AI Data Training Jobs

AI Data Training Jobs — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Zero-shot learning

  Zero-shot learning (ZSL) is a problem setup in deep learning where, at test time, a learner observes samples from classes which were not observed during training, and needs to predict the class that they belong to. The name is a play on words based on the earlier concept of one-shot learning, in which classification can be learned from only one, or a few, examples. Zero-shot methods generally work by associating observed and non-observed classes through some form of auxiliary information, which encodes observable distinguishing properties of objects. For example, given a set of images of animals to be classified, along with auxiliary textual descriptions of what animals look like, an artificial intelligence model which has been trained to recognize horses, but has never been given a zebra, can still recognize a zebra when it also knows that zebras look like striped horses. This problem is widely studied in computer vision, natural language processing, and machine perception. == Background and history == The first paper on zero-shot learning in natural language processing appeared in a 2008 paper by Chang, Ratinov, Roth, and Srikumar, at the AAAI'08, but the name given to the learning paradigm there was dataless classification. The first paper on zero-shot learning in computer vision appeared at the same conference, under the name zero-data learning. The term zero-shot learning itself first appeared in the literature in a 2009 paper from Palatucci, Hinton, Pomerleau, and Mitchell at NIPS'09. This terminology was repeated later in another computer vision paper and the term zero-shot learning caught on, as a take-off on one-shot learning that was introduced in computer vision years earlier. In computer vision, zero-shot learning models learned parameters for seen classes along with their class representations and rely on representational similarity among class labels so that, during inference, instances can be classified into new classes. In natural language processing, the key technical direction developed builds on the ability to "understand the labels"—represent the labels in the same semantic space as that of the documents to be classified. This supports the classification of a single example without observing any annotated data, the purest form of zero-shot classification. The original paper made use of the Explicit Semantic Analysis (ESA) representation but later papers made use of other representations, including dense representations. This approach was also extended to multilingual domains, fine entity typing and other problems. Moreover, beyond relying solely on representations, the computational approach has been extended to depend on transfer from other tasks, such as textual entailment and question answering. The original paper also points out that, beyond the ability to classify a single example, when a collection of examples is given, with the assumption that they come from the same distribution, it is possible to bootstrap the performance in a semi-supervised like manner (or transductive learning). Unlike standard generalization in machine learning, where classifiers are expected to correctly classify new samples to classes they have already observed during training, in ZSL, no samples from the classes have been given during training the classifier. It can therefore be viewed as an extreme case of domain adaptation. == Prerequisite information for zero-shot classes == Naturally, some form of auxiliary information has to be given about these zero-shot classes, and this type of information can be of several types. Learning with attributes: classes are accompanied by pre-defined structured description. For example, for bird descriptions, this could include "red head", "long beak". These attributes are often organized in a structured compositional way, and taking that structure into account improves learning. While this approach was used mostly in computer vision, there are some examples for it also in natural language processing. Learning from textual description. As pointed out above, this has been the key direction pursued in natural language processing. Here class labels are taken to have a meaning and are often augmented with definitions or free-text natural-language description. This could include for example a wikipedia description of the class. Class-class similarity. Here, classes are embedded in a continuous space. A zero-shot classifier can predict that a sample corresponds to some position in that space, and the nearest embedded class is used as a predicted class, even if no such samples were observed during training. == Generalized zero-shot learning == The above ZSL setup assumes that at test time, only zero-shot samples are given, namely, samples from new unseen classes. In generalized zero-shot learning, samples from both new and known classes, may appear at test time. This poses new challenges for classifiers at test time, because it is very challenging to estimate if a given sample is new or known. Some approaches to handle this include: a gating module, which is first trained to decide if a given sample comes from a new class or from an old one, and then, at inference time, outputs either a hard decision, or a soft probabilistic decision a generative module, which is trained to generate feature representation of the unseen classes—a standard classifier can then be trained on samples from all classes, seen and unseen. == Domains of application == Zero shot learning has been applied to the following fields: image classification semantic segmentation image generation object detection natural language processing computational biology abstract reasoning

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  View synthesis

  In computer graphics, view synthesis, or novel view synthesis, is a task which consists of generating images of a specific subject or scene from a specific point of view, when the only available information is pictures taken from different points of view. This task was only recently (late 2010s – early 2020s) tackled with significant success, mostly as a result of advances in machine learning. Notable successful methods are Neural radiance fields and 3D Gaussian Splatting. Applications of view synthesis are numerous, one of them being Free view point television. The technique has also been applied to real-estate marketing, where novel views of a listing's interior are generated from a limited set of photographs for use in virtual home staging.

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  Variable data publishing

  Variable-data publishing (VDP) (also known as database publishing) is a term referring to the output of a variable composition system. While these systems can produce both electronically viewable and hard-copy (print) output, the "variable-data publishing" term today often distinguishes output destined for electronic viewing, rather than that which is destined for hard-copy print (e.g. variable data printing). Essentially the same techniques are employed to perform variable-data publishing, as those utilized with variable data printing. The difference is in the interpretation for output. While variable-data printing may be interpreted to produce various print streams or page-description files (e.g. AFP/IPDS, PostScript, PCL), variable-data publishing produces electronically viewable files, most commonly seen in the forms of PDF, HTML, or XML. Variable-data composition involves the use of data to conditionally: exhibit text (static blocks and/or variable content) exhibit images select fonts select colors format page layouts & flows Variable-data may be as simple as an address block or salutation. However, it can be any or all of the document's textual content—including words, sentences, paragraphs, pages, or the entire document. In other words, it can make up as little or as much of the document as the composer desires. Variable data may also be used to exhibit various images, such as logos, products, or membership photos. Further, variable-data can be used to build rule-based design schemes, including fonts, colors, and page formats. The possibilities are vast. The variable-data tools available today, make it possible to perform variable-data composition at nearly every stage of document production. However, the level of control that can be achieved varies, based upon how far into the document production process a variable-data tool is deployed. For example, if variable-data insertion occurs just prior to output...it's not likely that the text flow or layout can be altered with nearly as much control as would be available at the time of initial document composition. Many organizations will produce multiple forms of output (aka: multi-channel output), for the same document. This ensures that the published content is available to recipients via any form of access method they might require. When multi-channel output is utilized, integrity between those output channels often becomes important. Variable-data publishing may be performed on everything from a personal computer to a mainframe system. However, the speed and practical output volumes which can be achieved are directly affected by the computer power utilized. == Origin of the concept == The term variable-data publishing was likely an offshoot of the term "variable-data printing", first introduced to the printing industry by Frank Romano, Professor Emeritus, School of Print Media, at the College of Imaging Arts and Sciences at Rochester Institute of Technology. However, the concept of merging static document elements and variable document elements predates the term and has seen various implementations ranging from simple desktop 'mail merge', to complex mainframe applications in the financial and banking industry. In the past, the term VDP has been most closely associated with digital printing machines. However, in the past 3 years the application of this technology has spread to web pages, emails, and mobile messaging.

