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  Space-based data center

  Space-based data centers or orbital AI infrastructure are proposed concepts to build AI data centers in the sun-synchronous orbit or other orbits utilizing space-based solar power. Electric power has become the main bottleneck for terrestrial AI infrastructure. Space-based edge computing has historical roots in military architectures designed to bypass the latency of ground-based targeting networks. In the 1980s, the Strategic Defense Initiative's Brilliant Pebbles program first envisioned autonomous on-orbit data processing for missile defense. In 2019, the Space Development Agency (SDA) began to revive this decentralized approach through its Proliferated Warfighter Space Architecture (PWSA). This ambitious "sensor-to-shooter" infrastructure is treated as a prerequisite for the modern Golden Dome program, which would rely on space-based data processing to continuously track targets. == History == Early thinking about space-based computing infrastructure grew out of mid-20th-century visions for large orbital industrial systems, most notably proposals for space-based solar power, which were popularized in both technical literature and science writing by figures such as Isaac Asimov in the 1940s. These ideas emphasized exploiting the vacuum, continuous solar energy, and thermal characteristics of space to support power-intensive activities that would be difficult or inefficient on Earth. In the 21st century, advances in small satellites, reusable launch vehicles, and high-performance computing revived interest in space-based data centers, with governments and private companies exploring orbital or near-space platforms for edge computing, secure data handling, and low-latency processing of Earth-observation data. In September 2024, Y Combinator-backed Starcloud released a white paper detailing plans to build multiple gigawatts of AI compute in orbit. It was the first widely cited proposal to actually start building large orbital data centers. In 2025, Starcloud deployed an NVIDIA H100-class system and became the first company to train an LLM in space and run a version of Google Gemini in space. In March 2025, Lonestar deployed a data backup machine on the surface of the moon. In early January 2026, a team from the University of Pennsylvania presented a tether-based architecture for orbital data centers at the AIAA SciTech conference. The design relied on gravity gradient tension and solar-pressure-based passive attitude stabilization to minimize the mass of MW-scale orbital data centers. In January 2026, SpaceX filed plans with the Federal Communications Commission (FCC) for millions of satellites, leveraging reusable launches and Starlink integration to extend cloud and AI computing into orbit. Around the same time, Blue Origin announced the TeraWave constellation of about 5,400 satellites, designed to provide high‑throughput networking for data centers, enterprise, and government customers. Meanwhile, China announced a 200,000‑satellite constellation, focusing on state coordination, data sovereignty, and in-orbit processing for secure, time-critical applications. In February 2026, Starcloud submitted a proposal to the FCC for a constellation of up to 88,000 satellites for orbital data centers. In March, it announced intentions to be the first to mine Bitcoin in space, flying bitcoin mining ASICs on its second satellite, Starcloud-2. In May 2026, Edge Aerospace was awarded a contract by the European Space Agency under its Space Cloud program to study use cases, architectures and implementation roadmap for orbital data centers. == Feasibility == In October 2025, Nature Electronics published a study led by a research group at Nanyang Technological University on the development of carbon-neutral data centres in space. In November 2025, Google published a feasibility study on space-based data centers. The authors argued that if launch costs to low earth orbit reached US$200/kg, the launch cost for data center satellites could be cost effective relative to current energy costs for ground-based data centers. They project this may occur around 2035 if SpaceX's Starship project scales to 180 launches/year by then. == Advantages == Some sun-synchronous orbit (SSO) planes have constant sunlight in the dawn/dusk which could provide continuous solar energy. SSO is a limited resource and proper management and sharing of it is required. Solar irradiance is 36% higher in Earth orbit than on the surface No Earth weather storms or clouds, however more exposed to Solar storms. No property tax or land-use regulation. Saves space for other land use. Ample space for scalability. Won't strain the power grid. Direct access to power source without additional infrastructure. == Disadvantages == The deployment of space-based data centers raises several technical, economic, and environmental concerns. Existing launch costs are substantial and remains main cost of space infrastructure deployment Cooling is limited to heat dissipation through radiation only, which made in inefficient in comparison to convection in terrestrial data centers Space infrastructure must be designed to survive launch and to work under environment conditions of radiation, wide range of temperatures, in vacuum and in microgravity In-space assembly is on early development stage to enable deployment of mega-structures Megastructures are particularly exposed to orbital debris Solar arrays efficiency decrease 0.5% to 0.8% per year due to exposure of ultraviolet rays, space weather and orbital thermal cycles Hardware is designed for limited lifespan. Maintenance and repair in space (known as On-Orbit Servicing (OOS)) is still on early stage of practical implementation. Disposable data centre: technology obsolescence of AI data centre being a concern and difficult maintenance in space imply the single-use purpose of those space data centres. To extend lifetime, space infrastructure will require either refueling or orbit rasie by the servicer, which is going to increase its operational costs The environmental impact on Earth has its own challenges: The environmental impact of launches need to be addressed. Deployment consumes Earth resources that cannot be recovered or recycled. Computers require lots of resources, some of which are strategic. Recycling e-waste is already a challenge on Earth and extremely unlikely in space. Space debris (orbit pollution) is another sustainability challenge for space: Orbits are, like any resources, a limited physical and electromagnetic resource and available for all mankind. The accumulation of satellites on a particular orbit reduces the use of space for other purposes. A consequence of the increase of satellite in orbit is a higher risk of the runaway of space debris (see Kessler syndrome). This means some orbits could become unusable. Latency and bandwidth are constrained in space, and consumes limited electromagnetic resources. Satellite flares could inhibit ground-based and space-based observational astronomy. == Size and power generated == It would take ~1 square mile solar array in earth orbit to produce 1 gigawatt of power at 30% cell efficiency. == Companies pursuing space-based AI infrastructure == Blue Origin Cowboy Space Corporation (formerly Aetherflux) Edge Aerospace Google – Project Suncatcher Nvidia OpenAI SpaceX Starcloud

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  Information history

  Information history may refer to the history of each of the categories listed below (or to combinations of them). It should be recognized that the understanding of, for example, libraries as information systems only goes back to about 1950. The application of the term information for earlier systems or societies is a retronym. == Academic discipline == Information history is an emerging discipline related to, but broader than, library history. An important introduction and review was made by Alistair Black (2006). A prolific scholar in this field is also Toni Weller, for example, Weller (2007, 2008, 2010a and 2010b). As part of her work Toni Weller has argued that there are important links between the modern information age and its historical precedents. A description from Russia is Volodin (2000). Alistair Black (2006, p. 445) wrote: "This chapter explores issues of discipline definition and legitimacy by segmenting information history into its various components: The history of print and written culture, including relatively long-established areas such as the histories of libraries and librarianship, book history, publishing history, and the history of reading. The history of more recent information disciplines and practice, that is to say, the history of information management, information systems, and information science. The history of contiguous areas, such as the history of the information society and information infrastructure, necessarily enveloping communication history (including telecommunications history) and the history of information policy. The history of information as social history, with emphasis on the importance of informal information networks." "Bodies influential in the field include the American Library Association’s Round Table on Library History, the Library History Section of the International Federation of Library Associations and Institutions (IFLA), and, in the U.K., the Library and Information History Group of the Chartered Institute of Library and Information Professionals (CILIP). Each of these bodies has been busy in recent years, running conferences and seminars, and initiating scholarly projects. Active library history groups function in many other countries, including Germany (The Wolfenbuttel Round Table on Library History, the History of the Book and the History of Media, located at the Herzog August Bibliothek), Denmark (The Danish Society for Library History, located at the Royal School of Library and Information Science), Finland (The Library History Research Group, University of Tamepere), and Norway (The Norwegian Society for Book and Library History). Sweden has no official group dedicated to the subject, but interest is generated by the existence of a museum of librarianship in Bods, established by the Library Museum Society and directed by Magnus Torstensson. Activity in Argentina, where, as in Europe and the U.S., a "new library history" has developed, is described by Parada (2004)." (Black (2006, p. 447). === Journals === Information & Culture (previously Libraries & the Cultural Record, Libraries & Culture) Library & Information History (until 2008: Library History; until 1967: Library Association. Library History Group. Newsletter) == Information technology (IT) == The term IT is ambiguous although mostly synonym with computer technology. Haigh (2011, pp. 432-433) wrote "In fact, the great majority of references to information technology have always been concerned with computers, although the exact meaning has shifted over time (Kline, 2006). The phrase received its first prominent usage in a Harvard Business Review article (Haigh, 2001b; Leavitt & Whisler, 1958) intended to promote a technocratic vision for the future of business management. Its initial definition was at the conjunction of computers, operations research methods, and simulation techniques. Having failed initially to gain much traction (unlike related terms of a similar vintage such as information systems, information processing, and information science) it was revived in policy and economic circles in the 1970s with a new meaning. Information technology now described the expected convergence of the computing, media, and telecommunications industries (and their technologies), understood within the broader context of a wave of enthusiasm for the computer revolution, post-industrial society, information society (Webster, 1995), and other fashionable expressions of the belief that new electronic technologies were bringing a profound rupture with the past. As it spread broadly during the 1980s, IT increasingly lost its association with communications (and, alas, any vestigial connection to the idea of anybody actually being informed of anything) to become a new and more pretentious way of saying "computer". The final step in this process is the recent surge in references to "information and communication technologies" or ICTs, a coinage that makes sense only if one assumes that a technology can inform without communicating". Some people use the term information technology about technologies used before the development of the computer. This is however to use the term as a retronym. =

