The New Classification Scheme for Chinese Libraries is a system of library classification developed by Lai Yung-hsiang since 1956. It is modified from "A System of Book Classification for Chinese Libraries" of Liu Guojun, which is based on the Dewey Decimal System. The scheme is developed for Chinese books and commonly used in Taiwan, Hong Kong and Macau. == Main classes == 000 Generalities 100 Philosophy 200 Religion 300 Sciences 400 Applied sciences 500 Social sciences 600 History of China and Geography of China 700 World history and Geography 800 Linguistics and Literature 900 Arts == Outline of the classification tables == 000 Generalities 000 Special collections 010 Bibliography; Literacy (Documentation) 020 Library and information science; Archive management 030 Sinology 040 General encyclopedia 050 Serial publications; Periodicals 060 General organization; Museology 070 General collected essays 080 General series 090 Collected Chinese classics 100 Philosophy 100 Philosophy: general 110 Thought; Learning 120 Chinese philosophy 130 Oriental philosophy 140 Western philosophy 150 Logic 160 Metaphysics 170 Psychology 180 Esthetics (Aesthetics) 190 Ethics 200 Religion 200 Religion: general 210 Science of religion 220 Buddhism 230 Taoism 240 Christianity 250 Islam (Mohammedanism) 260 Judaism 270 Other religions 280 Mythology 290 Astrology; Superstition 300 Sciences 300 Sciences: general 310 Mathematics 320 Astronomy 330 Physics 340 Chemistry 350 Earth science; Geology 360 Biological science 370 Botany 380 Zoology 390 Anthropology 400 Applied sciences 400 Applied sciences: general 410 Medical sciences 420 Home economics 430 Agriculture 440 Engineering 450 Mining and metallurgy 460 Chemical engineering 470 Manufacture 480 Commerce: various business 490 Commerce: administration and management 500 Social sciences 500 Social sciences: general 510 Statistics 520 Education 530 Rite and custom 540 Sociology 550 Economy 560 Finance 570 Political science 580 Law; Jurisprudence 590 Military science 600-700 History and geography 600 History and geography: General History and geography of China 610 General history of China 620 Chinese history by period 630 History of Chinese civilization 640 Diplomatic history of China 650 Historical sources 660 Geography of China 670 Local history 680 Topical topography 690 Chinese travels World history and geography 710 World: general history and geography 720 Oceans and seas 730 Asia: history and geography 740 Europe: history and geography 750 America: history and geography 760 Africa: history and geography 770 Oceania: history and geography 780 Biography 790 Antiquities and archaeology 800 Linguistics and literature 800 Linguistics: general 810 Literature: general 820 Chinese literature 830 Chinese literature: general collections 840 Chinese literature: individual works 850 Various Chinese literature 860 Oriental literature 870 Western literature 880 Other countries literatures 890 Journalism 900 Arts 900 Arts: general 910 Music 920 Architecture 930 Sculpture 940 Drawing and painting; Calligraphy 950 Photography; Computer art 960 Decorative arts 970 Arts and Crafts movement 980 Theatre 990 Recreation and leisure
Sequence labeling
In machine learning, sequence labeling is a type of pattern recognition task that involves the algorithmic assignment of a categorical label to each member of a sequence of observed values. A common example of a sequence labeling task is part of speech tagging, which seeks to assign a part of speech to each word in an input sentence or document. Sequence labeling can be treated as a set of independent classification tasks, one per member of the sequence. However, accuracy is generally improved by making the optimal label for a given element dependent on the choices of nearby elements, using special algorithms to choose the globally best set of labels for the entire sequence at once. As an example of why finding the globally best label sequence might produce better results than labeling one item at a time, consider the part-of-speech tagging task just described. Frequently, many words are members of multiple parts of speech, and the correct label of such a word can often be deduced from the correct label of the word to the immediate left or right. For example, the word "sets" can be either a noun or verb. In a phrase like "he sets the books down", the word "he" is unambiguously a pronoun, and "the" unambiguously a determiner, and using either of these labels, "sets" can be deduced to be a verb, since nouns very rarely follow pronouns and are less likely to precede determiners than verbs are. But in other cases, only one of the adjacent words is similarly helpful. In "he sets and then knocks over the table", only the word "he" to the left is helpful (cf. "...picks up the sets and then knocks over..."). Conversely, in "... and also sets the table" only the word "the" to the right is helpful (cf. "... and also sets of books were ..."). An algorithm that proceeds from left to right, labeling one word at a time, can only use the tags of left-adjacent words and might fail in the second example above; vice versa for an algorithm that proceeds from right to left. Most sequence labeling algorithms are probabilistic in nature, relying on statistical inference to find the best sequence. The most common statistical models in use for sequence labeling make a Markov assumption, i.e. that the choice of label for a particular word is directly dependent only on the immediately adjacent labels; hence the set of labels forms a Markov chain. This leads naturally to the hidden Markov model (HMM), one of the most common statistical models used for sequence labeling. Other common models in use are the maximum entropy Markov model and conditional random field.
