Physicalization of computer hardware (the opposite of virtualization), is a way to place multiple physical machines in a rack unit. It can be a way to reduce hardware costs, since in some cases, server processors cost more per core than energy efficient laptop processors, which may make up for added cost of board level integration. While Moore's law makes increasing integration less expensive, some jobs require much I/O bandwidth, which may be less expensive to provide using many less-integrated processors. Applications and services that are I/O bound are likely to benefit from such physicalized environments. This ensures that each operating system instance is running on a processor that has its own network interface card, host bus and I/O sub-system unlike in the case of a multi-core servers where a single I/O sub-system is shared between all the cores / VMs.
Symbaloo
Symbaloo is a cloud-based site that allows users to organize and categorize web links in the form of buttons. Symbaloo works from a web browser and can be configured as a homepage, allowing users to create a personalized virtual desktop accessible from any device with an Internet connection. Symbaloo users, which must be previously registered, have a page with a grid of buttons that can be configured to link to a specific page. The site allows users to assign different colors to the buttons for easy visual classification. Symbaloo allows a single user to create different pages or screens with buttons. These screens called webmix are useful to separate topics and links can be shared with other users, making them public and sending the link via email. As of 2015 Symbaloo has 6 million users worldwide and mainly used as an online education resource. Symbaloo's slogan is "Start Simple".
Constrained conditional model
A constrained conditional model (CCM) is a machine learning and inference framework that augments the learning of conditional (probabilistic or discriminative) models with declarative constraints. The constraint can be used as a way to incorporate expressive prior knowledge into the model and bias the assignments made by the learned model to satisfy these constraints. The framework can be used to support decisions in an expressive output space while maintaining modularity and tractability of training and inference. Models of this kind have recently attracted much attention within the natural language processing (NLP) community. Formulating problems as constrained optimization problems over the output of learned models has several advantages. It allows one to focus on the modeling of problems by providing the opportunity to incorporate domain-specific knowledge as global constraints using a first order language. Using this declarative framework frees the developer from low level feature engineering while capturing the problem's domain-specific properties and guarantying exact inference. From a machine learning perspective it allows decoupling the stage of model generation (learning) from that of the constrained inference stage, thus helping to simplify the learning stage while improving the quality of the solutions. For example, in the case of generating compressed sentences, rather than simply relying on a language model to retain the most commonly used n-grams in the sentence, constraints can be used to ensure that if a modifier is kept in the compressed sentence, its subject will also be kept. == Motivation == Making decisions in many domains (such as natural language processing and computer vision problems) often involves assigning values to sets of interdependent variables where the expressive dependency structure can influence, or even dictate, what assignments are possible. These settings are applicable not only to Structured Learning problems such as semantic role labeling, but also for cases that require making use of multiple pre-learned components, such as summarization, textual entailment and question answering. In all these cases, it is natural to formulate the decision problem as a constrained optimization problem, with an objective function that is composed of learned models, subject to domain- or problem-specific constraints. Constrained conditional models form a learning and inference framework that augments the learning of conditional (probabilistic or discriminative) models with declarative constraints (written, for example, using a first-order representation) as a way to support decisions in an expressive output space while maintaining modularity and tractability of training and inference. These constraints can express either hard restrictions, completely prohibiting some assignments, or soft restrictions, penalizing unlikely assignments. In most applications of this framework in NLP, following, Integer Linear Programming (ILP) was used as the inference framework, although other algorithms can be used for that purpose. == Formal Definition == Given a set of feature functions { ϕ i ( x , y ) } {\displaystyle \{\phi _{i}(x,y)\}} and a set of constraints { C i ( x , y ) } {\displaystyle \{C_{i}(x,y)\}} , defined over an input structure x ∈ X {\displaystyle x\in X} and an output structure y ∈ Y {\displaystyle y\in Y} , a constraint conditional model is characterized by two weight vectors, w and ρ {\displaystyle \rho } , and is defined as the solution to the following optimization problem: a r g m a x y ∑ i w i ϕ i ( x , y ) − ∑ ρ i C i ( x , y ) {\displaystyle argmax_{y}\sum _{i}w_{i}\phi _{i}(x,y)-\sum \rho _{i}C_{i}(x,y)} . Each constraint C i ∈ C {\displaystyle C_{i}\in C} is a boolean mapping indicating if the joint assignment ( x , y ) {\displaystyle (x,y)} violates a constraint, and ρ {\displaystyle \rho } is the penalty incurred for violating the constraints. Constraints assigned an infinite penalty are known as hard constraints, and represent unfeasible assignments to the optimization problem. == Training paradigms == === Learning local vs. global models === The objective function used by CCMs can be decomposed and learned in several ways, ranging from a complete joint training of the model along with the constraints to completely decoupling the learning and the inference stage. In the latter case, several local models are learned independently and the dependency between these models is considered only at decision time via a global decision process. The advantages of each approach are discussed in which studies the two training paradigms: (1) local models: L+I (learning + inference) and (2) global model: IBT (Inference based training), and shows both theoretically and experimentally that while IBT (joint training) is best in the limit, under some conditions (basically, ”good” components) L+I can generalize better. The ability of CCM to combine local models is especially beneficial in cases where joint learning is computationally intractable or when training data are not available for joint learning. This flexibility distinguishes CCM from the other learning frameworks that also combine statistical information