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  Qapital

  Qapital is a personal finance mobile application (app) for the iOS and Android operating systems, developed by Qapital, LLC. The app is designed to motivate users to save money through a gamification of their spending behavior. It moves money from a user's checking account to a separate Qapital account, when certain rules are triggered. Its database is used by psychology professor Dan Ariely to study consumer behavior. Qapital was released in Sweden in 2013, then in the US in early 2015. The application was later withdrawn from the Swedish market in April 2015, in order to focus on the US market. == History == The idea for Qapital was conceived by ex-bankers in Sweden. The software was designed by twin brothers Daniel and Andreas Källbom of Studio Källbom and released in Sweden in December 2013. The original software was a personal finance dashboard, similar to Mint.com, to show its users how they spent their money. Qapital introduced the app into the US market with a different design in 2014 and started focusing exclusively on the US market. The app was re-designed to focus on building savings rather than managing personal finances. The Swedish version shut down in April 2015. The app was initially restricted to the iOS platform, but an Android version was released at the end of 2015. Shortly after its US launch, Qapital invited psychology professor Dan Ariely to join its team as its "chief behavioral economist". He uses the app's database to conduct research into behavioral economics and Qapital in turn uses Ariely's research in design and programming decisions. In 2017, Qapital added checking and debit card services to the app. == Concept and features == Qapital is a free personal finance app for iOS and Android devices, intended to encourage its users to save money. Qapital directs each of its users to set savings goals, then automatically transfers money from their checking account to an account for savings, when a rule established in the app is met. It uses the "if this then that" (IFTTT) rule-based web-service. For example, one rule could be that if a user purchases a cup of coffee, then the app will round up the charge to the nearest dollar and deposit the difference into savings. Users connect their bank accounts to Qapital, so it knows when purchases are made. When a rule is met, money for savings are transferred to a Qapital account operated in partnership with Lincoln Savings Bank. As of 2015, Qapital can connect to more than 180 other apps, such as Facebook, Twitter, Dropbox and Instagram. For example, connecting to Jawbone allows the user to set a rule that if they take a certain number of steps during the day, a set amount of money is transferred to savings. The app also allows users to monitor activity among their other financial accounts, such as deposits and withdrawals. == Reception == In an October 2015 review, PC Magazine gave Qapital four out of five marks and an editor rating of "excellent." The review praised the app for having a "lovely design" and criticized it for being a, "bit simplistic in some of its rules." Bankrate, in a May 2015 review, gave the app a score of 3/5 for "ease of use," 5/5 for "features," 4/5 for "effectiveness," 4/5 for "value," for a total score of 16/20. The reviewer criticized Qapital's savings account for providing a low-interest rate, but concluded that its numerous features make the app "intriguing" and "it would be difficult to find a standard bank app more fun to use than Qapital."

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  Autognostics

  Autognostics is a new paradigm that describes the capacity for computer networks to be self-aware. It is considered one of the major components of Autonomic Networking. == Introduction == One of the most important characteristics of today's Internet that has contributed to its success is its basic design principle: a simple and transparent core with intelligence at the edges (the so-called "end-to-end principle"). Based on this principle, the network carries data without knowing the characteristics of that data (e.g., voice, video, etc.) - only the end-points have application-specific knowledge. If something goes wrong with the data, only the edge may be able to recognize that since it knows about the application and what the expected behavior is. The core has no information about what should happen with that data - it only forwards packets. Although an effective and beneficial attribute, this design principle has also led to many of today's problems, limitations, and frustrations. Currently, it is almost impossible for most end-users to know why certain network-based applications do not work well and what they need to do to make it better. Also, network operators who interact with the core in low-level terms such as router configuration have problems expressing their high-level goals into low-level actions. In high-level terms, this may be summarized as a weak coupling between the network and application layers of the overall system. As a consequence of the Internet end-to-end principle, the network performance experienced by a particular application is difficult to attribute based on the behavior of the individual elements. At any given moment, the measure of performance between any two points is typically unknown and applications must operate blindly. As a further consequence, changes to the configuration of given element, or changes in the end-to-end path, cannot easily be validated. Optimization and provisioning cannot then be automated except against only the simplest design specifications. There is an increasing interest in Autonomic Networking research, and a strong conviction that an evolution from the current networking status quo is necessary. Although to date there have not been any practical implementations demonstrating the benefits of an effective autonomic networking paradigm, there seems to be a consensus as to the characteristics which such implementations would need to demonstrate. These specifically include continuous monitoring, identifying, diagnosing and fixing problems based on high-level policies and objectives. Autognostics, as a major part of the autonomic networking concept, intends to bring networks to a new level of awareness and eliminate the lack of visibility which currently exists in today's networks. == Definition == Autognostics is a new paradigm that describes the capacity for computer networks to be self-aware, in part and as a whole, and dynamically adapt to the applications running on them by autonomously monitoring, identifying, diagnosing, resolving issues, subsequently verifying that any remediation was successful, and reporting the impact with respect to the application's use (i.e., providing visibility into the changes to networks and their effects). Although similar to the concept of network awareness, i.e., the capability of network devices and applications to be aware of network characteristics (see References section below), it is noteworthy that autognostics takes that concept one step further. The main difference is the auto part of autognostics, which entails that network devices are self-aware of network characteristics, and have the capability to adapt themselves as a result of continuous monitoring and diagnostics. == Path to autognostics == Autognostics, or in other words deep self-knowledge, can be best described as the ability of a network to know itself and the applications that run on it. This knowledge is used to autonomously adapt to dynamic network and application conditions such as utilization, capacity, quality of service/application/user experience, etc. In order to achieve autognosis, networks need a means to: Continuously monitor/test the network for application-specific performance Analyze the monitoring/test data to detect problems (e.g., performance degradation) Diagnose, identify and localize sources of degradation Automatically take actions to resolve problems via remediation/provisioning Verify the problems have been resolved (potentially rolling back changes if ineffective) Subsequently, continue to monitor/test for performance

