AI Google Grammar Checker

AI Google Grammar Checker — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  Nice (app)

  Nice is a photo-sharing mobile app developed by Nice App Mobile Technology Co., Ltd. (Chinese: 北京极赞科技有限公司) in China. The app allows users to tag specific locations on images, enabling detailed labeling of items such as clothing and accessories. The company received a $36 million investment in C-round funding in 2014. Nice had 30 million registered users and 12 million active users as of late 2015. As of January 2024, it remained a popular app, the 6th most-downloaded in the iOS App Store for China. == Official website == Official website

  Read more →
• 

  Dependency network (graphical model)

  Dependency networks (DNs) are graphical models, similar to Markov networks, wherein each vertex (node) corresponds to a random variable and each edge captures dependencies among variables. Unlike Bayesian networks, DNs may contain cycles. Each node is associated to a conditional probability table, which determines the realization of the random variable given its parents. == Markov blanket == In a Bayesian network, the Markov blanket of a node is the set of parents and children of that node, together with the children's parents. The values of the parents and children of a node evidently give information about that node. However, its children's parents also have to be included in the Markov blanket, because they can be used to explain away the node in question. In a Markov random field, the Markov blanket for a node is simply its adjacent (or neighboring) nodes. In a dependency network, the Markov blanket for a node is simply the set of its parents. == Dependency network versus Bayesian networks == Dependency networks have advantages and disadvantages with respect to Bayesian networks. In particular, they are easier to parameterize from data, as there are efficient algorithms for learning both the structure and probabilities of a dependency network from data. Such algorithms are not available for Bayesian networks, for which the problem of determining the optimal structure is NP-hard. Nonetheless, a dependency network may be more difficult to construct using a knowledge-based approach driven by expert-knowledge. == Dependency networks versus Markov networks == Consistent dependency networks and Markov networks have the same representational power. Nonetheless, it is possible to construct non-consistent dependency networks, i.e., dependency networks for which there is no compatible valid joint probability distribution. Markov networks, in contrast, are always consistent. == Definition == A consistent dependency network for a set of random variables X = ( X 1 , … , X n ) {\textstyle \mathbf {X} =(X_{1},\ldots ,X_{n})} with joint distribution p ( x ) {\displaystyle p(\mathbf {x} )} is a pair ( G , P ) {\displaystyle (G,P)} where G {\displaystyle G} is a cyclic directed graph, where each of its nodes corresponds to a variable in X {\displaystyle \mathbf {X} } , and P {\displaystyle P} is a set of conditional probability distributions. The parents of node X i {\displaystyle X_{i}} , denoted P a i {\displaystyle \mathbf {Pa_{i}} } , correspond to those variables P a i ⊆ ( X 1 , … , X i − 1 , X i + 1 , … , X n ) {\displaystyle \mathbf {Pa_{i}} \subseteq (X_{1},\ldots ,X_{i-1},X_{i+1},\ldots ,X_{n})} that satisfy the following independence relationships p ( x i ∣ p a i ) = p ( x i ∣ x 1 , … , x i − 1 , x i + 1 , … , x n ) = p ( x i ∣ x − x i ) . {\displaystyle p(x_{i}\mid \mathbf {pa_{i}} )=p(x_{i}\mid x_{1},\ldots ,x_{i-1},x_{i+1},\ldots ,x_{n})=p(x_{i}\mid \mathbf {x} -{x_{i}}).} The dependency network is consistent in the sense that each local distribution can be obtained from the joint distribution p ( x ) {\displaystyle p(\mathbf {x} )} . Dependency networks learned using large data sets with large sample sizes will almost always be consistent. A non-consistent network is a network for which there is no joint probability distribution compatible with the pair ( G , P ) {\displaystyle (G,P)} . In that case, there is no joint probability distribution that satisfies the independence relationships subsumed by that pair. == Structure and parameters learning == Two important tasks in a dependency network are to learn its structure and probabilities from data. Essentially, the learning algorithm consists of independently performing a probabilistic regression or classification for each variable in the domain. It comes from observation that the local distribution for variable X i {\displaystyle X_{i}} in a dependency network is the conditional distribution p ( x i | x − x i ) {\displaystyle p(x_{i}|\mathbf {x} -{x_{i}})} , which can be estimated by any number of classification or regression techniques, such as methods using a probabilistic decision tree, a neural network or a probabilistic support-vector machine. Hence, for each variable X i {\displaystyle X_{i}} in domain X {\displaystyle X} , we independently estimate its local distribution from data using a classification algorithm, even though it is a distinct method for each variable. Here, we will briefly show how probabilistic decision trees are used to estimate the local distributions. For each variable X i {\displaystyle X_{i}} in X {\displaystyle \mathbf {X} } , a probabilistic decision tree is learned where X i {\displaystyle X_{i}} is the target variable and X − X i {\displaystyle \mathbf {X} -X_{i}} are the input variables. To learn a decision tree structure for X i {\displaystyle X_{i}} , the search algorithm begins with a singleton root node without children. Then, each leaf node in the tree is replaced with a binary split on some variable X j {\displaystyle X_{j}} in X − X i {\displaystyle \mathbf {X} -X_{i}} , until no more replacements increase the score of the tree. == Probabilistic Inference == A probabilistic inference is the task in which we wish to answer probabilistic queries of the form p ( y ∣ z ) {\displaystyle p(\mathbf {y\mid z} )} , given a graphical model for X {\displaystyle \mathbf {X} } , where Y {\displaystyle \mathbf {Y} } (the 'target' variables) Z {\displaystyle \mathbf {Z} } (the 'input' variables) are disjoint subsets of X {\displaystyle \mathbf {X} } . One of the alternatives for performing probabilistic inference is using Gibbs sampling. A naive approach for this uses an ordered Gibbs sampler, an important difficulty of which is that if either p ( y ∣ z ) {\displaystyle p(\mathbf {y\mid z} )} or p ( z ) {\displaystyle p(\mathbf {z} )} is small, then many iterations are required for an accurate probability estimate. Another approach for estimating p ( y ∣ z ) {\displaystyle p(\mathbf {y\mid z} )} when p ( z ) {\displaystyle p(\mathbf {z} )} is small is to use modified ordered Gibbs sampler, where Z = z {\displaystyle \mathbf {Z=z} } is fixed during Gibbs sampling. It may also happen that y {\displaystyle \mathbf {y} } is rare, e.g. when Y {\displaystyle \mathbf {Y} } has many variables. So, the law of total probability along with the independencies encoded in a dependency network can be used to decompose the inference task into a set of inference tasks on single variables. This approach comes with the advantage that some terms may be obtained by direct lookup, thereby avoiding some Gibbs sampling. You can see below an algorithm that can be used for obtain p ( y | z ) {\displaystyle p(\mathbf {y|z} )} for a particular instance of y ∈ Y {\displaystyle \mathbf {y} \in \mathbf {Y} } and z ∈ Z {\displaystyle \mathbf {z} \in \mathbf {Z} } , where Y {\displaystyle \mathbf {Y} } and Z {\displaystyle \mathbf {Z} } are disjoint subsets. Algorithm 1: U := Y {\displaystyle \mathbf {U:=Y} } ( the unprocessed variables ) P := Z {\displaystyle \mathbf {P:=Z} } ( the processed and conditioning variables ) p := z {\displaystyle \mathbf {p:=z} } ( the values for P {\displaystyle \mathbf {P} } ) While U ≠ ∅ {\displaystyle \mathbf {U} \neq \emptyset } : Choose X i ∈ U {\displaystyle X_{i}\in \mathbf {U} } such that X i {\displaystyle X_{i}} has no more parents in U {\displaystyle U} than any variable in U {\displaystyle U} If all the parents of X {\displaystyle X} are in P {\displaystyle \mathbf {P} } p ( x i | p ) := p ( x i | p a i ) {\displaystyle p(x_{i}|\mathbf {p} ):=p(x_{i}|\mathbf {pa_{i}} )} Else Use a modified ordered Gibbs sampler to determine p ( x i | p ) {\displaystyle p(x_{i}|\mathbf {p} )} U := U − X i {\displaystyle \mathbf {U:=U} -X_{i}} P := P + X i {\displaystyle \mathbf {P:=P} +X_{i}} p := p + x i {\displaystyle \mathbf {p:=p} +x_{i}} Returns the product of the conditionals p ( x i | p ) {\displaystyle p(x_{i}|\mathbf {p} )} == Applications == In addition to the applications to probabilistic inference, the following applications are in the category of Collaborative Filtering (CF), which is the task of predicting preferences. Dependency networks are a natural model class on which to base CF predictions, once an algorithm for this task only needs estimation of p ( x i = 1 | x − x i = 0 ) {\displaystyle p(x_{i}=1|\mathbf {x} -{x_{i}}=0)} to produce recommendations. In particular, these estimates may be obtained by a direct lookup in a dependency network. Predicting what movies a person will like based on his or her ratings of movies seen; Predicting what web pages a person will access based on his or her history on the site; Predicting what news stories a person is interested in based on other stories he or she read; Predicting what product a person will buy based on products he or she has already purchased and/or dropped into his or her shopping basket. Another class of useful applications for dependency networks is related to data visualization, that is

