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  • Database-as-IPC

    Database-as-IPC

    In computer programming, Database-as-IPC may be considered an anti-pattern where a disk persisted table in a database is used as the message queue store for routine inter-process communication (IPC) or subscribed data processing. If database performance is of concern, alternatives include sockets, network socket, or message queue. British computer scientist, Junade Ali, defined the Database-as-IPC Anti-Pattern as using a database to "schedule jobs or queue up tasks to be completed", noting that this anti-pattern centres around using a database for temporary messages instead of persistent data. == Controversy == The issue arises if there is a performance issue, and if additional systems (and servers) can be justified. In terms of performance, recent advancements in database systems provide more efficient mechanisms for signaling and messaging, and database systems also support memory (non-persisted) tables. There are databases with built-in notification mechanisms, such as PostgreSQL, SQL Server, and Oracle. These mechanisms and future improvements of database systems can make queuing much more efficient and avoid the need to set up a separate signaling or messaging queue system along with the server and management overhead. While MySQL doesn't have direct support for notifications, some workarounds are possible. However, they would be seen as non-standard and therefore more difficult to maintain.

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  • Dan Hendrycks

    Dan Hendrycks

    Dan Hendrycks (born 1994 or 1995) is an American machine learning researcher. He serves as the director of the Center for AI Safety, a nonprofit research organization based in San Francisco, California. == Early life and education == Hendrycks was raised in a Christian evangelical household in Marshfield, Missouri. He received a B.S. from the University of Chicago in 2018 and a Ph.D. from the University of California, Berkeley in Computer Science in 2022. == Career and research == Hendrycks' research focuses on topics that include machine learning safety, machine ethics, and robustness. He credits his participation in the effective altruism (EA) movement-linked 80,000 Hours program for his career focus towards AI safety, though denies being an advocate for EA. Hendrycks is the main author of the research paper that introduced the activation function GELU in 2016, and of the paper that introduced the language model benchmark MMLU (Massive Multitask Language Understanding) in 2020. In February 2022, Hendrycks co-authored recommendations for the US National Institute of Standards and Technology (NIST) to inform the management of risks from artificial intelligence. In September 2022, Hendrycks wrote a paper providing a framework for analyzing the impact of AI research on societal risks. He later published a paper in March 2023 examining how natural selection and competitive pressures could shape the goals of artificial agents. This was followed by "An Overview of Catastrophic AI Risks", which discusses four categories of risks: malicious use, AI race dynamics, organizational risks, and rogue AI agents. Hendrycks is the safety adviser of xAI, an AI startup company founded by Elon Musk in 2023. To avoid any potential conflicts of interest, he receives a symbolic one-dollar salary and holds no company equity. In November 2024, he also joined Scale AI as an advisor collecting a one-dollar salary. Hendrycks is the creator of Humanity's Last Exam, a benchmark for evaluating the capabilities of large language models, which he developed in collaboration with Scale AI. In 2024, Hendrycks published the textbook Introduction to AI Safety, Ethics, and Society, based on courseware he had previously developed. == Selected publications == Hendrycks, Dan; Gimpel, Kevin (2020-07-08). "Gaussian Error Linear Units (GELUs)". arXiv:1606.08415 [cs.LG]. Hendrycks, Dan; Gimpel, Kevin (2018-10-03). "A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks". International Conference on Learning Representations 2017. arXiv:1610.02136. Hendrycks, Dan; Mazeika, Mantas; Dietterich, Thomas (2019-01-28). "Deep Anomaly Detection with Outlier Exposure". International Conference on Learning Representations 2019. arXiv:1812.04606. Hendrycks, Dan; Mazeika, Mantas; Zou, Andy (2021-10-25). "What Would Jiminy Cricket Do? Towards Agents That Behave Morally". Conference on Neural Information Processing Systems 2021. arXiv:2110.13136.

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  • Self-verifying finite automaton

    Self-verifying finite automaton

    In automata theory, a self-verifying finite automaton (SVFA) is a special kind of a nondeterministic finite automaton (NFA) with a symmetric kind of nondeterminism introduced by Hromkovič and Schnitger. Generally, in self-verifying nondeterminism, each computation path is concluded with any of the three possible answers: yes, no, and I do not know. For each input string, no two paths may give contradictory answers, namely both answers yes and no on the same input are not possible. At least one path must give answer yes or no, and if it is yes then the string is considered accepted. SVFA accept the same class of languages as deterministic finite automata (DFA) and NFA but have different state complexity. == Formal definition == An SVFA is represented formally by a 6-tuple, A=(Q, Σ, Δ, q0, Fa, Fr) such that (Q, Σ, Δ, q0, Fa) is an NFA, and Fa, Fr are disjoint subsets of Q. For each word w = a1a2 … an, a computation is a sequence of states r0,r1, …, rn, in Q with the following conditions: r0 = q0 ri+1 ∈ Δ(ri, ai+1), for i = 0, …, n−1. If rn ∈ Fa then the computation is accepting, and if rn ∈ Fr then the computation is rejecting. There is a requirement that for each w there is at least one accepting computation or at least one rejecting computation but not both. == Results == Each DFA is a SVFA, but not vice versa. Jirásková and Pighizzini proved that for every SVFA of n states, there exists an equivalent DFA of g ( n ) = Θ ( 3 n / 3 ) {\displaystyle g(n)=\Theta (3^{n/3})} states. Furthermore, for each positive integer n, there exists an n-state SVFA such that the minimal equivalent DFA has exactly g ( n ) {\displaystyle g(n)} states. Other results on the state complexity of SVFA were obtained by Jirásková and her colleagues.

