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Dhammin (Arabic: ضمّن) is a political platform that manages candidates' electoral campaigns for the National Assembly, Municipal Council or Cooperative Society councils of Kuwait. The platform was founded by Abdullah Al-Salloum and it is, according to news reports and interviews, the first within the field to apply distributed-systems' methodologies.
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Mark A. Heimann is an American chess grandmaster and machine learning researcher. == Chess career == Heimann began playing chess at the age of 5 after his father bought him and his twin brother Alexander a chess set. He then won several national grade-level championships as well as the Pennsylvania and Ohio state championships in middle school and high school. In October 2007, he was ranked as the national #2 under-14 player, only behind future grandmaster Marc Tyler Arnold. In the February 2008 national rankings, he moved up to being the top-ranked under-14 player. In December 2012, he played for Washington University St. Louis' "A" team in the Pan-American Intercollegiate Chess Championships, where he was the second-most successful player, recording 4 wins, 1 draw, and 1 loss. The university's team also won the Division II championship title. In three tournaments between September and December 2022, Heimann earned three international master title norms, earning the international master title at the age of 29. In November 2024, he scored a GM norm at the U.S. Masters Chess Championship. He finished the event in joint-6th place. The following week, at the Saint Louis Masters tournament, he earned his final grandmaster norm and crossed 2500 in live rating, achieving the Grandmaster title. It was formally awarded to him in April 2025. == Research career == He obtained a bachelor's degree from Washington University in St. Louis in the School of Arts and Sciences and got his PhD from the University of Michigan. He is a machine learning researcher at Lawrence Livermore National Laboratory. == Personal life == Outside of chess and research, he also plays several instruments and is a competitive powerlifter.
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Jian Ma (Chinese: 马坚) is an American computer scientist and computational biologist. He is the Ray and Stephanie Lane Professor of Computational Biology in the School of Computer Science at Carnegie Mellon University. He is a faculty member in the Ray and Stephanie Lane Computational Biology Department. His lab develops AI/ML methods to study the structure and function of the human genome and cellular organization and their implications for health and disease. During his Ph.D. and postdoc training, he developed algorithms to reconstruct the ancestral mammalian genome and evolutionary history. His research group has recently pioneered a series of new machine learning solutions for 3D genome organization, single-cell epigenomics, spatial omics, and complex molecular interactions. His lab also explores large language models to uncover gene regulatory mechanisms and the intricate connections among cellular components, with the aim of driving discovery and guiding experimentation. He received an NSF CAREER award in 2011. In 2020, he was awarded a Guggenheim Fellowship in Computer Science. He received the Allen Newell Award for Research Excellence (2025). He is an elected Fellow of the American Association for the Advancement of Science, the American Institute for Medical and Biological Engineering, the International Society for Computational Biology, and the Association for Computing Machinery. He leads an NIH 4D Nucleome Center to develop machine learning algorithms to better understand the cell nucleus. He served as the Program Chair for RECOMB 2024. He is also a member of the Scientific Advisory Board of the Chan Zuckerberg Biohub Chicago (CZ Biohub Chicago) and the RECOMB Steering Committee. In 2024, he launched the Center for AI-Driven Biomedical Research (AI4BIO) at CMU, which will be a catalyst for innovations at the intersection of AI and biomedicine across the School of Computer Science and campus. == Selected Recent Publications == Chen V#, Yang M#, Cui W, Kim JS, Talwalkar A, and Ma J. Applying interpretable machine learning in computational biology - pitfalls, recommendations and opportunities for new developments. Nature Methods, 21(8):1454-1461, 2024. Xiong K#, Zhang R#, and Ma J. scGHOST: Identifying single-cell 3D genome subcompartments. Nature Methods, 21(5):814-822, 2024. Zhou T, Zhang R, Jia D, Doty RT, Munday AD, Gao D, Xin L, Abkowitz JL, Duan Z, and Ma J. GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells. Nature Genetics, 56(8):1701-1711, 2024. Zhang Y, Boninsegna L, Yang M, Misteli T, Alber F, and Ma J. Computational methods for analysing multiscale 3D genome organization. Nature Reviews Genetics, 5(2):123-141, 2024. Chidester B#, Zhou T#, Alam S, and Ma J. SPICEMIX enables integrative single-cell spatial modeling of cell identity. Nature Genetics, 55(1):78-88, 2023. [Cover Article] Zhang R#, Zhou T#, and Ma J. Ultrafast and interpretable single-cell 3D genome analysis with Fast-Higashi. Cell Systems, 13(10):P798-807.E6, 2022. [Cover Article] Zhu X#, Zhang Y#, Wang Y, Tian D, Belmont AS, Swedlow JR, and Ma J. Nucleome Browser: An integrative and multimodal data navigation platform for 4D Nucleome. Nature Methods, 19(8):911-913, 2022. Zhang R, Zhou T, and Ma J. Multiscale and integrative single-cell Hi-C analysis with Higashi. Nature Biotechnology, 40:254–261, 2022.
