited public use" of the unpatched IE vulnerability in drive-by attacks against users who strayed onto malicious Web sites. According to Websense, the attack code it spotted is the same as the exploit that went public last week.[clarification needed] "Internet Explorer users currently face a real and present danger due to the public disclosure of the vulnerability and release of attack code, increasing the possibility of widespread attacks," said George Kurtz, chief technology officer of McAfee, in a blog update. Confirming this speculation, Websense Security Labs identified additional sites using the exploit on January 19. According to reports from Ahnlab, the second URL was spread through the Instant Messenger network Misslee Messenger, a popular IM client in South Korea. Researchers have created attack code that exploits the vulnerability in Internet Explorer 7 (IE7) and IE8—even when Microsoft's recommended defensive measure (Data Execution Prevention (DEP)) is turned on.[dubious – discuss] According to Dino Dai Zovi, a security vulnerability researcher, "even the newest IE8 isn't safe from attack if it's running on Windows XP Service Pack 2 (SP2) or earlier, or on Windows Vista RTM (release to manufacturing), the version Microsoft shipped in January 2007." Microsoft admitted that the security hole used had been known to them since September. Work on an update was prioritized and on Thursday, January 21, 2010, Microsoft released a security patch aiming to counter this weakness, the published exploits based on it and a number of other privately reported vulnerabilities. They did not state if any of the latter had been used or published by exploiters or whether these had any particular relation to the Aurora operation, but the entire cumulative update was termed critical for most versions of Windows, including Windows 7. Security researchers continued to investigate the attacks. HBGary, a security firm, released a report in which they claimed to have found some significant markers that might help identify the code developer. The firm also said that the code was Chinese language based but could not be specifically tied to any government entity. On February 19, 2010, a security expert investigating the cyber-attack on Google, has claimed that the people behind the attack were also responsible for the cyber-attacks made on several Fortune 100 companies in the past one and a half years. They have also tracked the attack back to its point of origin, which seems to be two Chinese schools, Shanghai Jiao Tong University and Lanxiang Vocational School. As highlighted by The New York Times, both of these schools have ties with the Chinese search engine Baidu, a rival of Google China. Both Lanxiang Vocational and Jiaotong University have denied the allegation. In March 2010, Symantec, which was helping investigate the attack for Google, identified Shaoxing as the source of 21.3% of all (12 billion) malicious emails sent throughout the world. Google retrospective On October 3, 2022, Google on YouTube released a six-episode series covering the events that occurred during Operation Aurora, with commentary from insiders who dealt with the attack, though the series primary focus was to reassure the Google-using public that measures are in place to counter hacking attempts.

Given this article about Operation Aurora, which companies were targeted in the attacks?

Google, Adobe Systems, Akamai Technologies, Juniper Networks, Rackspace, Yahoo, Symantec, Northrop Grumman, Morgan Stanley, and Dow Chemical appear to have been targted in these attacks. Some pubically confirmed this while others were claimed to be targeted in media reports.

Tell me whether these are countries or continents

Africa, Asia, Spain, Vietnam, China, Europe, North America

Sentiment analysis is a task that aims at recognizing in text the opinion of the writer. It is often modeled as a classification problem which relies on features extracted from the text in order to feed a classifier. Relevant features proposed in the literature span from microblogging artifacts including hashtags, emoticons BIBREF0 , BIBREF1 , intensifiers like all-caps words and character repetitions BIBREF2 , sentiment-topic features BIBREF3 , to the inclusion of polarity lexicons. The objective of the work presented in this paper is the creation of sentiment polarity lexicons. They are word lists or phrase lists with positive and negative sentiment labels. Sentiment lexicons allow to increase the feature space with more relevant and generalizing characteristics of the input. Unfortunately, creating sentiment lexicons requires human expertise, is time consuming, and often results in limited coverage when dealing with new domains. In the literature, it has been proposed to extend existing lexicons without supervision BIBREF4 , BIBREF5 , or to automatically translate existing lexicons from resourceful languages with statistical machine translation (SMT) systems BIBREF6 . While the former requires seed lexicons, the later are very interesting because they can automate the process of generating sentiment lexicons without any human expertise. But automatically translating sentiment lexicons leads to two problems: (1) out-of-vocabulary words, such as mis-spellings, morphological variants and slang, cannot be translated, and (2) machine translation performance strongly depends on available training resources such as bi-texts. In this paper, we propose to apply the method proposed in BIBREF7 for automatically mapping word embeddings across languages and use them to translate sentiment lexicons only given a small, general bilingual dictionary. After creating monolingual word embeddings in the source and target language, we train a linear transform on the bilingual dictionary and apply that transform to words for which we don't have a translation. We perform experiments on 3-class polarity classification in tweets, and report results on four different languages: French, Italian, Spanish and German. Existing English sentiment lexicons are translated to the target languages through the proposed approach, given gs trained on the respective Wikipedia of each language. Then, a SVM-based classifier is fed with lexicon features, comparing machine translation with embedding transfer. After presenting related work in Section SECREF2 , the extraction of word gs and their mapping across languages are detailed in Section SECREF3 . The corpus on which experiments are carried out and the results of our experiments are presented in Section SECREF4 . Finally, we conclude with a discussion of possible directions in Section SECREF5 . In this paper, we propose to apply the method proposed in [8] for for automatically mapping word embeddings across languages and use them to translate sentiment lexicons only given a small, general bilingual dictionary.

What tasks does the proposed method apply for?

It is applied for automatically mapping word embeddings across languages and translating sentiment lexicons only given a small, general bilingual dictionary.

In 2012 Safeway invested $350 million into retrofitting 800 locations with clinics that would offer in-store blood tests. After many missed deadlines and questionable results from a trial clinic at Safeway's corporate offices, the deal was terminated in 2015. In 2013, Theranos partnered with Walgreens to offer in-store blood tests at more than 40 locations. Although Theranos blood tests were reportedly used on drug trial patients for GlaxoSmithKline and Pfizer, both companies stated that there were no active projects with Theranos in October 2015. In June 2016, Walgreens terminated its partnership with Theranos. In November 2016 it filed suit against Theranos in federal court in Delaware for breach of contract. In June 2017, Theranos reported to investors that the suit, which originally sought $140 million in damages, was settled for less than $30 million. In March 2015 the Cleveland Clinic announced a partnership with Theranos to test its technology and decrease the cost of lab tests. In July 2015, Theranos became the lab-work provider for Pennsylvania insurers AmeriHealth Caritas and Capital Blue Cross. In July 2015 the Food and Drug Administration approved the use of the company's fingerstick blood testing device for the herpes simplex virus (HSV-1) outside a clinical laboratory setting. Theranos was named the 2015 Bioscience Company of the Year by the Arizona BioIndustry Association (AzBio).

Which companies did Theranos partner with in 2015?

Theranos partnered with Cleveland Clinic in March 2015 to test its technology and decrease the cost of lab tests. In July 2015, Theranos became the lab-work provider for Pennsylvania insurers AmeriHealth Caritas and Capital Blue Cross.

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

How does the module perform?

When applying the module to a sequence-to-sequence model with attention mechanism, it significantly increases its performance on both datasets.

Edward M. Gramlich (June 18, 1939 – September 5, 2007) was an American economist who served as a member of the Federal Reserve Board of Governors from 1997 to 2005. Gramlich was also an acting director of the Congressional Budget Office. Gramlich graduated from Williams College in 1961 and received a master's degree in 1962 and a Ph.D. in economics in 1965 from Yale University. He joined the Federal Reserve as a research economist from 1965 to 1970, and was a senior fellow at the Brookings Institution from 1973 to 1976. He then taught economics and public policy at the University of Michigan from 1976 to 1997, including a term as dean of the Gerald R. Ford School of Public Policy, and returned to Michigan as a professor in 2005.

Who was Edward Gramlich

Edward M. Gramlich (June 18, 1939 – September 5, 2007) was an American economist who served as a member of the Federal Reserve Board of Governors from 1997 to 2005. Gramlich was also an acting director of the Congressional Budget Office.

Which NBA franchises have the most championships?

The Los Angeles Lakers and Boston Celtics each have 17 NBA championships, the most of any team.

What is an AVA when it comes to wine?

AVA stands for American Viticultural Area, which is a designation for legally defined and protected areas (known as appellations) for wine vineyards. This tells a consumer where the grapes that were used in the wine were grown.

Venus is the second planet from the Sun. It is sometimes called Earth's "sister" or "twin" planet as it is almost as large and has a similar composition. As an interior planet to Earth, Venus (like Mercury) appears in Earth's sky never far from the Sun, either as morning star or evening star. Aside from the Sun and Moon, Venus is the brightest natural object in Earth's sky, capable of casting visible shadows on Earth in dark conditions and being visible to the naked eye in broad daylight. Venus is the second largest terrestrial object of the Solar System. It has a surface gravity slightly lower than on Earth and has a weak induced magnetosphere. The atmosphere of Venus consists mainly of carbon dioxide, and, at the planet’s surface, is the densest and hottest of the atmospheres of the four terrestrial planets. With an atmospheric pressure at the planet's surface of about 92 times the sea level pressure of Earth and a mean temperature of 737 K (464 °C; 867 °F), the carbon dioxide gas at Venus's surface is a supercritical fluid. Venus is shrouded by an opaque layer of highly reflective clouds of sulfuric acid, making it the planet with the highest albedo in the Solar System. It may have had water oceans in the past, but after these evaporated the temperature rose under a runaway greenhouse effect. The possibility of life on Venus has long been a topic of speculation but research has not produced convincing evidence thus far. Like Mercury, Venus does not have any moons. Solar days on Venus, with a length of 117 Earth days, are just about half as long as its solar year, orbiting the Sun every 224.7 Earth days. This Venusian daylength is a product of it rotating against its orbital motion, halving its full sidereal rotation period of 243 Earth days, the longest of all the Solar System planets. Venus and Uranus are the only planets with such a retrograde rotation, making the Sun move in their skies from their western horizon to their eastern. The orbit of Venus around the Sun is the closest to Earth's orbit, bringing them closer than any other pair of planets. This occurs during inferior conjunction with a synodic period of 1.6 years. However, Mercury is more frequently the closest to each.

Provide key stats about Venus in a bulleted list including days to orbit the sun, mean temperature, number of moons orbiting the planet, and primary element in its atmosphere

- 224.7 Earth days to orbit the sun - Mean temperature of 737 K (464 °C; 867 °F) - No moons orbit Venus - Carbon dioxide is the primary element in the atmosphere

Which country has won the most world cups in soccer?

Brazil has won 6 Fifa world cups and lead the pack followed by Germany and Italy who have won the coveted prize 4 times each. Argentina has won 3 Fifa world cups and are in 4th place for most world cups in soccer.

What country has hosted the Olympic Games the most often?

The United States has hosted the Olympic Games 8 times between summer and winter Games.

In recent years, the use of word representations, such as word2vec BIBREF0 , BIBREF1 and GloVe BIBREF2 , has become a key “secret sauce” for the success of many natural language processing (NLP), information retrieval (IR) and machine learning (ML) tasks. The empirical success of word embeddings raises an interesting research question: Beyond words, can we learn fixed-length distributed representations for pieces of texts? The texts can be of variable-length, ranging from paragraphs to documents. Such document representations play a vital role in a large number of downstream NLP/IR/ML applications, such as text clustering, sentiment analysis, and document retrieval, which treat each piece of text as an instance. Learning a good representation that captures the semantics of each document is thus essential for the success of such applications. In this paper, we introduce KeyVec, a neural network model that learns densely distributed representations for documents of variable-length. In order to capture semantics, the document representations are trained and optimized in a way to recover key information of the documents. In particular, given a document, the KeyVec model constructs a fixed-length vector to be able to predict both salient sentences and key words in the document. In this way, KeyVec conquers the problem of prior embedding models which treat every word and every sentence equally, failing to identify the key information that a document conveys. As a result, the vectorial representations generated by KeyVec can naturally capture the topics of the documents, and thus should yield good performance in downstream tasks. We evaluate our KeyVec on two text understanding tasks: document retrieval and document clustering. As shown in the experimental section SECREF5 , KeyVec yields generic document representations that perform better than state-of-the-art embedding models. In particular, given a document, the KeyVec model constructs a fixed-length vector to be able to predict both salient sentences and key words in the document.

