AI Data Center Texas

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  • Software diagnosis

    Software diagnosis

    Software diagnosis (also: software diagnostics) refers to concepts, techniques, and tools that allow for obtaining findings, conclusions, and evaluations about software systems and their implementation, composition, behaviour, and evolution. It serves as means to monitor, steer, observe and optimize software development, software maintenance, and software re-engineering in the sense of a business intelligence approach specific to software systems. It is generally based on the automatic extraction, analysis, and visualization of corresponding information sources of the software system. It can also be manually done and not automatic. == Applications == Software diagnosis supports all branches of software engineering, in particular project management, quality management, risk management as well as implementation and test. Its main strength is to support all stakeholders of software projects (in particular during software maintenance and for software re-engineering tasks) and to provide effective communication means for software development projects. For example, software diagnosis facilitates "bridging an essential information gap between management and development, improve awareness, and serve as early risk detection instrument". Software diagnosis includes assessment methods for "perfective maintenance" that, for example, apply "visual analysis techniques to combine multiple indicators for low maintainability, including code complexity and entanglement with other parts of the system, and recent changes applied to the code". == Characteristics == In contrast to manifold approaches and techniques in software engineering, software diagnosis does not depend on programming languages, modeling techniques, software development processes or the specific techniques used in the various stages of the software development process. Instead, software diagnosis aims at analyzing and evaluating the software system in its as-is state and based on system-generated information to bypass any subjective or potentially outdated information sources (e.g., initial software models). For it, software diagnosis combines and relates sources of information that are typically not directly linked. Examples: Source-code metrics are related with software developer activity to gain insight into developer-specific effects on software code quality. System structure and run-time execution traces are correlated to facilitate program comprehension through dynamic analysis in software maintenance tasks. == Principles == The core principle of software diagnosis is to automatically extract information from all available information sources of a given software projects such as source code base, project repository, code metrics, execution traces, test results, etc. To combine information, software-specific data mining, analysis, and visualization techniques are applied. Its strength results, among various reasons, from integrating decoupled information spaces in the scope of a typical software project, for example development and developer activities (recorded by the repository) and code and quality metrics (derived by analyzing source code) or key performance indicators (KPIs). == Examples == Examples of software diagnosis tools include software maps and software metrics. == Critics == Software diagnosis—in contrast to many approaches in software engineering—does not assume that developer capabilities, development methods, programming or modeling languages are right or wrong (or better or worse compared to each other): Software diagnosis aims at giving insight into a given software system and its status regardless of the methods, languages, or models used to create and maintain the system. === Related subjects === Cost estimation in software engineering Programming productivity Rapid application development Software design Software development Software documentation Software map Software release life cycle Systems design Systems Development Life Cycle

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  • Automated journalism

    Automated journalism

    Automated journalism, also known as algorithmic journalism or robot journalism, is a term that attempts to describe modern technological processes that are now in use in the journalistic profession, such as news articles and videos generated by computer programs. There are four main fields of application for automated journalism, namely automated content production, data mining, news dissemination and content optimization. Through generative artificial intelligence, stories are produced automatically by computers rather than human reporters. In the 2020s, generative pre-trained transformers have enabled the generation of articles, simply by providing prompts. Automated journalism is sometimes seen as an opportunity to free journalists from routine reporting, providing them with more time for complex tasks. It also allows efficiency and cost-cutting, alleviating some financial burden that many news organizations face. However, automated journalism is also perceived as a threat to the authorship and quality of news and a threat to the livelihoods of human journalists. == History == Historically, the process involved an algorithm that scanned large amounts of provided data, selected from an assortment of pre-programmed article structures, ordered key points, and inserted details such as names, places, amounts, rankings, statistics, and other figures. These programs interpret, organize, and present data in human-readable ways. The output can also be customized to fit a certain voice, tone, or style. Early implementations were mainly used for stories based on statistics and numerical figures. Common topics include sports recaps, weather, financial reports, real estate analysis, and earnings reviews. Data science and AI companies such as Automated Insights, Narrative Science, United Robots and Monok develop and provide these algorithms to news outlets. In 2016, early adopters included news providers such as the Associated Press, Forbes, ProPublica, and the Los Angeles Times. StatSheet, an online platform covering college basketball, runs entirely on an automated program. In 2006, Thomson Reuters announced their switch to automation to generate financial news stories on its online news platform. Reuters used a tool called Tracer. An algorithm called Quakebot published a story about a 2014 California earthquake on The Los Angeles Times website within three minutes after the shaking had stopped. The Associated Press began using automation to cover 10,000 minor baseball leagues games annually, using a program from Automated Insights and statistics from MLB Advanced Media. Outside of sports, the Associated Press also uses automation to produce stories on corporate earnings. Since 2014, Associated Press has been publishing quarterly financial stories with help from Automated Insights. In May 2020, Microsoft announced that a number of its MSN contract journalists would be replaced by robot journalism. On 8 September 2020, The Guardian published an article entirely written by the neural network GPT-3, although the published fragments were manually picked by a human editor. Agentic Tribune produces all of its news articles automatically using AI. News broadcasters in Kuwait, Greece, South Korea, India, China and Taiwan have presented news with anchors based on generative AI models, prompting concerns about job losses for human anchors and audience trust in news that has historically been influenced by parasocial relationships with broadcasters, content creators or social media influencers. Algorithmically generated anchors have also been used by allies of ISIS for their broadcasts. In 2023, Google reportedly pitched a tool to news outlets that claimed to "produce news stories" based on input data provided, such as "details of current events". Some news company executives who viewed the pitch described it as "[taking] for granted the effort that went into producing accurate and artful news stories." In February 2024, Google launched a program to pay small publishers to write three articles per day using a beta generative AI model. The program does not require the knowledge or consent of the websites that the publishers are using as sources, nor does it require the published articles to be labeled as being created or assisted by these models. Meta AI, a chatbot based on Llama 3 which summarizes news stories, was noted by The Washington Post to copy sentences from those stories without direct attribution and to potentially further decrease the traffic of online news outlets. == Benefits == === Speed === Robot reporters are built to produce large quantities of information at quicker speeds. The Associated Press announced that their use of automation has increased the volume of earnings reports from customers by more than ten times. With software from Automated Insights and data from other companies, they can produce 150 to 300-word articles in the same time it takes journalists to crunch numbers and prepare information. By automating routine stories and tasks, journalists are promised more time for complex jobs such as investigative reporting and in-depth analysis of events. Francesco Marconi of the Associated Press stated that, through automation, the news agency freed up 20 percent of reporters’ time to focus on higher-impact projects. This has also been stated by a spokesperson at Gannett, who stated "By leveraging AI, we are able to expand coverage and enable our journalists to focus on more in-depth reporting." GBH reports that AI tools help increase the reach of news publishers. Mike Carragi, a product manager at Patch, stated that they were able to increase their reach from 1200 communities to 7000 communities in just a few months without the need for new employees solely through the adoption of generative AI. In fact, many communities are served solely by AI generated content, which creates summaries of existing information within the community. === Cost === Automated journalism is cheaper because more content can be produced within less time. It also lowers labour costs for news organizations. Reduced human input means less expenses on wages or salaries, paid leaves, vacations, and employment insurance. Automation serves as a cost-cutting tool for news outlets struggling with tight budgets but still wish to maintain the scope and quality of their coverage. == Concerns == === Authorship === In an automated story, there is often confusion about who should be credited as the author. Several participants of a study on algorithmic authorship attributed the credit to the programmer; others perceived the news organization as the author, emphasizing the collaborative nature of the work. There is also no way for the reader to verify whether an article was written by a robot or human, which raises issues of transparency although such issues also arise with respect to authorship attribution between human authors too. === Credibility and quality === Concerns about the perceived credibility of automated news is similar to concerns about the perceived credibility of news in general. Critics doubt if algorithms are "fair and accurate, free from subjectivity, error, or attempted influence." Again, these issues about fairness, accuracy, subjectivity, error, and attempts at influence or propaganda has also been present in articles written by humans over thousands of years. A common criticism is that machines do not replace human capabilities such as creativity, humour, and critical-thinking. However, as the technology evolves, the aim is to mimic human characteristics. When the UK's Guardian newspaper used an AI to write an entire article in September 2020, commentators pointed out that the AI still relied on human editorial content. Austin Tanney, the head of AI at Kainos said: "The Guardian got three or four different articles and spliced them together. They also gave it the opening paragraph. It doesn’t belittle what it is. It was written by AI, but there was human editorial on that." The largest single study of readers' evaluations of news articles produced with and without the help of automation exposed 3,135 online news consumers to 24 articles. It found articles that had been automated were significantly less comprehensible, in part because they were considered to contain too many numbers. However, the automated articles were evaluated equally on other criteria including tone, narrative flow, and narrative structure. Beyond human evaluation, there are now numerous algorithmic methods to identify machine written articles although some articles may still contain errors that are obvious for a human to identify, they can at times score better with these automatic identifiers than human-written articles. A 2017 Nieman Reports article by Nicola Bruno discusses whether or not machines will replace journalists and addresses concerns around the concept of automated journalism practices. Ultimately, Bruno came to the conclusion that AI would assist journalist