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EDLUT (Event-Driven LookUp Table) is a computer application for simulating networks of spiking neurons. It was developed in the University of Granada and source code was released under GNU GPL version 3. EDLUT uses event-driven simulation scheme and lookup tables to efficiently simulate medium or large spiking neural networks. This allows this application to simulate detailed biological neuron models and to interface with experimental setups (such as a robotic arm) in real time.
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Waveform graphics is a simple vector graphics system introduced by Digital Equipment Corporation (DEC) on the VT55 and VT105 terminals in the mid-1970s. It was used to produce graphics output from mainframes and minicomputers. DEC used the term "waveform graphics" to refer specifically to the hardware, but it was used more generally to describe the whole system. The system was designed to use as little computer memory as possible. At any given X location it could draw two dots at given Y locations, making it suitable for producing two superimposed waveforms, line charts or histograms. Text and graphics could be mixed, and there were additional tools for drawing axes and markers. The waveform graphics system was used only for a short period of time before it was replaced by the more sophisticated ReGIS system, first introduced on the VT125 in 1981. ReGIS allowed the construction of arbitrary vectors and other shapes. Whereas DEC normally provided a backward compatible solution in newer terminal models, they did not choose to do this when ReGIS was introduced, and waveform graphics disappeared from later terminals. == Description == Waveform graphics was introduced on the VT55 terminal in October 1975, an era when memory was extremely expensive. Although it was technically possible to produce a bitmap display using a framebuffer using technology of the era, the memory needed to do so at a reasonable resolution was typically beyond the price point that made it practical. All sorts of systems were used to replace computer memory with other concepts, like the storage tubes used in the Tektronix 4010 terminals, or the zero memory racing-the-beam system used in the Atari 2600. DEC chose to attack this problem through a clever use of a small buffer representing only the vertical positions on the screen. Such a system could not draw arbitrary shapes, but would allow the display of graph data. The system was based on a 512 by 236 pixel display, producing 512 vertical columns along the X-axis, and 236 horizontal rows on the Y-axis. Y locations were counted up from the bottom, so the coordinate 0,0 was in the lower left, and 511, 235 in the upper right. Had this been implemented using a framebuffer with each location represented by a single bit, 512 × 236 × 1 = 120,832 bits, or 15,104 bytes, would have been required. At the time, memory cost about $50 per kilobyte, so the buffer alone would cost over $700 (equivalent to $4,570 in 2025). Instead, the waveform graphic system used one byte of memory for each X axis location, with the byte's value representing the Y location. This required only 512 bytes for each graph, a total of 1024 bytes for the two graphs. Drawing a line required the programmer to construct a series of Y locations and send them as individual points, the terminal could not connect the dots itself. To make this easier, the terminal automatically incremented the X location every time an Y coordinate was received, so a graph line could be sent as a long string of numbers for subsequent Y locations instead of having to repeatedly send the X location every time. Drawing normally started by sending a single instruction to set the initial X location, often 0 on the left, and then sending in data for the entire curve. The system also included storage for up to 512 markers on both lines. These were always drawn centered on the Y value of the line they were associated with, meaning that a simple on/off indication for X locations was all that was needed, requiring only 1024 bits, or 128 bytes, in total. The markers extended 16 pixels vertically, and could only be aligned on 16-pixel boundaries, so they were not necessarily centered across the underlying graph. Markers were used to indicate important points on the graph, where a symbol of some sort would normally be used. The system also allowed a vertical line to be drawn for every horizontal location and a horizontal one at every vertical location. These were also stored as simple on/off bits, requiring another 128 bytes of memory. These lines