Question (short story)
"Question" is a science fiction short story by American writer Isaac Asimov. The story first appeared in the March 1955 issue of Computers and Automation (thought to be the first computer magazine), and was reprinted in the April 30, 1957, issue of Science World. It is the first of a loosely connected series of stories concerning a fictional supercomputer called Multivac. The story concerns two technicians who are servicing Multivac, and their argument over whether or not the machine is truly intelligent and able to think. Multivac, however, supplies the answer on its own. After the reprint, another author, Robert Sherman Townes, noticed the climax in the last sentence was very similar to one of his own stories, "Problem for Emmy" (Startling Stories, June 1952), and wrote to Asimov about it. After searching in his library, Asimov did find the original story and, although he did not recall having read it, admitted that the endings were pretty similar. He then replied to Townes, apologizing and promising the story would never again be published, and it never was. Asimov mentioned "Question" in an editorial called "Plagiarism" which appeared in the August 1985 issue of Asimov's Science Fiction (although he did not mention Townes' name or the title of either story). "Plagiarism" was reprinted in Asimov's collection Gold (1995).
Mario Klingemann
Mario Klingemann (born 1970 in Laatzen, Lower Saxony) is a German artist best known for his work involving neural networks, code, and algorithms. Klingemann was a Google Arts and Culture resident from 2016 to 2018, and he is considered as a pioneer in the use of computer learning in the arts. His works examine creativity, culture, and perception through machine learning and artificial intelligence, and have appeared at the Ars Electronica Festival, the Museum of Modern Art New York, the Metropolitan Museum of Art New York, the Photographers’ Gallery London, the Centre Pompidou Paris, and the British Library. Today he lives in Munich, where, in addition to his art under the name "Dog & Pony", he still runs a creative free space between gallery and Wunderkammer with the paper artist Alexandra Lukaschewitz. In 2018 his work The Butcher's Son won the Lumen Prize Gold Award 2018 by working with figurative visual input. Mario Klingemann is part of ONKAOS, the new media artist support programme of SOLO. In collaboration with ONKAOS he has created works such as Memories of Passerby I, the first work made with AI to be auctioned at Sotheby's in 2019. In 2020, Mario Klingemann won an Honorary Mention in the Prix Ars Electronica with his AI installation Appropriate Response. In 2023, Klingemann presented A.I.C.C.A., a performative sculpture in the form of a dog capable of elaborating art critiques thanks to AI programming.
