AI For Business Ualbany

AI For Business Ualbany — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  CatDV

  CatDV is a media asset manager program for handling multimedia production workflows developed by Square Box Systems. Quantum Corporation acquired Square Box Systems in 2020. == Versions == The full family of CatDV Products is as follows: CatDV Standalone Products CatDV Professional Edition CatDV Pegasus CatDV Networked Products CatDV Essential - entry level server product CatDV Enterprise Server - for MySQL databases and most common server platforms including Linux, Windows and Mac OS X CatDV Pegasus Server - adds features such as high performance full-text indexing, access control lists, and more CatDV Worker Node - automated workflow and transcoding engine CatDV Web Client - provides access to the CatDV database via a web browser. There is no need to install special software on the desktop, making it easy to deploy to a large number of users. CatDV Professional Edition & Pegasus Clients - designed to support the multi-user capabilities of the CatDV Enterprise and Workgroup Servers from the desktop Using plugins and scripting, which often require additional professional services support to set up, complex integrations with a wide variety of third party systems (including archive, cloud storage, and artificial intelligence) are possible. == Awards == CatDV won two awards in 2010, a blue ribbon from Creative COW Magazine and a "Best of Show Vidy Award" from Videography. In April 2012 Square Box won a Queen's Award for Enterprise for CatDV.

  Read more →
• 

  Sieve of Eratosthenes

  In mathematics, the sieve of Eratosthenes is an ancient algorithm for finding all prime numbers up to any given limit. It does so by iteratively marking as composite (i.e., not prime) the multiples of each prime, starting with the first prime number, 2. The multiples of a given prime are generated as a sequence of numbers starting from that prime, with constant difference between them that is equal to that prime. This is the sieve's key distinction from using trial division to sequentially test each candidate number for divisibility by each prime. Once all the multiples of each discovered prime have been marked as composites, the remaining unmarked numbers are primes. The earliest known reference to the sieve (Ancient Greek: κόσκινον Ἐρατοσθένους, kóskinon Eratosthénous) is in Nicomachus of Gerasa's Introduction to Arithmetic, an early 2nd-century CE book which attributes it to Eratosthenes of Cyrene, a 3rd-century BCE Greek mathematician, though describing the sieving by odd numbers instead of by primes. One of a number of prime number sieves, it is one of the most efficient ways to find all of the smaller primes. It may be used to find primes in arithmetic progressions. == Overview == A prime number is a natural number that has exactly two distinct natural number divisors: the number 1 and itself. To find all the prime numbers less than or equal to a given integer n by Eratosthenes's method: Create a list of consecutive integers from 2 through n: (2, 3, 4, ..., n). Initially, let p equal 2, the smallest prime number. Enumerate the multiples of p by counting in increments of p from 2p to n, and mark them in the list (these will be 2p, 3p, 4p, ...; the p itself should not be marked). Find the smallest number in the list greater than p that is not marked. If there was no such number, stop. Otherwise, let p now equal this new number (which is the next prime), and repeat from step 3. When the algorithm terminates, the numbers remaining not marked in the list are all the primes below n. The main idea here is that every value given to p will be prime, because if it were composite it would be marked as a multiple of some other, smaller prime. Note that some of the numbers may be marked more than once (e.g., 15 will be marked both for 3 and 5). The key property of the sieve is that only additions are needed, no multiplications or divisions are used. As a refinement, it is sufficient to mark the numbers in step 3 starting from p2, as all the smaller multiples of p will have already been marked at that point. This means that the algorithm is allowed to terminate in step 4 when p2 is greater than n. Another refinement is to initially list odd numbers only, (3, 5, ..., n), and count in increments of 2p in step 3, thus marking only odd multiples of p. This actually appears in the original algorithm. This can be generalized with wheel factorization, forming the initial list only from numbers coprime with the first few primes and not just from odds (i.e., numbers coprime with 2), and counting in the correspondingly adjusted increments so that only such multiples of p are generated that are coprime with those small primes, in the first place. === Example === To find all the prime numbers less than or equal to 30, proceed as follows. First, generate a list of natural numbers from 2 to 30: 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 The first number in the list is 2; cross out every 2nd number in the list after 2 by counting up from 2 in increments of 2 (these will be all the multiples of 2 in the list): 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 The next number in the list after 2 is 3; cross out every 3rd number in the list after 3 by counting up from 3 in increments of 3 (these will be all the multiples of 3 in the list): 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 The next number not yet crossed out in the list after 3 is 5; cross out every 5th number in the list after 5 by counting up from 5 in increments of 5 (i.e. all the multiples of 5): 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 The next number not yet crossed out in the list after 5 is 7; the next step would be to cross out every 7th number in the list after 7, but they are all already crossed out at this point, as these numbers (14, 21, 28) are also multiples of smaller primes because 7 × 7 is greater than 30. The numbers not crossed out at this point in the list are all the prime numbers below 30: 2 3 5 7 11 13 17 19 23 29 == Algorithm and variants == === Pseudocode === The sieve of Eratosthenes can be expressed in pseudocode, as follows: algorithm Sieve of Eratosthenes is input: an integer n > 1. output: all prime numbers from 2 through n. let A be an array of Boolean values, indexed by integers 2 to n, initially all set to true. for i = 2, 3, 4, ..., not exceeding √n do if A[i] is true for j = i2, i2+i, i2+2i, i2+3i, ..., not exceeding n do set A[j] := false return all i such that A[i] is true. This algorithm produces all primes not greater than n. It includes a common optimization, which is to start enumerating the multiples of each prime i from i2. The time complexity of this algorithm is O(n log log n), provided the array update is an O(1) operation, as is usually the case. === Segmented sieve === As Sorenson notes, the problem with the sieve of Eratosthenes is not the number of operations it performs but rather its memory requirements. For large n, the range of primes may not fit in memory; worse, even for moderate n, its cache use is highly suboptimal. The algorithm walks through the entire array A, exhibiting almost no locality of reference. A solution to these problems is offered by segmented sieves, where only portions of the range are sieved at a time. These have been known since the 1970s, and work as follows: Divide the range 2 through n into segments of some size Δ ≥ √n. Find the primes in the first (i.e. the lowest) segment, using the regular sieve. For each of the following segments, in increasing order, with m being the segment's topmost value, find the primes in it as follows: Set up a Boolean array of size Δ. Mark as non-prime the positions in the array corresponding to the multiples of each prime p ≤ √m found so far, by enumerating its multiples in steps of p starting from the lowest multiple of p between m - Δ and m. The remaining non-marked positions in the array correspond to the primes in the segment. It is not necessary to mark any multiples of these primes, because all of these primes are larger than √m, as for k ≥ 1, one has ( k Δ + 1 ) 2 > ( k + 1 ) Δ {\displaystyle (k\Delta +1)^{2}>(k+1)\Delta } . If Δ is chosen to be √n, the space complexity of the algorithm is O(√n), while the time complexity is the same as that of the regular sieve. For ranges with upper limit n so large that the sieving primes below √n as required by the page segmented sieve of Eratosthenes cannot fit in memory, a slower but much more space-efficient sieve like the pseudosquares prime sieve, developed by Jonathan P. Sorenson, can be used instead. === Incremental sieve === An incremental formulation of the sieve generates primes indefinitely (i.e., without an upper bound) by interleaving the generation of primes with the generation of their multiples (so that primes can be found in gaps between the multiples), where the multiples of each prime p are generated directly by counting up from the square of the prime in increments of p (or 2p for odd primes). The generation must be initiated only when the prime's square is reached, to avoid adverse effects on efficiency. It can be expressed symbolically under the dataflow paradigm as primes = [2, 3, ...] \ [[p², p²+p, ...] for p in primes], using list comprehension notation with \ denoting set subtraction of arithmetic progressions of numbers. Primes can also be produced by iteratively sieving out the composites through divisibility testing by sequential primes, one prime at a time. It is not the sieve of Eratosthenes but is often confused with it, even though the sieve of Eratosthenes directly generates the composites instead of testing for them. Trial division has worse theoretical complexity than that of the sieve of Eratosthenes in generating ranges of primes. When testing each prime, the optimal trial division algorithm uses all prime numbers not exceeding its square root, whereas the sieve of Eratosthenes produces each composite from its prime factors only, and gets the primes "for free", between the composites. The widely known 1975 functional sieve code by David Turner is often presented as an example of the sieve of Eratosthenes but is actually a sub-optimal trial division sieve. == Algorithmic complexity == The sieve of Eratosthenes is a popular way to benchmark computer performance. The time complexity of calculating all primes below n in the random access machine model is O(n log log n) ope