were used to draw axes and scale lines, or could be used for a screen-spanning crosshair cursor. A separate set of two 7-bit registers held additional information about the drawing style and other settings. Although complex from the user's perspective, this system was easy to implement in hardware. A cathode ray tube produces a display by scanning the screen in a series of horizontal motions, moving down one vertical line after each horizontal scan. At any given instant during this process, the display hardware examines a few memory locations to see if anything needs to be displayed. For instance, it can determine whether to draw a marker on graph 0 by examining register 1 to see if markers are turned on, looking in the marker buffer to see if there is a 1 at the current X location, and then examining the Y location of graph 0 to see if it is within 16 pixels of the current scan line. If all of these are true, a spot is drawn to present that portion of the marker. As this will be true for 16 vertical locations during the scanning process, a 16-pixel high marker will be drawn. Sold alone, the VT55 was priced at $2,496 (equivalent to $16,295 in 2025),. Like other models of the VT50 series, the terminal could be equipped with an optional wet-paper printer in a panel on the right of the screen. This added $800 (equivalent to $5,223 in 2025) to the price. DEC also offered VT55 in a package with a small model of the PDP-11 to create one model of the DEClab 11/03 system. The DEClab normally sold for $14,000 (equivalent to $91,397 in 2025) with a DECwriter II (LA36) hard-copy terminal for $15,000 (equivalent to $97,925 in 2025), with the VT55. The system had I/O channels for up to 15 lab devices, and included libraries for FORTRAN and BASIC for reading the data and creating graphs. The fairly extensive VT55 Programmers Manual covered the latter in depth. == Commands and data == Data was sent to the terminal using an extended set of codes similar to those introduced on the VT52. VT52 codes generally started with the ESC character (octal 33, decimal 27) and was then followed by a single letter instruction. For instance, the string of four characters ESC H ESC J would reposition the cursor in the upper left (home) and then clear the screen from that point down. These codes were basically modeless; triggered by the ESC the resulting escape mode automatically exited again when the command was complete. Escape codes could be interspersed with display text anywhere in the stream of data. In contrast, the graphics system was entirely modal, with escape sequences being sent to cause the terminal to enter or exit graph drawing mode. Data sent between these two codes were interpreted by the graphics hardware, so text and graphics could not be mixed in a single stream of instructions. Graphics mode was entered by sending the string ESC 1, and exited again with the string ESC 2. Even the commands within the graphics mode were modal; characters were interpreted as being additional data for the previous load character (command) until another load character is seen. Ten load characters were available: @ - no operation, used to tell the terminal the last command is no longer active A - load data into register 0, selecting the drawing mode for the two graphs I - load data into register 1, selecting other drawing options H - load the starting X position (Horizontal) for the following commands B - load data for Y locations for graph 0 starting at the H position selected earlier J - load data for Y locations for graph 1 starting at the H position selected earlier C - store a marker on graph 0 at the following X location K - store a marker on graph 1 at the following X location D - draw a horizontal line at the given Y location L - draw a vertical line at the given X location X and Y locations were sent as 10-bit decimal numbers, encoded as ASCII characters, with 5 bits per character. This means that any number within the 1024 number space (210) can be stored as a string of two characters. To ensure the characters can be transmitted over 7-bit links, the pattern 01 is placed in front of both 5-bit numbers, producing 7-bit ASCII values that are always within the printable range. This results in a somewhat complex encoding algorithm. For instance, if one wanted to encode the decimal value 102, first you convert that to the 10-bit decimal pattern 0010010010. That is then split that into upper and lower 5-bit parts, 00100 and 10010. Then append 01 binary to produce 7-bit numbers 0100100 and 0110010. Individually convert back to decimal 40 and 50, and then look up those characters in an ASCII chart, finding ( and 2. These have to be sent to the terminal least significant character first. If these were being used to set the X coordinate, the complete string would be H2(. When used as X and Y locations for the graphs, extra digits were ignored. For instance, the 512 pixel X axis r
