Bainu ("how are you?") is a Chinese social networking website written in the Mongolian language. As of 2020 it had about 400,000 users, concentrated in Inner Mongolia. == Core features and positioning == Language and Cultural Characteristics Bainu is based on Traditional Mongolian Script and supports social interactions in the Mongolian language, including various message formats such as text, voice, images, and video. This design aims to preserve and promote Mongolian language and culture, particularly appealing to users in Inner Mongolia and other Mongolian-populated areas. Social Features Instant Messaging: Supports one-on-one private chats and group chats. Users can create interest-based groups or join local communities. Life Sharing: Through the "Chomorlig" feature (similar to Moments or a dynamic feed), users can share daily highlights to enhance community interaction. Location-Based Socializing: Recommends nearby users based on location, making it easier to connect with Mongolian friends in the same city or neighboring regions. Multilingual Support The app interface is available in English, Mongolian, and Simplified Chinese. == Technical Features and User Experience == Cross-Platform Compatibility Supports iPhone, iPad, Mac (with M1 chip or above), and Apple Vision Pro devices, covering users across the Apple ecosystem. Pricing Model Free download and basic features are available. Premium services (e.g., ad-free experience, extended social functions) require a subscription, with pricing options including $0.99/month, $2.99/quarter, and $6.99/year. User Feedback Positive Reviews: Some users praise it as the "best Mongolian-language chat app," recognizing its cultural value and social convenience. Negative Feedback: Reports of app crashes and technical issues, with some users calling for improved stability (e.g., frequent crashes in the iOS version). == Privacy and Data Policy == Bainu collects user data such as location, contact information, and device identifiers, which are linked to user identities. Additionally, user behavior may be tracked through third-party services, raising some privacy concerns. == Current Development and Challenges == User Base As of 2020, Bainu had approximately 400,000 users, primarily concentrated in Inner Mongolia. Policy Impact It was reported by Voice of America (VOA) that the Chinese authorities blocked Bainu on 23 August 2020 in order to prohibit Mongolians from discussing the issue of the authorities’ implementation of "bilingual education" in elementary schools. But now, in 2025, this software is completely available for download and use. see:https://bainu.com/
Realization (linguistics)
In linguistics, realization is the process by which some kind of surface representation is derived from its underlying representation; that is, the way in which some abstract object of linguistic analysis comes to be produced in actual language. Phonemes are often said to be realized by speech sounds. The different sounds that can realize a particular phoneme are called its allophones. Realization is also a subtask of natural language generation, which involves creating an actual text in a human language (English, French, etc.) from a syntactic representation. There are a number of software packages available for realization, most of which have been developed by academic research groups in NLG. The remainder of this article concerns realization of this kind. == Example == For example, the following Java code causes the simplenlg system [2] to print out the text The women do not smoke.: In this example, the computer program has specified the linguistic constituents of the sentence (verb, subject), and also linguistic features (plural subject, negated), and from this information the realiser has constructed the actual sentence. == Processing == Realisation involves three kinds of processing: Syntactic realisation: Using grammatical knowledge to choose inflections, add function words and also to decide the order of components. For example, in English the subject usually precedes the verb, and the negated form of smoke is do not smoke. Morphological realisation: Computing inflected forms, for example the plural form of woman is women (not womans). Orthographic realisation: Dealing with casing, punctuation, and formatting. For example, capitalising The because it is the first word of the sentence. The above examples are very basic, most realisers are capable of considerably more complex processing. == Systems == A number of realisers have been developed over the past 20 years. These systems differ in terms of complexity and sophistication of their processing, robustness in dealing with unusual cases, and whether they are accessed programmatically via an API or whether they take a textual representation of a syntactic structure as their input. There are also major differences in pragmatic factors such as documentation, support, licensing terms, speed and memory usage, etc. It is not possible to describe all realisers here, but a few of the emerging areas are: Simplenlg [3]: a document realizing engine with an api which intended to be simple to learn and use, focused on limiting scope to only finding the surface area of a document. KPML [4]: this is the oldest realiser, which has been under development under different guises since the 1980s. It comes with grammars for ten different languages. FUF/SURGE [5]: a realiser which was widely used in the 1990s, and is still used in some projects today OpenCCG [6]: an open-source realiser which has a number of nice features, such as the ability to use statistical language models to make realisation decisions.