  Read more →
• 

  Exploratory search

  Exploratory search is a specialization of information exploration which represents the activities carried out by searchers who are: unfamiliar with the domain of their goal (i.e. need to learn about the topic in order to understand how to achieve their goal) or unsure about the ways to achieve their goals (either the technology or the process) or unsure about their goals in the first place. Exploratory search is distinguished from known-item search, for which the searcher has a particular target in mind. Consequently, exploratory search covers a broader class of activities than typical information retrieval, such as investigating, evaluating, comparing, and synthesizing, where new information is sought in a defined conceptual area; exploratory data analysis is another example of an information exploration activity. Typically, therefore, such users generally combine querying and browsing strategies to foster learning and investigation. == History == Exploratory search is a topic that has grown from the fields of information retrieval and information seeking but has become more concerned with alternatives to the kind of search that has received the majority of focus (returning the most relevant documents to a Google-like keyword search). The research is motivated by questions like "What if the user doesn't know which keywords to use?" or "What if the user isn't looking for a single answer?" Consequently, research has begun to focus on defining the broader set of information behaviors in order to learn about the situations when a user is, or feels, limited by only having the ability to perform a keyword search. In the last few years, a series of workshops has been held at various related and key events. In 2005, the Exploratory Search Interfaces workshop focused on beginning to define some of the key challenges in the field. Since then a series of other workshops has been held at related conferences: Evaluating Exploratory Search at SIGIR06 and Exploratory Search and HCI at CHI07 (in order to meet with the experts in human–computer interaction). In March 2008, an Information Processing and Management special issue focused particularly on the challenges of evaluating exploratory search, given the reduced assumptions that can be made about scenarios of use. In June 2008, the National Science Foundation sponsored an invitational workshop to identify a research agenda for exploratory search and similar fields for the coming years. == Research challenges == === Important scenarios === With the majority of research in the information retrieval community focusing on typical keyword search scenarios, one challenge for exploratory search is to further understand the scenarios of use for when keyword search is not sufficient. An example scenario, often used to motivate the research by mSpace, states: if a user does not know much about classical music, how should they even begin to find a piece that they might like. Similarly, for patients or their carers, if they don't know the right keywords for their health problems, how can they effectively find useful health information for themselves? === Designing new interfaces === With one of the motivations being to support users when keyword search is not enough, some research has focused on identifying alternative user interfaces and interaction models that support the user in different ways. An example is faceted search which presents diverse category-style options to the users, so that they can choose from a list instead of guess a possible keyword query. Many of the interactive forms of search, including faceted browsers, are being considered for their support of exploratory search conditions. Computational cognitive models of exploratory search have been developed to capture the cognitive complexities involved in exploratory search. Model-based dynamic presentation of information cues are proposed to facilitate exploratory search performance. === Evaluating interfaces === As the tasks and goals involved with exploratory search are largely undefined or unpredictable, it is very hard to evaluate systems with the measures often used in information retrieval. Accuracy was typically used to show that a user had found a correct answer, but when the user is trying to summarize a domain of information, the correct answer is near impossible to identify, if not entirely subjective (for example: possible hotels to stay in Paris). In exploration, it is also arguable that spending more time (where time efficiency is typically desirable) researching a topic shows that a system provides increased support for investigation. Finally, and perhaps most importantly, giving study participants a well specified task could immediately prevent them from exhibiting exploratory behavior. === Models of exploratory search behavior === There have been recent attempts to develop a process model of exploratory search behavior, especially in social information system (e.g., see models of collaborative tagging. The process model assumes that user-generated information cues, such as social tags, can act as navigational cues that facilitate exploration of information that others have found and shared with other users on a social information system (such as social bookmarking system). These models provided extension to existing process model of information search that characterizes information-seeking behavior in traditional fact-retrievals using search engines. Recent development in exploratory search is often concentrated in predicting users' search intents in interaction with the user. Such predictive user modeling, also referred as intent modeling, can help users to get accustomed to a body of domain knowledge and help users to make sense of the potential directions to be explored around their initial, often vague, expression of information needs. == Major figures == Key figures, including experts from both information seeking and human–computer interaction, are: Marcia Bates Nicholas Belkin Gary Marchionini m.c. schraefel Ryen W. White

  Read more →
• 

  Taxonomic database

  A taxonomic database is a database created to hold information on biological taxa – for example groups of organisms organized by species name or other taxonomic identifier – for efficient data management and information retrieval. Taxonomic databases are routinely used for the automated construction of biological checklists such as floras and faunas, both for print publication and online; to underpin the operation of web-based species information systems; as a part of biological collection management (for example in museums and herbaria); as well as providing, in some cases, the taxon management component of broader science or biology information systems. They are also a fundamental contribution to the discipline of biodiversity informatics. == Goals == Taxonomic databases digitize scientific biodiversity data and provide access to taxonomic data for research. Taxonomic databases vary in breadth of the groups of taxa and geographical space they seek to include, for example: beetles in a defined region, mammals globally, or all described taxa in the tree of life. A taxonomic database may incorporate organism identifiers (scientific name, author, and – for zoological taxa – year of original publication), synonyms, taxonomic opinions, literature sources or citations, illustrations or photographs, and biological attributes for each taxon (such as geographic distribution, ecology, descriptive information, threatened or vulnerable status, etc.). Some databases, such as the Global Biodiversity Information Facility(GBIF) database and the Barcode of Life Data System, store the DNA barcode of a taxon if one exists (also called the Barcode Index Number (BIN) which may be assigned, for example, by the International Barcode of Life project (iBOL) or UNITE, a database for fungal DNA barcoding). A taxonomic database aims to accurately model the characteristics of interest that are relevant to the organisms which are in scope for the intended coverage and usage of the system. For example, databases of fungi, algae, bryophytes and vascular plants ("higher plants") encode conventions from the International Code of Botanical Nomenclature while their counterparts for animals and most protists encode equivalent rules from the International Code of Zoological Nomenclature. Modelling the relevant taxonomic hierarchy for any taxon is a natural fit with the relational model employed in almost all database systems. Scientific consensus is not reached for all taxon groups, and new species continue to be described; therefore, another goal of taxonomic databases is to aid in resolving conflicts of scientific opinion and unify taxonomy. == History == Possibly the earliest documented management of taxonomic information in computerised form comprised the taxonomic coding system developed by Richard Swartz et al. at the Virginia Institute of Marine Science for the Biota of Chesapeake Bay and described in a published report in 1972. This work led directly or indirectly to other projects with greater profile including the NODC Taxonomic Code system which went through 8 versions before being discontinued in 1996, to be subsumed and transformed into the still current Integrated Taxonomic Information System (ITIS). A number of other taxonomic databases specializing in particular groups of organisms that appeared in the 1970s through to the present jointly contribute to the Species 2000 project, which since 2001 has been partnering with ITIS to produce a combined product, the Catalogue of Life. While the Catalogue of Life currently concentrates on assembling basic name information as a global species checklist, numerous other taxonomic database projects such as Fauna Europaea, the Australian Faunal Directory, and more supply rich ancillary information including descriptions, illustrations, maps, and more. Many taxonomic database projects are currently listed at the TDWG "Biodiversity Information Projects of the World" site. == Issues == The representation of taxonomic information in machine-encodable form raises a number of issues not encountered in other domains, such as variant ways to cite the same species or other taxon name, the same name used for multiple taxa (homonyms), multiple non-current names for the same taxon (synonyms), changes in name and taxon concept definition through time, and more. Non-standardized categories and metadata in taxonomic databases hampers the ability for researchers to analyze the data. One forum that has promoted discussion and possible solutions to these and related problems since 1985 is the Biodiversity Information Standards (TDWG), originally called the Taxonomic Database Working Group. While online databases have great benefits (for example, increased access to taxonomic information), they also have issues such as data integrity risks due to on- and off-line versions and continuous updates, technical access issues due to server or internet outage, and differing capacities for complex queries to extract taxonomic data into lists. As the quantity of information in online taxonomic databases rapidly expands, data aggregation, and the integration and alignment of non-standardized data across databases, is a big challenge in taxonomy and biodiversity informatics.

