In automata theory (a branch of theoretical computer science), NFA minimization is the task of transforming a given nondeterministic finite automaton (NFA) into an equivalent NFA that has a minimum number of states, transitions, or both. While efficient algorithms exist for DFA minimization, NFA minimization is PSPACE-complete. No efficient (polynomial time) algorithms are known, and under the standard assumption that P ≠ PSPACE, none exist. The most efficient known algorithm is the Kameda–Weiner algorithm. == Non-uniqueness of minimal NFA == Unlike deterministic finite automata, minimal NFAs may not be unique. There may be multiple NFAs with the same number of states that accept the same regular language, but for which there is no equivalent NFA or DFA with fewer states.
List of color palettes
The following is a list that contains color palettes for notable computer graphics, terminals and video game consoles. Only a simulated image using a palette and its name are given. Main articles are linked from the name of each palette, test charts, sample colours, simulated images, and further technical details (including references). During older eras of computing, manufacturers developed many different display systems often in a competitive, non-collaborative basis (with a few exceptions in the VESA consortium), creating many proprietary, non-standard different instances of display hardware. Often, as with early personal and home computers, a given machine employed its unique display subsystem, also with its unique color palette. Furthermore, software developers had made use of the color abilities of distinct display systems in many different ways. The result is that there is no single common standard nomenclature or classification taxonomy which can encompass every computer color palette. In order to organize the material, color palettes have been grouped following certain criteria. First, generic monochrome and full RGB repertories common to various computer display systems are listed. Then, usual color repertories used for display systems that employ indexed color techniques. And finally, specific manufacturers' color palettes implemented in many representative early personal computers and video game consoles of various brands. The list for personal computer palettes is split into two categories: 8-bit and 16-bit machines. This is not intended as a true strict categorization of such machines, because mixed architectures also exist (16-bit processors with an 8-bit data bus or 32-bit processors with a 16-bit data bus, among others). The distinction is based more on broad 8-bit and 16-bit computer ages or generations (around 1975–1985 and 1985–1995, respectively) and their associated state of the art in color display capabilities. The following is the common color test chart and sample image used to render each palette in this list: See further details in the summary paragraph of the corresponding article. == List of monochrome and RGB palettes == In this article, the term monochrome palette means a set of intensities for a monochrome display, and the term RGB palette is defined as the complete set of combinations a given RGB display can offer by mixing all the possible intensities of the red, green, and blue primaries available in its hardware. These are generic complete repertories of colors to produce black and white and RGB color pictures by the display hardware, not necessarily the total number of such colors that can be simultaneously displayed in a given text or graphic mode of any machine. RGB is the most common method to produce colors for displays; so these complete RGB color repertories have every possible combination of R-G-B triplets within any given maximum number of levels per component. For specific hardware and different methods to produce colors than RGB, see the List of computer hardware palettes and the List of video game consoles sections. For various software arrangements and sorts of colors, including other possible full RGB arrangements within 8-bit depth displays, see the List of software palettes section. === Monochrome palettes === These palettes only have shades of gray. === Dichrome palettes === Each permuted pair of red, green, and blue (16-bit color palette, with 65,536 colors). For example, "additive red green" has zero blue and "subtractive red green" has full blue. === Regular RGB palettes === These full RGB palettes employ the same number of bits to store the relative intensity for the red, green and blue components of every image's pixel color. Thus, they have the same number of levels per channel and the total number of possible colors is always the cube of a power of two. It should be understood that 'when developed' many of these formats were directly related to the size of some host computers 'natural word length' in bytes—the amount of memory in bits held by a single memory address such that the CPU can grab or put it in one operation. === Non-regular RGB palettes === These are also RGB palettes, in the sense defined above (except for 4-bit RGBI, which has an intensity bit that affects all channels at once), but either they do not have the same number of levels for each primary channel, or the numbers are not powers of two, so are not represented as separate bit fields. All of these have been used in popular personal computers. == List of software palettes == Systems that use a 4-bit or 8-bit pixel depth can display up to 16 or 256 colors simultaneously. Many personal computers