What is the KeyVec model able to predict in the document?

It is able to predict both salient sentences and key words in the document.

Is Amazon EKS serverless?

Amazon EKS with AWS Fargate allows for Serverless Containers to be run.

What is Singlish?

Singlish (a portmanteau of Singapore and English) (officially similar and related to Singaporean English) is an English-based creole language spoken in Singapore. Singlish arose out of a situation of prolonged language contact between speakers of many different languages in Singapore, including Hokkien, Malay, Teochew, Cantonese and Tamil. Singlish originated with the arrival of the British and the establishment of English-medium education in Singapore. Elements of English quickly filtered out of schools and onto the streets, resulting in the development of a pidgin language spoken by non-native speakers as a lingua franca used for communication between speakers of the many different languages used in Singapore. Singlish evolved mainly among the working classes who learned elements of English without formal schooling, mixing in elements of their native languages. After some time, this new pidgin language, now combined with substantial influences from Indian English, Peranakan, southern varieties of Chinese, Malay, and Tamil, became the primary language of the streets. As Singlish grew in popularity, children began to acquire Singlish as their native language, a process known as creolization. Through this process of creolization, Singlish became a fully-formed, stabilized and independent creole language, acquiring a more robust vocabulary and more complex grammar, with fixed phonology, syntax, morphology, and syntactic embedding. Singlish shares many linguistic similarities with Manglish or Bazaar Malay of Malaysia, although a few distinctions can be made, particularly in vocabulary. Manglish generally receives more Malay influence and Singlish more Chinese (Mandarin, Hokkien, etc.) influence. Initially, Singlish and Manglish were essentially the same dialect evolving from the British Malaya economy, born in the trading ports of Singapore, Malacca and Penang when Singapore and Peninsular Malaysia were for many purposes a de facto (from 1963 to 1965, de jure) single entity. Ever since the two countries separated, a divergence has been observed. Like all languages, Singlish and other creole languages show consistent internal logic and grammatical complexity. Due to its origins, Singlish shares many similarities with other English-based creole languages. As with many other creole languages, it is sometimes incorrectly perceived to be a "broken" form of the lexifier language - in this case, English. The uniqueness of Singlish has been studied by linguistics experts beyond Singapore.

Who was Thales?

Thales was a greek mathematician. He is famous for the theorem named after him - Thales' theorem - stating that any triangle over a diameter touching a semicircle is rectangular.

A variety of approaches have been proposed for document quality assessment across different domains: Wikipedia article quality assessment, academic paper rating, content quality assessment in community question answering (cQA), and essay scoring. Among these approaches, some use hand-crafted features while others use neural networks to learn features from documents. For each domain, we first briefly describe feature-based approaches and then review neural network-based approaches. Wikipedia article quality assessment: Quality assessment of Wikipedia articles is a task that assigns a quality class label to a given Wikipedia article, mirroring the quality assessment process that the Wikipedia community carries out manually. Many approaches have been proposed that use features from the article itself, meta-data features (e.g., the editors, and Wikipedia article revision history), or a combination of the two. Article-internal features capture information such as whether an article is properly organized, with supporting evidence, and with appropriate terminology. For example, BIBREF3 use writing styles represented by binarized character trigram features to identify featured articles. BIBREF4 and BIBREF0 explore the number of headings, images, and references in the article. BIBREF5 use nine readability scores, such as the percentage of difficult words in the document, to measure the quality of the article. Meta-data features, which are indirect indicators of article quality, are usually extracted from revision history, and the interaction between editors and articles. For example, one heuristic that has been proposed is that higher-quality articles have more edits BIBREF6 , BIBREF7 . BIBREF8 use the percentage of registered editors and the total number of editors of an article. Article–editor dependencies have also been explored. For example, BIBREF9 use the authority of editors to measure the quality of Wikipedia articles, where the authority of editors is determined by the articles they edit. Deep learning approaches to predicting Wikipedia article quality have also been proposed. For example, BIBREF10 use a version of doc2vec BIBREF11 to represent articles, and feed the document embeddings into a four hidden layer neural network. BIBREF12 first obtain sentence representations by averaging words within a sentence, and then apply a biLSTM BIBREF13 to learn a document-level representation, which is combined with hand-crafted features as side information. BIBREF14 exploit two stacked biLSTMs to learn document representations. Academic paper rating: Academic paper rating is a relatively new task in NLP/AI, with the basic formulation being to automatically predict whether to accept or reject a paper. BIBREF2 explore hand-crafted features, such as the length of the title, whether specific words (such as outperform, state-of-the-art, and novel) appear in the abstract, and an embedded representation of the abstract as input to different downstream learners, such as logistic regression, decision tree, and random forest. BIBREF15 exploit a modularized hierarchical convolutional neural network (CNN), where each paper section is treated as a module. For each paper section, they train an attention-based CNN, and an attentive pooling layer is applied to the concatenated representation of each section, which is then fed into a softmax layer. Content quality assessment in cQA: Automatic quality assessment in cQA is the task of determining whether an answer is of high quality, selected as the best answer, or ranked higher than other answers. To measure answer content quality in cQA, researchers have exploited various features from different sources, such as the answer content itself, the answerer's profile, interactions among users, and usage of the content. The most common feature used is the answer length BIBREF16 , BIBREF17 , with other features including: syntactic and semantic features, such as readability scores. BIBREF18 ; similarity between the question and the answer at lexical, syntactic, and semantic levels BIBREF18 , BIBREF19 , BIBREF20 ; or user data (e.g., a user's status points or the number of answers written by the user). There have also been approaches using neural networks. For example, BIBREF21 combine CNN-learned representations with hand-crafted features to predict answer quality. BIBREF22 use a 2-dimensional CNN to learn the semantic relevance of an answer to the question, and apply an LSTM to the answer sequence to model thread context. BIBREF23 and BIBREF24 model the problem similarly to machine translation quality estimation, treating answers as competing translation hypotheses and the question as the reference translation, and apply neural machine translation to the problem. Essay scoring: Automated essay scoring is the task of assigning a score to an essay, usually in the context of assessing the language ability of a language learner. The quality of an essay is affected by the following four primary dimensions: topic relevance, organization and coherence, word usage and sentence complexity, and grammar and mechanics. To measure whether an essay is relevant to its “prompt” (the description of the essay topic), lexical and semantic overlap is commonly used BIBREF25 , BIBREF26 . BIBREF27 explore word features, such as the number of verb formation errors, average word frequency, and average word length, to measure word usage and lexical complexity. BIBREF28 use sentence structure features to measure sentence variety. The effects of grammatical and mechanic errors on the quality of an essay are measured via word and part-of-speech $n$ -gram features and “mechanics” features BIBREF29 (e.g., spelling, capitalization, and punctuation), respectively. BIBREF30 , BIBREF31 , and BIBREF32 use an LSTM to obtain an essay representation, which is used as the basis for classification. Similarly, BIBREF33 utilize a CNN to obtain sentence representation and an LSTM to obtain essay representation, with an attention layer at both the sentence and essay levels. Academic paper rating: Academic paper rating is a relatively new task in NLP/AI, with the basic formulation being to automatically predict whether to accept or reject a paper.

What is the Academic paper rating in this paper?

Academic paper rating is a relatively new task in NLP/AI, with the basic formulation being to automatically predict whether to accept or reject a paper.

2004.01894

Table TABREF31 summarizes the contribution of the images on text representations in test partition. The contribution is consistent through all text-based representations. We measure the absolute difference (Diff) and the error reduction (E.R) of each textual representation with the multimodal counterpart. For the comparison we chose the best text model for each representation. As expected we obtain the largest improvement ($22-26\%$ E.R) when text-based unsupervised models are combined with image representations. Note that unsupervised models are not learning anything about the specific task, so the more information in the representation, the better. In the case of use and vse++ the improvement is significant but not as large as the purely unsupervised models. The best text-only representation is the one fine-tuned on a multimodal task, VSE++, which is noteworthy, as it is better than a textual representation fine-tuned in a text-only inference task like USE. For the comparison we chose the best text model for each representation. As expected we obtain the largest improvement ($22-26\%$ E.R) when text-based unsupervised models are combined with image representations.

How much better is inference that has addition of image representation compared to text-only representations?

The answers are shown as follows: * largest improvement ($22-26\%$ E.R) when text-based unsupervised models are combined with image representations

What are the words of House Lannister?

"Hear Me Roar"

What city is Microsoft Based in?

Redmond, Wa

We evaluate the performance of CRAFT in the task of forecasting conversational derailment in both the Wikipedia and CMV scenarios. To this end, for each of these datasets we pre-train the generative component on the unlabeled portion of the data and fine-tune it on the labeled training split (data size detailed in Section SECREF3). In order to evaluate our sequential system against conversational-level ground truth, we need to aggregate comment level predictions. If any comment in the conversation triggers a positive prediction—i.e., $p_{\text{event}}(c_{n+1})$ is greater than a threshold learned on the development split—then the respective conversation is predicted to derail. If this forecast is triggered in a conversation that actually derails, but before the derailment actually happens, then the conversation is counted as a true positive; otherwise it is a false positive. If no positive predictions are triggered for a conversation, but it actually derails then it counts as a false negative; if it does not derail then it is a true negative. Fixed-length window baselines. We first seek to compare CRAFT to existing, fixed-length window approaches to forecasting. To this end, we implement two such baselines: Awry, which is the state-of-the-art method proposed in BIBREF9 based on pragmatic features in the first comment-reply pair, and BoW, a simple bag-of-words baseline that makes a prediction using TF-IDF weighted bag-of-words features extracted from the first comment-reply pair. Online forecasting baselines. Next, we consider simpler approaches for making forecasts as the conversations happen (i.e., in an online fashion). First, we propose Cumulative BoW, a model that recomputes bag-of-words features on all comments seen thus far every time a new comment arrives. While this approach does exhibit the desired behavior of producing updated predictions for each new comment, it fails to account for relationships between comments. This simple cumulative approach cannot be directly extended to models whose features are strictly based on a fixed number of comments, like Awry. An alternative is to use a sliding window: for a feature set based on a window of $W$ comments, upon each new comment we can extract features from a window containing that comment and the $W-1$ comments preceding it. We apply this to the Awry method and call this model Sliding Awry. For both these baselines, we aggregate comment-level predictions in the same way as in our main model. CRAFT ablations. Finally, we consider two modified versions of the CRAFT model in order to evaluate the impact of two of its key components: (1) the pre-training step, and (2) its ability to capture inter-comment dependencies through its hierarchical memory. To evaluate the impact of pre-training, we train the prediction component of CRAFT on only the labeled training data, without first pre-training the encoder layers with the unlabeled data. We find that given the relatively small size of labeled data, this baseline fails to successfully learn, and ends up performing at the level of random guessing. This result underscores the need for the pre-training step that can make use of unlabeled data. To evaluate the impact of the hierarchical memory, we implement a simplified version of CRAFT where the memory size of the context encoder is zero (CRAFT $-$ CE), thus effectively acting as if the pre-training component is a vanilla seq2seq model. In other words, this model cannot capture inter-comment dependencies, and instead at each step makes a prediction based only on the utterance encoding of the latest comment. Results. Table TABREF17 compares CRAFT to the baselines on the test splits (random baseline is 50%) and illustrates several key findings. First, we find that unsurprisingly, accounting for full conversational context is indeed helpful, with even the simple online baselines outperforming the fixed-window baselines. On both datasets, CRAFT outperforms all baselines (including the other online models) in terms of accuracy and F1. Furthermore, although it loses on precision (to CRAFT $-$ CE) and recall (to Cumulative BoW) individually on the Wikipedia data, CRAFT has the superior balance between the two, having both a visibly higher precision-recall curve and larger area under the curve (AUPR) than the baselines (Figure FIGREF20). This latter property is particularly useful in a practical setting, as it allows moderators to tune model performance to some desired precision without having to sacrifice as much in the way of recall (or vice versa) compared to the baselines and pre-existing solutions. To evaluate the impact of the hierarchical memory, we implement a simplified version of CRAFT where the memory size of the context encoder is zero (CRAFT − CE), thus effectively acting as if the pre-training component is a vanilla seq2seq model. In other words, this model cannot capture inter-comment dependencies, and instead at each step makes a prediction based only on the utterance encoding of the latest comment.