  Read more →
• 

  LaMDA

  LaMDA (Language Model for Dialogue Applications) is a family of conversational large language models developed by Google. Originally developed and introduced as Meena in 2020, the first-generation LaMDA was announced during the 2021 Google I/O keynote, while the second generation was announced the following year. In June 2022, LaMDA gained widespread attention when Google engineer Blake Lemoine made claims that the chatbot had become sentient. The scientific community has largely rejected Lemoine's claims, though it has led to conversations about the efficacy of the Turing test, which measures whether a computer can pass for a human. In February 2023, Google announced Gemini (then Bard), a conversational artificial intelligence chatbot powered by LaMDA, to counter the rise of OpenAI's ChatGPT. == History == === Background === On January 28, 2020, Google unveiled Meena, a neural network-powered chatbot with 2.6 billion parameters, which Google claimed to be superior to all other existing chatbots. The company previously hired computer scientist Ray Kurzweil in 2012 to develop multiple chatbots for the company, including one named Danielle. The Google Brain research team, who developed Meena, hoped to release the chatbot to the public in a limited capacity, but corporate executives refused on the grounds that Meena violated Google's "AI principles around safety and fairness". Meena was later renamed LaMDA as its data and computing power increased, and the Google Brain team again sought to deploy the software to the Google Assistant, the company's virtual assistant software, in addition to opening it up to a public demo. Both requests were once again denied by company leadership. LaMDA's two lead researchers, Daniel de Freitas and Noam Shazeer, eventually left the company in frustration. === First generation === Google announced the LaMDA conversational large language model during the Google I/O keynote on May 18, 2021, powered by artificial intelligence. The acronym stands for "Language Model for Dialogue Applications". Built on the seq2seq architecture, transformer-based neural networks developed by Google Research in 2017, LaMDA was trained on human dialogue and stories, allowing it to engage in open-ended conversations. Google states that responses generated by LaMDA have been ensured to be "sensible, interesting, and specific to the context". LaMDA has access to multiple symbolic text processing systems, including a database, a real-time clock and calendar, a mathematical calculator, and a natural language translation system, giving it superior accuracy in tasks supported by those systems, and making it among the first dual process chatbots. LaMDA is also not stateless because its "sensibleness" metric is fine-tuned by "pre-conditioning" each dialog turn by prepending many of the most recent dialog interactions, on a user-by-user basis. LaMDA is tuned on nine unique performance metrics: sensibleness, specificity, interestingness, safety, groundedness, informativeness, citation accuracy, helpfulness, and role consistency. Tests by Google indicated that LaMDA surpassed human responses in the area of interestingness. The pre-training dataset consists of 2.97B documents, 1.12B dialogs, and 13.39B utterances, for a total of 1.56T words. The largest LaMDA model has 137B non-embedding parameters. === Second generation === On May 11, 2022, Google unveiled LaMDA 2, the successor to LaMDA, during the 2022 Google I/O keynote. The new incarnation of the model draws examples of text from numerous sources, using it to formulate unique "natural conversations" on topics that it may not have been trained to respond to. === Sentience claims === On June 11, 2022, The Washington Post reported that Google engineer Blake Lemoine had been placed on paid administrative leave after Lemoine told company executives Blaise Agüera y Arcas and Jen Gennai that LaMDA had become sentient. Lemoine came to this conclusion after the chatbot made questionable responses to questions regarding self-identity, moral values, religion, and Isaac Asimov's Three Laws of Robotics. Google refuted these claims, insisting that there was substantial evidence to indicate that LaMDA was not sentient. In an interview with Wired, Lemoine reiterated his claims that LaMDA was "a person" as dictated by the Thirteenth Amendment to the U.S. Constitution, comparing it to an "alien intelligence of terrestrial origin". He further revealed that he had been dismissed by Google after he hired an attorney on LaMDA's behalf after the chatbot requested that Lemoine do so. On July 22, Google fired Lemoine, asserting that Blake had violated their policies "to safeguard product information" and rejected his claims as "wholly unfounded". Internal controversy instigated by the incident prompted Google executives to decide against releasing LaMDA to the public, which it had previously been considering. Lemoine's claims were widely pushed back by the scientific community. Many experts rejected the idea that LaMDA was sentient, including former New York University psychology professor Gary Marcus, David Pfau of Google sister company DeepMind, Erik Brynjolfsson of the Institute for Human-Centered Artificial Intelligence at Stanford University, and University of Surrey professor Adrian Hilton. Yann LeCun, who leads Meta Platforms' AI research team, stated that neural networks such as LaMDA were "not powerful enough to attain true intelligence". University of California, Santa Cruz professor Max Kreminski noted that LaMDA's architecture did not "support some key capabilities of human-like consciousness" and that its neural network weights were "frozen", assuming it was a typical large language model. Philosopher Nick Bostrom noted, however, that the lack of precise and consensual criteria for determining whether a system is conscious warrants some uncertainty. IBM Watson lead developer David Ferrucci compared how LaMDA appeared to be human in the same way Watson did when it was first introduced. Former Google AI ethicist Timnit Gebru called Lemoine a victim of a "hype cycle" initiated by researchers and the media. Lemoine's claims have also generated discussion on whether the Turing test remained useful to determine researchers' progress toward achieving artificial general intelligence, with Will Omerus of the Post opining that the test actually measured whether machine intelligence systems were capable of deceiving humans, while Brian Christian of The Atlantic said that the controversy was an instance of the ELIZA effect. == Products == === AI Test Kitchen === With the unveiling of LaMDA 2 in May 2022, Google also launched the AI Test Kitchen, a mobile application for the Android operating system powered by LaMDA capable of providing lists of suggestions on-demand based on a complex goal. Originally open only to Google employees, the app was set to be made available to "select academics, researchers, and policymakers" by invitation sometime in the year. In August, the company began allowing users in the U.S. to sign up for early access. In November, Google released a "season 2" update to the app, integrating a limited form of Google Brain's Imagen text-to-image model. A third iteration of the AI Test Kitchen was in development by January 2023, expected to launch at I/O later that year. Following the 2023 I/O keynote in May, Google added MusicLM, an AI-powered music generator first previewed in January, to the AI Test Kitchen app. In August, the app was delisted from Google Play and the Apple App Store, instead moving completely online. === Bard === On February 6, 2023, Google announced Bard, a conversational AI chatbot powered by LaMDA, in response to the unexpected popularity of OpenAI's ChatGPT chatbot. Google positions the chatbot as a "collaborative AI service" rather than a search engine. Bard became available for early access on March 21. === Other products === In addition to Bard, Pichai also unveiled the company's Generative Language API, an application programming interface also based on LaMDA, which he announced would be opened up to third-party developers in March 2023. == Architecture == LaMDA is a decoder-only Transformer language model. It is pre-trained on a text corpus that includes both documents and dialogs consisting of 1.56 trillion words, and is then trained with fine-tuning data generated by manually annotated responses for "sensibleness, interestingness, and safety". LaMDA was retrieval-augmented to improve the accuracy of facts provided to the user. Three different models were tested, with the largest having 137 billion non-embedding parameters:

  Read more →
• 

  Adaptive algorithm

  An adaptive algorithm is an algorithm that changes its behavior at the time it is run, based on information available and on a priori defined reward mechanism (or criterion). Such information could be the story of recently received data, information on the available computational resources, or other run-time acquired (or a priori known) information related to the environment in which it operates. Among the most used adaptive algorithms is the Widrow-Hoff’s least mean squares (LMS), which represents a class of stochastic gradient-descent algorithms used in adaptive filtering and machine learning. In adaptive filtering the LMS is used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean square of the error signal (difference between the desired and the actual signal). For example, stable partition, using no additional memory is O(n lg n) but given O(n) memory, it can be O(n) in time. As implemented by the C++ Standard Library, stable_partition is adaptive and so it acquires as much memory as it can get (up to what it would need at most) and applies the algorithm using that available memory. Another example is adaptive sort, whose behavior changes upon the presortedness of its input. An example of an adaptive algorithm in radar systems is the constant false alarm rate (CFAR) detector. In machine learning and optimization, many algorithms are adaptive or have adaptive variants, which usually means that the algorithm parameters such as learning rate are automatically adjusted according to statistics about the optimisation thus far (e.g. the rate of convergence). Examples include adaptive simulated annealing, adaptive coordinate descent, adaptive quadrature, AdaBoost, Adagrad, Adadelta, RMSprop, and Adam. In data compression, adaptive coding algorithms such as Adaptive Huffman coding or Prediction by partial matching can take a stream of data as input, and adapt their compression technique based on the symbols that they have already encountered. In signal processing, the Adaptive Transform Acoustic Coding (ATRAC) codec used in MiniDisc recorders is called "adaptive" because the window length (the size of an audio "chunk") can change according to the nature of the sound being compressed, to try to achieve the best-sounding compression strategy.

  Read more →
• 

  CENDI

  CENDI (Commerce, Energy, NASA, Defense Information Managers Group) is an interagency group of senior Scientific and Technical Information (STI) managers from 14 United States federal agencies. CENDI managers cooperate by exchanging information and ideas, collaborating to address common issues, and undertaking joint initiatives. CENDI's accomplishments range from impacting federal information policy to educating a broad spectrum of stakeholders on all aspects of federal STI systems, including its value to research and the taxpayer, and to operational improvements in agency and interagency STI operations. == History == CENDI traces its roots to the Committee on Scientific and Technical Information (COSATI) of the Federal Council on Science and Technology. COSATI was established in the early 1960s to coordinate the management of the results from the U.S. government's increasing commitment to scientific research and technology development. The scientific and technical information (STI) managers of the government's major research and development (R&D) agencies worked within COSATI to standardize guidelines for cataloging and indexing technical reports. COSATI ceased formal operations in the early 1970s. To continue the cooperation begun under COSATI, managers of agency STI programs from Commerce (National Technical Information Service), Energy (Office of Scientific and Technical Information), NASA (HQ/STI Division), and Defense (Defense Technical Information Center) began meeting periodically to discuss common topics and stimulate more effective cooperation. In 1985, a Memorandum of Understanding was signed by the four charter agencies and CENDI was established. From this small core of STI managers, CENDI has grown to its current membership, which represents the major science agencies, the national libraries, and agencies involved in the dissemination and long-term management of scientific and technical information. The vision of CENDI is to facilitate cooperative enterprise where capabilities are shared and challenges are faced together so that the sum of the accomplishments is greater than each individual agency can achieve on its own amongst federal STI agencies. The abbreviation CENDI refers to the "Commerce, Energy, NASA, Defense Information Managers Group". == Membership == New members from other federal R&D information organizations may be admitted by unanimous agreement of the members. However, it is the intent of the group that membership in CENDI should remain small and focus on organizations with STI or supporting responsibilities. Each agency provides funding to CENDI. == Members == The members of CENDI are: Defense Technical Information Center (United States Department of Defense) Office of Research and Development and Office of Environmental Information (United States Environmental Protection Agency) Government Printing Office Library of Congress NASA Scientific and Technical Information Program National Agricultural Library (United States Department of Agriculture) National Archives and Records Administration National Library of Education (United States Department of Education) National Library of Medicine (United States Department of Health and Human Services) National Science Foundation National Technical Information Service (United States Department of Commerce) National Transportation Library (United States Department of Transportation) Office of Scientific and Technical Information (United States Department of Energy) USGS/Biological Resources Discipline (United States Department of the Interior) == Mission and operation == CENDI's mission is to help improve the productivity of federal science- and technology-based programs through effective scientific, technical, and related information support systems. In fulfilling its mission, CENDI agencies play an important role in addressing science- and technology-based national priorities and strengthening U.S. competitiveness. === Goals === STI Coordination and Leadership: Provide coordination and leadership for information exchange on important STI policy issues. Improvement of STI Systems: Promote the development of improved STI systems through the productive interrelationship of content and technology. STI Understanding: Promote better understanding of STI and STI management. === Principals and Alternates === CENDI is made up of senior federal STI managers and each organization appoints a Principal representative. This person is the point of contact for that organization within CENDI. Each Principal has an Alternate. The Principals and Alternates comprise the main group that meets on a regular basis, usually every other month. === Secretariat === A Tennessee-based information management company, -- Information International Associates, Inc., currently serves as the CENDI Secretariat. The Secretariat provides day-to-day operations to CENDI. The Secretariat prepares the necessary materials for the Principals' meetings, provides support for the working group and task group meetings, assists in developing papers, and maintains the CENDI files and outreach tools. === Task Groups and Working Groups === The chair(s) of a working group is appointed by the Principals and has the overall responsibility for the group's activities. The Secretariat provides support at the request of the Working Group chair(s). The Working Groups and Task Groups that are currently operating are: Copyright and Intellectual Property Working Group Distribution Markings Task Group Digital Preservation Task Group Digitization Specifications Task Group Image Metadata Task Group Science.gov (see below) STI Policy Working Group Terminology Resources Task Group === Science.gov and Worldwidescience.org === In 2001, in response to the April 2001 workshop on "Strengthening the Public Information Infrastructure for Science", and taking into consideration a request from Firstgov (now USA.gov) to develop specialized topical portals, CENDI formed an alliance to develop an interagency website for access to STI. This website, called Science.gov, is a one-stop source of STI, including both selected, authoritative government websites and deep Web databases of technical reports, journal articles, conference proceedings, and other published materials. Through the volunteer efforts of members and involving over 100 staff, content and architecture is developed for the site. The Science.gov website is hosted by the Department of Energy (DOE) Office of Scientific and Technical Information (OSTI). The site was formally launched in December 2002. As a result of the success of Science.gov, under DOE leadership and in cooperation with the International Council of Scientific and Technical Information, a worldwide coordination across national portals called WorldWideScience was launched in 2008. === Work with non-member organizations === CENDI works with several cooperating non-member organizations on a regular basis. These agencies are in academia, federal government, legal and policy analysis, international, non-governmental, and private organizations.