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  Generative art

  Generative art is post-conceptual art that has been created (in whole or in part) with the use of an autonomous system. An autonomous system in this context is generally one that is non-human and can independently determine features of an artwork that would otherwise require decisions made directly by the artist. In some cases the human creator may claim that the generative system represents their own artistic idea, and in others that the system takes on the role of the creator. "Generative art" often refers to algorithmic art (algorithmically determined computer generated artwork) and synthetic media (general term for any algorithmically generated media), but artists can also make generative art using systems of chemistry, biology, mechanics and robotics, smart materials, manual randomization, mathematics, data mapping, symmetry, and tiling. Generative algorithms, algorithms programmed to produce artistic works through predefined rules, stochastic methods, or procedural logic, often yielding dynamic, unique, and contextually adaptable outputs—are central to many of these practices. == History == The use of the word "generative" in the discussion of art has developed over time. The use of "Artificial DNA" defines a generative approach to art focused on the construction of a system able to generate unpredictable events, all with a recognizable common character. The use of autonomous systems, required by some contemporary definitions, focuses a generative approach where the controls are strongly reduced. This approach is also named "emergent". Margaret Boden and Ernest Edmonds have noted the use of the term "generative art" in the broad context of automated computer graphics in the 1960s, beginning with artwork exhibited by Georg Nees and Frieder Nake in 1965: A. Michael Noll did his initial computer art, combining randomness with order, in 1962, and exhibited it along with works by Bell Julesz in 1965. The terms "generative art" and "computer art" have been used in tandem, and more or less interchangeably, since the very earliest days. The first such exhibition showed the work of Nees in February 1965, which some claim was titled "Generative Computergrafik". While Nees does not himself remember, this was the title of his doctoral thesis published a few years later. The correct title of the first exhibition and catalog was "computer-grafik". "Generative art" and related terms was in common use by several other early computer artists around this time, including Manfred Mohr and Ken Knowlton. Vera Molnár (born 1924) is a French media artist of Hungarian origin. Molnar is widely considered to be a pioneer of generative art, and is also one of the first women to use computers in her art practice. The term "Generative Art" with the meaning of dynamic artwork-systems able to generate multiple artwork-events was clearly used the first time for the "Generative Art" conference in Milan in 1998. The term has also been used to describe geometric abstract art where simple elements are repeated, transformed, or varied to generate more complex forms. Thus defined, generative art was practiced by the Argentinian artists Eduardo Mac Entyre and Miguel Ángel Vidal in the late 1960s. In 1972 the Romanian-born Paul Neagu created the Generative Art Group in Britain. It was populated exclusively by Neagu using aliases such as "Hunsy Belmood" and "Edward Larsocchi". In 1972 Neagu gave a lecture titled 'Generative Art Forms' at the Queen's University, Belfast Festival. In 1970 the School of the Art Institute of Chicago created a department called Generative Systems. As described by Sonia Landy Sheridan the focus was on art practices using the then new technologies for the capture, inter-machine transfer, printing and transmission of images, as well as the exploration of the aspect of time in the transformation of image information. Also noteworthy is John Dunn, first a student and then a collaborator of Sheridan. In 1988 Clauser identified the aspect of systemic autonomy as a critical element in generative art: It should be evident from the above description of the evolution of generative art that process (or structuring) and change (or transformation) are among its most definitive features, and that these features and the very term 'generative' imply dynamic development and motion. (the result) is not a creation by the artist but rather the product of the generative process - a self-precipitating structure. In 1989 Celestino Soddu defined the Generative Design approach to Architecture and Town Design in his book Citta' Aleatorie. In 1989 Franke referred to "generative mathematics" as "the study of mathematical operations suitable for generating artistic images." From the mid-1990s Brian Eno popularized the terms generative music and generative systems, making a connection with earlier experimental music by Terry Riley, Steve Reich and Philip Glass. From the end of the 20th century, communities of generative artists, designers, musicians and theoreticians began to meet, forming cross-disciplinary perspectives. The first meeting about generative Art was in 1998, at the inaugural International Generative Art conference at Politecnico di Milano University, Italy. In Australia, the Iterate conference on generative systems in the electronic arts followed in 1999. On-line discussion has centered around the eu-gene mailing list, which began late 1999, and has hosted much of the debate which has defined the field. These activities have more recently been joined by the Generator.x conference in Berlin starting in 2005. In 2012 the new journal GASATHJ, Generative Art Science and Technology Hard Journal was founded by Celestino Soddu and Enrica Colabella jointing several generative artists and scientists in the editorial board. Some have argued that as a result of this engagement across disciplinary boundaries, the community has converged on a shared meaning of the term. As Boden and Edmonds put it in 2011: Today, the term "Generative Art" is still current within the relevant artistic community. Since 1998 a series of conferences have been held in Milan with that title (Generativeart.com), and Brian Eno has been influential in promoting and using generative art methods (Eno, 1996). Both in music and in visual art, the use of the term has now converged on work that has been produced by the activation of a set of rules and where the artist lets a computer system take over at least some of the decision-making (although, of course, the artist determines the rules). In the call of the Generative Art conferences in Milan (annually starting from 1998), the definition of Generative Art by Celestino Soddu: Generative Art is the idea realized as genetic code of artificial events, as construction of dynamic complex systems able to generate endless variations. Each Generative Project is a concept-software that works producing unique and non-repeatable events, like music or 3D Objects, as possible and manifold expressions of the generating idea strongly recognizable as a vision belonging to an artist / designer / musician / architect /mathematician. Discussion on the eu-gene mailing list was framed by the following definition by Adrian Ward from 1999: Generative art is a term given to work which stems from concentrating on the processes involved in producing an artwork, usually (although not strictly) automated by the use of a machine or computer, or by using mathematic or pragmatic instructions to define the rules by which such artworks are executed. A similar definition is provided by Philip Galanter: Generative art refers to any art practice where the artist creates a process, such as a set of natural language rules, a computer program, a machine, or other procedural invention, which is then set into motion with some degree of autonomy contributing to or resulting in a completed work of art. Around the 2020s, generative AI models learned to imitate the distinct style of particular authors. For example, a generative image model such as Stable Diffusion is able to model the stylistic characteristics of an artist like Pablo Picasso (including his particular brush strokes, use of colour, perspective, and so on), and a user can engineer a prompt such as "an astronaut riding a horse, by Picasso" to cause the model to generate a novel image applying the artist's style to an arbitrary subject. Generative image models have received significant backlash from artists who object to their style being imitated without their permission, arguing that this harms their ability to profit from their own work. The emergence of text-to-image generative AI systems has expanded debates over authorship, copyright, and artistic labor. The main issues in these debates include the eligibility of AI-generated outputs for copyright protection and the legal and ethical questions of using existing copyrighted works as training data for generative AI systems. == Types == === Music === Johann Kirnberger's Mu

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  Visual analytics

  Visual analytics is a multidisciplinary science and technology field that emerged from information visualization and scientific visualization. It focuses on how analytical reasoning can be facilitated by interactive visual interfaces. == Overview == Visual analytics is "the science of analytical reasoning facilitated by interactive visual interfaces." It can address problems whose size, complexity, and need for closely coupled human and machine analysis may make them otherwise intractable. Visual analytics advances scientific and technological development across multiple domains, including analytical reasoning, human–computer interaction, data transformations, visual representation for computation and analysis, analytic reporting, and the transition of new technologies into practice. As a research agenda, visual analytics brings together several scientific and technical communities from computer science, information visualization, cognitive and perceptual sciences, interactive design, graphic design, and social sciences. Visual analytics integrates new computational and theory-based tools with innovative interactive techniques and visual representations to enable human-information discourse. The design of the tools and techniques is based on cognitive, design, and perceptual principles. This science of analytical reasoning provides the reasoning framework upon which one can build both strategic and tactical visual analytics technologies for threat analysis, prevention, and response. Analytical reasoning is central to the analyst's task of applying human judgments to reach conclusions from a combination of evidence and assumptions. Visual analytics has some overlapping goals and techniques with information visualization and scientific visualization. There is currently no clear consensus on the boundaries between these fields, but broadly speaking the three areas can be distinguished as follows: Scientific visualization deals with data that has a natural geometric structure (e.g., MRI data, wind flows). Information visualization handles abstract data structures such as trees or graphs. Visual analytics is especially concerned with coupling interactive visual representations with underlying analytical processes (e.g., statistical procedures, data mining techniques) such that high-level, complex activities can be effectively performed (e.g., sense making, reasoning, decision making). Visual analytics seeks to marry techniques from information visualization with techniques from computational transformation and analysis of data. Information visualization forms part of the direct interface between user and machine, amplifying human cognitive capabilities in six basic ways: by increasing cognitive resources, such as by using a visual resource to expand human working memory, by reducing search, such as by representing a large amount of data in a small space, by enhancing the recognition of patterns, such as when information is organized in space by its time relationships, by supporting the easy perceptual inference of relationships that are otherwise more difficult to induce, by perceptual monitoring of a large number of potential events, and by providing a manipulable medium that, unlike static diagrams, enables the exploration of a space of parameter values These capabilities of information visualization, combined with computational data analysis, can be applied to analytic reasoning to support the sense-making process. == History == As an interdisciplinary approach, visual analytics has its roots in information visualization, cognitive sciences, and computer science. The term and scope of the field was defined in the early 2000s through researchers such as Jim Thomas, Kristin A. Cook, John Stasko, Pak Chung Wong, Daniel A. Keim and David S. Ebert. As a reaction to the September 11, 2001 attacks the United States Department of Homeland Security was established in late 2002, combining dozens of previously separated government agencies. Building upon earlier work on visual data mining by Daniel A. Keim starting in the late 1990s, this simultaneously lead to the development of a research agenda for visual analytics. As part of these efforts the National Visualization and Analytics Center (NVAC) at Pacific Northwest National Laboratory was established in 2004, whose charter was to develop system to mitigate information overload after the September 11, 2001 attacks in the intelligence community. Their research work determined core challenges, posed open research questions, and positioned visual analytics as a new research domain, in particular through the 2005 research agenda Illuminating the Path. In 2006, the IEEE VIS community led by Pak Chung Wong and Daniel A. Keim launched the annual IEEE Conference on Visual Analytics Science and Technology (VAST), providing a dedicated venue for research into visual analytics, which in 2020 merged to form the IEEE Visualization conference. In 2008, scope and challenges of visual analytics were conceptually defined by Daniel A. Keim and Jim Thomas in their influential book about visual data mining. The domain was further refined as part of the European Commissions FP7 VisMaster program in the late 2000s. == Topics == === Scope === Visual analytics is a multidisciplinary field that includes the following focus areas: Analytical reasoning techniques that enable users to obtain deep insights that directly support assessment, planning, and decision making Data representations and transformations that convert all types of conflicting and dynamic data in ways that support visualization and analysis Techniques to support production, presentation, and dissemination of the results of an analysis to communicate information in the appropriate context to a variety of audiences. Visual representations and interaction techniques that take advantage of the human eye's broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once. === Analytical reasoning techniques === Analytical reasoning techniques are the method by which users obtain deep insights that directly support situation assessment, planning, and decision making. Visual analytics must facilitate high-quality human judgment with a limited investment of the analysts’ time. Visual analytics tools must enable diverse analytical tasks such as: Understanding past and present situations quickly, as well as the trends and events that have produced current conditions Identifying possible alternative futures and their warning signs Monitoring current events for emergence of warning signs as well as unexpected events Determining indicators of the intent of an action or an individual Supporting the decision maker in times of crisis. These tasks will be conducted through a combination of individual and collaborative analysis, often under extreme time pressure. Visual analytics must enable hypothesis-based and scenario-based analytical techniques, providing support for the analyst to reason based on the available evidence. === Data representations === Data representations are structured forms suitable for computer-based transformations. These structures must exist in the original data or be derivable from the data themselves. They must retain the information and knowledge content and the related context within the original data to the greatest degree possible. The structures of underlying data representations are generally neither accessible nor intuitive to the user of the visual analytics tool. They are frequently more complex in nature than the original data and are not necessarily smaller in size than the original data. The structures of the data representations may contain hundreds or thousands of dimensions and be unintelligible to a person, but they must be transformable into lower-dimensional representations for visualization and analysis. === Theories of visualization === Theories of visualization include: Jacques Bertin's Semiology of Graphics (1967) Nelson Goodman's Languages of Art (1977) Jock D. Mackinlay's Automated design of optimal visualization (APT) (1986) Leland Wilkinson's Grammar of Graphics (1998) Hadley Wickham's Layered Grammar of Graphics (2010) === Visual representations === Visual representations translate data into a visible form that highlights important features, including commonalities and anomalies. These visual representations make it easy for users to perceive salient aspects of their data quickly. Augmenting the cognitive reasoning process with perceptual reasoning through visual representations permits the analytical reasoning process to become faster and more focused. == Process == The input for the data sets used in the visual analytics process are heterogeneous data sources (i.e., the internet, newspapers, books, scientific experiments, expert systems). From these rich sources, the data sets S = S1, ..., Sm are chosen, whereas each Si , i ∈ (1, ..., m) consists of attrib