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  Novell Storage Manager

  Novell Storage Manager is a system software package released by Novell in 2004 that uses identity, policy and directory events to automate full lifecycle management of file storage for individual users and organizational groups. By tying storage management to an organization's existing identity infrastructure, it has been pointed out, Novell Storage Manager enables the administration of users across all file servers "as a single pool rather than [in] separate independently managed domains." Novell Storage Manager is a component of the Novell File Management Suite. == How It Works == Novell Storage Manager dynamically manages and provisions storage based on user and group events that occur in the directory, including user creations, group assignments, moves, renames, and deletions. When a change happens in the directory that affects a user’s file storage needs or user storage policy, Storage Manager applies the appropriate policy and makes the necessary changes at the file system level to address those storage needs. The following key components comprise Novell Storage Manager's identity and policy-driven state machine architecture: Directory services; Storage policies; Novell Storage Manager event monitors; Novell Storage Manager policy engine; Novell Storage Manager agents; and Action objects. This state machine architecture enables the engine to properly deal with transient waits with directory synchronization issues. It also allows recovery from failures involving network communications, a target server or a server running a component of Storage Manager—including the policy engine itself. If a failure or interruption occurs at any point during operation, Storage Manager will be able to successfully continue the operation from where it was when the interruption occurred. == Reviews == Jon Toigo called Novell Storage Manager "a robust and smart approach to corralling user files... into an organized and efficient management scheme". He also said it was "best in class" of the products he'd reviewed.

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  Shapiro–Senapathy algorithm

  The Shapiro—Senapathy algorithm (S&S) is a computational method for identifying splice sites in eukaryotic genes. The algorithm employs a Position Weight Matrix (PWM) scoring formula to predict donor and acceptor splice sites in any given gene. This methodology has been used to discover splice sites and disease-causing splice site mutations in the human genome, and has become a standard tool in clinical genomics. The S&S algorithm has been cited in thousands of clinical studies, according to Google Scholar. It has also formed the basis of widely used software, including Human Splicing Finder, SROOGLE, and Alamut, which identify splice sites and splice site mutations that cause disease. The algorithm has uncovered splicing mutations in diseases ranging from cancers to inherited disorders, and predicted the deleterious effects of these mutations including exon skipping, intron retention, and cryptic splice site activation. == The algorithm == A splice site defines the boundary between a coding exon and a non-coding intron in eukaryotic genes. The S&S algorithm employs a sliding window, corresponding to the length of the splice site motif, to scan a gene sequence and detect potential splice sites. For each sliding window, the algorithm calculates a score by comparing the nucleotide sequence to a Position Weight Matrix (PWM) derived from known splice sites. This formula generates a percentile score, indicating the likelihood that a given sequence functions as a donor or acceptor splice site. The majority of disease-causing mutations in the human genome are located in splice sites. Clinical genomics studies analyze the splice site scores generated by the S&S algorithm to predict the consequences of splice site mutations including exon skipping and intron retention. The algorithm's sensitivity to single-nucleotide changes allows it to determine mutations that may impact RNA splicing and contribute to disease. In addition to identifying real splice sites, the S&S algorithm has been used to discover cryptic splice sites — alternative splice sites activated by mutations — which may disrupt normal splicing. The algorithm detects mutations that lead to the activation of cryptic splice sites, which may be located proximal to real splice sites or deep within non-coding introns. It has thus been used to determine the causes of numerous diseases that are due to cryptic splicing. == Cancer gene discovery using S&S == The S&S algorithm has been used to identify splice-site mutations in genes associated with several cancers. For example, genes causing commonly occurring cancers including breast cancer, ovarian cancer, colorectal cancer, leukemia, head and neck cancers, prostate cancer, retinoblastoma, squamous cell carcinoma, gastrointestinal cancer, melanoma, liver cancer, Lynch syndrome, skin cancer, and neurofibromatosis have been found. In addition, splicing mutations in genes causing less commonly known cancers including gastric cancer, gangliogliomas, Li-Fraumeni syndrome, Loeys–Dietz syndrome, Osteochondromas (bone tumor), Nevoid basal cell carcinoma syndrome, and Pheochromocytomas have been identified. Specific mutations in different splice sites in various genes causing breast cancer (e.g., BRCA1, PALB2), ovarian cancer (e.g., SLC9A3R1, COL7A1, HSD17B7), colon cancer (e.g., APC, MLH1, DPYD), colorectal cancer (e.g., COL3A1, APC, HLA-A), skin cancer (e.g., COL17A1, XPA, POLH), and Fanconi anemia (e.g., FANC, FANA) have been uncovered. The mutations in the donor and acceptor splice sites in different genes causing a variety of cancers that have been identified by S&S are shown in Table 1. == Discovery of genes causing inherited disorders using S&S == Specific mutations in different splice sites in various genes that cause inherited disorders, including, for example, Type 1 diabetes (e.g., PTPN22, TCF1 (HCF-1A)), hypertension (e.g., LDL, LDLR, LPL), Marfan syndrome (e.g., FBN1, TGFBR2, FBN2), cardiac diseases (e.g., COL1A2, MYBPC3, ACTC1), eye disorders (e.g., EVC, VSX1) have been uncovered. A few example mutations in the donor and acceptor splice sites in different genes causing a variety of inherited disorders identified using S&S are shown in Table 2. == Genes causing immune system disorders == More than 100 immune system disorders affect humans, including inflammatory bowel diseases, multiple sclerosis, systemic lupus erythematosus, bloom syndrome, familial cold autoinflammatory syndrome, and dyskeratosis congenita. The Shapiro–Senapathy algorithm has been used to discover genes and mutations involved in many immune disorder diseases, including Ataxia telangiectasia, B-cell defects, epidermolysis bullosa, and X-linked agammaglobulinemia. Xeroderma pigmentosum, an autosomal recessive disorder is caused by faulty proteins formed due to new preferred splice donor site identified using S&S algorithm and resulted in defective nucleotide excision repair. Type I Bartter syndrome (BS) is caused by mutations in the gene SLC12A1. S&S algorithm helped in disclosing the presence of two novel heterozygous mutations c.724 + 4A > G in intron 5 and c.2095delG in intron 16 leading to complete exon 5 skipping. Mutations in the MYH gene, which is responsible for removing the oxidatively damaged DNA lesion are cancer-susceptible in the individuals. The IVS1+5C plays a causative role in the activation of a cryptic splice donor site and the alternative splicing in intron 1, S&S algorithm shows, guanine (G) at the position of IVS+5 is well conserved (at the frequency of 84%) among primates. This also supported the fact that the G/C SNP in the conserved splice junction of the MYH gene causes the alternative splicing of intron 1 of the β type transcript. Splice site scores were calculated according to S&S to find EBV infection in X-linked lymphoproliferative disease. Identification of Familial tumoral calcinosis (FTC) is an autosomal recessive disorder characterized by ectopic calcifications and elevated serum phosphate levels and it is because of aberrant splicing. == Application of S&S in hospitals for clinical practice and research == The Shapiro–Senapathy (S&S) algorithm has played a significant role in advancing the diagnosis and treatment of human diseases through its application in modern clinical genomics. With the widespread adoption of next-generation sequencing (NGS) technologies, the S&S algorithm is now routinely integrated into clinical practice by geneticists and diagnostic laboratories. It is implemented in various computational tools such as Human Splicing Finder (HSF), Splice Site Finder (SSF), and Alamut Visual, which assist in interpreting the functional impact of genetic variants on RNA splicing. The algorithm is particularly useful in identifying pathogenic splice site mutations in cases where the clinical presentation is unclear or where conventional diagnostic methods have failed to identify a causative gene. Its utility has been demonstrated across diverse patient cohorts, including individuals from different ethnic backgrounds with various cancers and inherited genetic disorders. The following are selected examples illustrating its application in clinical research. === Cancers === === Inherited disorders === == S&S - Algorithm for identifying splice sites, exons and split genes == The Shapiro–Senapathy algorithm (SSA) was developed to identify splice sites in uncharacterized genomic sequences, with early applications in the Human Genome Project. The method introduced a Position Weight Matrix (PWM)-based approach to analyze splicing sequences across eukaryotic organisms, marking the first computational framework to systematically define splice sites using probabilistic scoring. Key innovations of the algorithm included: Exon Detection – Exons were defined as sequences bounded by acceptor and donor splice sites with S&S scores above a threshold, requiring an open reading frame (ORF) for validation. Gene Prediction – The method enabled the identification of complete genes by assembling predicted exons, forming a basis for later gene-finding tools. Mutation Analysis – The algorithm distinguishes deleterious splice-site mutations (which disrupt protein function by lowering S&S scores) from neutral variations. This capability allowed researchers to study disease-linked cryptic splice sites in humans, animals, and plants. SSA's PWM-based framework influenced subsequent computational methods, including machine learning and neural network approaches, for splice-site prediction and alternative splicing research. It remains a foundational tool in genomics and disease studies. == Discovering the mechanisms of aberrant splicing in diseases == The Shapiro–Senapathy algorithm has been used to determine the various aberrant splicing mechanisms in genes due to deleterious mutations in the splice sites, which cause numerous diseases. Deleterious splice site mutations impair the normal splicing of the gene transcripts, and thereby make the encoded protei