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  • Best AI Art Generators in 2026

    Best AI Art Generators in 2026

    Curious about the best AI art generator? An AI art generator is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI art generator slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  • Artificial intelligence arms race

    Artificial intelligence arms race

    A military artificial intelligence arms race is a technological, economic, and military competition between two or more states to develop and deploy advanced AI technologies and lethal autonomous weapons systems (LAWS). The goal is to gain a strategic or tactical advantage over rivals, similar to previous arms races involving nuclear or conventional military technologies. Since the mid-2010s, many analysts have noted the emergence of such an arms race between superpowers for better AI technology and military AI, driven by increasing geopolitical and military tensions. An AI arms race is sometimes placed in the context of an AI Cold War between the United States and China. Several influential figures and publications have emphasized that whoever develops artificial general intelligence (AGI) first could dominate global affairs in the 21st century. Russian President Vladimir Putin stated that the leader in AI will "rule the world." Researchers and experts, such as Leopold Aschenbrenner and Adrian Pecotic respectively, warn that the AGI race between major powers like the U.S. and China could reshape geopolitical power. This includes AI for surveillance, autonomous weapons, decision-making systems, cyber operations, and more. == Terminology == Lethal autonomous weapons systems use artificial intelligence to identify and kill human targets without human intervention. LAWS have colloquially been called "slaughterbots" or "killer robots". Broadly, any competition for superior AI is sometimes framed as an "arms race". Advantages in military AI overlap with advantages in other sectors, as countries pursue both economic and military advantages, as per previous arms races throughout history. == History == In 2014, AI specialist Steve Omohundro warned that "An autonomous weapons arms race is already taking place". According to Siemens, worldwide military spending on robotics was US$5.1 billion in 2010 and US$7.5 billion in 2015. China became a top player in artificial intelligence research in the 2010s. According to the Financial Times, in 2016, for the first time, China published more AI research papers than the entire European Union. When restricted to number of AI papers in the top 5% of cited papers, China overtook the United States in 2016 but lagged behind the European Union. 23% of the researchers presenting at the 2017 American Association for the Advancement of Artificial Intelligence (AAAI) conference were Chinese. Eric Schmidt, the former chairman and chief executive officer of Alphabet, has predicted China will be the leading country in AI by 2025. == Risks == One risk concerns the AI race itself, whether or not the race is won by any one group. There are strong incentives for development teams to cut corners with regard to the safety of the system, increasing the risk of critical failures and unintended consequences. This is in part due to the perceived advantage of being the first to develop advanced AI technology. One team appearing to be on the brink of a breakthrough can encourage other teams to take shortcuts, ignore precautions and deploy a system that is less ready. Some argue that using "race" terminology at all in this context can exacerbate this effect. Another potential danger of an AI arms race is the possibility of losing control of the AI systems; the risk is compounded in the case of a race to artificial general intelligence, which may present an existential risk. In 2023, a United States Air Force official reportedly said that during a computer test, a simulated AI drone killed the human character operating it. The USAF later said the official had misspoken and that it never conducted such simulations. A third risk of an AI arms race is whether or not the race is actually won by one group. The concern is regarding the consolidation of power and technological advantage in the hands of one group. A US government report argued that "AI-enabled capabilities could be used to threaten critical infrastructure, amplify disinformation campaigns, and wage war":1, and that "global stability and nuclear deterrence could be undermined".:11 == By nation == === United States === In 2014, former Secretary of Defense Chuck Hagel posited the "Third Offset Strategy" that rapid advances in artificial intelligence will define the next generation of warfare. According to data science and analytics firm Govini, the U.S. Department of Defense (DoD) increased investment in artificial intelligence, big data and cloud computing from $5.6 billion in 2011 to $7.4 billion in 2016. However, the civilian NSF budget for AI saw no increase in 2017. Japan Times reported in 2018 that the United States private investment is around $70 billion per year. The November 2019 'Interim Report' of the United States' National Security Commission on Artificial Intelligence confirmed that AI is critical to US technological military superiority. The U.S. has many military AI combat programs, such as the Sea Hunter autonomous warship, which is designed to operate for extended periods at sea without a single crew member, and to even guide itself in and out of port. From 2017, a temporary US Department of Defense directive requires a human operator to be kept in the loop when it comes to the taking of human life by autonomous weapons systems. On October 31, 2019, the United States Department of Defense's Defense Innovation Board published the draft of a report recommending principles for the ethical use of artificial intelligence by the Department of Defense that would ensure a human operator would always be able to look into the 'black box' and understand the kill-chain process. However, a major concern is how the report will be implemented. The Joint Artificial Intelligence Center (JAIC) (pronounced "jake") is an American organization on exploring the usage of AI (particularly edge computing), Network of Networks, and AI-enhanced communication, for use in actual combat. It is a subdivision of the United States Armed Forces and was created in June 2018. The organization's stated objective is to "transform the US Department of Defense by accelerating the delivery and adoption of AI to achieve mission impact at scale. The goal is to use AI to solve large and complex problem sets that span multiple combat systems; then, ensure the combat Systems and Components have real-time access to ever-improving libraries of data sets and tools." In 2023, Microsoft pitched the DoD to use DALL-E models to train its battlefield management system. OpenAI, the developer of DALL-E, removed the blanket ban on military and warfare use from its usage policies in January 2024. The Biden administration imposed restrictions on the export of advanced NVIDIA chips and GPUs to China in an effort to limit China's progress in artificial intelligence and high-performance computing. The policy aimed to prevent the use of cutting-edge U.S. technology in military or surveillance applications and to maintain a strategic advantage in the global AI race. In 2025, under the second Trump administration, the United States began a broad deregulation campaign aimed at accelerating growth in sectors critical to artificial intelligence, including nuclear energy, infrastructure, and high-performance computing. The goal was to remove regulatory barriers and attract private investment to boost domestic AI capabilities. This included easing restrictions on data usage, speeding up approvals for AI-related infrastructure projects, and incentivizing innovation in cloud computing and semiconductors. Companies like NVIDIA, Oracle, and Cisco played a central role in these efforts, expanding their AI research, data center capacity, and partnerships to help position the U.S. as a global leader in AI development. ==== Project Maven ==== Project Maven is a Pentagon project involving using machine learning and engineering talent to distinguish people and objects in drone videos, apparently giving the government real-time battlefield command and control, and the ability to track, tag and spy on targets without human involvement. Initially the effort was led by Robert O. Work who was concerned about China's military use of the emerging technology. Reportedly, Pentagon development stops short of acting as an AI weapons system capable of firing on self-designated targets. The project was established in a memo by the U.S. Deputy Secretary of Defense on 26 April 2017. Also known as the Algorithmic Warfare Cross Functional Team, it is, according to Lt. Gen. of the United States Air Force Jack Shanahan in November 2017, a project "designed to be that pilot project, that pathfinder, that spark that kindles the flame front of artificial intelligence across the rest of the [Defense] Department". Its chief, U.S. Marine Corps Col. Drew Cukor, said: "People and computers will work symbiotically to increase the ability of weapon systems to detect objects." Project Maven has been noted by allies, such as Australia's Ian Langford, for the