  Read more →
• 

  Irish logarithm

  The Irish logarithm was a system of number manipulation invented by Percy Ludgate for machine multiplication. The system used a combination of mechanical cams as lookup tables and mechanical addition to sum pseudo-logarithmic indices to produce partial products, which were then added to produce results. The technique is similar to Zech logarithms (also known as Jacobi logarithms), but uses a system of indices original to Ludgate. == Concept == Ludgate's algorithm compresses the multiplication of two single decimal numbers into two table lookups (to convert the digits into indices), the addition of the two indices to create a new index which is input to a second lookup table that generates the output product. Because both lookup tables are one-dimensional, and the addition of linear movements is simple to implement mechanically, this allows a less complex mechanism than would be needed to implement a two-dimensional 10×10 multiplication lookup table. Ludgate stated that he deliberately chose the values in his tables to be as small as he could make them; given this, Ludgate's tables can be simply constructed from first principles, either via pen-and-paper methods, or a systematic search using only a few tens of lines of program code. They do not correspond to either Zech logarithms, Remak indexes or Korn indexes. == Pseudocode == The following is an implementation of Ludgate's Irish logarithm algorithm in the Python programming language: Table 1 is taken from Ludgate's original paper; given the first table, the contents of Table 2 can be trivially derived from Table 1 and the definition of the algorithm. Note since that the last third of the second table is entirely zeros, this could be exploited to further simplify a mechanical implementation of the algorithm.