Dos the simplified version of CRAFT they used can capture inter-comment dependencies?

No, it can only make a prediction based on the utterance encoding of the latest comment.

The phrase "black swan" derives from a Latin expression; its oldest known occurrence is from the 2nd-century Roman poet Juvenal's characterization in his Satire VI of something being "rara avis in terris nigroque simillima cygno" ("a rare bird in the lands and very much like a black swan").: 165  When the phrase was coined, the black swan was presumed not to exist. The importance of the metaphor lies in its analogy to the fragility of any system of thought. A set of conclusions is potentially undone once any of its fundamental postulates is disproved. In this case, the observation of a single black swan would be the undoing of the logic of any system of thought, as well as any reasoning that followed from that underlying logic. Juvenal's phrase was a common expression in 16th century London as a statement of impossibility. The London expression derives from the Old World presumption that all swans must be white because all historical records of swans reported that they had white feathers. In that context, a black swan was impossible or at least nonexistent. However, in 1697, Dutch explorers led by Willem de Vlamingh became the first Europeans to see black swans, in Western Australia.[citation needed] The term subsequently metamorphosed to connote the idea that a perceived impossibility might later be disproven. Taleb notes that in the 19th century, John Stuart Mill used the black swan logical fallacy as a new term to identify falsification. Black swan events were discussed by Nassim Nicholas Taleb in his 2001 book Fooled By Randomness, which concerned financial events. His 2007 book The Black Swan extended the metaphor to events outside of financial markets. Taleb regards almost all major scientific discoveries, historical events, and artistic accomplishments as "black swans"—undirected and unpredicted. He gives the rise of the Internet, the personal computer, World War I, the dissolution of the Soviet Union, and the September 11, 2001 attacks as examples of black swan events.

From the above passage, provide some of the examples of black swan events. List the events in a bulleted list.

As per the above passage, here are a few examples of black swan events: Rise of the Internet The personal computer World War I The dissolution of the Soviet Union The September 11, 2001 attacks

1602.01208

Table TABREF54 shows the results of PAR. Table TABREF55 presents examples of the word segmentation results of the three considered methods. We found that the unsupervised morphological analyzer capable of using lattices improved the accuracy of phoneme recognition and word segmentation. Consequently, this result suggests that this word segmentation method considers the multiple hypothesis of speech recognition as a whole and reduces uncertainty such as variability in recognition by using the syllable recognition results in the lattice format. We found that the unsupervised morphological analyzer capable of using lattices improved the accuracy of phoneme recognition and word segmentation. Consequently, this result suggests that this word segmentation method considers the multiple hypothesis of speech recognition as a whole and reduces uncertainty such as variability in recognition by using the syllable recognition results in the lattice format.

How do they show that acquiring names of places helps self-localization?

The answers are shown as follows: * unsupervised morphological analyzer capable of using lattices improved the accuracy of phoneme recognition and word segmentation * Consequently, this result suggests that this word segmentation method considers the multiple hypothesis of speech recognition as a whole and reduces uncertainty such as variability in recognition

What does the term ACID mean with respect to databases?

ACID covers 4 properties that Database transactions should have. A stands for Atomicity - either all the work done as part of a transaction should finish or none of it should. C stands for consistency i.e. any changes done by a transaction should pass all validation rules . I stands for Isolation which refers to the idea that transactions should be able to execute correctly independently of other concurrent transactions. D is for Durability. Any work done by a transaction that commits should persist despite failures. Each of these terms requires a more detailed explanation but this is a high level overview of the term ACID in databases

How can I read and write data with Delta Lake?

You can use your favorite Apache Spark APIs to read and write data with Delta Lake. See Read a table and Write to a table.

The International Brazilian Jiu-Jitsu Federation (IBJJF) is a for-profit company that hosts several of the biggest Brazilian jiu-jitsu (BJJ) tournaments in the world, including the World Jiu-Jitsu Championship, World No-Gi Championship, Pan Jiu-Jitsu Championship, and European Open Jiu-Jitsu Championship. The federation was created by Carlos Gracie, Jr., who is the head of one of the largest Brazilian jiu-jitsu associations, Gracie Barra. The IBJJF uses the ruleset of the Confederação Brasileira de Jiu-Jitsu (CBJJ). On October 11, 2020, the IBJJF announced that they will begin to allow both heel hooks and knee-reaping for all brown and black belts competing in no-gi tournaments, starting on an undisclosed date in 2021.

Given a reference text about the IBJJF, provide two tournaments organized by IBJJ and the ruleset used by these tournaments.

World No-Gi Championship and Pan Jiu-Jitsu Championship are two tournaments organized by the IBJJF. They follow the ruleset of the CBJJ.

Describe a plan for driving from Florida to New York with some great places to stop with American History.

The drive from Florida to New York is close to 1,200 miles and will take a minimum of 17 hours in a car via interstate 95. There are many great stops along the way to learn more about American History. The states you will drive through starting from Florida are Georgia, South Carolina, North Carolina, Virgina, Maryland, Delaware and New Jersey before getting to New York. A few destinations to stop along the journey are Savannah Georgia which is known as America's first planned city, Charleston South Carolina Fort Sumter where the Civil War Started, Richmond where The Richmond Union Passenger Railway was the first electric streetcar in the U.S, Washington D.C. to visit our nation's capital, Baltimore Maryland where Fort McHenry is located and the Star-Spangled Banner was written and Philadelphia where the Liberty Bell is located.

The Rakaia Gorge is located on the Rakaia River in inland Canterbury in New Zealand's South Island.

Where is Rakaia Gorge located?

The Rakaia Gorge is located on the Rakaia River in inland Canterbury in New Zealand's South Island. Like its neighbour, the Waimakariri River, the Rakaia runs through wide shingle beds for much of its length, but is forced through a narrow canyon as it approaches the Canterbury Plains.

Do you have to get training to go scuba diving?

Scuba diving requires training, and normally people gain a scuba diving certification before doing this activity

How many people live in the Netherlands

The Netherlands has a population of 17.4 million people.

Identify which instrument is string or percussion: Naqareh, Liuqin

Liuqin is string, Nagareh is percussion.

Neural machine translation (NMT) BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 is a deep learning-based method for translation that has recently shown promising results as an alternative to statistical approaches. NMT systems directly model the probability of the next word in the target sentence simply by conditioning a recurrent neural network on the source sentence and previously generated target words. While both simple and surprisingly accurate, NMT systems typically need to have very high capacity in order to perform well: Sutskever2014 used a 4-layer LSTM with 1000 hidden units per layer (herein INLINEFORM0 ) and Zhou2016 obtained state-of-the-art results on English INLINEFORM1 French with a 16-layer LSTM with 512 units per layer. The sheer size of the models requires cutting-edge hardware for training and makes using the models on standard setups very challenging. This issue of excessively large networks has been observed in several other domains, with much focus on fully-connected and convolutional networks for multi-class classification. Researchers have particularly noted that large networks seem to be necessary for training, but learn redundant representations in the process BIBREF6 . Therefore compressing deep models into smaller networks has been an active area of research. As deep learning systems obtain better results on NLP tasks, compression also becomes an important practical issue with applications such as running deep learning models for speech and translation locally on cell phones. Existing compression methods generally fall into two categories: (1) pruning and (2) knowledge distillation. Pruning methods BIBREF7 , BIBREF8 , BIBREF9 , zero-out weights or entire neurons based on an importance criterion: LeCun1990 use (a diagonal approximation to) the Hessian to identify weights whose removal minimally impacts the objective function, while Han2016 remove weights based on thresholding their absolute values. Knowledge distillation approaches BIBREF0 , BIBREF10 , BIBREF1 learn a smaller student network to mimic the original teacher network by minimizing the loss (typically INLINEFORM0 or cross-entropy) between the student and teacher output. In this work, we investigate knowledge distillation in the context of neural machine translation. We note that NMT differs from previous work which has mainly explored non-recurrent models in the multi-class prediction setting. For NMT, while the model is trained on multi-class prediction at the word-level, it is tasked with predicting complete sequence outputs conditioned on previous decisions. With this difference in mind, we experiment with standard knowledge distillation for NMT and also propose two new versions of the approach that attempt to approximately match the sequence-level (as opposed to word-level) distribution of the teacher network. This sequence-level approximation leads to a simple training procedure wherein the student network is trained on a newly generated dataset that is the result of running beam search with the teacher network. We run experiments to compress a large state-of-the-art INLINEFORM0 LSTM model, and find that with sequence-level knowledge distillation we are able to learn a INLINEFORM1 LSTM that roughly matches the performance of the full system. We see similar results compressing a INLINEFORM2 model down to INLINEFORM3 on a smaller data set. Furthermore, we observe that our proposed approach has other benefits, such as not requiring any beam search at test-time. As a result we are able to perform greedy decoding on the INLINEFORM4 model 10 times faster than beam search on the INLINEFORM5 model with comparable performance. Our student models can even be run efficiently on a standard smartphone. Finally, we apply weight pruning on top of the student network to obtain a model that has INLINEFORM6 fewer parameters than the original teacher model. We have released all the code for the models described in this paper. Furthermore, we observe that our proposed approach has other benefits, such as not requiring any beam search at test-time.

Is beam search required at test-time by using their method?

No.

Josh Logan (born 1980), born Josh Corder, is a singer-songwriter, vocalist and guitarist best known for his performances on the television shows Rock Star: Supernova and The Voice. By the age of 15, Logan was on-stage as the front man for the local New England rock band Gunshy. The band released one album in 1996, but broke up when Logan was 18. After the breakup, Logan played at local venues for two years as a solo artist, hosting open mic nights as well as performing, to hone his skills as a solo musician.

How old was Josh Logan when Gunshy released its album?