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  • The Best Free AI Analytics Tool for Beginners

    The Best Free AI Analytics Tool for Beginners

    Trying to pick the best AI analytics tool? An AI analytics tool is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI analytics tool slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  • Pumping lemma for regular languages

    Pumping lemma for regular languages

    In the theory of formal languages, the pumping lemma for regular languages is a lemma that describes an essential property of all regular languages. Informally, it says that all sufficiently long strings in a regular language may be pumped—that is, have a middle section of the string repeated an arbitrary number of times—to produce a new string that is also part of the language. The pumping lemma is useful for proving that a specific language is not a regular language, by showing that the language does not have the property. Specifically, the pumping lemma says that for any regular language L {\displaystyle L} , there exists a constant p {\displaystyle p} such that any string w {\displaystyle w} in L {\displaystyle L} with length at least p {\displaystyle p} can be split into three substrings x {\displaystyle x} , y {\displaystyle y} and z {\displaystyle z} ( w = x y z {\displaystyle w=xyz} , with y {\displaystyle y} being non-empty), such that the strings x z , x y z , x y y z , x y y y z , . . . {\displaystyle xz,xyz,xyyz,xyyyz,...} are also in L {\displaystyle L} . The process of repeating y {\displaystyle y} zero or more times is known as "pumping". Moreover, the pumping lemma guarantees that the length of x y {\displaystyle xy} will be at most p {\displaystyle p} , thus giving a "small" substring x y {\displaystyle xy} that has the desired property. Languages with a finite number of strings vacuously satisfy the pumping lemma by having p {\displaystyle p} equal to the maximum string length in L {\displaystyle L} plus one. By doing so, no strings at all in L {\displaystyle L} have length at least p {\displaystyle p} . The pumping lemma was first proven by Michael Rabin and Dana Scott in 1959, and rediscovered shortly after by Yehoshua Bar-Hillel, Micha A. Perles, and Eli Shamir in 1961, as a simplification of their pumping lemma for context-free languages. == Formal statement == Let L {\displaystyle L} be a regular language. Then there exists an integer p ≥ 1 {\displaystyle p\geq 1} depending only on L {\displaystyle L} such that every string w {\displaystyle w} in L {\displaystyle L} of length at least p {\displaystyle p} ( p {\displaystyle p} is called the "pumping length") can be written as w = x y z {\displaystyle w=xyz} (i.e., w {\displaystyle w} can be divided into three substrings), satisfying the following conditions: | y | ≥ 1 {\displaystyle |y|\geq 1} | x y | ≤ p {\displaystyle |xy|\leq p} ( ∀ n ≥ 0 ) ( x y n z ∈ L ) {\displaystyle (\forall n\geq 0)(xy^{n}z\in L)} y {\displaystyle y} is the substring that can be pumped (removed or repeated any number of times, and the resulting string is always in L {\displaystyle L} ). (1) means the loop y {\displaystyle y} to be pumped must be of length at least one, that is, not an empty string; (2) means the loop must occur within the first p {\displaystyle p} characters. | x | {\displaystyle |x|} must be smaller than p {\displaystyle p} (conclusion of (1) and (2)), but apart from that, there is no restriction on x {\displaystyle x} and z {\displaystyle z} . In simple words, for any regular language L {\displaystyle L} , any sufficiently long string w {\displaystyle w} (in L {\displaystyle L} ) can be split into 3 parts, i.e. w = x y z {\displaystyle w=xyz} , such that all the strings x y n z {\displaystyle xy^{n}z} for n ≥ 0 {\displaystyle n\geq 0} are also in L {\displaystyle L} . Below is a formal expression of the pumping lemma. ∀ L ⊆ Σ ∗ , regular ( L ) ⟹ ∃ p ≥ 1 , ∀ w ∈ L , | w | ≥ p ⟹ ∃ x , y , z ∈ Σ ∗ , ( w = x y z ) ∧ ( | y | ≥ 1 ) ∧ ( | x y | ≤ p ) ∧ ( ∀ n ≥ 0 , x y n z ∈ L ) {\displaystyle {\begin{array}{l}\forall L\subseteq \Sigma ^{},{\mbox{regular}}(L)\implies \\\quad \exists p\geq 1,\forall w\in L,|w|\geq p\implies \\\qquad \exists x,y,z\in \Sigma ^{},(w=xyz)\land (|y|\geq 1)\land (|xy|\leq p)\land (\forall n\geq 0,xy^{n}z\in L)\end{array}}} == Use of the lemma to prove non-regularity == The pumping lemma is often used to prove that a particular language is non-regular: a proof by contradiction may consist of exhibiting a string (of the required length) in the language that lacks the property outlined in the pumping lemma. Example: The language L = { a n b n : n ≥ 0 } {\displaystyle L=\{a^{n}b^{n}:n\geq 0\}} over the alphabet Σ = { a , b } {\displaystyle \Sigma =\{a,b\}} can be shown to be non-regular as follows: Assume that some constant p ≥ 1 {\displaystyle p\geq 1} exists as required by the lemma. Let w {\displaystyle w} in L {\displaystyle L} be given by w = a p b p {\displaystyle w=a^{p}b^{p}} , which is a string longer than p {\displaystyle p} . By the pumping lemma, there must exist a decomposition w = x y z {\displaystyle w=xyz} with | x y | ≤ p {\displaystyle |xy|\leq p} and | y | ≥ 1 {\displaystyle |y|\geq 1} such that x y i z {\displaystyle xy^{i}z} in L {\displaystyle L} for every i ≥ 0 {\displaystyle i\geq 0} . Since | x y | ≤ p {\displaystyle |xy|\leq p} , the string y {\displaystyle y} only consists of instances of a {\displaystyle a} . Because | y | ≥ 1 {\displaystyle |y|\geq 1} , it contains at least one instance of the letter a {\displaystyle a} . Pumping y {\displaystyle y} to give x y 2 z {\displaystyle xy^{2}z} gives a word with more instances of the letter a {\displaystyle a} than the letter b {\displaystyle b} , since some instances of a {\displaystyle a} but none of b {\displaystyle b} were added. Therefore, x y 2 z {\displaystyle xy^{2}z} is not in L {\displaystyle L} which contradicts the pumping lemma. Therefore, L {\displaystyle L} cannot be regular. The proof that the language of balanced (i.e., properly nested) parentheses is not regular follows the same idea. Given p {\displaystyle p} , there is a string of balanced parentheses that begins with more than p {\displaystyle p} left parentheses, so that y {\displaystyle y} will consist entirely of left parentheses. By repeating y {\displaystyle y} , a string can be produced that does not contain the same number of left and right parentheses, and so they cannot be balanced. == Proof of the pumping lemma == For every regular language there is a finite-state automaton (FSA) that accepts the language. The number of states in such an FSA are counted and that count is used as the pumping length p {\displaystyle p} . For a string of length at least p {\displaystyle p} , let q 0 {\displaystyle q_{0}} be the start state and let q 1 , . . . , q p {\displaystyle q_{1},...,q_{p}} be the sequence of the next p {\displaystyle p} states visited as the string is emitted. Because the FSA has only p {\displaystyle p} states, within this sequence of p + 1 {\displaystyle p+1} visited states there must be at least one state that is repeated. Write q s {\displaystyle q_{s}} for such a state. The transitions that take the machine from the first encounter of state q s {\displaystyle q_{s}} to the second encounter of state q s {\displaystyle q_{s}} match some string. This string is called y {\displaystyle y} in the lemma, and since the machine will match a string without the y {\displaystyle y} portion, or with the string y {\displaystyle y} repeated any number of times, the conditions of the lemma are satisfied. For example, the following image shows an FSA. The FSA accepts the string: abcd. Since this string has a length at least as large as the number of states, which is four (so the total number of states that the machine passes through to scan abcd would be 5), the pigeonhole principle indicates that there must be at least one repeated state among the start state and the next four visited states. In this example, only q 1 {\displaystyle q_{1}} is a repeated state. Since the substring bc takes the machine through transitions that start at state q 1 {\displaystyle q_{1}} and end at state q 1 {\displaystyle q_{1}} , that portion could be repeated and the FSA would still accept, giving the string abcbcd. Alternatively, the bc portion could be removed and the FSA would still accept giving the string ad. In terms of the pumping lemma, the string abcd is broken into an x {\displaystyle x} portion a, a y {\displaystyle y} portion bc and a z {\displaystyle z} portion d. As a side remark, the problem of checking whether a given string can be accepted by a given nondeterministic finite automaton without visiting any state repeatedly, is NP hard. == General version of pumping lemma for regular languages == If a language L {\displaystyle L} is regular, then there exists a number p ≥ 1 {\displaystyle p\geq 1} (the pumping length) such that every string u w v {\displaystyle uwv} in L {\displaystyle L} with | w | ≥ p {\displaystyle |w|\geq p} can be written in the form u w v = u x y z v {\displaystyle uwv=uxyzv} with strings x {\displaystyle x} , y {\displaystyle y} and z {\displaystyle z} such that | x y | ≤ p {\displaystyle |xy|\leq p} , | y | ≥ 1 {\displaystyle |y|\geq 1} and u x y i z v {\displaystyle uxy^{i}zv} is in L {\displaystyle L} for every integer i ≥ 0 {\displaystyle i\geq 0} . From this, the above standard v