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  • Skyline operator

    Skyline operator

    The skyline operator is the subject of an optimization problem and computes the Pareto optimum on tuples with multiple dimensions. This operator is an extension to SQL proposed by Börzsönyi et al. to filter results from a database to keep only those objects that are not dominated by any other point on all dimensions. The name skyline comes from the view on Manhattan from the Hudson River, where those buildings can be seen that are not hidden by any other. A building is visible if it is not dominated by a building that is taller or closer to the river (two dimensions, distance to the river minimized, height maximized). Another application of the skyline operator involves selecting a hotel for a holiday. The user wants the hotel to be both cheap and close to the beach. However, hotels that are close to the beach may also be expensive. In this case, the skyline operator would only present those hotels that are not worse than any other hotel in both price and distance to the beach. == Formal specification == The skyline operator returns tuples that are not dominated by any other tuple. A tuple dominates another if it is at least as good in all dimensions and better in at least one dimension. Formally, we can think of each tuple as a vector p , q ∈ R n {\displaystyle p,q\in \mathbb {R} ^{n}} . p {\displaystyle p} dominates q {\displaystyle q} (written: p ≻ q {\displaystyle p\succ q} ) if p {\displaystyle p} is at least as good as q {\displaystyle q} in every dimension, and superior in at least one: p ≻ q ⇔ ∀ i ∈ [ n ] . p [ i ] ⪰ q [ i ] ∧ ∃ j ∈ [ n ] . p [ j ] ≻ q [ j ] . {\displaystyle p\succ q\Leftrightarrow \forall i\in [n].p[i]\succeq q[i]\wedge \exists j\in [n].p[j]\succ q[j].} Dominance ( p ≻ q {\displaystyle p\succ q} ) can be defined as any strict partial ordering, for example greater (with ≻:=> {\displaystyle \succ :=>} and ⪰:=≥ {\displaystyle \succeq :=\geq } ) or less (with ≻:=< {\displaystyle \succ :=<} and ⪰:=≤ {\displaystyle \succeq :=\leq } ). Assuming two dimensions and defining dominance in both dimensions as greater, we can compute the skyline in SQL-92 as follows: == Proposed syntax == As an extension to SQL, Börzsönyi et al. proposed the following syntax for the skyline operator: where d1, ... dm denote the dimensions of the skyline and MIN, MAX and DIFF specify whether the value in that dimension should be minimised, maximised or simply be different. Without an SQL extension, the SQL query requires an antijoin with not exists: == Implementation == The skyline operator can be implemented directly in SQL using current SQL constructs, but this has been shown to be very slow in disk-based database systems. Other algorithms have been proposed that make use of divide and conquer, indices, MapReduce and general-purpose computing on graphics cards. Skyline queries on data streams (i.e. continuous skyline queries) have been studied in the context of parallel query processing on multicores, owing to their wide diffusion in real-time decision making problems and data streaming analytics. Exasol features a native implementation.

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  • Five safes

    Five safes

    The Five Safes is a framework for helping make decisions about making effective use of data which is confidential or sensitive. It is mainly used to describe or design research access to statistical data held by government and health agencies, and by data archives such as the UK Data Service. It is not an internationally accepted standard. Two of the Five Safes refer to statistical disclosure control, and so the Five Safes is usually used to contrast statistical and non-statistical controls when comparing data management options. == Concept == The Five Safes proposes that data management decisions be considered as solving problems in five 'dimensions': projects, people, settings, data and outputs. The combination of the controls leads to 'safe use'. These are most commonly expressed as questions, for example: These dimensions are scales, not limits. That is, solutions can have a mix of more or fewer controls in each dimension, but the overall aim of 'safe use' independent of the particular mix. For example, a public use file available for open download cannot control who uses it, where or for what purpose, and so all the control (protection) must be in the data itself. In contrast, a file which is only accessed through a secure environment with certified users can contain very sensitive information: the non-statistical controls allow the data to be 'unsafe'. One academic likened the process to a graphic equalizer, where bass and treble can be combined independently to produce a sound the listener likes, which has proven to be a very useful metaphor. This 2023 Data Foundation webinar is an expert discussion of how the elements interact, including an excellent introductory representation. There is no 'order' to the Five Safes, in that one is necessarily more important than the others. However, Ritchie argued that the 'managerial' controls (projects, people, setting) should be addressed before the 'statistical' controls (data, output). The Five Safes concept is associated with other topics which developed from the same programme at ONS, although these are not necessarily implemented. Safe people is associated with 'active researcher management', while safe outputs is linked with principles-based output statistical disclosure control. The Five Safes is a positive framework, describing what is and is not. The EDRU ('evidence-based, default-open, risk-managed, user-centred') attitudinal model is sometimes used to give a normative context == The 'data access spectrum' == From 2003 the Five Safes was also represented in a simpler form as a 'Data Access Spectrum'. The non-data controls (project, people, setting, outputs) tend to work together, in that organisations often see these as a complementary set of restrictions on access. These can then be contrasted with choices about data anonymisation to present a linear representation of data access options. This presentation is consistent with the idea of 'data as a residual', as well as data protection laws of the time which often characterised data simply as anonymous or not anonymous. A similar idea had already been developed independently in 2001 by Chuck Humphrey of the Canadian RDC network, the 'continuum of access'. More recently, The Open Data Institute has developed a 'Data Spectrum toolkit' which includes industry-specific examples. == History and terminology == The Five Safes was devised in the winter of 2002/2003 by Felix Ritchie at the UK Office for National Statistics (ONS) to describe its secure remote-access Virtual Microdata Laboratory (VML). It was described at this time as the 'VML Security Model'. This was adopted by the NORC data enclave, and more widely in the US, as the 'portfolio model' (although this is now also used to refer to a slightly different legal/statistical/educational breakdown). In 2012 the framework as was still being referred to as the 'VML security model', but its increasing use among non-UK organisations led to the adoption of the more general and informative phrase 'Five Safes'. The original framework only had four safes (projects, people, settings and outputs): the framework was used to describe highly detailed data access through a secure environment, and so the 'data' dimension was irrelevant. From 2007 onwards, 'safe data' was included as the framework was used to a describe a wider range of ONS activities. As the US version was based upon the 2005 specification, some US iterations uses have the original four dimensions (eg). Some discussions, such as the OECD, use the term 'secure' instead 'safe'. However, the use of both these terms can cause presentational problems: less control in a particular dimension could be seen to imply 'unsafe users' or 'insecure settings', for example, which distracts from the main message. Hence, the Australian government uses the term "five data sharing principles". The 'Anonymisation Decision-Making Framework' uses a framework based on the Five Safes but relabelling "projects", "people", and "settings" as "governance", "agency" and "infrastructure", respectively; "Output" is omitted, and "safe use" becomes "functional anonymisation". There is no reference to the Five Safes or any associated literature. The Australian version was required to include references to the Five Safes, and presented it as an alternative without comment. == Application == The framework has had three uses: pedagogical, descriptive, and design. Since 2016, it has also been used, directly and indirectly in legislation. See for more detailed examples. === Pedagogy === The first significant use of the framework, other than internal administrative use, was to structure researcher training courses at the UK Office for National Statistics from 2003. UK Data Archive, Administrative Data Research Network, Eurostat, Statistics New Zealand, the Mexican National Institute of Statistics and Geography, NORC, Statistics Canada and the Australian Bureau of Statistics, amongst others, have also used this framework. Most of these courses are for researchers using restricted-access facilities; the Eurostat courses are unusual in that they are designed for all users of sensitive data. === Description === The framework is often used to describe existing data access solutions (e.g. UK HMRC Data Lab, UK Data Service, Statistics New Zealand) or planned/conceptualised ones (e.g. Eurostat in 2011). An early use was to help identify areas where ONS' still had 'irreducible risks' in its provision of secure remote access. The framework is mostly used for confidential social science data. To date it appears to have made little impact on medical research planning, although it is now included in the revised guidelines on implementing HIPAA regulations in the US, and by Cancer Research UK and the Health Foundation in the UK. It has also been used to describe a security model for the Scottish Health Informatics Programme. === Design === In general the Five Safes has been used to describe solutions post-factum, and to explain/justify choices made, but an increasing number of organisations have used the framework to design data access solutions. For example, the Hellenic Statistical Agency developed a data strategy built around the Five Safes in 2016; the UK Health Foundation used the Five Safes to design its data management and training programmes. Use in the private sector is less common but some organisations have incorporated the Five Safes into consulting services. In 2015 the UK Data Service organized a workshop to encourage data users from the academic and private sectors to think about how to manage confidential research data, using the Five Safes to demonstrate alternative options and best practice. Early adopters for strategic design use were in Australia: both the Australian Bureau of Statistics and the Australian Department of Social Service used the Five Safes as an ex ante design tool. In 2017 the Australian Productivity Commission recommended adopting a version of the framework to support cross-government data sharing and re-use. This underwent extensive consultation and culminated in the DAT Act 2022. Since 2020 the Five Safes has been the overriding framework for the design of new secure facilities and data sharing arrangements in the UK for public health and social sciences. This has been promoted by the Office for Statistics Regulation, the UK Statistics Authority, NHS DIgital, and the research funding bodies Administrative Data Research UK and DARE UK. === Regulation and legislation === Three laws have incorporated the Fives Safes. They are explicit in the South Australian Public Sector (Data Sharing) Act 2016, and implicit in the research provisions of the UK Digital Economy Act 2017. The Australian Data Availability and Transparency Act 2022 renames the Five Safes as the Five Data Sharing Principles.A 2025 statutory review of the DAT Act 2022 found "that the DAT Act has not been effective in achieving its objectives.". The review includes specific referen

