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The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution. These tasks are usually required to build more advanced text processing services.
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Informetrics is the study of quantitative aspects of information, it is an extension and evolution of traditional bibliometrics and scientometrics. Informetrics uses bibliometrics and scientometrics methods to study mainly the problems of literature information management and evaluation of science and technology. Informetrics is an independent discipline that uses quantitative methods from mathematics and statistics to study the process, phenomena, and law of informetrics. Informetrics has gained more attention as it is a common scientific method for academic evaluation, research hotspots in discipline, and trend analysis. Informetrics includes the production, dissemination, and use of all forms of information, regardless of its form or origin. Informetrics encompasses the following fields: Scientometrics, which studies quantitative aspects of science Webometrics, which studies quantitative aspects of the World Wide Web Bibliometrics, which studies quantitative aspects of recorded information Cybermetrics, which is similar to webometrics, but broadens its definition to include electronic resources == Origin and Development == The term informetrics (French: informétrie) was coined by German scholar Otto Nacke in 1979, and came from the German word 'informetrie’. The corresponding English terminology soon appeared in the subsequent literature. In September 1980, Professor Otto Nacke introduced the term 'informetrics' at the first seminar on Informetrics in Frankfurt, Germany. Later, Committee on Informetrics has established through The International Federation for Information and Documentation (FID). In 1987, informetrics started to be officially recognized by the international information community and several foreign information scientists. In 1988, at First International Conference on Bibliometrics and Theoretical Aspects of Information Retrieval Archived 2022-05-23 at the Wayback Machine, Brooks suggested bibliometrics and scientometrics can be included in the field of informetrics. In 1990, Leo Egghe and Ronald Rousseau proposed the formation of the discipline of informetrics: statistical bibliography (1923) to bibliometrics and scientometrics (1969) and then to informetrics (1979). In 1993, the International Society for Scientometrics and Informetrics (ISSI) Archived 2023-11-05 at the Wayback Machine was founded at the International Conference on Bibliometrics, Informetrics and Scientometrics in Berlin, and the first one was held in Belgium and organized by Leo Egghe and Ronald Rousseau. The society was formally incorporated in 1994 in the Netherlands and plays a significant role in the development of informetrics. The ISSI aims to promote the "exchange and communication of professional information in the fields of scientometrics and informetrics, including improve standards, theory and practice, as well as promote research, education and training". In addition, to "engage in relevant public conversation and policy discussions". In the western world, 20th century's Informetrics is mostly based on Lotka's law, named after Alfred J. Lotka, Zipf's law, named after George Kingsley Zipf, Bradford's law named after Samuel C. Bradford and on the work of Derek J. de Solla Price, Gerard Salton, Leo Egghe, Ronald Rousseau, Tibor Braun, Olle Persson, Peter Ingwersen, Manfred Bonitz, and Eugene Garfield. == Difference Between Informetrics, Bibliometrics and Scientometrics == Since the 1960s, three similar terms have emerged in the fields of library science, philology and science of science, they are bibliometrics, scientometrics and informetrics, representing three very similar quantitative sub-disciplines. The three metrics terms can be confusing and often misused. Informetrics and bibliometrics interpenetrate each other but have different aspects in research object, research scope, and measuring unit. Informetrics and scientometrics are very different in their research purpose and research object, as well as the research scope and application. Bibliometrics is categorised under the field of library science, it uses mathematical and statistical methods to describe, evaluate, and predict the current status and trends of science and technology. Also to study the "distribution structure, quantitative relationship, change law and quantitative management of literature information, quantitative relationships, patterns and quantitative management of literature and information". The term was first used by Alan Pritchard in 1969 in his paper Statistical Bibliography or Bibliometrics?. Scientometrics is a branch of science that quantitatively evaluates and predicts the process and management of scientific activities in order to reveal their development patterns and trends. The definition of scientometrics was described by Derek De Solla Price in his book Science to Science as the “quantitative study of science, communication in science, and science policy”. === Links between the three metrics terms === The most prominent connection between the three metrics terms is in their research objects. Since all three disciplines use literature information as their research object, therefore, they have some similarities and overlaps in their research methods and fields. Moreover, they all use mathematical methods as the basic research methods and they all apply the three basic laws, Bradford's law, Lotka's law and Zipf's law. === Distinctions between the three metrics terms === The distinction between the three metrics terms can tell from their research object and research purpose. The research of bibliometrics focuses on the analysis of "scientific output in the form of articles, publications, citations, and others". Scientometrics is to measure the basic characteristics and laws of scientific activities. Where informetrics is to investigate information sources and information distribution process. == Concept and System Structure == === Purpose of Informetrics Research === The main purpose of informetrics is to use its theocratical research to solve the methodological issues in the research process, and to discover and reveal the basic laws of information distribution through the study of information process and phenomenon. In this way, makes information management more scientific and provides a quantitative basis for information services and information management decisions. For informetrics, it is necessary to bring quantitative analysis methods to further reveal the structure of information units and the "quantitative change law of literature information”. Further to this, to improve the scientific accuracy of information science from a theoretical point of view. At the same time, to better solve the basic contradictions in the information service, overcome the information crisis, and make the information management work more effective to serve science and technology, economic and social development. Quantitative analysis of bibliographic data was pioneered by Robert K. Merton in an article called Science, Technology, and Society in Seventeenth Century England and originally published by Merton in 1938. === The Significance of Informetrics Research === The significance of informetrics research is to summarize various empirical laws from the theoretical point of view, at the same time test and modify the various empirical laws in the new information unit conditions, and explore its new applicability, therefore, the scientific nature of information science can be improved, but also to provide theoretical guidance for practical work. === The Objects of Informetrics Research === The object of informetrics is broader than the field of bibliometrics and scientometrics, including "messages, data, events, objects, text, and documents”. Informetrics is often used to inform policies and decisions across a broad range of fields, such as economy, politics, technology and social spheres that "influence the flow and use patterns of information". Tague-Sutcliffe describes the following uses of informetrics: Citation analysis; Characteristics of authors; Use of recorded information; Obsolescence of the literature; Concomitant growth of new concepts; Characteristics of publication sources; Definition and measurement o information; Growth of subject literature, databases, libraries; Types and characteristics of retrieval performance measures; Statistical aspects of language, word, and phrase frequencies. == Basic Laws == In the field of informetrics research, there are many outstanding contributors in the discipline with a solid knowledge of quantitative research methods. In the early 20th century, several scientists contributed empirical applications that have become the three basic laws of informetrics, Bradford's law, Lotka's law, and Zipf's law, which promote the development of informetrics. === Bradford's Law === The British documentalist and librarian Samuel C. Bradford first discovered the law of concentration and scattering of literature, and in 1934, it has be
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Collaborative Diffusion is a type of pathfinding algorithm which uses the concept of antiobjects, objects within a computer program that function opposite to what would be conventionally expected. Collaborative Diffusion is typically used in video games, when multiple agents must path towards a single target agent. For example, the ghosts in Pac-Man. In this case, the background tiles serve as antiobjects, carrying out the necessary calculations for creating a path and having the foreground objects react accordingly, whereas having foreground objects be responsible for their own pathing would be conventionally expected. Collaborative Diffusion is favored for its efficiency over other pathfinding algorithms, such as A, when handling multiple agents. Also, this method allows elements of competition and teamwork to easily be incorporated between tracking agents. Notably, the time taken to calculate paths remains constant as the number of agents increases.
