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Ampere Computing

Ampere Computing LLC is an American fabless semiconductor company that designs ARM-based central processing units (CPUs) with high core counts for use in cloud computing and data center environments. Founded in 2017 by former Intel president Renée James, the company is headquartered in Santa Clara, California, and operates as an independent subsidiary of SoftBank Group since November 2025. == History == Ampere Computing was founded in fall 2017 by Renée James, ex-President of Intel, with funding from The Carlyle Group. James acquired a team from MACOM Technology Solutions (formerly AppliedMicro) in addition to several industry hires to start the company. Ampere Computing is an ARM architecture licensee and develops its own server microprocessors. Ampere fabricates its products at TSMC. In April 2019, Ampere announced its second major investment round, including investment from Arm Holdings and Oracle Corporation. In June 2019, Nvidia announced a partnership with Ampere to bring support for Compute Unified Device Architecture (CUDA). In November 2019, Nvidia announced a reference design platform for graphics processing unit (GPU)-accelerated ARM-based servers including Ampere. In the first half of 2020, Ampere announced Ampere Altra, an 80-core processor, and Ampere Altra Max, a 128-core processor, without the use of simultaneous multithreading. In March 2020, the company announced a partnership with Oracle. In September 2020, Oracle said it would launch bare-metal and virtual machine instances in early 2021 based on Ampere Altra. In November 2020, Ampere was named one of the top 10 hottest semiconductor startups by CRN. In May 2021, the company announced a partnership with Microsoft. In April 2022, Ampere said that it had filed a confidential prospectus with the U.S. Securities and Exchange Commission, signaling its intent to go public. In June 2022, HPE announced their Gen11 ProLiant system would use Ampere Altra and Ampere Altra Max Cloud Native Processors. In July 2022, Google announced T2A instances using Ampere Altra in the Google cloud and in August 2022 Microsoft announced their instances of Ampere running in Azure. On March 19, 2025, investment holding company SoftBank Group announced it will acquire Ampere Computing for $6.5 billion. The deal finalized in November 2025, with Ampere remaining as an independent subsidiary with its headquarters in Santa Clara, California. == Products == Ampere develops ARM-based computer processors and CPU cores under their Altra brands. These are used in databases, media encoding, web services, network acceleration, mobile gaming, AI inference processing, and other applications and programs that need to scale. On February 5, 2018, Ampere announced the eMAG 8180 featuring 32x Skylark cores fabricated on TSMC's 16FF+ process. It supports a turbo of up to 3.3 GHz with a TDP of 125 W, 8ch 64-bit DDR4, up to 1 TB DDR4 per socket, and 42x PCIe 3.0 Lanes. The Skylark cores were based on AppliedMicro's X-Gene 3. Packet offers servers with the eMAG 8180 and 128 GB DRAM, 480 GB SSD, and 2x 10 Gbit/s networking. On September 19, 2018, Ampere announced the availability of a version featuring 16x Skylark cores. === 2020 === On March 3, 2020, Ampere announced the Ampere Altra featuring 80 cores fabricated on TSMC's N7 process for hyperscale computing. It was the first server-grade processor to include 80 cores and the Q80-30 conserves power by running at 161 W in use. The cores are semi-custom Arm Neoverse N1 cores with Ampere modifications. It supports a frequency of up to 3.3 GHz with TDP of 250 W, 8ch 72-bit DDR4, up to 4 TB DDR4-3200 per socket, 128x PCIe 4.0 Lanes, 1 MB L2 per core and 32 MB SLC. Ampere also announced their roadmap with Ampere Altra Max (2021) in development and AmpereOne (2022) defined. === 2021 === The 128-core Altra Max was released in 2021 and targeted hyperscale cloud providers. It uses the same server socket and platforms as Ampere Altra, and both products have one thread per core. The Altra Max CPUs provide 128 Arm v8.2+ cores per chip and run up to 3.0 GHz. They also support eight channels of DDR4-3200 memory and 128 lanes of PCIe Gen4. Also in 2021, Oracle launched its Oracle Cloud Infrastructure (OCI) using Ampere Altra processors. === 2022 === In February 2022, Ampere and Rigetti Computing announced a strategic partnership to create hybrid quantum-classical computers. The companies will combine Ampere's Altra Max CPUs with Rigetti's Quantum Processing Units (QPU) in cloud-based High-Performance Computing (HPC) environments. In April, Microsoft previewed its Azure Virtual Machines running on the Ampere Altra. The VMs run scale-out workloads, web servers, application servers, open source databases, cloud native .NET applications, Java applications, gaming servers, media servers, and other processes. In May, Ampere announced the sampling of AmpereOne CPUs, 5 nanometer chips based on its in-house Ampere-developed core. AmpereOne will add support for DDR5 main memory and PCIe Gen5 peripherals. On June 28, 2022, HPE became first tier-one server provider to offer compute with optimized cloud-native silicon for service providers and enterprises embracing cloud-native development with new line of HPE ProLiant RL Gen11 servers, using Ampere® Altra® and Ampere® Altra® Max processors, delivering high performance and power efficiency. === 2023 === During April 2023, Ampere released the Altra developer's kit, an IoT Prototype Kit based on Ampere Altra, aimed at cloud developers, available in 32-core, 64-core, and 80-core formats. === 2024 === In May 2024, Ampere updated its AmpereOne roadmap to 256 cores and announced a joint effort with Qualcomm on CPUs and accelerators. == Customers == Ampere's customers include Microsoft Azure, Tencent Cloud, Oracle, ByteDance, Hewlett Packard Enterprise (HPE), Cloudflare, Equinix, Kingsoft Cloud, Meituan, Scaleway, UCloud, Foxconn Industrial Internet, Gigabyte, Inspur, Cruise, Hetzner, Project Ronin, Wiwynn and Google Cloud Platform Cruise uses an Ampere Altra variant for its autonomous driving unit. The CPU was selected because of its throughput and low power consumption. In 2021, Oracle, Microsoft, Tencent, and ByteDance committed to using Ampere's customized chips, first announced in May. In April 2022, Microsoft previewed Ampere Altra processors in its new Azure D-and E- series virtual machines. The Dpsv5 series is built for Linux enterprise application types, and the Epsv5 series is for memory-intensive Linux workloads. They provide up to 64 vCPUs, include VM sizes with 2GiB, 4GiB, and 8GiB per vCPU memory configurations, up to 40 Gbit/s networking, and high-performance local SSD storage. In 2022, Microsoft's Ampere Altra-based Azure servers became the first cloud solution provider server to be Arm SystemReady SR certified. The Azure VMs, powered by Altra processors, were also the first to be SystemReady Virtual Environment standard certified. SystemReady defines a set of firmware and hardware standards as a baseline for system development for software developers, original equipment vendors, and chipmakers.
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How to Choose an AI Paragraph Rewriter

