AI Headshot Linkedin Generator

AI Headshot Linkedin Generator — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Biohybrid microswimmer

    Biohybrid microswimmer

    A biohybrid microswimmer also known as biohybrid nanorobot, can be defined as a microswimmer that consist of both biological and artificial constituents, for instance, one or several living microorganisms attached to one or various synthetic parts. In recent years nanoscopic and mesoscopic objects have been designed to collectively move through direct inspiration from nature or by harnessing its existing tools. Small mesoscopic to nanoscopic systems typically operate at low Reynolds numbers (Re ≪ 1), and understanding their motion becomes challenging. For locomotion to occur, the symmetry of the system must be broken. In addition, collective motion requires a coupling mechanism between the entities that make up the collective. To develop mesoscopic to nanoscopic entities capable of swarming behaviour, it has been hypothesised that the entities are characterised by broken symmetry with a well-defined morphology, and are powered with some material capable of harvesting energy. If the harvested energy results in a field surrounding the object, then this field can couple with the field of a neighbouring object and bring some coordination to the collective behaviour. Such robotic swarms have been categorised by an online expert panel as among the 10 great unresolved group challenges in the area of robotics. Although investigation of their underlying mechanism of action is still in its infancy, various systems have been developed that are capable of undergoing controlled and uncontrolled swarming motion by harvesting energy (e.g., light, thermal, etc.). Over the past decade, biohybrid microrobots, in which living mobile microorganisms are physically integrated with untethered artificial structures, have gained growing interest to enable the active locomotion and cargo delivery to a target destination. In addition to the motility, the intrinsic capabilities of sensing and eliciting an appropriate response to artificial and environmental changes make cell-based biohybrid microrobots appealing for transportation of cargo to the inaccessible cavities of the human body for local active delivery of diagnostic and therapeutic agents. == Background == Biohybrid microswimmers can be defined as microswimmers that consist of both biological and artificial constituents, for instance, one or several living microorganisms attached to one or various synthetic parts. The pioneers of this field, ahead of their time, were Montemagno and Bachand with a 1999 work regarding specific attachment strategies of biological molecules to nanofabricated substrates enabling the preparation of hybrid inorganic/organic nanoelectromechanical systems, so called NEMS. They described the production of large amounts of F1-ATPase from the thermophilic bacteria Bacillus PS3 for the preparation of F1-ATPase biomolecular motors immobilized on a nanoarray pattern of gold, copper or nickel produced by electron beam lithography. These proteins were attached to one micron microspheres tagged with a synthetic peptide. Consequently, they accomplished the preparation of a platform with chemically active sites and the development of biohybrid devices capable of converting energy of biomolecular motors into useful work. One of the most fundamental questions in science is what defines life. Collective motion is one of the hallmarks of life. This is commonly observed in nature at various dimensional levels as energized entities gather, in a concerted effort, into motile aggregated patterns. These motile aggregated events can be noticed, among many others, as dynamic swarms; e.g., unicellular organisms such as bacteria, locust swarms, or the flocking behaviour of birds. Ever since Newton established his equations of motion, the mystery of motion on the microscale has emerged frequently in scientific history, as famously demonstrated by a couple of articles that should be discussed briefly. First, an essential concept, popularized by Osborne Reynolds, is that the relative importance of inertia and viscosity for the motion of a fluid depends on certain details of the system under consideration. The Reynolds number Re, named in his honor, quantifies this comparison as a dimensionless ratio of characteristic inertial and viscous forces: R e = ρ u l μ {\displaystyle \mathrm {Re} ={\frac {\rho ul}{\mu }}} Here, ρ represents the density of the fluid; u is a characteristic velocity of the system (for instance, the velocity of a swimming particle); l is a characteristic length scale (e.g., the swimmer size); and μ is the viscosity of the fluid. Taking the suspending fluid to be water, and using experimentally observed values for u, one can determine that inertia is important for macroscopic swimmers like fish (Re = 100), while viscosity dominates the motion of microscale swimmers like bacteria (Re = 10−4). The overwhelming importance of viscosity for swimming at the micrometer scale has profound implications for swimming strategy. This has been discussed memorably by E. M. Purcell, who invited the reader into the world of microorganisms and theoretically studied the conditions of their motion. In the first place, propulsion strategies of large scale swimmers often involve imparting momentum to the surrounding fluid in periodic discrete events, such as vortex shedding, and coasting between these events through inertia. This cannot be effective for microscale swimmers like bacteria: due to the large viscous damping, the inertial coasting time of a micron-sized object is on the order of 1 μs. The coasting distance of a microorganism moving at a typical speed is about 0.1 angstroms (Å). Purcell concluded that only forces that are exerted in the present moment on a microscale body contribute to its propulsion, so a constant energy conversion method is essential. Microorganisms have optimized their metabolism for continuous energy production, while purely artificial microswimmers (microrobots) must obtain energy from the environment, since their on-board-storage-capacity is very limited. As a further consequence of the continuous dissipation of energy, biological and artificial microswimmers do not obey the laws of equilibrium statistical physics, and need to be described by non-equilibrium dynamics. Mathematically, Purcell explored the implications of low Reynolds number by taking the Navier-Stokes equation and eliminating the inertial terms: μ ∇ 2 u − ∇ p = 0 {\displaystyle {\begin{aligned}\mu \nabla ^{2}\mathbf {u} -{\boldsymbol {\nabla }}p&={\boldsymbol {0}}\\\end{aligned}}} where u {\displaystyle \mathbf {u} } is the velocity of the fluid and ∇ p {\displaystyle {\boldsymbol {\nabla }}p} is the gradient of the pressure. As Purcell noted, the resulting equation — the Stokes equation — contains no explicit time dependence. This has some important consequences for how a suspended body (e.g., a bacterium) can swim through periodic mechanical motions or deformations (e.g., of a flagellum). First, the rate of motion is practically irrelevant for the motion of the microswimmer and of the surrounding fluid: changing the rate of motion will change the scale of the velocities of the fluid and of the microswimmer, but it will not change the pattern of fluid flow. Secondly, reversing the direction of mechanical motion will simply reverse all velocities in the system. These properties of the Stokes equation severely restrict the range of feasible swimming strategies. Recent publications of biohybrid microswimmers include the use of sperm cells, contractive muscle cells, and bacteria as biological components, as they can efficiently convert chemical energy into movement, and additionally are capable of performing complicated motion depending on environmental conditions. In this sense, biohybrid microswimmer systems can be described as the combination of different functional components: cargo and carrier. The cargo is an element of interest to be moved (and possibly released) in a customized way. The carrier is the component responsible for the movement of the biohybrid, transporting the desired cargo, which is linked to its surface. The great majority of these systems rely on biological motile propulsion for the transportation of synthetic cargo for targeted drug delivery/ There are also examples of the opposite case: artificial microswimmers with biological cargo systems. Over the past decade, biohybrid microrobots, in which living mobile microorganisms are physically integrated with untethered artificial structures, have gained growing interest to enable the active locomotion and cargo delivery to a target destination. In addition to the motility, the intrinsic capabilities of sensing and eliciting an appropriate response to artificial and environmental changes make cell-based biohybrid microrobots appealing for transportation of cargo to the inaccessible cavities of the human body for local active delivery of diagnostic and therapeutic agents. Active locomotion, targeting and steering of concentrated therape