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Trying to pick the best AI image generator? An AI image generator 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 image generator 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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Ashutosh Saxena is an Indian-American computer scientist, researcher, and entrepreneur known for his contributions to the field of artificial intelligence and large-scale robot learning. His interests include building enterprise AI agents and embodied AI. Saxena is the co-founder and CEO of Caspar.AI, where generative AI parses data from ambient 3D radar sensors to predict 20+ health & wellness markers for pro-active patient care. Prior to Caspar.AI, Ashutosh co-founded Cognical Katapult (NSDQ: KPLT), which provides a no credit required alternative to traditional financing for online and omni-channel retail. Before Katapult, Saxena was an assistant professor in the Computer Science Department and faculty director of the RoboBrain Project (a large-scale AI model for robotics) at Cornell University. == Education == In 2009, with artificial intelligence pioneer Andrew Ng as his advisor, Saxena received both his M.S. and Ph.D. in computer science with an emphasis on artificial intelligence from Stanford University. Saxena received his bachelor's degree in electrical engineering from the Indian Institute of Technology, Kanpur in 2004. == Career == Saxena was the chief scientist of New York-based Holopad, where he worked with Steven Spielberg's team to create walkthroughs and 3D experiences for his movie TinTin. His past experiences include building acoustic AI models at Bose Corporation. Once Ashutosh completed his undergraduate degree, he became a researcher at the Commonwealth Scientific and Industrial Research Organization, where he developed AI models for medical devices. Before Caspar, Saxena pursued other entrepreneurial ventures, such as ZunaVision, an artificial intelligence startup he co-founded with Andrew Ng that uses AI to embed advertising space within videos. Ashutosh served as the CTO of ZunaVision from 2008 to 2010. After ZunaVision, Saxena co-founded Cognical Katapult, which provided financing solutions to nonprime and underbanked consumers powered by artificial intelligence. From 2014 to 2016, Saxena served as the faculty director of the RoboBrain project, which was a joint venture that he started between Stanford University, Cornell University, Brown University, and the University of California, Berkeley that made a knowledge engine for robots. Saxena co-founded Brain of Things in 2015 with David Cheriton, who serves as chief scientist, and was listed as the fastest growing private company reaching an annual recurring revenue of $8 million in three years. It has been widely covered in several outlets including Forbes Japan, and MIT Technology Review. Saxena's work on deep learning won test of time award in 2023 by Robotics Science and Systems. Ashutosh has been recognized for his work by receiving the Alfred P. Sloan Fellow in 2011, Google Faculty Research Award in 2012, Microsoft Faculty Fellowship in 2012, NSF Career award in 2013, One of the Eight Innovators to Watch by the Smithsonian Institution in 2015, and received TR35 Innovator Award by MIT Technology Review in 2018. He was named by San Francisco Business Times as a 40 under 40 young business leader. == Research == Saxena has authored over 100 published papers in the areas of large-scale robot learning and artificial intelligence, with 20,000+ citations. His work in the fields of computer vision and deep learning have been featured in press releases and academic journal reviews. Ashutosh's early work includes the Stanford Artificial Intelligence Robot (STAIR), an AI models that enables to perform tasks such as unload items from a dishwasher, which was covered on the front-page of New York Times. His work on Make3D, was the first work that estimated 3D depth from a single still image. At Cornell University, Ashutosh led the Robot Learning Lab, which used a machine learning approach to train robots to perform tasks in human environments such as generalizing manipulation in 3D point-clouds where robots learn to transfer manipulation trajectories to novel objects utilizing a large sample of demonstrations from crowdsourcing.