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  • World model (artificial intelligence)

    World model (artificial intelligence)

    A world model in artificial intelligence is a machine learning system that builds an internal representation of an environment. The model predicts how that environment changes over time in response to actions. Researchers design world models to help agents plan, reason, and act without constant real-world trial and error. World models differ from systems that merely classify or generate outputs. They simulate dynamics such as physics, object interactions, and causality. Early ideas date to the 1990s. Modern versions power robots, autonomous driving, and interactive video generation. == History == Jürgen Schmidhuber introduced the term world model in machine learning in 1990. He proposed recurrent neural networks that predict future states from observations and use those predictions to train agents. David Ha and Schmidhuber revived the concept in a 2018 paper. Their agents learned to drive virtual cars and play video games inside self-generated simulations. Yann LeCun advanced the idea in a 2022 position paper titled "A Path Towards Autonomous Machine Intelligence". He argued that intelligence requires predictive models of the world rather than pure pattern matching. LeCun proposed the joint embedding predictive architecture (JEPA) as a practical foundation. LeCun and collaborators developed several JEPA variants. V-JEPA 2 reached state-of-the-art performance on video understanding and physical reasoning at the time. It supports zero-shot robot control in unfamiliar environments. Introduced in March 2026, LeWorldModel trains stably end-to-end from raw pixels and uses two loss terms and avoids hand-crafted heuristics. LeCun founded Advanced Machine Intelligence Labs in 2026 to further develop world models. Google DeepMind introduced Genie in 2024. The model learned interactive environments from unlabeled internet videos. Genie 2 followed in late 2024 and added three-dimensional generation. The Genie series set benchmarks for general-purpose simulation. Genie 3 was introduced in August 2025. It produces photorealistic, real-time interactive worlds from text prompts which are displayed at 24 frames per second and explored in real time with text or image prompts. The model supports persistent three-dimensional worlds and real-time interaction. Waymo adopted Genie 3 in February 2026 and used it to create a specialized world model for autonomous driving simulation, called the Waymo World Model. It produces synchronized camera and lidar outputs and creates edge cases that real robotaxis rarely encounter. The edge cases were reported to be unusual by PCMag. General Intuition announced a $133.7 million seed round. World Labs raised $1 billion. AMI raised $1.03 billion. In April 2026, Alibaba announced Happy Oyster, its world model designed for real-time and “flowy” world model. It includes a directing mode for world building based on text and image prompts and a wandering mode for exploring the resulting world. It can generate 3-minute in-world video clips. Also in April, World Labs, co-founded by Li Fei Fei, unveiled Spark 2.0, an open-source 3D Gaussian splatting rendering engine that targets smartphone-class devices. In June 2026, Nvidia released Cosmos 3, a family of open-weight models. It combines previously independent physical reasoning, world simulation, and action generation. Cosmos 3 integrates can process and generate text, image, video, audio, and action sequences. The model employs a Mixture-of-Transformers" (MoT) approach. An autoregressive (AR) transformer handles reasoning and next-token prediction, while a diffusion transformer (DT) does multimodal generation. Encoders (ViT for vision, VAE for visual/audio, and domain-specific for actions) and generate a shared representation space using 3D multi-dimensional rotary position embedding (mRoPE) for spatial and temporal information. The family includes Cosmos3-Nano (16B parameters) for workstations; Cosmos3-Super (64B parameters) for research. == Architecture == World models process raw sensory data such as video frames or lidar scans. They compress this input into compact latent representations. The system then predicts future representations rather than pixel-by-pixel reconstructions. Many modern world models use joint embedding predictive architecture (JEPA). An encoder turns observations into embeddings. A predictor estimates one or a suite of embeddings from the current one and an action. In some cases a critic chooses one embedding as the best result. A regularizer keeps embeddings well-behaved. The model trains by minimizing prediction error in embedding space. This approach avoids the high cost of generating every detail. Some architectures add explicit components. A fast reactive path handles immediate responses. A slower deliberative path performs longer-horizon planning. Video prediction accuracy or robot success rates are key metrics, but do not always predict real-world performance. Generative world models such as Genie 3 combine these with a simulator. They accept text prompts or layouts and output consistent video, lidar, or three-dimensional scenes. World models often train with self-supervised learning. They use large unlabeled datasets of video or robot interactions. Self-supervised learning can speed learning. Reinforcement learning can fine-tune a model for specific tasks. == Applications == World models support robot learning. Agents train inside simulations and transfer skills to the physical world. This reduces the need for dangerous or expensive real-world trials. Autonomous vehicles use world models to test rare events. Waymo's system simulates tornadoes or unusual pedestrian behavior. Companies train planners without putting vehicles on public roads. Interactive entertainment benefits from world models. Genie 3 lets users generate playable environments from simple descriptions. Game studios prototype levels faster. Scientific simulation gains from these models. Researchers model physical systems or biological processes at scale. Planners in logistics or urban design test strategies inside accurate digital twins. == Comparison with large language models == Both world models and large language models (LLMs) use inferencing on their inputs to make predictions. LLMs operate on textual inputs. They predict the next token in text sequences. They excel at language-oriented tasks such as translation or summarization. However, they lack understanding of physics. World models operate on sensor inputs such as pixels. They predict state changes in that data in latent space. This design supports planning and causal reasoning. LLMs generate fluent text but often fail at consistent physical predictions. Their architecture employs transformers with refinements such as mixture of experts. World models divide an inferencing task into work performed by encoders, predictors, simulators, and other pieces. They typically handle multimodal inputs such as video, lidar, radar, and audio, guided by textual prompting. LLMs power chatbots and code assistants. World models drive embodied agents that act in dynamic environments, such as autonomous driving. The two may be combined in hybrid systems. For example, a LLM handles instructions, while a world model manages low-level control. World model proponents such as LeCun claim that because LLMs are trained only on text, they have no ability to predict anything beyond text, such as real-world events. == Benchmarks == World model benchmarks test physical understanding, long-term consistency, planning, and generalization from sensor data. Meta introduced three benchmarks for V-JEPA 2. IntPhys 2 measures a model's ability to detect physics violations. It presents pairs of videos that diverge when one breaks physical rules. Humans score near 100% accuracy. V-JEPA 2 achieves little better than random chance on many conditions. Minimal Video Pairs (MVPBench) tests physical understanding through multiple-choice questions based on short video clips. It probes object interactions and causality. Something-Something tests action recognition. Epic-Kitchens-100 tests human action anticipation. DeepMind benchmark: Interactive evaluation measures consistency over minutes of interaction, memory of off-screen objects, and response to user actions or text prompts. Waymo benchmark: Output generation quality: Metrics include realism, controllability (via text prompts), and usefulness for training planners in simulated worlds. However, pixel reconstruction error rate with episodic rewards often fails. Other: Epic-Kitchens-100 (often measured with Recall@5) Ego4D 50 Salads, Breakfast, etc. Potential benchmarks: Zero-shot transfer to robots Long-horizon planning Implausible prediction rate