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  • MedSLT

    MedSLT

    MedSLT is a medium-ranged open source spoken language translator developed by the University of Geneva. It is funded by the Swiss National Science Foundation. The system has been designed for the medical domain. It currently covers the doctor-patient diagnosis dialogues for the domains of headache, chest and abdominal pain in English, French, Japanese, Spanish, Catalan and Arabic. The vocabulary used ranges from 350 to 1000 words depending on the domain and language pair. == Motivation for creating MedSLT == With more than 6000 languages worldwide, language barriers become an increasing problem for healthcare. The lack of medical interpreters can lead to disastrous consequences. These range from prolonged hospital stays to wrong diagnosis and medication. A study found that only about half of the 23 million people with limited proficiency in English in the United States had been provided with a medical interpreter. Millions of refugees and immigrants worldwide face similar problems, although not always as severe. The gap between need and availability of language services might be closed with speech translation systems. == Challenges == The biggest challenge is and was to develop an ideal system, though it is not possible to do so at this moment. This system would fit the needs of doctors and the patients alike, and would provide accurate and flexible translation. A realisation of an ideal translation tool is impossible without the use of unrestricted language and a large vocabulary. Medical professionals demand high reliability from translation. This favours rule-based architectures over data-driven. The latter are more suitable for inexperienced users. Rule-based architectures achieve higher accuracy especially if used by experts. Though it is highly desirable to build a bidirectional system supporting a two-way dialogue, which concentrates on patient-centered communication, the patients will have difficult access to the system. Most patients have no experience with such systems. Less reliable results for translation from the patient-to-doctor direction are the outcome. To overcome this the system needs to provide either easy access or an integrated help tool to guide the users through the process. Although controlled rule-based systems achieve good results, they are brittle. To receive good translations the user needs to be familiar with the system and has to know what is covered by the grammar. Covering different sub-domains (headache, chest and abdominal pain) and language pairs presents additional problems. A shared structure and grammar for all subdomains and language pairs minimises development and maintenance costs. The integration of new doctor and patient languages is also a key challenge. Adding new languages should be quick and rather simple, because he system has to be used in many countries to cover multiple language pairs. Direct translation from source to target language proves to be rather difficult. Using interlingua for unidirectional translation instead of a bidirectional approach helps to simplify the translation process. On top of this, the system has to run on different platforms, because mobility is a key issue for many attending physicians. A portable version addresses these issues, but has to deal with the heavy load of the translation process. == The MedSLT system == The system's speech recognition is based on the Nuance 8.5 platform that supports grammar-based language models. All grammars used for recognition, analysis and generation are compiled from a small set of unification grammars. These core grammars are created by the open-source Regulus Grammar Compiler and are automatically specialised using corpus-driven methods. The specialisation considers both the task (recognition, analysis and generation) and the sub-domain (headache, chest and abdominal pain). The specialisation uses the explanation-based learning algorithm to create a treebank from the training corpus. These examples are divided into sets of subtrees by using domain- and grammar-specific rules (also known as "operationality criteria" in machine translation). The subtree rules are combined into a single rule, creating a specialised unification grammar. The grammar is compiled to an executable form, for analysis and generation by a parser or generator, and for recognition of a CFG grammar. A CFG grammar is required for the Nuance engine. Compilation by Nuance-specific criteria turns the grammar into speech recognition packages. The final step uses the training corpus again for statistical tuning of the language model. MedSLT translation processes are based on a rule-based interlingua. The interlingua is treated as an actual language (it is a very simple version of English) and is specified by a Regulus grammar. This grammar does not take account of complex surface syntax phenomena of real languages like movement or agreement. A set of rules is the base for translating the source language semantic representation to interlingua. Another set of rules covers the translation from interlingua to the target language. The semantic representations are converted to surface words using a target language grammar. Defining semantics for a specific domain enables the developers to specify interlingua with a small, tightly constraint semantic grammar. The translations based on interlingua match direct translations almost perfectly, because the development shifts to a decoupled monolingual architecture. A set of combined interlingua corpora, with one corpus per sub-domain, is the core of this architecture. All source language development corpora are translated to interlingua. These are sorted and grouped together with the corresponding source language examples. The interlingua forms are then translated into each target language, and the results are attached together. This organisation improves the translation process. There is no duplicated effort for multilingual regression testing, because each parsing and generation step is performed once. This allows more frequent testing. The representation language used for all forms is Almost Flat Functional semantics. AFF is derived from the Spoken Language Translator, the precursor of MEdSLT. SLT uses Quasi Logical Form, a logical based representation language. QLF is an expressive yet very complex language, causing high development and maintenance costs. A minimal solution was planned for the medical translator. Early versions of the system utilised a language using simple feature-value lists. These lists were supplemented with an optional level of nesting to represent subordinate clauses (i.e. embedded clauses). Determiners were not included, because they are hard to translate and it is difficult to reliably distinguish and recognise them. This way, translation rules became a lot simpler, because only a list of feature-value pairs had to be mapped to another list of pairs. The language turned out to be underconstrained. Adding natural sortal constraints to the grammar solved this problem, but also returned the language to a more expressive formalism. The newly created AFF combines elements of QLF and the feature-value list semantics. This version of flat semantics is enhanced with additional functional markings. This together with a relatively small vocabulary solved the ambiguity problem of the original flat representation language without creating overly complex rules. In addition, the syntactic structures are treated carefully by a compromise of linguistic and engineering traditions. The grammars are in fact retrieved from linguistically motivated resource, using corpus-based methods. They are driven by small sets of examples. This results in simpler and flatter domain-specific grammars. The semantics are less sophisticated and represent a minimal approach in the engineering tradition. Each lexical item contributes a set of feature-value pairs. This leads to simple-to-write translation rules. There are only lists of features-value pairs to map to other feature-value pairs. However, as a result the machine translation channel model becomes underspecified and is weakened, whereas the target language model is strengthened. An intelligent help module is integrated into the system to support users in utilising the full coverage of the grammars. This tool provides the user with examples as close as possible to the users original utterance. The output is based on a library. Each sub-domain and language pair has its own library. The contents are extracted from the combined interlingua corpora. The help module scans the corpus for the tagged source language form mapped with the corresponding target language form. Additionally a second statistical recogniser is used as backup. The results are used to select similar examples from the library. According to the generation preferences, one of the derived strings is picked and the target language string is realised as spoken language. Some statistical corpus based meth

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  • HTK Limited

    HTK Limited

    HTK Limited is a software-as-a-service company that provides mobile phone messaging and IVR services. Founded in 1996, HTK is headquartered in Ipswich, Suffolk, UK. HTK provide mass notification services. Specifically, the "Police Direct" messaging service to Suffolk and Norfolk Constabularies. In 2010 the HTK Horizon SaaS platform was selected by the Scottish Environment Protection Agency (SEPA) for their Floodline Warnings Direct service. == History == HTK was founded in 1996 by Marlon Bowser and Adrian Gregory and from the outset focused on what has now become commonly known as Software-as-a-Service. in 2004, according to the Deloitte Fast 50 (UK), HTK was the 17th fastest growing company in the East of England. In 2005 The Times listed HTK 65th nationally and 4th in the East of England in the Sunday Times & Microsoft "Tech Track 100" awards. In 2009 the company was approved as a supplier to UK Government under a new framework agreement. In 2010 HTK launched version 2.2 of its Horizon platform, with a feature set that signals a shift from mass notification into the customer service automation market.

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  • Glushkov's construction algorithm