Josh Logan was 16 years old. He was born in 1980 and the album released in 1996, so 1996-1980 = 16

Career Amateur years As an amateur, Liang's major feats were as follows: 2003 IBSF World Snooker Championship, men's division, quarter-finalist 2004 IBSF World Snooker Championship, under-21 division, semi-finalist 2005 International Open Series, under-21 division, no. 4 runner-up Liang built on the positive start to his snooker career, winning an individual silver medal and a team gold medal at the 2006 Asian Games. 2004/2005 Liang began his professional career during the 2004–05 snooker season playing on the Challenge Tour, which is the tier below the World Snooker Association Main Tour. He finished a mediocre 104th out of 168 competitors, having only accumulated 2150 points. 2005/2006 Liang received a wildcard nomination to the Main Tour despite not qualifying directly; this was because he won the 2005 IBSF World Under-21 Championship, and also because not all of the players that were eligible for the Main Tour took their places. In his first ranking tournament, the Grand Prix, he lost in the first qualifying round to Rory McLeod 2–5. He fared better in the next ranking event, the UK Championship, where he almost whitewashed Alfred Burden in the first qualifying round 9–1, but subsequently lost in the second qualifying round to Marcus Campbell by the narrowest of margins, 8–9. Liang qualified for his first ranking event at the Welsh Open, beating Sean Storey, Jamie Burnett and Rory McLeod to reach the main draw. He defeated Nigel Bond in the first round 5–0, but his run was halted when he lost to Graeme Dott 3–5. At the Malta Cup, however, he lost in the first qualifying round to Paul Davies 3–5. At the China Open, he beat David McDonnell and Matthew Couch before losing against Adrian Gunnell 3–5 in the third qualifying round. He ended the season falling at the first hurdle at the World Championship, losing to Joe Delaney 5–10 in the first qualifying round. Liang ended his debut season on the professional tour ranked 78th, a position that would not guarantee a place in the following season's tour; however, he had finished inside the top 8 of the one year ranking list, which qualified him for a place on the main tour for the next season. 2006/2007 During the 2006–07 season, Liang reached at least the second round of qualifying in every ranking event. At the Northern Ireland Trophy, he beat Robert Stephen 5–0 before falling to David Gilbert 0–5 in qualifying. However, at the Grand Prix, Liang came top of his qualifying group, above more experienced players such as Gerard Greene and Barry Pinches. He finished fourth in his group at the round-robin stage, and although he did not progress to the next round, he did beat former world champion and world number one Stephen Hendry 3–0. At the UK Championship, he lost in the second round of qualifying to Jamie Burnett 7–9. In the following ranking event, the Malta Cup, he lost to Joe Jogia 3–5, again in the second round of qualifying. He qualified for the Welsh Open, his third ranking tournament, by beating Dene O'Kane, Joe Jogia and Mark Davis. He met Nigel Bond again in the last 48, this time losing only 3–5. At the China Open, he continued his run of reaching the second round of qualifying in every ranking tournament, and beat Robert Stephen before losing to Finland's Robin Hull. At the World Championship, he beat Jeff Cundy before losing to Mike Dunn. After a modest season, he improved on his tour ranking by finishing in 66th place, just outside the top 64; and he topped the one year ranking list to ensure his place on the WSA Tour for next season. 2007/2008 Liang started the season by almost qualifying for the Shanghai Masters, however Nigel Bond beat him 5–3 in the last qualifying round, preventing him from appearing at his home tournament. At the Grand Prix, he could not repeat the success of last season and failed to qualify, finishing third on frame difference. He had more luck at the next tournament, the Northern Ireland Trophy, where he won through the qualifying rounds, beating Fraser Patrick, Joe Delaney and Rory McLeod on the way. He faced Gerard Greene in the last 48, but lost 2–5. He had less success at the UK Championship, losing in the second qualifying round to David Roe 2–9. He also failed to qualify for the Welsh Open, when he was dispatched in the last qualifying round by Andrew Norman 2–5. He fell at the first hurdle at his other home tournament, the China Open, losing in the first qualifying round to Steve Mifsud, who at the end of this season was ranked 54 places below Liang. At the World Championship, Liang was the third Chinese player to qualify for the main draw, defeating Ben Woollaston, Rod Lawler, David Gilbert and Ian McCulloch in the qualifying rounds. He met Ken Doherty in the first round of the championship, and defeated him 10–5. Before the start of this match, he accidentally entered the arena at the same time as the match officials and had to hurry back; he subsequently received a warm ovation when he entered the arena for a second time after being introduced by MC Rob Walker. For every session thereafter, Walker introduced him as "Should he stay or should he go... Liang Wenbo", despite the rhyme occurring due to a mispronunciation of his name ("bo" is pronounced "bwor" in Chinese). Liang faced Northern Ireland's Joe Swail in the last 16 of the tournament. In a humorous incident, Liang fluked a red after scattering the balls, but failed to notice and went back to his seat. To the amusement of the spectators, Swail pointed out the mistake and the referee called Liang back to the table. In the 23rd frame, with a 12–10 lead, Liang prematurely celebrated winning the match after potting "match ball", only to then lose the frame due to a snooker; Swail came back to level the match at 12–12. In the final frame, Liang made early breaks of 34 and 30. He missed the final yellow but snookered Swail, leaving the cue ball in the jaws of the pocket. Liang followed up with a safety shot but Swail snookered him behind the blue; Liang failed to hit the yellow ball so Swail had the white replaced. In his second attempt, Liang hit the yellow directly and went on to win the frame 74–34, and thus the match, 13–12. The incident in the last frame proved controversial as the referee replaced the cue ball in the wrong position, giving Liang a better sight of the yellow. At the time, Swail nodded his assent to the referee, but he complained in a post-match interview that Liang had behaved unprofessionally by not pointing out the referee's error. Commentators countered that Swail should have queried the placement of the ball before Liang took his shot, and that, given the tension of the situation, Liang could be forgiven for not thinking clearly. Liang faced eventual champion Ronnie O'Sullivan in the quarter-final, taking the first two frames with a break of 80 in the first, but O'Sullivan had levelled the match 4–4 by the end of the first session. O'Sullivan eased ahead in the second session and eventually won the match 13–7. Liang's run to the quarter-finals of the World Championship gained him 5000 ranking points, boosting his final ranking to number 40 in the world. This guaranteed that he would only have to win two qualifying matches to enter the main draw of the ranking tournaments the following season. 2008/2009 Liang began the new season by qualifying for the last 48 of the Northern Ireland Trophy. He then beat Steve Davis and Peter Ebdon to reach the last 16, where he lost to John Higgins 1–5. This result lifted him to a provisional career high of 26 in the world. He reached the main draw of the Grand Prix by winning two qualifying matches, but then succumbed to Ronnie O'Sullivan in the first round of the main draw. He then made a 147 and three other centuries (including two total clearances of 139) in a 5–1 victory over Martin Gould in the third qualifying round of the Bahrain Championship. However, he failed to qualify for the main draw, losing 2–5 to Michael Judge. For the two Chinese events on this season's tour, Liang's two qualifying matches were held over until the venue stages. At the 2008 Shanghai Masters, he defeated Atthasit Mahitthi and Mark Allen to reach the main draw, but lost to Ryan Day 0–5 in the first round. Ironically, his second qualifying match for the Welsh Open was held over to ensure that his Welsh opponent Dominic Dale played at the main venue in Newport. Liang ended the season at the World Championship, after defeating Dave Harold 10–3 in the last qualifying round. He lost in the first round of the main draw 8–10 against Ding Junhui. 2009/2010 In July 2009, Liang won his first professional title, the Beijing International Challenge, beating world number 2 Stephen Maguire 7–6 in the final. He made a further breakthrough in August when he reached the final of the Shanghai Masters, becoming only the second Chinese player, and the fourth Asian man, to reach a ranking final. He ultimately finished runner-up to Ronnie O'Sullivan. Liang qualified for the Grand Prix but was defeated 2–5 by Peter Ebdon in the first round, winning the first two frames and losing the last five in succession. Prior to this meeting, he had played Ebdon twice, winning 5–1 and 5–0. He reached the quarter-finals of the UK Championship after defeating Ryan Day 9–3, and Mark King 9–2. He went on to lose 2–9 to John Higgins in the quarter-finals. He failed to qualify for the Welsh Open and the China Open, as he lost 3–5 against Michael Judge and 2–5 against Andrew Higginson respectively. Liang qualified for the World Championships by defeating Rod Lawler 10–2. He was drawn against Ronnie O'Sullivan, but lost 7–10. After the quarter-finals of the event, it was clear that Liang would be ranked as number 16 the following season, the only new player entering the top 16 that season. 2010/2011 Liang began the season at the Wuxi Classic, where he lost in the first round 2–5 against Marco Fu. He participated at the Beijing International Challenge to defend his first professional title, but lost in the semi-finals 4–6 against eventual winner Tian Pengfei. Liang failed to qualify for the World Open, as he lost 1–3 against Andrew Higginson and lost his first round match at the Shanghai Masters 3–5 against Matthew Stevens. Having started the season inside the top 16, his results were not sufficient to maintain that position and he slipped out of the top 16. After this, he lost his qualifying matches in the five ranking tournaments. Liang also participated at the Players Tour Championship, his best performance coming at the third European event, where he reached the final, but lost 0–4 against Marcus Campbell. After 12 out of 12 events, he was ranked 14th in the Players Tour Championship Order of Merit. He lost his first round match at the Finals 1–4 against Ricky Walden. 2011/2012 Liang and Ding Junhui represented China at the World Cup and they won in the final 4–2 against the Northern Ireland team. During his match against Matthew Stevens in the first round of Australian Goldfields Open, Liang had a chance to make his second maximum break in his career, but he snookered himself on the yellow ball, and the break ended at 120. He won the match 5–4 to reach the second round, where he lost 4–5 against Ken Doherty. He also qualified for the Shanghai Masters, but lost in the first round 1–5 against Neil Robertson. After this, he was unable to qualify for the next two ranking events, as he lost 2–6 against Michael White at the UK Championship, and 3–5 against Liu Chuang at the German Masters. In December 2011, Liang joined Romford-based snooker Academy and management team Grove Leisure. He beat David Gilbert and Rory McLeod, both 4–1, to reach the first round of the Welsh Open, where he faced John Higgins and was this time on the wrong end of a 4–1 scoreline. He narrowly missed out on a place in the World Open as he was defeated 4–5 by Mark King in the final round of qualifying. He also lost in qualifying for the China Open to the eventual winner of the event Peter Ebdon 0–5. Liang played in eleven of the twelve minor-ranking PTC events throughout the season, with a best finish in Event 10, where he lost in the quarter-finals to Dominic Dale. He also reached the last 16 in two other events to finish 38th in the PTC Order of Merit, outside of the top 24 who qualified for the Finals. He qualified for the World Championship by defeating Marcus Campbell 10–9. He had led the match 8–2 and 9–5 before Campbell took it to 9–9, at which point the match was suspended to allow players on the other tables to begin their sessions. Liang subsequently won the decider with a 72 break and played defending champion Higgins again in the first round. He was involved in another final-frame decider, but was this time on the losing end as he bowed out of the tournament on the opening day, 9–10. He finished the season ranked world number 37. 2012/2013 Liang lost in qualifying for both the Wuxi Classic and the Shanghai Masters to Rod Lawler. He reached the venue stage of the Australian Goldfields Open by beating Thanawat Thirapongpaiboon, but lost 3–5 in the first round against Matthew Stevens. Liang beat Anthony McGill and Andrew Higginson to qualify for the UK Championship, where he saw a 4–3 lead in the first round against Barry Hawkins turn into a 4–6 defeat. Liang failed to qualify for the next two events, but won two matches to enter the main draw of both the Welsh Open and the China Open. In Wales he lost 2–4 to local favourite Stevens in the first round, and in his homeland of China he beat Lu Ning 5–1 in the wildcard round, before losing 3–5 to Stuart Bingham in the first round. Liang had a very consistent season in the Players Tour Championship series as he advanced to, but not past, the last 16 in five of the ten events. This saw him placed 27th on the PTC Order of Merit, one spot short of making the Finals. Liang's season ended when he was beaten 6–10 by Mark Davis in the final round of World Championship Qualifying. His end of year ranking was world number 32. 2013/2014 Liang Wenbo at the 2014 German Masters In July 2013, Liang reached the final of the World Games but lost in the gold medal match 0–3 to Aditya Mehta. He had an excellent season in the Asian Tour events by reaching the semi-finals of the Zhangjiagang Open where he was defeated 1–4 by Michael Holt and at the Zhengzhou Open, where Liang won his first individual title on the World Snooker Tour. He beat Anthony McGill 4–3 in the semi-finals before whitewashing Lü Haotian 4–0 to claim the £10,000 first prize. In the full ranking events, Liang won five successive frames against defending world champion Ronnie O'Sullivan in the second round of the International Championship to triumph 6–4 which marked his first ever success over his opponent having lost in all four previous attempts. He then thrashed Mark Davis 6–1 to reach the quarter-finals of a ranking event for the first time in four years, where he lost 3–6 against Graeme Dott. Liang reached the last 16 of both the German Masters and Welsh Open, losing 2–5 to Mark Davis and 2–4 to Barry Hawkins respectively. A second final on the Asian Tour followed at the Dongguan Open where Stuart Bingham made four breaks above 50 to defeat him 4–1, but Liang still topped the Order of Merit to qualify for the Finals. There, he was beaten 2–4 by Yu Delu and Liang was defeated 7–10 by Martin Gould in the final round of World Championship qualifying. 2014/2015 Liang overcame Jamie Burnett 5–1 in the first round of the 2014 Wuxi Classic, and then inflicted the first defeat on Mark Selby since he won the World Championship, beating him 5–3. In the last 16, Liang was knocked out 2–5 by Joe Perry. He lost 3–5 against Robert Milkins in the opening round of the Australian Goldfields Open, and in the wildcard round of the Shanghai Masters 1–5 to Yan Bingtao. He failed to get past the last 64 of the next two ranking events, but won two matches to reach the German Masters, where he eliminated Li Hang 5–1 in the first round. He reached the quarter-finals by coming back from 3–4 down against Stuart Bingham to win 5–4 on the final pink. He repeated this form when he edged Ryan Day 5–4 to play in his second career ranking event semi-final, where he took four frames in a row to hold a narrow 4–3 advantage over Shaun Murphy, before losing three successive frames in a 4–6 defeat. Liang did not drop a single frame in seeing off Cao Xinlong and Gerard Greene at the Welsh Open, but was then the victim of a whitewash by John Higgins in the third round. At the inaugural World Grand Prix, he lost 3–4 to Graeme Dott in the second round. In the final round of World Championship qualifying, he lost the last three frames against compatriot Zhang Anda to be narrowly defeated 9–10. 2015/2016 Liang was heavily beaten 2–8 by Thepchaiya Un-Nooh in the final of the 2015 Six-red World Championship. In the third round of the UK Championship, he took advantage of a collapse from Judd Trump to win 6–4 after trailing 1–4. He then saw off Tom Ford 6–5, after which Ford accused Liang of "boring him off the table" with slow play. Liang responded by opening his quarter-final match against Marco Fu with three centuries and hung on to edge it 6–5, then came from 2–4 behind to reach the final by beating David Grace 6–4. It was the first final in the history of the event to feature two players from outside the United Kingdom; Liang lost the match 5–10 to eventual UK champion Neil Robertson. A week later, he progressed to the semi-finals of the Gibraltar Open, but was whitewashed 0–4 by Fu. Liang's UK final helped him break back into the top 16 in the world rankings to make his debut at the Masters, where he was knocked out 4–6 by John Higgins in the opening round. He was whitewashed 0–4 by Shaun Murphy in the quarter-finals of the World Grand Prix. After winning three matches to qualify for the World Championship, Liang lost 8–10 to Judd Trump after an earlier 7–3 lead. 2016/2017 In the first round of the Shanghai Masters, Liang was narrowly beaten 4–5 by Ronnie O'Sullivan. He won five matches at the English Open, which included a 4–3 second round victory over Shaun Murphy, to reach the semi-finals. From 3–4 down, Liang made breaks of 134 and 138 and Stuart Bingham made a 116 to lock the score at 5–5. Liang took the decider and then ended Judd Trump's 14-match winning streak in the final to win his maiden ranking title 9–6. He became the second player from mainland China to win a ranking event and thanked O'Sullivan (with whom he practises daily) for his help. The win also gave him entry to his first Champion of Champions where he lost 0–4 in the opening round to Mark Selby. In the Scottish Open, Liang reached the quarter-finals, where he lost to Yu Delu 4–5. In the Masters, he missed the final black, which would have seen him eliminate O'Sullivan 6–4 in the opening round of the Masters, instead going on to lose 5–6. A 4–0 thrashing of Dominic Dale and a pair of 4–3 victories over Mark Allen and Joe Perry moved Liang into the semi-finals of the World Grand Prix, where he was defeated 1–6 by Barry Hawkins. In the second round of the World Championship, Liang trailed Ding Junhui 2–6 after the first session and 7–9 after the second. He then won a trio of frames to take the lead in the match for the first time and would have been 12–11 ahead, one frame away from reaching the quarter-finals for the second time, but instead lost 12–13. After the event, his end-of-season ranking rose to 11, which was a career-high. 2017/2018 Though Liang had a decent campaign this season, he was unable to reach the same height as previous season. In his first round match against Ronnie O'Sullivan at the Masters. In a best -of-11 match, Liang led 5-4 up and only needed the final black in the 10th frame to guarantee his place in the quarter final; however, he missed the relatively easy black, and subsequently lost the frame and eventually the match. In his match against Rod Lawler in the World Championship Qualifying round, Liang made a 147 in the 10th frame, the third 147 in his career. But interestingly, he also had a chance to make another 147 in the 12th frame, but he missed the tricky final black. 2018/2019 Liang had a somewhat of a down season in the 2018/19 campaign. His best results came in the European Masters, China Open, and China Championship, in which he advanced to the third round. Liang only earned a total of 78,500 ranking points, which was 141,100 points shy from his best season in 2016/2017. 2019/2020 This season Liang saw some improvement in this form. His best result came in the UK Championship, in which he defeated Dominic Dale, David Grace, Eden Sharav, and Li Hang, before losing to the eventual champion Ding Junhui in the quarter final. Liang also reached the quarter final of the Gibraltar Open, losing 4–1 to Judd Trump.