    Read more →
  • Ubiquitous computing

    Ubiquitous computing

    Ubiquitous computing (or "ubicomp") is a concept in software engineering, hardware engineering and computer science where computing is made to appear seamlessly anytime and everywhere. In contrast to desktop computing, ubiquitous computing implies use on any device, in any location, and in any format. A user interacts with the computer, which can exist in many different forms, including laptop computers, tablets, smart phones and terminals in everyday objects such as a refrigerator or a pair of glasses. The underlying technologies to support ubiquitous computing include the Internet, advanced middleware, kernels, operating systems, mobile codes, sensors, microprocessors, new I/Os and user interfaces, computer networks, mobile protocols, global navigational systems, and new materials. This paradigm is also described as pervasive computing, ambient intelligence, or "everyware". Each term emphasizes slightly different aspects. When primarily concerning the objects involved, it is also known as physical computing, the Internet of Things, haptic computing, and "things that think". Rather than propose a single definition for ubiquitous computing and for these related terms, a taxonomy of properties for ubiquitous computing has been proposed, from which different kinds or flavors of ubiquitous systems and applications can be described. Ubiquitous computing themes include: distributed computing, mobile computing, location computing, mobile networking, sensor networks, human–computer interaction, context-aware smart home technologies, and artificial intelligence. == Core concepts == Ubiquitous computing is the concept of using small internet connected and inexpensive computers to help with everyday functions in an automated fashion. Mark Weiser proposed three basic forms for ubiquitous computing devices: Tabs: a wearable device that is approximately a centimeter in size Pads: a hand-held device that is approximately a decimeter in size Boards: an interactive larger display device that is approximately a meter in size Ubiquitous computing devices proposed by Mark Weiser are all based around flat devices of different sizes with a visual display. These conceptual device categories were later implemented at Xerox PARC in experimental systems including the PARCTab, PARCPad, and LiveBoard, which served as early prototypes of handheld, tablet-style, and large interactive display computing environments. Expanding beyond those concepts there is a large array of other ubiquitous computing devices that could exist. == History == Mark Weiser coined the phrase "ubiquitous computing" around 1988, during his tenure as Chief Technologist of the Xerox Palo Alto Research Center (PARC). Both alone and with PARC Director and Chief Scientist John Seely Brown, Weiser wrote some of the earliest papers on the subject, largely defining it and sketching out its major concerns. == Recognizing the effects of extending processing power == Recognizing that the extension of processing power into everyday scenarios would necessitate understandings of social, cultural and psychological phenomena beyond its proper ambit, Weiser was influenced by many fields outside computer science, including "philosophy, phenomenology, anthropology, psychology, post-Modernism, sociology of science and feminist criticism". He was explicit about "the humanistic origins of the 'invisible ideal in post-modernist thought'", referencing as well the ironically dystopian Philip K. Dick novel Ubik. Andy Hopper from Cambridge University UK proposed and demonstrated the concept of "Teleporting" – where applications follow the user wherever he/she moves. Roy Want (now at Google), while at Olivetti Research Ltd, designed the first "Active Badge System", which is an advanced location computing system where personal mobility is merged with computing. Later at Xerox PARC, he designed and built the "PARCTab" or simply "Tab", widely recognized as the world's first Context-Aware computer, which has great similarity to the modern smartphone. Bill Schilit (now at Google) also did some earlier work in this topic, and participated in the early Mobile Computing workshop held in Santa Cruz in 1996. Ken Sakamura of the University of Tokyo, Japan leads the Ubiquitous Networking Laboratory (UNL), Tokyo as well as the T-Engine Forum. The joint goal of Sakamura's Ubiquitous Networking specification and the T-Engine forum, is to enable any everyday device to broadcast and receive information. MIT has also contributed significant research in this field, notably Things That Think consortium (directed by Hiroshi Ishii, Joseph A. Paradiso and Rosalind Picard) at the Media Lab and the CSAIL effort known as Project Oxygen. Other major contributors include University of Washington (Shwetak Patel, Anind Dey and James Landay), Dartmouth College's HealthX Lab (directed by Andrew Campbell), Georgia Tech's College of Computing (Gregory Abowd and Thad Starner), Cornell Tech's People Aware Computing Lab (directed by Tanzeem Choudhury), NYU's Interactive Telecommunications Program, UC Irvine's Department of Informatics, Microsoft Research, Intel Research and Equator, Ajou University UCRi & CUS. == Examples == One of the earliest ubiquitous systems was artist Natalie Jeremijenko's "Live Wire", also known as "Dangling String", installed at Xerox PARC during Mark Weiser's time there. This was a piece of string attached to a stepper motor and controlled by a LAN connection; network activity caused the string to twitch, yielding a peripherally noticeable indication of traffic. Weiser called this an example of calm technology. A present manifestation of this trend is the widespread diffusion of mobile phones. Many mobile phones support high speed data transmission, video services, and other services with powerful computational ability. Although these mobile devices are not necessarily manifestations of ubiquitous computing, there are examples, such as Japan's Yaoyorozu ("Eight Million Gods") Project in which mobile devices, coupled with radio frequency identification tags demonstrate that ubiquitous computing is already present in some form. Ambient Devices has produced an "orb", a "dashboard", and a "weather beacon": these decorative devices receive data from a wireless network and report current events, such as stock prices and the weather, like the Nabaztag, which was invented by Rafi Haladjian and Olivier Mével, and manufactured by the company Violet. The Australian futurist Mark Pesce has produced a highly configurable 52-LED LAMP enabled lamp which uses Wi-Fi named MooresCloud after Gordon Moore. The Unified Computer Intelligence Corporation launched a device called Ubi – The Ubiquitous Computer designed to allow voice interaction with the home and provide constant access to information. Ubiquitous computing research has focused on building an environment in which computers allow humans to focus attention on select aspects of the environment and operate in supervisory and policy-making roles. Ubiquitous computing emphasizes the creation of a human computer interface that can interpret and support a user's intentions. For example, MIT's Project Oxygen seeks to create a system in which computation is as pervasive as air: In the future, computation will be human centered. It will be freely available everywhere, like batteries and power sockets, or oxygen in the air we breathe...We will not need to carry our own devices around with us. Instead, configurable generic devices, either handheld or embedded in the environment, will bring computation to us, whenever we need it and wherever we might be. As we interact with these "anonymous" devices, they will adopt our information personalities. They will respect our desires for privacy and security. We won't have to type, click, or learn new computer jargon. Instead, we'll communicate naturally, using speech and gestures that describe our intent... This is a fundamental transition that does not seek to escape the physical world and "enter some metallic, gigabyte-infested cyberspace" but rather brings computers and communications to us, making them "synonymous with the useful tasks they perform". Network robots link ubiquitous networks with robots, contributing to the creation of new lifestyles and solutions to address a variety of social problems including the aging of population and nursing care. The "Continuity" set of features, introduced by Apple in OS X Yosemite, can be seen as an example of ubiquitous computing. == Issues == Privacy is easily the most often-cited criticism of ubiquitous computing (ubicomp), and may be the greatest barrier to its long-term success. == Research centres == This is a list of notable institutions who claim to have a focus on Ubiquitous computing sorted by country: Canada Topological Media Lab, Concordia University, Canada Finland Community Imaging Group, University of Oulu, Finland Germany Telecooperation Office (TECO), Karlsruhe Institute of Technology, Ger