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  Digital curation

  Digital curation is the selection, preservation, maintenance, collection, and archiving of digital assets. It is a process that establishes, maintains, and adds value to repositories of digital data for present and future use. The implementation of digital curation is often carried out by archivists, librarians, scientists, historians, and scholars to ensure users have access to reliable, high-quality resources. Enterprises are also starting to adopt digital curation as a means to improve the quality of information and data within their operational and strategic processes. A successful digital curation initiative will help to mitigate digital obsolescence, keeping the information accessible to users indefinitely. Digital curation includes various aspects, including digital asset management, data curation, digital preservation, and electronic records management. == Word History == Much like the word archive has layered meanings and uses, the word curation is both a noun and a verb, used originally in the field of museology to represent a wide range of activities, most often associated with collection care, long-term preservation, and exhibition design. Curation can be a reference to physical repositories that store cultural heritage or natural resource collections (e.g., a curatorial repository) or a representation of varied policies and processes involved with the long-term care and management of heritage collections, digital archives, and research data (e.g, curatorial/collections management plans, curation life-cycle, and data curation). Yet curation is also associated with short-term objectives and processes of selection and interpretation for the purposes of presentation, such as for gallery exhibitions and websites, which contribute to knowledge creation. It has also been applied to interaction with social media including compiling digital images, web links, and movie files. The term curation entered the legal framework through federal historic preservation laws, starting with the National Historic Preservation Act of 1966, and was further defined and coded into federal regulations through 36 CFR Part 79: Curation of Federally-owned and Administered Archaeological Collections. Curation has since permeated into an array of disciplines but remains closely tied to heritage and information management. == Core Principles and Activities == The term "digital curation" was first used in the e-science and biological science fields as a means of differentiating the additional suite of activities ordinarily employed by library and museum curators to add value to their collections and enable its reuse from the smaller subtask of simply preserving the data, a significantly more concise archival task. Additionally, the historical understanding of the term "curator" demands more than simple care of the collection. A curator is expected to command academic mastery of the subject matter as a requisite part of appraisal and selection of assets and any subsequent adding of value to the collection through application of metadata. === Principles === There are five commonly accepted principles that govern the occupation of digital curation: Manage the complete birth-to-retirement life cycle of the digital asset. Evaluate and cull assets for inclusion in the collection. Apply preservation methods to strengthen the asset’s integrity and reusability for future users. Act proactively throughout the asset life cycle to add value to both the digital asset and the collection. Facilitate the appropriate degree of access to users. === Methodology === The Digital Curation Center offers the following step-by-step life cycle procedures for putting the above principles into practice: Sequential Actions: Conceptualize: Consider what digital material you will be creating and develop storage options. Take into account websites, publications, email, among other types of digital output. Create: Produce digital material and attach all relevant metadata, typically the more metadata the more accessible the information. Appraise and select: Consult the mission statement of the institution or private collection and determine what digital data is relevant. There may also be legal guidelines in place that will guide the decision process for a particular collection. Ingest: Send digital material to the predetermined storage solution. This may be an archive, repository or other facility. Preservation action: Employ measures to maintain the integrity of the digital material. Store: Secure data within the predetermined storage facility. Access, use, and reuse: Determine the level of accessibility for the range of digital material created. Some material may be accessible only by password and other material may be freely accessible to the public. Routinely check that material is still accessible for the intended audience and that the material has not been compromised through multiple uses. Transform: If desirable or necessary the material may be transferred into a different digital format. Occasional Actions: Dispose: Discard any digital material that is not deemed necessary to the institution. Reappraise: Reevaluate material to ensure that is it still relevant and is true to its original form. Migrate: Migrate data to another format in order to protect data for using better in the future. == Related terms == The term "digital curation" is sometimes used interchangeably with terms such as "digital preservation" and "digital archiving." While digital preservation does focus a significant degree of energy on optimizing reusability, preservation remains a subtask to the concept of digital archiving, which is in turn a subtask of digital curation. For example, archiving is a part of curation, but so are subsequent tasks such as themed collection-building, which is not considered an archival task. Similarly, preservation is a part of archiving, as are the tasks of selection and appraisal that are not necessarily part of preservation. Data curation is another term that is often used interchangeably with digital curation, however common usage of the two terms differs. While "data" is a more all-encompassing term that can be used generally to indicate anything recorded in binary form, the term "data curation" is most common in scientific parlance and usually refers to accumulating and managing information relative to the process of research. Data-driven research of education request the role of information professional gradually develop tradition of digital service to data curation particularly at the management of digital research data. So, while documents and other discrete digital assets are technically a subset of the broader concept of data, in the context of scientific vernacular digital curation represents a broader purview of responsibilities than data curation due to its interest in preserving and adding value to digital assets of any kind. == Challenges == === Rate of creation of new data and data sets === The ever lowering cost and increasing prevalence of entirely new categories of technology has led to a quickly growing flow of new data sets. These come from well established sources such as business and government, but the trend is also driven by new styles of sensors becoming embedded in more areas of modern life. This is particularly true of consumers, whose production of digital assets is no longer relegated strictly to work. Consumers now create wider ranges of digital assets, including videos, photos, location data, purchases, and fitness tracking data, just to name a few, and share them in wider ranges of social platforms. Additionally, the advance of technology has introduced new ways of working with data. Some examples of this are international partnerships that leverage astronomical data to create "virtual observatories," and similar partnerships have also leveraged data resulting from research at the Large Hadron Collider at CERN and the database of protein structures at the Protein Data Bank. === Storage format evolution and obsolescence === By comparison, archiving of analog assets is notably passive in nature, often limited to simply ensuring a suitable storage environment. Digital preservation requires a more proactive approach. Today’s artifacts of cultural significance are notably transient in nature and prone to obsolescence when social trends or dependent technologies change. This rapid progression of technology occasionally makes it necessary to migrate digital asset holdings from one file format to another in order to mitigate the dangers of hardware and software obsolescence which would render the asset unusable. === Underestimation of human labor costs === Modern tools for program planning often underestimate the amount of human labor costs required for adequate digital curation of large collections. As a result cost-benefit assessments often paint an inaccurate picture of both the amount of work involved and the true cost to the institution for bot

