Omni-Path Architecture (OPA) is a high-performance communication architecture developed by Intel. It aims for low communication latency, low power consumption and a high throughput. It directly competes with InfiniBand. Intel planned to develop technology based on this architecture for exascale computing. The current owner of Omni-Path is Cornelis Networks. == History == Production of Omni-Path products started in 2015 and delivery of these products started in the first quarter of 2016. In November 2015, adapters based on the 2-port "Wolf River" ASIC were announced, using QSFP28 connectors with channel speeds up to 100 Gbit/s. Simultaneously, switches based on the 48-port "Prairie River" ASIC were announced. First models of that series were available starting in 2015. In April 2016, implementation of the InfiniBand "verbs" interface for the Omni-Path fabric was discussed. In October 2016, IBM, Hewlett Packard Enterprise, Dell, Lenovo, Samsung, Seagate Technology, Micron Technology, Western Digital and SK Hynix announced a joint consortium called Gen-Z to develop an open specification and architecture for non-volatile storage and memory products—including Intel's 3D Xpoint technology—which might in part compete against Omni-Path. Intel offered their Omni-Path products and components via other (hardware) vendors. For example, Dell EMC offered Intel Omni-Path as Dell Networking H-series, following the naming-standard of Dell Networking in 2017. In July 2019, Intel announced it would not continue development of Omni-Path networks and canceled OPA 200 series (200-Gbps variant of Omni-Path). In September 2020, Intel announced that the Omni-Path network products and technology would be spun out into a new venture with Cornelis Networks. Intel would continue to maintain support for legacy Omni-Path products, while Cornelis Networks continues the product line, leveraging existing Intel intellectual property related to Omni-Path architecture. In 2021, Cornelis announced Omni-Path Express, which replaces PSM2-based drivers and middleware, which trace back to PathScale's PSM created in 2003, for the existing Omni-Path hardware, with a native libfabric provider.
Recursive transition network
A recursive transition network ("RTN") is a graph theoretical schematic used to represent the rules of a context-free grammar. RTNs have application to programming languages, natural language and lexical analysis. Any sentence that is constructed according to the rules of an RTN is said to be "well-formed". The structural elements of a well-formed sentence may also be well-formed sentences by themselves, or they may be simpler structures. This is why RTNs are described as recursive. == Notes and references ==
Muller automaton
In automata theory, a Muller automaton is a type of an ω-automaton. The acceptance condition separates a Muller automaton from other ω-automata. The Muller automaton is defined using a Muller acceptance condition, i.e. the set of all states visited infinitely often must be an element of the acceptance set. Both deterministic and non-deterministic Muller automata recognize the ω-regular languages. They are named after David E. Muller, an American mathematician and computer scientist, who invented them in 1963. == Formal definition == Formally, a deterministic Muller-automaton is a tuple A = (Q,Σ,δ,q0,F) that consists of the following information: Q is a finite set. The elements of Q are called the states of A. Σ is a finite set called the alphabet of A. δ: Q × Σ → Q is a function, called the transition function of A. q0 is an element of Q, called the initial state. F is a set of sets of states. Formally, F ⊆ P(Q) where P(Q) is powerset of Q. F defines the acceptance condition. A accepts exactly those runs in which the set of infinitely often occurring states is an element of F In a non-deterministic Muller automaton, the transition function δ is replaced with a transition relation Δ that returns a set of states and the initial state q0 is replaced by a set of initial states Q0. Generally, 'Muller automaton' refers to a non-deterministic Muller automaton. For more comprehensive formalisation look at ω-automaton. == Equivalence with other ω-automata == The Muller automata are equally expressive as parity automata, Rabin automata, Streett automata, and non-deterministic Büchi automata, to mention some, and strictly more expressive than the