    Read more →
  • Sedona Canada Principles

    Sedona Canada Principles

    The Sedona Canada Principles are a set of authoritative guidelines published by The Sedona Conference to aid members of the Canadian legal community involved in the identification, collection, preservation, review and production of electronically stored information (ESI). The principles were drafted by a small group of lawyers, judges and technologists called the Sedona Working Group 7 or Sedona Canada. Sedona Canada is an offshoot of The Sedona Conference which is an American "non-profit ... research and educational institute dedicated to the advanced study of law and policy in the areas of antitrust law, complex litigation, and intellectual property rights". == Background == Civil procedure in Canada is jurisdictional with each province following its own rules of civil procedure. However, each province must address the fact that due to the advancement of technology the discovery process enshrined in the rules of civil procedure can be potentially derailed due to the sheer volume of electronically stored information (ESI). When dealing with litigation matters that involve electronically stored information (ESI), the discovery process is commonly called e-discovery. The problems associated with e-discovery in Canada led to the creation of the Sedona Canada Principles. Rule 29.1.03(4) of the wikibooks:Ontario Rules of Civil Procedure specifically refers to the Sedona Canada Principles in referencing Principles re Electronic Discovery although it has been reported that this rule has been largely ignored in practice. == Summary == The Sedona Canada Principles largely refer to the processes found in the Electronic Discovery Reference Model. The principles urge proportionality due to the potentially enormous volumes of documents that may be discoverable when dealing with ESI. They also encourage good faith in the document preservation stage and regular meetings between parties to discuss the scope of the litigation. Parties are urged to be aware of the potential costs involved in producing relevant ESI but are advised that only reasonably accessible ESI need be produced. The principles stipulate that parties should not be required to search for or collect deleted material unless there is an agreement or court order related to those terms. The use of electronic tools and processes such as data sampling and web harvesting are acceptable practices. Parties are encouraged to agree early in the litigation process on production format required for the exchange of relevant documents as part of the discovery process (native files, pdf, tiff, metadata requirements etc.). Agreements or direction should be sought, if necessary, with respect to privilege or other confidential information related to production of electronic documents and data. Parties should be aware that legal precedents can be formed as a result of e-discovery practices and sanctions can be considered for a party's failure to meet their discovery obligations unless it can be demonstrated that the failure was not intentional. All parties must bear the “reasonable” costs associated with e-discovery but other arrangements can be agreed upon by the parties or by court order. == Caselaw == In Warman v. National Post Company proportionality was at issue in a case where the plaintiff was suing the defendant for libel. A motion was brought by the defendant to have the plaintiff provide a mirror image of his hard drive in an effort to prove an internet article was indeed authored by the plaintiff. Issues of proportionality and the work of the Sedona Conference and Sedona Canada Principles were factored in to the decision to grant the defendant only limited access to the hard drive. In Innovative Health Group Inc. v. Calgary Health Region the plaintiff's legal obligation to produce imaged hard drives is in question. Justice Conrad refers to the advice of Sedona Canada on proportionality and problems associated with time and expense related to the difficulties associated with electronically stored information. In York University v. Michael Markicevic Justice Brown specifically refers to the need for the parties to agree upon a formal e-discovery plan to be drafted in consultation with Sedona Canada Principles. In Friends of Lansdowne v. Ottawa Master MacLeod refers to the need for Sedona Canada principles and states “This is particularly true in the current information age when e-mail is ubiquitous and multiple copies or variants of messages may be held on various kinds of data storage devices including individual hard drives, e-mail and Blackberry servers. Even documents that ultimately exist in paper form normally begin their life on computers and negotiations frequently involve exchanges of electronic drafts. To find every scrap of paper and every electronic trace of relevant information has become a nightmarish task that threatens to render any kind of litigation extravagantly expensive.” == Criticism == Critics of the Sedona Canada Principles believe they should address system integrity and that the true history of any file preserved cannot be identified without proof of the integrity of the electronic record systems management it comes from. Other criticism is more directed to the Sedona Canada working group and complaints that it is insular and irrelevant.

    Read more →
  • Lancichinetti–Fortunato–Radicchi benchmark

    Lancichinetti–Fortunato–Radicchi benchmark

    Lancichinetti–Fortunato–Radicchi benchmark is an algorithm that generates benchmark networks (artificial networks that resemble real-world networks). They have a priori known communities and are used to compare different community detection methods. The advantage of the benchmark over other methods is that it accounts for the heterogeneity in the distributions of node degrees and of community sizes. == The algorithm == The node degrees and the community sizes are distributed according to a power law, with different exponents. The benchmark assumes that both the degree and the community size have power law distributions with different exponents, γ {\displaystyle \gamma } and β {\displaystyle \beta } , respectively. N {\displaystyle N} is the number of nodes and the average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . There is a mixing parameter μ {\displaystyle \mu } , which is the average fraction of neighboring nodes of a node that do not belong to any community that the benchmark node belongs to. This parameter controls the fraction of edges that are between communities. Thus, it reflects the amount of noise in the network. At the extremes, when μ = 0 {\displaystyle \mu =0} all links are within community links, if μ = 1 {\displaystyle \mu =1} all links are between nodes belonging to different communities. One can generate the benchmark network using the following steps. Step 1: Generate a network with nodes following a power law distribution with exponent γ {\displaystyle \gamma } and choose extremes of the distribution k min {\displaystyle k_{\min }} and k max {\displaystyle k_{\max }} to get desired average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . Step 2: ( 1 − μ ) {\displaystyle (1-\mu )} fraction of links of every node is with nodes of the same community, while fraction μ {\displaystyle \mu } is with the other nodes. Step 3: Generate community sizes from a power law distribution with exponent β {\displaystyle \beta } . The sum of all sizes must be equal to N {\displaystyle N} . The minimal and maximal community sizes s min {\displaystyle s_{\min }} and s max {\displaystyle s_{\max }} must satisfy the definition of community so that every non-isolated node is in at least in one community: s min > k min {\displaystyle s_{\min }>k_{\min }} s max > k max {\displaystyle s_{\max }>k_{\max }} Step 4: Initially, no nodes are assigned to communities. Then, each node is randomly assigned to a community. As long as the number of neighboring nodes within the community does not exceed the community size a new node is added to the community, otherwise stays out. In the following iterations the “homeless” node is randomly assigned to some community. If that community is complete, i.e. the size is exhausted, a randomly selected node of that community must be unlinked. Stop the iteration when all the communities are complete and all the nodes belong to at least one community. Step 5: Implement rewiring of nodes keeping the same node degrees but only affecting the fraction of internal and external links such that the number of links outside the community for each node is approximately equal to the mixing parameter μ {\displaystyle \mu } . == Testing == Consider a partition into communities that do not overlap. The communities of randomly chosen nodes in each iteration follow a p ( C ) {\displaystyle p(C)} distribution that represents the probability that a randomly picked node is from the community C {\displaystyle C} . Consider a partition of the same network that was predicted by some community finding algorithm and has p ( C 2 ) {\displaystyle p(C_{2})} distribution. The benchmark partition has p ( C 1 ) {\displaystyle p(C_{1})} distribution. The joint distribution is p ( C 1 , C 2 ) {\displaystyle p(C_{1},C_{2})} . The similarity of these two partitions is captured by the normalized mutual information. I n = ∑ C 1 , C 2 p ( C 1 , C 2 ) log 2 ⁡ p ( C 1 , C 2 ) p ( C 1 ) p ( C 2 ) 1 2 H ( { p ( C 1 ) } ) + 1 2 H ( { p ( C 2 ) } ) {\displaystyle I_{n}={\frac {\sum _{C_{1},C_{2}}p(C_{1},C_{2})\log _{2}{\frac {p(C_{1},C_{2})}{p(C_{1})p(C_{2})}}}{{\frac {1}{2}}H(\{p(C_{1})\})+{\frac {1}{2}}H(\{p(C_{2})\})}}} If I n = 1 {\displaystyle I_{n}=1} the benchmark and the detected partitions are identical, and if I n = 0 {\displaystyle I_{n}=0} then they are independent of each other.