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  • Bartels–Stewart algorithm

    Bartels–Stewart algorithm

    In numerical linear algebra, the Bartels–Stewart algorithm is used to numerically solve the Sylvester matrix equation A X − X B = C {\displaystyle AX-XB=C} . Developed by R.H. Bartels and G.W. Stewart in 1971, it was the first numerically stable method that could be systematically applied to solve such equations. The algorithm works by using the real Schur decompositions of A {\displaystyle A} and B {\displaystyle B} to transform A X − X B = C {\displaystyle AX-XB=C} into a triangular system that can then be solved using forward or backward substitution. In 1979, G. Golub, C. Van Loan and S. Nash introduced an improved version of the algorithm, known as the Hessenberg–Schur algorithm. It remains a standard approach for solving Sylvester equations when X {\displaystyle X} is of small to moderate size. == The algorithm == Let X , C ∈ R m × n {\displaystyle X,C\in \mathbb {R} ^{m\times n}} , and assume that the eigenvalues of A {\displaystyle A} are distinct from the eigenvalues of B {\displaystyle B} . Then, the matrix equation A X − X B = C {\displaystyle AX-XB=C} has a unique solution. The Bartels–Stewart algorithm computes X {\displaystyle X} by applying the following steps: 1.Compute the real Schur decompositions R = U T A U , {\displaystyle R=U^{T}AU,} S = V T B T V . {\displaystyle S=V^{T}B^{T}V.} The matrices R {\displaystyle R} and S {\displaystyle S} are block-upper triangular matrices, with diagonal blocks of size 1 × 1 {\displaystyle 1\times 1} or 2 × 2 {\displaystyle 2\times 2} . 2. Set F = U T C V . {\displaystyle F=U^{T}CV.} 3. Solve the simplified system R Y − Y S T = F {\displaystyle RY-YS^{T}=F} , where Y = U T X V {\displaystyle Y=U^{T}XV} . This can be done using forward substitution on the blocks. Specifically, if s k − 1 , k = 0 {\displaystyle s_{k-1,k}=0} , then ( R − s k k I ) y k = f k + ∑ j = k + 1 n s k j y j , {\displaystyle (R-s_{kk}I)y_{k}=f_{k}+\sum _{j=k+1}^{n}s_{kj}y_{j},} where y k {\displaystyle y_{k}} is the k {\displaystyle k} th column of Y {\displaystyle Y} . When s k − 1 , k ≠ 0 {\displaystyle s_{k-1,k}\neq 0} , columns [ y k − 1 ∣ y k ] {\displaystyle [y_{k-1}\mid y_{k}]} should be concatenated and solved for simultaneously. 4. Set X = U Y V T . {\displaystyle X=UYV^{T}.} === Computational cost === Using the QR algorithm, the real Schur decompositions in step 1 require approximately 10 ( m 3 + n 3 ) {\displaystyle 10(m^{3}+n^{3})} flops, so that the overall computational cost is 10 ( m 3 + n 3 ) + 2.5 ( m n 2 + n m 2 ) {\displaystyle 10(m^{3}+n^{3})+2.5(mn^{2}+nm^{2})} . === Simplifications and special cases === In the special case where B = − A T {\displaystyle B=-A^{T}} and C {\displaystyle C} is symmetric, the solution X {\displaystyle X} will also be symmetric. This symmetry can be exploited so that Y {\displaystyle Y} is found more efficiently in step 3 of the algorithm. == The Hessenberg–Schur algorithm == The Hessenberg–Schur algorithm replaces the decomposition R = U T A U {\displaystyle R=U^{T}AU} in step 1 with the decomposition H = Q T A Q {\displaystyle H=Q^{T}AQ} , where H {\displaystyle H} is an upper-Hessenberg matrix. This leads to a system of the form H Y − Y S T = F {\displaystyle HY-YS^{T}=F} that can be solved using forward substitution. The advantage of this approach is that H = Q T A Q {\displaystyle H=Q^{T}AQ} can be found using Householder reflections at a cost of ( 5 / 3 ) m 3 {\displaystyle (5/3)m^{3}} flops, compared to the 10 m 3 {\displaystyle 10m^{3}} flops required to compute the real Schur decomposition of A {\displaystyle A} . == Software and implementation == The subroutines required for the Hessenberg-Schur variant of the Bartels–Stewart algorithm are implemented in the SLICOT library. These are used in the MATLAB control system toolbox. == Alternative approaches == For large systems, the O ( m 3 + n 3 ) {\displaystyle {\mathcal {O}}(m^{3}+n^{3})} cost of the Bartels–Stewart algorithm can be prohibitive. When A {\displaystyle A} and B {\displaystyle B} are sparse or structured, so that linear solves and matrix vector multiplies involving them are efficient, iterative algorithms can potentially perform better. These include projection-based methods, which use Krylov subspace iterations, methods based on the alternating direction implicit (ADI) iteration, and hybridizations that involve both projection and ADI. Iterative methods can also be used to directly construct low rank approximations to X {\displaystyle X} when solving A X − X B = C {\displaystyle AX-XB=C} .