  Read more →
• 

  Knowledge organization

  Knowledge organization (KO), organization of knowledge, organization of information, or information organization is an intellectual discipline concerned with activities such as document description, indexing, and classification that serve to provide systems of representation and order for knowledge and information objects. According to The Organization of Information by Joudrey and Taylor, information organization: examines the activities carried out and tools used by people who work in places that accumulate information resources (e.g., books, maps, documents, datasets, images) for the use of humankind, both immediately and for posterity. It discusses the processes that are in place to make resources findable, whether someone is searching for a single known item or is browsing through hundreds of resources just hoping to discover something useful. Information organization supports a myriad of information-seeking scenarios. Issues related to knowledge sharing can be said to have been an important part of knowledge management for a long time. Knowledge sharing has received a lot of attention in research and business practice both within and outside organizations and its different levels. Sharing knowledge is not only about giving it to others, but it also includes searching, locating, and absorbing knowledge. Unawareness of the employees' work and duties tends to provoke the repetition of mistakes, the waste of resources, and duplication of the same projects. Motivating co-workers to share their knowledge is called knowledge enabling. It leads to trust among individuals and encourages a more open and proactive relationship that grants the exchange of information easily. Knowledge sharing is part of the three-phase knowledge management process which is a continuous process model. The three parts are knowledge creation, knowledge implementation, and knowledge sharing. The process is continuous, which is why the parts cannot be fully separated. Knowledge creation is the consequence of individuals' minds, interactions, and activities. Developing new ideas and arrangements alludes to the process of knowledge creation. Using the knowledge which is present at the company in the most effective manner stands for the implementation of knowledge. Knowledge sharing, the most essential part of the process for our topic, takes place when two or more people benefit by learning from each other. Traditional human-based approaches performed by librarians, archivists, and subject specialists are increasingly challenged by computational (big data) algorithmic techniques. KO as a field of study is concerned with the nature and quality of such knowledge-organizing processes (KOP) (such as taxonomy and ontology) as well as the resulting knowledge organizing systems (KOS). == Theoretical approaches == === Traditional approaches === Among the major figures in the history of KO are Melvil Dewey (1851–1931) and Henry Bliss (1870–1955). Dewey's goal was an efficient way to manage library collections; not an optimal system to support users of libraries. His system was meant to be used in many libraries as a standardized way to manage collections. The first version of this system was created in 1876. An important characteristic in Henry Bliss' (and many contemporary thinkers of KO) was that the sciences tend to reflect the order of Nature and that library classification should reflect the order of knowledge as uncovered by science: The implication is that librarians, in order to classify books, should know about scientific developments. This should also be reflected in their education: Again from the standpoint of the higher education of librarians, the teaching of systems of classification ... would be perhaps better conducted by including courses in the systematic encyclopedia and methodology of all the sciences, that is to say, outlines which try to summarize the most recent results in the relation to one another in which they are now studied together. ... (Ernest Cushing Richardson, quoted from Bliss, 1935, p. 2) Among the other principles, which may be attributed to the traditional approach to KO are: Principle of controlled vocabulary Cutter's rule about specificity Hulme's principle of literary warrant (1911) Principle of organizing from the general to the specific Today, after more than 100 years of research and development in LIS, the "traditional" approach still has a strong position in KO and in many ways its principles still dominate. === Facet analytic approaches === The date of the foundation of this approach may be chosen as the publication of S. R. Ranganathan's colon classification in 1933. The approach has been further developed by, in particular, the British Classification Research Group. The best way to explain this approach is probably to explain its analytico-synthetic methodology. The meaning of the term "analysis" is: breaking down each subject into its basic concepts. The meaning of the term synthesis is: combining the relevant units and concepts to describe the subject matter of the information package in hand. Given subjects (as they appear in, for example, book titles) are first analyzed into a few common categories, which are termed "facets". Ranganathan proposed his PMEST formula: Personality, Matter, Energy, Space and Time: Personality is the distinguishing characteristic of a subject. Matter is the physical material of which a subject may be composed. Energy is any action that occurs with respect to the subject. Space is the geographic component of the location of a subject. Time is the period associated with a subject. === The information retrieval tradition (IR) === Important in the IR-tradition have been, among others, the Cranfield experiments, which were founded in the 1950s, and the TREC experiments (Text Retrieval Conferences) starting in 1992. It was the Cranfield experiments, which introduced the measures "recall" and "precision" as evaluation criteria for systems efficiency. The Cranfield experiments found that classification systems like UDC and facet-analytic systems were less efficient compared to free-text searches or low level indexing systems ("UNITERM"). The Cranfield I test found, according to Ellis (1996, 3–6) the following results: Although these results have been criticized and questioned, the IR-tradition became much more influential while library classification research lost influence. The dominant trend has been to regard only statistical averages. What has largely been neglected is to ask: Are there certain kinds of questions in relation to which other kinds of representation, for example, controlled vocabularies, may improve recall and precision? === User-oriented and cognitive views === The best way to define this approach is probably by method: Systems based upon user-oriented approaches must specify how the design of a system is made on the basis of empirical studies of users. User studies demonstrated very early that users prefer verbal search systems as opposed to systems based on classification notations. This is one example of a principle derived from empirical studies of users. Adherents of classification notations may, of course, still have an argument: That notations are well-defined and that users may miss important information by not considering them. Folksonomies is a recent kind of KO based on users' rather than on librarians' or subject specialists' indexing. === Bibliometric approaches === These approaches are primarily based on using bibliographical references to organize networks of papers, mainly by bibliographic coupling (introduced by Kessler 1963) or co-citation analysis ( independently suggested by Marshakova 1973 and Small 1973). In recent years it has become a popular activity to construe bibliometric maps as structures of research fields. Two considerations are important in considering bibliometric approaches to KO: The level of indexing depth is partly determined by the number of terms assigned to each document. In citation indexing this corresponds to the number of references in a given paper. On the average, scientific papers contain 10–15 references, which provide quite a high level of depth. The references, which function as access points, are provided by the highest subject-expertise: The experts writing in the leading journals. This expertise is much higher than that which library catalogs or bibliographical databases typically are able to draw on. === The domain analytic approach === Domain analysis is a sociological-epistemological standpoint that advocates that the indexing of a given document should reflect the needs of a given group of users or a given ideal purpose. In other words, any description or representation of a given document is more or less suited to the fulfillment of certain tasks. A description is never objective or neutral, and the goal is not to standardize descriptions or make one description once and for all for different target groups. The develo

  Read more →
• 

  Mixed raster content

  Mixed raster content (MRC) is a method for compressing images that contain both binary-compressible text and continuous-tone components, using image segmentation methods to improve the level of compression and the quality of the rendered image. By separating the image into components with different compressibility characteristics, the most efficient and accurate compression algorithm for each component can be applied. MRC-compressed images are typically packaged into a hybrid file format such as DjVu and sometimes PDF. This allows for multiple images, and the instructions to properly render and reassemble them, to be stored within a single file. Some image scanners optionally support MRC when scanning to PDF. A typical manual states that without MRC, the image is generated in a single process, with text and graphics not distinguished. With MRC, separate processes are used for text, graphics, and other elements, producing clearer graphics and sharper text, at the price of slightly slower processing. MRC is recommended to optimise the scanning of documents with harder-to-read text or lower-quality graphics. MRC can also reduce the size of the scanned file, though higher compression using JBIG2 can sometimes lead to character substitution errors in scanned documents. == File format == A form of MRC is defined by international standard bodies as ISO/IEC 16485, or ITU recommendation T.44 (accessible free of charge). It defines a file format with bilevel masks and two data layers in each "stripe" of the image. The mask can be encoded in ITU T.4, JBIG1, or JBIG2, while the images can be JPEG, JBIG1, or run-length encoded color. The format is loosely based on JPEG, with a APP13 segment registered for this purpose. It is not known whether this file format is actually used, as formats like DjVu and PDF have their own ways of defining layers and masks.