What professional tournaments did Liang win during his career?

In July 2009, Liang won his first professional title, the Beijing International Challenge. Liang also won the 2016 English Open.

Which of Dany's dragons is the largest?

Daenerys Targaryen's dragon, Drogon, is the largest of her three dragons.

What is the difference between medicare and medicaid?

Medicare and medicaid are government-run health insurance programs designed to provide health care coverage for different qualifying groups. Medicare is a federal program that provides healthcare coverage for people 65 years of age or older. It can also provide coverage for people under 65 years old if they have a qualifying disability. There are no income limits in order to qualify for medicare. Medicaid is a state and federal program that provides health insurance for people with low income and assets. Individual states establish financial eligibility guidelines to determine who is eligible for coverage. It's possible to enroll in medicare and medicaid at the same time if all qualifications are met for both programs.

Who planted the first wine vineyard at California's Mission San Juan Capistrano?

Father Junípero Serra

What is the difference between software engineer and systems engineer?

Systems engineers often have more experience working with hardware and networks while software engineers focus on coding and developing applications.

Is 95 degrees Fahrenheit considered to be hot weather?

Yes, 95 degrees would typically be considered to be hot

Identify which instrument is string or percussion: Tingsha, Bazantar

Tingsha is percussion, Bazantar is string.

Identify which car manufacturer is British or American: Rolls-Royce, Ford

Ford is American, Rolls-Royce is British

Which nation placed its astronauts on the moon first?

The United States won the race for a manned space mission to the moon.

“I'm supposed to trust the opinion of a MS minion? The people that produced Windows ME, Vista and 8? They don't even understand people, yet they think they can predict the behavior of new, self-guiding AI?” –anonymous “I think an AI would make it easier for Patients to confide their information because by nature, a robot cannot judge them. Win-win? :D”' –anonymous Dogmatism describes the tendency to lay down opinions as incontrovertibly true, without respect for conflicting evidence or the opinions of others BIBREF0 . Which user is more dogmatic in the examples above? This question is simple for humans. Phrases like “they think” and “they don't even understand,” suggest an intractability of opinion, while “I think” and “win-win?” suggest the opposite. Can we train computers to draw similar distinctions? Work in psychology has called out many aspects of dogmatism that can be modeled computationally via natural language, such as over-confidence and strong emotions BIBREF1 . We present a statistical model of dogmatism that addresses two complementary goals. First, we validate psychological theories by examining the predictive power of feature sets that guide the model's predictions. For example, do linguistic signals of certainty help to predict a post is dogmatic, as theory would suggest? Second, we apply our model to answer four questions: R1: What kinds of topics (e.g., guns, LGBT) attract the highest levels of dogmatism? R2: How do dogmatic beliefs cluster? R3: How does dogmatism influence a conversation on social media? R4: How do other user behaviors (e.g., frequency and breadth of posts) relate to dogmatism? We train a predictive model to classify dogmatic posts from Reddit, one of the most popular discussion communities on the web. Posts on Reddit capture discussion and debate across a diverse set of domains and topics – users talk about everything from climate change and abortion, to world news and relationship advice, to the future of artificial intelligence. As a prerequisite to training our model, we have created a corpus of 5,000 Reddit posts annotated with levels of dogmatism, which we are releasing to share with other researchers. Using the model, we operationalize key domain-independent aspects of psychological theories of dogmatism drawn from the literature. We find these features have predictive power that largely supports the underlying theory. For example, posts that use less confident language tend to be less dogmatic. We also discover evidence for new attributes of dogmatism. For example, dogmatic posts tend not to verbalize cognition, through terms such as “I think,” “possibly,” or “might be.” Our model is trained on only 5,000 annotated posts, but once trained, we use it to analyze millions of other Reddit posts to answer our research questions. We find a diverse set of topics are colored by dogmatic language (e.g., people are dogmatic about religion, but also about LGBT issues). Further, we find some evidence for dogmatism as a deeper personality trait – people who are strongly dogmatic about one topic are more likely to express dogmatic views about others as well. Finally, in conversation, we discover that one user's dogmatism tends to bring out dogmatism in their conversational partner, forming a vicious cycle. As a prerequisite to training our model, we have created a corpus of 5,000 Reddit posts annotated with levels of dogmatism, which we are releas_x005f_x001f_ing to share with other researchers.

What is the corpus created with？

It is created with 5,000 Reddit posts annotated with levels of dogmatism.

2001.11268

Sentences 1 and 2 are labelled incorrectly, and clearly appear far away from the population class centroid. Sentence 3 is an example of an ambiguous case. It appears very close to the population centroid, but neither its label nor its position reflect the intervention content. This supports a need for multiple tags per sentence, and the fine-tuning of weights within the network. FLOAT SELECTED: Figure 2: Visualization of training sentences using BERTbase. The x and y-axis represent the two most dominant dimensions in the hidden state output, as selected by the t-SNE algorithm. This visualization uses the sixth layer from the top, and shows three examples of labelled P sentences and their embedded positions. Sentence 3 is an example of an ambiguous case. It appears very close to the population centroid, but neither its label nor its position reflect the intervention content. FLOAT SELECTED: Figure 2: Visualization of training sentences using BERTbase. The x and y-axis represent the two most dominant dimensions in the hidden state output, as selected by the t-SNE algorithm. This visualization uses the sixth layer from the top, and shows three examples of labelled P sentences and their embedded positions.