Comparing the best AI paragraph rewriter? An AI paragraph rewriter is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI paragraph rewriter slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
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AI Website Builders Reviews: What Actually Works in 2026

Trying to pick the best AI website builder? An AI website builder 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 website builder slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
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Model inversion attack

Model inversion attack is a type of adversarial machine learning attack where an attacker tries to reconstruct or infer sensitive information about a model's training data by analyzing the outputs of a trained machine learning model. Instead of directly querying the underlying dataset, attackers query the model (usually via APIs or prediction interfaces), and leverage patterns in the model responses to infer properties of the original inputs. These attacks leverage the fact that machine learning models encode statistical information about their training data in their parameters and outputs, which can unintentionally leak private or proprietary information. Depending on the access level to the target model, model inversion attacks can be performed in both black-box and white-box settings. In a generic attack, an adversary makes several queries to a model and leverages the responses (e.g. confidence scores, predictions) to train a surrogate or inversion model that learns to approximate the inverse mapping from outputs to inputs. This process may enable the reconstruction of sensitive attributes, e.g., facial features, medical data, or user behavior patterns, from models trained on such data. The technique has been demonstrated against various models like deep neural networks, classification systems etc. The technique has significant privacy risks in areas like healthcare, finance, biometric identification etc. Mitigation strategies include restricting model access, reducing output granularity, using differential privacy and monitoring anomalous query patterns.
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Availability zone

In cloud computing, an availability region is a group of data centres that are located in the same geographical region. Availability regions comprise multiple availability zones, which are groups of data centres that are located far enough from each other to prevent large-scale outages in the event of failure of a single zone, whilst still being close enough to each other to enable low-latency connections. Distributed systems spanning multiple availability zones allow for high availability, even in the event of catastrophic failure, such as natural disasters. Services offering distinct availability zones include Amazon Web Services, Microsoft Azure and Google Cloud.
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Corinna Cortes

Corinna Cortes (born 31 March 1961) is a Danish computer scientist known for her contributions to machine learning. She is a Vice President at Google Research in New York City. Cortes is an ACM Fellow and a recipient of the Paris Kanellakis Award for her work on theoretical foundations of support vector machines. == Early life and education == Corinna Cortes was born in 1961 in Denmark. Cortes received her Master of Science degree in physics from University of Copenhagen in 1989. She received her PhD in computer science from the University of Rochester in 1993 for research supervised by Randal C. Nelson. == Career and research == Cortes joined AT&T Bell Labs as a researcher in 1993. Since 2003, she has served as Vice President of Google Research, New York City, and since 2011, as adjunct professor at the UCPH Department of Computer Science. She is serves as an editorial board member of the journal Machine Learning. Cortes' research covers a wide range of topics in machine learning, including support vector machines (SVM) and data mining. SVM is one of the most frequently used algorithms in machine learning, which is used in many practical applications, including medical diagnosis and weather forecasting. At AT&T, Cortes was a contributor to the design of Hancock programming language. === Awards and honours === In 2008, she jointly with Vladimir Vapnik received the Paris Kanellakis Award for the development of a highly effective algorithm for supervised learning known as support vector machines (SVM). She was named an ACM Fellow in 2023 for theoretical and practical contributions to machine learning, industrial leadership and service to the field. == Personal life == Corinna has two children and is also a competitive runner.
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Deepti Gurdasani