  Read more →
• 

  Informationist

  An informationist (or information specialist in context) provides research and knowledge management services in the context of clinical care or biomedical research. Although there is no one educational pathway or formalized set of skills or knowledge for informationists, one way to think of the informationist is as one who possesses the knowledge and skill of a medical librarian with extensive research specialization and some formal clinical or public health education that goes beyond on-the-job osmosis. Medical librarians and other biomedical professional organizations have been exploring the possibilities for evaluating how informationists are being used and whether their activities supplement or replace medical library activity. More generally, an informationist is a professional who works with information within a particular business, analytic or scientific context to drive toward outcomes based on evidence, analysis, prediction and execution. For example, an extension of the term is increasingly emerging in financial services, life sciences and health care industries. Though still nascently in use, its adoption applies to individuals with extensive industry expertise, acute familiarity with organizational structures and processes, deep domain level information mastery and information systems technical savvy. Informationists in this context support transformational initiatives within and across functional areas of an enterprise as architects, governance experts, continuous improvement advocates and strategists. == Background == The term was proposed in 2000 by Davidoff & Florance. Their editorial suggested that physicians should be delegating their information needs to informationists, just as they currently order CT scans from radiologists or cardiac catheterizations from cardiologists. They conceived of an information professional who was embedded in (and indeed, supported by) the clinical departments. Supporters of the concept see it as a means for librarians to reinvigorate connections with the faculty/clinicians, as well as provide superior service by dint of informationists' biomedical training. Critics complained that the idea is nothing new; librarians already provide in-depth, high quality information services and clinical medical librarians have been working alongside physicians, nurses and other clinicians for years. Large informationist programs in the U.S. exist at the National Institutes of Health and at Vanderbilt University. Welch Medical Library at Johns Hopkins University (JHU) is developing an informationist service model in which its 10 clinical and public health librarians are moving from serving as liaison librarians for assigned departments toward becoming embedded informationists within their departments. To prepare for the embedded informationist role, librarians are undertaking education as needed to supplement their backgrounds. For example, librarians bring experience in clinical behavior counseling, public health, nursing, and more. Informationist training can then focus upon filling gaps in research methods knowledge more so than on gaining additional knowledge in the librarian's area of expertise. Courses, seminars and workshops being undertaken include those covering systematic reviews, evidence-based medicine, critical appraisal, medical language, anatomy and physiology, biostatistics, and clinical research. The term informationist is related to that of informatician—also informaticist—and many informationists do possess skills in clinical topics, bioinformatics, and biomedical informatics. Harvard University, the University of Pittsburgh, and Washington University in St. Louis are examples of institutional libraries which have hired PhD-level scientists (who may or may not have library degrees) to provide informatics support for biomedical research.

  Read more →
• 

  AIVA

  AIVA (Artificial Intelligence Virtual Artist) is an electronic composer recognized by the SACEM. == Description == Created in February 2016, AIVA specializes in classical and symphonic music composition. It became the world's first virtual composer to be recognized by a music society (SACEM). By reading a large collection of existing works of classical music (written by human composers such as Bach, Beethoven, Mozart) AIVA is capable of detecting regularities in music and on this base composing on its own. The algorithm AIVA is based on deep learning and reinforcement learning architectures. Since January 2019, the company offers a commercial product, Music Engine, capable of generating short (up to 3 minutes) compositions in various styles (rock, pop, jazz, fantasy, shanty, tango, 20th century cinematic, modern cinematic, and Chinese). AIVA was presented at TED by Pierre Barreau. == Discography == AIVA is a published composer; its first studio album "Genesis" was released in November 2016. Second album "Among the Stars" in 2018. 2016 CD album « Genesis » Hv-Com – LEPM 048427. Track listing "Genesis": 2018 CD album « Among the Stars » Hv-Com – LEPM 048708 Avignon Symphonic Orchestra [ORAP] also performed Aiva's compositions [2] in April 2017.

  Read more →
• 

  Pointer jumping

  Pointer jumping or path doubling is a design technique for parallel algorithms that operate on pointer structures, such as linked lists and directed graphs. Pointer jumping allows an algorithm to follow paths with a time complexity that is logarithmic with respect to the length of the longest path. It does this by "jumping" to the end of the path computed by neighbors. The basic operation of pointer jumping is to replace each neighbor in a pointer structure with its neighbor's neighbor. In each step of the algorithm, this replacement is done for all nodes in the data structure, which can be done independently in parallel. In the next step when a neighbor's neighbor is followed, the neighbor's path already followed in the previous step is added to the node's followed path in a single step. Thus, each step effectively doubles the distance traversed by the explored paths. Pointer jumping is best understood by looking at simple examples such as list ranking and root finding. == List ranking == One of the simpler tasks that can be solved by a pointer jumping algorithm is the list ranking problem. This problem is defined as follows: given a linked list of N nodes, find the distance (measured in the number of nodes) of each node to the end of the list. The distance d(n) is defined as follows, for nodes n that point to their successor by a pointer called next: If n.next is nil, then d(n) = 0. For any other node, d(n) = d(n.next) + 1. This problem can easily be solved in linear time on a sequential machine, but a parallel algorithm can do better: given n processors, the problem can be solved in logarithmic time, O(log N), by the following pointer jumping algorithm: The pointer jumping occurs in the last line of the algorithm, where each node's next pointer is reset to skip the node's direct successor. It is assumed, as in common in the PRAM model of computation, that memory access are performed in lock-step, so that each n.next.next memory fetch is performed before each n.next memory store; otherwise, processors may clobber each other's data, producing inconsistencies. The following diagram follows how the parallel list ranking algorithm uses pointer jumping for a linked list with 11 elements. As the algorithm describes, the first iteration starts initialized with all ranks set to 1 except those with a null pointer for next. The first iteration looks at immediate neighbors. Each subsequent iteration jumps twice as far as the previous. Analyzing the algorithm yields a logarithmic running time. The initialization loop takes constant time, because each of the N processors performs a constant amount of work, all in parallel. The inner loop of the main loop also takes constant time, as does (by assumption) the termination check for the loop, so the running time is determined by how often this inner loop is executed. Since the pointer jumping in each iteration splits the list into two parts, one consisting of the "odd" elements and one of the "even" elements, the length of the list pointed to by each processor's n is halved in each iteration, which can be done at most O(log N) time before each list has a length of at most one. == Root finding == Following a path in a graph is an inherently serial operation, but pointer jumping reduces the total amount of work by following all paths simultaneously and sharing results among dependent operations. Pointer jumping iterates and finds a successor — a vertex closer to the tree root — each time. By following successors computed for other vertices, the traversal down each path can be doubled every iteration, which means that the tree roots can be found in logarithmic time. Pointer doubling operates on an array successor with an entry for every vertex in the graph. Each successor[i] is initialized with the parent index of vertex i if that vertex is not a root or to i itself if that vertex is a root. At each iteration, each successor is updated to its successor's successor. The root is found when the successor's successor points to itself. The following pseudocode demonstrates the algorithm. algorithm Input: An array parent representing a forest of trees. parent[i] is the parent of vertex i or itself for a root Output: An array containing the root ancestor for every vertex for i ← 1 to length(parent) do in parallel successor[i] ← parent[i] while true for i ← 1 to length(successor) do in parallel successor_next[i] ← successor[successor[i]] if successor_next = successor then break for i ← 1 to length(successor) do in parallel successor[i] ← successor_next[i] return successor The following image provides an example of using pointer jumping on a small forest. On each iteration the successor points to the vertex following one more successor. After two iterations, every vertex points to its root node. == History and examples == Although the name pointer jumping would come later, JáJá attributes the first uses of the technique in early parallel graph algorithms and list ranking. The technique has been described with other names such as shortcutting, but by the 1990s textbooks on parallel algorithms consistently used the term pointer jumping. Today, pointer jumping is considered a software design pattern for operating on recursive data types in parallel. As a technique for following linked paths, graph algorithms are a natural fit for pointer jumping. Consequently, several parallel graph algorithms utilizing pointer jumping have been designed. These include algorithms for finding the roots of a forest of rooted trees, connected components, minimum spanning trees, and biconnected components. However, pointer jumping has also shown to be useful in a variety of other problems including computer vision, image compression, and Bayesian inference.