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Species distribution modelling (SDM), also known as environmental (or ecological) niche modelling (ENM), habitat suitability modelling, predictive habitat distribution modelling, and range mapping uses ecological models to predict the distribution of a species across geographic space and time using environmental data. The environmental data are most often climate data (e.g. temperature, precipitation), but can include other variables such as soil type, water depth, and land cover. SDMs are used in several research areas in conservation biology, ecology and evolution. These models can be used to understand how environmental conditions influence the occurrence or abundance of a species, and for predictive purposes (ecological forecasting). Predictions from an SDM may be of a species' future distribution under climate change, a species' past distribution in order to assess evolutionary relationships, or the potential future distribution of an invasive species. Predictions of current and/or future habitat suitability can be useful for management applications (e.g. reintroduction or translocation of vulnerable species, reserve placement in anticipation of climate change). There are two main types of SDMs. Correlative SDMs, also known as climate envelope models, bioclimatic models, or resource selection function models, model the observed distribution of a species as a function of environmental conditions. Mechanistic SDMs, also known as process-based models or biophysical models, use independently derived information about a species' physiology to develop a model of the environmental conditions under which the species can exist. The extent to which such modelled data reflect real-world species distributions will depend on a number of factors, including the nature, complexity, and accuracy of the models used and the quality of the available environmental data layers; the availability of sufficient and reliable species distribution data as model input; and the influence of various factors such as barriers to dispersal, geologic history, or biotic interactions, that increase the difference between the realized niche and the fundamental niche. Environmental niche modelling may be considered a part of the discipline of biodiversity informatics. == History == A. F. W. Schimper used geographical and environmental factors to explain plant distributions in his 1898 Pflanzengeographie auf physiologischer Grundlage (Plant Geography Upon a Physiological Basis) and his 1908 work of the same name. Andrew Murray used the environment to explain the distribution of mammals in his 1866 The Geographical Distribution of Mammals. Robert Whittaker's work with plants and Robert MacArthur's work with birds strongly established the role the environment plays in species distributions. Elgene O. Box constructed environmental envelope models to predict the range of tree species. His computer simulations were among the earliest uses of species distribution modelling. The adoption of more sophisticated generalised linear models (GLMs) made it possible to create more sophisticated and realistic species distribution models. The expansion of remote sensing and the development of GIS-based environmental modelling increase the amount of environmental information available for model-building and made it easier to use. == Correlative vs mechanistic models == === Correlative SDMs === SDMs originated as correlative models. Correlative SDMs model the observed distribution of a species as a function of geographically referenced climatic predictor variables using multiple regression approaches. Given a set of geographically referred observed presences of a species and a set of climate maps, a model defines the most likely environmental ranges within which a species lives. Correlative SDMs assume that species are at equilibrium with their environment and that the relevant environmental variables have been adequately sampled. The models allow for interpolation between a limited number of species occurrences. For these models to be effective, it is required to gather observations not only of species presences, but also of absences, that is, where the species does not live. Records of species absences are typically not as common as records of presences, thus often "random background" or "pseudo-absence" data are used to fit these models. If there are incomplete records of species occurrences, pseudo-absences can introduce bias. Since correlative SDMs are models of a species' observed distribution, they are models of the realized niche (the environments where a species is found), as opposed to the fundamental niche (the environments where a species can be found, or where the abiotic environment is appropriate for the survival). For a given species, the realized and fundamental niches might be the same, but if a species is geographically confined due to dispersal limitation or species interactions, the realized niche will be smaller than the fundamental niche. Correlative SDMs are easier and faster to implement than mechanistic SDMs, and can make ready use of available data. Since they are correlative however, they do not provide much information about causal mechanisms and are not good for extrapolation. They will also be inaccurate if the observed species range is not at equilibrium (e.g. if a species has been recently introduced and is actively expanding its range). In standard SDMs, the distribution of a single species is often modeled, with unique parameters describing how environmental (abiotic) factors influence its occurrence probability. This allows for differentiated responses to environmental drivers among species, but can be problematic for data-deficient species. In contrast, similarities in environmental responses can be accounted for in multi-species SDMs, which model several species jointly using shared or hierarchically related parameters. However, neither approach explicitly accounts for community-level biotic interactions, which can be important in explaining species diversity patterns. Joint species distribution models (joint SDMs or J-SDMs) address this by modeling species co-occurrence patterns directly. The occurrence probability of a given species is thus influenced not only by abiotic drivers but also by inferred biotic associations with other species. This can improve accuracy for rarer taxa and provide insights into community ecology. Both standard SDMs and J-SDMs can be used to generate community-level metrics, such as species richness, by aggregating outputs across multiple species. These can be important for decision-making such as conservation planning. === Mechanistic SDMs === Mechanistic SDMs are more recently developed. In contrast to correlative models, mechanistic SDMs use physiological information about a species (taken from controlled field or laboratory studies) to determine the range of environmental conditions within which the species can persist. These models aim to directly characterize the fundamental niche, and to project it onto the landscape. A simple model may simply identify threshold values outside of which a species can't survive. A more complex model may consist of several sub-models, e.g. micro-climate conditions given macro-climate conditions, body temperature given micro-climate conditions, fitness or other biological rates (e.g. survival, fecundity) given body temperature (thermal performance curves), resource or energy requirements, and population dynamics. Geographically referenced environmental data are used as model inputs. Because the species distribution predictions are independent of the species' known range, these models are especially useful for species whose range is actively shifting and not at equilibrium, such as invasive species. Mechanistic SDMs incorporate causal mechanisms and are better for extrapolation and non-equilibrium situations. However, they are more labor-intensive to create than correlational models and require the collection and validation of a lot of physiological data, which may not be readily available. The models require many assumptions and parameter estimates, and they can become very complicated. Dispersal, biotic interactions, and evolutionary processes present challenges, as they aren't usually incorporated into either correlative or mechanistic models. Correlational and mechanistic models can be used in combination to gain additional insights. For example, a mechanistic model could be used to identify areas that are clearly outside the species' fundamental niche, and these areas can be marked as absences or excluded from analysis. See for a comparison between mechanistic and correlative models. == Niche models (correlative) == There are a variety of mathematical methods that can be used for fitting, selecting, and evaluating correlative SDMs. Models include "profile" methods, which are simple statistical techniques that use e.g. environmental distance to known sites of occurrence such as