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  Artificial consciousness

  Artificial consciousness, also known as machine consciousness, synthetic consciousness, or digital consciousness, is consciousness hypothesized to be possible for artificial intelligence. It is also the corresponding field of study, which draws insights from philosophy of mind, philosophy of artificial intelligence, cognitive science and neuroscience. The term "sentience" can be used when specifically designating ethical considerations stemming from a form of phenomenal consciousness (P-consciousness, or the ability to feel qualia). Since sentience involves the ability to experience ethically positive or negative (i.e., valenced) mental states, it may justify welfare concerns and legal protection, as with non-human animals. Some scholars believe that consciousness is generated by the interoperation of various parts of the brain; these mechanisms are labeled the neural correlates of consciousness (NCC). Some further believe that constructing a system (e.g., a computer system) that can emulate this NCC interoperation would result in a system that is conscious. Some scholars reject the possibility of non-biological conscious beings. == Philosophical views == As there are many hypothesized types of consciousness, there are many potential implementations of artificial consciousness. In the philosophical literature, perhaps the most common taxonomy of consciousness is into "access" and "phenomenal" variants. Access consciousness concerns those aspects of experience that can be apprehended, while phenomenal consciousness concerns those aspects of experience that seemingly cannot be apprehended, instead being characterized qualitatively in terms of "raw feels", "what it is like" or qualia. === Plausibility debate === Type-identity theorists and other skeptics hold the view that consciousness can be realized only in particular physical systems because consciousness has properties that necessarily depend on physical constitution. In his 2001 article "Artificial Consciousness: Utopia or Real Possibility," Giorgio Buttazzo says that a common objection to artificial consciousness is that, "Working in a fully automated mode, they [the computers] cannot exhibit creativity, unreprogrammation (which means can 'no longer be reprogrammed', from rethinking), emotions, or free will. A computer, like a washing machine, is a slave operated by its components." For other theorists (e.g., functionalists), who define mental states in terms of causal roles, any system that can instantiate the same pattern of causal roles, regardless of physical constitution, will instantiate the same mental states, including consciousness. ==== Thought experiments ==== David Chalmers proposed two thought experiments intending to demonstrate that "functionally isomorphic" systems (those with the same "fine-grained functional organization", i.e., the same information processing) will have qualitatively identical conscious experiences, regardless of whether they are based on biological neurons or digital hardware. The "fading qualia" is a reductio ad absurdum thought experiment. It involves replacing, one by one, the neurons of a brain with a functionally identical component, for example based on a silicon chip. Chalmers makes the hypothesis, knowing it in advance to be absurd, that "the qualia fade or disappear" when neurons are replaced one-by-one with identical silicon equivalents. Since the original neurons and their silicon counterparts are functionally identical, the brain's information processing should remain unchanged, and the subject's behaviour and introspective reports would stay exactly the same. Chalmers argues that this leads to an absurd conclusion: the subject would continue to report normal conscious experiences even as their actual qualia fade away. He concludes that the subject's qualia actually don't fade, and that the resulting robotic brain, once every neuron is replaced, would remain just as sentient as the original biological brain. Similarly, the "dancing qualia" thought experiment is another reductio ad absurdum argument. It supposes that two functionally isomorphic systems could have different perceptions (for instance, seeing the same object in different colors, like red and blue). It involves a switch that alternates between a chunk of brain that causes the perception of red, and a functionally isomorphic silicon chip, that causes the perception of blue. Since both perform the same function within the brain, the subject would not notice any change during the switch. Chalmers argues that this would be highly implausible if the qualia were truly switching between red and blue, hence the contradiction. Therefore, he concludes that the equivalent digital system would not only experience qualia, but it would perceive the same qualia as the biological system (e.g., seeing the same color). Greg Egan's short story Learning To Be Me (mentioned in §In fiction), illustrates how undetectable duplication of the brain and its functionality could be from a first-person perspective. Critics object that Chalmers' proposal begs the question in assuming that all mental properties and external connections are already sufficiently captured by abstract causal organization. Van Heuveln et al. argue that the dancing qualia argument contains an equivocation fallacy, conflating a "change in experience" between two systems with an "experience of change" within a single system. Mogensen argues that the fading qualia argument can be resisted by appealing to vagueness at the boundaries of consciousness and the holistic structure of conscious neural activity, which suggests consciousness may require specific biological substrates rather than being substrate-independent. Anil Seth argues that the complexity of brain neurons intrinsically matters in addition to their function and that it is not possible to replace any part of the brain with a perfect silicon equivalent. He points out that some of biological neurons exhibit activity aimed at cleaning up metabolic waste products, and writes that a perfect silicon replacement would require a silicon-based metabolism, but silicon is not suitable for creating such artificial metabolism. ==== In large language models ==== In 2022, Google engineer Blake Lemoine made a viral claim that Google's LaMDA chatbot was sentient. Lemoine supplied as evidence the chatbot's humanlike answers to many of his questions; however, the chatbot's behavior was judged by the scientific community as likely a consequence of mimicry, rather than machine sentience. Lemoine's claim was widely derided for being ridiculous. Moreover, attributing consciousness based solely on the basis of LLM outputs or the immersive experience created by an algorithm is considered a fallacy. However, while philosopher Nick Bostrom states that LaMDA is unlikely to be conscious, he additionally poses the question of "what grounds would a person have for being sure about it?" One would have to have access to unpublished information about LaMDA's architecture, and also would have to understand how consciousness works, and then figure out how to map the philosophy onto the machine: "(In the absence of these steps), it seems like one should be maybe a little bit uncertain. [...] there could well be other systems now, or in the relatively near future, that would start to satisfy the criteria." David Chalmers argued in 2023 that LLMs today display impressive conversational and general intelligence abilities, but are likely not conscious yet, as they lack some features that may be necessary, such as recurrent processing, a global workspace, and unified agency. Nonetheless, he considers that non-biological systems can be conscious, and suggested that future, extended models (LLM+s) incorporating these elements might eventually meet the criteria for consciousness, raising both profound scientific questions and significant ethical challenges. However, the view that consciousness can exist without biological phenomena is controversial and some reject it. Kristina Šekrst cautions that anthropomorphic terms such as "hallucination" can obscure important ontological differences between artificial and human cognition. While LLMs may produce human-like outputs, she argues that it does not justify ascribing mental states or consciousness to them. Instead, she advocates for an epistemological framework (such as reliabilism) that recognizes the distinct nature of AI knowledge production. She suggests that apparent understanding in LLMs may be a sophisticated form of AI hallucination. She also questions what would happen if an LLM were trained without any mention of consciousness. === Testing === Sentience is an inherently first-person phenomenon. Because of that, and due to the lack of an empirical definition of sentience, directly measuring it may be impossible. Although systems may display numerous behaviors correlated with sentience, determining whether a system is sentient is known as the hard pr

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  Online analytical processing