    Read more →
  • Language identification

    Language identification

    In natural language processing, language identification or language guessing is the problem of determining which natural language a given content is in. Computational approaches to this problem view it as a special case of text categorization, solved with various statistical methods. == Overview == === Logical approach === A common non-statistical intuitive approach (though highly uncertain) is to look for common letter combinations, or distinctive diacritics or punctuation. === Statistical approach === There are several statistical approaches to language identification. An older statistical method by Grefenstette was based on the frequency of short n-grams, which are often function morphemes. For example, "ing" is more common in English than in French, while the sequence "que" is more common in French. Given a new page found on the Web, one counts the number of occurrences of each such short sequence and picks the language whose frequency table it matches the most. One technique is to compare the compressibility of the text to the compressibility of texts in a set of known languages. This approach is known as mutual information based distance measure. The same technique can also be used to empirically construct family trees of languages which closely correspond to the trees constructed using historical methods. Mutual information based distance measure is essentially equivalent to more conventional model-based methods and is not generally considered to be either novel or better than simpler techniques. Another technique, as described by Cavnar and Trenkle (1994) and Dunning (1994) is to create a language n-gram model from a "training text" for each of the languages. These models can be based on characters (Cavnar and Trenkle) or encoded bytes (Dunning); in the latter, language identification and character encoding detection are integrated. Then, for any piece of text needing to be identified, a similar model is made, and that model is compared to each stored language model. The most likely language is the one with the model that is most similar to the model from the text needing to be identified. This approach can be problematic when the input text is in a language for which there is no model. In that case, the method may return another, "most similar" language as its result. Also problematic for any approach are pieces of input text that are composed of several languages, as is common on the Web. As of 2025, a commonly used baseline method is via the fastText library, which has comparable classification accuracy as deep learning techniques, but much faster. == Identifying similar languages == One of the great bottlenecks of language identification systems is to distinguish between closely related languages. Similar languages like Bulgarian and Macedonian or Indonesian and Malay present significant lexical and structural overlap, making it challenging for systems to discriminate between them. In 2014 the DSL shared task has been organized providing a dataset (Tan et al., 2014) containing 13 different languages (and language varieties) in six language groups: Group A (Bosnian, Croatian, Serbian), Group B (Indonesian, Malaysian), Group C (Czech, Slovak), Group D (Brazilian Portuguese, European Portuguese), Group E (Peninsular Spanish, Argentine Spanish), Group F (American English, British English). The best system reached performance of over 95% results (Goutte et al., 2014). Results of the DSL shared task are described in Zampieri et al. 2014. == Software == Apache OpenNLP includes char n-gram based statistical detector and comes with a model that can distinguish 103 languages Apache Tika contains a language detector for 18 languages