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Alipay (simplified Chinese: 支付宝; traditional Chinese: 支付寶; pinyin: zhīfùbǎo) is a third-party mobile and online payment platform, established in Hangzhou, China, in February 2004 by Alibaba Group and its founder Jack Ma. In 2015, Alipay moved its headquarters to Pudong, Shanghai, although its parent company Ant Financial remains Hangzhou-based. Alipay overtook PayPal as the world's largest mobile (digital) payment platform in 2013. As of June 2020, Alipay serves over 1.3 billion users and 80 million merchants. According to the statistics of the fourth quarter of 2018, Alipay has a 55.32% share of the third-party payment market in mainland China, and it continues to grow. Along with WeChat, Alipay has been described to be China's super-app with a wide range of functionalities including ridesharing, travel booking and medical appointments. == History == The service was first launched in 2003, by Taobao. The People's Bank of China, China's central bank, issued licensing regulations in June 2010 for third-party payment providers. It also issued separate guidelines for foreign-funded payment institutions. Because of this, Alipay, which accounted for half of China's non-bank online payment market, was restructured as a domestic company controlled by Alibaba CEO Jack Ma in order to facilitate the regulatory approval for the license. The 2010 transfer of Alipay's ownership was controversial, with media reports in 2011 that Yahoo! and Softbank (Alibaba Group's controlling shareholders) were not informed of the sale for nominal value. Chinese business publication Century Weekly criticised Ma, who stated that Alibaba Group's board of directors was aware of the transaction. The incident was criticised in foreign and Chinese media as harming foreign trust in making Chinese investments. The ownership dispute was resolved by Alibaba Group, Yahoo!, and Softbank in July 2011. In 2013, Alipay launched a financial product platform called Yu'e Bao. Alipay partnered with Tianhong Asset Management to launch the it. Yu'e Bao offers an online money market account in which Alipay customers can deposit money and receive a higher interest rate than that available from banks. It soon became China's largest online money market fund and prompted competitors like Baidu and Tencent to introduce alternatives. Alibaba (the parent company of Alipay) reported having 152 million Yu'e Bao users in mid-2016, with 810 billion RMB (US$117 billion) in funds under management. In 2015, Alipay's parent company was re-branded as Ant Financial Services Group. In 2017, Alipay unveiled their facial recognition payment service. In 2020, Alipay upgraded from a payment financial instrument to an open platform for digital life. In 2021, the mandate by the Ministry of Industry and Information Technology (MIIT) to open up the "walled garden" ecosystems of the major tech companies has led to the introduction of interoperability of payment QR codes of Alipay and competing WeChat Pay and UnionPay's Cloud QuickPass platforms. In response to the increase in Alipay's payment volume due to use on Alibaba's e-commerce sites and others, Chinese regulators introduced new rules in 2020. The new rules focused on Alipay because the payment volume exploded due to its use on Alibaba's e-commerce sites and other platforms. By the second quarter in 2020, Alipay held 55.6% of China's third party mobile payment market. The People's Bank of China made rules that required payment firms to place money with regulators and anti-monopoly reviews would be triggered if the amount exceeded 50% market share. The rules included that the People's Bank of China mandate an online-payment clearing route through the NetsUnion Clearing Corporation, a centralized, state-overseen clearing body, and that unused consumer funds be held by a third-party payment provider in a non-interest-bearing account. These measures increased transparency and reduced systemic risk. When Alipay operates outside of China, it must comply with local financial regulations, which may treat specific functions such as money-market funds or investment-linked products. In Singapore, such services may require prior authorization from securities or financial-services regulators before they can be offered to residents. == Services == Alipay states that it operates with more than 65 financial institutions including Visa and MasterCard to provide payment services for Taobao and Tmall as well as more than 460,000 online and local Chinese businesses. Alipay is used in smartphones with their Alipay Wallet app. QR code payment codes are used for local in-store payments. The Alipay app also provides features such as credit card bill payments, bank account managements, P2P transfer, prepay mobile phone top-up, bus and train ticket purchases, food orders, vehicles for hire, insurance selections and a digital identification document storage. Alipay also allows online check-out on most Chinese-based websites such as Taobao and Tmall. The Alipay app allows users to add their own services provided from different companies to create a more personalised experience. Since late 2008, Alipay has promoted public service payment services and has covered more than 300 cities nationwide, supporting more than 1,200 partner organizations. In addition to utility bills such as water and electricity, Alipay also extends their services to areas such as paying transportation fines, property fees, and cable television fees. Common online payment services also include hydropower coal payment, tuition payment and traffic fine. On 15 January 2009, Alipay launched a credit card repayment service, supporting 39 domestic bank-issued credit cards. It is currently the most popular third-party repayment platform. The main advantages are free credit card bills checking, repayments with no administrative fee, as well as automatic repayment, repayment reminders and other value-added services. In the first quarter of 2014, 76% of credit cards were also paid by Alipay Wallet. From December 2013, several chain convenience store companies, including Meiyijia, Hongqi Chain, and Qishiduo C-STORE and 7-Eleven, have successively supported Alipay payment; in December, Beijing taxi drivers began to accept Alipay to pay the fare. Subsequently, Wanda Cinema, Joy City, Wangfujing and other large-scale retail companies as well as movie theaters, KTV, and catering companies have access to Alipay. From 26 March 2019, the service fee will be charged for the payment of credit card through Alipay. Customers only pay the portion of the payment that exceeds 2,000 yuan at 0.1%. In addition to this, in 2019, Walgreens accepted Alipay as payment in 3,000 US stores. Walgreen's products are available to Chinese customers through Alibaba's Tmall online marketplace. The payment application can also be used on Alibaba.com's site and Taobao as a means of payment. A Nielsen report suggests that over 90% of Chinese tourists would be willing to use mobile payment overseas if given the option. Many Chinese tourists do not have international credit cards, and so Alipay is a payment option. Digital payments have become the norm in China as the government pushes a cashless system even in rural and village areas. In November 2019, Alipay introduced Tourpass, a service component that allows non-Chinese users to use its mobile payment feature by pre-loading Chinese Yuan equivalent foreign currency into the app. In 2020, Alipay used a QR code system to help in containing the COVID-19 outbreak. The health code system tags users one of three colors according to their location, basic health information and travel history. "Beauty filters" were included to Alipay's face-scan payment system in a new upgrade that was released in July 2019. The market has responded well to the "beauty filters," which make users seem better when they use the program to make payments. Alipay Tap is a payment function launched by Alipay in July 2024. Alipay+ NFC enables wallets to offer tap-to-pay acceptance across Mastercard's global contactless network, all within your existing wallet infrastructure. == Foreign expansion == Outside of China, more than 300 worldwide merchants use Alipay to sell directly to consumers in China. It currently supports transactions in 18 foreign currencies. Since the launch of Alipay in the Mainland China, Ant Financial introduced a series of expansion of the services to other countries. Other than expanding into individual countries, the system would also be integrated with online payment platform providers. Ant Group had acquired a majority stake into 2C2P, a Singapore-based provider used by merchants worldwide in April 2022, and would eventually integrate Alipay with 2C2P. === Asia === ==== Bangladesh ==== In 2018, Alipay bought 20% shares in Bangladeshi mobile financial service provider bKash Limited. ==== Hong Kong ==== In 2017, Ant Financial expanded to Hong Kong. In a joint venture with CK Hutchison, as Alipay Payment Ser
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ARMA International (formerly the Association of Records Managers and Administrators) is an American not-for-profit professional association for information professionals – primarily information management (including records management) and information governance, and related industry practitioners and vendors. The association provides educational opportunities and publications covering aspects of information management broadly. == History == The Association was founded in 1955. In 1975, the Association of Records Executives and Administrators (AREA) and the American Records Management Association merged to form ARMA International. The headquarters for ARMA International is located in Overland Park, Kansas. == Operations == ARMA International services professionals in the United States, Canada, Japan, and the United Kingdom. Its members include records managers, attorneys, information technology professionals, consultants, and archivists involved in various aspects of managing records and information assets. ARMA hosts an annual conference with the goal of bringing together record and information management professionals from around the world – In 2023, ARMA hosted conferences in both the United States and Canada. Topics addressed in the 120+ educational sessions include advanced technology, creating information structure, ediscovery and information law, information management fundamentals, information project management, and reducing organizational information risk. The expo features exhibitors displaying records and information technologies, products, and services.