DARPA Prize Competitions
Over the years, the U.S. Defense Advanced Research Projects Agency (DARPA) has conducted numerous prize competitions to spur innovation. A prize competition allows DARPA to establish an ambitious goal, opening the door to novel approaches from the public that might otherwise appear too risky for experts in a particular field to pursue. == Statutory authorities == In 1999, Congress provided prize competition authority to DARPA in the National Defense Authorization Act for Fiscal Year 2000 (P.L. 106–65), 10 U.S.C. § 4025, formerly 10 U.S.C. §2374a. DARPA also conducts prize competitions under the America COMPETES Act, 15 U.S.C. § 3719. == Recent prize competitions == DARPA Grand Challenge (2004 and 2005) was a prize competition to spur the development of autonomous vehicle technologies. The $1 million prize went unclaimed as no vehicles could complete the challenging desert route from Barstow, CA, to Primm, NV, on March 13, 2004. A year later, on October 8, 2005, the Stanford Racing Team won the $2 million prize during the second competition of the Grand Challenge in the desert Southwest near the California/Nevada state line. DARPA Urban Challenge (2007) required the competitors to build an autonomous vehicle capable of driving in traffic and performing complex maneuvers such as merging, passing, parking, and negotiating intersections. On November 3, 2007, the Carnegie Mellon Team won the $2 million prize, and its vehicle became the first autonomous vehicle that interacted with both manned and unmanned vehicle traffic in an urban environment. DARPA Network Challenge (Red Balloon Challenge) (2009) explored the roles that the Internet and social networking play in solving broad-scope, time-critical problems. On December 5, 2009, the Massachusetts Institute of Technology team won $40,000 by locating the ten moored, eight-foot, red weather balloons at ten places in the United States within seven hours. DARPA Digital Manufacturing Analysis, Correlation and Estimation Challenge (DMACE) (2010) was a three-month contest to showcase the potential of digital manufacturing of advanced materials. The University of California at Santa Barbara team won a $50,000 prize for crushing 180 digitally manufactured (DM) titanium mesh spheres with the most accurate predictive model of the components’ properties. DARPA Shredder Challenge (2011) was to identify and assess potential capabilities and vulnerabilities to sensitive information in the national security community. Participating teams must download the images of the documents shredded into more than 10,000 pieces from the Challenge website, reconstruct the documents, and solve the five puzzles. Of almost 9,000 teams, the San Francisco-based All Your Shreds Are Belong to U.S team won the $50,000 prize. DARPA UAVForge Challenge (2011-2012) aimed to build and test a user-intuitive, backpack-portable unmanned aerial vehicle (UAV) that could quietly fly in and out of critical environments to conduct sustained surveillance for up to three hours. The $100,000 prize was not claimed because none of the 140 teams met the technical matrix. DARPA Cash for Locating & Identifying Quick Response Codes (CLIQR) Quest Challenge (2012) explored the role the Internet and social media played in the timely communication, wide-area team-building, and urgent mobilization required to solve broad scope, time-critical problems. The challenge offered $40,000 to the first individual or team that could locate seven posters appearing in U.S. cities bearing the DARPA logo and a quick response code (QR) within 15 days. No team found and submitted all seven codes. DARPA Fast Adaptable Next-Generation Ground Vehicle (FANG) Challenge (2012-2013) was to use three competitions for the design of an infantry fighting vehicle, culminating in prototypes. In April 2013, DARPA awarded US$1 million to a three-man team during the first competition. DARPA decided not to proceed with the second and third competitions as originally planned and transitioned the technologies to the defense and commercial industry through the Digital Manufacturing and Design Innovation Institute (DMDII). DARPA Spectrum Challenge (2013-2014) sought to demonstrate how a software-defined radio can use a given communication channel in the presence of other users and interfering signals. Three teams emerged as the overall winners, winning a total of $150,000 in prizes. DARPA Chikungunya (CHIKV) Challenge (2014-2015) was a health-related effort to develop the most accurate predictions of CHIKV cases for all Western Hemisphere countries and territories between September 2014 and March 2015. On May 12, 2015, DARPA awarded $500,000 in prizes to the 11 winners of the competition during a scientific review DARPA Robotics Challenge (DRC) (2013-2015) aimed to develop semi-autonomous ground robots that could do "complex tasks in dangerous, degraded, human-engineered environments." A South Korean team won the first prize of $2 million, and two U.S. teams won $1 million and $500,000 as second and third winners. DARPA Cyber Grand Challenge (CGC) (2014 - 2016) was to “create automatic defensive systems capable of reasoning about flaws, formulating patches and deploying them on a network in real time.” The top three winners were awarded prizes of $2 million, $1 million, and $750,000, respectively. DARPA Spectrum Collaboration Challenge (SC2) (2016-2019) aimed to encourage the development of AI-enabled wireless networks to “ensure that the exponentially growing number of military and civilian wireless