in the later 1980s and early 1990s displayed at most 256 different colors, freely selected by software (either by the user or by a program) from their wider hardware's color palette. Usual selections of colors in limited subsets (generally 16 or 256) of the full palette includes some RGB level arrangements commonly used with the 8 bpp palettes as master palettes or universal palettes (i.e., palettes for multipurpose uses). These are some representative software palettes, but any selection can be made in such types of systems. === System specific palettes === These are selections of colors officially employed as system palettes in some popular operating systems for personal computers that feature 8-bit displays. === RGB arrangements === These are selections of colors based on evenly ordered RGB levels, mainly used as master palettes to display any kind of image within the limitations of the 8-bit pixel depth. === Other common uses of software palettes === == List of computer hardware palettes == In old personal computers and terminals that offered color displays, some color palettes were chosen algorithmically to provide the most diverse set of colors for a given palette size, and others were chosen to assure the availability of certain colors. In many early home computers, especially when the palette choices were determined at the hardware level by resistor combinations, the palette was determined by the manufacturer. Many early models output composite video colors. When seen on TV devices, the perception of the colors may not correspond with the value levels for the color values employed (most noticeable with NTSC TV color system). For current RGB display systems for PCs (Super VGA, etc.), see the 16-bit RGB and 24-bit RGB for High Color (thousands) and True Color (millions of colors) modes. For video game consoles, see the List of video game consoles section. For every model, their main different graphical color modes are listed based exclusively in the way they handle colors on screen, not all their different screen modes. The list is organized roughly historically by video hardware, not by branch. They are listed according to the original model of each system, which means that extended versions, clones, and compatibles also support the original palette. === Terminals and 8-bit machines === === 16-bit machines === === Video game console palettes === Color palettes of some of the most popular video game consoles. The criteria are the same as those of the List of computer hardware palettes section.
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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.
Jaime Carbonell
Jaime Guillermo Carbonell (July 29, 1953 – February 28, 2020) was a computer scientist who made seminal contributions to the development of natural language processing tools and technologies. His research in machine translation resulted in the development of several state-of-the-art language translation and artificial intelligence systems. He earned his B.S. degrees in Physics and in Mathematics from MIT in 1975 and did his Ph.D. under Dr. Roger Schank at Yale University in 1979. He joined Carnegie Mellon University as an assistant professor of computer science in 1979 and moved to Pittsburgh. He was affiliated with the Language Technologies Institute, Computer Science Department, Machine Learning Department, and Computational Biology Department at Carnegie Mellon. His interests spanned several areas of artificial intelligence, language technologies and machine learning. In particular, his research focused on areas such as text mining (extraction, categorization, novelty detection) and in new theoretical frameworks such as a unified utility-based theory bridging information retrieval, summarization, free-text question-answering and related tasks. He also worked on machine translation, both high-accuracy knowledge-based MT and machine learning for corpus-based MT (such as generalized example-based MT). == Career == Carbonell was the Allen Newell Professor of Computer Science and head of the Language Technologies Institute at Carnegie Mellon University. He joined Carnegie Mellon in 1979, and became a key faculty member in the artificial intelligence area. He was appointed full professor in 1987, Newell Chair in 1995, and University Professor in 2012. He completed his undergraduate studies at MIT. He received dual degrees in Mathematics and Physics. He received his Ph.D. in computer science from Yale University in 1979. At the time of his appointment, Carbonell was the youngest chaired professor in the School of Computer Science at CMU. His research spanned several areas of computer science, mostly in artificial intelligence, including: machine learning, data and text mining, natural language processing, very-large-scale knowledge bases, translingual information retrieval and automated summarization. He wrote more than 300 technical papers and gave over 500 invited or refereed-paper presentations (colloquia, seminars, panels, conferences, keynotes, etc.). He died following a long illness on February 28, 2020. Mona Talat Diab became the director of CMU's Language Technologies Institute in 2023. == Research == Carbonell created MMR (maximal marginal relevance) technology for text summarization and informational novelty detection in search engines, invention of transformational analogy, a generalized method for case-based reasoning (CBR) to re-use, modify and compose past successful plans for increasingly