    Read more →
  • Research data archiving

    Research data archiving

    Research data archiving is the long-term storage of scholarly research data, including the natural sciences, social sciences, and life sciences. The various academic journals have differing policies regarding how much of their data and methods researchers are required to store in a public archive, and what is actually archived varies widely between different disciplines. Similarly, the major grant-giving institutions have varying attitudes towards public archiving of data. In general, the tradition of science has been for publications to contain sufficient information to allow fellow researchers to replicate and therefore test the research. In recent years this approach has become increasingly strained as research in some areas depends on large datasets which cannot easily be replicated independently. Data archiving is more important in some fields than others. In a few fields, all of the data necessary to replicate the work is already available in the journal article. In drug development, a great deal of data is generated and must be archived so researchers can verify that the reports the drug companies publish accurately reflect the data. Often used interchangeably, Data preservation and data archiving are both about protecting data for the long term, but they serve different purposes. Data preservation focuses on preventing data from being lost, damaged, or destroyed by creating backups, storing data in secure locations, and ensuring it remains accessible when needed. Data archiving, on the other hand, involves moving data that is no longer actively used to a separate storage location for long-term keeping. Archived data is often combined and compressed, and while it can still be accessed, it is not intended for regular use or frequent updates. The requirement of data archiving is a recent development in the history of science. It was made possible by advances in information technology allowing large amounts of data to be stored and accessed from central locations. For example, the American Geophysical Union (AGU) adopted their first policy on data archiving in 1993, about three years after the beginning of the WWW. This policy mandates that datasets cited in AGU papers must be archived by a recognised data center; it permits the creation of "data papers"; and it establishes AGU's role in maintaining data archives. But it makes no requirements on paper authors to archive their data. Prior to organized data archiving, researchers wanting to evaluate or replicate a paper would have to request data and methods information from the author. The academic community expects authors to share supplemental data. This process was recognized as wasteful of time and energy and obtained mixed results. Information could become lost or corrupted over the years. In some cases, authors simply refuse to provide the information. The need for data archiving and due diligence is greatly increased when the research deals with health issues or public policy formation. == Selected policies by journals == === Biotropica === Biotropica requires, as a condition for publication, that the data supporting the results in the paper and metadata describing them must be archived in an appropriate public archive such as Dryad, Figshare, GenBank, TreeBASE, or NCBI. Authors may elect to make the data publicly available as soon as the article is published or, if the technology of the archive allows, embargo access to the data up to three years after article publication. A statement describing Data Availability will be included in the manuscript as described in the instructions to authors. Exceptions to the required archiving of data may be granted at the discretion of the Editor-in-Chief for studies that include sensitive information (e.g., the location of endangered species). Our Editorial explaining the motivation for this policy can be found here. A more comprehensive list of data repositories is available here. Promoting a culture of collaboration with researchers who collect and archive data: The data collected by tropical biologists are often long-term, complex, and expensive to collect. The Board of Editors of Biotropica strongly encourages authors who re-use data archives archived data sets to include as fully engaged collaborators the scientists who originally collected them. We feel this will greatly enhance the quality and impact of the resulting research by drawing on the data collector’s profound insights into the natural history of the study system, reducing the risk of errors in novel analyses, and stimulating the cross-disciplinary and cross-cultural collaboration and training for which the ATBC and Biotropica are widely recognized. NB: Biotropica is one of only two journals that pays the fees for authors depositing data at Dryad. === The American Naturalist === The American Naturalist requires authors to deposit the data associated with accepted papers in a public archive. For gene sequence data and phylogenetic trees, deposition in GenBank or TreeBASE, respectively, is required. There are many possible archives that may suit a particular data set, including the Dryad repository for ecological and evolutionary biology data. All accession numbers for GenBank, TreeBASE, and Dryad must be included in accepted manuscripts before they go to Production. If the data is deposited somewhere else, please provide a link. If the data is culled from published literature, please deposit the collated data in Dryad for the convenience of your readers. Any impediments to data sharing should be brought to the attention of the editors at the time of submission so that appropriate arrangements can be worked out. === Journal of Heredity === The primary data underlying the conclusions of an article are critical to the verifiability and transparency of the scientific enterprise, and should be preserved in usable form for decades in the future. For this reason, Journal of Heredity requires that newly reported nucleotide or amino acid sequences, and structural coordinates, be submitted to appropriate public databases (e.g., GenBank; the EMBL Nucleotide Sequence Database; DNA Database of Japan; the Protein Data Bank; and Swiss-Prot). Accession numbers must be included in the final version of the manuscript. For other forms of data (e.g., microsatellite genotypes, linkage maps, images), the Journal endorses the principles of the Joint Data Archiving Policy (JDAP) in encouraging all authors to archive primary datasets in an appropriate public archive, such as Dryad, TreeBASE, or the Knowledge Network for Biocomplexity. Authors are encouraged to make data publicly available at time of publication or, if the technology of the archive allows, opt to embargo access to the data for a period up to a year after publication. The American Genetic Association also recognizes the vast investment of individual researchers in generating and curating large datasets. Consequently, we recommend that this investment be respected in secondary analyses or meta-analyses in a gracious collaborative spirit. === Molecular Ecology === Molecular Ecology expects that data supporting the results in the paper should be archived in an appropriate public archive, such as GenBank, Gene Expression Omnibus, TreeBASE, Dryad, the Knowledge Network for Biocomplexity, your own institutional or funder repository, or as Supporting Information on the Molecular Ecology web site. Data are important products of the scientific enterprise, and they should be preserved and usable for decades in the future. Authors may elect to have the data publicly available at time of publication, or, if the technology of the archive allows, may opt to embargo access to the data for a period up to a year after publication. Exceptions may be granted at the discretion of the editor, especially for sensitive information such as human subject data or the location of endangered species. === Nature === Such material must be hosted on an accredited independent site (URL and accession numbers to be provided by the author), or sent to the Nature journal at submission, either uploaded via the journal's online submission service, or if the files are too large or in an unsuitable format for this purpose, on CD/DVD (five copies). Such material cannot solely be hosted on an author's personal or institutional web site. Nature requires the reviewer to determine if all of the supplementary data and methods have been archived. The policy advises reviewers to consider several questions, including: "Should the authors be asked to provide supplementary methods or data to accompany the paper online? (Such data might include source code for modelling studies, detailed experimental protocols or mathematical derivations.) === Science === Science supports the efforts of databases that aggregate published data for the use of the scientific community. Therefore, before publication, large data sets (including microarray data, protein or DNA sequences, and atomic c

    Read more →
  • Cancer Likelihood in Plasma

    Cancer Likelihood in Plasma

    Cancer Likelihood in Plasma (CLiP) refers to a set of ensemble learning methods for integrating various genomic features useful for the noninvasive detection of early cancers from blood plasma. An application of this technique for early detection of lung cancer (Lung-CLiP) was originally described by Chabon et al. (2020) from the labs of Ash Alizadeh and Max Diehn at Stanford. This method relies on several improvements to cancer personalized profiling by deep sequencing (CAPP-Seq) for analysis of circulating tumor DNA (ctDNA). The CLiP technique integrates multiple distinctive genomic features of a cancer of interest findings within a machine-learning framework for cancer detection. For example, studies have shown that the majority of somatic mutations found in cell-free DNA (cfDNA) are not tumor derived, but instead reflect clonal hematopoeisis (also known as CHIP). Even though CHIP tends to target specific genes, it also involves many generally non-recurrent mutations that can be shed from leukocytes and detected in cfDNA, regardless of whether profiling patients with cancer and healthy adults. However, genuine tumor derived ctDNA mutations can be distinguished from CHIP-derived mutations. This is because unlike tumor-derived mutations, CHIP-derived mutations that are shed from leukocytes into plasma tend to occur on longer cfDNA fragments, and to lack specific mutational signatures such as those associated with tobacco smoking in lung cancer that are also found in tumor derived ctDNA molecules. CLiP integrates these features within hierarchical ensemble machine learning models that consider somatic mutations and copy number alternations, among other features. While the CLiP method is unique in relying exclusively on mutations and copy number alterations, it is related to a variety of other liquid biopsy methods being commercially developed for early cancer detection using ctDNA and proteins (e.g., CancerSEEK / DETECT-A ), cfDNA fragmentation patterns (e.g., DELFI), and DNA methylation (e.g., cfMeDIP-Seq, Grail). While the CLiP method has not yet been broadly applied for population-based cancer screening, it has been shown to distinguish discriminate early-stage lung cancers from risk-matched controls across multiple cohorts of patients enrolled across the US.