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  IT operations analytics

  In the fields of information technology (IT) and systems management, IT operations analytics (ITOA) is an approach or method to retrieve, analyze, and report data for IT operations. ITOA may apply big data analytics to large datasets to produce business insights. In 2014, Gartner predicted its use might increase revenue or reduce costs. By 2017, it predicted that 15% of enterprises will use IT operations analytics technologies. == Definition == IT operations analytics (ITOA) (also known as advanced operational analytics, or IT data analytics) technologies are primarily used to discover complex patterns in high volumes of often "noisy" IT system availability and performance data. Forrester Research defined IT analytics as "The use of mathematical algorithms and other innovations to extract meaningful information from the sea of raw data collected by management and monitoring technologies." Note, ITOA is different than AIOps, which focuses on applying artificial intelligence and machine learning to the applications of ITOA. == History == Operations research as a discipline emerged from the Second World War to improve military efficiency and decision-making on the battlefield. However, only with the emergence of machine learning tech in the early 2000s could an artificially intelligent operational analytics platform actually begin to engage in the high-level pattern recognition that could adequately serve business needs. A critical catalyst towards ITOA development was the rise of Google, which pioneered a predictive analytics model that represented the first attempt to read into patterns of human behavior on the Internet. IT specialists then applied predictive analytics to the IT Industry, coming forward with platforms that can sift through data to generate insights without the need for human intervention. Due to the mainstream embrace of cloud computing and the increasing desire for businesses to adopt more big data practices, the ITOA industry has grown significantly since 2010. A 2016 ExtraHop survey of large and mid-size corporations indicates that 65 percent of the businesses surveyed will seek to integrate their data silos either this year or the next. The current goals of ITOA platforms are to improve the accuracy of their APM services, facilitate better integration with the data, and to enhance their predictive analytics capabilities. == Applications == ITOA systems tend to be used by IT operations teams, and Gartner describes seven applications of ITOA systems: Root cause analysis: The models, structures and pattern descriptions of IT infrastructure or application stack being monitored can help users pinpoint fine-grained and previously unknown root causes of overall system behavior pathologies. Proactive control of service performance and availability: Predicts future system states and the impact of those states on performance. Problem assignment: Determines how problems may be resolved or, at least, direct the results of inferences to the most appropriate individuals, or communities in the enterprise for problem resolution. Service impact analysis: When multiple root causes are known, the analytics system's output is used to determine and rank the relative impact, so that resources can be devoted to correcting the fault in the most timely and cost-effective way possible. Complement best-of-breed technology: The models, structures and pattern descriptions of IT infrastructure or application stack being monitored are used to correct or extend the outputs of other discovery-oriented tools to improve the fidelity of information used in operational tasks (e.g., service dependency maps, application runtime architecture topologies, network topologies). Real time application behavior learning: Learns & correlates the behavior of Application based on user pattern and underlying Infrastructure on various application patterns, create metrics of such correlated patterns and store it for further analysis. Dynamically baselines threshold: Learns behavior of Infrastructure on various application user patterns and determines the Optimal behavior of the Infra and technological components, bench marks and baselines the low and high water mark for the specific environments and dynamically changes the bench mark baselines with the changing infra and user patterns without any manual intervention. == Types == In their Data Growth Demands a Single, Architected IT Operations Analytics Platform, Gartner Research describes five types of analytics technologies: Log analysis Unstructured text indexing, search and inference (UTISI) Topological analysis (TA) Multidimensional database search and analysis (MDSA) Complex operations event processing (COEP) Statistical pattern discovery and recognition (SPDR) == Tools and ITOA platforms == A number of vendors operate in the ITOA space:

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  Screen space directional occlusion

  Screen space directional occlusion (SSDO) is a computer graphics technique enhancing screen space ambient occlusion (SSAO) by taking direction into account to sample the ambient light (both the light coming directly at an object, as well as the light reflected off of the object directly behind it), to better approximate global illumination. SSDO was introduced by Tobias Ritschel, Thorsten Grosch, and Hans-Peter Seidel in their 2009 ACM Symposium on Interactive 3D Graphics and Games paper Approximating dynamic global illumination in image space, which describes it as extending SSAO to directional occlusion with one diffuse indirect bounce of light; later literature notes that SSDO still suffers from common screen-space artifacts such as noise and banding. == Method == The original SSDO paper describes a two-pass screen-space approach, with one pass for direct lighting and a second pass for indirect bounces. Later literature describes SSDO as assuming a general shadowing direction that allows color bleeding and a single light bounce.

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  Glossary of computer graphics