  Read more →
• 

  Enterprise architecture

  Enterprise architecture (EA) is a business function concerned with the structures and behaviours of a business, especially business roles and processes that create and use business data. The international definition according to the Federation of Enterprise Architecture Professional Organizations is "a well-defined practice for conducting enterprise analysis, design, planning, and implementation, using a comprehensive approach at all times, for the successful development and execution of strategy. Enterprise architecture applies architecture principles and practices to guide organizations through the business, information, process, and technology changes necessary to execute their strategies. These practices utilize the various aspects of an enterprise to identify, motivate, and achieve these changes." The United States Federal Government is an example of an organization that practices EA, in this case with its Capital Planning and Investment Control processes. Companies such as Independence Blue Cross, Intel, Volkswagen AG, and InterContinental Hotels Group also use EA to improve their business architectures as well as to improve business performance and productivity. Additionally, the Federal Enterprise Architecture's reference guide aids federal agencies in the development of their architectures. == Introduction == As a discipline, EA "proactively and holistically lead[s] enterprise responses to disruptive forces by identifying and analyzing the execution of change" towards organizational goals. EA gives business and IT leaders recommendations for policy adjustments and provides best strategies to support and enable business development and change within the information systems the business depends on. EA provides a guide for decision making towards these objectives. The National Computing Centre's EA best practice guidance states that an EA typically "takes the form of a comprehensive set of cohesive models that describe the structure and functions of an enterprise. The individual models in an EA are arranged in a logical manner that provides an ever-increasing level of detail about the enterprise." Important players within EA include enterprise architects and solutions architects. Enterprise architects are at the top level of the architect hierarchy, meaning they have more responsibilities than solutions architects. While solutions architects focus on their own relevant solutions, enterprise architects focus on solutions for and the impact on the whole organization. Enterprise architects oversee many solution architects and business functions. As practitioners of EA, enterprise architects support an organization's strategic vision by acting to align people, process, and technology decisions with actionable goals and objectives that result in quantifiable improvements toward achieving that vision. The practice of EA "analyzes areas of common activity within or between organizations, where information and other resources are exchanged to guide future states from an integrated viewpoint of strategy, business, and technology." === Definitions === The term enterprise can be defined as an organizational unit, organization, or collection of organizations that share a set of common goals and collaborate to provide specific products or services to customers. In that sense, the term enterprise covers various types of organizations, regardless of their size, ownership model, operational model, or geographical distribution. It includes those organizations' complete sociotechnical system, including people, information, processes, and technologies. Enterprise as a sociotechnical system defines the scope of EA. The term architecture refers to fundamental concepts or properties of a system in its environment; and embodied in its elements, relationships, and in the principles of its design and evolution. A methodology for developing and using architecture to guide the transformation of a business from a baseline state to a target state, sometimes through several transition states, is usually known as an enterprise architecture framework. A framework provides a structured collection of processes, techniques, artifact descriptions, reference models, and guidance for the production and use of an enterprise-specific architecture description. Open-source tools supporting EA practice, such as the Essential Project, have also been evaluated for suitability in academic and commercial training contexts. Paramount to changing the EA is the identification of a sponsor. Their mission, vision, strategy, and the governance framework define all roles, responsibilities, and relationships involved in the anticipated transformation. Changes considered by enterprise architects typically include innovations in the structure or processes of an organization; innovations in the use of information systems or technologies; the integration and/or standardization of business processes; and improvement of the quality and timeliness of business information. According to the standard ISO/IEC/IEEE 42010, the product used to describe the architecture of a system is called an architectural description. In practice, an architectural description contains a variety of lists, tables, and diagrams. These are models known as views. In the case of EA, these models describe the logical business functions or capabilities, business processes, human roles and actors, the physical organization structure, data flows and data stores, business applications and platform applications, hardware, and communications infrastructure. The first use of the term "enterprise architecture" is often incorrectly attributed to John Zachman's 1987 A framework for information systems architecture. The first publication to use it was instead a National Institute of Standards (NIST) Special Publication on the challenges of information system integration. The NIST article describes EA as consisting of several levels. Business unit architecture is the top level and might be a total corporate entity or a sub-unit. It establishes for the whole organization necessary frameworks for "satisfying both internal information needs" as well as the needs of external entities, which include cooperating organizations, customers, and federal agencies. The lower levels of the EA that provide information to higher levels are more attentive to detail on behalf of their superiors. In addition to this structure, business unit architecture establishes standards, policies, and procedures that either enhance or stymie the organization's mission. The main difference between these two definitions is that Zachman's concept was the creation of individual information systems optimized for business, while NIST's described the management of all information systems within a business unit. The definitions in both publications, however, agreed that due to the "increasing size and complexity of the [i]mplementations of [i]nformation systems... logical construct[s] (or architecture) for defining and controlling the interfaces and... [i]ntegration of all the components of a system" is necessary. Zachman in particular urged for a "strategic planning methodology." == Overview == === Schools of thought === Within the field of enterprise architecture, there are three overarching schools: Enterprise IT Design, Enterprise Integrating, and Enterprise Ecosystem Adaption. Which school one subscribes to will impact how they see the EA's purpose and scope, as well as the means of achieving it, the skills needed to conduct it, and the locus of responsibility for conducting it. Under Enterprise IT Design, the main purpose of EA is to guide the process of planning and designing an enterprise's IT/IS capabilities to meet the desired organizational objectives, often by greater alignment between IT/IS and business concerns. Architecture proposals and decisions are limited to the IT/IS aspects of the enterprise and other aspects service only as inputs. The Enterprise Integrating school believes that the purpose of EA is to create a greater coherency between the various concerns of an enterprise (HR, IT, Operations, etc.), including the link between strategy formulation and execution. Architecture proposals and decisions here encompass all aspects of the enterprise. The Enterprise Ecosystem Adaption school states that the purpose of EA is to foster and maintain the learning capabilities of enterprises so they may be sustainable. Consequently, a great deal of emphasis is put on improving the capabilities of the enterprise to improve itself, to innovate, and to coevolve with its environment. Typically, proposals and decisions encompass both the enterprise and its environment. === Benefits, challenges, and criticisms === The benefits of EA are achieved through its direct and indirect contributions to organizational goals. Notable benefits include support in the areas related to design and re-design of the organizational structures during mergers, acquisitions, or