  Read more →
• 

  Python (programming language)

  Python is a high-level, general-purpose programming language that emphasizes code readability, simplicity, and ease-of-writing with the use of significant indentation, "plain English" naming, an extensive ("batteries-included") standard library, and garbage collection. Python supports multiple programming paradigms but with an emphasis on object-oriented programming and dynamic typing. Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language. Python 3.0, released in 2008, was a major revision and not completely backward-compatible with earlier versions. Beginning with Python 3.5, capabilities and keywords for typing were added to the language, allowing optional static typing. As of 2026, the Python Software Foundation supports Python 3.10, 3.11, 3.12, 3.13, and 3.14, following the project's annual release cycle and five-year support policy. Python 3.15 is currently in the alpha development phase, and the stable release is expected to launch in October 2026. Earlier versions in the 3.x series have reached end-of-life and no longer receive security updates. Python has gained extensive use in the machine learning community. It is widely taught as an introductory programming language. Since 2003, Python has consistently ranked among the top ten most popular programming languages in the TIOBE Programming Community Index, which ranks programming languages based on searches across 24 platforms. == History == Python was conceived in the late 1980s by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands. It was designed as a successor to the ABC programming language, which was inspired by SETL, capable of exception handling and interfacing with the Amoeba operating system. Python implementation began in December 1989. Van Rossum first released it in 1991 as Python 0.9.0. Van Rossum assumed sole responsibility for the project, as the lead developer, until 12 July 2018, when he announced his "permanent vacation" from responsibilities as Python's "benevolent dictator for life" (BDFL); this title was bestowed on him by the Python community to reflect his long-term commitment as the project's chief decision-maker. (He has since come out of retirement and is self-titled "BDFL-emeritus".) In January 2019, active Python core developers elected a five-member Steering Council to lead the project. The name Python derives from the British comedy series Monty Python's Flying Circus. (See § Naming.) Python 2.0 was released on 16 October 2000, featuring many new features such as list comprehensions, cycle-detecting garbage collection, reference counting, and Unicode support. Python 2.7's end-of-life was initially set for 2015, and then postponed to 2020 out of concern that a large body of existing code could not easily be forward-ported to Python 3. It no longer receives security patches or updates. While Python 2.7 and older versions are officially unsupported, a different unofficial Python implementation, PyPy, continues to support Python 2, i.e., "2.7.18+" (plus 3.11), with the plus signifying (at least some) "backported security updates". Python 3.0 was released on 3 December 2008, and was a major revision and not completely backward-compatible with earlier versions, with some new semantics and changed syntax. Python 2.7.18, released in 2020, was the last release of Python 2. Several releases in the Python 3.x series have added new syntax to the language, and made a few (considered very minor) backward-incompatible changes. As of May 2026, Python 3.14.5 is the latest stable release. All older 3.x versions had a security update down to Python 3.9.24 then again with 3.9.25, the final version in 3.9 series. Python 3.10 is, since November 2025, the oldest supported branch. Python 3.15 has an alpha released, and Android has an official downloadable executable available for Python 3.14. Releases receive two years of full support followed by three years of security support. == Design philosophy and features == Python is a multi-paradigm programming language. Object-oriented programming and structured programming are fully supported, and many of their features support functional programming and aspect-oriented programming – including metaprogramming and metaobjects. Many other paradigms are supported via extensions, including design by contract and logic programming. Python is often referred to as a 'glue language' because it is purposely designed to be able to integrate components written in other languages. Python uses dynamic typing and a combination of reference counting and a cycle-detecting garbage collector for memory management. It uses dynamic name resolution (late binding), which binds method and variable names during program execution. Python's design offers some support for functional programming in the "Lisp tradition". It has filter, map, and reduce functions; list comprehensions, dictionaries, sets, and generator expressions. The standard library has two modules (itertools and functools) that implement functional tools borrowed from Haskell and Standard ML. Python's core philosophy is summarized in the Zen of Python (PEP 20) written by Tim Peters, which includes aphorisms such as these: Explicit is better than implicit. Simple is better than complex. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity, errors should never pass silently, unless explicitly silenced. There should be one-- and preferably only one --obvious way to do it. However, Python has received criticism for violating these principles and adding unnecessary language bloat. Responses to these criticisms note that the Zen of Python is a guideline rather than a rule. The addition of some new features had been controversial: Guido van Rossum resigned as Benevolent Dictator for Life after conflict about adding the assignment expression operator in Python 3.8. Nevertheless, rather than building all functionality into its core, Python was designed to be highly extensible through modules. This compact modularity has made it particularly popular as a means of adding programmable interfaces to existing applications. Van Rossum's vision of a small core language with a large standard library and an easily extensible interpreter stemmed from his frustrations with ABC, which represented the opposite approach. Python claims to strive for a simpler, less-cluttered syntax and grammar, while giving developers a choice in their coding methodology. Python lacks do .. while loops, which Rossum considered harmful. In contrast to Perl's motto "there is more than one way to do it", Python advocates an approach where "there should be one – and preferably only one – obvious way to do it". In practice, however, Python provides many ways to achieve a given goal. There are at least three ways to format a string literal, with no certainty as to which one a programmer should use. Alex Martelli is a Fellow at the Python Software Foundation and Python book author; he wrote that "To describe something as 'clever' is not considered a compliment in the Python culture." Python's developers typically prioritize readability over performance. For example, they reject patches to non-critical parts of the CPython reference implementation that would offer increases in speed that do not justify the cost of clarity and readability. Execution speed can be improved by moving speed-critical functions to extension modules written in languages such as C, or by using a just-in-time compiler like PyPy. Also, it is possible to transpile to other languages. However, this approach either fails to achieve the expected speed-up, since Python is a very dynamic language, or only a restricted subset of Python is compiled (with potential minor semantic changes). Python is meant to be a fun language to use. This goal is reflected in the name – a tribute to the British comedy group Monty Python – and in playful approaches to some tutorials and reference materials. For instance, some code examples use the terms "spam" and "eggs" (in reference to a Monty Python sketch), rather than the typical terms "foo" and "bar". A common neologism in the Python community is pythonic, which has a broad range of meanings related to program style: Pythonic code may use Python idioms well; be natural or show fluency in the language; or conform with Python's minimalist philosophy and emphasis on readability. === Enhancement Proposals === Python Enhancement Proposals are a design document for either providing information to the Python community, or proposal for new feature in Python. PEPs are intented to explain new processes in Python, provide naming conventions or document the processes in the language. PEPs are overseen by Python Steering Council. There are 3 kinds of PEPs, with those are being standards track PEP, Informational PEP and Process PEPs which has their own unique meanings. They were firstly introduced in 2000, in