  This is a glossary of terms relating to computer graphics. For more general computer hardware terms, see glossary of computer hardware terms. == 0–9 == 2D convolution Operation that applies linear filtering to image with a given two-dimensional kernel, able to achieve e.g. edge detection, blurring, etc. 2D image 2D texture map A texture map with two dimensions, typically indexed by UV coordinates. 2D vector A two-dimensional vector, a common data type in rasterization algorithms, 2D computer graphics, graphical user interface libraries. 2.5D Also pseudo 3D. Rendering whose result looks 3D while actually not being 3D or having great limitations, e.g. in camera degrees of freedom. 3D graphics pipeline A graphics pipeline taking 3D models and producing a 2D bitmap image result. 3D paint tool A 3D graphics application for digital painting of multiple texture map image channels directly onto a rotated 3D model, such as zbrush or mudbox, also sometimes able to modify vertex attributes. 3D scene A collection of 3D models and lightsources in world space, into which a camera may be placed, describing a scene for 3D rendering. 3D unit vector A unit vector in 3D space. 4D vector A common datatype in graphics code, holding homogeneous coordinates or RGBA data, or simply a 3D vector with unused W to benefit from alignment, naturally handled by machines with 4-element SIMD registers. 4×4 matrix A matrix commonly used as a transformation of homogeneous coordinates in 3D graphics pipelines. 7e3 format A packed pixel format supported by some graphics processing units (GPUs) where a single 32-bit word encodes three 10-bit floating-point color channels, each with seven bits of mantissa and three bits of exponent. == A == AABB Axis-aligned bounding box (sometimes called "axis oriented"), a bounding box stored in world coordinates; one of the simplest bounding volumes. Additive blending A compositing operation where d s t = d s t + s r c , {\displaystyle dst=dst+src,} without the use of an alpha channel, used for various effects. Also known as linear dodge in some applications. Affine texture mapping Linear interpolation of texture coordinates in screen space without taking perspective into account, causing texture distortion. Aliasing Unwanted effect arising when sampling high-frequency signals, in computer graphics appearing e.g. when downscaling images. Antialiasing methods can prevent it. Alpha channel An additional image channel (e.g. extending an RGB image) or standalone channel controlling alpha blending. Ambient lighting An approximation to the light entering a region from a wide range of directions, used to avoid needing an exact solution to the rendering equation. Ambient occlusion (AO) Effect approximating, in an inexpensive way, one aspect of global illumination by taking into account how much ambient light is blocked by nearby geometry, adding visual clues about the shape. Analytic model A mathematical model for a phenomenon to be simulated, e.g. some approximation to surface shading. Contrasts with Empirical models based purely on recorded data. Anisotropic filtering Advanced texture filtering improving on mipmapping, preventing aliasing while reducing blur in textured polygons at oblique angles to the camera. Anti-aliasing Methods for filtering and sampling to avoid visual artifacts associated with the uniform pixel grid in 3D rendering. Array texture A form of texture map containing an array of 2D texture slices selectable by a 3rd 'W' texture coordinate; used to reduce state changes in 3D rendering. Augmented reality Computer-rendered content inserted into the user's view of the real world. AZDO Approaching zero driver overhead, a set of techniques aimed at reducing the CPU overhead in preparing and submitting rendering commands in the OpenGL pipeline. A compromise between the traditional GL API and other high-performance low-level rendering APIs. == B == Back-face culling Culling (discarding) of polygons that are facing backwards from the camera. Baking Performing an expensive calculation offline, and caching the results in a texture map or vertex attributes. Typically used for generating lightmaps, normal maps, or low level of detail models. Barycentric coordinates Three-element coordinates of a point inside a triangle. Beam tracing Modification of ray tracing which instead of lines uses pyramid-shaped beams to address some of the shortcomings of traditional ray tracing, such as aliasing. Bicubic interpolation Extension of cubic interpolation to 2D, commonly used when scaling textures. Bilinear interpolation Linear interpolation extended to 2D, commonly used when scaling textures. Binding Selecting a resource (texture, buffer, etc.) to be referenced by future commands. Billboard A textured rectangle that keeps itself oriented towards the camera, typically used e.g. for vegetation or particle effects. Binary space partitioning (BSP) A data structure that can be used to accelerate visibility determination, used e.g. in Doom engine. Bit depth The number of bits per pixel, sample, or texel in a bitmap image (holding one or more image channels, typical values being 4, 8, 16, 24, 32) Bitmap Image stored by pixels. Bit plane A format for bitmap images storing 1 bit per pixel in a contiguous 2D array; Several such parallel arrays combine to produce the a higher-bit-depth image. Opposite of packed-pixel format. Blend operation A render state controlling alpha blending, describing a formula for combining source and destination pixels. Bone Coordinate systems used to control surface deformation (via Weight maps) during skeletal animation. Typically stored in a hierarchy, controlled by key frames, and other procedural constraints. Bounding box One of the simplest type of bounding volume, consisting of axis-aligned or object-aligned extents. Bounding volume A mathematically simple volume, such as a sphere or a box, containing 3D objects, used to simplify and accelerate spatial tests (e.g. for visibility or collisions). BRDF Bidirectional reflectance distribution functions (BRDFs), empirical models defining 4D functions for surface shading indexed by a view vector and light vector relative to a surface. Bump mapping Technique similar to normal mapping that instead of normal maps uses so called bump maps (height maps). BVH Bounding volume hierarchy is a tree structure on a set of geometric objects. == C == Camera A virtual camera from which rendering is performed, also sometimes referred to as 'eye'. Camera space A space with the camera at the origin, aligned with the viewer's direction, after the application of the world transformation and view transformation. Cel shading Cartoon-like shading effect. Clipping Limiting specific operations to a specific region, usually the view frustum. Clipping plane A plane used to clip rendering primitives in a graphics pipeline. These may define the view frustum or be used for other effects. Clip space Coordinate space in which clipping is performed. Clip window A rectangular region in screen space, used during clipping. A clip window may be used to enclose a region around a portal in portal rendering. CLUT A table of RGB color values to be indexed by a lower-bit-depth image (typically 4–8 bits), a form of vector quantization. Color bleeding Unwanted effect in texture mapping. A color from a border of unmapped region of the texture may appear (bleed) in the mapped result due to interpolation. Color channels The set of channels in a bitmap image representing the visible color components, i.e. distinct from the alpha channel or other information. Color resolution Command buffer A region of memory holding a set of instructions for a graphics processing unit for rendering a scene or portion of a scene. These may be generated manually in bare metal programming, or managed by low level rendering APIs, or handled internally by high level rendering APIs. Command list A group of rendering commands ready for submission to a graphics processing unit, see also Command buffer. Compute API An API for efficiently processing large amounts of data. Compute shader A compute kernel managed by a rendering API, with easy access to rendering resources. Cone tracing Modification of ray tracing which instead of lines uses cones as rays in order to achieve e.g. antialiasing or soft shadows. Connectivity information Indices defining [rendering primitive]s between vertices, possibly held in index buffers. describes geometry as a graph or hypergraph. CSG Constructive solid geometry, a method for generating complex solid models from boolean operations combining simpler modelling primitives. Cube mapping A form of environment reflection mapping in which the environment is captured on a surface of a cube (cube map). Culling Before rendering begins, culling removes objects that don't significantly contribute to the rendered result (e.g. being obscured or outside camera view). == D == Decal A "sticker" picture applied onto a surface (e.g. a

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  DigitaltMuseum

  DigitaltMuseum (lit. 'The Digital Museum') is a website database in Norwegian and Swedish for art, images and cultural history museums. The service was established in 2009 after a trial period. The database is developed and operated by KulturIT. KulturIT ANS was established by the Norwegian Museum of Cultural History and Maihaugen in consultation with the Norwegian Archive, Library and Museum Authority (ABM) in 2007. In 2015, the company underwent a corporate transformation and KulturIT AS was established on 12 February. The website has per 2025 around 2,548,022 images. Many of the images are in the public domain or under Creative Commons licenses and are being imported into Wikimedia Commons. The website's API was developed in 2012. == Institutions == As of 2025, there are 223 collaborating museums. == Mission == DigitaltMuseum aims to make the museums' collections accessible to all interested parties, regardless of time and place. The website aims to facilitate easy use of the collections through various methods including image searches, research, teaching and joint knowledge development. DigitaltMuseum contains collections from several hundred Norwegian and Swedish museums, totalling around five million objects. The website contains both historical images from the areas and themes covered by the museums, as well as images of artefacts from the collections. Parts of the collection have previously only been shown in the museums' exhibitions and books and have therefore rarely or never been shown to the public.