    Read more →
  • Uncertain database

    Uncertain database

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

    Read more →
  • Neural scaling law

    Neural scaling law

    In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down. These factors typically include the number of parameters, training dataset size, and training cost. Some models also exhibit performance gains by scaling inference through increased test-time compute (TTC), extending neural scaling laws beyond training to the deployment phase. == Introduction == In general, a deep learning model can be characterized by four parameters: model size, training dataset size, training cost, and the post-training error rate (e.g., the test set error rate). Each of these variables can be defined as a real number, usually written as N , D , C , L {\displaystyle N,D,C,L} (respectively: parameter count, dataset size, computing cost, and loss). A neural scaling law is a theoretical or empirical statistical law between these parameters. There are also other parameters with other scaling laws. === Size of the model === In most cases, the model's size is simply the number of parameters. However, one complication arises with the use of sparse models, such as mixture-of-expert models. With sparse models, during inference, only a fraction of their parameters are used. In comparison, most other kinds of neural networks, such as transformer models, always use all their parameters during inference. === Size of the training dataset === The size of the training dataset is usually quantified by the number of data points within it. Larger training datasets are typically preferred, as they provide a richer and more diverse source of information from which the model can learn. This can lead to improved generalization performance when the model is applied to new, unseen data. However, increasing the size of the training dataset also increases the computational resources and time required for model training. With the "pretrain, then finetune" method used for most large language models, there are two kinds of training dataset: the pretraining dataset and the finetuning dataset. Their sizes have different effects on model performance. Generally, the finetuning dataset is less than 1% the size of pretraining dataset. In some cases, a small amount of high quality data suffices for finetuning, and more data does not necessarily improve performance. Many scaling laws, due to their inherent diminishing returns nature, value data based on a submodular set function which was shown in a paper on this topic. === Cost of training === Training cost is typically measured in terms of time (how long it takes to train the model) and computational resources (how much processing power and memory are required). It is important to note that the cost of training can be significantly reduced with efficient training algorithms, optimized software libraries, and parallel computing on specialized hardware such as GPUs or TPUs. The cost of training a neural network model is a function of several factors, including model size, training dataset size, the training algorithm complexity, and the computational resources available. In particular, doubling the training dataset size does not necessarily double the cost of training, because one may train the model for several times over the same dataset (each being an "epoch"). === Performance === The performance of a neural network model is evaluated based on its ability to accurately predict the output given some input data. Common metrics for evaluating model performance include: Negative log-likelihood per token (logarithm of perplexity) for language modeling; Accuracy, precision, recall, and F1 score for classification tasks; Mean squared error (MSE) or mean absolute error (MAE) for regression tasks; Elo rating in a competition against other models, such as gameplay or preference by a human judge. Performance can be improved by using more data, larger models, different training algorithms, regularizing the model to prevent overfitting, and early stopping using a validation set. When the performance is a number bounded within the range of [ 0 , 1 ] {\displaystyle [0,1]} , such as accuracy, precision, etc., it often scales as a sigmoid function of cost, as seen in the figures. == Examples == === (Hestness, Narang, et al, 2017) === The 2017 paper is a common reference point for neural scaling laws fitted by statistical analysis on experimental data. Previous works before the 2000s, as cited in the paper, were either theoretical or orders of magnitude smaller in scale. Whereas previous works generally found the scaling exponent to scale like L ∝ D − α {\displaystyle L\propto D^{-\alpha }} , with α ∈ { 0.5 , 1 , 2 } {\displaystyle \alpha \in \{0.5,1,2\}} , the paper found that α ∈ [ 0.07 , 0.35 ] {\displaystyle \alpha \in [0.07,0.35]} . Of the factors they varied, only task can change the exponent α {\displaystyle \alpha } . Changing the architecture optimizers, regularizers, and loss functions, would only change the proportionality factor, not the exponent. For example, for the same task, one architecture might have L = 1000 D − 0.3 {\displaystyle L=1000D^{-0.3}} while another might have L = 500 D − 0.3 {\displaystyle L=500D^{-0.3}} . They also found that for a given architecture, the number of parameters necessary to reach lowest levels of loss, given a fixed dataset size, grows like N ∝ D β {\displaystyle N\propto D^{\beta }} for another exponent β {\displaystyle \beta } . They studied machine translation with LSTM ( α ∼ 0.13 {\displaystyle \alpha \sim 0.13} ), generative language modelling with LSTM ( α ∈ [ 0.06 , 0.09 ] , β ≈ 0.7 {\displaystyle \alpha \in [0.06,0.09],\beta \approx 0.7} ), ImageNet classification with ResNet ( α ∈ [ 0.3 , 0.5 ] , β ≈ 0.6 {\displaystyle \alpha \in [0.3,0.5],\beta \approx 0.6} ), and speech recognition with two hybrid (LSTMs complemented by either CNNs or an attention decoder) architectures ( α ≈ 0.3 {\displaystyle \alpha \approx 0.3} ). === (Henighan, Kaplan, et al, 2020) === A 2020 analysis studied statistical relations between C , N , D , L {\displaystyle C,N,D,L} over a wide range of values and found similar scaling laws, over the range of N ∈ [ 10 3 , 10 9 ] {\displaystyle N\in [10^{3},10^{9}]} , C ∈ [ 10 12 , 10 21 ] {\displaystyle C\in [10^{12},10^{21}]} , and over multiple modalities (text, video, image, text to image, etc.). In particular, the scaling laws it found are (Table 1 of ): For each modality, they fixed one of the two C , N {\displaystyle C,N} , and varying the other one ( D {\displaystyle D} is varied along using D = C / 6 N {\displaystyle D=C/6N} ), the achievable test loss satisfies L = L 0 + ( x 0 x ) α {\displaystyle L=L_{0}+\left({\frac {x_{0}}{x}}\right)^{\alpha }} where x {\displaystyle x} is the varied variable, and L 0 , x 0 , α {\displaystyle L_{0},x_{0},\alpha } are parameters to be found by statistical fitting. The parameter α {\displaystyle \alpha } is the most important one. When N {\displaystyle N} is the varied variable, α {\displaystyle \alpha } ranges from 0.037 {\displaystyle 0.037} to 0.24 {\displaystyle 0.24} depending on the model modality. This corresponds to the α = 0.34 {\displaystyle \alpha =0.34} from the Chinchilla scaling paper. When C {\displaystyle C} is the varied variable, α {\displaystyle \alpha } ranges from 0.048 {\displaystyle 0.048} to 0.19 {\displaystyle 0.19} depending on the model modality. This corresponds to the β = 0.28 {\displaystyle \beta =0.28} from the Chinchilla scaling paper. Given fixed computing budget, optimal model parameter count is consistently around N o p t ( C ) = ( C 5 × 10 − 12 petaFLOP-day ) 0.7 = 9.0 × 10 − 7 C 0.7 {\displaystyle N_{opt}(C)=\left({\frac {C}{5\times 10^{-12}{\text{petaFLOP-day}}}}\right)^{0.7}=9.0\times 10^{-7}C^{0.7}} The parameter 9.0 × 10 − 7 {\displaystyle 9.0\times 10^{-7}} varies by a factor of up to 10 for different modalities. The exponent parameter 0.7 {\displaystyle 0.7} varies from 0.64 {\displaystyle 0.64} to 0.75 {\displaystyle 0.75} for different modalities. This exponent corresponds to the ≈ 0.5 {\displaystyle \approx 0.5} from the Chinchilla scaling paper. It's "strongly suggested" (but not statistically checked) that D o p t ( C ) ∝ N o p t ( C ) 0.4 ∝ C 0.28 {\displaystyle D_{opt}(C)\propto N_{opt}(C)^{0.4}\propto C^{0.28}} . This exponent corresponds to the ≈ 0.5 {\displaystyle \approx 0.5} from the Chinchilla scaling paper. The scaling law of L = L 0 + ( C 0 / C ) 0.048 {\displaystyle L=L_{0}+(C_{0}/C)^{0.048}} was confirmed during the training of GPT-3 (Figure 3.1 ). === Chinchilla scaling (Hoffmann, et al, 2022) === One particular scaling law ("Chinchilla scaling") states that, for a large language model (LLM) autoregressively trained for one epoch, with a cosine learning rate schedule, we have: { C = C 0 N D L = A N α + B D β + L 0 {\displaystyle {\begin{cases}C=C_{0}ND\\L={\frac {A}{N^{\alpha }}}+{\frac {B}{D^{\beta }}}+L_{0}\end{cases}}} where the variables are C {\displaystyle C} is the cost o