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  • Artificial intelligence in marketing

    Artificial intelligence in marketing

    Artificial intelligence marketing (AI marketing) is a form of marketing that uses artificial intelligence concepts and models such as machine learning, natural language processing, and computer vision to achieve marketing goals. The main difference between AI marketing and traditional forms of marketing reside in the reasoning, which is performed through a computer algorithm rather than a human. Each form of marketing has a different technique to the core of the marketing theory. Traditional marketing directly focuses on the needs of consumers; meanwhile some believe the shift AI may cause will lead marketing agencies to manage consumer needs instead. AI is used in various digital marketing spaces, such as content marketing, email marketing, online advertisement (in combination with machine learning), social media marketing, affiliate marketing, and beyond. == Historical development == AI in marketing has a long history, which goes all the way back to the 1980s. At this time, AI research was focusing on expert systems and robotics. Despite the initial research and the studies that were carried out, AI adoption remained limited. Research on it came to a stop for a while, until research was revived two decades later with the advancement in technology, the rise of big data, and a significant increase in computational power. Eventually, AI became very popular in the marketing world, and caught the eyes of many researchers as well as professionals. A large‐scale bibliometric study covering 1,580 peer‑reviewed papers published between 1982 and 2020 confirms that scholarly output on AI in marketing has surged since 2017, with Expert Systems with Applications emerging as the most prolific outlet. Prior to the application of artificial Intelligence in marketing, there was something called "collaborative filtering". This was used as early as 1998 by Amazon, and one of the first ways companies predicted consumer behavior, which enabled millions of recommendations to different customers. Personalized recommender systems are now widely used, for example to suggest music on Spotify, or TV shows on Netflix. A big milestone in AI marketing happened in 2014, when programmatic ad buying gained much greater popularity. Marketing consists of numerous manual tasks such as researching target markets, insertion orders, and managing high budgets as well as prices. In order to cut costs, and remove the need for these tedious tasks, many companies started to automate the marketing process with AI. In 2015, Google introduced RankBrain, a machine learning component of its search algorithm designed to interpret the intent behind user queries. RankBrain was followed by further AI-based search updates, including BERT in 2019, which improved the understanding of conversational queries, and the Multitask Unified Model (MUM) in 2021, which is multimodal and processes information across 75 languages. These advances shifted search engine optimization practice away from keyword matching toward content that satisfies user intent. Artificial intelligence is increasingly used in marketing to personalize user experiences and automate decision-making. For example, Netflix uses AI algorithms to recommend content based on viewing history, while Sephora employs chatbots to assist customers with product selection and availability. Programmatic advertising platforms like Google Ads leverage machine learning to optimize bidding strategies and target audiences more effectively. These applications demonstrate how AI enhances efficiency, engagement, and conversion rates across digital channels. === Artificial neural networks === An artificial neural network is a form of computer program modeled on the brain and nervous system of humans. Neural networks are composed of a series of interconnected processing neurons that function in unison to achieve certain outcomes. Using “human-like trial and error learning methods neural networks detect patterns existing within a data set ignoring data that is not significant while emphasizing the data which is most influential”. From a marketing perspective, neural networks are a form of software tool used to assist in decision making. Neural networks are effective in gathering and extracting information from large data sources and have the ability to identify cause and effect within tha data. These neural nets through the process of learning, identify relationships and connections between databases. Once knowledge has been accumulated, neural networks can be relied on to provide generalizations and can apply past knowledge and learning to a variety of situations. Neural networks help fulfill the role of marketing companies through effectively aiding in market segmentation and measurement of performance while reducing costs and improving accuracy. Due to their learning ability, flexibility, adaption, and knowledge discovery, neural networks offer many advantages over traditional models. Neural networks can be used to assist in pattern classification, forecasting and marketing analysis. == Tools and uses == Classification of customers can be facilitated through the neural network approach allowing companies to make informed marketing decisions. An example of this was employed by Spiegel Inc., a firm dealing in direct-mail operations that used neural networks to improve efficiencies. Using software developed by NeuralWare Inc., Spiegel identified the demographics of customers who had made a single purchase and those customers who had made repeat purchases. Neural networks where then able to identify the key patterns and consequently identify the customers that were most likely to repeat purchase. Understanding this information allowed Spiegel to streamline marketing efforts, and reduced costs. Sales forecasting “is the process of estimating future events with the goal of providing benchmarks for monitoring actual performance and reducing uncertainty". Artificial intelligence techniques have emerged to facilitate the process of forecasting through increasing accuracy in the areas of demand for products, distribution, employee turnover, performance measurement, and inventory control. An example of forecasting using neural networks is the Airline Marketing Assistant/Tactician; an application developed by BehabHeuristics which allows for the forecasting of passenger demand and consequent seat allocation through neural networks. This system has been used by National air Canada and USAir. Neural networks provide a useful alternative to traditional statistical models due to their reliability, time-saving characteristics and ability to recognize patterns from incomplete or noisy data. Examples of marketing analysis systems includes the Target Marketing System developed by Churchull Systems for Veratex Corporation. This support system scans a market database to identify dormant customers allowing management to make decisions regarding which key customers to target. When performing marketing analysis, neural networks can assist in the gathering and processing of information ranging from consumer demographics and credit history to the purchase patterns of consumers. Predictive analytics is a form of analytics involving the use of historical data and artificial intelligence algorithms to predict future trends and outcomes. It serves as a tool for anticipating and understanding user behavior based on patterns found in data. Predictive analytics uses artificial intelligence machine learning algorithms to recognize and predict patterns within data. Machine learning algorithms analyze the data, recognize patterns, and make predictions through continuous learning and adaptation. Predictive analytics is widely used across businesses and industries as a way to identify opportunities, avoid risks, and anticipate customer needs based on information derived from the analysis of user data. By analyzing historical customer data, artificial intelligence algorithms can deliver relevant and targeted marketing content. Recent systematic reviews show that generative large‑language models such as GPT‑3 and GPT‑4 are now routinely embedded in predictive‑analytics pipelines to mine unstructured market data and anticipate customer intent with greater precision. Personalization engines use artificial intelligence and machine learning to provide content or advertisements that are relevant to the user. User data is gathered, which then gets processed with machine learning, and patterns and trends among the users are identified. Users with shared characteristics or behaviors are then segmented into groups, and the personalization engine adjusts content and advertisements to match each segment's preferences. By processing a large amount of data, personalization engines are able to match users to advertisements and recommendations that align with their interests or preferences. Field evidence from consumer‑goods and electronics firms indicates that AI‑driven personalization can raise