Deepti Gurdasani is a British-Indian clinical epidemiologist and statistical geneticist who is a senior lecturer in machine learning at the Queen Mary University of London. Her research considers the genetic diversity of African Populations. Throughout the COVID-19 pandemic, Gurdasani has provided the public with her analysis of the evolving situation mainly on the Twitter platform. == Early life and education == Gurdasani was an undergraduate and medical student at the Christian Medical College Vellore at Tamil Nadu Dr. M.G.R. Medical University. After earning her medical degree and qualifying in internal medicine, she moved to the United Kingdom, where she worked toward a research doctorate in genetic epidemiology at Wolfson College, Cambridge. Her doctoral research involved the design of strategies to understand complex diseases in diverse populations. == Research and career == In 2013, Gurdasani joined the Wellcome Sanger Institute as a postdoctoral fellow, where she worked on the genomic diversity of African populations and how this diversity impacts susceptibility to disease. She makes use of dense genotypes and whole genome sequences to better understand how population movements determined genetic structure. In particular, Gurdasani develops machine learning algorithms to large-scale clinical data sets. At the Sanger Gurdasani co-led the African Genome Variation Project and the Uganda Resource Project. Gurdasani moved to Queen Mary University of London in 2019, where she created deep learning approaches for clinical prediction and the identification of novel, genome-based drug targets. During the COVID-19 pandemic Gurdasani has provided public commentary on the pandemic, making use of both Twitter and print media to share information on the evolving situation. She has researched the incidence of long covid in the UK. In 2021 Gurdasani started to write for The Guardian. == Selected publications == Deepti Gurdasani; Tommy Carstensen; Fasil Tekola-Ayele; et al. (3 December 2014). "The African Genome Variation Project shapes medical genetics in Africa". Nature. 517 (7534): 327–332. doi:10.1038/NATURE13997. ISSN 1476-4687. PMC 4297536. PMID 25470054. Wikidata Q34979569. Nisreen A Alwan; Rochelle Ann Burgess; Simon Ashworth; et al. (15 October 2020). "Scientific consensus on the COVID-19 pandemic: we need to act now". The Lancet. doi:10.1016/S0140-6736(20)32153-X. ISSN 0140-6736. PMC 7557300. PMID 33069277. Wikidata Q100697134. Deepti Gurdasani; Inês Barroso; Eleftheria Zeggini; Manjinder S Sandhu (24 June 2019). "Genomics of disease risk in globally diverse populations". Nature Reviews Genetics. 20 (9): 520–535. doi:10.1038/S41576-019-0144-0. ISSN 1471-0056. PMID 31235872. Wikidata Q93000887. (erratum)
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Maghi King

Margaret (Maghi) Daniel King is a retired British computational linguist known for her work on evaluating the quality of machine translation. She is an honorary professor in the Department of Translation Technology of the University of Geneva in Switzerland, and the former director of the Dalle Molle Institute for Semantic and Cognitive Studies at the University of Geneva. == Education and career == King read classics, Ancient History and Philosophy (Greats) at the University of Oxford, worked as a computer programmer, and became a lecturer in the Department of Computation at the University of Manchester Institute of Science and Technology. She moved to the Dalle Molle Institute for Semantic and Cognitive Studies (ISSCO) in 1974. In 1976, ISSCO became part of the University of Geneva, and she continued there, becoming ISSCO's director in 1978. She remained director until her retirement in 2006. == Recognition == King is a Fellow of the European Association for Artificial Intelligence (formerly ECCAI), elected in 1999.
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Automation