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Trying to pick the best AI photo editor? An AI photo editor 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 photo editor 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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Bottlenose.com, also known as Bottlenose, is an enterprise trend intelligence company that analyzes big data and business data to detect trends for brands. It helps Fortune 500 enterprises discover, and track emerging trends that affect their brands. The company uses natural language processing, sentiment analysis, statistical algorithms, data mining, and machine learning heuristics to determine trends, and has a search engine that gathers information from social networks. KPMG Capital has invested a "substantial amount" in the company. Bottlenose processed 72 billion messages per day, in real-time, from across social and broadcast media, as of December 2014. == History == The company is based in Los Angeles, CA. Bottlenose is a real-time trend intelligence tool that measures social media campaigns and trends. The company also provides a free version of its Sonar tool that shows real-time trends across social media. In October 2012, the company received $1 million of funding from ff Venture Capital and Prosper Capital. By 2014, the company raised about $7 million in funding. In December 2014, KPMG Capital announced further investment in the company. In February 2015, the company confirmed it had raised $13.4 million in Series B funding led by KPMG Capital. Bottlenose partnered with the nonprofit No Labels during the 2014 State of the Union Address to analyze Twitter conversations for bipartisanship. The company also partnered with media monitoring company Critical Mention to analyze broadcast analytics. The Bottlenose Nerve Center integrated with the Critical Mention API to analyze real-time trends in television and radio broadcasts. In June 2014, Bottlenose updated its trend detection product to Nerve Center 2.0. It creates a newsfeed to show changes in trends and sends alerts when trends occur. It also has "emotion detection," which will display the emotions associated with specific comments on trending topics. In 2016, Bottlenose released its Nerve Center 3.0 platform, which was designed to automate the work of data scientists and lower the cost of artificial intelligence for businesses.
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Various methods for the evaluation for machine translation have been employed. This article focuses on the evaluation of the output of machine translation, rather than on performance or usability evaluation. == Round-trip translation == A typical way for lay people to assess machine translation quality is to translate from a source language to a target language and back to the source language with the same engine. Though intuitively this may seem like a good method of evaluation, it has been shown that round-trip translation is a "poor predictor of quality". The reason why it is such a poor predictor of quality is reasonably intuitive. A round-trip translation is not testing one system, but two systems: the language pair of the engine for translating into the target language, and the language pair translating back from the target language. Consider the following examples of round-trip translation performed from English to Italian and Portuguese from Somers (2005): In the first example, where the text is translated into Italian then back into English—the English text is significantly garbled, but the Italian is a serviceable translation. In the second example, the text translated back into English is perfect, but the Portuguese translation is meaningless; the program thought "tit" was a reference to a tit (bird), which was intended for a "tat", a word it did not understand. While round-trip translation may be useful to generate a "surplus of fun," the methodology is deficient for serious study of machine translation quality. == Human evaluation == This section covers two of the large scale evaluation studies that have had significant impact on the field—the ALPAC 1966 study and the ARPA study. === Automatic Language Processing Advisory Committee (ALPAC) === One of the constituent parts of the ALPAC report was a study comparing different levels of human translation with machine translation output, using human subjects as judges. The human judges were specially trained for the purpose. The evaluation study compared an MT system translating from Russian into English with human translators, on two variables. The variables studied were "intelligibility" and "fidelity". Intelligibility was a measure of how "understandable" the sentence was, and was measured on a scale of 1–9. Fidelity was a measure of how much information the translated sentence retained compared to the original, and was measured on a scale of 0–9. Each point on the scale was associated with a textual description. For example, 3 on the intelligibility scale was described as "Generally unintelligible; it tends to read like nonsense