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  • Tuple

    Tuple

    In mathematics, a tuple is a finite sequence (or ordered list) of numbers. More generally, it is a sequence of mathematical objects, called the elements of the tuple. An n-tuple is a tuple of n elements, where n is a non-negative integer. There is only one 0-tuple, called the empty tuple. A 1-tuple and a 2-tuple are commonly called a singleton and an ordered pair, respectively. The term "infinite tuple" is occasionally used for "infinite sequences". Tuples are usually written by listing the elements within parentheses "( )" and separated by commas; for example, (2, 7, 4, 1, 7) denotes a 5-tuple. Other types of brackets are sometimes used, although they may have a different meaning. An n-tuple can be formally defined as the image of a function that has the set of the first n natural numbers as its domain (1, 2, ..., n). Tuples may be also defined from ordered pairs by a recurrence starting from an ordered pair; indeed, an n-tuple can be identified with the ordered pair of its (n − 1) first elements and its nth element, for example, ( ( ( 1 , 2 ) , 3 ) , 4 ) = ( 1 , 2 , 3 , 4 ) {\displaystyle \left(\left(\left(1,2\right),3\right),4\right)=\left(1,2,3,4\right)} . In computer science, tuples come in many forms. Most typed functional programming languages implement tuples directly as product types, tightly associated with algebraic data types, pattern matching, and destructuring assignment. Many programming languages offer an alternative to tuples, known as record types, featuring unordered elements accessed by label. A few programming languages combine ordered tuple product types and unordered record types into a single construct, as in C structs and Haskell records. Relational databases may formally identify their rows (records) as tuples. Tuples also occur in relational algebra; when programming the semantic web with the Resource Description Framework (RDF); in linguistics; and in philosophy. == Etymology == The term originated as an abstraction of the sequence: single, couple/double, triple, quadruple, quintuple, sextuple, septuple, octuple, ..., n‑tuple, ..., where the prefixes are taken from the Latin names of the numerals. The unique 0-tuple is called the null tuple or empty tuple. A 1‑tuple is called a single (or singleton), a 2‑tuple is called an ordered pair or couple, and a 3‑tuple is called a triple (or triplet). The number n can be any nonnegative integer. For example, a complex number can be represented as a 2‑tuple of reals, a quaternion can be represented as a 4‑tuple, an octonion can be represented as an 8‑tuple, and a sedenion can be represented as a 16‑tuple. Although these uses treat ‑tuple as the suffix, the original suffix was ‑ple as in "triple" (three-fold) or "decuple" (ten‑fold). This originates from medieval Latin plus (meaning "more") related to Greek ‑πλοῦς, which replaced the classical and late antique ‑plex (meaning "folded"), as in "duplex". == Properties == The general rule for the identity of two n-tuples is ( a 1 , a 2 , … , a n ) = ( b 1 , b 2 , … , b n ) {\displaystyle (a_{1},a_{2},\ldots ,a_{n})=(b_{1},b_{2},\ldots ,b_{n})} if and only if a 1 = b 1 , a 2 = b 2 , … , a n = b n {\displaystyle a_{1}=b_{1},{\text{ }}a_{2}=b_{2},{\text{ }}\ldots ,{\text{ }}a_{n}=b_{n}} . Thus a tuple has properties that distinguish it from a set: A tuple may contain multiple instances of the same element, so tuple ( 1 , 2 , 2 , 3 ) ≠ ( 1 , 2 , 3 ) {\displaystyle (1,2,2,3)\neq (1,2,3)} ; but set { 1 , 2 , 2 , 3 } = { 1 , 2 , 3 } {\displaystyle \{1,2,2,3\}=\{1,2,3\}} . Tuple elements are ordered: tuple ( 1 , 2 , 3 ) ≠ ( 3 , 2 , 1 ) {\displaystyle (1,2,3)\neq (3,2,1)} , but set { 1 , 2 , 3 } = { 3 , 2 , 1 } {\displaystyle \{1,2,3\}=\{3,2,1\}} . A tuple has a finite number of elements, while a set or a multiset may have an infinite number of elements. == Definitions == There are several definitions of tuples that give them the properties described in the previous section. === Tuples as functions === The 0 {\displaystyle 0} -tuple may be identified as the empty function. For n ≥ 1 , {\displaystyle n\geq 1,} the n {\displaystyle n} -tuple ( a 1 , … , a n ) {\displaystyle \left(a_{1},\ldots ,a_{n}\right)} may be identified with the surjective function F : { 1 , … , n } → { a 1 , … , a n } {\displaystyle F~:~\left\{1,\ldots ,n\right\}~\to ~\left\{a_{1},\ldots ,a_{n}\right\}} with domain domain ⁡ F = { 1 , … , n } = { i ∈ N : 1 ≤ i ≤ n } {\displaystyle \operatorname {domain} F=\left\{1,\ldots ,n\right\}=\left\{i\in \mathbb {N} :1\leq i\leq n\right\}} and with codomain codomain ⁡ F = { a 1 , … , a n } , {\displaystyle \operatorname {codomain} F=\left\{a_{1},\ldots ,a_{n}\right\},} that is defined at i ∈ domain ⁡ F = { 1 , … , n } {\displaystyle i\in \operatorname {domain} F=\left\{1,\ldots ,n\right\}} by F ( i ) := a i . {\displaystyle F(i):=a_{i}.} That is, F {\displaystyle F} is the function defined by 1 ↦ a 1 ⋮ n ↦ a n {\displaystyle {\begin{alignedat}{3}1\;&\mapsto &&\;a_{1}\\\;&\;\;\vdots &&\;\\n\;&\mapsto &&\;a_{n}\\\end{alignedat}}} in which case the equality ( a 1 , a 2 , … , a n ) = ( F ( 1 ) , F ( 2 ) , … , F ( n ) ) {\displaystyle \left(a_{1},a_{2},\dots ,a_{n}\right)=\left(F(1),F(2),\dots ,F(n)\right)} necessarily holds. Tuples as sets of ordered pairs Functions are commonly identified with their graphs, which is a certain set of ordered pairs. Indeed, many authors use graphs as the definition of a function. Using this definition of "function", the above function F {\displaystyle F} can be defined as: F := { ( 1 , a 1 ) , … , ( n , a n ) } . {\displaystyle F~:=~\left\{\left(1,a_{1}\right),\ldots ,\left(n,a_{n}\right)\right\}.} === Tuples as nested ordered pairs === Another way of modeling tuples in set theory is as nested ordered pairs. This approach assumes that the notion of ordered pair has already been defined. The 0-tuple (i.e. the empty tuple) is represented by the empty set ∅ {\displaystyle \emptyset } . An n-tuple, with n > 0, can be defined as an ordered pair of its first entry and an (n − 1)-tuple (which contains the remaining entries when n > 1): ( a 1 , a 2 , a 3 , … , a n ) = ( a 1 , ( a 2 , a 3 , … , a n ) ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=(a_{1},(a_{2},a_{3},\ldots ,a_{n}))} This definition can be applied recursively to the (n − 1)-tuple: ( a 1 , a 2 , a 3 , … , a n ) = ( a 1 , ( a 2 , ( a 3 , ( … , ( a n , ∅ ) … ) ) ) ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=(a_{1},(a_{2},(a_{3},(\ldots ,(a_{n},\emptyset )\ldots ))))} Thus, for example: ( 1 , 2 , 3 ) = ( 1 , ( 2 , ( 3 , ∅ ) ) ) ( 1 , 2 , 3 , 4 ) = ( 1 , ( 2 , ( 3 , ( 4 , ∅ ) ) ) ) {\displaystyle {\begin{aligned}(1,2,3)&=(1,(2,(3,\emptyset )))\\(1,2,3,4)&=(1,(2,(3,(4,\emptyset ))))\\\end{aligned}}} A variant of this definition starts "peeling off" elements from the other end: The 0-tuple is the empty set ∅ {\displaystyle \emptyset } . For n > 0: ( a 1 , a 2 , a 3 , … , a n ) = ( ( a 1 , a 2 , a 3 , … , a n − 1 ) , a n ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=((a_{1},a_{2},a_{3},\ldots ,a_{n-1}),a_{n})} This definition can be applied recursively: ( a 1 , a 2 , a 3 , … , a n ) = ( ( … ( ( ( ∅ , a 1 ) , a 2 ) , a 3 ) , … ) , a n ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=((\ldots (((\emptyset ,a_{1}),a_{2}),a_{3}),\ldots ),a_{n})} Thus, for example: ( 1 , 2 , 3 ) = ( ( ( ∅ , 1 ) , 2 ) , 3 ) ( 1 , 2 , 3 , 4 ) = ( ( ( ( ∅ , 1 ) , 2 ) , 3 ) , 4 ) {\displaystyle {\begin{aligned}(1,2,3)&=(((\emptyset ,1),2),3)\\(1,2,3,4)&=((((\emptyset ,1),2),3),4)\\\end{aligned}}} === Tuples as nested sets === Using Kuratowski's representation for an ordered pair, the second definition above can be reformulated in terms of pure set theory: The 0-tuple (i.e. the empty tuple) is represented by the empty set ∅ {\displaystyle \emptyset } ; Let x {\displaystyle x} be an n-tuple ( a 1 , a 2 , … , a n ) {\displaystyle (a_{1},a_{2},\ldots ,a_{n})} , and let x → b ≡ ( a 1 , a 2 , … , a n , b ) {\displaystyle x\rightarrow b\equiv (a_{1},a_{2},\ldots ,a_{n},b)} . Then, x → b ≡ { { x } , { x , b } } {\displaystyle x\rightarrow b\equiv \{\{x\},\{x,b\}\}} . (The right arrow, → {\displaystyle \rightarrow } , could be read as "adjoined with".) In this formulation: ( ) = ∅ ( 1 ) = ( ) → 1 = { { ( ) } , { ( ) , 1 } } = { { ∅ } , { ∅ , 1 } } ( 1 , 2 ) = ( 1 ) → 2 = { { ( 1 ) } , { ( 1 ) , 2 } } = { { { { ∅ } , { ∅ , 1 } } } , { { { ∅ } , { ∅ , 1 } } , 2 } } ( 1 , 2 , 3 ) = ( 1 , 2 ) → 3 = { { ( 1 , 2 ) } , { ( 1 , 2 ) , 3 } } = { { { { { { ∅ } , { ∅ , 1 } } } , { { { ∅ } , { ∅ , 1 } } , 2 } } } , { { { { { ∅ } , { ∅ , 1 } } } , { { { ∅ } , { ∅ , 1 } } , 2 } } , 3 } } {\displaystyle {\begin{array}{lclcl}()&&&=&\emptyset \\&&&&\\(1)&=&()\rightarrow 1&=&\{\{()\},\{(),1\}\}\\&&&=&\{\{\emptyset \},\{\emptyset ,1\}\}\\&&&&\\(1,2)&=&(1)\rightarrow 2&=&\{\{(1)\},\{(1),2\}\}\\&&&=&\{\{\{\{\emptyset \},\{\emptyset ,1\}\}\},\\&&&&\{\{\{\emptyset \},\{\emptyset ,1\}\},2\}\}\\&&&&\\(1,2,3)&=&(1,2)\rightarrow 3&=&\{\{(1,2)\},\{(1,2),3\}\}\\&&&=&\{\{\{\{\{\{\empty