  In computing, online analytical processing (OLAP) (), is an approach to quickly answer multi-dimensional analytical (MDA) queries. The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP). OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture. OLAP tools enable users to analyse multidimensional data interactively from multiple perspectives. OLAP consists of three basic analytical operations: consolidation (roll-up), drill-down, and slicing and dicing. Consolidation involves the aggregation of data that can be accumulated and computed in one or more dimensions. For example, all sales offices are rolled up to the sales department or sales division to anticipate sales trends. By contrast, the drill-down is a technique that allows users to navigate through the details. For instance, users can view the sales by individual products that make up a region's sales. Slicing and dicing is a feature whereby users can take out (slicing) a specific set of data of the OLAP cube and view (dicing) the slices from different viewpoints. These viewpoints are sometimes called dimensions (such as looking at the same sales by salesperson, or by date, or by customer, or by product, or by region, etc.). Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad hoc queries with a rapid execution time. They borrow aspects of navigational databases, hierarchical databases and relational databases. OLAP is typically contrasted to OLTP (online transaction processing), which is generally characterized by much less complex queries, in a larger volume, to process transactions rather than for the purpose of business intelligence or reporting. Whereas OLAP systems are mostly optimized for read, OLTP has to process all kinds of queries (read, insert, update and delete). == Overview of OLAP systems == At the core of any OLAP system is an OLAP cube (also called a 'multidimensional cube' or a hypercube). It consists of numeric facts called measures that are categorized by dimensions. The measures are placed at the intersections of the hypercube, which is spanned by the dimensions as a vector space. The usual interface to manipulate an OLAP cube is a matrix interface, like Pivot tables in a spreadsheet program, which performs projection operations along the dimensions, such as aggregation or averaging. The cube metadata is typically created from a star schema or snowflake schema or fact constellation of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables. Each measure can be thought of as having a set of labels, or meta-data associated with it. A dimension is what describes these labels; it provides information about the measure. A simple example would be a cube that contains a store's sales as a measure, and Date/Time as a dimension. Each Sale has a Date/Time label that describes more about that sale. For example: Sales Fact Table +-------------+----------+ | sale_amount | time_id | +-------------+----------+ Time Dimension | 930.10| 1234 |----+ +---------+-------------------+ +-------------+----------+ | | time_id | timestamp | | +---------+-------------------+ +---->| 1234 | 20080902 12:35:43 | +---------+-------------------+ === Multidimensional databases === Multidimensional structure is defined as "a variation of the relational model that uses multidimensional structures to organize data and express the relationships between data". The structure is broken into cubes and the cubes are able to store and access data within the confines of each cube. "Each cell within a multidimensional structure contains aggregated data related to elements along each of its dimensions". Even when data is manipulated it remains easy to access and continues to constitute a compact database format. The data still remains interrelated. Multidimensional structure is quite popular for analytical databases that use online analytical processing (OLAP) applications. Analytical databases use these databases because of their ability to deliver answers to complex business queries swiftly. Data can be viewed from different angles, which gives a broader perspective of a problem unlike other models. === Aggregations === It has been claimed that for complex queries OLAP cubes can produce an answer in around 0.1% of the time required for the same query on OLTP relational data. The most important mechanism in OLAP which allows it to achieve such performance is the use of aggregations. Aggregations are built from the fact table by changing the granularity on specific dimensions and aggregating up data along these dimensions, using an aggregate function (or aggregation function). The number of possible aggregations is determined by every possible combination of dimension granularities. The combination of all possible aggregations and the base data contains the answers to every query which can be answered from the data. Because usually there are many aggregations that can be calculated, often only a predetermined number are fully calculated; the remainder are solved on demand. The problem of deciding which aggregations (views) to calculate is known as the view selection problem. View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both. The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time. View selection is NP-complete. Many approaches to the problem have been explored, including greedy algorithms, randomized search, genetic algorithms and A search algorithm. Some aggregation functions can be computed for the entire OLAP cube by precomputing values for each cell, and then computing the aggregation for a roll-up of cells by aggregating these aggregates, applying a divide and conquer algorithm to the multidimensional problem to compute them efficiently. For example, the overall sum of a roll-up is just the sum of the sub-sums in each cell. Functions that can be decomposed in this way are called decomposable aggregation functions, and include COUNT, MAX, MIN, and SUM, which can be computed for each cell and then directly aggregated; these are known as self-decomposable aggregation functions. In other cases, the aggregate function can be computed by computing auxiliary numbers for cells, aggregating these auxiliary numbers, and finally computing the overall number at the end; examples include AVERAGE (tracking sum and count, dividing at the end) and RANGE (tracking max and min, subtracting at the end). In other cases, the aggregate function cannot be computed without analyzing the entire set at once, though in some cases approximations can be computed; examples include DISTINCT COUNT, MEDIAN, and MODE; for example, the median of a set is not the median of medians of subsets. These latter are difficult to implement efficiently in OLAP, as they require computing the aggregate function on the base data, either computing them online (slow) or precomputing them for possible rollouts (large space). == Types == OLAP systems have been traditionally categorized using the following taxonomy. === Multidimensional OLAP (MOLAP) === MOLAP (multi-dimensional online analytical processing) is the classic form of OLAP and is sometimes referred to as just OLAP. MOLAP stores this data in an optimized multi-dimensional array storage, rather than in a relational database. Some MOLAP tools require the pre-computation and storage of derived data, such as consolidations – the operation known as processing. Such MOLAP tools generally utilize a pre-calculated data set referred to as a data cube. The data cube contains all the possible answers to a given range of questions. As a result, they have a very fast response to queries. On the other hand, updating can take a long time depending on the degree of pre-computation. Pre-computation can also lead to what is known as data explosion. Other MOLAP tools, particularly those that implement the functional database model do not pre-compute derived data but make all calculations on demand other than those that were previously requested and stored in a cache. Advantages of MOLAP Fast query performance due to optimized storage, multidimensional indexing and caching. Smaller on-disk size of data compared to data stored in relational database due to compression techniques. Automated computation of higher-level aggregates of the data. It is very compact for low dimension data se

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  Run-time algorithm specialization

  In computer science, run-time algorithm specialization is a methodology for creating efficient algorithms for costly computation tasks of certain kinds. The methodology originates in the field of automated theorem proving and, more specifically, in the Vampire theorem prover project. The idea is inspired by the use of partial evaluation in optimising program translation. Many core operations in theorem provers exhibit the following pattern. Suppose that we need to execute some algorithm a l g ( A , B ) {\displaystyle {\mathit {alg}}(A,B)} in a situation where a value of A {\displaystyle A} is fixed for potentially many different values of B {\displaystyle B} . In order to do this efficiently, we can try to find a specialization of a l g {\displaystyle {\mathit {alg}}} for every fixed A {\displaystyle A} , i.e., such an algorithm a l g A {\displaystyle {\mathit {alg}}_{A}} , that executing a l g A ( B ) {\displaystyle {\mathit {alg}}_{A}(B)} is equivalent to executing a l g ( A , B ) {\displaystyle {\mathit {alg}}(A,B)} . The specialized algorithm may be more efficient than the generic one, since it can exploit some particular properties of the fixed value A {\displaystyle A} . Typically, a l g A ( B ) {\displaystyle {\mathit {alg}}_{A}(B)} can avoid some operations that a l g ( A , B ) {\displaystyle {\mathit {alg}}(A,B)} would have to perform, if they are known to be redundant for this particular parameter A {\displaystyle A} . In particular, we can often identify some tests that are true or false for A {\displaystyle A} , unroll loops and recursion, etc. == Difference from partial evaluation == The key difference between run-time specialization and partial evaluation is that the values of A {\displaystyle A} on which a l g {\displaystyle {\mathit {alg}}} is specialised are not known statically, so the specialization takes place at run-time. There is also an important technical difference. Partial evaluation is applied to algorithms explicitly represented as codes in some programming language. At run-time, we do not need any concrete representation of a l g {\displaystyle {\mathit {alg}}} . We only have to imagine a l g {\displaystyle {\mathit {alg}}} when we program the specialization procedure. All we need is a concrete representation of the specialized version a l g A {\displaystyle {\mathit {alg}}_{A}} . This also means that we cannot use any universal methods for specializing algorithms, which is usually the case with partial evaluation. Instead, we have to program a specialization procedure for every particular algorithm a l g {\displaystyle {\mathit {alg}}} . An important advantage of doing so is that we can use some powerful ad hoc tricks exploiting peculiarities of a l g {\displaystyle {\mathit {alg}}} and the representation of A {\displaystyle A} and B {\displaystyle B} , which are beyond the reach of any universal specialization methods. == Specialization with compilation == The specialized algorithm has to be represented in a form that can be interpreted. In many situations, usually when a l g A ( B ) {\displaystyle {\mathit {alg}}_{A}(B)} is to be computed on many values of B {\displaystyle B} in a row, a l g A {\displaystyle {\mathit {alg}}_{A}} can be written as machine code instructions for a special abstract machine, and it is typically said that A {\displaystyle A} is compiled. The code itself can then be additionally optimized by answer-preserving transformations that rely only on the semantics of instructions of the abstract machine. The instructions of the abstract machine can usually be represented as records. One field of such a record, an instruction identifier (or instruction tag), would identify the instruction type, e.g. an integer field may be used, with particular integer values corresponding to particular instructions. Other fields may be used for storing additional parameters of the instruction, e.g. a pointer field may point to another instruction representing a label, if the semantics of the instruction require a jump. All instructions of the code can be stored in a traversable data structure such as an array, linked list, or tree. Interpretation (or execution) proceeds by fetching instructions in some order, identifying their type, and executing the actions associated with said type. In many programming languages, such as C and C++, a simple switch statement may be used to associate actions with different instruction identifiers. Modern compilers usually compile a switch statement with constant (e.g. integer) labels from a narrow range by storing the address of the statement corresponding to a value i {\displaystyle i} in the i {\displaystyle i} -th cell of a special array, as a means of efficient optimisation. This can be exploited by taking values for instruction identifiers from a small interval of values. == Data-and-algorithm specialization == There are situations when many instances of A {\displaystyle A} are intended for long-term storage and the calls of a l g ( A , B ) {\displaystyle {\mathit {alg}}(A,B)} occur with different B {\displaystyle B} in an unpredictable order. For example, we may have to check a l g ( A 1 , B 1 ) {\displaystyle {\mathit {alg}}(A_{1},B_{1})} first, then a l g ( A 2 , B 2 ) {\displaystyle {\mathit {alg}}(A_{2},B_{2})} , then a l g ( A 1 , B 3 ) {\displaystyle {\mathit {alg}}(A_{1},B_{3})} , and so on. In such circumstances, full-scale specialization with compilation may not be suitable due to excessive memory usage. However, we can sometimes find a compact specialized representation A ′ {\displaystyle A^{\prime }} for every A {\displaystyle A} , that can be stored with, or instead of, A {\displaystyle A} . We also define a variant a l g ′ {\displaystyle {\mathit {alg}}^{\prime }} that works on this representation and any call to a l g ( A , B ) {\displaystyle {\mathit {alg}}(A,B)} is replaced by a l g ′ ( A ′ , B ) {\displaystyle {\mathit {alg}}^{\prime }(A^{\prime },B)} , intended to do the same job faster.