but, with a considerable amount of reflection and study, one can at least hypothesize the idea intended by the sentence". Intelligibility was measured without reference to the original, while fidelity was measured indirectly. The translated sentence was presented, and after reading it and absorbing the content, the original sentence was presented. The judges were asked to rate the original sentence on informativeness. So, the more informative the original sentence, the lower the quality of the translation. The study showed that the variables were highly correlated when the human judgment was averaged per sentence. The variation among raters was small, but the researchers recommended that at the very least, three or four raters should be used. The evaluation methodology managed to separate translations by humans from translations by machines with ease. The study concluded that, "highly reliable assessments can be made of the quality of human and machine translations". === Advanced Research Projects Agency (ARPA) === As part of the Human Language Technologies Program, the Advanced Research Projects Agency (ARPA) created a methodology to evaluate machine translation systems, and continues to perform evaluations based on this methodology. The evaluation programme was instigated in 1991, and continues to this day. Details of the programme can be found in White et al. (1994) and White (1995). The evaluation programme involved testing several systems based on different theoretical approaches; statistical, rule-based and human-assisted. A number of methods for the evaluation of the output from these systems were tested in 1992 and the most recent suitable methods were selected for inclusion in the programmes for subsequent years. The methods were; comprehension evaluation, quality panel evaluation, and evaluation based on adequacy and fluency. Comprehension evaluation aimed to directly compare systems based on the results from multiple choice comprehension tests, as in Church et al. (1993). The texts chosen were a set of articles in English on the subject of financial news. These articles were translated by professional translators into a series of language pairs, and then translated back into English using the machine translation systems. It was decided that this was not adequate for a standalone method of comparing systems and as such abandoned due to issues with the modification of meaning in the process of translating from English. The idea of quality panel evaluation was to submit translations to a panel of expert native English speakers who were professional translators and get them to evaluate them. The evaluations were done on the basis of a metric, modelled on a standard US government metric used to rate human translations. This was good from the point of view that the metric was "externally motivated", since it was not specifically developed for machine translation. However, the quality panel evaluation was very difficult to set up logistically, as it necessitated having a number of experts together in one place for a week or more, and furthermore for them to reach consensus. This method was also abandoned. Along with a modified form of the comprehension evaluation (re-styled as informativeness evaluation), the most popular method was to obtain ratings from monolingual judges for segments of a document. The judges were presented with a segment, and asked to rate it for two variables, adequacy and fluency. Adequacy is a rating of how much information is transferred between the original and the translation, and fluency is a rating of how good the English is. This technique was found to cover the relevant parts of the quality panel evaluation, while at the same time being easier to deploy, as it didn't require expert judgment. Measuring systems based on adequacy and fluency, along with informativeness is now the standard methodology for the ARPA evaluation program. == Automatic evaluation == In the context of this article, a metric is a measurement. A metric that evaluates machine translation output represents the quality of the output. The quality of a translation is inherently subjective, there is no objective or quantifiable "good." Therefore, any metric must assign quality scores so they correlate with the human judgment of quality. That is, a metric should score highly translations that humans score highly, and give low scores to those humans give low scores. Human judgment is the benchmark for assessing automatic metrics, as humans are the end-users of any translation output. The measure of evaluation for metrics is correlation with human judgment. This is generally done at two levels, at the sentence level, where scores are calculated by the metric for a set of translated sentences, and then correlated against human judgment for the same sentences. And at the corpus level, where scores over the sentences are aggregated for both human judgments and metric judgments, and these aggregate scores are then correlated. Figures for correlation at the sentence level are rarely reported, although Banerjee