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  Random feature

  Random features (RF) are a technique used in machine learning to approximate kernel methods, introduced by Ali Rahimi and Ben Recht in their 2007 paper "Random Features for Large-Scale Kernel Machines", and extended by. RF uses a Monte Carlo approximation to kernel functions by randomly sampled feature maps. It is used for datasets that are too large for traditional kernel methods like support vector machine, kernel ridge regression, and gaussian process. == Mathematics == === Kernel method === Given a feature map ϕ : R d → V {\textstyle \phi :\mathbb {R} ^{d}\to V} , where V {\textstyle V} is a Hilbert space (more specifically, a reproducing kernel Hilbert space), the kernel trick replaces inner products in feature space ⟨ ϕ ( x i ) , ϕ ( x j ) ⟩ V {\displaystyle \langle \phi (x_{i}),\phi (x_{j})\rangle _{V}} by a kernel function k ( x i , x j ) : R d × R d → R {\displaystyle k(x_{i},x_{j}):\mathbb {R} ^{d}\times \mathbb {R} ^{d}\to \mathbb {R} } Kernel methods replaces linear operations in high-dimensional space by operations on the kernel matrix: K X := [ k ( x i , x j ) ] i , j ∈ 1 : N {\displaystyle K_{X}:=[k(x_{i},x_{j})]_{i,j\in 1:N}} where N {\textstyle N} is the number of data points. === Random kernel method === The problem with kernel methods is that the kernel matrix K X {\textstyle K_{X}} has size N × N {\textstyle N\times N} . This becomes computationally infeasible when N {\textstyle N} reaches the order of a million. The random kernel method replaces the kernel function k {\textstyle k} by an inner product in low-dimensional feature space R D {\textstyle \mathbb {R} ^{D}} : k ( x , y ) ≈ ⟨ z ( x ) , z ( y ) ⟩ {\displaystyle k(x,y)\approx \langle z(x),z(y)\rangle } where z {\textstyle z} is a randomly sampled feature map z : R d → R D {\textstyle z:\mathbb {R} ^{d}\to \mathbb {R} ^{D}} . This converts kernel linear regression into linear regression in feature space, kernel SVM into SVM in feature space, etc. Since we have K X ≈ Z X T Z X {\displaystyle K_{X}\approx Z_{X}^{T}Z_{X}} where Z X = [ z ( x 1 ) , … , z ( x N ) ] {\displaystyle Z_{X}=[z(x_{1}),\dots ,z(x_{N})]} , these methods no longer involve matrices of size O ( N 2 ) {\textstyle O(N^{2})} , but only random feature matrices of size O ( D N ) {\textstyle O(DN)} . == Random Fourier feature == === Radial basis function kernel === The radial basis function (RBF) kernel on two samples x i , x j ∈ R d {\displaystyle x_{i},x_{j}\in \mathbb {R} ^{d}} is defined as k ( x i , x j ) = exp ⁡ ( − ‖ x i − x j ‖ 2 2 σ 2 ) {\displaystyle k(x_{i},x_{j})=\exp \left(-{\frac {\|x_{i}-x_{j}\|^{2}}{2\sigma ^{2}}}\right)} where ‖ x i − x j ‖ 2 {\displaystyle \|x_{i}-x_{j}\|^{2}} is the squared Euclidean distance and σ {\displaystyle \sigma } is a free parameter defining the shape of the kernel. It can be approximated by a random Fourier feature map z : R d → R 2 D {\displaystyle z:\mathbb {R} ^{d}\to \mathbb {R} ^{2D}} : z ( x ) := 1 D [ cos ⁡ ⟨ ω 1 , x ⟩ , sin ⁡ ⟨ ω 1 , x ⟩ , … , cos ⁡ ⟨ ω D , x ⟩ , sin ⁡ ⟨ ω D , x ⟩ ] T {\displaystyle z(x):={\frac {1}{\sqrt {D}}}[\cos \langle \omega _{1},x\rangle ,\sin \langle \omega _{1},x\rangle ,\ldots ,\cos \langle \omega _{D},x\rangle ,\sin \langle \omega _{D},x\rangle ]^{T}} where ω 1 , . . . , ω D {\displaystyle \omega _{1},...,\omega _{D}} are IID samples from the multidimensional normal distribution N ( 0 , σ − 2 I ) {\displaystyle N(0,\sigma ^{-2}I)} . Since cos , sin {\displaystyle \cos ,\sin } are bounded, there is a stronger convergence guarantee by Hoeffding's inequality. === Random Fourier features === By Bochner's theorem, the above construction can be generalized to arbitrary positive definite shift-invariant kernel k ( x , y ) = k ( x − y ) {\displaystyle k(x,y)=k(x-y)} . Define its Fourier transform p ( ω ) = 1 2 π ∫ R d e − j ⟨ ω , Δ ⟩ k ( Δ ) d Δ {\displaystyle p(\omega )={\frac {1}{2\pi }}\int _{\mathbb {R} ^{d}}e^{-j\langle \omega ,\Delta \rangle }k(\Delta )d\Delta } then ω 1 , . . . , ω D {\displaystyle \omega _{1},...,\omega _{D}} are sampled IID from the probability distribution with probability density p {\displaystyle p} . This applies for other kernels like the Laplace kernel and the Cauchy kernel. === Neural network interpretation === Given a random Fourier feature map z {\displaystyle z} , training the feature on a dataset by featurized linear regression is equivalent to fitting complex parameters θ 1 , … , θ D ∈ C {\displaystyle \theta _{1},\dots ,\theta _{D}\in \mathbb {C} } such that f θ ( x ) = R e ( ∑ k θ k e i ⟨ ω k , x ⟩ ) {\displaystyle f_{\theta }(x)=\mathrm {Re} \left(\sum _{k}\theta _{k}e^{i\langle \omega _{k},x\rangle }\right)} which is a neural network with a single hidden layer, with activation function t ↦ e i t {\displaystyle t\mapsto e^{it}} , zero bias, and the parameters in the first layer frozen. In the overparameterized case, when 2 D ≥ N {\displaystyle 2D\geq N} , the network linearly interpolates the dataset { ( x i , y i ) } i ∈ 1 : N {\displaystyle \{(x_{i},y_{i})\}_{i\in 1:N}} , and the network parameters is the least-norm solution: θ ^ = arg ⁡ min θ ∈ C D , f θ ( x k ) = y k ∀ k ∈ 1 : N ‖ θ ‖ {\displaystyle {\hat {\theta }}=\arg \min _{\theta \in \mathbb {C} ^{D},f_{\theta }(x_{k})=y_{k}\forall k\in 1:N}\|\theta \|} At the limit of D → ∞ {\displaystyle D\to \infty } , the L2 norm ‖ θ ^ ‖ → ‖ f K ‖ H {\displaystyle \|{\hat {\theta }}\|\to \|f_{K}\|_{H}} where f K {\displaystyle f_{K}} is the interpolating function obtained by the kernel regression with the original kernel, and ‖ ⋅ ‖ H {\displaystyle \|\cdot \|_{H}} is the norm in the reproducing kernel Hilbert space for the kernel. == Other examples == === Random binning features === A random binning features map partitions the input space using randomly shifted grids at randomly chosen resolutions and assigns to an input point a binary bit string that corresponds to the bins in which it falls. The grids are constructed so that the probability that two points x i , x j ∈ R d {\displaystyle x_{i},x_{j}\in \mathbb {R} ^{d}} are assigned to the same bin is proportional to K ( x i , x j ) {\displaystyle K(x_{i},x_{j})} . The inner product between a pair of transformed points is proportional to the number of times the two points are binned together, and is therefore an unbiased estimate of K ( x i , x j ) {\displaystyle K(x_{i},x_{j})} . Since this mapping is not smooth and uses the proximity between input points, Random Binning Features works well for approximating kernels that depend only on the L 1 {\displaystyle L_{1}} distance between datapoints. === Orthogonal random features === Orthogonal random features uses a random orthogonal matrix instead of a random Fourier matrix. == Historical context == In NIPS 2006, deep learning had just become competitive with linear models like PCA and linear SVMs for large datasets, and people speculated about whether it could compete with kernel SVMs. However, there was no way to train kernel SVM on large datasets. The two authors developed the random feature method to train those. It was then found that the O ( 1 / D ) {\displaystyle O(1/D)} variance bound did not match practice: the variance bound predicts that approximation to within 0.01 {\displaystyle 0.01} requires D ∼ 10 4 {\displaystyle D\sim 10^{4}} , but in practice required only ∼ 10 2 {\displaystyle \sim 10^{2}} . Attempting to discover what caused this led to the subsequent two papers.