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  Synthetic data

  Synthetic data are artificially generated data not produced by real-world events. Typically created using algorithms, synthetic data can be deployed to validate mathematical models and to train machine learning models. Data generated by a computer simulation can be seen as synthetic data. This encompasses most applications of physical modeling, such as music synthesizers or flight simulators. The output of such systems approximates the real thing, but is fully algorithmically generated. Synthetic data is used in a variety of fields as a filter for information that would otherwise compromise the confidentiality of particular aspects of the data. In many sensitive applications, datasets theoretically exist but cannot be released to the general public; synthetic data sidesteps the privacy issues that arise from using real consumer information without permission or compensation. == Usefulness == Synthetic data is generated to meet specific needs or certain conditions that may not be found in the original, real data. One of the hurdles in applying up-to-date machine learning approaches for complex scientific tasks is the scarcity of labeled data, a gap effectively bridged by the use of synthetic data, which closely replicates real experimental data. This can be useful when designing many systems, from simulations based on theoretical value, to database processors, etc. This helps detect and solve unexpected issues such as information processing limitations. Synthetic data are often generated to represent the authentic data and allows a baseline to be set. Another benefit of synthetic data is to protect the privacy and confidentiality of authentic data, while still allowing for use in testing systems. Computer security experts claim generated synthetic data "... enables us to create realistic behavior profiles for users and attackers. The data is used to train the fraud detection system itself, thus creating the necessary adaptation of the system to a specific environment." In defense and military contexts, synthetic data is seen as a potentially valuable tool to develop and improve complex AI systems, particularly in contexts where high-quality real-world data is scarce. At the same time, synthetic data together with the testing approach can give the ability to model real-world scenarios. == History == Scientific modelling of physical systems has a long history that runs concurrent with the history of physics. For example, research into synthesis of audio and voice can be traced back to the 1930s and before, driven forward by the developments of the telephone and audio recording technologies. Digitization gave rise to software synthesizers from the 1970s onwards. In the context of privacy-preserving statistical analysis, in 1993, the idea of original fully synthetic data was created by Donald Rubin. Rubin originally designed this to synthesize the Decennial Census long form responses for the short form households. He then released samples that did not include any actual long form records - in this he preserved anonymity of the household. Later that year, the idea of original partially synthetic data was created by Little. Little used this idea to synthesize the sensitive values on the public use file. A 1993 work fitted a statistical model to 60,000 MNIST digits, then it was used to generate over 1 million examples. Those were used to train a LeNet-4 to reach state of the art performance. In 1994, Stephen Fienberg introduced 'critical refinement', in which a parametric posterior predictive distribution (instead of a Bayes bootstrap) is used to do the sampling. Later, other important contributors to the development of synthetic data generation were Trivellore Raghunathan, Jerry Reiter, Donald Rubin, John M. Abowd, and Jim Woodcock. Collectively they came up with a solution for how to treat partially synthetic data with missing data. Similarly, they developed the technique of Sequential Regression Multivariate Imputation. == Calculations == Researchers test the framework on synthetic data, which is "the only source of ground truth on which they can objectively assess the performance of their algorithms". Synthetic data can be generated through the use of random lines, having different orientations and starting positions. Datasets can get fairly complicated. A more complicated dataset can be generated by using a synthesizer build. To create a synthesizer build, first use the original data to create a model or equation that fits the data the best. This model or equation will be called a synthesizer build. This build can be used to generate more data. Constructing a synthesizer build involves constructing a statistical model. In a linear regression line example, the original data can be plotted, and a best fit linear line can be created from the data. This line is a synthesizer created from the original data. The next step will be generating more synthetic data from the synthesizer build or from this linear line equation. In this way, the new data can be used for studies and research, and it protects the confidentiality of the original data. David Jensen from the Knowledge Discovery Laboratory explains how to generate synthetic data: "Researchers frequently need to explore the effects of certain data characteristics on their data model." To help construct datasets exhibiting specific properties, such as auto-correlation or degree disparity, proximity can generate synthetic data having one of several types of graph structure: random graphs that are generated by some random process; lattice graphs having a ring structure; lattice graphs having a grid structure, etc. In all cases, the data generation process follows the same process: Generate the empty graph structure. Generate attribute values based on user-supplied prior probabilities. Since the attribute values of one object may depend on the attribute values of related objects, the attribute generation process assigns values collectively. == Applications == === Fraud detection and confidentiality systems === Testing and training fraud detection and confidentiality systems are devised using synthetic data. Specific algorithms and generators are designed to create realistic data, which then assists in teaching a system how to react to certain situations or criteria. For example, intrusion detection software is tested using synthetic data. This data is a representation of the authentic data and may include intrusion instances that are not found in the authentic data. The synthetic data allows the software to recognize these situations and react accordingly. If synthetic data was not used, the software would only be trained to react to the situations provided by the authentic data and it may not recognize another type of intrusion. === Scientific research === Researchers doing clinical trials or any other research may generate synthetic data to aid in creating a baseline for future studies and testing. Real data can contain information that researchers may not want released, so synthetic data is sometimes used to protect the privacy and confidentiality of a dataset. Using synthetic data reduces confidentiality and privacy issues since it holds no personal information and cannot be traced back to any individual. Beyond privacy protection, synthetic data is also being explored for methodological innovation in drug development. For instance, synthetic data may be used to construct synthetic control arms as an alternative to conventional external control arms based on real-world data (RWD) or randomized controlled trials (RCTs). Collectively, regulatory agencies such as the FDA and EMA appear to be at various stages of recognizing and integrating AI-generated synthetic data into their methodologies. While there is growing consensus on the potential of such data to support model development and the broader lifecycle of medicinal products, to date no drug or medical device has been approved using solely or predominantly synthetic data—particularly not as a comparator arm generated entirely via data-driven algorithms. The quality and statistical handling of synthetic data are expected to become more prominent in future regulatory discussions, particularly in contexts such as predictive modeling (e.g., digital twins), where innovative approaches have already been referenced. === Machine learning === Synthetic data is increasingly being used for machine learning applications: a model is trained on a synthetically generated dataset with the intention of transfer learning to real data. Efforts have been made to enable more data science experiments via the construction of general-purpose synthetic data generators, such as the Synthetic Data Vault. In general, synthetic data has several natural advantages: once the synthetic environment is ready, it is fast and cheap to produce as much data as needed; synthetic data can have perfectly accurate labels, including labeling that may be very expensive or impo

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  Luminance HDR

  Luminance HDR, formerly Qtpfsgui, is graphics software used for the creation and manipulation of high-dynamic-range images. Released under the terms of the GPL, it is available for Linux, Microsoft Windows, and Mac OS X (Intel only). Luminance HDR supports several High Dynamic Range (HDR) as well as Low Dynamic Range (LDR) file formats. == Functionality == Prerequisite of HDR photography are several narrow-range digital images with different exposures. Luminance HDR combines these images and calculates a high-contrast image. In order to view this image on a regular computer monitor, Luminance HDR can convert it into a displayable LDR image format using a variety of methods, such as tone mapping. Currently fifteen different tone mapping operators (algorithms) are available, each one with its tunable parameters. Different image processing techniques can be applied to the generated HDR images, such as resizing, cropping, rotating and a number of projective transformations. The software also provides batch processing functionality for creating HDR images and for tone mapping them in a non-interactive way. A module for copying Exif data among sets of images is also provided. For users who prefers the command line, a non-GUI, non-graphical interface is also available on all supported platforms. A common problem with HDR photography is that images need to be aligned exactly. If the subject is static, this can be achieved using a tripod or a stable surface on which the camera is placed. In the case of image data that does not align exactly, an automatic alignment can be performed using a tool provided by the Hugin project. If this automation doesn't provide the desired result, the user may improve it manually. == Supported formats == HDR images are images with a high dynamic range and, using Luminance HDR, they can be created as well as edited. The following HDR graphic formats are supported: OpenEXR Radiance HDR Tag Image File Format (TIFF) Format: 16 Bit, 32 Bit (Float) and LogLuv Raw PFS native Luminance HDR can create an HDR image from several LDR images and tonemap an HDR into an LDR. The following LDR formats are supported: JPG PNG Portable Pixmap (PPM) Portable Bitmap (PBM) TIFF (8 Bit)