et al. (2005) do give correlation figures that show that, at least for their metric, sentence-level correlation is substantially worse than corpus level correlation. While not widely reported, it has been noted that the genre, or domain, of a text has an effect on the correlation obtained when using metrics. Coughlin (2003) reports that comparing the candidate text against a single reference translation does not adversely affect the correlation of metrics when working in a restricted domain text. Even if a metric correlates well with human judgment in one study on one corpus, this successful correlation may not carry over to another corpus. Good metric performance, across text types or domains, is important for the reusability of the metric. A metric that only works for text in a specific domain is useful, but less useful than one that works across many domains—because creating a new metric for every new evaluation or domain is undesirable. Another important factor in the usefulness of an evaluation metric is to have a good correlation, even when working with small amounts of data, that is candidate sentences and reference translations. Turian et al. (2003) point out that, "Any MT evaluation measure is less reliable on shorter translations", and
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Theano is a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially matrix-valued ones. In Theano, computations are expressed using a NumPy-esque syntax and compiled to run efficiently on either CPU or GPU architectures. == History == Theano is an open source project primarily developed by the Montreal Institute for Learning Algorithms (MILA) at the Université de Montréal. The name of the software references the ancient philosopher Theano, long associated with the development of the golden mean. On 28 September 2017, Pascal Lamblin posted a message from Yoshua Bengio, Head of MILA: major development would cease after the 1.0 release due to competing offerings by strong industrial players. Theano 1.0.0 was then released on 15 November 2017. On 17 May 2018, Chris Fonnesbeck wrote on behalf of the PyMC development team that the PyMC developers will officially assume control of Theano maintenance once the MILA development team steps down. On 29 January 2021, they started using the name Aesara for their fork of Theano. On 29 Nov 2022, the PyMC development team announced that the PyMC developers will fork the Aesara project under the name PyTensor. == Sample code == The following code is the original Theano's example. It defines a computational graph with 2 scalars a and b of type double and an operation between them (addition) and then creates a Python function f that does the actual computation. == Examples == === Matrix Multiplication (Dot Product) === The following code demonstrates how to perform matrix multiplication using Theano, which is essential for linear algebra operations in many machine learning tasks. === Gradient Calculation === The following code uses Theano to compute the gradient of a simple operation (like a neuron) with respect to its input. This is useful in training machine learning models (backpropagation). === Building a Simple Neural Network === The following code shows how to start building a simple neural network. This is a very basic neural network with one hidden layer. === Broadcasting in Theano === The following code demonstrates how broadcasting works in Theano. Broadcasting allows operations between arrays of different shapes without needing to explicitly reshape them.
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Svetlana Lazebnik (born 1979) is a Ukrainian-American researcher in computer vision who works as a professor of computer science and Willett Faculty Scholar at the University of Illinois at Urbana–Champaign. Her research involves interactions between image understanding and natural language processing, including the automated captioning of images, and the development of a benchmark database of textually grounded images. == Education and career == Lazebnik was born in Kyiv in 1979 to a family of Ukrainian Jews, and emigrated with her family to the US as a teenager. She majored in computer science at DePaul University, minoring in mathematics and graduating with the highest honors in 2000. She completed her Ph.D. in 2006 at the University of Illinois at Urbana–Champaign, with the dissertation Local, Semi-Local and Global Models for Texture, Object and Scene Recognition supervised by Jean Ponce. After postdoctoral research at the University of Illinois, she became an assistant professor at the University of North Carolina at Chapel Hill in 2007. She returned to the University of Illinois as a faculty member in 2012. She is a co-editor-in-chief of the International Journal of Computer Vision. == Recognition == Lazebnik was named an IEEE Fellow in 2021, "for contributions to computer vision". With Cordelia Schmid and Jean Ponce, she won the Longuet-Higgins Prize in 2016 for the best work in computer vision from ten years earlier, for their work on spatial pyramid matching.