    Glushkov's construction algorithm

    In computer science theory – particularly formal language theory – Glushkov's construction algorithm, invented by Victor Mikhailovich Glushkov, transforms a given regular expression into an equivalent nondeterministic finite automaton (NFA). Thus, it forms a bridge between regular expressions and nondeterministic finite automata: two abstract representations of the same class of formal languages. A regular expression may be used to conveniently describe an advanced search pattern in a "find and replace"–like operation of a text processing utility. Glushkov's algorithm can be used to transform it into an NFA, which furthermore is small by nature, as the number of its states equals the number of symbols of the regular expression, plus one. Subsequently, the NFA can be made deterministic by the powerset construction and then be minimized to get an optimal automaton corresponding to the given regular expression. The latter format is best suited for execution on a computer. From another, more theoretical point of view, Glushkov's algorithm is a part of the proof that NFA and regular expressions both accept exactly the same languages; that is, the regular languages. The converse of Glushkov's algorithm is Kleene's algorithm, which transforms a finite automaton into a regular expression. The automaton obtained by Glushkov's construction is the same as the one obtained by Thompson's construction algorithm, once its ε-transitions are removed. Glushkov's construction algorithm is also called The algorithm of Berry-Sethi, named after Gérard Berry and Ravi Sethi who worked on this construction. == Construction == Given a regular expression e, the Glushkov Construction Algorithm creates a non-deterministic automaton that accepts the language L ( e ) {\displaystyle L(e)} accepted by e. The construction uses four steps: === Step 1 === Linearisation of the expression. Each letter of the alphabet appearing in the expression e is renamed, so that each letter occurs at most once in the new expression e ′ {\displaystyle e'} . Glushkov's construction essentially relies on the fact that e ′ {\displaystyle e'} represents a local language L ( e ′ ) {\displaystyle L(e')} . Let A be the old alphabet and let B be the new one. === Step 2a === Computation of the sets P ( e ′ ) {\displaystyle P(e')} , D ( e ′ ) {\displaystyle D(e')} , and F ( e ′ ) {\displaystyle F(e')} . The first, P ( e ′ ) {\displaystyle P(e')} , is the set of letters which occurs as first letter of a word of L ( e ′ ) {\displaystyle L(e')} . The second, D ( e ′ ) {\displaystyle D(e')} , is the set of letters that can end a word of L ( e ′ ) {\displaystyle L(e')} . The last one, F ( e ′ ) {\displaystyle F(e')} , is the set of letter pairs that can occur in words of L ( e ′ ) {\displaystyle L(e')} , i.e. it is the set of factors of length two of the words of L ( e ′ ) {\displaystyle L(e')} . Those sets are mathematically defined by P ( e ′ ) = { x ∈ B ∣ x B ∗ ∩ L ( e ′ ) ≠ ∅ } {\displaystyle P(e')=\{x\in B\mid xB^{}\cap L(e')\neq \emptyset \}} , D ( e ′ ) = { y ∈ B ∣ B ∗ y ∩ L ( e ′ ) ≠ ∅ } {\displaystyle D(e')=\{y\in B\mid B^{}y\cap L(e')\neq \emptyset \}} , F ( e ′ ) = { u ∈ B 2 ∣ B ∗ u B ∗ ∩ L ( e ′ ) ≠ ∅ } {\displaystyle F(e')=\{u\in B^{2}\mid B^{}uB^{}\cap L(e')\neq \emptyset \}} . They are computed by induction over the structure of the expression, as explained below. === Step 2b === Computation of the set Λ ( e ′ ) {\displaystyle \Lambda (e')} which contains the empty word ε {\displaystyle \varepsilon } if this word belongs to L ( e ′ ) {\displaystyle L(e')} , and is the empty set otherwise. Formally, this is Λ ( e ′ ) = { ε } ∩ L ( e ′ ) {\displaystyle \Lambda (e')=\{\varepsilon \}\cap L(e')} . === Step 3 === Computation of automaton recognizing the local language, as defined by P ( e ′ ) {\displaystyle P(e')} , D ( e ′ ) {\displaystyle D(e')} , F ( e ′ ) {\displaystyle F(e')} , and Λ ( e ′ ) {\displaystyle \Lambda (e')} . By definition, the local language defined by the sets P, D, and F is the set of words which begin with a letter of P, end by a letter of D, and whose factors of length 2 belong to F, optionally also including the empty word; that is, it is the language: L ′ = ( P B ∗ ∩ B ∗ D ) ∖ B ∗ ( B 2 ∖ F ) B ∗ ∪ Λ ( e ′ ) {\displaystyle L'=(PB^{}\cap B^{}D)\setminus B^{}(B^{2}\setminus F)B^{}\cup \Lambda (e')} . Strictly speaking, it is the computation of the automaton for the local language denoted by this linearised expression that is Glushkov's construction. === Step 4 === Remove the linearisation, replacing each indexed letter B by the original letter of A. == Example == Consider the regular expression e = ( a ( a b ) ∗ ) ∗ + ( b a ) ∗ {\displaystyle e=(a(ab)^{})^{}+(ba)^{}} . == Computation of the set of letters == The computation of the sets P, D, F, and Λ is done inductively over the regular expression e ′ {\displaystyle e'} . One must give the values for ∅, ε (the symbols for the empty language and the singleton language containing the empty word), the letters, and the results of the operations + , ⋅ , ∗ {\displaystyle +,\cdot ,^{}} . The most costly operations are the cartesian products of sets for the computation of F. == Properties == The obtained automaton is non-deterministic, and it has as many states as the number of letters of the regular expression, plus one. It has been proven that every Thompson's automaton can be transformed into Glushkov's automaton via a ε-transitions elimination method. == Applications and deterministic expressions == The computation of the automaton by the expression occurs often; it has been systematically used in search functions, in particular by the Unix grep command. Similarly, XML's specification also uses such constructions; for more efficiency, regular expressions of a certain kind, called deterministic expressions, have been studied.