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  Wearable technology

  Wearable technology is a category of small electronic and mobile devices with wireless communications capability designed to be worn on the human body and are incorporated into gadgets, accessories, or clothes. Common types of wearable technology include smartwatches, fitness trackers, and smartglasses. Wearable electronic devices are often close to or on the surface of the skin, where they detect, analyze, and transmit information such as vital signs, and/or ambient data and which allow in some cases immediate biofeedback to the wearer. Wearable devices collect vast amounts of data from users making use of different behavioral and physiological sensors, which monitor their health status and activity levels. Wrist-worn devices include smartwatches with a touchscreen display, while wristbands are mainly used for fitness tracking but do not contain a touchscreen display. Wearable devices such as activity trackers are an example of the Internet of things, since "things" such as electronics, software, sensors, and connectivity are effectors that enable objects to exchange data (including data quality) through the internet with a manufacturer, operator, and/or other connected devices, without requiring human intervention. Wearable technology offers a wide range of possible uses, from communication and entertainment to improving health and fitness, however, there are worries about privacy and security because wearable devices have the ability to collect personal data. Wearable technology has a variety of use cases which is growing as the technology is developed and the market expands. It can be used to encourage individuals to be more active and improve their lifestyle choices. Healthy behavior is encouraged by tracking activity levels and providing useful feedback to enable goal setting. This can be shared with interested stakeholders such as healthcare providers. Wearables are popular in consumer electronics, most commonly in the form factors of smartwatches, smart rings, and implants. Apart from commercial uses, wearable technology is being incorporated into navigation systems, advanced textiles (e-textiles), and healthcare. As wearable technology is being proposed for use in critical applications, like other technology, it is vetted for its reliability and security properties. == History == In the 1500s, German inventor Peter Henlein (1485–1542) created small watches that were worn as necklaces. A century later, pocket watches grew in popularity as waistcoats became fashionable for men. Wristwatches were created in the late 1600s but were worn mostly by women as bracelets. Pedometers were developed around the same time as pocket watches. The concept of a pedometer was described by Leonardo da Vinci around 1500, and the Germanic National Museum in Nuremberg has a pedometer in its collection from 1590. In the late 1800s, the first wearable hearing aids were introduced. In 1904, aviator Alberto Santos-Dumont pioneered the modern use of the wristwatch. In 1949, American biophysicist Norman Holter invented the very first health monitoring device. His invention, the Holter monitor, was groundbreaking as one of the first wearable devices capable of tracking vital health data outside of a clinical setting. In the 1970s, calculator watches became available, reaching the peak of their popularity in the 1980s. From the early 2000s, wearable cameras were being used as part of a growing sousveillance movement. Expectations, operations, usage and concerns about wearable technology was floated on the first International Conference on Wearable Computing. In 2008, Ilya Fridman incorporated a hidden Bluetooth microphone into a pair of earrings. Big tech companies such as Apple, Samsung, and Fitbit have expanded on this idea by interfacing with smartphones and personal computer software to collect a wide variety of data. Wearable devices include dedicated health monitors, fitness bands, and smartwatches. In 2010, Fitbit released its first step counter. Wearable technology which tracks information such as walking and heart rate is part of the quantified self movement. In 2013, McLear, also known as NFC Ring, released a "smart ring". The smart ring could make bitcoin payments, unlock other devices, and transfer personally identifying information, and also had other features. In 2013, one of the first widely available smartwatches was the Samsung Galaxy Gear. Apple followed in 2015 with the Apple Watch. === Prototypes === From 1991 to 1997, Rosalind Picard and her students, Steve Mann and Jennifer Healey, at the MIT Media Lab designed, built, and demonstrated data collection and decision making from "Smart Clothes" that monitored continuous physiological data from the wearer. These "smart clothes", "smart underwear", "smart shoes", and smart jewellery collected data that related to affective state and contained or controlled physiological sensors and environmental sensors like cameras and other devices. At the same time, also at the MIT Media Lab, Thad Starner and Alex "Sandy" Pentland develop augmented reality. In 1997, their smartglass prototype is featured on 60 Minutes and enables rapid web search and instant messaging. Though the prototype's glasses are nearly as streamlined as modern smartglasses, the processor was a computer worn in a backpack – the most lightweight solution available at the time. In 2009, Sony Ericsson teamed up with the London College of Fashion for a contest to design digital clothing. The winner was a cocktail dress with Bluetooth technology making it light up when a call is received. Zach "Hoeken" Smith of MakerBot fame made keyboard pants during a "Fashion Hacking" workshop at a New York City creative collective. The Tyndall National Institute in Ireland developed a "remote non-intrusive patient monitoring" platform which was used to evaluate the quality of the data generated by the patient sensors and how the end users may adopt to the technology. More recently, London-based fashion company CuteCircuit created costumes for singer Katy Perry featuring LED lighting so that the outfits would change color both during stage shows and appearances on the red carpet such as the dress Katy Perry wore in 2010 at the MET Gala in NYC. In 2012, CuteCircuit created the world's first dress to feature Tweets, as worn by singer Nicole Scherzinger. In 2010, McLear, also known as NFC Ring, developed prototypes of its "smart ring" devices, before a Kickstarter fundraising in 2013. In 2014, graduate students from the Tisch School of Arts in New York designed a hoodie that sent pre-programmed text messages triggered by gesture movements. Around the same time, prototypes for digital eyewear with heads up display (HUD) began to appear. The US military employs headgear with displays for soldiers using a technology called holographic optics. In 2010, Google started developing prototypes of its optical head-mounted display Google Glass, which went into customer beta in March 2013. == Usage == In the consumer space, sales of smart wristbands (aka activity trackers such as the Jawbone UP and Fitbit Flex) started accelerating in 2013. One in five American adults have a wearable device, according to the 2014 PriceWaterhouseCoopers Wearable Future Report. As of 2009, decreasing cost of processing power and other components was facilitating widespread adoption and availability. In professional sports, wearable technology has applications in monitoring and real-time feedback for athletes. Examples of wearable technology in sport include accelerometers, pedometers, and GPS's which can be used to measure an athlete's energy expenditure and movement pattern. In cybersecurity and financial technology, secure wearable devices have captured part of the physical security key market. McLear, also known as NFC Ring, and VivoKey developed products with one-time pass secure access control. In health informatics, wearable devices have enabled better capturing of human health statistics for data driven analysis. This has facilitated data-driven machine learning algorithms to analyse the health condition of users. In business, wearable technology helps managers easily supervise employees by knowing their locations and what they are currently doing. Employees working in a warehouse also have increased safety when working around chemicals or lifting something. Smart helmets are employee safety wearables that have vibration sensors that can alert employees of possible danger in their environment. == Wearable technology and health == Wearable technology is often used to monitor a user's health. Given that such a device is in close contact with the user, it can easily collect data. It started as soon as 1980 where first wireless ECG was invented. In the last decades, there has been substantial growth in research of e.g. textile-based, tattoo, patch, and contact lenses as well as circulation of a notion of "quantified self", transhumanism-related ideas, and growth of life ex

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  EdgeRank

  EdgeRank is the name commonly given to the algorithm that Facebook uses to determine what articles should be displayed in a user's News Feed. As of 2011, Facebook has stopped using the EdgeRank system and uses a machine learning algorithm that, as of 2013, takes more than 100,000 factors into account. EdgeRank was developed and implemented by Serkan Piantino. == Formula and factors == In 2010, a simplified version of the EdgeRank algorithm was presented as: ∑ e d g e s e u e w e d e {\displaystyle \sum _{\mathrm {edges\,} e}u_{e}w_{e}d_{e}} where: u e {\displaystyle u_{e}} is user affinity. w e {\displaystyle w_{e}} is how the content is weighted. d e {\displaystyle d_{e}} is a time-based decay parameter. User Affinity: The User Affinity part of the algorithm in Facebook's EdgeRank looks at the relationship and proximity of the user and the content (post/status update). Content Weight: What action was taken by the user on the content. Time-Based Decay Parameter: New or old. Newer posts tend to hold a higher place than older posts. Some of the methods that Facebook uses to adjust the parameters are proprietary and not available to the public. A study has shown that it is possible to hypothesize a disadvantage of the "like" reaction and advantages of other interactions (e.g., the "haha" reaction or "comments") in content algorithmic ranking on Facebook. The "like" button can decrease the organic reach as a "brake effect of viral reach". The "haha" reaction, "comments" and the "love" reaction could achieve the highest increase in total organic reach. == Impact == EdgeRank and its successors have a broad impact on what users actually see out of what they ostensibly follow: for instance, the selection can produce a filter bubble (if users are exposed to updates which confirm their opinions etc.) or alter people's mood (if users are shown a disproportionate amount of positive or negative updates). As a result, for Facebook pages, the typical engagement rate is less than 1% (or less than 0.1% for the bigger ones), and organic reach 10% or less for most non-profits. As a consequence, for pages, it may be nearly impossible to reach any significant audience without paying to promote their content.