Automation describes a wide range of technologies that reduce human intervention in processes, mainly by predetermining decision criteria, subprocess relationships, and related actions, as well as embodying those predeterminations in machines. Automation has been achieved by various means including mechanical, hydraulic, pneumatic, electrical, electronic devices, and computers, usually in combination. Complicated systems, such as modern factories, airplanes, and ships typically use combinations of all of these techniques. The benefits of automation includes labor savings, reducing waste, savings in electricity costs, savings in material costs, and improvements to quality, accuracy, and precision. Automation includes the use of various equipment and control systems such as machinery, processes in factories, boilers, and heat-treating ovens, switching on telephone networks, steering, stabilization of ships, aircraft and other applications and vehicles with reduced human intervention. Examples range from a household thermostat controlling a boiler to a large industrial control system with tens of thousands of input measurements and output control signals. In the simplest type of an automatic control loop, a controller compares a measured value of a process with a desired set value and processes the resulting error signal to change some input to the process, in such a way that the process stays at its set point despite disturbances. This closed-loop control is an application of negative feedback to a system. The mathematical basis of control theory began in the 18th century and advanced rapidly in the 20th. The term automation, inspired by the earlier word automatic (coming from automaton), was not widely used before 1947, when Ford established an automation department. It was during this time that the industry was rapidly adopting feedback controllers, Technological advancements introduced in the 1930s revolutionized various industries significantly. The World Bank's World Development Report of 2019 shows evidence that the new industries and jobs in the technology sector outweigh the economic effects of workers being displaced by automation. Job losses and downward mobility blamed on automation have been cited as one of many factors in the resurgence of nationalist, protectionist and populist politics in the US, UK and France, among other countries since the 2010s. == History == === Early history === It was a preoccupation of the Greeks and Arabs (in the period between about 300 BC and about 1200 AD) to keep an accurate track of time. In Ptolemaic Egypt, about 270 BC, Ctesibius described a float regulator for a water clock, a device not unlike the ball and cock in a modern flush toilet. This was the earliest feedback-controlled mechanism. The appearance of the mechanical clock in the 14th century made the water clock and its feedback control system obsolete. The Persian Banū Mūsā brothers, in their Book of Ingenious Devices (850 AD), described a number of automatic controls. Two-step level controls for fluids, a form of discontinuous variable structure controls, were developed by the Banu Musa brothers. They also described a feedback controller. The design of feedback control systems up through the Industrial Revolution was by trial-and-error, together with a great deal of engineering intuition. It was not until the mid-19th century that the stability of feedback control systems was analyzed using mathematics, the formal language of automatic control theory. The centrifugal governor was invented by Christiaan Huygens in the seventeenth century, and used to adjust the gap between millstones. === Industrial Revolution in Western Europe === The introduction of prime movers, or self-driven machines advanced grain mills, furnaces, boilers, and the steam engine created a new requirement for automatic control systems including temperature regulators (invented in 1624; see Cornelius Drebbel), pressure regulators (1681), float regulators (1700) and speed control devices. Another control mechanism was used to tent the sails of windmills. It was patented by Edmund Lee in 1745. Also in 1745, Jacques de Vaucanson invented the first automated loom. Around 1800, Joseph Marie Jacquard created a punch-card system to program looms. In 1771 Richard Arkwright invented the first fully automated spinning mill driven by water power, known at the time as the water frame. An automatic flour mill was developed by Oliver Evans in 1785, making it the first completely automated industrial process. A centrifugal governor was used by Mr. Bunce of England in 1784 as part of a model steam crane. The centrifugal governor was adopted by James Watt for use on a steam engine in 1788 after Watt's partner Boulton saw one at a flour mill Boulton & Watt were building. The governor could not actually hold a set speed; the engine would assume a new constant speed in response to load changes. The governor was able to handle smaller variations such as those caused by fluctuating heat load to the boiler. Also, there was a tendency for oscillation whenever there was a speed change. As a consequence, engines equipped with this governor were not suitable for operations requiring constant speed, such as cotton spinning. Several improvements to the governor, plus improvements to valve cut-off timing on the steam engine, made the engine suitable for most industrial uses before the end of the 19th century. Advances in the steam engine stayed well ahead of science, both thermodynamics and control theory. The governor received relatively little scientific attention until James Clerk Maxwell published a paper that established the beginning of a theoretical basis for understanding control theory. === 20th century === Relay logic was introduced with factory electrification, which underwent rapid adaptation from 1900 through the 1920s. Central electric power stations were also undergoing rapid growth and the operation of new high-pressure boilers, steam turbines and electrical substations created a great demand for instruments and controls. Central control rooms became common in the 1920s, but as late as the early 1930s, most process controls were on-off. Operators typically monitored charts drawn by recorders that plotted data from instruments. To make corrections, operators manually opened or closed valves or turned switches on or off. Control rooms also used color-coded lights to send signals to workers in the plant to manually make certain changes. The development of the electronic amplifier during the 1920s, which was important for long-distance telephony, required a higher signal-to-noise ratio, which was solved by negative feedback noise cancellation. This and other telephony applications contributed to the control theory. In the 1940s and 1950s, German mathematician Irmgard Flügge-Lotz developed the theory of discontinuous automatic controls, which found military applications during the Second World War to fire control systems and aircraft navigation systems. Controllers, which were able to make calculated changes in response to deviations from a set point rather than on-off control, began being introduced in the 1930s. Controllers allowed manufacturing to continue showing productivity gains to offset the declining influence of factory electrification. Factory productivity was greatly increased by electrification in the 1920s. U.S. manufacturing productivity growth fell from 5.2%/yr 1919–29 to 2.76%/yr 1929–41. Alexander Field notes that spending on non-medical instruments increased significantly from 1929 to 1933 and remained strong thereafter. The First and Second World Wars saw major advancements in the field of mass communication and signal processing. Other key advances in automatic controls include differential equations, stability theory and system theory (1938), frequency domain analysis (1940), ship control (1950), and stochastic analysis (1941). Starting in 1958, various systems based on solid-state digital logic modules for hard-wired programmed logic controllers (the predecessors of programmable logic controllers [PLC]) emerged to replace electro-mechanical relay logic in industrial control systems for process control and automation, including early Telefunken/AEG Logistat, Siemens Simatic, Philips/Mullard/Valvo Norbit, BBC Sigmatronic, ACEC Logacec, Akkord Estacord, Krone Mibakron, Bistat, Datapac, Norlog, SSR, or Procontic systems. In 1959 Texaco's Port Arthur Refinery became the first chemical plant to use digital control. Conversion of factories to digital control began to spread rapidly in the 1970s as the price of computer hardware fell. === Significant applications === The automatic telephone switchboard was introduced in 1892 along with dial telephones. By 1929, 31.9% of the Bell system was automatic. Automatic telephone switching originally used vacuum tube amplifiers and electro-mechanical switches, which consumed a large amount of electricity. Call volume eve
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Multiline optical-character reader