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  • Katz's back-off model

    Katz's back-off model

    Katz back-off is a generative n-gram language model that estimates the conditional probability of a word given its history in the n-gram. It accomplishes this estimation by backing off through progressively shorter history models under certain conditions. By doing so, the model with the most reliable information about a given history is used to provide the better results. The model was introduced in 1987 by Slava M. Katz. Prior to that, n-gram language models were constructed by training individual models for different n-gram orders using maximum likelihood estimation and then interpolating them together. == Method == The equation for Katz's back-off model is: P b o ( w i ∣ w i − n + 1 ⋯ w i − 1 ) = { d w i − n + 1 ⋯ w i C ( w i − n + 1 ⋯ w i − 1 w i ) C ( w i − n + 1 ⋯ w i − 1 ) if C ( w i − n + 1 ⋯ w i ) > k α w i − n + 1 ⋯ w i − 1 P b o ( w i ∣ w i − n + 2 ⋯ w i − 1 ) otherwise {\displaystyle {\begin{aligned}&P_{bo}(w_{i}\mid w_{i-n+1}\cdots w_{i-1})\\[4pt]={}&{\begin{cases}d_{w_{i-n+1}\cdots w_{i}}{\dfrac {C(w_{i-n+1}\cdots w_{i-1}w_{i})}{C(w_{i-n+1}\cdots w_{i-1})}}&{\text{if }}C(w_{i-n+1}\cdots w_{i})>k\\[10pt]\alpha _{w_{i-n+1}\cdots w_{i-1}}P_{bo}(w_{i}\mid w_{i-n+2}\cdots w_{i-1})&{\text{otherwise}}\end{cases}}\end{aligned}}} where C(x) = number of times x appears in training wi = ith word in the given context Essentially, this means that if the n-gram has been seen more than k times in training, the conditional probability of a word given its history is proportional to the maximum likelihood estimate of that n-gram. Otherwise, the conditional probability is equal to the back-off conditional probability of the (n − 1)-gram. The more difficult part is determining the values for k, d and α. k {\displaystyle k} is the least important of the parameters. It is usually chosen to be 0. However, empirical testing may find better values for k. d {\displaystyle d} is typically the amount of discounting found by Good–Turing estimation. In other words, if Good–Turing estimates C {\displaystyle C} as C ∗ {\displaystyle C^{}} , then d = C ∗ C {\displaystyle d={\frac {C^{}}{C}}} To compute α {\displaystyle \alpha } , it is useful to first define a quantity β, which is the left-over probability mass for the (n − 1)-gram: β w i − n + 1 ⋯ w i − 1 = 1 − ∑ { w i : C ( w i − n + 1 ⋯ w i ) > k } d w i − n + 1 ⋯ w i C ( w i − n + 1 ⋯ w i − 1 w i ) C ( w i − n + 1 ⋯ w i − 1 ) {\displaystyle \beta _{w_{i-n+1}\cdots w_{i-1}}=1-\sum _{\{w_{i}:C(w_{i-n+1}\cdots w_{i})>k\}}d_{w_{i-n+1}\cdots w_{i}}{\frac {C(w_{i-n+1}\cdots w_{i-1}w_{i})}{C(w_{i-n+1}\cdots w_{i-1})}}} Then the back-off weight, α, is computed as follows: α w i − n + 1 ⋯ w i − 1 = β w i − n + 1 ⋯ w i − 1 ∑ { w i : C ( w i − n + 1 ⋯ w i ) ≤ k } P b o ( w i ∣ w i − n + 2 ⋯ w i − 1 ) {\displaystyle \alpha _{w_{i-n+1}\cdots w_{i-1}}={\frac {\beta _{w_{i-n+1}\cdots w_{i-1}}}{\sum _{\{w_{i}:C(w_{i-n+1}\cdots w_{i})\leq k\}}P_{bo}(w_{i}\mid w_{i-n+2}\cdots w_{i-1})}}} The above formula only applies if there is data for the "(n − 1)-gram". If not, the algorithm skips n-1 entirely and uses the Katz estimate for n-2. (and so on until an n-gram with data is found) == Discussion == This model generally works well in practice, but fails in some circumstances. For example, suppose that the bigram "a b" and the unigram "c" are very common, but the trigram "a b c" is never seen. Since "a b" and "c" are very common, it may be significant (that is, not due to chance) that "a b c" is never seen. Perhaps it's not allowed by the rules of the grammar. Instead of assigning a more appropriate value of 0, the method will back off to the bigram and estimate P(c | b), which may be too high.

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  • Dan Klein

    Dan Klein

    Daniel Klein (born c. 1976) is an American computer scientist and professor of computer science at the University of California, Berkeley. His research focuses on natural language processing and artificial intelligence. He was educated at Mt. Lebanon High School in Mt. Lebanon Township, Pennsylvania and earned a B.A. in mathematics, computer science, and linguistics from Cornell University (1998), a MSt in linguistics by Oxford University (1999) and a Ph.D. from Stanford University (2004), under Christopher D. Manning. He attended Oxford on a Marshall Scholarship. In addition to the Marshall scholarship, he has been awarded the ACM's Grace Murray Hopper Award, the Sloan Research Fellowship, the NSF CAREER Award, and the Microsoft New Faculty Fellowship.