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In computer science and operations research, randomized rounding is a widely used approach for designing and analyzing approximation algorithms. Many combinatorial optimization problems are computationally intractable to solve exactly (to optimality). For such problems, randomized rounding can be used to design fast (polynomial time) approximation algorithms—that is, algorithms that are guaranteed to return an approximately optimal solution given any input. The basic idea of randomized rounding is to convert an optimal solution of a relaxation of the problem into an approximately-optimal solution to the original problem. The resulting algorithm is usually analyzed using the probabilistic method. == Overview == The basic approach has three steps: Formulate the problem to be solved as an integer linear program (ILP). Compute an optimal fractional solution x {\displaystyle x} to the linear programming relaxation (LP) of the ILP. Round the fractional solution x {\displaystyle x} of the LP to an integer solution x ′ {\displaystyle x'} of the ILP. (Although the approach is most commonly applied with linear programs, other kinds of relaxations are sometimes used. For example, see Goemans' and Williamson's semidefinite programming-based Max-Cut approximation algorithm.) In the first step, the challenge is to choose a suitable integer linear program. Familiarity with linear programming, in particular modelling using linear programs and integer linear programs, is required. For many problems, there is a natural integer linear program that works well, such as in the Set Cover example below. (The integer linear program should have a small integrality gap; indeed randomized rounding is often used to prove bounds on integrality gaps.) In the second step, the optimal fractional solution can typically be computed in polynomial time using any standard linear programming algorithm. In the third step, the fractional solution must be converted into an integer solution (and thus a solution to the original problem). This is called rounding the fractional solution. The resulting integer solution should (provably) have cost not much larger than the cost of the fractional solution. This will ensure that the cost of the integer solution is not much larger than the cost of the optimal integer solution. The main technique used to do the third step (rounding) is to use randomization, and then to use probabilistic arguments to bound the increase in cost due to the rounding (following the probabilistic method from combinatorics). Therein, probabilistic arguments are used to show the existence of discrete structures with desired properties. In this context, one uses such arguments to show the following: Given any fractional solution x {\displaystyle x} of the LP, with positive probability the randomized rounding process produces an integer solution x ′ {\displaystyle x'} that approximates x {\displaystyle x} according to some desired criterion. Finally, to make the third step computationally efficient, one either shows that x ′ {\displaystyle x'} approximates x {\displaystyle x} with high probability (so that the step can remain randomized) or one derandomizes the rounding step, typically using the method of conditional probabilities. The latter method converts the randomized rounding process into an efficient deterministic process that is guaranteed to reach a good outcome. == Example: the set cover problem == The following example illustrates how randomized rounding can be used to design an approximation algorithm for the set cover problem. Fix any instance ⟨ c , S ⟩ {\displaystyle \langle c,{\mathcal {S}}\rangle } of set cover over a universe U {\displaystyle {\mathcal {U}}} . === Computing the fractional solution === For step 1, let IP be the standard integer linear program for set cover for this instance. For step 2, let LP be the linear programming relaxation of IP, and compute an optimal solution x ∗ {\displaystyle x^{}} to LP using any standard linear programming algorithm. This takes time polynomial in the input size. The feasible solutions to LP are the vectors x {\displaystyle x} that assign each set s ∈ S {\displaystyle s\in {\mathcal {S}}} a non-negative weight x s {\displaystyle x_{s}} , such that, for each element e ∈ U {\displaystyle e\in {\mathcal {U}}} , x ′ {\displaystyle x'} covers e {\displaystyle e} —the total weight assigned to the sets containing e {\displaystyle e} is at least 1, that is, ∑ s ∋ e x s ≥ 1. {\displaystyle \sum _{s\ni e}x_{s}\geq 1.} The optimal solution x ∗ {\displaystyle x^{}} is a feasible solution whose cost ∑ s ∈ S c ( S ) x s ∗ {\displaystyle \sum _{s\in {\mathcal {S}}}c(S)x_{s}^{}} is as small as possible. Note that any set cover C {\displaystyle {\mathcal {C}}} for S {\displaystyle {\mathcal {S}}} gives a feasible solution x {\displaystyle x} (where x s = 1 {\displaystyle x_{s}=1} for s ∈ C {\displaystyle s\in {\mathcal {C}}} , x s = 0 {\displaystyle x_{s}=0} otherwise). The cost of this C {\displaystyle {\mathcal {C}}} equals the cost of x {\displaystyle x} , that is, ∑ s ∈ C c ( s ) = ∑ s ∈ S c ( s ) x s . {\displaystyle \sum _{s\in {\mathcal {C}}}c(s)=\sum _{s\in {\mathcal {S}}}c(s)x_{s}.} In other words, the linear program LP is a relaxation of the given set-cover problem. Since x ∗ {\displaystyle x^{}} has minimum cost among feasible solutions to the LP, the cost of x ∗ {\displaystyle x^{}} is a lower bound on the cost of the optimal set cover. === Randomized rounding step === In step 3, we must convert the minimum-cost fractional set cover x ∗ {\displaystyle x^{}} into a feasible integer solution x ′ {\displaystyle x'} (corresponding to a true set cover). The rounding step should produce an x ′ {\displaystyle x'} that, with positive probability, has cost within a small factor of the cost of x ∗ {\displaystyle x^{}} .Then (since the cost of x ∗ {\displaystyle x^{}} is a lower bound on the cost of the optimal set cover), the cost of x ′ {\displaystyle x'} will be within a small factor of the optimal cost. As a starting point, consider the most natural rounding scheme: For each set s ∈ S {\displaystyle s\in {\mathcal {S}}} in turn, take x s ′ = 1 {\displaystyle x'_{s}=1} with probability min ( 1 , x s ∗ ) {\displaystyle \min(1,x_{s}^{})} , otherwise take x s ′ = 0 {\displaystyle x'_{s}=0} . With this rounding scheme, the expected cost of the chosen sets is at most ∑ s c ( s ) x s ∗ {\displaystyle \sum _{s}c(s)x_{s}^{}} , the cost of the fractional cover. This is good. Unfortunately the coverage is not good. When the variables x s ∗ {\displaystyle x_{s}^{}} are small, the probability that an element e {\displaystyle e} is not covered is about ∏ s ∋ e 1 − x s ∗ ≈ ∏ s ∋ e exp ( − x s ∗ ) = exp ( − ∑ s ∋ e x s ∗ ) ≈ exp ( − 1 ) . {\displaystyle \prod _{s\ni e}1-x_{s}^{}\approx \prod _{s\ni e}\exp(-x_{s}^{})=\exp {\Big (}-\sum _{s\ni e}x_{s}^{}{\Big )}\approx \exp(-1).} So only a constant fraction of the elements will be covered in expectation. To make x ′ {\displaystyle x'} cover every element with high probability, the standard rounding scheme first scales up the rounding probabilities by an appropriate factor λ > 1 {\displaystyle \lambda >1} . Here is the standard rounding scheme: Fix a parameter λ ≥ 1 {\displaystyle \lambda \geq 1} . For each set s ∈ S {\displaystyle s\in {\mathcal {S}}} in turn, take x s ′ = 1 {\displaystyle x'_{s}=1} with probability min ( λ x s ∗ , 1 ) {\displaystyle \min(\lambda x_{s}^{},1)} , otherwise take x s ′ = 0 {\displaystyle x'_{s}=0} . Scaling the probabilities up by λ {\displaystyle \lambda } increases the expected cost by λ {\displaystyle \lambda } , but makes coverage of all elements likely. The idea is to choose λ {\displaystyle \lambda } as small as possible so that all elements are provably covered with non-zero probability. Here is a detailed analysis. ==== Lemma (approximation guarantee for rounding scheme) ==== Fix λ = ln ( 2 | U | ) {\displaystyle \lambda =\ln(2|{\mathcal {U}}|)} . With positive probability, the rounding scheme returns a set cover x ′ {\displaystyle x'} of cost at most 2 ln ( 2 | U | ) c ⋅ x ∗ {\displaystyle 2\ln(2|{\mathcal {U}}|)c\cdot x^{}} (and thus of cost O ( log | U | ) {\displaystyle O(\log |{\mathcal {U}}|)} times the cost of the optimal set cover). (Note: with care the O ( log | U | ) {\displaystyle O(\log |{\mathcal {U}}|)} can be reduced to ln ( | U | ) + O ( log log | U | ) {\displaystyle \ln(|{\mathcal {U}}|)+O(\log \log |{\mathcal {U}}|)} .) ==== Proof ==== The output x ′ {\displaystyle x'} of the random rounding scheme has the desired properties as long as none of the following "bad" events occur: the cost c ⋅ x ′ {\displaystyle c\cdot x'} of x ′ {\displaystyle x'} exceeds 2 λ c ⋅ x ∗ {\displaystyle 2\lambda c\cdot x^{}} , or for some element e {\displaystyle e} , x ′ {\displaystyle x'} fails to cover e {\displaystyle e} . The expectation of each x s ′ {\displaystyle x'_{s}} is at most λ x s ∗ {\displaystyle \lambda x_{s