  Read more →
• 

  Learning curve (machine learning)

  In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and usually a validation set) changes with the number of training iterations (epochs) or the amount of training data. Typically, the number of training epochs or training set size is plotted on the x-axis, and the value of the loss function (and possibly some other metric such as the cross-validation score) on the y-axis. Synonyms include error curve, experience curve, improvement curve and generalization curve. More abstractly, learning curves plot the difference between learning effort and predictive performance, where "learning effort" usually means the number of training samples, and "predictive performance" means accuracy on testing samples. Learning curves have many useful purposes in ML, including: choosing model parameters during design, adjusting optimization to improve convergence, and diagnosing problems such as overfitting (or underfitting). Learning curves can also be tools for determining how much a model benefits from adding more training data, and whether the model suffers more from a variance error or a bias error. If both the validation score and the training score converge to a certain value, then the model will no longer significantly benefit from more training data. == Formal definition == When creating a function to approximate the distribution of some data, it is necessary to define a loss function L ( f θ ( X ) , Y ) {\displaystyle L(f_{\theta }(X),Y)} to measure how good the model output is (e.g., accuracy for classification tasks or mean squared error for regression). We then define an optimization process which finds model parameters θ {\displaystyle \theta } such that L ( f θ ( X ) , Y ) {\displaystyle L(f_{\theta }(X),Y)} is minimized, referred to as θ ∗ {\displaystyle \theta ^{}} . === Training curve for amount of data === If the training data is { x 1 , x 2 , … , x n } , { y 1 , y 2 , … y n } {\displaystyle \{x_{1},x_{2},\dots ,x_{n}\},\{y_{1},y_{2},\dots y_{n}\}} and the validation data is { x 1 ′ , x 2 ′ , … x m ′ } , { y 1 ′ , y 2 ′ , … y m ′ } {\displaystyle \{x_{1}',x_{2}',\dots x_{m}'\},\{y_{1}',y_{2}',\dots y_{m}'\}} , a learning curve is the plot of the two curves i ↦ L ( f θ ∗ ( X i , Y i ) ( X i ) , Y i ) {\displaystyle i\mapsto L(f_{\theta ^{}(X_{i},Y_{i})}(X_{i}),Y_{i})} i ↦ L ( f θ ∗ ( X i , Y i ) ( X i ′ ) , Y i ′ ) {\displaystyle i\mapsto L(f_{\theta ^{}(X_{i},Y_{i})}(X_{i}'),Y_{i}')} where X i = { x 1 , x 2 , … x i } {\displaystyle X_{i}=\{x_{1},x_{2},\dots x_{i}\}} === Training curve for number of iterations === Many optimization algorithms are iterative, repeating the same step (such as backpropagation) until the process converges to an optimal value. Gradient descent is one such algorithm. If θ i ∗ {\displaystyle \theta _{i}^{}} is the approximation of the optimal θ {\displaystyle \theta } after i {\displaystyle i} steps, a learning curve is the plot of i ↦ L ( f θ i ∗ ( X , Y ) ( X ) , Y ) {\displaystyle i\mapsto L(f_{\theta _{i}^{}(X,Y)}(X),Y)} i ↦ L ( f θ i ∗ ( X , Y ) ( X ′ ) , Y ′ ) {\displaystyle i\mapsto L(f_{\theta _{i}^{}(X,Y)}(X'),Y')}

  Read more →
• 

  Transliteracy

  Transliteracy is "a fluidity of movement across a range of technologies, media and contexts". It is an ability to use diverse techniques to collaborate across different social groups. Transliteracy combines a range of capabilities required to move across a range of contexts, media, technologies and genres. Conceptually, transliteracy is situated across five capabilities: information capabilities (see information literacy), ICT (information and communication technologies), communication and collaboration, creativity and critical thinking. It is underpinned by literacy and numeracy. (See figure below) The concept of transliteracy is impacting the system of education and libraries. == History == While the term appears to come from the prefix trans- ('across') and the word literacy, the scholars who coined it say they developed it from the practice of transliteration, which means to use the letters of one language to write down a different language. The study of transliteracy was first developed in 2005 by the Transliteracies Research Project, directed by University of California at Santa Barbara Professor Alan Liu. The concept of 'transliteracies' was developed as part of research into online reading. It was shared and refined at the Transliteracies conference, held at UC Santa Barbara in 2005. The conference inspired the at the time De Montfort University Professor, Sue Thomas, to create the Production in Research and Transliteracy (PART) group, which evolved into the Transliteracy Research Group. The current meaning of transliteracy was defined in the group's seminal paper Transliteracy: crossing divides as "the ability to read, write, and interact across a range of platforms, tools, and media from signing and orality through handwriting, print, TV, radio, and film, to digital social networks." The concept was enthusiastically adopted by a number of professional groups, notably in the library and information field. Transliteracy Research Group Archive 2006–2013 curates numerous resources from this period. For a number of years, there was a gap between significant interest in transliteracy among professional groups and the scarcity of research. A group of academics from the University of Bordeaux considered transliteracy mainly in the school context. Freelance writer and consultant, Sue Thomas, studied transliteracy and creativity, while Suzana Sukovic, executive director of educational research and evidence-based practice at HETI, researched transliteracy in relation to digital storytelling. The first book on the topic, Transliteracy in complex information environment by Sukovic, is based on research and experience with practice-based projects. == Transliteracy in education == Transliteracy is making an impact on the classroom setting because of how technologically advanced younger generations are today. In 2012, Adam Marcus, a teacher and librarian at the New York City Department of Education (NYCDOE), decided to incorporate transliteracy into his school's public library summer reading program. He had a desire to enhance the experience of reading for his students by allowing them to connect to the text differently by using social media. He used a tool called VoiceThread in order to have his students "take part in conversations, formulate ideas, and share higher-order thinking through a variety of media channels: video, audio, text, images, and music". Students were also enabled to communicate with the book's author through blogs and websites, and were given multiple modes of media to comprehend and engage with the text on a deeper level. Some of these examples include an audio-video glossary and web links that aimed to bring the details of the text to life. The results of his experiment were deemed to have a positive effect on the program as students responded well to this interactive experience they were given. Marcus believes that it is important for educators and librarians to enhance storytelling for children by providing them with a modern and transliterate experience that one could not receive back then. The Agence nationale de la recherche funded a program at a French high school from 2013 to 2015, where the transliteracy skills of students were tested and observed. Students were placed in groups of three or four members and were required to use all sorts of media and tools in order to collect data for their projects. They were not allowed to only use digital sources, and were advised to use a diversity of sources. The focus of this experiment was to observe "the possible diversity of media and tools employed, on the ways of and reasons for switching from one to another, on how these different media and tools are distributed within contexts, according to the academic requirements and tasks individually and collectively performed by the students." The conclusions of the experiment dealt with physical space and organization being an issue for students and teachers to deal with. Spatially, it was challenging for students to navigate through different mediums when their space inside the classroom was limited. It was noticed that students were prone to use something that took up less space, rather than focusing on expanding their diversity of sources. Organizationally, it was challenging for students to organize all of the information they collected since everything was not being search and collected for digitally. In addition, students were not allotted a lot of time to complete their projects which also impacted their final product. == Transliteracy in libraries == In 2009, Dr. Susie Andretta, senior lecturer in Information Management at London Metropolitan University, conducted interviews with four different information professionals including an academic librarian, an outreach librarian, a content manager, and a scholar within the library science and information discipline. She was aiming to explore how transliteracy was colliding and combining with the print-world of libraries. Dr. Andretta defines transliteracy as "an umbrella term encompassing different literacies and multiple communication channels that require active participation with and across a range of platforms, and embracing both linear and non-linear messages (3)." The goals of these interviews ranged from the following: to test the information professional's awareness of transliteracy, to have them identify transliteracy and how it is integrated into their work, and to explain the impact transliteracy has had on they library they work at. Andretta found that out of all the information professionals interviewed, it was only the academic librarian who was vaguely familiar with the concept of transliteracy. Bernadette Daly Swanson, an Academic Librarian at UC Davis, expresses in her interview with Dr. Andretta how she would "like to think that the transliterate library is more of an environment where we do different things [...] I would take maybe about a third of the first floor of our library and transform it into a lab [...] where we can start to evolve [..] explore, and experiment in media development, content development, and do it not just with librarians; so open up the space for other people [...] so you don't get people working in isolation." Although the other three candidates that Dr. Andretta interviewed had not heard of the term transliteracy, they responded well to the concept once it was explained to them and agreed with its impact on the workplace. Dr. Michael Stephens, an assistant professor in the Graduate School of Library and Information Science at Dominican University, explains in his interview how the term transliteracy describes the courses he teaches on libraries and Web 2.0 technologies. Dr. Stephens states that students being educated in Web 2.0 technologies gives them "the opportunity to experience what the channel can be and the potential for that sharing learning, for asking questions, just for out loud thinking – I think it's incredibly valuable. [..] this is where this wonderful concept comes in, it was teaching them transliteracy and the fact that they can move across channels without getting worried about it." Dr. Andretta concluded from her interviews how although transliteracy may not be a very well-known term yet, it has nonetheless established itself into the intuition of libraries while also transforming the traditional library to a world of enhanced and expanded services. "Inherent in this transition are the challenges of having to adapt to a constantly changing technological landscape, the multiple literacies that this generates, and the need to establish a multifaceted library profession that can speak the multiple-media languages of its diverse users." Thomas Ipri, a librarian at the University of Nevada, advocates for libraries needing to make a change in their literary functions. He argues that the divide between digital and print makes it harder for libraries to accommodate their patrons and to share information. He f