Non-native speech database
A non-native speech database is a speech database of non-native pronunciations of English. Such databases are used in the development of: multilingual automatic speech recognition systems, text to speech systems, pronunciation trainers, and second language learning systems. == List == The actual table with information about the different databases is shown in Table 2. === Legend === In the table of non-native databases some abbreviations for language names are used. They are listed in Table 1. Table 2 gives the following information about each corpus: The name of the corpus, the institution where the corpus can be obtained, or at least further information should be available, the language which was actually spoken by the speakers, the number of speakers, the native language of the speakers, the total amount of non-native utterances the corpus contains, the duration in hours of the non-native part, the date of the first public reference to this corpus, some free text highlighting special aspects of this database and a reference to another publication. The reference in the last field is in most cases to the paper which is especially devoted to describe this corpus by the original collectors. In some cases it was not possible to identify such a paper. In these cases a paper is referenced which is using this corpus is. Some entries are left blank and others are marked with unknown. The difference here is that blank entries refer to attributes where the value is just not known. Unknown entries, however, indicate that no information about this attribute is available in the database itself. As an example, in the Jupiter weather database no information about the origin of the speakers is given. Therefore this data would be less useful for verifying accent detection or similar issues. Where possible, the name is a standard name of the corpus, for some of the smaller corpora, however, there was no established name and hence an identifier had to be created. In such cases, a combination of the institution and the collector of the database is used. In the case where the databases contain native and non-native speech, only attributes of the non-native part of the corpus are listed. Most of the corpora are collections of read speech. If the corpus instead consists either partly or completely of spontaneous utterances, this is mentioned in the Specials column.
Eaze
Eaze is an American company based in San Francisco, California that launched a medical cannabis delivery app of the same name in 2014. == History == Eaze was launched in 2014 by Keith McCarty to deliver medical marijuana to patients in California. McCarty started the company in his San Francisco apartment with four employees. The company provides a mobile app to connect users with cannabis dispensaries, but does not grow or sell marijuana itself, and has been nicknamed “the Uber of Weed”. As of 2017, the company operates in more than 100 cities within California. In 2017, Eaze reported 300 percent growth over the previous year. It has 81 employees, and performs 120,000 deliveries per month to 250,000 users. A survey of Eaze users revealed that 66% are male, 57% are between 22 and 34, just over half have a bachelor's degree, and 49% have an annual income over $75,000. The company's vaporizer cartridge sales reached $1 million in sales in 4 months, and 31% of customers had ordered a vaporizer by the end of 2016. In 2016, Eaze founder Keith McCarty stepped down from his position as CEO and was replaced by Jim Patterson, who served as the company's chief product and technology officer. == EazeMD == EazeMD is a service that helps people acquire a medical marijuana card. It is a California-based telemedicine service in which physicians assess patients through an online video chat. It is California's largest telemedicine service for marijuana referrals. In June 2017, a former employee of one of these physicians accessed patient data in the physician's records system, causing a security breach. However, there was no evidence that Eaze data was accessed. == Eaze Insights == Eaze Insights conducts surveys of their users and compiles data into reports on cannabis use. Statistics from their reports have been cited in Seattle Weekly, Forbes, The Huffington Post, Business Insider, Fortune, and other general interest publications. == Financing == The company announced its $10 million Series A funding in April 2015 by multiple venture capital firms, including the Snoop Dogg-backed Casa Verde Capital. In October 2016, Eaze announced its series B funding in the amount of $13 million from five investors, making the company "the highest-funded startup in the history of the cannabis industry, as well as its fastest-growing one". In September 2017, the company raised another $27 million in venture funding. The Series B funding was led by Bailey Capital, joined by DCM Ventures, Kaya Ventures, and FJ Labs. According to the company' officials in 2017, Eaze managed to raise more than $52 million since its inception in 2014.
Irwin Sobel
Irwin Sobel (born September 12, 1940) is a scientist and researcher in digital image processing. == Biography == Irwin Sobel was born in New York City. He graduated from MIT in 1961 and completed his Ph.D. research at the Stanford Artificial Intelligence Project (SAIL) with thesis Camera Models and Machine Perception. His Ph.D. advisor was Jerome A. Feldman. Starting in 1973, he spent nine years doing postdoctoral research at Columbia University. After 1982, he worked as a Senior Researcher at HP Labs. == Sobel operator == In 1968, Sobel gave a talk entitled "An Isotropic 3x3 Image Gradient Operator" at SAIL; this method became known as the Sobel operator. It was developed jointly with a colleague, Gary Feldman, also at SAIL.