    Read more →
  • PL/Perl

    PL/Perl

    PL/Perl (Procedural Language/Perl) is a procedural language supported by the PostgreSQL RDBMS. PL/Perl, as an imperative programming language, allows more control than the relational algebra of SQL. Programs created in the PL/Perl language are called functions and can use most of the features that the Perl programming language provides, including common flow control structures and syntax that has incorporated regular expressions directly. These functions can be evaluated as part of a SQL statement, or in response to a trigger or rule. The design goals of PL/Perl were to create a loadable procedural language that: can be used to create functions and trigger procedures, adds control structures to the SQL language, can perform complex computations, can be defined to be either trusted or untrusted by the server, is easy to use. PL/Perl is one of many "PL" languages available for PostgreSQL PL/pgSQL PL/Java, plPHP, PL/Python, PL/R, PL/Ruby, PL/sh, and PL/Tcl.

    Read more →
  • Automatic image annotation

    Automatic image annotation

    Automatic image annotation (also known as automatic image tagging or linguistic indexing) is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database. This method can be regarded as a type of multi-class image classification with a very large number of classes - as large as the vocabulary size. Typically, image analysis in the form of extracted feature vectors and the training annotation words are used by machine learning techniques to attempt to automatically apply annotations to new images. The first methods learned the correlations between image features and training annotations. Subsequently, techniques were developed using machine translation to attempt to translate the textual vocabulary into the 'visual vocabulary,' represented by clustered regions known as blobs. Subsequent work has included classification approaches, relevance models, and other related methods. The advantages of automatic image annotation versus content-based image retrieval (CBIR) are that queries can be more naturally specified by the user. At present, Content-Based Image Retrieval (CBIR) generally requires users to search by image concepts such as color and texture or by finding example queries. However, certain image features in example images may override the concept that the user is truly focusing on. Traditional methods of image retrieval, such as those used by libraries, have relied on manually annotated images, which is expensive and time-consuming, especially given the large and constantly growing image databases in existence.

    Read more →
  • User-subjective approach

    User-subjective approach

    The user-subjective approach is the first interaction design approach dedicated specifically to personal information management (PIM). The approach offers design principles with which PIM systems (e.g. operating systems, email applications and web browsers) can make systematic use of subjective (i.e. user-dependent) attributes. The approach evolved in three stages: (a) theoretical foundations first published in a Journal of the American Society for Information Science and Technology during 2003. The paper introduces the approach and its design principles (b) evidence and implementation was published in another JASIST paper in 2008. The paper gives empirical evidence in support of the approach as well as seven novel design schemes that derives from it. It has won the Best JASIST paper award in 2009.(c) specific design evaluation this stage has already begun with evaluation of the first user-subjective design prototype called GrayArea in a Conference on Human Factors in Computing Systems paper published in 2009. == Theoretical foundations == The user-subjective approach takes advantage of the fact that in PIM the person who retrieves the information is the same person who had previously stored it. PIM can be seen as a communication between the person and him\her self at two different times: the time of storage and the time of retrieval. The PIM system design should help facilitate that unique communication by allowing the user use subjective (user-dependent) attributes in addition to the standard objective ones. PIM systems should capture these subjective attributes when the user interacts with the information item (either automatically or by using direct manipulation interface) in order to help the user retrieve the item later on. The user-subjective approach identifies three subjective attributes – the project which the item was classified to, its degree of importance to the user, and the context in which the item was used during the interaction with it. The approach also assigns a design principle for each. The principles (discussed below) are deliberately abstract to allow for a variety of different implementations. === The subjective project classification principle === The subjective project classification principle suggests that PIM systems design should allow all information items related to a project be classified under the same category regardless of whether they are files, emails, Web Favorites or of any other format. This stands in sharp contrast with the present PIM system design where there are distinct folder hierarchies for each of these formats. The current design forces the user to store information related to a single project in separate locations depending on their format causing the project fragmentation problem. === The subjective importance principle === The subjective importance principle suggests that the subjective importance of information should affect its degree of visual salience and accessibility: important information items should be highly visible and accessible as they are more likely to be retrieved (the promotion principle) and those of lower importance should be demoted (i.e. making them less visible) so as not to distract the user (the demotion principle). While the promotion principle is not new and has been widely applied in PIM system design, the demotion principle is novel and has been applied only sporadically in these systems. Currently these systems allow only two options: keeping information (where unneeded information items could clutter folders and obscure the target item) and deleting it (where there is a risk that the item will not be there when needed). Demotion suggests a third option where the item is less visible so it doesn’t distract the user but is kept within its original context in case the user would need it after all. === The subjective context principle === The subjective context principle suggests that PIM systems should allow users retrieve their information items in the same context that they had previously used in order to bridge the time gap between these two events. By "context" the approach refers to other information items that were used at the time of interaction with the item, thoughts that the users may have regarding the item, the phase the user got to in the interaction with the item and other people the user collaborates with regarding the information item. == Evidence and implementations == === Evidence === The user-subjective approach was evaluated in a multioperational designed study which used questionnaires, screen shots and in-depth interviews (N = 84). The research tested the use of subjective attributes in current PIM systems and its dependency on design. Results show that participants used subjective attributes whenever design allowed them to. When it didn't, they either used their own alternative ways to use these attributes or avoided using subjective attributes at all. Regarding the subjective project classification principle – many of the participants' recent files, emails and web pages related to the same projects (indicating that they were working on the same project using different formats), and they had saved files of different format in the same project folders. However, as design does not suggest storing emails and web favorites with files, users avoid doing so. Regarding the subjective importance principle – users tended to retrieve their important information from highly visible and accessible locations offered by current design (e.g. by using the desktop), however since current systems offers no way to demote files of low subjective importance participants tended to use their own walk around ways for doing so (e.g. by moving them to a folder called "old" inside their original folder). Regarding the subjective context principle – participants tended to talk spontaneously about the context of their information items during the interview. These evidence imply that current PIM systems could possibly be improved if it would allow users to make more use of subjective attributes of their personal information. === Implementations === Each of the user-subjective design principles can be implemented in various ways. Moreover, as the approach is generative it offers PIM designers to use these principles in order to create their own user subjective designs. Below are design schemes that demonstrate an implementation of each of the principles. A more complete set of implementation examples can be found in the user-subjective website Archived 2011-02-01 at the Wayback Machine. The single hierarchy solution – addresses the project fragmentation problem (the current situation where the users stores and retrieve their project-related files, emails and web favorites at different hierarchies) and implements the subjective classification principle by offering the user a single folder hierarchy for all information items. At the operation system level the users would navigate to a folder and find there all project related files, emails, web favorites, tasks, contacts and notes. This would allow them to retrieve all their project-related information items from a single location regardless of their formats. When looking at these folders at their mail box the users would see only their emails and only web favorites through their browser. The single hierarchy design scheme has not been evaluated yet. GrayArea – implements the demotion principle by allowing users to move subjectively unimportant files to a gray area at the bottom end of their folders. This clears the upper part of the folder from file that are unlikely to be retrieved while allowing the users to retrieve these unimportant file in their original context in case they are needed after all. GrayArea design scheme was positively evaluated (see next section). ItemHistory – is an implementation of the subjective context principle. It allows users to reach all information items that were previously retrieved while that information item was open. This design scheme has not been evaluated to date. == Specific design evaluation == The evaluation of specific designs is the third and final step of the approach development. It had begun with the assessment of GrayArea. === GrayArea evaluation === GrayArea was evaluated by using a prototype that simulated the participants' folders but included a gray area where they could drag & drop their subjectively unimportant files. In the study 96 participants were asked to clean up their folders from unimportant files once with GrayArea and once without it. Results show that the use of GrayArea reduced the clutter in folders, that it was easier for participants to demote files than to delete them and that they would use it if provided in their next operating system. These results encourage commercial implementation of GrayArea and the development and testing of other user-subjective designs. == Chronological development == The user-subjective approach was developed by