  Read more →
• 

  Open Compute Project

  The Open Compute Project (OCP) is an organization that facilitates the sharing of data center product designs and industry best practices among companies. Founded in 2011, OCP has significantly influenced the design and operation of large-scale computing facilities worldwide. As of February 2025, over 400 companies across the world are members of OCP, including Arm, Meta, IBM, Wiwynn, Intel, Nokia, Google, Microsoft, Seagate Technology, Dell, Rackspace, Hewlett Packard Enterprise, NVIDIA, Cisco, Goldman Sachs, Fidelity, Lenovo, Accton Technology Corporation and Alibaba Group. == Structure == The Open Compute Project Foundation is a 501(c)(6) non-profit incorporated in the state of Delaware, United States. OCP has multiple committees, including the board of directors, advisory board and steering committee to govern its operations. As of July 2020, there are seven members who serve on the board of directors which is made up of one individual member and six organizational members. Mark Roenigk (Facebook) is the Foundation's president and chairman. Andy Bechtolsheim is the individual member. In addition to Mark Roenigk who represents Facebook, other organizations on the Open Compute board of directors include Intel (Rebecca Weekly), Microsoft (Kushagra Vaid), Google (Partha Ranganathan), and Rackspace (Jim Hawkins). A list of members can be found on the OCP website. == History == The Open Compute Project began at Facebook (now Meta) in 2009 as an internal project called "Project Freedom". The hardware designs and engineering teams were led by Amir Michael (Manager, Hardware Design) and sponsored by Jonathan Heiliger (VP, Technical Operations) and Frank Frankovsky (Director, Hardware Design and Infrastructure). The three would later open source the designs of Project Freedom and co-found the Open Compute Project. The project was announced at a press event at Facebook's headquarters in Palo Alto on April 7, 2011. == OCP projects == The Open Compute Project Foundation maintains a number of OCP projects, such as: === Server designs === In 2013, two years after the Open Compute Project had started, it was noted that the goal of a more modular server design was "still a long way from live data centers". However, by then some aspects published had been used in Facebook's Prineville data center to improve energy efficiency, as measured by the power usage effectiveness index defined by The Green Grid. Efforts to advance server compute node designs included one for Intel processors and one for AMD processors. Also in 2013, Calxeda contributed a design with ARM architecture processors. Since then, several generations of OCP server designs have been deployed: Wildcat (Intel), Spitfire (AMD), Windmill (Intel E5-2600), Watermark (AMD), Winterfell (Intel E5-2600 v2) and Leopard (Intel E5-2600 v3). === OCP Accelerator Module === OCP Accelerator Module (OAM) is a design specification for hardware architectures that implement artificial intelligence systems that require high module-to-module bandwidth. OAM is used in some of AMD's Instinct accelerator modules. === Rack and power designs === Designs for a mechanical mounting system to replace standard 19-inch racks have been published, with a cabinet the same outside width (600 mm) and depth as existing racks, but with an interior space allowing for wider equipment chassis with a 537 mm width (21 inches). This allows more equipment to fit in the same volume and improves air flow. Compute chassis sizes are defined in multiples of an OpenU or OU, which is 48 mm, slightly taller than the 44 mm rack unit defined for 19-inch racks. As of March 2026, the most current base mechanical definition is the Open Rack V3.1 Specification. At the time the base specification was released, Meta also defined in greater depth the specifications for the rectifiers and power shelf. Specifications for the power monitoring interface (PMI), a communications interface enabling upstream communications between the rectifiers and battery backup unit(BBU) were published by Meta that same year, with Delta Electronics as the main technical contributor to the BBU spec. However, since 2022 the AI boom in the data center has created higher power requirements in order to satisfy the demands of AI accelerators that have been released. As of September 2024, Meta is in the process of updating its Open Rack v3 rectifier, power shelf, battery backup and power management interface specifications to accommodate this increased energy demand. In May 2024, at an Open Compute regional summit, Meta and Rittal outlined their plans for development of their High Power Rack (HPR) ecosystem in conjunction with rack, power and cable partners, increasing power capacity in the rack to 92 kilowatts or more. At the same meeting, Delta Electronics and Advanced Energy reported on their progress in developing new Open Compute standard specifications for power shelf and rectifier designs for HPR applications. Rittal also outlined their collaboration with Meta in designing airflow containment, busbar designs and grounding schemes for the new HPR requirements. === Data storage === Open Vault storage building blocks (also called "Knox") offer high disk densities, with 30 drives in a 2 OU Open Rack chassis designed for easy disk drive replacement. The 3.5 inch disks are stored in two drawers, five across and three deep in each drawer, with connections via serial attached SCSI. There is a "cold storage" variant where idle disks power down to reduce energy consumption. Another design concept was contributed by Hyve Solutions, a division of Synnex, in 2012. At the OCP Summit 2016 Facebook, together with Taiwanese ODM Wistron's spin-off Wiwynn, introduced "Lightning", a flexible NVMe JBOF (just a bunch of flash), based on the existing Open Vault (Knox) design. === Energy efficient data centers === The OCP has published data center designs for energy efficiency. These include power distribution at three-phase 277/480 VAC, which eliminates one transformer stage in typical North American data centers, a single voltage (12.5 VDC) power supply designed to work with 277/480 VAC input, and 48 VDC battery backup. For European (and other 230V countries) datacenters, there is a specification for 230/400 VAC power distribution and its conversion to 12.5 VDC. === Open networking switches === On May 8, 2013, an effort to define an open network switch was announced. The plan was to allow Facebook to load its own operating system software onto its top-of-rack switches. Press reports predicted that more expensive and higher-performance switches would continue to be popular, while less expensive products treated more like a commodity. The first attempt at an open networking switch by Facebook was designed together with Taiwanese ODM Accton using Broadcom Trident II chip and is called "Wedge"; the Linux OS that it runs is called "FBOSS". Later switch contributions include "6-pack" and Wedge-100, based on Broadcom Tomahawk chips. Similar switch hardware designs have been contributed by: Accton Technology Corporation (and its Edgecore Networks subsidiary), Mellanox Technologies, Interface Masters Technologies, Agema Systems. Capable of running Open Network Install Environment (ONIE)-compatible network operating systems such as Cumulus Linux, Switch Light OS by Big Switch Networks, or PICOS by Pica8. A similar project for a custom switch for the Google platform had been rumored, and evolved to use the OpenFlow protocol. === Servers === A sub-project for Mezzanine (NIC) OCP NIC 3.0 specification 1v00 was released in late 2019 establishing three form factors: SFF, TSFF, and LFF. == Litigation == In March, 2015, BladeRoom Group Limited and Bripco (UK) Limited sued Facebook, Emerson Electric Co. and others alleging that Facebook has disclosed BladeRoom and Bripco's trade secrets for prefabricated data centers in the Open Compute Project. Facebook petitioned for the lawsuit to be dismissed, but this was rejected in 2017. A confidential mid-trial settlement was agreed in April 2018.