Decision tree pruning
Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting. One of the questions that arises in a decision tree algorithm is the optimal size of the final tree. A tree that is too large risks overfitting the training data and poorly generalizing to new samples. A small tree might not capture important structural information about the sample space. However, it is hard to tell when a tree algorithm should stop because it is impossible to tell if the addition of a single extra node will dramatically decrease error. This problem is known as the horizon effect. A common strategy is to grow the tree until each node contains a small number of instances then use pruning to remove nodes that do not provide additional information. Pruning should reduce the size of a learning tree without reducing predictive accuracy as measured by a cross-validation set. There are many techniques for tree pruning that differ in the measurement that is used to optimize performance. == Techniques == Pruning processes can be divided into two types (pre- and post-pruning). Pre-pruning procedures prevent a complete induction of the training set by replacing a stop () criterion in the induction algorithm (e.g. max. Tree depth or information gain (Attr)> minGain). Pre-pruning methods are considered to be more efficient because they do not induce an entire set, but rather trees remain small from the start. Prepruning methods share a common problem, the horizon effect. This is to be understood as the undesired premature termination of the induction by the stop () criterion. Post-pruning (or just pruning) is the most common way of simplifying trees. Here, nodes and subtrees are replaced with leaves to reduce complexity. Pruning can not only significantly reduce the size but also improve the classification accuracy of unseen objects. It may be the case that the accuracy of the assignment on the train set deteriorates, but the accuracy of the classification properties of the tree increases overall. The procedures are differentiated on the basis of their approach in the tree (top-down or bottom-up). === Bottom-up pruning === These procedures start at the last node in the tree (the lowest point). Following recursively upwards, they determine the relevance of each individual node. If the relevance for the classification is not given, the node is dropped or replaced by a leaf. The advantage is that no relevant sub-trees can be lost with this method. These methods include Reduced Error Pruning (REP), Minimum Cost Complexity Pruning (MCCP), or Minimum Error Pruning (MEP). === Top-down pruning === In contrast to the bottom-up method, this method starts at the root of the tree. Following the structure below, a relevance check is carried out which decides whether a node is relevant for the classification of all n items or not. By pruning the tree at an inner node, it can happen that an entire sub-tree (regardless of its relevance) is dropped. One of these representatives is pessimistic error pruning (PEP), which brings quite good results with unseen items. == Pruning algorithms == === Reduced error pruning === One of the simplest forms of pruning is reduced error pruning. Starting at the leaves, each node is replaced with its most popular class. If the prediction accuracy is not affected then the change is kept. While somewhat naive, reduced error pruning has the advantage of simplicity and speed. === Cost complexity pruning === Cost complexity pruning generates a series of trees T 0 … T m {\displaystyle T_{0}\dots T_{m}} where T 0 {\displaystyle T_{0}} is the initial tree and T m {\displaystyle T_{m}} is the root alone. At step i {\displaystyle i} , the tree is created by removing a subtree from tree i − 1 {\displaystyle i-1} and replacing it with a leaf node with value chosen as in the tree building algorithm. The subtree that is removed is chosen as follows: Define the error rate of tree T {\displaystyle T} over data set S {\displaystyle S} as err ( T , S ) {\displaystyle \operatorname {err} (T,S)} . The subtree t {\displaystyle t} that minimizes err ( prune ( T , t ) , S ) − err ( T , S ) | leaves ( T ) | − | leaves ( prune ( T , t ) ) | {\displaystyle {\frac {\operatorname {err} (\operatorname {prune} (T,t),S)-\operatorname {err} (T,S)}{\left\vert \operatorname {leaves} (T)\right\vert -\left\vert \operatorname {leaves} (\operatorname {prune} (T,t))\right\vert }}} is chosen for removal. The function prune ( T , t ) {\displaystyle \operatorname {prune} (T,t)} defines the tree obtained by pruning the subtrees t {\displaystyle t} from the tree T {\displaystyle T} . Once the series of trees has been created, the best tree is chosen by generalized accuracy as measured by a training set or cross-validation. == Examples == Pruning could be applied in a compression scheme of a learning algorithm to remove the redundant details without compromising the model's performances. In neural networks, pruning removes entire neurons or layers of neurons.
GamePigeon
GamePigeon is a mobile app for iOS devices, developed by Vitalii Zlotskii and released on September 13, 2016. The game takes advantage of the iOS 10 update, which expanded how users could interact with Apple's Messages app. GamePigeon is only available through the Messages app, which allows players to start and respond to different party games in conversations. == Release == The app was first released on September 13, 2016, coinciding with the launch of iOS 10. The app was released for free, although it includes in-app purchases to unlock additional items, such as cosmetic skins, avatar items, new game modes, and an option to remove ads. == Games in the app == The following is a list of games that users can play within GamePigeon: Sources: Poker was one of the games included in GamePigeon at launch, although it has since been removed and is no longer listed on the game's App Store description. == Reception == GamePigeon has enjoyed commercial success, with VentureBeat noting that GamePigeon was ranked number-one in the "Top Free" category of the iMessage App Store, six months after its release. Critically, GamePigeon has been generally well received, being highlighted by online media publications early on shortly after the iOS 10 launch. It has since been included on many "best iMessage apps" lists. Based on over 162,000 ratings, the game holds a 4.0 out of 5 rating on the App Store. Julian Chokkattu of Digital Trends wrote "GamePigeon should be like the pre-installed versions of Solitaire and Minesweeper that used to come with older iterations of Windows." On its launch day, Boy Genius Report included it on a list of "10 of the best iMessage apps, games and stickers for iOS 10 on launch day." The Daily Dot wrote, "GamePigeon is easily the best current gaming option within iMessages." 8-ball and cup pong have been particularly well received by media outlets. The Daily Dot had specific praise for the app's billiards game: "8-Ball controls shockingly smoothly with your fingers, and there’s nothing quite like destroying a dear friend in poker." During his 2020 U.S. presidential campaign, Cory Booker was cited as playing the game with his family. In 2017, CNBC cited one teenager who expressed that GamePigeon was one of just a few reasons that those in her age range use the iMessage app. The game has received particular positive reception for allowing introverted individuals to exercise a form social activity; similarly, the game was highlighted as a way to maintain social distancing guidelines during the COVID-19 pandemic. As an April Fools' Day joke in 2020, The Chronicle, a Duke University newspaper, published that Duke's athletic program adopted GamePigeon's Cup Pong as an official varsity sport.