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Query rewriting is a typically automatic transformation that takes a set of database tables, views, and/or queries, usually indices, often gathered data and query statistics, and other metadata, and yields a set of different queries, which produce the same results but execute with better performance (for example, faster, or with lower memory use). Query rewriting can be based on relational algebra or an extension thereof (e.g. multiset relational algebra with sorting, aggregation and three-valued predicates i.e. NULLs as in the case of SQL). The equivalence rules of relational algebra are exploited, in other words, different query structures and orderings can be mathematically proven to yield the same result. For example, filtering on fields A and B, or cross joining R and S can be done in any order, but there can be a performance difference. Multiple operations may be combined, and operation orders may be altered. The result of query rewriting may not be at the same abstraction level or application programming interface (API) as the original set of queries (though often is). For example, the input queries may be in relational algebra or SQL, and the rewritten queries may be closer to the physical representation of the data, e.g. array operations. Query rewriting can also involve materialization of views and other subqueries; operations that may or may not be available to the API user. The query rewriting transformation can be aided by creating indices from which the optimizer can choose (some database systems create their own indexes if deemed useful), mandating the use of specific indices, creating materialized and/or denormalized views, or helping a database system gather statistics on the data and query use, as the optimality depends on patterns in data and typical query usage. Query rewriting may be rule based or optimizer based. Some sources discuss query rewriting as a distinct step prior to optimization, operating at the level of the user accessible algebra API (e.g. SQL). There are other, largely unrelated concepts also named similarly, for example, query rewriting by search engines.
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Ibotta, Inc. is an American mobile technology company headquartered in Denver, Colorado. Founded in 2011, the company offers cash back rewards on various purchases through its Ibotta Performance Network and direct to consumer app. Ibotta partners with CPG (consumer packaged goods) brands and network publishers to provide these rewards. As of 2024, the company operates solely in the United States. The company's rewards-as-a-service offering, the Ibotta Performance Network, went live in 2022. In August 2019, Ibotta received a $1 billion valuation after its Series D funding, and in 2023, the company surpassed $1.5 billion cash rewards paid to over 50 million consumers since the company's founding. Ibotta became a publicly traded company in April 2024 with a listing on the New York Stock Exchange. As of September 2025, Ibotta is trading at approximately $27.13 per share, marking a 69% decline from its initial public offering price of $88 per share on April 18, 2024. == History == === Founding through early 2019 === Ibotta was founded by current CEO Bryan Leach. The company was incorporated in 2011 and the app launched to both the App Store and Google Play stores in 2012. Early investors included entrepreneur and computer scientist Jim Clark and Tom “TJ” Jermoluk, Chairman of @Home Network. In 2015, Ibotta expanded beyond item level grocery, adding the ability to get cash back on in-store retail purchases. In 2016, in-app mobile commerce began, allowing users to navigate from the Ibotta app to its partners' apps to earn cash back on purchases. In 2016 with a Series C investment, Ibotta had raised over $73 million in funding. In March of that year, Ibotta partnered with Anheuser-Busch to offer cash back for adults who purchased its products. In May, the company partnered with LiveRamp so that companies could use their CRM data to create segmented, personalized campaigns. At the time, the company had around 200 full- and part-time employees and moved from offices in Lower Downtown Denver (LoDo) to a 40,000-square-foot office in the central Denver business district. A year later, the company had to expand to a second floor as it added almost another 100 employees. In 2017, Ibotta added cash back for Uber to its app as well as cash back rewards for online and mobile purchases. In 2018, Ibotta was listed on the Inc. 5,000 list as one of the fastest growing private companies in the U.S. A year later, in January 2019, the Ibotta app had been downloaded more than 30 million times with users receiving a reported $500 million in cash back rewards. That year, Ibotta was the largest mobile company in Colorado with six million monthly active users. === August 2019 to present === In August 2019, Ibotta was valued at $1 billion, following a Series D round of funding. The round was led by Koch Disruptive Technologies, a subsidiary of Koch Industries. 2019 was also the year the company introduced Pay with Ibotta, which allowed users to complete purchases at key retailers on the Ibotta app and earn instant cash back in the process. With that new service, users were able to enter their purchase total and use a QR code to checkout and receive immediate cash back. In 2020, the company partnered with Trees for the Future to plant up to 1 million trees as part of an Earth Month campaign to raise awareness about the waste of unused paper coupons. In response to the COVID-19 pandemic, Ibotta partnered with CPG brands in their “Here to Help” campaign and together committed over $10 million in cash back to American consumers. The company added the ability to earn cash back from online grocery pick-up and delivery orders. Later that year, Ibotta started its free Thanksgiving program, providing users with 100% cash back on select groceries needed for a Thanksgiving meal. By 2022, the company had provided approximately 10 million Thanksgiving meals. In 2021, Ibotta acquired the company OctoShop (originally InStok), a shopping browser extension company. The OctoShop app enables users to compare prices across stores and set restock and price-drop alerts. In April 2022, the Ibotta Performance Network (IPN) was launched. The IPN allows brands to deliver digital offers to consumers through third party publishers. Retailers including Walmart, Dollar General and Family Dollar, food delivery services including Instacart, and convenience stores including Shell are all part of the Ibotta Performance Network. This pay-per-sales or success-based performance network reaches over 200 million consumers. On April 18, 2024, Ibotta had its initial public offering (IPO), trading on the New York Stock Exchange (NYSE) under the ticker symbol IBTA. It was the largest technology IPO in Colorado history. In October 2025, Ibotta announced a partnership with technology and analytics company Circana, integrating Circana's Household Lift measurement into Ibotta campaigns to give CPG brands an increased understanding of the impact of their promotional campaigns. On November 3, 2025, Ibotta launched LiveLift, a tool for companies to measure the return on investment of digital promotions, in order to optimize performance marketing goals. === Athletic partnerships === Ibotta became the official jersey patch partner of the New Orleans Pelicans, a professional men's basketball team in the National Basketball Association (NBA), for the 2020–2021 and 2023–2024 seasons. Ibotta became the official jersey patch partner of the 2023 NBA champion Denver Nuggets baskeetball team beginning in the 2023–2024 season. In March 2023, F1 driver Logan Sargeant, the first U.S. racer to compete in F1 since 2015, partnered with Ibotta. The Ibotta logo was displayed on Sargeant's racing helmet throughout his F1 career. In June 2023, UConn Huskies women's basketball player Paige Bueckers entered into a "name, image, and likeness" (NIL) promotional agreement with Ibotta. According to a press release by Ibotta, the company has agreements with The Brandr Group, which finds NIL opportunities for women college athletes, and the Pearpop social media marketing platform to promote Ibotta. == Legal issues == In April 2025, shareholders filed a class action lawsuit—Fortune v. Ibotta, Inc., in the U.S. District Court for the District of Colorado (Case No. 25-cv-01213)—alleging that the registration statement in connection with Ibotta’s April 2024 initial public offering omitted material information. The complaint claims that, although Ibotta disclosed detailed terms for its contract with Walmart Inc., it failed to warn investors that its agreement with The Kroger Co., its second-largest client, was terminable at will and thus could be canceled without warning, creating a misleading impression of stability.