deterministic Büchi automata. The equivalence of the above automata and non-deterministic Muller automata can be shown very easily as the accepting conditions of these automata can be emulated using the acceptance condition of Muller automata and vice versa. McNaughton's theorem demonstrates the equivalence of non-deterministic Büchi automaton and deterministic Muller automaton. Thus, deterministic and non-deterministic Muller automata are equivalent in terms of the languages they can accept. == Transformation to non-deterministic Muller automata == Following is a list of automata constructions that each transforms a type of ω-automata to a non-deterministic Muller automaton. From Büchi automata If B is the set of final states in a Büchi automaton with the set of states Q, we can construct a Muller automaton with same set of states, transition function and initial state with the Muller accepting condition as F = { X | X ∈ P(Q) ∧ X ∩ B ≠ ∅}. From Rabin automata/parity automata Similarly, the Rabin conditions ( E j , F j ) {\displaystyle (E_{j},F_{j})} can be emulated by constructing the acceptance set in the Muller automaton as all sets F ⊆ Q {\displaystyle F\subseteq Q} that satisfy F ∩ E j = ∅ {\displaystyle F\cap E_{j}=\emptyset } and F ∩ F j ≠ ∅ {\displaystyle F\cap F_{j}\neq \emptyset } , for some j. Note that this covers the case of parity automata too, as the parity acceptance condition can be expressed as a Rabin acceptance condition easily. From Streett automata The Streett conditions ( E j , F j ) {\displaystyle (E_{j},F_{j})} can be emulated by constructing the acceptance set in the Muller automaton as all sets F ⊆ Q {\displaystyle F\subseteq Q} that satisfy F ∩ F j = ∅ ⟹ F ∩ E j = ∅ {\displaystyle F\cap F_{j}=\emptyset \implies F\cap E_{j}=\emptyset } , for all j. == Transformation to deterministic Muller automata == From Büchi automaton McNaughton's theorem provides a procedure to transform any non-deterministic Büchi automaton into a deterministic Muller automaton.
Isabelle Guyon
Isabelle Guyon (French pronunciation: [izabɛl ɡɥijɔ̃]; born August 15, 1961) is a French-born researcher in machine learning known for her work on support-vector machines, artificial neural networks and bioinformatics. She is a Chair Professor at the University of Paris-Saclay. Guyon serves as the Director of Research at Google DeepMind since October 2022. She is considered to be a pioneer in the field, with her contribution to the support-vector machines with Vladimir Vapnik and Bernhard Boser. == Biography == After graduating from the French engineering school ESPCI Paris in 1985, she joined the group of Gerard Dreyfus at the Université Pierre-et-Marie-Curie to do a PhD on neural networks architectures and training. Guyon defended her thesis in 1988 and was hired the year after at AT&T Bell Laboratories, first as a post-doc, then as a group leader. She worked at Bell Labs for six years, where she explored several research areas, from neural networks to pattern recognition and computational learning theory, with application to handwriting recognition. She collaborated with Yann LeCun, Léon Bottou, Vladimir Vapnik, Corinna Cortes, Yoshua Bengio, Patrice Simard, and met her future husband, Bernhard Boser. In 1996, Guyon left Bell Labs and raised her children at Berkeley, California. In Berkeley, she created her own machine learning consulting company, Clopinet. She became interested in medical applications, and used her previous work to classify the genes responsible for different types of cancers. Since 2003, Guyon has organized many challenges in data science, in order to stimulate research in this field. She founded ChaLearn in 2011, a non-profit organization aimed at creating machine learning challenges open to everyone. She was Program Chair of NeurIPS 2016 and became General Chair of NeurIPS in 2017. She is also Action Editor for the Journal of Machine Learning Research and Series Editor for Series: Challenges in Machine Learning. She is a member of the European Laboratory for Learning and Intelligent Systems. In 2016, Guyon came back to France to take the Chair Professorship in Big data between the University of Paris-Saclay and INRIA. She works in TAU (TAckling the Underspecified), a research collaboration of the Laboratoire de recherche en informatique. Together with Bernhard Schölkopf and Vladimir Vapnik, she received in 2020 the BBVA