What are the problems related to ambiguity in PICO sentence prediction tasks?

Some sentences are associated to ambiguous dimensions in the hidden state output

2002.02224

Overall, through the process described in Section SECREF3, we have retrieved three datasets of extracted references - one dataset per each of the apex courts. These datasets consist of the individual pairs containing the identification of the decision from which the reference was retrieved, and the identification of the referred documents. As we only extracted references to other judicial decisions, we obtained 471,319 references from Supreme Court decisions, 167,237 references from Supreme Administrative Court decisions and 264,463 references from Constitutional Court Decisions. These are numbers of text spans identified as references prior the further processing described in Section SECREF3. As we only extracted references to other judicial decisions, we obtained 471,319 references from Supreme Court decisions, 167,237 references from Supreme Administrative Court decisions and 264,463 references from Constitutional Court Decisions. These are numbers of text spans identified as references prior the further processing described in Section SECREF3.

How big is the dataset?

903019 references

A group of friends and putting together a D&D group and would like a balance of ranged and melee character classes. Here are the classes they are considering: Ranger, Rogue, Fighter, Barbarian, Monk, Warlock, Paladin, and Wizard. Please create a dot point list of Ranged and Melee classes in alphabetical order, so that I can share that with my friends.

Below is the breakdown of classes, listed in alphabetical order in Melee and Ranged groups. Melee classes: - Barbarian - Fighter - Monk - Paladin - Rogue Ranged classes: - Ranger - Warlock - Wizard

Venezuela (/ˌvɛnəˈzweɪlə/; American Spanish: [beneˈswela] (listen)), officially the Bolivarian Republic of Venezuela (Spanish: República Bolivariana de Venezuela), is a country on the northern coast of South America, consisting of a continental landmass and many islands and islets in the Caribbean Sea. It has a territorial extension of 916,445 km2 (353,841 sq mi), and its population was estimated at 29 million in 2022. The capital and largest urban agglomeration is the city of Caracas.

What is Venezuela?

Venezuela (/ˌvɛnəˈzweɪlə/; American Spanish: [beneˈswela] (listen)), officially the Bolivarian Republic of Venezuela (Spanish: República Bolivariana de Venezuela), is a country on the northern coast of South America, consisting of a continental landmass and many islands and islets in the Caribbean Sea. It has a territorial extension of 916,445 km2 (353,841 sq mi), and its population was estimated at 29 million in 2022. The capital and largest urban agglomeration is the city of Caracas. The continental territory is bordered on the north by the Caribbean Sea and the Atlantic Ocean, on the west by Colombia, Brazil on the south, Trinidad and Tobago to the north-east and on the east by Guyana. The Venezuelan government maintains a claim against Guyana to Guayana Esequiba. Venezuela is a federal presidential republic consisting of 23 states, the Capital District and federal dependencies covering Venezuela's offshore islands. Venezuela is among the most urbanized countries in Latin America; the vast majority of Venezuelans live in the cities of the north and in the capital. The territory of Venezuela was colonized by Spain in 1522 amid resistance from indigenous peoples. In 1811, it became one of the first Spanish-American territories to declare independence from the Spanish and to form part, as a department, of the first federal Republic of Colombia (historiographically known as Gran Colombia). It separated as a full sovereign country in 1830. During the 19th century, Venezuela suffered political turmoil and autocracy, remaining dominated by regional military dictators until the mid-20th century. Since 1958, the country has had a series of democratic governments, as an exception where most of the region was ruled by military dictatorships, and the period was characterized by economic prosperity. Economic shocks in the 1980s and 1990s led to major political crises and widespread social unrest, including the deadly Caracazo riots of 1989, two attempted coups in 1992, and the impeachment of a President for embezzlement of public funds charges in 1993. The collapse in confidence in the existing parties saw the 1998 Venezuelan presidential election, the catalyst for the Bolivarian Revolution, which began with a 1999 Constituent Assembly, where a new Constitution of Venezuela was imposed. The government's populist social welfare policies were bolstered by soaring oil prices, temporarily increasing social spending, and reducing economic inequality and poverty in the early years of the regime. However, poverty began to increase in the 2010s. The 2013 Venezuelan presidential election was widely disputed leading to widespread protest, which triggered another nationwide crisis that continues to this day. Venezuela has experienced democratic backsliding, shifting into an authoritarian state. It ranks low in international measurements of freedom of the press and civil liberties and has high levels of perceived corruption. Venezuela is a developing country and ranks 113th on the Human Development Index. It has the world's largest known oil reserves and has been one of the world's leading exporters of oil. Previously, the country was an underdeveloped exporter of agricultural commodities such as coffee and cocoa, but oil quickly came to dominate exports and government revenues. The excesses and poor policies of the incumbent government led to the collapse of Venezuela's entire economy. The country struggles with record hyperinflation, shortages of basic goods, unemployment, poverty, disease, high child mortality, malnutrition, severe crime and corruption. These factors have precipitated the Venezuelan migrant crisis where more than three million people have fled the country. By 2017, Venezuela was declared to be in default regarding debt payments by credit rating agencies. The crisis in Venezuela has contributed to a rapidly deteriorating human rights situation, including increased abuses such as torture, arbitrary imprisonment, extrajudicial killings and attacks on human rights advocates. Venezuela is a charter member of the UN, Organization of American States (OAS), Union of South American Nations (UNASUR), ALBA, Mercosur, Latin American Integration Association (LAIA) and Organization of Ibero-American States (OEI).

Who invented the game of basketball?

The game of basketball as it is known today was created by Dr. James Naismith in December 1891 in Springfield, Massachusetts, to condition young athletes during cold months.

1902.00330

We conduct experiments on several different types of public datasets including news and encyclopedia corpus. The training set is AIDA-Train and Wikipedia datasets, where AIDA-Train contains 18448 mentions and Wikipedia contains 25995 mentions. In order to compare with the previous methods, we evaluate our model on AIDA-B and other datasets. These datasets are well-known and have been used for the evaluation of most entity linking systems. The statistics of the datasets are shown in Table 1. AIDA-CoNLL BIBREF14 is annotated on Reuters news articles. It contains training (AIDA-Train), validation (AIDA-A) and test (AIDA-B) sets. ACE2004 BIBREF15 is a subset of the ACE2004 Coreference documents. MSNBC BIBREF16 contains top two stories in the ten news categories(Politics, Business, Sports etc.) AQUAINT BIBREF17 is a news corpus from the Xinhua News Service, the New York Times, and the Associated Press. WNED-CWEB BIBREF18 is randomly picked from the FACC1 annotated ClueWeb 2012 dataset. WNED-WIKI BIBREF18 is crawled from Wikipedia pages with its original hyperlink annotation. OURSELF-WIKI is crawled by ourselves from Wikipedia pages. We conduct experiments on several different types of public datasets including news and encyclopedia corpus. AIDA-CoNLL BIBREF14 is annotated on Reuters news articles. It contains training (AIDA-Train), validation (AIDA-A) and test (AIDA-B) sets. ACE2004 BIBREF15 is a subset of the ACE2004 Coreference documents. MSNBC BIBREF16 contains top two stories in the ten news categories(Politics, Business, Sports etc.) AQUAINT BIBREF17 is a news corpus from the Xinhua News Service, the New York Times, and the Associated Press. WNED-CWEB BIBREF18 is randomly picked from the FACC1 annotated ClueWeb 2012 dataset. WNED-WIKI BIBREF18 is crawled from Wikipedia pages with its original hyperlink annotation. OURSELF-WIKI is crawled by ourselves from Wikipedia pages.

What datasets used for evaluation?

The answers are shown as follows: * AIDA-CoNLL * ACE2004 * MSNBC * AQUAINT * WNED-CWEB * WNED-WIKI * OURSELF-WIKI

The islands are located in the eastern Pacific Ocean, 973 km (605 mi) off the west coast of South America. The majority of islands are also more broadly part of the South Pacific. The closest land mass is that of mainland Ecuador, the country to which they belong, 926 km (500 nmi) to the east. Orthographic projection centered over the Galápagos. School of scalloped hammerheads, Wolf Island, Galápagos Islands. Grapsus grapsus on the rocks. Satellite photo of the Galápagos islands overlaid with the names of the visible main islands. Isabela seen from Spot Satellite. Waved albatrosses on Española. Galápagos marine iguana. Main Street on San Cristóbal Island. An animated tour of the Galápagos. NASA oceanographer Gene Carl Feldman reflects on his unique perspective on this region. The islands are found at the coordinates 1°40'N–1°36'S, 89°16'–92°01'W. Straddling the equator, islands in the chain are located in both the northern and southern hemispheres, with Volcán Wolf and Volcán Ecuador on Isla Isabela being directly on the equator. Española Island, the southernmost islet of the archipelago, and Darwin Island, the northernmost one, are spread out over a distance of 220 km (137 mi). The International Hydrographic Organization (IHO) considers them wholly within the South Pacific Ocean, however The Galápagos Archipelago consists of 7,880 km2 (3,040 sq mi) of land spread over 45,000 km2 (17,000 sq mi) of ocean. The largest of the islands, Isabela, measures 2,250 square miles (5,800 km2)and makes up close to three-quarters of the total land area of the Galápagos. Volcán Wolf on Isabela is the highest point, with an elevation of 1,707 m (5,600 ft) above sea level. The group consists of 18 main islands, 3 smaller islands, and 107 rocks and islets. The islands are located at the Galapagos Triple Junction. The archipelago is located on the Nazca Plate (a tectonic plate), which is moving east/southeast, diving under the South American Plate at a rate of about 2.5 inches (6.4 cm) per year.It is also atop the Galápagos hotspot, a place where the Earth's crust is being melted from below by a mantle plume, creating volcanoes. The first islands formed here at least 8 million and possibly up to 90 million years ago. While the older islands have disappeared below the sea as they moved away from the mantle plume, the youngest islands, Isabela and Fernandina, are still being formed. In April 2009, lava from the volcanic island Fernandina started flowing both towards the island's shoreline and into the center caldera. In late June 2018, Sierra Negra, one of five volcanoes on Isabela and one of the most active in the Galapagos archipelago, began erupting for the first time since 2005. Lava flows made their way to the coastline, prompting the evacuation of about fifty nearby residents and restricting tourist access.

Where is Galapogas Island located? What is the area of Galapogas? How many islands are there in total? When was the first island formed? What are few Volcanos in Galapogas?

The islands are located in the eastern Pacific Ocean, 973 km off the west coast of South America. The islands are found at the coordinates 1°40'N–1°36'S, 89°16'–92°01'W 7,880 km2 of land is the total area of Galapogos Island. The group consists of 18 main islands, 3 smaller islands. The first islands formed here at least 8 million and possibly up to 90 million years ago Sierra Negra is one of the five volcanoes.

Horizon Zero Dawn is an action role-playing game played from a third-person view. Players take control of Aloy, a hunter who ventures through a post-apocalyptic land ruled by robotic creatures. Aloy can kill enemies in a variety of ways – shooting them with arrows, setting traps such as tripwires using the Tripcaster, using explosives, and a spear. Machine components, including electricity and the metal they are composed of, are vital to Aloy's survival; she can loot their remains for crafting resources. Ammo, resource satchels, pouches, quivers, resistance, antidotes, health potions, and traps can all be crafted. Weapons have modification slots for dealing more damage. Aloy wears a Focus, a small head piece that scans machines to determine their susceptibilities, identify their location, their level, and the nature of loot they will drop. One machine, the Stalker, can enable cloaking technology to avert the gaze of Aloy's Focus scan. Machines attack with defensive and offensive measures, and in either case react to a perceived threat by charging at it with brute force or projectiles. Exhibiting the behaviour of wild animals, some machines are inclined to move in herds, and others, with the ability to fly, do so in flocks. Unless hacked with the Override Tool, or sufficiently hit with corruption arrows, machines do not exert aggressive force against each other. Aloy also engages in battle with human enemies, such as bandits and the Eclipse cult.