A multiline optical-character reader, or MLOCR, is a type of mail sorting machine that uses optical character recognition (OCR) technology to determine how to route mail through the postal system. MLOCRs work by capturing images of the front of letter-sized mailpieces, and extracting the entire address from each piece. It looks up the postal code within each address in a master database, prints a barcode representing this information on the mailpiece, and performs an initial sort. All of this occurs in a fraction of a second as the mailpiece passes through the machine. After this point, mail is further sorted by barcode sorters that read this barcode to determine its destination throughout its journey all the way down to the walk sequence of the mail carrier. The United States Postal Service has used remote bar coding since 1992. In the United States, if the MLOCR is not able to decode the address, then the mailpiece is placed on "hold" by printing a unique fluorescent barcode on the back of the mailpiece, and the mailpiece is then set aside for further processing by the Remote Bar Coding System (formerly called Remote Video Encoding). An image of the mailpiece is sent to a Remote Encoding Center where a human data conversion operator manually inspects the image. The operator converts the information on the mailpiece into abbreviated codes and enters the data into the computer. This data is sent back to the MLOCR site where it is matched with the unique barcode on the back of the un-coded mailpiece, and a barcode is then printed on the mailpiece like the rest of the mail. All this effort is invested up front into deciphering the destination of each mailpiece and printing the correct barcode, so that the mailpiece will never need to be manually examined again until it reaches the hands of the letter carrier who will carry it to the final delivery point. A Delivery Bar Code Sorter is repeatedly used at each point in the USPS system to read the barcode and sort the mailpiece to a tray corresponding to the next leg of its journey towards its final destination. The United States Postal Service is the largest user of these machines; however, large volume mailers and mail consolidators also have their own MLOCR systems to barcode outgoing mail in order to receive significant postage discounts. An option called FASTforward can be added to an MLOCR that allows it to automatically forward mail to a new address. This additional computer hardware/software combination looks up decoded addresses in the National Change of Address database to see if the recipient has recently moved. If so, a POSTNET barcode representing the new address is sprayed on the mailpiece thus routing it to new address although the old address is still visible—a testament to the degree at which mail can be mechanically sorted. Generally, all OCR-equipped letter sorting machines ordered since the late 1980s have been equipped with OCR systems capable of reading multiple lines of address.
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Richard Zemel

Richard Stanley Zemel (born 1963) is a Canadian-American computer scientist and professor at Columbia University, Department of Computer Science, and a leading figure in the field of machine learning and computer vision. Zemel studied the history of science at Harvard University and obtained his B.A. in 1984. He continued his study at the Department of Computer Science of the University of Toronto under the supervision of Geoffrey Hinton. He obtained his M.Sc. and Ph.D. both in computer science in 1989 and 1994, respectively.
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Anil K. Jain (computer scientist, born 1948)