Foundation Frontiers of Knowledge Awards for her work in machine learning. == Scientific work == Guyon has worked in many subfields of machine learning, including neural networks, support-vector machines, feature selection and applications of machine learning to biology. === Support-vector machines === Among her most notable contributions, Guyon co-invented support-vector machines (SVM) in 1992, with Bernhard Boser and Vladimir Vapnik. SVM is a supervised machine learning algorithm, comparable to neural networks or decision trees, which has quickly become a classical technique in machine learning. SVMs have especially contributed to the popularization of kernel methods. === Neural networks === During her years at Bell Labs, Guyon took part of numerous projects involving neural networks. In particular, she wrote some of the first papers on the use of neural network for handwriting recognition using the MNIST database. She is also a co-inventor of the siamese neural networks, a neural network architecture used to learn similarities, with applications to signature, face or object recognition. === Machine learning for biology === Guyon is the author of many publications at the intersection of biology (cancer research and genomics) and artificial intelligence. She has notably introduced the use of support-vector machines to detect cancer using genes. === Machine learning challenges === Through her non-profit organization ChaLearn, Guyon has organized and directed challenges open to everyone in order to solve open problems in machine learning, including computer vision, neurosciences, particle physics, feature selection, causality and automated machine learning. Most of the challenges organized by ChaLearn have resulted in publications. Among the most cited ones are: Guyon et al., Result analysis of the NIPS 2003 feature selection challenge, Advances in neural information processing systems, 2005, link Escalera et al., ChaLearn Looking at People Challenge 2014: Dataset and Results, Computer Vision - ECCV 2014 Workshops, Springer International Publishing, 2014, link Guyon et al., A brief Review of the ChaLearn AutoML Challenge, JMLR: Workshop and Conference Proceedings 64:21-30, 2016, link Adam-Bourdario et al., The Higgs boson machine learning challenge, JMLR: Workshop and Conference Proceedings 42:19-55, 2015, link == Private life == She is married to Bernhard Boser, a professor at UC Berkeley. She has twins and one daughter, all three of whom have completed a science degree. Guyon has three citizenships: French by birth, Swiss by marriage and American by naturalization. == Awards and honors == Nomination at the French Academy of technologies (2024) Recipient of the BBVA Foundation Frontiers of Knowledge Awards (2020) American Medical Informatics Association Fellow (2011) == Publications == Bernhard Boser, Isabelle Guyon and Vladmir Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, 1992, doi:10.1145/130385.130401 Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger and Roopak Shah, Signature verification using a" siamese" time delay neural network, Advances in Neural Information Processing Systems, 1994. Isabelle Guyon and André Elisseeff, An introduction to variable and feature selection, Journal of Machine Learning Research, 2003. Isabelle Guyon, Jason Weston, Stephen Barnhill and Vladimir Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning, Kluwer Academic Publishers, 2002, doi:10.1023/A:1012487302797
Top 10 AI Code Generators Compared (2026)
Curious about the best AI code generator? An AI code 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 code 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.
Resolution enhancement technology
Resolution enhancement technology (RET) is a form of image processing technology used to manipulate dot characteristics popular among laser printer and inkjet printer manufacturers. Closely related RET techniques are also used in VLSI photolithography manufacturing technology, in particular in relation to 90 nanometre technology. Resolution refers to the sharpness of image detail, smoothness of curved lines, and the faithful reproduction of an image. In both cases, RET uses pre-compensation of the image in order to try to mitigate the effects of the printing process. Among the major issues in RET in VLSI technology are the fundamental properties of a wave: amplitude, phase, and direction.