devices would have full access to the increasingly crowded electromagnetic spectrum.” A team from the University of Florida won the overall top prize of US$2 million at the final SC2 competition. DARPA Subterranean (SubT) Challenge (2017-2021) was to develop robotic technologies to map, navigate, search and exploit complex underground environments. The first-place winners of the system final competition and of the virtual final competition were awarded $2 million and $750,000, respectively, with multiple prizes awarded to the second and third-place winners. DARPA Launch Challenge (2018-2020) was a $12 million satellite launch challenge to demonstrate responsive and flexible space launch capabilities from the small launch providers and was to culminate in two separate launch competitions where the competitors must launch a satellite to low Earth orbit (LEO) within days of each other at different locations in the United States. The competition ended without a winner. DARPA Forecasting Floats in Turbulence (FFT) Challenge (2021) was to spur technologies that could predict the location of sea drifters or floats within 10 days. DARPA awarded $25,000 for first place, with prizes of $15,000 and $10,000 for second place and third place. DARPA Artificial Intelligence Cyber Challenge (AIxCC) (2023–2025) was a two-year challenge and asks competitors to design novel AI systems to secure critical software code on which Americans rely. The total prize money is $29.5 million. In March 2024, the Advanced Research Projects Agency for Health (ARPA-H) partnered with DARPA, contributing an additional $20 million to the competition's prize pool to address software vulnerabilities in medical devices, hospital IT, and biotech equipment. AIxCC collaborates with Google, Microsoft, OpenAI, Anthropic, Linux Foundation, Open Source Security Foundation, Black Hat USA, and DEF CON, all of which provide AIxCC with access to large language models. In August 2024, AIxCC held the semifinal at DEF CON in Las Vegas. DARPA and ARPA-H tested all 42 submissions by running them through various open-source coding projects with deliberately injected vulnerabilities and scored the tools based on their effectiveness in identifying and fixing security flaws. Seven teams, each winning $2 million in the semifinals, competed in the final round of the AIxCC at the August 2025 DEF CON conference. Team Atlanta won first place with a $4 million prize for its cyber reasoning systems, which identified and patched vulnerabilities across 54 million lines of code. DARPA Triage Challenge (2023 – 2026) aims to spur the development of novel physiological features for medical triage, with a total prize money of $7 million. In October 2024, Challenge Event 1 was held in Perry, Georgia, featuring to-scale replicas of disaster sites such as an airplane crash and Hurricane Katrina, and teams competed based on how closely their data aligned with the agency’s official data and how quickly and accurately their autonomous systems could identify individuals most urgently in need of medical care. DARPA concluded the second year of competitions and, in November 2025, named the top performers in systems and data categories, which will advance to the final 2026 competition. The DARPA Lift Challenge (2025-2026) is for participants to design unmanned aerial systems capable of carrying up to four times their own weight, with a minimum payload of 110 pounds. Acco
Scene statistics
Scene statistics is a discipline within the field of perception. It is concerned with the statistical regularities related to scenes. It is based on the premise that a perceptual system is designed to interpret scenes. Biological perceptual systems have evolved in response to physical properties of natural environments. Therefore natural scenes receive a great deal of attention. Natural scene statistics are useful for defining the behavior of an ideal observer in a natural task, typically by incorporating signal detection theory, information theory or estimation theory. == Within-domain versus across-domain == Geisler (2008) distinguishes between four kinds of domains: (1) Physical environments (2) Images/Scenes (3) Neural responses and (4) Behavior. Within the domain of images/scenes one can study the characteristics of information related to redundancy and efficient coding. Across-domain statistics determine how an autonomous system should make inferences about its environment, process information and control its behavior. To study these statistics it is necessary to sample or register information in multiple domains simultaneously. == Applications == === Prediction of picture and video quality === One of the most successful applications of Natural Scenes Statistics Models has been perceptual picture and video quality prediction. For example, the Visual Information Fidelity (VIF) algorithm, which is used to measure the degree of distortion of pictures and videos, is used extensively by the image and video processing communities to assess perceptual quality. This is often after processing, such as compression, which can degrade the appearance of a visual signal. The premise is that the scene statistics are changed by distortion and that the visual system is sensitive to the changes in the scene statistics. VIF is heavily used in the streaming television industry. Other popular picture quality models that use natural scene statistics include BRISQUE and NIQE, both of which are no-reference since they do not require any reference picture to measure quality against.