  Read more →
• 

  Mathematical knowledge management

  Mathematical knowledge management (MKM) is the study of how society can effectively make use of the vast and growing literature on mathematics. It studies approaches such as databases of mathematical knowledge, automated processing of formulae and the use of semantic information, and artificial intelligence. Mathematics is particularly suited to a systematic study of automated knowledge processing due to the high degree of interconnectedness between different areas of mathematics.

  Read more →
• 

  Manhattan address algorithm

  The Manhattan address algorithm is a series of formulas used to estimate the closest east–west cross street for building numbers on north–south avenues in the New York City borough of Manhattan. == Algorithm == To find the approximate number of the closest cross street, divide the building number by a divisor (generally 20) and add (or subtract) the "tricky number" from the table below: For the north–south avenues, there are typically 20 address numbers between consecutive east–west streets (10 on either side of the avenue). A standard land lot on each avenue was originally 20 feet (6.1 m) wide, and there is about 200 feet (61 m) between each pair of east–west streets, for ten land lots between each pair of streets. The exceptions are Riverside Drive, as well as Fifth Avenue and Central Park West between 59th and 110th streets, which use a divisor of 10. These avenues all have buildings only on one side of the street, with a park on the other side. The "tricky number" often corresponds to a street near the southern end of the avenue. There are some notable exceptions: York Avenue address numbers are continuations of Avenue A address numbers, since the avenue was originally called Avenue A. East End Avenue address numbers are continuations of Avenue B address numbers, since the avenue was originally called Avenue B. Sixth Avenue and Broadway start south of Houston Street, the southern boundary of the Manhattan street numbering system. Although Park Avenue's southern terminus is at 32nd Street, a homeowner at 34th Street wanted the address "1 Park Avenue" (this was later changed). === Examples === For example, if you are at 62 Avenue B, 62 ÷ 20 ≈ 3 {\displaystyle 62\div 20\approx 3} , then add the "tricky number" 3 {\displaystyle 3} to give 6 {\displaystyle 6} . The nearest cross street to 62 Avenue B is East 6th Street. If you are at 78 Riverside Drive, 78 ÷ 10 ≈ 8 {\displaystyle 78\div 10\approx 8} , then add the "tricky number" 72 {\displaystyle 72} to give 80 {\displaystyle 80} . The nearest cross street to 78 Riverside Drive is West 80th Street. If you are at 501 5th Avenue, 501 ÷ 20 ≈ 25 {\displaystyle 501\div 20\approx 25} , then add the "tricky number" 18 {\displaystyle 18} to give 43 {\displaystyle 43} . The nearest cross street to 501 5th Avenue is actually 42nd Street, not 43rd Street, as the Manhattan address algorithm only gives approximate answers.