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  Grid-oriented storage

  Grid-oriented Storage (GOS) was a term used for data storage by a university project during the era when the term grid computing was popular. == Description == GOS was a successor of the term network-attached storage (NAS). GOS systems contained hard disks, often RAIDs (redundant arrays of independent disks), like traditional file servers. GOS was designed to deal with long-distance, cross-domain and single-image file operations, which is typical in Grid environments. GOS behaves like a file server via the file-based GOS-FS protocol to any entity on the grid. Similar to GridFTP, GOS-FS integrates a parallel stream engine and Grid Security Infrastructure (GSI). Conforming to the universal VFS (Virtual Filesystem Switch), GOS-FS can be pervasively used as an underlying platform to best utilize the increased transfer bandwidth and accelerate the NFS/CIFS-based applications. GOS can also run over SCSI, Fibre Channel or iSCSI, which does not affect the acceleration performance, offering both file level protocols and block level protocols for storage area network (SAN) from the same system. In a grid infrastructure, resources may be geographically distant from each other, produced by differing manufacturers, and have differing access control policies. This makes access to grid resources dynamic and conditional upon local constraints. Centralized management techniques for these resources are limited in their scalability both in terms of execution efficiency and fault tolerance. Provision of services across such platforms requires a distributed resource management mechanism and the peer-to-peer clustered GOS appliances allow a single storage image to continue to expand, even if a single GOS appliance reaches its capacity limitations. The cluster shares a common, aggregate presentation of the data stored on all participating GOS appliances. Each GOS appliance manages its own internal storage space. The major benefit of this aggregation is that clustered GOS storage can be accessed by users as a single mount point. GOS products fit the thin-server categorization. Compared with traditional “fat server”-based storage architectures, thin-server GOS appliances deliver numerous advantages, such as the alleviation of potential network/grid bottle-necks, CPU and OS optimized for I/O only, ease of installation, remote management and minimal maintenance, low cost and Plug and Play, etc. Examples of similar innovations include NAS, printers, fax machines, routers and switches. An Apache server has been installed in the GOS operating system, ensuring an HTTPS-based communication between the GOS server and an administrator via a Web browser. Remote management and monitoring makes it easy to set up, manage, and monitor GOS systems. == History == Frank Zhigang Wang and Na Helian proposed a funding proposal to the UK government titled “Grid-Oriented Storage (GOS): Next Generation Data Storage System Architecture for the Grid Computing Era” in 2003. The proposal was approved and granted one million pounds in 2004. The first prototype was constructed in 2005 at Centre for Grid Computing, Cambridge-Cranfield High Performance Computing Facility. The first conference presentation was at IEEE Symposium on Cluster Computing and Grid (CCGrid), 9–12 May 2005, Cardiff, UK. As one of the five best work-in-progress, it was included in the IEEE Distributed Systems Online. In 2006, the GOS architecture and its implementations was published in IEEE Transactions on Computers, titled “Grid-oriented Storage: A Single-Image, Cross-Domain, High-Bandwidth Architecture”. Starting in January 2007, demonstrations were presented at Princeton University, Cambridge University Computer Lab and others. By 2013, the Cranfield Centre still used future tense for the project. Peer-to-peer file sharings use similar techniques.

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  Onshape

  Onshape is a computer-aided design (CAD) software system, delivered over the Internet via a software as a service (SaaS) model. It makes extensive use of cloud computing, with compute-intensive processing and rendering performed on Internet-based servers, and users are able to interact with the system via a web browser or the iOS and Android apps. As a SaaS system, Onshape upgrades are released directly to the web interface, and the software does not require maintenance by the user. Onshape allows teams to collaborate on a single shared design, the same way multiple writers can work together editing a shared document via cloud services. It is primarily focused on mechanical CAD (MCAD) and is used for product and machinery design across many industries, including consumer electronics, mechanical machinery, medical devices, 3D printing, machine parts, and industrial equipment. As of 2025, Onshape is popularly used as a CAD suite for the FIRST Robotics Competition (FRC) alongside the MKCad application available in the Onshape App Store. == Company history == Onshape was developed by a company with the same name. Founded in 2012, Onshape was based in Cambridge, Massachusetts (USA), with offices in Singapore and Pune, India. Its leadership team includes several engineers and executives who originated from SolidWorks, a popular 3D CAD program that runs on Microsoft Windows. Onshape’s co-founders include two former SolidWorks CEOs, Jon Hirschtick and John McEleney. In November 2012, former SolidWorks CEOs Jon Hirschtick and John McEleney led six co-founders launching Belmont Technology, a placeholder name that was later changed to Onshape. The company’s first round of funding was $9 million from North Bridge Venture Partners and Commonwealth Capital. In March 2015, Onshape released the public beta version of its cloud CAD software, after pre-production testing with more than a thousand CAD professionals in 52 countries. Included in the beta launch was Onshape for iPhone. In August 2015, the company released its Onshape for Android app. In December 2015, Onshape launched its full commercial release. The company also launched the Onshape App Store, offering CAM, simulation, rendering and other cloud-based engineering tools. The Onshape App Store was launched with 24 developer partners. In April 2016, Onshape introduced its Education Plan, with a free version of Onshape Professional geared for college students and educators. In May 2016, Onshape released FeatureScript, a new open source (MIT licensed) programming language for creating and customizing CAD features. In October 2019, Onshape agreed to be acquired by PTC. The acquisition closed in November 2019 for $470 million. In February 2024, Onshape released iOS support for the Apple Vision Pro, allowing for real world applications of CAD models and prototypes. In January 2025, Onshape released the CAM studio, allowing users to generate G-code for up to 5-axis Simultaneous milling. == Funding == Onshape was a venture-backed company with investments from firms including Andreessen Horowitz, Commonwealth Capital Ventures, New Enterprise Associates (NEA) and North Bridge Venture Partners. Total venture funding amounted to $169 million. == Supported file formats == === Modelling === ==== Importing ==== As of May 2025, Onshape supported importing (opening) the following common CAD file formats: Parasolid X_T (Preferred) STEP (ISO 10303) ISO JT (ISO 14306) ACIS IGES CATIA v4, v5, v6 Autodesk Inventor Part (.IPT) Assembly (.IAM) Presentation (.IPN) Drawing (.IDW) Pro/ENGINEER, Creo Rhinoceros 3D: .3dm .STL .OBJ SolidWorks file formats Siemens NX file formats Drawings (.DXF/.DWG) ==== Exporting ==== Onshape supports exporting to the following formats: STEP (ISO 10303) Parasolid XT ACIS IGES SolidWorks file formats .STL Rhinoceros 3D: .3dm Collada XML-spec based textual file === Drawing === Ordinary engineering or technical drawing can be exported as .PDF file. === Other Formats === In addition to CAD file formats, Onshape supports importing some Non-CAD file formats for viewing and referencing. === Assembly === Assemblies can be imported and exported to: STEP (ISO 10303) Parasolid XT ACIS Pro/ENGINEER, Creo ISO JT Rhinoceros 3D: .3dm Siemens NX file formats SolidWorks Pack and Go zip file File formats that assemblies can be only-exported to, are: IGES .STL Collada XML-spec based textual file

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  AI-assisted reverse engineering