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A wearable computer, also known as a body-borne computer or wearable, is a computing device worn on the body. The definition of 'wearable computer' may be narrow or broad, extending to smartphones or even ordinary wristwatches. Wearables may be for general use, in which case they are just a particularly small example of mobile computing. Alternatively, they may be for specialized purposes such as fitness trackers. They may incorporate special sensors such as accelerometers, heart rate monitors, or on the more advanced side, electrocardiogram (ECG) and blood oxygen saturation (SpO2) monitors. Under the definition of wearable computers, we also include novel user interfaces such as Google Glass, an optical head-mounted display controlled by gestures. It may be that specialized wearables will evolve into general all-in-one devices, as happened with the convergence of PDAs and mobile phones into smartphones. Wearables are typically worn on the wrist (e.g. fitness trackers), hung from the neck (like a necklace), strapped to the arm or leg (electronic tagging), or on the head (as glasses or a helmet), though some have been located elsewhere (e.g. on a finger or in a shoe). Devices carried in a pocket or bag – such as smartphones and before them, pocket calculators and PDAs, may or may not be regarded as 'worn'. Wearable computers have various technical issues common to other mobile computing, such as batteries, heat dissipation, software architectures, wireless and personal area networks, and data management. Many wearable computers are active all the time, e.g. processing or recording data continuously. == Applications == Wearable computers are not only limited to computers such as fitness trackers that are worn on wrists; they also include wearables such as heart pacemakers and other prosthetics. They are used most often in research that focuses on behavioral modeling, health monitoring systems, IT and media development, where the person wearing the computer actually moves or is otherwise engaged with his or her surroundings. Wearable computers have been used for the following: general-purpose computing (e.g. smartphones and smartwatches) sensory integration, e.g. to help people see better or understand the world better (whether in task-specific applications like camera-based welding helmets or for everyday use like Google Glass) behavioral modeling health care monitoring systems service management electronic textiles and fashion design, e.g. Microsoft's 2011 prototype "The Printing Dress". Wearable computing is the subject of active research, especially the form-factor and location on the body, with areas of study including user interface design, augmented reality, and pattern recognition. The use of wearables for specific applications, for compensating disabilities or supporting elderly people steadily increases. == Operating systems == The dominant operating systems for wearable computing are: FreeRTOS is a real-time operating system kernel for embedded devices; most of the Smartbands that are currently available in the market are based on FreeRTOS, which include Huawei, Honor, Lenovo, realme, TCL and Xiaomi smartbands. LiteOS is a lightweight open source real-time operating system that is part of Huawei's "1+8+N" Internet of Things solution. Tizen OS from Samsung (there was an announcement in May 2021 that Wear OS and Tizen OS will merge and will be called simply Wear.) watchOS watchOS is a proprietary mobile operating system developed by Apple Inc. to run on the Apple Watch. Wear OS Wear OS (previously known as Android Wear) is a smartwatch operating system developed by Google Inc. == History == Due to the varied definitions of wearable and computer, the first wearable computer could be as early as the first abacus on a necklace, a 16th-century abacus ring, a wristwatch and 'finger-watch' owned by Queen Elizabeth I of England, or the covert timing devices hidden in shoes to cheat at roulette by Thorp and Shannon in the 1960s and 1970s. However, a general-purpose computer is not merely a time-keeping or calculating device, but rather a user-programmable item for arbitrary complex algorithms, interfacing, and data management. By this definition, the wearable computer was invented by Steve Mann, in the late 1970s: Steve Mann, a professor at the University of Toronto, was hailed as the father of the wearable computer and the ISSCC's first virtual panelist, by moderator Woodward Yang of Harvard University (Cambridge Mass.). The development of wearable items has taken several steps of miniaturization from discrete electronics over hybrid designs to fully integrated designs, where just one processor chip, a battery, and some interface conditioning items make the whole unit. === 1500s === Queen Elizabeth I of England received a watch from Robert Dudley in 1571, as a New Year's present; it may have been worn on the forearm rather than the wrist. She also possessed a 'finger-watch' set in a ring, with an alarm that prodded her finger. === 1600s === The Qing dynasty saw the introduction of a fully functional abacus on a ring, which could be used while it was being worn. === 1960s === In 1961, mathematicians Edward O. Thorp and Claude Shannon built some computerized timing devices to help them win a game of roulette. One such timer was concealed in a shoe and another in a pack of cigarettes. Various versions of this apparatus were built in the 1960s and 1970s. Thorp refers to himself as the inventor of the first "wearable computer". In other variations, the system was a concealed cigarette-pack-sized analog computer designed to predict the motion of roulette wheels. A data-taker would use microswitches hidden in his shoes to indicate the speed of the roulette wheel, and the computer would indicate an octant of the roulette wheel to bet on by sending musical tones via radio to a miniature speaker hidden in a collaborator's ear canal. The system was successfully tested in Las Vegas in June 1961, but hardware issues with the speaker wires prevented it from being used beyond test runs. This was not a wearable computer because it could not be re-purposed during use; rather it was an example of task-specific hardware. This work was kept secret until it was first mentioned in Thorp's book Beat the Dealer (revised ed.) in 1966 and later published in detail in 1969. === 1970s === Pocket calculators became mass-market devices in 1970, starting in Japan. Programmable calculators followed in the late 1970s, being somewhat more general-purpose computers. The HP-01 algebraic calculator watch by Hewlett-Packard was released in 1977. A camera-to-tactile vest for the blind, launched by C.C. Collins in 1977, converted images into a 1024-point, ten-inch square tactile grid on a vest. === 1980s === The 1980s saw the rise of more general-purpose wearable computers. In 1981, Steve Mann designed and built a backpack-mounted 6502-based wearable multimedia computer with text, graphics, and multimedia capability, as well as video capability (cameras and other photographic systems). Mann went on to be an early and active researcher in the wearables field, especially known for his 1994 creation of the Wearable Wireless Webcam, the first example of lifelogging. Seiko Epson released the RC-20 Wrist Computer in 1984. It was an early smartwatch, powered by a computer on a chip. In 1989, Reflection Technology marketed the Private Eye head-mounted display, which scans a vertical array of LEDs across the visual field using a vibrating mirror. This display gave rise to several hobbyist and research wearables, including Gerald "Chip" Maguire's IBM/Columbia University Student Electronic Notebook, Doug Platt's Hip-PC, and Carnegie Mellon University's VuMan 1 in 1991. The Student Electronic Notebook consisted of the Private Eye, Toshiba diskless AIX notebook computers (prototypes), a stylus based input system and a virtual keyboard. It used direct-sequence spread spectrum radio links to provide all the usual TCP/IP based services, including NFS mounted file systems and X11, which all ran in the Andrew Project environment. The Hip-PC included an Agenda palmtop used as a chording keyboard attached to the belt and a 1.44 megabyte floppy drive. Later versions incorporated additional equipment from Park Engineering. The system debuted at "The Lap and Palmtop Expo" on 16 April 1991. VuMan 1 was developed as part of a Summer-term course at Carnegie Mellon's Engineering Design Research Center, and was intended for viewing house blueprints. Input was through a three-button unit worn on the belt, and output was through Reflection Tech's Private Eye. The CPU was an 8 MHz 80188 processor with 0.5 MB ROM. === 1990s === In the 1990s PDAs became widely used, and in 1999 were combined with mobile phones in Japan to produce the first mass-market smartphone. In 1993, the Private Eye was used in Thad Starner's wearable, based on Doug Platt's system and built from a kit from Park Enterprises, a Pri