Extract the different types of enemies Aloy has to fight with and separate them with commas.

Machines, humans (like bandits and the Eclipse cult)

Laurens Willy Symons (born 28 August 2001) is a Belgian professional footballer who plays as a forward for the reserve team of Mechelen. On 5 February 2018, Symons signed his first professional contract with Lokeren, having joined their youth academy in 2015. Symons made his professional debut for Lokeren in a 2–1 Belgian First Division A loss to Waasland-Beveren on 22 December 2018.

Which country is Laurens Willy Symons from?

Belgium

In the UK, the recommended daily values for magnesium are 300 mg for men and 270 mg for women. In the U.S. the Recommended Dietary Allowances (RDAs) are 400 mg for men ages 19–30 and 420 mg for older; for women 310 mg for ages 19–30 and 320 mg for older.

Given this reference test, what is the daily recommended amount of magnesium for men in the UK?

The daily recommended amount of magnesium for men in the UK is 300 mg.

1805.02400

We developed an AdaBoost-based classifier to detect our new fake reviews, consisting of 200 shallow decision trees (depth 2). The features we used are recorded in Table~\ref{table:features_adaboost} (Appendix). We developed an AdaBoost-based classifier to detect our new fake reviews, consisting of 200 shallow decision trees (depth 2).

What kind of model do they use for detection?

The answers are shown as follows: * AdaBoost-based classifier

Classify each of the following words as either a noun or a verb

Brian, running, chair, eating, listen, dog

What is a snowman?

A snowman is a replica of a human made from snow, typically by stacking three large balls of snow on top of each other. The top ball represents the head, the middle ball is the body and the lower ball are the lower extremities of the snowman. Often, a snowman will have sticks for arms and a carrot for a nose.

How does manufacturing analytics improve production?

Manufacturers often used data-driven techniques such as Six Sigma to improve manufacturing performance. Through the continual aggregation of manufacturing data, analytics can be developed to baseline machine performance and determine anomalous behavior in order to address product quality and machine maintenance issues. Data and analytics are key to improving production metrics in repeatable manner.

What are the top reasons to become a vegan?

-climate change -animal agriculture -health

Ernest George 'Ernie' Wilson (18 October 1900 – 7 June 1982) was an Australian rules footballer who played for Collingwood in the Victorian Football League (VFL). Wilson featured in the Collingwood side during a strong era, playing in no less than six Grand Finals. He was on a half forward flank in their 1919 premiership but was used mostly as a defender. His other Grand Final appearances came in 1920, 1922, 1925 and 1926 while he was a half back flanker in the Collingwood premiership team of 1927. He could have added another in 1928, but lost his place in the side after the drawn semi final against Melbourne which would be his last game. In 1929 he played with Northcote in the VFA. He was reported in the last few minutes of the Grand Final which Northcote won by 5 points. He received a 12-month suspension for the offense and returned to Collingwood in 1930 where he captain coached the reserve team until the end of the 1933 season. He also represented Victoria at interstate football, a total of nine times, including in the 1924 Hobart Carnival. He remains the only player to have represented Victoria whilst under suspension from the VFL.

In which years did Ernie Wilson play his first and last grand finals?

He played his first grand final in 1919, and his last in 1927.

Which Russian author to read first?

I'd recommend something like Crime and Punishment (Dostoevsky) or Anna Karenina (Tolstoy) for someone starting out with Russian novels. Branch out from there to something like Master and Margarita (Bulgakov) or Fathers and Children (Turgenev) afterward.

Give me a list of the best takeaway foods when youre hungry.

Burgers, pizza, tacos, sandwiches, pasta and bento boxes.

Gabriel José de la Concordia García Márquez (American Spanish: [ɡaˈβɾjel ɣaɾˈsi.a ˈmaɾkes] (listen); 6 March 1927 – 17 April 2014) was a Colombian novelist, short-story writer, screenwriter, and journalist, known affectionately as Gabo ([ˈɡaβo]) or Gabito ([ɡaˈβito]) throughout Latin America. Considered one of the most significant authors of the 20th century, particularly in the Spanish language, he was awarded the 1972 Neustadt International Prize for Literature and the 1982 Nobel Prize in Literature. He pursued a self-directed education that resulted in leaving law school for a career in journalism. From early on he showed no inhibitions in his criticism of Colombian and foreign politics. In 1958, he married Mercedes Barcha Pardo; they had two sons, Rodrigo and Gonzalo. García Márquez started as a journalist and wrote many acclaimed non-fiction works and short stories, but is best known for his novels, such as One Hundred Years of Solitude (1967), Chronicle of a Death Foretold (1981), and Love in the Time of Cholera (1985). His works have achieved significant critical acclaim and widespread commercial success, most notably for popularizing a literary style known as magic realism, which uses magical elements and events in otherwise ordinary and realistic situations. Some of his works are set in the fictional village of Macondo (mainly inspired by his birthplace, Aracataca), and most of them explore the theme of solitude. He is the most-translated Spanish-language author. Upon García Márquez's death in April 2014, Juan Manuel Santos, the president of Colombia, called him "the greatest Colombian who ever lived."

What are some noteworthy facts from Gabriel García Márquez present on the text?

Gabriel García Márquez was born in Colombia and is recognized as one of the most significant authors of the 20th century. He won a Nobel Prize in Literature in 1982. His work's literary style known as magic realism brought him critical acclaim and commercial success. Some of his works used places of his live as inspiration, and most of them explore the theme of solitude. He is the most-translated Spanish-language author. He was called "the greates Colombian who ever lived"by the president of Colombia at the time of his death.

Who is Oren Middlebrook

Oren Middlebrook (born January 23, 1953) is a former American football wide receiver. He played for the Philadelphia Eagles in 1978.

Which is a species of fish? Perch or Purge

Perch

Why do kids like to play Fortnite so much?

Fortnite is an extremely popular video game produced by Epic Games. Fortnite's popularity is largely due to its cartoonish animation style and competitive gameplay. The most popular game mode in Fortnite is called "Battle Royale", where players battle to be the last player standing.

The Wold Bison Jump is an archeological site in Johnson County, Wyoming, United States. An area at the site was listed on the National Register of Historic Places in 2015. The site was used during the 1300s to 1600s by Native Americans to kill bison by stampeding them over a cliff. The site is relatively attractive for that purpose because the cliff is not visible to the bison from afar, and the routes by which the bison must be channeled are relatively efficiently managed

When was the Wold Bison Jump in active use?

The Wold Bison Jump was estimated to be active from 1300s to 1600s.

What makes a good cocktail?

There are many elements to a good cocktail, and these can often be modified to fit individual tastes. In general, a cocktail contains a base spirit, flavors from bitters or amaros, and something to sweeten. There are optional ingredients to add as well, like fruit, juice, or herbs. The amount of each ingredient can vary the flavor significantly, so it is important to understand the impact of each ingredient. For example, very strong flavors like bitters or absinthe should be used in very small quantities. Sometimes even a rinse in the cocktail glass or spritz on the top of the prepared cocktail is enough to add the desired amount of flavor. A good cocktail comes about from a balance in flavors that build and develop off one another. The deep wooden flavor in whiskey is brightened by the citrus twist of an old fashioned. The sweetness from pineapple juice is deepened by the bitterness of campari in a jungle bird. The bitterness of ginger syrup is balanced with the tartness of lemon juice in a penicillin. Many newer cocktails are spins off a classic. Each builds off a flavor profile and modifies some of the more subtle flavors therein. If one wants to experiment with creating a new cocktail, starting with a classic favorite could be a good place to start. It is also possible to create the same balanced flavors without the use of spirits (or with the use of alcohol free spirits).

who is a feminist writer : simone de beauvoir, simone veil, virginia woolf, Gisèle halimi, Voltaire, Marquis de Sade

simone de beauvoir, simone veil, virginia woolf, Gisèle halimi sont des pionnières du féminisme

What is the difference between a GPU and a CPU?

Central processing unit (CPU) and a graphics processing unit (GPU) have very different roles. The CPU can have multiple processing cores and is commonly referred to as the brain of the computer. It is essential to all modern computing systems as it executes the commands and processes needed for your computer and operating system. The CPU is also important in determining how fast programs can run, from surfing the web to building spreadsheets. The GPU is a processor that is made up of many smaller and more specialized cores. By working together, the cores deliver massive performance when a processing task can be divided up and processed across many cores. While CPUs and GPUs have a lot in common - Both are critical computing engines. Both are silicon-based microprocessors. And both handle data - they do have different architectures and are built for different purposes. The CPU is suited to a wide variety of workloads, especially those for which latency or per-core performance are important. A powerful execution engine, the CPU focuses its smaller number of cores on individual tasks and on getting things done quickly. This makes it uniquely well equipped for jobs ranging from serial computing to running databases. GPUs began as specialized ASICs developed to accelerate specific 3D rendering tasks. Over time, these fixed-function engines became more programmable and more flexible. While graphics and the increasingly lifelike visuals of today’s top games remain their principal function, GPUs have evolved to become more general-purpose parallel processors as well, handling a growing range of applications.

How many compute services does Azure Synapse have?

Azure Synapse provides five different compute services: 1. Dedicated SQL Pools (previously known as "Synapse Data Warehouse"), 2. Serverless SQL Pools (i.e. a query service over the data lake), 3. Data Explorer Pools (for real-time & time series processing), 4. Spark Pools (Microsoft's version of Spark), and 5. Pipelines (a variant of ADF)

Why is Austin Texas a popular place to live

Austin Texas is an increasingly popular place to live in North America for a number of contributory factors. Weather: Austin is located in Texas and typically has a very warm climate year round. During the summer months temperatures can exceed 100 degrees Fahrenheit which may be too hot for some people. Economy: Austin has a diverse economy including Education, Healthcare, Agriculture and in recent years a burgeoning Technology sector. Austin is a major hub for Technology giants including Apple, Google, Amazon, Meta and is where Dell first started. Cost of Living: Despite its' strong economy and resulting low unemployment, Austin has a relatively low cost of living compared to many US cities, resulting in a higher quality of living.