  AI-assisted reverse engineering (AIARE) is a branch of computer science that leverages artificial intelligence (AI), notably machine learning (ML) strategies, to augment and automate the process of reverse engineering. The latter involves breaking down a product, system, or process to comprehend its structure, design, and functionality. AIARE was primarily introduced in the early years of the 21st century, witnessing substantial advancements from the mid-2010s onwards. == Overview == Conventionally, reverse engineering is conducted by specialists who dismantle a system to grasp its working principles, often for the purposes of reproduction, modification, enhancement of compatibility, or forensic examination. This method, while efficient, can be laborious and time-intensive, particularly when dealing with intricate software or hardware systems. AIARE integrates machine learning algorithms to either partially automate or augment this process. It is capable of detecting patterns, relationships, structures, and potential vulnerabilities within the analyzed system, frequently surpassing human experts in speed and accuracy. This has rendered AIARE a critical tool in numerous fields, including cybersecurity, software development, and hardware design and analysis. == Techniques == AIARE encompasses several AI methodologies: === Supervised learning === Supervised learning employs tagged data to train models to recognize system components, their operations, and their interconnections. This method is particularly helpful in software analysis to discover vulnerabilities or enhance compatibility. === Unsupervised learning === Unsupervised learning is utilized to detect concealed patterns and structures in untagged data. It proves beneficial in comprehending complex systems where there's no evident labeling or mapping of components. === Reinforcement learning === Reinforcement learning is employed to build models that progressively refine their system understanding through a process of trial and error. This method is often implemented when deciphering a system's functionality under various circumstances or configurations. === Deep learning === Deep learning is employed for analysis of high-dimensional data. For instance, deep learning techniques can aid in examining the layout and connections of integrated circuits (ICs), substantially reducing the manual effort required for reverse engineering. == Benefits == === Usable Security === AIARE expands usable security as reverse engineering is traditionally slow and highly specialized as it produces dense, low-level information (usually in Assembly or C) when using tools like Ghidra. The use of multiple different methods to interface with models today (such as through chat bots like ChatGPT) greatly reduces the barrier to entry by providing a clear way to interact with the user and even providing meaningful decompiled source code. In addition, either done automatically or through prompt engineering, a model is capable of producing a high-level summary and explanation of its reverse engineering efforts in human-readable form that doesn't require much knowledge on code. === Speedup === AIARE is capable of processing data much faster than humans, providing a boost in speed when analyzing said data. In the context of computer security, this can greatly speed up incident management or response and malware detection as AIARE can be automated to drastically reduce the manual effort usually associated with reverse engineering. == Limitations == In an effort to improve readability for reverse engineering, AI-generated code may introduce erroneous bugs not present in the source. This compromises the correctness of the code if not carefully validated and will throw off reverse engineering efforts. Additionally, AIARE's weakness in zero-shot prompting makes gathering accurate data without reference data in the prompt more inconsistent, thus requiring a user to provide some quality data of their own that hurts its usability.

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  Personal, Inc.

  Personal (also referred to as Personal.com or Personal, Inc.) was a consumer personal data service and identity management system for individuals to aggregate, manage and reuse their own data. It merged with digi.me in August 2017, a business in Europe that has the same business model. The combined company is called digi.me. One of its product lines, a collaborative data management and information security solution for the workplace called TeamData, was spun off as a new company as a result of the merger. == History == Personal was founded in 2009 in Washington, DC by the management team that built The Map Network, a location data and mapping platform that was acquired by Nokia/NAVTEQ in 2006. Personal was the first online consumer-facing company to be named an Ambassador for Privacy by Design for its technical, business and legal commitments to providing users with control over the data they store in Personal's service. Called a “life management platform” by The Economist and a “personal encrypted cloud service” by TIME for its user-centric approach to data, the company has been associated with both the Infomediary model originated in 1999 by John Hagel III and Mark Singer, as well as the vendor relationship management (VRM) model developed by Doc Searls. Personal raised $30m in funding to develop its platform and products from such leading investors as Steve Case's Revolution Ventures, Grotech Ventures, Allen & Company, Ted Leonsis, Neil Ashe, Jonathan Miller, Bill Miller of Legg Mason, Esther Dyson of EDventures, and Eric C. Anderson. The company received recognition for its user agreement, called the Owner Data Agreement, which acted like a reverse license agreement when data was shared between registered parties and emphasized that data ownership resides with the user. Doc Searls wrote in The Intention Economy: When Customers Take Charge that the Owner Data Agreement “had no precedent and modeled a new legal position, both for vendors and for intermediaries.” Personal was early to embrace “small data,” which it defines as “big data for the benefit of individuals.” The term “small data” may have been originally coined by Jeremie Miller of Sing.ly, who mentioned it in a talk at the Web 2.0 Summit in November 2011 and is cited in The Intention Economy. In 2011, Personal was a part of the first group of companies to join the Personal Data Ecosystem Consortium's Startup Circle. A Small Data Meetup group has also formed in New York City, bringing together technology, legal and business experts to exchange ideas about user-centric and user-driven models for internet products and services. Personal has been included in case studies by Ctrl-Shift and Forrester regarding Personal Data Stores and Personal Identity Management. In 2011, Personal received the Innovator Spotlight Award at Privacy Identity Innovation Conference (pii2011) and participated in the Technology Showcase at pii2012. In 2012, TechHive named Personal as one of the top five apps or web services of SXSW. Personal won the 2013 Campus Technology Innovators Award with Lone Star College in July 2013. Personal was included in a list of Executive Travel Magazine's favorite travel apps for 2013 in its May/June issue. In 2013, Personal was also included as part of NYU GovLab's Open Data 500 and was named by J. Walter Thompson as one of 100 things to watch for in 2014. In 2015, the National Law Journal named Company Chief Policy Officer and General Counsel, Joshua P. Galper, as one of their 50 "Cybersecurity & Privacy Trailblazers." == Products and services == === Overview === The Personal Platform was a privacy- and security-by-design platform for individuals to manage and reuse their own data and information. The Fill It app was a 1-click form-filling solution for web and mobile logins, checkouts and forms, and the Data Vault app served as the main cloud-based repository for a user's data. Personal helped individuals take control and benefit from their information while knowing that the information in their Data Vault remained legally theirs and could not be used without their permission. === Data Vault with Cloud Sync === Personal spent two years building the Personal Platform before launching its Data Vault product in beta in November 2011. Following Privacy by Design principles, Personal only enabled users to see or share the sensitive data and all the files they stored in their Data Vault. Such information was encrypted, and could only be decrypted with a user's password. Only users could choose and know their passwords to their vault because Personal did not store user passwords – and therefore could not reset them without deleting a user's sensitive data and all files stored in their vault. All Personal apps and services were linked to a user's private Data Vault. The Data Vault featured automatic synchronization of data and files added on any device logged into Personal. It also featured a “Secure Share” function that created a live, private network, allowing registered users to share access to data and files through an exchange of encrypted keys without the risk of transmitting the data or files through non-secure, direct means. It also allowed users to immediately update data across their own network and revoke access to it when they choose. Fast Company called the Data Vault “a tool that will simplify our lives.” Personal launched its Android app on November 30, 2011. The iOS Data Vault app was released on May 7, 2012. Personal officially launched its application programming interface (APIs) on October 2, 2012 at the Mashery Business of APIs Conference. A review by CNET highlighted the challenges of getting people to trust such a new service with their sensitive data and spending the time required entering enough data to make it useful. === Fill It App and Form Index === When the Data Vault was launched in November 2011, Mashable posed the question: “Never Fill Out a Form Again?” The World Economic Forum in its February 2013 report highlighted the possibility of saving 10 billion hours globally “and improv[ing] the delivery of public and private sector services” through automated form-filling tools, specifically citing Personal's Fill It app. In January 2013, Personal launched Fill It in beta as a web bookmarklet for automatic form-filling. On June 11, 2014, Personal released Fill It as a web extension and announced that it was publishing an index of over 140,000 1-click online forms at www.fillit.com. The company also announced that a mobile version of the product will launch later in the year. According to a story in Tech Cocktail about the launch, Personal's “web extension and mobile app are able to support over 1,200 different types of reusable data, even enabling them to unlock more confidential information so they can complete longer forms, including patient registrations, job applications, event registrations, school admissions, insurance and bank applications, and government forms.” In November 2014, a mobile version of Fill It was launched that could autofill mobile forms using APIs. Personal's form portal ultimately indexed more than 500,000 forms with three components, which, together, allowed data to be captured and reused across any of the forms: (1) a form graph, which mapped individual form fields to the Personal ontology; (2) a semantic layer, which determined how data was required on a form (e.g. one field vs. three fields for a U.S. telephone number); and (3) a correlations graph, which helped individuals match their specific data to a form without looking at the data value (e.g. knowing which phone number is a mobile phone number, which address is a billing address, or that a person uses their middle name as a first name on most forms). === Monetizing personal data === With the initial public offering of Facebook in May 2012, there was media interest in the question of the monetary value of personal data and whether tools and services might emerge to help consumers monetize their own data. Personal was frequently cited as a company that could potentially offer such a service. Articles and pieces focusing on this subject have appeared in The New York Times, AdWeek, the MIT Technology Review, and on CNN and National Public Radio. Company Co-founder and CEO Shane Green was quoted as saying that “the average American consumer would soon be able to realize over $1,000 per year” by granting limited, anonymous access to their data to marketers, but that figure was never supported by Green or the company. === Launch of TeamData === In May 2016, Personal shifted its product focus to TeamData, which focuses on the problem of securing and collaboratively managing data in the workplace. It is now a separate business.

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