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  • Medical imaging

    Medical imaging

    Medical imaging is the technique and process of imaging the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues (physiology). Medical imaging seeks to reveal internal structures hidden by the skin and bones, as well as to diagnose and treat disease. Medical imaging also establishes a database of normal anatomy and physiology to make it possible to identify abnormalities. Although imaging of removed organs and tissues can be performed for medical reasons, such procedures are usually considered part of pathology instead of medical imaging. Measurement and recording techniques that are not primarily designed to produce images, such as electroencephalography (EEG), magnetoencephalography (MEG), electrocardiography (ECG), and others, represent other technologies that produce data susceptible to representation as a parameter graph versus time or maps that contain data about the measurement locations. In a limited comparison, these technologies can be considered forms of medical imaging in another discipline of medical instrumentation. As of 2010, 5 billion medical imaging studies had been conducted worldwide. Radiation exposure from medical imaging in 2006 made up about 50% of total ionizing radiation exposure in the United States. Medical imaging equipment is manufactured using technology from the semiconductor industry, including CMOS integrated circuit chips, power semiconductor devices, sensors such as image sensors (particularly CMOS sensors) and biosensors, and processors such as microcontrollers, microprocessors, digital signal processors, media processors and system-on-chip devices. As of 2015, annual shipments of medical imaging chips amount to 46 million units and $1.1 billion. The term "noninvasive" is used to denote a procedure where no instrument is introduced into a patient's body, which is the case for most imaging techniques used. == History == In 1972, engineer Godfrey Hounsfield from the British company EMI invented the X-ray computed tomography device for head diagnosis, which is commonly referred to as computed tomography (CT). The CT nucleus method is based on the projecting X-rays through a section of the human head, which are then processed by computer to reconstruct the cross-sectional image, known as image reconstruction. In 1975, EMI successfully developed a CT device for the entire body, enabling the clear acquisition of tomographic images of various parts of the human body. This revolutionary diagnostic technique earned Hounsfield and physicist Allan Cormack the Nobel Prize in Physiology or Medicine in 1979. Digital image processing technology for medical applications was inducted into the Space Foundation's Space Technology Hall of Fame in 1994. By 2010, over 5 billion medical imaging studies had been conducted worldwide. Radiation exposure from medical imaging in 2006 accounted for about 50% of total ionizing radiation exposure in the United States. Medical imaging equipment is manufactured using technology from the semiconductor industry, including CMOS integrated circuit chips, power semiconductor devices, sensors such as image sensors (particularly CMOS sensors) and biosensors, as well as processors like microcontrollers, microprocessors, digital signal processors, media processors and system-on-chip devices. As of 2015, annual shipments of medical imaging chips reached 46 million units, generating a market value of $1.1 billion. == Types == In the clinical context, "invisible light" medical imaging is generally equated to radiology or "clinical imaging". "Visible light" medical imaging involves digital video or still pictures that can be seen without special equipment. Dermatology and wound care are two modalities that use visible light imagery. Interpretation of medical images is generally undertaken by a physician specialising in radiology known as a radiologist; however, this may be undertaken by any healthcare professional who is trained and certified in radiological clinical evaluation. Increasingly interpretation is being undertaken by non-physicians, for example radiographers frequently train in interpretation as part of expanded practice. Diagnostic radiography designates the technical aspects of medical imaging and in particular the acquisition of medical images. The radiographer (also known as a radiologic technologist) is usually responsible for acquiring medical images of diagnostic quality; although other professionals may train in this area, notably some radiological interventions performed by radiologists are done so without a radiographer. As a field of scientific investigation, medical imaging constitutes a sub-discipline of biomedical engineering, medical physics or medicine depending on the context: Research and development in the area of instrumentation, image acquisition (e.g., radiography), modeling and quantification are usually the preserve of biomedical engineering, medical physics, and computer science; Research into the application and interpretation of medical images is usually the preserve of radiology and the medical sub-discipline relevant to medical condition or area of medical science (neuroscience, cardiology, psychiatry, psychology, etc.) under investigation. Many of the techniques developed for medical imaging also have scientific and industrial applications. === Radiography === Two forms of radiographic images are in use in medical imaging. Projection radiography and fluoroscopy, with the latter being useful for catheter guidance. These 2D techniques are still in wide use despite the advance of 3D tomography due to the low cost, high resolution, and depending on the application, lower radiation dosages with 2D technique. This imaging modality uses a wide beam of X-rays for image acquisition and is the first imaging technique available in modern medicine. Fluoroscopy produces real-time images of internal structures of the body in a similar fashion to radiography, but employs a constant input of X-rays, at a lower dose rate. Contrast media, such as barium, iodine, and air are used to visualize internal organs as they work. Fluoroscopy is also used in image-guided procedures when constant feedback during a procedure is required. An image receptor is required to convert the radiation into an image after it has passed through the area of interest. Early on, this was a fluorescing screen, which gave way to an Image Amplifier (IA) which was a large vacuum tube that had the receiving end coated with cesium iodide, and a mirror at the opposite end. Eventually the mirror was replaced with a TV camera. Projectional radiographs, more commonly known as X-rays, are often used to determine the type and extent of a fracture as well as for detecting pathological changes in the lungs. With the use of radio-opaque contrast media, such as barium, they can also be used to visualize the structure of the stomach and intestines – this can help diagnose ulcers or certain types of colon cancer. === Magnetic resonance imaging === A magnetic resonance imaging instrument (MRI scanner), or "nuclear magnetic resonance (NMR) imaging" scanner as it was originally known, uses powerful magnets to polarize and excite hydrogen nuclei (i.e., single protons) of water molecules in human tissue, producing a detectable signal that is spatially encoded, resulting in images of the body. The MRI machine emits a radio frequency (RF) pulse at the resonant frequency of the hydrogen atoms on water molecules. Radio frequency antennas ("RF coils") send the pulse to the area of the body to be examined. The RF pulse is absorbed by protons, causing their direction with respect to the primary magnetic field to change. When the RF pulse is turned off, the protons "relax" back to alignment with the primary magnet and emit radio waves in the process. This radio-frequency emission from the hydrogen atoms on water is what is detected and reconstructed into an image. The resonant frequency of a spinning magnetic dipole (of which protons are one example) is called the Larmor frequency and is determined by the strength of the main magnetic field and the chemical environment of the nuclei of interest. MRI uses three electromagnetic fields: a very strong (typically 1.5 to 3 teslas) static magnetic field to polarize the hydrogen nuclei, called the primary field; gradient fields that can be modified to vary in space and time (on the order of 1 kHz) for spatial encoding, often simply called gradients; and a spatially homogeneous radio-frequency (RF) field for manipulation of the hydrogen nuclei to produce measurable signals, collected through an RF antenna. Like CT, MRI traditionally creates a two-dimensional image of a thin "slice" of the body and is therefore considered a tomographic imaging technique. Modern MRI instruments are capable of producing images in the form of 3D blocks, which may be considered a generalization of the single-slice

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  • Bibliotheca Polyglotta

    Bibliotheca Polyglotta

    The Bibliotheca Polyglotta is a Norwegian database for Multilingualism project, lingua franca and science per global history at the University of Oslo. The aim of the project is according to pages is "producing a web corpus of Buddhist texts for using in multilingual lexicography. More generally, will the texts used for the study Sanskrit, Chinese and Tibetan."

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  • Co-occurrence

    Co-occurrence

    In linguistics, co-occurrence or cooccurrence (in older texts often shown with diacritic as coöccurrence) is an above-chance frequency of ordered occurrence of two adjacent terms in a text corpus. Co-occurrence in this linguistic sense can be interpreted as an indicator of semantic proximity or an idiomatic expression. Corpus linguistics and its statistical analyses can reveal (regularity of) patterns of co-occurrences within a language and enable the working out of typical collocations for its lexical items. A co-occurrence restriction is identified when linguistic elements never occur together. Analysis of these restrictions can lead to discoveries about the structure and development of a language. Co-occurrence can be seen an extension of word counting in higher dimensions. Co-occurrence can be quantitatively described using measures like a massive correlation or mutual information. Co-occurrence information and knowledge of co-occurring words may be relevant in analysis of language for the purposes of large language models, part of the emerging field of artificial intelligence, and helpful in word games such as scrabble.

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