Categorize the following ingredients as meat, cheese, or spread: mayonnaise, ham, swiss, muenster, turkey, mustard

mayonnaise - spread ham - meat swiss - cheese muenster - cheese turkey - meat mustard - spread

1903.09588

Construction of the auxiliary sentence For simplicity, we mainly describe our method with TABSA as an example. We consider the following four methods to convert the TABSA task into a sentence pair classification task: The sentence we want to generate from the target-aspect pair is a question, and the format needs to be the same. For example, for the set of a target-aspect pair (LOCATION1, safety), the sentence we generate is “what do you think of the safety of location - 1 ?" For the NLI task, the conditions we set when generating sentences are less strict, and the form is much simpler. The sentence created at this time is not a standard sentence, but a simple pseudo-sentence, with (LOCATION1, safety) pair as an example: the auxiliary sentence is: “location - 1 - safety". For QA-B, we add the label information and temporarily convert TABSA into a binary classification problem ( INLINEFORM0 ) to obtain the probability distribution. At this time, each target-aspect pair will generate three sequences such as “the polarity of the aspect safety of location - 1 is positive", “the polarity of the aspect safety of location - 1 is negative", “the polarity of the aspect safety of location - 1 is none". We use the probability value of INLINEFORM1 as the matching score. For a target-aspect pair which generates three sequences ( INLINEFORM2 ), we take the class of the sequence with the highest matching score for the predicted category. The difference between NLI-B and QA-B is that the auxiliary sentence changes from a question to a pseudo-sentence. The auxiliary sentences are: “location - 1 - safety - positive", “location - 1 - safety - negative", and “location - 1 - safety - none". After we construct the auxiliary sentence, we can transform the TABSA task from a single sentence classification task to a sentence pair classification task. As shown in Table TABREF19 , this is a necessary operation that can significantly improve the experimental results of the TABSA task. Construction of the auxiliary sentence For simplicity, we mainly describe our method with TABSA as an example. We consider the following four methods to convert the TABSA task into a sentence pair classification task: The sentence we want to generate from the target-aspect pair is a question, and the format needs to be the same. For example, for the set of a target-aspect pair (LOCATION1, safety), the sentence we generate is “what do you think of the safety of location - 1 ?" For the NLI task, the conditions we set when generating sentences are less strict, and the form is much simpler. The sentence created at this time is not a standard sentence, but a simple pseudo-sentence, with (LOCATION1, safety) pair as an example: the auxiliary sentence is: “location - 1 - safety". For QA-B, we add the label information and temporarily convert TABSA into a binary classification problem ( INLINEFORM0 ) to obtain the probability distribution. At this time, each target-aspect pair will generate three sequences such as “the polarity of the aspect safety of location - 1 is positive", “the polarity of the aspect safety of location - 1 is negative", “the polarity of the aspect safety of location - 1 is none". We use the probability value of INLINEFORM1 as the matching score. For a target-aspect pair which generates three sequences ( INLINEFORM2 ), we take the class of the sequence with the highest matching score for the predicted category. The difference between NLI-B and QA-B is that the auxiliary sentence changes from a question to a pseudo-sentence. The auxiliary sentences are: “location - 1 - safety - positive", “location - 1 - safety - negative", and “location - 1 - safety - none". After we construct the auxiliary sentence, we can transform the TABSA task from a single sentence classification task to a sentence pair classification task. As shown in Table TABREF19 , this is a necessary operation that can significantly improve the experimental results of the TABSA task.

How do they generate the auxiliary sentence?

The answers are shown as follows: * The sentence we want to generate from the target-aspect pair is a question, and the format needs to be the same. * For the NLI task, the conditions we set when generating sentences are less strict, and the form is much simpler. * For QA-B, we add the label information and temporarily convert TABSA into a binary classification problem ( INLINEFORM0 ) to obtain the probability distribution * auxiliary sentence changes from a question to a pseudo-sentence

Bhopal (/boʊˈpɑːl/; Hindi: [bʱoːpaːl] ( listen)) is the capital city of the Indian state of Madhya Pradesh and the administrative headquarters of both Bhopal district and Bhopal division. It is known as the City of Lakes due to its various natural and artificial lakes. It is also one of the greenest cities in India. It is the 16th largest city in India and 131st in the world. After the formation of Madhya Pradesh, Bhopal was part of the Sehore district. It was bifurcated in 1972 and a new district, Bhopal, was formed. Flourishing around 1707, the city was the capital of the former Bhopal State, a princely state of the British ruled by the Nawabs of Bhopal. Numerous heritage structures from this period include the Taj-ul-Masajid and Taj Mahal palace. In 1984, the city was struck by the Bhopal disaster, one of the worst industrial disasters in history

Based on the paragraph given, list down some important points regarding Bhopal city

1. Bhopal is the capital city of Madhya Pradesh in India, and the administrative headquarters of both Bhopal district and Bhopal division. 2. Bhopal is also known as “The city of lakes” . It is also the 16th largest city in India and 131st in the world. 3. Bhopal was formed as a new district from existing Sehore district in 1972. 4. In 1707 Bhopal was the capital of the former Bhopal State, which British ruled by the Nawabs of Bhopal. 5. Bhopal has Numerous heritage structures from 1700’s include the Taj-ul-Masjid and Taj Mahal palace. 6. One of the worst industrial disasters in history happened in 1984 in Bhopal , also known as Bhopal Disaster.

What are the top 5 most expensive luxury brands?

-Louis Vuitton -Chanel -Hermes -Dior -Saint Laurent

Inspired by BIBREF6 , the first embedding layer is a vector representing each target word INLINEFORM0 obtained by concatenating the embedding of that word with those of the aligned words INLINEFORM1 in the source. If a target word is aligned to multiple source words, we average the embedding of all the source words, and concatenate the target word embedding with its average source embedding. The immediate left and right contexts for source and target words are also concatenated, enriching the local context information of the embedding of target word INLINEFORM2 . Thus, the embedding of target word INLINEFORM3 , denoted as INLINEFORM4 , is a INLINEFORM5 dimensional vector, where INLINEFORM6 is the dimension of the word embeddings. The source and target words use the same embedding parameters, and thus identical words in both languages, such as digits and proper nouns, have the same embedding vectors. This allows the model to easily identify identical words in both languages. Similarly, the POS tags in both languages share the same embedding parameters. Table TABREF4 shows the statistics of the set of POS tags over all language pairs. The source and target words use the same embedding parameters, and thus identical words in both languages, such as digits and proper nouns, have the same embedding vectors. This allows the model to easily identify identical words in both languages.

What benefit is brought by the source and target words using the same embedding parameters?

This allows the model to easily identify identical words in both languages.

Describe a camping plan with your family especially when you have young children

Before planning a camping trip, make sure you know the camping site and the facilities provided there. Having an electric campsite for cooking hot meals for the kids is critical. You must ensure the tent is big enough to accommodate your family and the bags you will carry. And lastly, carry extra clothes, food packets, packaged water, and batteries for your equipment.

From the Tropics to the Snow is a 1964 Australian short documentary film. It was one of the best known Australian films of the 1960s. It was produced under the auspices of the Commonwealth Film Unit (CFU), later reincorporated as Film Australia. It was co-directed by Jack Lee and Richard (Dick) Mason, and featured Reg Livermore (in his first film role) as one of the 'narrators'. One of the regular duties of the CFU at this time was the production of short films that were purpose-made for overseas distribution and were intended to promote Australia as an attractive destination for migrants and tourists. From the Tropics to the Snow marked a significant break with the traditional style of such features, and is noted for its subversive and satirical approach to its subject. Rather than using the stilted, authoritative single-voice narration that was typical of such 'promotion documentaries' at the time, Mason and Lee opted for a strikingly reflexive approach, using a lively (and frequently tongue-in-cheek) multi-voice narration, which is introduced through the dramatic device of a production planning meeting between the film's Producer-in-Chief (Alexander Archdale), the director (Alastair Smart) and the screenwriter (Livermore). This allowed the film to become an ironic critique of itself, humorously examining the mechanics of "documentary" film construction, and the competing pressures and choices that faced filmmakers when creating such films. It also gently parodied Lee and Mason's own situation as "young turks" charged with turning out what were in essence "production-line" propaganda films for a hidebound government department. The film co-won the Australian Film Institute's Gold Medal for Best Film in 1965.

Did Tropics to the Snow win any awards?

Yes, Tropics to the Snow was an Australian Film Institute's Gold Medal co-winner for Best Film in 1965.

Classify each of the following as a weapon or a toy: gun, lego, knife, bomb, blocks, doll

Gun: Weapon Lego: Toy Knife: Weapon Bomb: Weapon Blocks: Toy Doll: Toy

What are some of the best ways to generate passive income?

1) Rental Property Income 2) Crypto staking 3) Peer-to-Peer lending 4) Dividend Income 5) Create a digital course or product 6) Write an e-book and sell on Amazon 7) Affiliate Marketing 8) Invest in a REIT

We have presented a method for quantifying the similarity and relatedness between two terms that integrates pair–wise similarity scores into second–order vectors. The goal of this approach is two–fold. First, we restrict the context used by the vector measure to words that exist in the biomedical domain, and second, we apply larger weights to those word pairs that are more similar to each other. Our hypothesis was that this combination would reduce the amount of noise in the vectors and therefore increase their correlation with human judgments. We evaluated our method on datasets that have been manually annotated for relatedness and similarity and found evidence to support this hypothesis. In particular we discovered that guiding the creation of a second–order context vector by selecting term pairs from biomedical text based on their semantic similarity led to improved levels of correlation with human judgment. We also explored using a threshold cutoff to include only those term pairs that obtained a sufficiently large level of similarity. We found that eliminating less similar pairs improved the overall results (to a point). In the future, we plan to explore metrics to automatically determine the threshold cutoff appropriate for a given dataset and measure. We also plan to explore additional features that can be integrated with a second–order vector measure that will reduce the noise but still provide sufficient information to quantify relatedness. We are particularly interested in approaches that learn word, phrase, and sentence embeddings from structured corpora such as literature BIBREF23 and dictionary entries BIBREF24 . Such embeddings could be integrated into a second–order vector or be used on their own. Finally, we compared our proposed method to other distributional approaches, focusing on those that used word embeddings. Our results showed that integrating semantic similarity measures into second–order co–occurrence vectors obtains the same or higher correlation with human judgments as do various different word embedding approaches. However, a direct comparison was not possible due to variations in the subsets of the UMNSRS evaluation dataset used. In the future, we would not only like to conduct a direct comparison but also explore integrating semantic similarity into various kinds of word embeddings by training on pair–wise values of semantic similarity as well as co–occurrence statistics. Our hypothesis was that this combination would reduce the amount of noise in the vectors and therefore increase their correlation with human judgments.

What was the hypothesis for the team's study?

Their hypothesis was that this combination would reduce the amount of noise in the vectors and therefore increase their correlation with human judgments.

Help me come up with 3 ideas for a new business and how I can start it

There are many businesses you could think about creating. Here are a few examples: - Services: you can create a firm that provides services such as marketing, technical consulting, or strategic advising. You can start this business by examining your professional expertise, building marketing materials, and networking to find your first customers. - Product: you can create a product in a domain you're passionate about is a great way of starting a business. This could be small scale boutique items such as candles or soaps made at home and sold at local markets or on a website like Etsy. This could also include something mass manufactured, either contracted out or involving significant capital expenditure in order to start development. - Training: you can start a training or education program in an industry where you have expertise. There are online platforms for hosting content and managing payments. You can also leverage social media in order to build a funnel of customers. There are many different potential businesses you might create but services, product, and training businesses are good domains to examine as you look to start your business.

1601.06068

We use GraphParser without paraphrases as our baseline. This gives an idea about the impact of using paraphrases. We compare our paraphrasing models with monolingual machine translation based model for paraphrase generation BIBREF24 , BIBREF36 . In particular, we use Moses BIBREF37 to train a monolingual phrase-based MT system on the Paralex corpus. Finally, we use Moses decoder to generate 10-best distinct paraphrases for the test questions. We use GraphParser without paraphrases as our baseline. This gives an idea about the impact of using paraphrases We compare our paraphrasing models with monolingual machine translation based model for paraphrase generation BIBREF24 , BIBREF36 . In particular, we use Moses BIBREF37 to train a monolingual phrase-based MT system on the Paralex corpus. Finally, we use Moses decoder to generate 10-best distinct paraphrases for the test questions.

What are the baselines?

The answers are shown as follows: * GraphParser without paraphrases * monolingual machine translation based model for paraphrase generation