Anil Kumar Jain (born 1948) is an Indian-American computer scientist and University Distinguished Professor in the Department of Computer Science and Engineering at Michigan State University. He is one of the most highly cited researchers in computer science, and is internationally recognized for his foundational contributions to pattern recognition, computer vision, and biometric recognition, particularly in fingerprint recognition and face recognition. Jain is a member of the United States National Academy of Engineering, a Foreign Member of the Chinese Academy of Sciences, and a Foreign Fellow of the Indian National Academy of Engineering. He is a Fellow of the ACM, IEEE, AAAS, IAPR, and SPIE. His research has shaped the field of biometrics and has been applied in systems used worldwide for identity verification, law enforcement, and border security. In 2024, he was awarded the BBVA Foundation Frontiers of Knowledge Award in the category of Information and Communication Technologies. == Early life and education == Born in Basti, India, Jain received his Bachelor of Technology in electrical engineering from the Indian Institute of Technology, Kanpur in 1969. He then moved to the United States, where he earned his M.S. in 1970 and Ph.D. in 1973 from Ohio State University. His doctoral dissertation, titled Some Aspects of Dimensionality and Sample Size Problems in Statistical Pattern Recognition, was supervised by Robert B. McGhee and laid the groundwork for his subsequent research in pattern recognition. == Career == Jain began his academic career at Wayne State University, where he taught from 1972 to 1974. In 1974, he joined the faculty of Michigan State University, where he has remained for over five decades and currently holds the position of University Distinguished Professor. Throughout his career, Jain has conducted pioneering research in data clustering, fingerprint recognition, and face recognition. His work has been published in leading scientific journals including Scientific American, Nature, IEEE Spectrum, and MIT Technology Review. He served as Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence from 1991 to 1994. Jain has also contributed to national security and policy through his service on several advisory bodies. He served as a member of the U.S. National Academies panels on Information Technology, Whither Biometrics, and Improvised Explosive Devices (IED). He has also served on the Defense Science Board, the Forensic Science Standards Board, and the AAAS Latent Fingerprint Working Group. In 2014, Jain was named Innovator of the Year at Michigan State University for transferring several technologies on face and fingerprint recognition to major players in the biometrics industry. He holds eight U.S. and Korean patents related to biometric technologies. == Research contributions == Jain's research spans pattern recognition, computer vision, machine learning, and biometric recognition. His contributions have been particularly influential in several areas: === Biometric recognition === Jain is considered one of the foremost authorities on biometric recognition systems. His research group at Michigan State University has developed algorithms and systems for fingerprint, face, and iris recognition that have been widely adopted in both academic research and commercial applications. His work on fingerprint matching algorithms has been instrumental in establishing standards for automated fingerprint identification systems (AFIS) used by law enforcement agencies worldwide. In recent years, Jain and his research team have made significant advances in child fingerprint recognition, demonstrating that digital scans of a young child's fingerprint can be correctly recognized one year later with over 99 percent accuracy for children as young as six months old. This research has important implications for child identification in developing countries, where it can be used to track immunization records and provide access to medical care. === Data clustering === Jain's survey article "Data clustering: a review" (1999), co-authored with M. N. Murty and P. J. Flynn, is one of the most highly cited papers in computer science. His 2010 paper "Data Clustering: 50 Years Beyond K-Means" provided a comprehensive overview of the evolution of clustering methods and remains an essential reference in the field. === Statistical pattern recognition === Jain's work on statistical pattern recognition, including his influential survey "Statistical pattern recognition: A review" (2000) with R. P. W. Duin and Jianchang Mao, has shaped the theoretical foundations of the field. == Citation metrics and academic impact == Jain is among the most highly cited researchers in computer science. Based on his Google Scholar profile, he had an h-index of 200 in 2020, which was the highest among computer scientists identified in a survey published by UCLA at the time. As of August 2023, his h-index on Google Scholar is 211. He has since been surpassed by Yoshua Bengio, a researcher of similar subjects (neural networks and deep learning for artificial intelligence), who had an h-index of 224 as of August 2023. Another source reported that as of December 2022, he had the highest discipline h-index (D-index) in computer science. == Honors and awards == Jain has received numerous awards and honors recognizing his contributions to computer science and engineering: === Academy memberships === Member, United States National Academy of Engineering (2016) — elected "for contributions to the engineering and practice of biometrics" Foreign Fellow, Indian National Academy of Engineering (2016) Foreign Member, Chinese Academy of Sciences (2019) Member, The World Academy of Sciences (2019) Fellow, National Academy of Inventors === Professional society fellowships === Fellow, ACM Fellow, IEEE (1988) — for contributions to image processing Fellow, AAAS Fellow, International Association for Pattern Recognition Fellow, SPIE === Major awards === BBVA Foundation Frontiers of Knowledge Award in Information and Communication Technologies (2024) IAPR King-Sun Fu Prize (2008) IEEE W. Wallace McDowell Award (2007) — the highest technical honor awarded by the IEEE Computer Society, for pioneering contributions to theory, technique, and practice of pattern recognition, computer vision, and biometric recognition systems IEEE Computer Society Technical Achievement Award (2003) IAPR Pierre Devijver Award (2002) Humboldt Research Award (2002) Guggenheim Fellowship (2001) Fulbright Fellowship (1998) IEEE ICDM Research Contribution Award (2008) === Best paper awards === IEEE Transactions on Neural Networks (1996) Pattern Recognition journal (1987, 1991, 2005) === Honorary doctorates === Universidad Autónoma de Madrid (2018) Hong Kong University of Science and Technology (2021) == Legacy and endowments == Two endowed funds have been established in Jain's honor at Michigan State University, recognizing his lasting impact on the field and the university. In 2015, a former visiting scholar from Jain's laboratory made an anonymous $400,000 gift to create the Anil K. Jain Endowed Graduate Fellowship, which supports doctoral-level research in pattern recognition, computer vision, and biometric recognition. In 2022, the Anil K. and Nandita K. Jain Endowed Professorship was established through $1 million in contributions from multiple donors, including a substantial gift from the Jain family, to support faculty recruitment and retention in the Department of Computer Science and Engineering. == Selected publications == === Books === 1988. Algorithms For Clustering Data. With Richard C. Dubes. Prentice Hall. 1993. Markov Random Fields: Theory and Applications. With Rama Chellappa eds. Academic Press. 1999. Biometrics: Personal Identification in Networked Society. With Ruud M. Bolle and Sharath Pankanti eds. Springer. 2003. Handbook of Fingerprint Recognition. (2nd edition 2009). With D. Maio, D. Maltoni, S. Prabhakar. Springer. 2005. Handbook of Face Recognition. (2nd edition 2011). With S. Z. Li ed. Springer. 2006. Handbook of Multibiometrics. With A. Ross and K. Nandakumar. Springer. 2007. Handbook of Biometrics. With P. Flynn and A. Ross eds. Springer. 2011. Introduction to Biometrics. With A. Ross and K. Nandakumar. Springer. 2015. Encyclopedia of Biometrics (Second Edition). With Stan Li. Springer. === Research articles === Cross, George R. and Anil K. Jain. "Markov random field texture models". IEEE Transactions on Pattern Analysis and Machine Intelligence (1983): 25–39. Jain, Anil K., and Farshid Farrokhnia. "Unsupervised texture segmentation using Gabor filters". Pattern Recognition 24.12 (1991): 1167–1186. Jain, Anil K., and Douglas Zongker. "Feature selection: Evaluation, application, and small sample performance". IEEE Transactions on Pattern Analysis and Machine Intelligence, 19.2 (1997): 153–158. Jain, Anil K., L. Hong, S. Pankanti, R. Bolle. "An Identity-A
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InciWeb