Structure mapping engine
In artificial intelligence and cognitive science, the structure mapping engine (SME) is an implementation in software of an algorithm for analogical matching based on the psychological theory of Dedre Gentner. The basis of Gentner's structure-mapping idea is that an analogy is a mapping of knowledge from one domain (the base) into another (the target). The structure-mapping engine is a computer simulation of the analogy and similarity comparisons. The theory is useful because it ignores surface features and finds matches between potentially very different things if they have the same representational structure. For example, SME could determine that a pen is like a sponge because both are involved in dispensing liquid, even though they do this very differently. == Structure mapping theory == Structure mapping theory is based on the systematicity principle, which states that connected knowledge is preferred over independent facts. Therefore, the structure mapping engine should ignore isolated source-target mappings unless they are part of a bigger structure. The SME, the theory goes, should map objects that are related to knowledge that has already been mapped. The theory also requires that mappings be done one-to-one, which means that no part of the source description can map to more than one item in the target and no part of the target description can be mapped to more than one part of the source. The theory also requires that if a match maps subject to target, the arguments of subject and target must also be mapped. If both these conditions are met, the mapping is said to be "structurally consistent." == Concepts in SME == SME maps knowledge from a source into a target. SME calls each description a dgroup. Dgroups contain a list of entities and predicates. Entities represent the objects or concepts in a description — such as an input gear or a switch. Predicates are one of three types and are a general way to express knowledge for SME. Relation predicates contain multiple arguments, which can be other predicates or entities. An example relation is: (transmit (what from to)). This relation has a functor transmit and takes three arguments: what, from, and to. Attribute predicates are the properties of an entity. An example of an attribute is (red gear) which means that gear has the attribute red. Function predicates map an entity into another entity or constant. An example of a function is (joules power source) which maps the entity power source onto the numerical quantity joules. Functions and attributes have different meanings, and consequently SME processes them differently. For example, in SME's true analogy rule set, attributes differ from functions because they cannot match unless there is a higher-order match between them. The difference between attributes and functions will be explained further in this section's examples. All predicates have four parameters. They have (1) a functor, which identifies it, and (2) a type, which is either relation, attribute, or function. The other two parameters (3 and 4) are for determining how to process the arguments in the SME algorithm. If the arguments have to be matched in order, commutative is false. If the predicate can take any number of arguments, N-ary is false. An example of a predicate definition is: (sme:defPredicate behavior-set (predicate) relation :n-ary? t :commutative? t) The predicate's functor is “behavior-set,” its type is “relation,” and its n-ary and commutative parameters are both set to true. The “(predicate)” part of the definition specifies that there will be one or more predicates inside an instantiation of behavior-set. == Algorithm details == The algorithm has several steps. The first step of the algorithm is to create a set of match hypotheses between source and target dgroups. A match hypothesis represents a possible mapping between any part of the source and the target. This mapping is controlled by a set of match rules. By changing the match rules, one can change the type of reasoning SME does. For example, one set of match rules may perform a kind of analogy called literal similarity, and another performs a kind of analogy called true-analogy. These rules are not the place where domain-dependent information is added, but rather where the analogy process is tweaked, depending on the type of cognitive function the user is trying to emulate. For a given match rule, there are two types of rules that further define how it will be applied: filter rules and intern rules. Intern rules use only the arguments of the expressions in the match hypotheses that the filter rules identify. This limitation makes the processing more efficient by constraining the number of match hypotheses that are generated. At the same time, it also helps to build the structural consistencies that are needed later on in the algorithm. An example of a filter rule from the true-analogy rule set creates match hypotheses between predicates that have the same functor. The true-analogy rule set has an intern rule that iterates over the arguments of any match hypothesis, creating more match hypotheses if the arguments are entities or functions, or if the arguments are attributes and have the same functor. In order to illustrate how the match rules produce match hypotheses consider these two predicates: transmit torque inputgear secondgear (p1) transmit signal switch div10 (p2) Here we use true analogy for the type of reasoning. The filter match rule generates a match between p1 and p2 because they share the same functor, transmit. The intern rules then produce three more match hypotheses: torque to signal, inputgear to switch, and secondgear to div10. The intern rules created these match hypotheses because all the arguments were entities. If the arguments were functions or attributes instead of entities, the predicates would be expressed as: transmit torque (inputgear gear) (secondgear gear) (p3) transmit signal (switch circuit) (div10 circuit) (p4) These additional predicates make inputgear, secondgear, switch, and div10 functions or attributes depending on the value defined in the language input file. The representation also contains additional entities for gear and circuit. Depending on what type inputgear, secondgear, switch, and div10 are, their meanings change. As attributes, each one is a property of the gear or circuit. For example, the gear has two attributes, inputgear and secondgear. The circuit has two attributes, switch and circuit. As functions inputgear, secondgear, switch, and div10 become quantities of the gear and circuit. In this example, the functions inputgear and secondgear now map to the numerical quantities “torque from inputgear” and “torque from secondgear,” For the circuit the quantities map to logical quantity “switch engaged” and the numerical quantity “current count on the divide by 10 counter.” SME processes these differently. It does not allow attributes to match unless they are part of a higher-order relation, but it does allow functions to match, even if they are not part of such a relation. It allows functions to match because they indirectly refer to entities and thus should be treated like relations that involve no entities. However, as next section shows, the intern rules assign lower weights to matches between functions than to matches between relations. The reason SME does not match attributes is because it is trying to create connected knowledge based on relationships and thus satisfy the systematicity principle. For example, if both a clock and a car have inputgear attributes, SME will not mark them as similar. If it did, it would be making a match between the clock and car based on their appearance — not on the relationships between them. When the additional predicates in p3 and p4 are functions, the results from matching p3 and p4 are similar to the results from p1 and p2 except there is an additional match between gear and circuit and the values for the match hypotheses between (inputgear gear) and (switch circuit), and (secondgear gear) and (div10 circuit), are lower. The next section describes the reason for this in more detail. If the inputgear, secondgear, switch, and div10 are attributes instead of entities, SME does not find matches between any of the attributes. It finds matches only between the transmit predicates and between torque and signal. Additionally, the structural-evaluation scores for the remaining two matches decrease. In order to get the two predicates to match, p3 would need to be replaced by p5, which is demonstrated below. transmit torque (inputgear gear) (div10 gear) (p5) Since the true-analogy rule set identifies that the div10 attributes are the same between p5 and p4 and because the div10 attributes are both part of the higher-relation match between torque and signal, SME makes a match between (div10 gear) and (div10 circuit) — which leads to a match between gear and circuit. Being part of a higher-order match is a requiremen