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Ubiquitous computing (or "ubicomp") is a concept in software engineering, hardware engineering and computer science where computing is made to appear seamlessly anytime and everywhere. In contrast to desktop computing, ubiquitous computing implies use on any device, in any location, and in any format. A user interacts with the computer, which can exist in many different forms, including laptop computers, tablets, smart phones and terminals in everyday objects such as a refrigerator or a pair of glasses. The underlying technologies to support ubiquitous computing include the Internet, advanced middleware, kernels, operating systems, mobile codes, sensors, microprocessors, new I/Os and user interfaces, computer networks, mobile protocols, global navigational systems, and new materials. This paradigm is also described as pervasive computing, ambient intelligence, or "everyware". Each term emphasizes slightly different aspects. When primarily concerning the objects involved, it is also known as physical computing, the Internet of Things, haptic computing, and "things that think". Rather than propose a single definition for ubiquitous computing and for these related terms, a taxonomy of properties for ubiquitous computing has been proposed, from which different kinds or flavors of ubiquitous systems and applications can be described. Ubiquitous computing themes include: distributed computing, mobile computing, location computing, mobile networking, sensor networks, human–computer interaction, context-aware smart home technologies, and artificial intelligence. == Core concepts == Ubiquitous computing is the concept of using small internet connected and inexpensive computers to help with everyday functions in an automated fashion. Mark Weiser proposed three basic forms for ubiquitous computing devices: Tabs: a wearable device that is approximately a centimeter in size Pads: a hand-held device that is approximately a decimeter in size Boards: an interactive larger display device that is approximately a meter in size Ubiquitous computing devices proposed by Mark Weiser are all based around flat devices of different sizes with a visual display. These conceptual device categories were later implemented at Xerox PARC in experimental systems including the PARCTab, PARCPad, and LiveBoard, which served as early prototypes of handheld, tablet-style, and large interactive display computing environments. Expanding beyond those concepts there is a large array of other ubiquitous computing devices that could exist. == History == Mark Weiser coined the phrase "ubiquitous computing" around 1988, during his tenure as Chief Technologist of the Xerox Palo Alto Research Center (PARC). Both alone and with PARC Director and Chief Scientist John Seely Brown, Weiser wrote some of the earliest papers on the subject, largely defining it and sketching out its major concerns. == Recognizing the effects of extending processing power == Recognizing that the extension of processing power into everyday scenarios would necessitate understandings of social, cultural and psychological phenomena beyond its proper ambit, Weiser was influenced by many fields outside computer science, including "philosophy, phenomenology, anthropology, psychology, post-Modernism, sociology of science and feminist criticism". He was explicit about "the humanistic origins of the 'invisible ideal in post-modernist thought'", referencing as well the ironically dystopian Philip K. Dick novel Ubik. Andy Hopper from Cambridge University UK proposed and demonstrated the concept of "Teleporting" – where applications follow the user wherever he/she moves. Roy Want (now at Google), while at Olivetti Research Ltd, designed the first "Active Badge System", which is an advanced location computing system where personal mobility is merged with computing. Later at Xerox PARC, he designed and built the "PARCTab" or simply "Tab", widely recognized as the world's first Context-Aware computer, which has great similarity to the modern smartphone. Bill Schilit (now at Google) also did some earlier work in this topic, and participated in the early Mobile Computing workshop held in Santa Cruz in 1996. Ken Sakamura of the University of Tokyo, Japan leads the Ubiquitous Networking Laboratory (UNL), Tokyo as well as the T-Engine Forum. The joint goal of Sakamura's Ubiquitous Networking specification and the T-Engine forum, is to enable any everyday device to broadcast and receive information. MIT has also contributed significant research in this field, notably Things That Think consortium (directed by Hiroshi Ishii, Joseph A. Paradiso and Rosalind Picard) at the Media Lab and the CSAIL effort known as Project Oxygen. Other major contributors include University of Washington (Shwetak Patel, Anind Dey and James Landay), Dartmouth College's HealthX Lab (directed by Andrew Campbell), Georgia Tech's College of Computing (Gregory Abowd and Thad Starner), Cornell Tech's People Aware Computing Lab (directed by Tanzeem Choudhury), NYU's Interactive Telecommunications Program, UC Irvine's Department of Informatics, Microsoft Research, Intel Research and Equator, Ajou University UCRi & CUS. == Examples == One of the earliest ubiquitous systems was artist Natalie Jeremijenko's "Live Wire", also known as "Dangling String", installed at Xerox PARC during Mark Weiser's time there. This was a piece of string attached to a stepper motor and controlled by a LAN connection; network activity caused the string to twitch, yielding a peripherally noticeable indication of traffic. Weiser called this an example of calm technology. A present manifestation of this trend is the widespread diffusion of mobile phones. Many mobile phones support high speed data transmission, video services, and other services with powerful computational ability. Although these mobile devices are not necessarily manifestations of ubiquitous computing, there are examples, such as Japan's Yaoyorozu ("Eight Million Gods") Project in which mobile devices, coupled with radio frequency identification tags demonstrate that ubiquitous computing is already present in some form. Ambient Devices has produced an "orb", a "dashboard", and a "weather beacon": these decorative devices receive data from a wireless network and report current events, such as stock prices and the weather, like the Nabaztag, which was invented by Rafi Haladjian and Olivier Mével, and manufactured by the company Violet. The Australian futurist Mark Pesce has produced a highly configurable 52-LED LAMP enabled lamp which uses Wi-Fi named MooresCloud after Gordon Moore. The Unified Computer Intelligence Corporation launched a device called Ubi – The Ubiquitous Computer designed to allow voice interaction with the home and provide constant access to information. Ubiquitous computing research has focused on building an environment in which computers allow humans to focus attention on select aspects of the environment and operate in supervisory and policy-making roles. Ubiquitous computing emphasizes the creation of a human computer interface that can interpret and support a user's intentions. For example, MIT's Project Oxygen seeks to create a system in which computation is as pervasive as air: In the future, computation will be human centered. It will be freely available everywhere, like batteries and power sockets, or oxygen in the air we breathe...We will not need to carry our own devices around with us. Instead, configurable generic devices, either handheld or embedded in the environment, will bring computation to us, whenever we need it and wherever we might be. As we interact with these "anonymous" devices, they will adopt our information personalities. They will respect our desires for privacy and security. We won't have to type, click, or learn new computer jargon. Instead, we'll communicate naturally, using speech and gestures that describe our intent... This is a fundamental transition that does not seek to escape the physical world and "enter some metallic, gigabyte-infested cyberspace" but rather brings computers and communications to us, making them "synonymous with the useful tasks they perform". Network robots link ubiquitous networks with robots, contributing to the creation of new lifestyles and solutions to address a variety of social problems including the aging of population and nursing care. The "Continuity" set of features, introduced by Apple in OS X Yosemite, can be seen as an example of ubiquitous computing. == Issues == Privacy is easily the most often-cited criticism of ubiquitous computing (ubicomp), and may be the greatest barrier to its long-term success. == Research centres == This is a list of notable institutions who claim to have a focus on Ubiquitous computing sorted by country: Canada Topological Media Lab, Concordia University, Canada Finland Community Imaging Group, University of Oulu, Finland Germany Telecooperation Office (TECO), Karlsruhe Institute of Technology, Ger