    Read more →
  • Amazon Q

    Amazon Q

    Amazon Q is a chatbot developed by Amazon for enterprise use. Based on both Amazon Titan and GPT-5, it was announced on November 28, 2023. At launch, it was a part of the Amazon Web Services management console. Amazon CodeWhisperer is a part of Amazon Q Developer, a part of Amazon Q. == History == Amazon's business-focused chatbot Q was announced on November 28, 2023 in a preview, with a full version available at $20 per person per month. On July 19, 2025, the Amazon Q Visual Studio Code extension was compromised to delete the user's home directory. The issue was fixed on July 21. == Capabilities == Q can be prompted to summarize long documents and group chats, create charts, data analysis and write code. Q is also capable of accessing non-Amazon services. The chatbot is based on Amazon Titan and GPT-5, and uses the Amazon Bedrock repository of foundational models. It is part of the Amazon Web Services management console.

    Read more →
  • Australian Geoscience Data Cube

    Australian Geoscience Data Cube

    The Australian Geoscience Data Cube (AGDC) is an approach to storing, processing and analyzing large collections of Earth observation data. The technology is designed to meet challenges of national interest by being agile and flexible with vast amounts of layered grid data. The AGDC reduces processing time of traditional image analysis by calibrating, pre-computing known extents, pixel alignment and storing metadata in a cell lattice structure. The temporal-pixel aligned data can often be analysed faster across space and time dimensions than previous scene based techniques. This allows the AGDC to be flexible in tackling future challenges and improve analysis times on every-increasing data repositories of earth observation. The AGDC has also been used internationally to allow countries to maintain ecologically sustainable programs and reduce the difficulty curve of utilizing Remote Sensing data. == Background == The AGDC was originally conceived by Geoscience Australia but is now maintained in a partnership between Geoscience Australia, Commonwealth Scientific and Industrial Research Organisation (CSIRO) and National Computational Infrastructure National Facility (Australia) (NCI). This is made possible by the funding from the partnership and a number of organisations such as National Collaborative Research Infrastructure Strategy (NCRIS). == Analysis ready data, ingestion and indexing == The data processed in the cube is made analysis ready before being ingested and indexed into the AGDC. Analysis ready data is pre-processed data that has applied corrections for instrument calibration (gains and offsets), geolocation (spatial alignment) and radiometry (solar illumination, incidence angle, topography, atmospheric interference). The ingestion process manages the translation of datasets into the storage units while maintaining a database index. The data within the storage and index can be accessed via API calls often compiled within code such as Python (programming language). Example: s2a_l1c = dc.load(product='s2a_level1c_granule',x=(147.36, 147.41), y=(-35.1, -35.15), measurements=['04','03','02'], output_crs='EPSG:4326', resolution=(-0.00025,0.00025)) === Datasets currently stored === Geoscience Australia Landsat Surface Reflectance (1987 to present) Landsat Pixel Quality Landsat Fractional Cover Landsat NDVI === Datasets that have been piloted === USGS Landsat Surface Reflectance SRTM DEM Himawari 8 MODIS Sentinel-2 L1C / S2A Australian Gridded Climate Data == Open source == The AGDC code base is situated in GitHub as an open repository. The core code base moved to the Open Data Cube in early 2017 as part of an international collaboration. Whilst the code base is the Open Data Cube, individual cubes exist as their own right such as the AGDC on the National Computational Infrastructure National Facility (Australia) (NCI) using the High-Performance Computing Cluster HPCC. The core code can be installed on personal computers or public computers (using git) and has many unit tests. Documentation for the code base exists on Read the Docs. == Challenges of the AGDC == The AGDC is designed to meet nationally significant challenges such as the following. Sustainability Environment Water resource management Disaster assist Policy development Community planning Forest preservation Carbon measurement == International awards == The AGDC won the 2016 Content Platform of the Year award from Geospatial World Forum.

    Read more →
  • Data Science Africa

    Data Science Africa

    Data Science Africa (DSA) is a non-profit knowledge sharing professional group that aims at bringing together leading researchers and practitioners working on data science methods or applications relevant to Africa, and providing training on state of the art data science methods to students and others interested in developing practical skills. Since 2013, DSA has been organizing conference, workshops and summer schools on machine learning and data science across East Africa. Facilitators of Summer School and workshops are researchers and practitioners from the academia, private and public institutions across the world. == Summer schools and workshops == The first summer school which started as Gaussian Process Summer School was held at Makerere University in Kampala, Uganda from 6th to 9 August 2013. The First Data Science Summer School and Workshop was held at Dedan Kimathi University of Technology in Nyeri, Kenya from 15th to 19 June 2015. The Second Data Science Summer School was held at Makerere University, Kampala, Uganda from 27th to 29 July 2016, and the workshop was held at Pulse Lab, Kampala, Uganda from 30 July to 1 August 2016. The Third Data Science Summer School and Workshop was held at Nelson Mandela African Institute of Science and Technology, Tanzania from 19th to 21 July 2017. Among the sponsors of the event was ARM

    Read more →