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  • ClearForest

    ClearForest

    ClearForest was an Israeli software company that developed and marketed text analytics and text mining solutions. == History == Founded in 1998, ClearForest had its headquarters just outside Boston and a development center in Or Yehuda. The company was acquired by Reuters in April, 2007. It now markets its services under the names Calais, OpenCalais, and OneCalais. ClearForest was previously venture-backed; its last funding round was led by Greylock Ventures and closed in 2005. Other investors included DB Capital Partners, Pitango, Walden Israel, Booz Allen, JP Morgan Partners and HarbourVest Partners. On February 7, 2008 Reuters announced the launch of Open Calais, a named-entity recognition and semantic analysis service that uses ClearForest technology. On April 30, 2007, Reuters announced that it would acquire ClearForest. Sources estimate the acquisition to be for $25 Million. == Solutions and products == ClearForest offers several hosted solutions, including: OpenCalais, a free web service and open API (for commercial and non-commercial use) that performs named-entity recognition and enables automatic metadata generation using the ClearForest financial module. Semantic Web Services (SWS), an on-demand service that makes ClearForest's natural language processing tools available as a standard web service. A subset of ClearForest's capabilities is available via SWS at no cost. Gnosis, a free Firefox extension that uses SWS to analyze the content of a web page. Gnosis identifies named entities such as people, companies, organizations, geographies and products on the page being viewed. Gnosis also automatically processes pages from Wikipedia, providing additional links for people, geographies and other entities which were not explicitly linked within the subject article. Harvest, a real-time machine-readable news service that uses SWS to process a company's news and document feeds and return machine-readable information about people, companies, locations and over 200 other entities facts and events. ClearForest also offers Text Analytics solutions targeted at specific business problems, including: Equity valuation for hedge funds and alternative investments firms Metadata & database creation for publishers and information providers/services Tapping "voice of customer" for market and survey research firms Quality Early Warning for vehicle, capital equipment & durable goods manufacturers

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  • Pointer algorithm

    Pointer algorithm

    In computer science, a pointer algorithm (sometimes called a pointer machine, or a reference machine; see the article Pointer machine for a close but non-identical concept) is a type of algorithm that manages a linked data structure. This concept is used as a model for lower-bound proofs and specific restrictions on the linked data structure and on the algorithm's access to the structure vary. This model has been used extensively with problems related to the disjoint-set data structure. Thus, Tarjan and La Poutré used this model to prove lower bounds on the amortized complexity of a disjoint-set data structure (La Poutré also addressed the interval split-find problem). Blum used this model to prove a lower bound on the single operation worst-case time of disjoint set data structure. Blum and Rochow proved a worst-case lower bound for the interval union-find problem. == Example == In Tarjan's lower bound for the disjoint set union problem, the assumptions on the algorithm are: The algorithm maintains a linked structure of nodes. Each element of the problem is associated with a node. Each set is represented by a node. The nodes of each set constitute a distinct connected component in the structure (this property is called separability). The find operation is performed by following links from the element node to the set node. Under these assumptions, the lower bound of Ω ( m α ( m , n ) ) {\displaystyle \Omega (m\alpha (m,n))} on the cost of a sequence of m operations is proven.

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  • E-Science librarianship

    E-Science librarianship

    E-Science librarianship refers to a role for librarians in e-Science. == Early scholars == Early references to e-Science and librarianship involve information studies scholars researching cyberinfrastructure and emerging networked information and knowledge communities. Notably Christine Borgman, Professor and Presidential Chair in Information Studies at the University of California, Los Angeles (UCLA) was a key player in bringing e-Science, and the idea of networked knowledge communities, to the attention of the library profession. In 2004, as a visiting fellow at the Oxford Internet Institute, she conducted research and lectured publicly on e-Science, Digital Libraries, and Knowledge Communities. In 2007 Anna K. Gold, formerly of MIT and Cal Poly, San Luis Obispo, authored a series of articles in D-Lib Magazine that opened the door for academic libraries to begin exploring roles, skills, and strategies for engaging in e-Science: Cyberinfrastructure, Data, and Libraries, Part 1: A Cyberinfrastructure Primer for Librarians and Cyberinfrastructure, Data, and Libraries, Part 2: Libraries and the Data Challenge: Roles and Actions for Libraries. == Academic research and health sciences libraries == In 2007, the Association of Research Libraries (ARL) e-Science task force issued its report on e-Science and librarianship. The ARL's report encouraged its member libraries to position themselves to engage with researchers involved in e-Science (eScience) by cultivating new research support strategies and developing their digital scholarship infrastructure. E-Science has multiple attributes; Tony and Jessie Hey framed e-Science for the library community by characterizing it as a research methodology: "e-Science is not a new scientific discipline in its own right: e-Science is shorthand for the set of tools and technologies required to support collaborative, networked science". In addition to academic libraries' interests in providing support for their researchers engaging in e-Science, the health sciences library community also emerged as a major proponent for creating librarian positions for supporting the information needs of large-scale, networked, research collaborations on their campuses. Neil Rambo, current director of NYU's Health Sciences Library and former director of University of Washington Health Sciences Library, was the first to use the term in the Journal of the Medical Library Association, in his 2009 editorial e-Science and the Biomedical Library. Rambo's definition of e-Science highlighted the potential e-Science held for creating data as a research product: "E-science is a new research methodology, fueled by networked capabilities and the practical possibility of gathering and storing vast amounts of data." In response to this article the University of Massachusetts Medical School Lamar Soutter Library and National Network of Libraries of Medicine, New England Region encouraged health sciences libraries to cooperate to identify skills and develop a program for training e-Science Librarians. Then, in 2013, Shannon Bohle, an archivist who was employed in the library at Cold Spring Harbor Laboratory, an NCI-designated basic cancer research facility, used experience gained there and previous papers and presentations about preserving scientific archival materials to expand the traditional definition of e-Science by including the terms, principles, and practices used in archival science. These included in the definition the "long-term storage and accessibility of all materials generated through the scientific process," as well as examples of material types traditionally preserved in archives, like "electronic/digitized laboratory notebooks, raw and fitted data sets, manuscript production and draft versions, pre-prints," as well as library materials ("print and/or electronic publications"). == Roles == Many areas of science are about to be transformed by the availability of vast amounts of new scientific data that can potentially provide insights at a level of detail never before envisaged. However, this new data dominant era brings new challenges for the scientists and they will need the skills and technologies both of computer scientists and of the library community to manage, search and curate these new data resources. Libraries will not be immune from change in this new world of research. Karen Williams identifies roles in the following areas for librarians in the developing world of e-Science. Campus Engagement Content/Collection Development and Management Teaching and Learning Scholarly Communication E-Scholarship and Digital Tools Reference/Help Services Outreach Fund Raising Exhibit and Event Planning Leadership == Challenges for research libraries == E-science tends toward inter- and multidisciplinary approaches that depend on computation and computer science. Research libraries have traditionally been discipline focused and, although increasingly technologically sophisticated, do not have systems of the scale or complexity of the e-science environment. E-science is data intensive, but research libraries have not typically been responsible for scientific data. E-science is frequently conducted in a team context, often distributed across multiple institutions and on a global scale. The primary constituency of libraries generally comprises those affiliated with the local institution. Licenses for electronic content are typically restricted to a particular institutional community, and the infrastructure to move institutional licenses into a multi-institutional environment is not well developed. E-science challenges all these traditional paradigms of research library organization and services. == Skills == Garritano & Carlson were among the first to outline a skill set for librarians seeking to support the data needs of e-Science; they identified five skill categories librarians new to this area should expect to adapt or develop when participating on such projects: Library and information science expertise Subject expertise Partnerships and outreach (both internal and external) Participating in sponsored research Balancing workload An example of librarians reconfiguring traditional librarian skills to meet the needs of researchers engaging in e-Science is Witt & Carlson's adaptation of the traditional reference interview into a "data interview" in order to provide effective data management and e-Science services. This interview consists of ten practical queries necessary for understanding the provenance and expectations for the preservation of datasets typical of e-Science that also help illustrate some of the educational tools and skills needed by a librarian new to e-Science. "What is the story of the data? What form and format are the data in? What is the expected lifespan of the dataset? How could the data be used, reused, and repurposed? How large is the dataset, and what is its rate of growth? Who are the potential audiences for the data? Who owns the data? Does the dataset include any sensitive information? What publications or discoveries have resulted from the data? How should the data be made accessible?" == Resources == In 2009 the Lamar Soutter Library at the University of Massachusetts Medical School (UMMS) and the National Network of Libraries of Medicine, New England Region (NN/LM NER) funded an e-Science program for building the skills highlighted above for librarians. Elaine Russo Martin, Director of Library Services at the Lamar Soutter Library and Director of the NN/LM NER developed this comprehensive e-Science program to build librarians' subject expertise in the sciences, developing their data management skills, and their familiarity with cyberinfrastructure and e-Science. Three major products of this program are the e-Science web portal for librarians, the E-Science Symposium, and the New England Collaborative Data Management Curriculum (NECDMC). This portal includes educational resources for specific tools and subject/discipline tutorials and modules to assist librarians new to e-Science. UMMS and NN/LM NER also publish an open access journal called the Journal of eScience Librarianship.