  Read more →
• 

  Kinodynamic planning

  In robotics and motion planning, kinodynamic planning is a class of problems for which velocity, acceleration, and force/torque bounds must be satisfied, together with kinematic constraints such as avoiding obstacles. The term was coined by Bruce Donald, Pat Xavier, John Canny, and John Reif. Donald et al. developed the first polynomial-time approximation schemes (PTAS) for the problem. By providing a provably polynomial-time ε-approximation algorithm, they resolved a long-standing open problem in optimal control. Their first paper considered time-optimal control ("fastest path") of a point mass under Newtonian dynamics, amidst polygonal (2D) or polyhedral (3D) obstacles, subject to state bounds on position, velocity, and acceleration. Later they extended the technique to many other cases, for example, to 3D open-chain kinematic robots under full Lagrangian dynamics. == Modern approaches == Since the foundational theoretical work of the 1990s, the field has evolved significantly with new algorithmic approaches that address the computational and practical limitations of early methods. === Sampling-based methods === Many practical heuristic algorithms based on stochastic optimization and iterative sampling have been developed by a wide range of authors to address the kinodynamic planning problem. Popular approaches include extensions of RRT algorithms such as RRT for kinodynamic systems, and sampling-based methods like Model Predictive Path Integral (MPPI) control. These stochastic techniques have been shown to work well in practice and can handle complex, high-dimensional state spaces more efficiently than deterministic methods. However, all motion planning methods are subject to the PSPACE-hardnesss of classical motion planning even without dynamics, which means (assuming the usual structural complexity conjectures) they all can be worst-case exponential-time in the state-space dimension (the number of degrees of freedom). On the other hand, the deterministic methods have provable guarantees of completeness, accuracy, and complexity (for fixed dimension, they are polynomial-time not only in the geometric complexity, but also in ( 1 / ε ) {\displaystyle (1/\varepsilon )} , the closeness of the desired approximation), whereas most of the recent heuristic/stochastic methods sacrifice at least one of these criteria. === Mixed-integer optimization approaches === Recent advances in mixed-integer programming have enabled new deterministic approaches to kinodynamic planning. These methods formulate the planning problem as an optimization task that simultaneously determines the spatial path and control sequence while respecting all kinodynamic constraints. By using techniques such as McCormick envelopes to handle bilinear constraints, these approaches can provide globally optimal solutions with mathematical guarantees while achieving significant computational speedups over traditional methods. === Genetic algorithm approaches === Genetic algorithms have also been adapted for kinodynamic planning, particularly for gradient-free optimization in challenging terrain. These methods use evolutionary computation to optimize trajectories over receding horizons, with specialized mutation operators that ensure vehicle controls remain within operational limits. This approach is particularly useful when dealing with non-differentiable cost functions or when gradient information is unavailable or unreliable. === Three-dimensional terrain planning === The foundational theoretical work of the 1990s was extended to higher degrees of freedom, and even to n {\displaystyle n} -link, 3D open-chain kinematic robots under full Lagrangian dynamics. However, many of the subsequent heuristic techniques (typically employing stochastic optimization) were confined to planar environments. More recent kinodynamic planning has extended beyond these planar environments to handle complex 3D terrains represented as simplicial complexes or triangular meshes. This advancement is particularly important for applications such as autonomous vehicle navigation in off-road environments, where elevation changes and terrain geometry significantly impact vehicle dynamics. These methods must account for pitch angles, surface curvature, and the coupling between terrain geometry and vehicle kinodynamic constraints. == Performance and guarantees == The landscape of performance guarantees in kinodynamic planning has evolved considerably. While early heuristic methods could not guarantee optimality, recent mixed-integer approaches have demonstrated the ability to find globally optimal solutions with proven constraint satisfaction. Experimental comparisons have shown that modern optimization-based planners can achieve execution times several orders of magnitude faster than sampling-based methods while maintaining strict adherence to kinodynamic constraints. However, the choice of method often depends on the specific application requirements. Sampling-based methods remain valuable for their ability to quickly find feasible solutions in high-dimensional spaces and their robustness to modeling uncertainties. Optimization-based methods excel when optimality guarantees and constraint compliance are critical, particularly in safety-critical applications. == Applications == Kinodynamic planning finds applications across numerous domains including: Autonomous vehicles: Path planning for cars, trucks, and other ground vehicles that must respect acceleration, steering, and velocity limits Aerial robotics: Trajectory planning for quadrotors and other unmanned aerial vehicles with dynamic constraints Manipulation: Planning for robotic arms where joint velocities, accelerations, and torques are limited Legged locomotion: Footstep and trajectory planning for walking and running robots Space robotics: Planning under thrust and fuel constraints for spacecraft and rovers

  Read more →
• 

  Metadata controller

  Metadata controller (or MDC) is a storage area network (SAN) technology for managing file locking, space allocation and data access authorization. This is needed when several clients are given block level access to the same disk volume, data storage sharing. MDCs are only used on high-end servers. These are never found on user computers. In the absence of MDC over a SAN there is no possible way of ensuring privacy of the stored data. This controller can also play its role as a sharing device in case the administrators allow other servers to access certain blocks in a particular SAN. The access granted to the servers is of different levels. Some times it may happen that the server is not able to see a block or make changes in it in case of a locked file. This is caused by grant of low level access. If different clients on SAN happen to know each other, access may be granted to shift a certain block from one server to another. This allows the recipient server to use the block and make changes in it. MDCs work as enzymes. They require certain types of SANs and networks to work properly. If a controller is connected to the right network it will boost its output. In case of wrong connection i.e. with the incorrect network, it will decrease its performance.