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Multiple satellite imaging is the process of using multiple satellites to gather more information than a single satellite so that a better estimate of the desired source is possible. Something that cannot be resolved with one telescope might be visible with two or more telescopes. == Background == Interferometry is the process of combining waves in such a way that they constructively interfere. When two or more independent sources detect a signal at the same given frequency those signals can be combined and the result is better than each one individually. An overview of Astronomical interferometers and a History of astronomical interferometry can be referenced from their respective pages. The NASA Origins Program was created in the 1990s to ultimately search for the origin of the universe. The theory that the Origins Program is based on is: since light travels at a constant speed until it is absorbed by something; there is still light that was part of the first light ever created traveling about the universe and ultimately some of that light is coming in the general direction of Earth. So a satellite system capable of collecting light from the beginning of the universe would be able to tell us more about where we came from. There is also the constant search for life in other worlds. A satellite system using the interferometric technologies mentioned above would be able to have a much higher resolution than any of the current deep space imaging systems. == Future == NASA is currently focused on the Vision for Space Exploration and has reduced current funding for scientific unmanned space exploration in favor of human exploration. These budget cuts have slowed the multiple satellite imaging development and relevant scientific missions as Project Prometheus and Terrestrial Planet Finder have ended as well but research continues.
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Looking for the best AI photo editor? An AI photo editor is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI photo editor 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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Dissociated press is a parody generator (a computer program that generates nonsensical text). The generated text is based on another text using the Markov chain technique. The name is a play on "Associated Press" and the psychological term dissociation (although word salad is more typical of conditions like aphasia and schizophrenia – which is, however, frequently confused with dissociative identity disorder by laypeople). An implementation of the algorithm is available in Emacs. Another implementation is available as a Perl module in CPAN, Games::Dissociate. == The algorithm == The algorithm starts by printing a number of consecutive words (or letters) from the source text. Then it searches the source text for an occurrence of the few last words or letters printed out so far. If multiple occurrences are found, it picks a random one, and proceeds with printing the text following the chosen occurrence. After a predetermined length of text is printed out, the search procedure is repeated for the newly printed ending. Considering that words and phrases tend to appear in specific grammatical contexts, the resulting text usually seems correct grammatically, and if the source text is uniform in style, the result appears to be of similar style and subject, and takes some effort on the reader's side to recognize as not genuine. Still, the randomness of the assembly process deprives it of any logical flow - the loosely related parts are connected in a nonsensical way, creating a humorously abstract, random result. == Examples == Here is a short example of word-based Dissociated Press applied to the Jargon File: wart: n. A small, crocky feature that sticks out of an array (C has no checks for this). This is relatively benign and easy to spot if the phrase is bent so as to be not worth paying attention to the medium in question. Here is a short example of letter-based Dissociated Press applied to the same source: window sysIWYG: n. A bit was named aften /bee´t@/ prefer to use the other guy's re, especially in every cast a chuckle on neithout getting into useful informash speech makes removing a featuring a move or usage actual abstractionsidered interj. Indeed spectace logic or problem! == History == The dissociated press algorithm is described in HAKMEM (1972) Item #176. The name "dissociated press" is first known to have been associated with the Emacs implementation. Brian Hayes discussed a Travesty algorithm in Scientific American in November 1983. The article provided a garbled William Faulkner passage: When he got on the table, he come in. He never come out of my own pocket as a measure of protecting the company against riot and bloodshed. And when he said. "You tell me a bus ticket, let alone write out no case histories. Then the law come back with a knife!" Hugh Kenner and Joseph O'Rourke of Johns Hopkins University discussed their frequency table-based Travesty generator for microcomputers in BYTE in November 1984. The article included the Turbo Pascal source for two versions of the generator, one using Hayes' algorithm and another using Claude Shannon's Hellbat algorithm. Murray Lesser offered a compiled BASIC version in the magazine in July 1985, in September 1985 Peter Wayner offered a version that used tree data structures instead of frequency tables, and in December 1985 Neil J. Rubenking offered a version written in Turbo Pascal that stored frequency information in a B-tree.
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