Sophia Ananiadou
Sophia Ananiadou is a Greek-British computer scientist and computational linguist. She led the development of and directs the National Centre for Text Mining (NaCTeM) in the United Kingdom. She is also Professor in Computer Science in the Department of Computer Science at the University of Manchester. Her research focusses on biomedical text mining and natural language processing and has fed into the development of numerous applications that, for example, facilitate the discovery of new knowledge, enable exploration of historical archives, allow semantic search of biomedical literature, reduce human effort in screening search hits for production of systematic reviews, enable enrichment of metabolic pathway models with evidence from the literature, allow discovery of risk in the construction industry from health and safety incident reports and enable interoperability of components in text mining workflows. == Education == Ananiadou was educated at the Lycée français St Joseph in Athens, Greece (1969–1975). She received a Bachelor of Arts (Ptychion) from the University of Athens (1979), a Master of Advanced Studies (DEA) in Linguistics from Paris VII, Jussieu, France (1980), a DEA in Literature from Paris IV, Sorbonne, France (1984) and a PhD in Computational linguistics from the University of Manchester Institute of Science and Technology (UMIST), in 1988. == Career and research == Ananiadou was a research assistant at Dalle Molle Institute for Semantic and Cognitive Studies (ISSCO, 1983–1984), a research assistant (1985–1988) then research associate (1988–1993) in the department of language engineering at UMIST, senior lecturer at Manchester Metropolitan University (1993–1999), senior lecturer then reader in the School of Computing Science and Engineering, University of Salford (2000–2005), then reader in the School of Computer Science, University of Manchester (2005–2009). Since 2009, she has served as professor in computer science in the Department of Computer Science at the University of Manchester. In July 2025, she became deputy director of the Christabel Pankhurst Institute for health technology research and innovation, University of Manchester. From 2018–2026, she served as the deputy director of the Institute for Data Science and Artificial Intelligence, University of Manchester. She is a senior lead researcher of the ARCHIMEDES research unit of the Athena Research Centre, Greece. ARCHIMEDES is a research and innovation hub fostering international collaboration and knowledge exchange on Artificial Intelligence and Data Science. On February 7, 2025, she was appointed a member of the Artificial Intelligence Sectoral Scientific Council of the Greek Ministry of Development (announcement of appointment in Greek). She is also a Visiting Distinguished Research Fellow in the Knowledge and Information Research Team at the Artificial Intelligence Research Center (AIRC), Japan, which is a research unit of the Japanese National Institute of Advanced Industrial Science and Technology (AIST). In addition, she was appointed to the honorary position of Adjunct Professor of Wuhan University, People's Republic of China, for the period October 2025 to October 2028, collaborating with the School of Artificial Intelligence. Ananiadou has published since 1986, has an h-index of 81 and a Research.com United Kingdom ranking in Computer Science of 104. She is also ranked number 1 internationally in text mining by ScholarGPS. In addition, she is included in the Stanford/Elsevier Top 2% Scientist Rankings for 2025. Ananiadou received a Diplôme de traducteur (Diploma of Translator) from the Institut français d'Athènes, Greece (1979) and a Certificate in Counselling from the University of Salford, UK (2004). === Awards and honours === In 2019, in recognition of her contributions in Artificial Intelligence and text mining for Biomedicine, Ananiadou received an honorary doctorate from the University of the Aegean, on the 20th anniversary of its Department of Mediterranean Studies, Rhodes. Ananiadou received the Unstructured Information Management Architecture (UIMA) innovation award from IBM three years running (2006, 2007 & 2008). She was awarded the Daiwa Adrian Prize in 2004 and also received a Japan Trust award from the Ministry of Education, Japan in 1997. Ananiadou was a Turing Fellow of the Alan Turing Institute in London from 2018 to 2023. Since 2021, she is a member and, since 2024, a Fellow, of the ELLIS Society, the professional society of the cross-national European Laboratory for Learning and Intelligent Systems. Ananiadou served as vice president (VP) of the European Association for Terminology from 1997 to 1999. At the 28th International Conference on Computational Linguistics (COLING 2020), she received, with M. Li and H. Takamura, an Outstanding Paper designation for the paper "A Neural Model for Aggregating Coreference Annotation in Crowdsourcing".