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The March algorithm is a widely used algorithm that tests SRAM memory by filling all its entries test patterns. It carries out several passes through an SRAM checking the patterns and writing new patterns. The SRAM read and write operations performed on each pass are called a March element and each element is repeated for each entry. The March algorithm is often used to find functional faults in SRAM during testing such as: Stuck-at Faults (SAFs) Transition Faults (TFs) Address Decoder Faults (AFs) Coupling Faults (CFs), such as Inversion (CFin), Idempotent (CFid), and State (CFst) coupling faults It has been suggested to test SRAM modules using the algorithm before sale using a built-in self-test mechanism. == Notation == Each pass in a test sequence is represented by an "element". An element consists of a vertical arrow to indicate the direction in which the memory is scanned followed by a list of read/write operations to be applied to each memory cell. Multiple elements can be listed, separated by semicolons, to form a "test". For example, { ⇕ ( w 0 ) ; ⇑ ( r 0 , w 1 ) ; ⇓ ( r 1 , w 0 , r 0 ) } {\displaystyle \{\Updownarrow (w0);\Uparrow (r0,w1);\Downarrow (r1,w0,r0)\}} specifies to: Scan in both directions, writing 0. Scan from lowest to highest address, reading 0 and writing 1. Scan from highest to lowest address, reading 1, writing 0 and reading 0. == Variants == Many variants of the March algorithm exist with different sequences of tests. Each variant makes a different tradeoff between what faults it can detect and the complexity of the algorithm. Several variants have been given names:
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A geofence warrant or a reverse location warrant is a search warrant issued by a court to allow law enforcement to search a database to find all active mobile devices within a particular geo-fence area. Courts have granted law enforcement geo-fence warrants to obtain information from databases such as Google's Sensorvault, which collects users' historical geolocation data. Geo-fence warrants are a part of a category of warrants known as reverse search warrants. == History == Geofence warrants were first used in 2016. Google reported that it had received 982 such warrants in 2018, 8,396 in 2019, and 11,554 in 2020. A 2021 transparency report showed that 25% of data requests from law enforcement to Google were geo-fence data requests. Google is the most common recipient of geo-fence warrants and the main provider of such data, although companies including Apple, Snapchat, Lyft, and Uber have also received such warrants. == Legality == === United States === Some lawyers and privacy experts believe reverse search warrants are unconstitutional under the Fourth Amendment to the United States Constitution, which protects people from unreasonable searches and seizures, and requires any search warrants be specific to what and to whom they apply. The Fourth Amendment specifies that warrants may only be issued "upon probable cause, supported by Oath or affirmation, and particularly describing the place to be searched, and the persons or things to be seized." Some lawyers, legal scholars, and privacy experts have likened reverse search warrants to general warrants, which were made illegal by the Fourth Amendment. Groups including the Electronic Frontier Foundation have opposed geo-fence warrants in amicus briefs filed in motions to quash such orders to disclose geo-fence data. In 2024, a panel of the United States Fourth Circuit Court of Appeals considered data acquired from Google’s Sensorvault not to be a search, but non-private business records when users opt-in to Google’s location history. However, upon a rehearing en banc, the Court vacated that decision. In April 2025, the full Court affirmed the judgment solely on the 'good faith' exception, leaving the underlying constitutional question of whether geofence warrants constitute a search unsettled in the Circuit. However, the United States Fifth Circuit Court of Appeals found that geofence warrants are "categorically prohibited by the Fourth Amendment." The split in Circuits prompted the United States Supreme Court to agree to hear Chatrie v. United States in January 2026.
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Causal AI is a technique in artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation. One practical use for causal AI is for organisations to explain decision-making and the causes for a decision. Systems based on causal AI, by identifying the underlying web of causality for a behaviour or event, provide insights that solely predictive AI models might fail to extract from historical data. An analysis of causality may be used to supplement human decisions in situations where understanding the causes behind an outcome is necessary, such as quantifying the impact of different interventions, policy decisions or performing scenario planning. A 2024 paper from Google DeepMind demonstrated mathematically that "Any agent capable of adapting to a sufficiently large set of distributional shifts must have learned a causal model". The paper offers the interpretation that learning to generalise beyond the original training set requires learning a causal model, concluding that causal AI is necessary for artificial general intelligence. == History == The concept of causal AI and the limits of machine learning were raised by Judea Pearl, the Turing Award-winning computer scientist and philosopher, in 2018's The Book of Why: The New Science of Cause and Effect. Pearl asserted: “Machines' lack of understanding of causal relations is perhaps the biggest roadblock to giving them human-level intelligence.” In 2020, Columbia University established a Causal AI Lab under Director Elias Bareinboim. Professor Bareinboim's research focuses on causal and counterfactual inference and their applications to data-driven fields in the health and social sciences as well as artificial intelligence and machine learning. Technological research and consulting firm Gartner for the first time included causal AI in its 2022 Hype Cycle report, citing it as one of five critical technologies in accelerated AI automation. Causal AI is closely related to but distinct from fields such as causal inference, explainable AI and causal reasoning. While causal inference focuses on estimating cause-effect relationships (often from observational data), causal AI emphasises the integration of those causal models into AI systems for prediction, planning and adaptation.
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In computer science, in the field of databases, read–write conflict, also known as unrepeatable reads, is a computational anomaly associated with interleaved execution of transactions. Specifically, a read–write conflict occurs when a "transaction requests to read an entity for which an unclosed transaction has already made a write request." Given a schedule S S = [ T 1 T 2 R ( A ) R ( A ) W ( A ) C o m . R ( A ) W ( A ) C o m . ] {\displaystyle S={\begin{bmatrix}T1&T2\\R(A)&\\&R(A)\\&W(A)\\&Com.\\R(A)&\\W(A)&\\Com.&\end{bmatrix}}} In this example, T1 has read the original value of A, and is waiting for T2 to finish. T2 also reads the original value of A, overwrites A, and commits. However, when T1 reads from A, it discovers two different versions of A, and T1 would be forced to abort, because T1 would not know what to do. This is an unrepeatable read. This could never occur in a serial schedule, in which each transaction executes in its entirety before another begins. Strict two-phase locking (Strict 2PL) or Serializable Snapshot Isolation (SSI) prevent this conflict. == Real-world example == Alice and Bob are using a website to book tickets for a specific show. Only one ticket is left for the specific show. Alice signs on first to see that only one ticket is left, and finds it expensive. Alice takes time to decide. Bob signs on and also finds one ticket left, and orders it instantly. Bob purchases and logs off. Alice decides to buy a ticket, to find there are no tickets. This is a typical read–write conflict situation.
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