  Read more →
• 

  Dark data

  Dark data is data which is acquired through various computer network operations but not used in any manner to derive insights or for decision making. The ability of an organisation to collect data can exceed the throughput at which it can analyse the data. In some cases the organisation may not even be aware that the data is being collected. IBM estimate that roughly 90 percent of data generated by sensors and analog-to-digital conversions never get used. In an industrial context, dark data can include information gathered by sensors and telematics. Organizations retain dark data for a multitude of reasons, and it is estimated that most companies are only analyzing 1% of their data. Often it is stored for regulatory compliance and record keeping. Some organizations believe that dark data could be useful to them in the future, once they have acquired better analytic and business intelligence technology to process the information. Because storage is inexpensive, storing data is easy. However, storing and securing the data usually entails greater expenses (or even risk) than the potential return profit. In academic discourse, the term dark data was essentially coined by Bryan P. Heidorn. He uses it to describe research data, especially from the long tail of science (the many, small research projects), which are not or no longer available for research because they disappear in a drawer without adequate data management. Without this, the data become dark, and further reasons for this are e.g. missing metadata annotation, missing data management plans and data curators. == Analysis == The term "dark data" very often refers to data that is not amenable to computer processing. For example, a company might have a great deal of data that exists only as scanned page-images. Even the bare text in such documents is not available without something like Optical character recognition, which can vary greatly in accuracy. Even with OCR, the significance of each part of the data is unavailable. An obvious examples is whether a capitalized word is a name or not, and if so, whether it represents a person, place, organization, or even a work of art. Bibliographic and other references, data within tables (that may be labeled quite adequately for humans, but not for processing), and countless assertions represented with the full complexity and ambiguity of human language. A lot of unused data is very valuable, and would be used if it could be; but is blocked because it is in formats that are difficult to process, categorise, identify, and analyse. Often the reason that business does not use their dark data is because of the amount of resources it would take and the difficulty of having that data analysed. In other words, the data is "dark" not because it is not used, but because it cannot (feasibly or affordably) be used, given its poor representation. There are many data representations that can make data much more accessible for automation. However, a great deal of information lacks any such identification of information items or relationships; and much more loses it during "downhill" conversion such as saving to page-oriented representations, printing, scanning, or faxing. The journey back "uphill" can be costly. According to Computer Weekly, 60% of organisations believe that their own business intelligence reporting capability is "inadequate" and 65% say that they have "somewhat disorganised content management approaches". == Relevance == Useful data may become dark data after it becomes irrelevant, as it is not processed fast enough. This is called "perishable insights" in "live flowing data". For example, if the geolocation of a customer is known to a business, the business can make offer based on the location, however if this data is not processed immediately, it may be irrelevant in the future. According to IBM, about 60 percent of data loses its value immediately. == Storage == According to the New York Times, 90% of energy used by data centres is wasted. If data was not stored, energy costs could be saved. Furthermore, there are costs associated with the underutilisation of information and thus missed opportunities. According to Datamation, "the storage environments of EMEA organizations consist of 54 percent dark data, 32 percent redundant, obsolete and trivial data and 14 percent business-critical data. By 2020, this can add up to $891 billion in storage and management costs that can otherwise be avoided." The continuous storage of dark data can put an organisation at risk, especially if this data is sensitive. In the case of a breach, this can result in serious repercussions. These can be financial, legal and can seriously hurt an organisation's reputation. For example, a breach of private records of customers could result in the stealing of sensitive information, which could result in identity theft. Another example could be the breach of the company's own sensitive information, for example relating to research and development. These risks can be mitigated by assessing and auditing whether this data is useful to the organisation, employing strong encryption and security and finally, if it is determined to be discarded, then it should be discarded in a way that it becomes unretrievable. == Future == It is generally considered that as more advanced computing systems for analysis of data are built, the higher the value of dark data will be. It has been noted that "data and analytics will be the foundation of the modern industrial revolution". Of course, this includes data that is currently considered "dark data" since there are not enough resources to process it. All this data that is being collected can be used in the future to bring maximum productivity and an ability for organisations to meet consumers' demand. Technology advancements are helping to leverage this dark data affordably. Furthermore, many organisations do not realise the value of dark data right now, for example in healthcare and education organisations deal with large amounts of data that could create a significant "potential to service students and patients in the manner in which the consumer and financial services pursue their target population".

  Read more →
• 

  Applied Information Science in Economics

  The Applied Information Science in Economics (Russian: Прикладная информатика в Экономике) or Applied Computer Science in Economics is a professional qualification generally awarded in Russian Federation. The degree inherited from the U.S.S.R. education system also known as Specialist degree. The degree is awarded after five years of full-time study and includes several internships, course-works, thesis writing and defense. The degree has similarities with German Magister Artium or Diplom degree. However, due to the Bologna Process number of such degrees are declining. Degree focuses on applying mathematical methods in economics involving maximum information technology. It is very close to applied mathematics, but includes also major part of computer science. == List of specialty codes in the education system == 080801 - Applied computer science in economics 351400 - Applied computer science == Fields of activity == Organization and management; Project design; Experimental research; Marketing; Consulting; Operational and Maintenance. == Major == Information Science and Programming. High Level Methods of Information Science and Programming. Information Technologies in Economics. Computer Systems, Networks and Telecommunications Services. Operational Environments, Systems and Shells. Architecture and Design of Information Systems for Companies. Data Bases. Information security. Information Management. Imitative Simulation.

  Read more →
• 

  Uncertain database

  An uncertain database is a kind of database studied in database theory. The goal of uncertain databases is to manage information on which there is some uncertainty. Uncertain databases make it possible to explicitly represent and manage uncertainty on the data, usually in a succinct way. == Formal definition == At the basis of uncertain databases is the notion of possible world. Specifically, a possible world of an uncertain database is a (certain) database which is one of the possible realizations of the uncertain database. A given uncertain database typically has more than one, and potentially infinitely many, possible worlds. A formalism to represent uncertain databases then explains how to succinctly represent a set of possible worlds into one uncertain database. == Types of uncertain databases == Uncertain database models differ in how they represent and quantify these possible worlds: Incomplete databases are a compact representation of the set of possible worlds – the use of NULL in SQL, arguably the most commonplace instantiation of uncertain databases, is an example of incomplete database model. Probabilistic databases are a compact representation of a probability distribution over the set of possible worlds. Fuzzy databases are a compact representation of a fuzzy set of the possible worlds. Though mostly studied in the relational setting, uncertain database models can also be defined in other relational models such as graph databases or XML databases. === Incomplete database === The most common database model is the relational model. Multiple incomplete database models have been defined over the relational model, that form extensions to the relational algebra. These have been called Imieliński–Lipski algebras: Relations with NULL values, also called Codd tables c-tables v-tables === Example === The following table is a relation of an incomplete database, described in the formalism of NULL values: There are infinitely many possible worlds for this incomplete database, obtained by replacing the "NULL" values with concrete values. For instance, the following relation is a possible world:

  Read more →