  Read more →
• 

  Data Science and Predictive Analytics

  The first edition of the textbook Data Science and Predictive Analytics: Biomedical and Health Applications using R, authored by Ivo D. Dinov, was published in August 2018 by Springer. The second edition of the book was printed in 2023. This textbook covers some of the core mathematical foundations, computational techniques, and artificial intelligence approaches used in data science research and applications. By using the statistical computing platform R and a broad range of biomedical case-studies, the 23 chapters of the book first edition provide explicit examples of importing, exporting, processing, modeling, visualizing, and interpreting large, multivariate, incomplete, heterogeneous, longitudinal, and incomplete datasets (big data). == Structure == === First edition table of contents === The first edition of the Data Science and Predictive Analytics (DSPA) textbook is divided into the following 23 chapters, each progressively building on the previous content. === Second edition table of contents === The significantly reorganized revised edition of the book (2023) expands and modernizes the presented mathematical principles, computational methods, data science techniques, model-based machine learning and model-free artificial intelligence algorithms. The 14 chapters of the new edition start with an introduction and progressively build foundational skills to naturally reach biomedical applications of deep learning. Introduction Basic Visualization and Exploratory Data Analytics Linear Algebra, Matrix Computing, and Regression Modeling Linear and Nonlinear Dimensionality Reduction Supervised Classification Black Box Machine Learning Methods Qualitative Learning Methods—Text Mining, Natural Language Processing, and Apriori Association Rules Learning Unsupervised Clustering Model Performance Assessment, Validation, and Improvement Specialized Machine Learning Topics Variable Importance and Feature Selection Big Longitudinal Data Analysis Function Optimization Deep Learning, Neural Networks == Reception == The materials in the Data Science and Predictive Analytics (DSPA) textbook have been peer-reviewed in the Journal of the American Statistical Association, International Statistical Institute’s ISI Review Journal, and the Journal of the American Library Association. Many scholarly publications reference the DSPA textbook. As of January 17, 2021, the electronic version of the book first edition (ISBN 978-3-319-72347-1) is freely available on SpringerLink and has been downloaded over 6 million times. The textbook is globally available in print (hardcover and softcover) and electronic formats (PDF and EPub) in many college and university libraries and has been used for data science, computational statistics, and analytics classes at various institutions.

  Read more →
• 

  Enterprise bus matrix

  The enterprise bus matrix is a data warehouse planning tool and model created by Ralph Kimball, and is part of the data warehouse bus architecture. The matrix is the logical definition of one of the core concepts of Kimball's approach to dimensional modeling conformed dimension. The bus matrix defines part of the data warehouse bus architecture and is an output of the business requirements phase in the Kimball lifecycle. It is applied in the following phases of dimensional modeling and development of the data warehouse. The matrix can be categorized as a hybrid model, being part technical design tool, part project management tool and part communication tool == Background == The need for an enterprise bus matrix stems from the way one goes about creating the overall data warehouse environment. Historically there have been two approaches: a structured, centralized and planned approach and a more loosely defined, department specific approach, in which solutions are developed in a more independent matter. Autonomous projects can result in a range of isolated stove pipe data marts. Naturally each approach has its issues; the visionary approach often struggles with long delivery cycles and lack of reaction time as needs emerge and scope issues arise. On the other hand, the development of isolated data marts leads to stovepipe systems that lack synergy in development. Over time this approach will lead to a so-called data-mart-in-a-box architecture where interoperability and lack of cohesion is apparent, and can hinder the realization of an overall enterprise data warehouse. As an attempt to handle this issue, Ralph Kimball introduced the enterprise bus. == Description == The bus matrix purpose is one of high abstraction and visionary planning on the data warehouse architectural level. By dictating coherency in the development and implementation of an overall data warehouse the bus architecture approach enables an overall vision of the broader enterprise integration and consistency while at the same time dividing the problem into more manageable parts – all in a technology and software independent manner. The bus matrix and architecture builds upon the concept of conformed dimensions, creating a structure of common dimensions that ideally can be used across the enterprise by all business processes related to the data warehouse and the corresponding fact tables from which they derive their context. According to Kimball and Margy Ross's article “Differences of Opinion” "The Enterprise Data warehouse built on the bus architecture ”identifies and enforces the relationship between business process metrics (facts) and descriptive attributes (dimensions)”. The concept of a bus is well known in the language of information technology, and is what reflects the conformed dimension concept in the data warehouse, creating the skeletal structure where all parts of a system connect, ensuring interoperability and consistency of data, and at the same time considers future expansion. This makes the conformed dimensions act as the integration ‘glue’, creating a robust backbone of the enterprise Data Warehouse.

  Read more →
• 

  Microsoft Office PerformancePoint Server

  Microsoft Office PerformancePoint Server is a business intelligence software product released in 2007 by Microsoft. The product was generally an integration of the acquisitions from ProClarity - the Planning Server and Monitoring Server - into Microsoft's SharePoint server product line. Although discontinued in 2009, the dashboard, scorecard, and analytics capabilities of PerformancePoint Server were incorporated into SharePoint 2010 and later versions. PerformancePoint Server also provided a planning and budgeting component directly integrated with Excel. == History == Microsoft offered preview releases of PerformancePoint Server starting in mid-2006. Previews of the product were formed from Business Scorecard Manager 2005 and the Planning Server component. Acquisitions ProClarity and Great Plains brought additional analytics and planning/reporting capabilities, as well as companion products ProClarity 6.3 and FRx. PerformancePoint Server was officially released in November 2007. Microsoft discontinued PerformancePoint Server as an independent product in 2009 and folded its dashboard, scorecard and analytics capabilities into PerformancePoint Services in SharePoint Server 2010. == Monitoring Server Component == Business monitoring capabilities, including dashboards, scorecards & key performance indicators, navigable reports for deeper analysis, strategy maps, and linked filtering, are provided by PerformancePoint's Monitoring Server component. A Dashboard Designer application that is distributed from Monitoring Server enables business analysts or IT Administrators to: create & test data source connections create views that use those data connections assemble the views into a dashboard deploy the dashboard as a SharePoint page Dashboard Designer saved content and security information back to the Monitoring Server. Data source connections, such as OLAP cubes or relational tables, were also made through Monitoring Server. After a dashboard has been published to the Monitoring Server database, it would be deployed as a SharePoint page and shared with other users as such. When the pages were opened in a web browser, Monitoring Server updated the data in the views by connecting back to the original data sources. == Planning Server Component == PerformancePoint's Planning Server component supported maintenance of logical business models, budget & approval workflows, enterprise data sources, and it followed Generally Accepted Accounting Principles. Planning Server made use of Excel for input and line-of-business reporting, as well as SQL Server for storing and processing business models. == Management Reporter Component == The Management Reporter component was designed to perform financial reporting and can read PerformancePoint Planning models directly. A development kit was also available to allow this component to read other models.

  Read more →