complex problems and knowledge-based interlingual machine translation. He was instrumental in setting up the Computational Biolinguistics Program, a joint venture between Carnegie Mellon and the University of Pittsburgh, which combines Language Technologies and Machine Learning to model and predict genomic, proteomic and glycomic 3D structures. Carbonell also did work in machine learning. He organized the first four machine learning conferences, starting with CMU in 1981. The Language Technologies Institute (LTI), founded and directed by Carbonell, achieved top honors in multiple areas. These areas include machine translation, search engines (including founding of Lycos by Michael Mauldin, one of Carbonell’s PhD students), speech synthesis, and education. LTI remains the original, largest and best-known institute for language technologies, with over $12M in annual funding and 200 researchers (faculty, staff, PhD students, MS students, visiting scholars etc.). Carbonell made major technical contributions in several fields, including (1) Creation of MMR (maximal marginal relevance) technology for text summarization and informational novelty detection in search engines,(2) Proactive machine learning for multi-source cost-sensitive active learning, (3) Linked conditional random fields for predicting tertiary and quaternary protein folds, (4) Symmetric optimal phrasal alignment method for trainable example-based and statistical machine translation, (5) Series- anomaly modeling for financial fraud detection and syndromic surveillance, (6) Knowledge-based interlingual machine translation, (7) Robust case-frame parsing, (8) Seeded version-space learning and (9) Invention of transformational and derivational analogy, generalized methods for case-based reasoning (CBR) to re-use, modify and compose past successful plans for increasingly complex problems. The teams led by Carbonell achieved top honors in many areas such as first scalable high-accuracy interlingual machine translation (1991), first speech-to-speech machine translation (1992), first large-scale spider and search engine (1994), and first trainable, large-scale protein-structure topology predictor (2005). Modern machine learning, co-founded by Carbonell, Michalski and Mitchell, is a fundamental enabling technology in search engines, data mining and social networking. Starting in 1980, he co-edited the first three books on ML, launched the ML conferences and was a co-founder and editor-in-chief of ML Journal. Carbonell’s innovations have led to several successful start-ups: Carnegie Group (AI expertsystems), Lycos (web search), Wisdom (financial optimization & ML), Carnegie Speech (spoken-language tutoring), Dynamix (data mining and pattern discovery), and Meaningful Machines (context-based machine translation). Carbonell was the founding director of The Language Technology Institute, the preeminent global institution in language studies, unparalleled in size and scope and has since been adopted/imitated in Germany (DFKI), Japan (Tokyo Univ.), and the US (Johns Hopkins). == Awards and honors == Okawa Prize, 2015 Best paper award, “Translingual Search” w/Yang, International Joint Conference on AI, 1997 Allen Newell endowed chair, Carnegie Mellon University, 1995 Elected fellow of AAAI, 1991 Computer Science teaching award, Carnegie Mellon University, 1987 Sperry Fellowship for excellence in AI research, 1986 Herbert Simon teaching award, 1986 "Recognition of Service" award from the ACM for the SIGART presidency, 1983–1985 Provided congressional testimony on machine translation, 1990 == Selected works == === Books === 1983. (with Ryszard S. Michalski & Tom M. Mitchell, Eds.) Machine learning: An artificial intelligence approach. Los Altos, CA: Morgan Kaufmann. 1986. (with Ryszard S. Michalski & Tom Mitchell, Eds.) Machine learning: An artificial intelligence approach. Vol. II. Los Altos, CA: Morgan-Kaufmann. 1986. (with Ryszard S. Michalski & Tom Mitchell, Eds.) Machine Learning: A Guide to Current Research. Kluwer Academic Publishers. == Contributions == “Protein Quaternary Fold Recognition Using Conditional Graphical Models” IJCAI 2007 (w/Liu et al.) “Context-Based Machine Translation” AMTA 2006 (w/Klein et al.) “SCRFs: A New Approach for Protein Fold Recognition,’’ Journal of Computational Biology, 13,2, 2006 (w/Liu et al) “MT for Resource-Poor Languages Using Elicitation-Based Learning” Machine Translation, 2004 ‘‘Learning Approaches for Detecting and Tracking News Events,’’ IEEE Trans I.S., 14, 4, 2000 (w/Yang)
Semantic analysis (machine learning)
In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Semantic analysis strategies include: Metalanguages based on first-order logic, which can analyze the speech of humans. Understanding the semantics of a text is symbol grounding: if language is grounded, it is equal to recognizing a machine-readable meaning. For the restricted domain of spatial analysis, a computer-based language understanding system was demonstrated. Latent semantic analysis (LSA), a class of techniques where documents are represented as vectors in a term space. A prominent example is probabilistic latent semantic analysis (PLSA). Latent Dirichlet allocation, which involves attributing document terms to topics. n-grams and hidden Markov models, which work by representing the term stream as a Markov chain, in which each term is derived from preceding terms. == Stochastic semantic analysis ==
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