InciWeb is an interagency all-risk incident web information management system provided by the United States Forest Service released in 2004. It was originally developed for wildland fire emergencies, but can be also used for other emergency incidents (natural disasters, such as earthquakes, floods, hurricanes, and tornadoes). == Introduction == It was developed with two primary missions: 1. Provide the public a single source of incident related information 2. Provide a standardized reporting tool for the Public Affairs community Official announcements include evacuations, road closures, news releases, maps, photographs, and basic info and current situation about the incident. Incident information can be accessed by: web browser at https://inciweb.wildfire.gov/ Twitter RSS web feed == Technical == The original application was hosted at the United States Forest Service - Wildland Fire Training and Conference Center, at McClellan Airfield, California, comprising three servers: Database server Administrative server Load balancer for the public content which routes traffic to a pool of eight servers. Web traffic averages 2 million plus hits daily during the fire season with the ability to handle 3.5 million hits. The servers were moved to the National information Technology Center (NITC), Kansas City, Missouri on July 16, 2008, along with the release of version 2.0; the current version is 2.2. == Availability issues == InciWeb was having technical difficulties due to the high volume of Internet users trying to access the site during the September–October 2006 Day Fire and the Summer 2008 California wildfires. == Participating agencies == United States Forest Service Bureau of Land Management Bureau of Indian Affairs Fish and Wildlife Service National Park Service National Oceanic & Atmospheric Administration Department of the Interior Office of Aircraft Services National Association of State Foresters United States Fire Administration These same agencies are also in the National Interagency Fire Center.
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Moore machine