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  Zolostays

  Zolostays is a real-tech co-living focused startup that provides ready-to-move rooms/beds. It was founded in 2015 by Nikhil Sikri, Akhil Sikri and Sneha Choudhry. == Overview == During the pandemic, Zolo provided 75 of rent-free accommodation to those who lost their jobs. Zolo uses bulk inventory in usually residential township and ties up with real estate companies to make the rooms/beds available. Zolostays has both revenue sharing and leased model. == History == Zolostays was founded in 2015 to solve the problem of students and young professionals who would move to temporarily go to other cities to study and work and look for affordable housing. In 2020, it was operating in 10 Indian cities. It has four round of funding, with total $98 million.

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  Film-out

  Film-out is the process in the computer graphics, video production and filmmaking disciplines of transferring images or animation from videotape or digital files to a traditional film print. Film-out is a broad term that encompasses the conversion of frame rates, color correction, as well as the actual printing, also called scannior recording. The film-out process is different depending on the regional standard of the master videotape in question – NTSC, PAL, or SECAM – or likewise on the several emerging region-independent formats of high definition video (HD video); thus each type is covered separately, taking into account regional film-out industries, methods and technical considerations. == Live action video == Many modern documentaries and low-budget films are shot on videotape or other digital video media, instead of film stock, and completed as digital video. Video production means substantially lower costs than 16 mm or 35 mm film production on all levels. Until recently, the relatively low cost of video ended when the issue of a theatrical presentation was raised, which required a print for film projection. With the growing presence of digital projection, this is becoming less of a factor. === Standard definition (SD) video === Film-out of standard-definition video – or any source that has an incompatible frame rate – is the up-conversion of video media to film for theatrical viewing. The video-to-film conversion process consists of two major steps: first, the conversion of video into digital film frames which are then stored on a computer or on HD videotape; and secondly, the printing of these digital film frames onto actual film. To understand these two steps, it is important to understand how video and film differ. Film (sound film, at least) has remained unchanged for almost a century and creates the illusion of moving images through the rapid projection of still images, frames, upon a screen, typically 24 per second. Traditional interlaced SD video has no real frame rate, (though the term frame is applied to video, it has a different meaning). Instead, video consists of a very fast succession of horizontal lines that continually cascade down the television screen – streaming top to bottom, before jumping back to the top and then streaming down to the bottom again, repeatedly, almost 60 alternating screen-fulls every second for NTSC, or exactly 50 such screen-fulls per second for PAL and SECAM. Since visual movement in video is infused in this continuous cascade of scan lines, there is no discrete image or real frame that can be identified at any one time. Therefore, when transferring video to film, it is necessary to invent individual film frames, 24 for every second of elapsed time. The bulk of the work done by a film-out company is this first step, creating film frames out of the stream of interlaced video. Each company employs its own (often proprietary) technology for turning interlaced video into high-resolution digital video files of 24 discrete images every second, called 24 progressive video or 24p. The technology must filter out all the visually unappealing artifacting that results from the inherent mismatch between video and film movement. Moreover, the conversion process usually requires human intervention at every edit point of a video program, so that each type of scene can be calibrated for maximum visual quality. The use of archival footage in video especially calls for extra attention. Step two, the scanning to film, is the rote part of the process. This is the mechanical step where lasers print each of the newly created frames of the 24p video, stored on computer files or HD videotape, onto rolls of film. Most companies that do film-out, do all the stages of the process themselves for a lump sum. The job includes converting interlaced video into 24p and often a color correction session – (calibrating the image for theatrical projection), before scanning to physical film, (possibly followed by color correction of the film print made from the digital intermediary) – is offered. At the very least, film-out can be understood as the process of converting interlaced video to 24p and then scanning it to film. ==== NTSC video ==== NTSC is the most challenging of the formats when it comes to standards conversion and, specifically, converting to film prints. NTSC runs at the approximate rate of 29.97 video frames (consisting of two interlaced screen-fulls of scan lines, called fields, per frame) per second. In this way, NTSC resolves actual live action movement at almost – but not quite – 60 alternating half-resolution images every second. Because of this 29.97 rate, no direct correlation to film frames at 24 frames per second can be achieved. NTSC is hardest to reconcile with film, thus motivating its own unique processes. ==== PAL and SECAM video ==== PAL and SECAM run at 25 interlaced video frames per second, which can be slowed down or frame-dropped, then deinterlaced, to correlate frame for frame with film running at 24 actual frames per second. PAL and SECAM are less complex and demanding than NTSC for film-out. PAL and SECAM conversions do agitate, though, with the unpleasant choice between slowing down video (and audio pitch, noticeably) by four percent, from 25 to 24 frames per second, in order to maintain a 1:1 frame match, slightly changing the rhythm and feel of the program; or maintaining original speed by periodically dropping frames, thereby creating jerkiness and possible loss of vital detail in fast-moving action or precise edits. === High definition (HD) digital video === High definition digital video can be shot at a variety of frame rates, including 29.97 interlaced (like NTSC) or progressive; or 25 interlaced (like PAL) or progressive; or even 24-progressive (just like film). HD, if shot in 24-progressive, scans nearly perfectly to film without the need for a frame or field conversion process. Other issues remain though, based on the different resolutions, color spaces, and compression schemes that exist in the high-definition video world. == Computer graphics and animation == Artists working with CGI-Computer-generated imagery animation computers create pictures frame by frame. Once the finished product is done, the frames are outputted, normally in a DPX file. These picture data files can then be put on to film using a film recorder for film out. SGI computers started the high-end CGI-Computer-generated imagery animation systems, but with faster computers and the growth of Linux-based systems, many others are on the market now. Movies fully rendered and animated in CGI such as Toy Story, and Antz utilize the film-out method to produce 35mm copies for archival and release prints. Most CGI work is done in 2K Display resolution files (about the size of QXGA) and then output to the Film-out device for creation of 35 mm elements. With 4K Display resolution digital intermediates on the rise, newer types of film-out recorders are being developed to accept 4k resolution files. A 2K movie requires a Storage Area Network storage several terabytes in size to be properly stored and played out. Computer graphics files are handled the same way but in single frames and may use DPX, TIFF or other file formats. == Digital intermediates == Film-out-recording is the last step of digital intermediate workflow. DPX files that were scanned on a motion picture film scanner are stored on a storage area network (often abbreviated as SAN). The scanned DPX footage is edited and composited-FX on workstations, then mastered back on film. Film restoration is also done this way. A "film intermediate" is an analog variation of a digital intermediate, where a project shot on digital video is printed onto film stock and transferred back to digital video to emulate film. The term was coined after it was used on the Oscar-winning 2012 short film "Curfew". The process was also used on the films Dune (2021) and The Batman (2022). == Images for graphic design and print industries == The days of newspapers and magazines shooting 35mm film are almost gone. Digital cameras can now shoot all the images needed, storing them as files (e.g. JPEG, DPX or another format) that are readily edited prior to use. Once the final copy is approved, it can be filmed out for publishing. Digital stills are not the only way to get pictures used in the graphic design and print industries. Film scanners and computer graphics programs are also common sources for graphic design and print industries. == Types of devices == The following devices are used in film-out processes: CRT recorder. Camera and a special TV display Kinescope – early type Electronic Video Recording or EVR – early type EBR Electron Beam Film Recorder 16 mm by 3M Laser film recorder, like Kodak's high-end Lightning II recorder and Arri's Arrilaser. DLP Film recorder, like Cinevation's real-time Cinevator. == History == Lately it has become possible to transfer video images, inclu

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