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  • Informationist

    Informationist

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

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  • Shepp–Logan phantom

    Shepp–Logan phantom

    The Shepp–Logan phantom is a standard test image created by Larry Shepp and Benjamin F. Logan for their 1974 paper "The Fourier Reconstruction of a Head Section". It serves as the model of a human head in the development and testing of image reconstruction algorithms. == Definition == The function describing the phantom is defined as the sum of 10 ellipses inside a 2×2 square:

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  • Literature review

    Literature review

    A literature review is an overview of previously published works on a particular topic. The term can refer to a full scholarly paper or a section of a scholarly work such as books or articles. Either way, a literature review provides the researcher/author and the audiences with general information of an existing knowledge of a particular topic. A good literature review has a proper research question, a proper theoretical framework, and/or a chosen research method. It serves to situate the current study within the body of the relevant literature and provides context for the reader. In such cases, the review usually precedes the methodology and results sections of the work. Producing a literature review is often part of a graduate and post-graduate requirement, included in the preparation of a thesis, dissertation, or a journal article. Literature reviews are also common in a research proposal or prospectus (the document approved before a student formally begins a dissertation or thesis). A literature review can be a type of a review article. In this sense, it is a scholarly paper that presents the current knowledge including substantive findings as well as theoretical and methodological contributions to a particular topic. Literature reviews are secondary sources and do not report new or original experimental work. Most often associated with academic-oriented literature, such reviews are found in academic journals and are not to be confused with book reviews, which may also appear in the same publication. Literature reviews are a basis for research in nearly every academic field. == Types == Since the concept of a systematic review was formalized in the 1970s, a basic division among types of reviews is the dichotomy of narrative reviews versus systematic reviews. The main types of narrative reviews are evaluative, exploratory, and instrumental. A fourth type of review of literature (the scientific literature) is the systematic review but it is not called a literature review, which absent further specification, conventionally refers to narrative reviews. A systematic review focuses on a specific research question to identify, appraise, select, and synthesize all high-quality research evidence and arguments relevant to that question. A meta-analysis is typically a systematic review using statistical methods to effectively combine the data used on all selected studies to produce a more reliable result. Torraco (2016) describes an integrative literature review. The purpose of an integrative literature review is to generate new knowledge on a topic through the process of review, critique, and synthesis of the literature under investigation. George et al (2023) offer an extensive overview of review approaches. They also propose a model for selecting an approach by looking at the purpose, object, subject, community, and practices of the review. They describe six different types of review, each with their own unique purposes: Exploratory or scoping reviews focus on breadth as opposed to depth Systematic or integrative reviews integrate empirical studies on a topic Meta-narrative reviews are qualitative and use literature to compare research or practice communities Problematizing or critical reviews propose new perspectives on a concept by association with other literature Meta-analyses and meta-regressions integrate quantitative studies and identify moderators Mixed research syntheses combine other review approaches in the same paper == Process and product == Shields and Rangarajan (2013) distinguish between the process of reviewing the literature and a finished work or product known as a literature review. The process of reviewing the literature is often ongoing and informs many aspects of the empirical research project. The process of reviewing the literature requires different kinds of activities and ways of thinking. Shields and Rangarajan (2013) and Granello (2001) link the activities of doing a literature review with Benjamin Bloom's revised taxonomy of the cognitive domain (ways of thinking: remembering, understanding, applying, analyzing, evaluating, and creating). === Use of artificial intelligence in a literature review === Artificial intelligence (AI) is reshaping traditional literature reviews across various disciplines. Generative pre-trained transformers, such as ChatGPT, are often used by students and academics for review purposes. Since 2023, an increasing number of tools powered by large language models and other artificial intelligence technologies have been developed to assist, automate, or generate literature reviews. Nevertheless, the employment of ChatGPT in academic reviews is problematic due to ChatGPT's propensity to "hallucinate". In response, efforts are being made to mitigate these hallucinations through the integration of plugins. For instance, Rad et al. (2023) used ScholarAI for review in cardiothoracic surgery.

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  • Quality of Data

    Quality of Data

    Quality of Data (QoD) is a designation coined by L. Veiga, that specifies and describes the required Quality of Service of a distributed storage system from the Consistency point of view of its data. It can be used to support big data management frameworks, Workflow management, and HPC systems (mainly for data replication and consistency). It takes into account data semantics, namely the Time interval of data freshness, the Sequence of tolerable number of outstanding versions of the data read before ore refresh, and the Value divergence allowed before displaying it. Initially it was based on a model from an existing research work regarding vector-field Consistency, awarded the best-paper prize in the ACM/IFIP/Usenix Middleware Conference 2007 and later enhanced for increased scalability and fault-tolerance. This consistency model has been successfully applied and proven in big data key/value store Apache HBase, initially designed as a middleware module seating between clusters from separate data centres. The HBase-QoD coupling minimises bandwidth usage and optimises resources allocation during replication achieving the desired consistency level at a more fine-grained level. QoD is defined by the three-dimensions of vector k=(θ,σ,ν), but with a broader view of the issue, applicable also to large-scale data management techniques in regards to their timely delivery. == Other descriptions == Quality of Data should not be confused with other definitions for data quality such as completeness, validity, and accuracy.

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