In the theory of computation, a Moore machine is a finite-state machine whose current output values are determined only by its current state. This is in contrast to a Mealy machine, whose output values are determined both by its current state and by the values of its inputs. Like other finite state machines, in Moore machines, the input typically influences the next state. Thus the input may indirectly influence subsequent outputs, but not the current or immediate output. The Moore machine is named after Edward F. Moore, who presented the concept in a 1956 paper, “Gedanken-experiments on Sequential Machines.” == Formal definition == A Moore machine can be defined as a 6-tuple ( S , s 0 , Σ , Λ , δ , G ) {\displaystyle (S,s_{0},\Sigma ,\Lambda ,\delta ,G)} consisting of the following: A finite set of states S {\displaystyle S} A start state (also called initial state) s 0 {\displaystyle s_{0}} which is an element of S {\displaystyle S} A finite set called the input alphabet Σ {\displaystyle \Sigma } A finite set called the output alphabet Λ {\displaystyle \Lambda } A transition function δ : S × Σ → S {\displaystyle \delta :S\times \Sigma \rightarrow S} mapping a state and the input alphabet to the next state An output function G : S → Λ {\displaystyle G:S\rightarrow \Lambda } mapping each state to the output alphabet "Evolution across time" is realized in this abstraction by having the state machine consult the time-changing input symbol at discrete "timer ticks" t 0 , t 1 , t 2 , . . . {\displaystyle t_{0},t_{1},t_{2},...} and react according to its internal configuration at those idealized instants, or else having the state machine wait for a next input symbol (as on a FIFO) and react whenever it arrives. A Moore machine can be regarded as a restricted type of finite-state transducer. == Visual representation == === Table === A state transition table is a table listing all the triples in the transition relation δ : S × Σ → S {\displaystyle \delta :S\times \Sigma \rightarrow S} . === Diagram === The state diagram for a Moore machine, or Moore diagram, is a state diagram that associates an output value with each state. == Relationship with Mealy machines == As Moore and Mealy machines are both types of finite-state machines, they are equally expressive: either type can be used to parse a regular language. The difference between Moore machines and Mealy machines is that in the latter, the output of a transition is determined by the combination of current state and current input ( S × Σ {\displaystyle S\times \Sigma } as the domain of G {\displaystyle G} ), as opposed to just the current state ( S {\displaystyle S} as the domain of G {\displaystyle G} ). When represented as a state diagram, for a Moore machine, each node (state) is labeled with an output value; for a Mealy machine, each arc (transition) is labeled with an output value. Every Moore machine M {\displaystyle M} is equivalent to the Mealy machine with the same states and transitions and the output function G ( s , σ ) = G M ( δ M ( s , σ ) ) {\displaystyle G(s,\sigma )=G_{M}(\delta _{M}(s,\sigma ))} , which takes each state-input pair ( s , σ ) {\displaystyle (s,\sigma )} and yields G M ( δ M ( s , σ ) ) {\displaystyle G_{M}(\delta _{M}(s,\sigma ))} , where G M {\displaystyle G_{M}} is M {\displaystyle M} 's output function and δ M {\displaystyle \delta _{M}} is M {\displaystyle M} 's transition function. However, not every Mealy machine can be converted to an equivalent Moore machine. Some can be converted only to an almost equivalent Moore machine, with outputs shifted in time. This is due to the way that state labels are paired with transition labels to form the input/output pairs. Consider a transition s i → s j {\displaystyle s_{i}\rightarrow s_{j}} from state s i {\displaystyle s_{i}} to state s j {\displaystyle s_{j}} . The input causing the transition s i → s j {\displaystyle s_{i}\rightarrow s_{j}} labels the edge ( s i , s j ) {\displaystyle (s_{i},s_{j})} . The output corresponding to that input, is the label of state s i {\displaystyle s_{i}} . Notice that this is the source state of the transition. So for each input, the output is already fixed before the input is received, and depends solely on the present state. This is the original definition by E. Moore. It is a common mistake to use the label of state s j {\displaystyle s_{j}} as output for the transition s i → s j {\displaystyle s_{i}\rightarrow s_{j}} . == Examples == Types according to number of inputs/outputs. === Simple === Simple Moore machines have one input and one output: edge detector using XOR binary adding machine clocked sequential systems (a restricted form of Moore machine where the state changes only when the global clock signal changes) Most digital electronic systems are designed as clocked sequential systems. Clocked sequential systems are a restricted form of Moore machine where the state changes only when the global clock signal changes. Typically the current state is stored in flip-flops, and a global clock signal is connected to the "clock" input of the flip-flops. Clocked sequential systems are one way to solve metastability problems. A typical electronic Moore machine includes a combinational logic chain to decode the current state into the outputs (lambda). The instant the current state changes, those changes ripple through that chain, and almost instantaneously the output gets updated. There are design techniques to ensure that no glitches occur on the outputs during that brief period while those changes are rippling through the chain, but most systems are designed so that glitches during that brief transition time are ignored or are irrelevant. The outputs then stay the same indefinitely (LEDs stay bright, power stays connected to the motors, solenoids stay energized, etc.), until the Moore machine changes state again. ==== Worked example ==== A sequential network has one input and one output. The output becomes 1 and remains 1 thereafter when at least two 0's and two 1's have occurred as inputs. A Moore machine with nine states for the above description is shown on the right. The initial state is state A, and the final state is state I. The state table for this example is as follows: === Complex === More complex Moore machines can have multiple inputs as well as multiple outputs. == Gedanken-experiments == In Moore's 1956 paper "Gedanken-experiments on Sequential Machines", the ( n ; m ; p ) {\displaystyle (n;m;p)} automata (or machines) S {\displaystyle S} are defined as having n {\displaystyle n} states, m {\displaystyle m} input symbols and p {\displaystyle p} output symbols. Nine theorems are proved about the structure of S {\displaystyle S} , and experiments with S {\displaystyle S} . Later, " S {\displaystyle S} machines" became known as "Moore machines". At the end of the paper, in Section "Further problems", the following task is stated: Another directly following problem is the improvement of the bounds given at the theorems 8 and 9. Moore's Theorem 8 is formulated as: Given an arbitrary ( n ; m ; p ) {\displaystyle (n;m;p)} machine S {\displaystyle S} , such that every two of its states are distinguishable from one another, then there exists an experiment of length n ( n − 1 ) 2 {\displaystyle {\tfrac {n(n-1)}{2}}} which determines the state of S {\displaystyle S} at the end of the experiment. In 1957, A. A. Karatsuba proved the following two theorems, which completely solved Moore's problem on the improvement of the bounds of the experiment length of his "Theorem 8". Theorem A. If S {\displaystyle S} is an ( n ; m ; p ) {\displaystyle (n;m;p)} machine, such that every two of its states are distinguishable from one another, then there exists a branched experiment of length at most ( n − 1 ) ( n − 2 ) 2 + 1 {\displaystyle {\tfrac {(n-1)(n-2)}{2}}+1} through which one may determine the state of S {\displaystyle S} at the end of the experiment. Theorem B. There exists an ( n ; m ; p ) {\displaystyle (n;m;p)} machine, every two states of which are distinguishable from one another, such that the length of the shortest experiments establishing the state of the machine at the end of the experiment is equal to ( n − 1 ) ( n − 2 ) 2 + 1 {\displaystyle {\tfrac {(n-1)(n-2)}{2}}+1} . Theorems A and B were used for the basis of the course work of a student of the fourth year, A. A. Karatsuba, "On a problem from the automata theory", which was distinguished by testimonial reference at the competition of student works of the faculty of mechanics and mathematics of Moscow State University in 1958. The paper by Karatsuba was given to the journal Uspekhi Mat. Nauk on 17 December 1958 and was published there in June 1960. Until the present day (2011), Karatsuba's result on the length of experiments is the only exact nonlinear result, both in automata theory, and in similar problems of computational complexity theory.
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Is an AI Blog Writer Worth It in 2026?

Trying to pick the best AI blog writer? An AI blog writer 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 blog writer 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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