with declarative constraints, such as Markov logic network, that emphasize joint training. === Minimally supervised CCM === CCM can help reduce supervision by using domain knowledge (expressed as constraints) to drive learning. These settings were studied in and. These works introduce semi-supervised Constraints Driven Learning (CODL) and show that by incorporating domain knowledge the performance of the learned model improves significantly. === Learning over latent representations === CCMs have also been applied to latent learning frameworks, where the learning problem is defined over a latent representation layer. Since the notion of a correct representation is inherently ill-defined, no gold-standard labeled data regarding the representation decision is available to the learner. Identifying the correct (or optimal) learning representation is viewed as a structured prediction process and therefore modeled as a CCM. This problem was covered in several papers, in both supervised and unsupervised settings. In all cases research showed that explicitly modeling the interdependencies between representation decisions via constraints results in an improved performance. == Integer linear programming for natural language processing applications == The advantages of the CCM declarative formulation and the availability of off-the-shelf solvers have led to a large variety of natural language processing tasks being formulated within the framework, including semantic role labeling, syntactic parsing, coreference resolution, summarization, transliteration, natural language generation and joint information extraction. Most of these works use an integer linear programming (ILP) solver to solve the decision problem. Although theoretically solving an Integer Linear Program is exponential in the size of the decision problem, in practice using state-of-the-art solvers and approximate inference techniques large scale problems can be solved efficiently. The key advantage of using an ILP solver for solving the optimization problem defined by a constrained conditional model is the declarative formulation used as input for the ILP solver, consisting of a linear objective function and a set of linear constraints. == Resources == CCM Tutorial Predicting Structures in NLP: Constrained Conditional Models and Integer Linear Programming in NLP
Katia Sycara
Ekaterini Panagiotou Sycara (Greek: Κάτια Συκαρά) is a Greek computer scientist. She is an Edward Fredkin Research Professor of Robotics in the Robotics Institute, School of Computer Science at Carnegie Mellon University internationally known for her research in artificial intelligence, particularly in the fields of negotiation, autonomous agents and multi-agent systems. She directs the Advanced Agent-Robotics Technology Lab at Robotics Institute, Carnegie Mellon University. She also serves as academic advisor for PhD students at both Robotics Institute and Tepper School of Business. == Education and early life == Born in Greece, she went to the United States to pursue advanced education through various scholarships, including a Fulbright (1965-1969). She received a B.S. in applied mathematics from Brown University, M.S. in electrical engineering from the University of Wisconsin–Milwaukee, and PhD in computer science from Georgia Institute of Technology. == Research and career == Sycara is a pioneer in the field of semantic web, case-based reasoning, autonomous agents and multi-agent systems. She has authored or co-authored more than 700 technical papers dealing with multi-agent systems, software agents, web services, semantic web, human–computer interaction, human-robot interaction, negotiation, case-based reasoning and the application of these techniques to crisis action planning, scheduling, manufacturing, healthcare management, financial planning and e-commerce.[1] She has led multimillion-dollar research effort funded by DARPA, NASA, AFOSR, ONR, AFRL, NSF and industry. Through an ONR MURI program and though the COABS DARPA program, Prof. Sycara's group has developed the RETSINA multiagent infrastructure, a toolkit that enables the development of heterogeneous software agents that can dynamically coordinate in open information environments (e.g. the Internet). RETSINA has been used in multiple applications including supporting human joint mission teams for crisis response; creating autonomous agents for situation awareness and information fusion; financial portfolio management, negotiations and coalition formation for e-commerce, and coordinating robots for Urban Search and Rescue. Sycara is one of the contributors to the development of OWL-S, the Darpa-sponsored language for Semantic Web services, as well as matchmaking and brokering software for agent discovery, service integration and semantic interoperation. === Academic service === Sycara is the founding Editor-in-Chief of the journal Autonomous Agents and Multi-Agent Systems; Editor-in-Chief, of the Springer Series on Agents; and Area Editor of AI and Management Science, the journal "Group Decision and Negotiation." She is a member of the Editorial Board, the Kluwer book series on "Multiagent Systems, Artificial Societies and Simulated Organizations"; member of the editorial board, the journals "Agent Oriented Software Engineering", "Web Intelligence and Agent Technologies", "Journal of Infonomics", "Fundamenda Informaticae", and "Concurrent Engineering: Research and Applications"; and member of the editorial board of the "ETAI journal on the Semantic Web" (1998–2001). She was on the Editorial Board of "IEEE Intelligent Systems and their Applications" (1992–1996), and "AI in Engineering" (1990–1996). She is a member of the Scientific Advisory Board of France Telecom, 2003-2009; member of the Scientific Advisory Board of the Institute of Informatics and Telecommunications of the Greek National Research Center Demokritos, 2004-2012; member of the AAAI Executive Council (1996–99); member of the OASIS Technical committee on the development of UDDI (Universal Description and Discovery for Interoperability) software which is an industry standard; and an invited expert for W3C (the World Wide Web Consortium) Working Group on Web Services Architecture. She was a founding member of the Board of Directors of the International Foundation of Multiagent Systems (IFMAS), and founding member of the Semantic Web Science Association. Sycara served as the program chair of the Second International Semantic Web Conference (ISWC 2003); general chair, of the Second International Conference on Autonomous Agents (Agents 98); chair of the Steering Committee of the Agents Conference (1999–2001); scholarship chair of AAAI (1993–1999); and the US co-chair for the US-Europe Semantic Web Services Initiative. === Awards and honors === Sycara is a Fellow of Institute of Electrical and Electronics Engineers (IEEE), and a Fellow of American Association for Artificial Intelligence (AAAI). Sycara is the recipient of the 2002 ACM/SIGART Agents Research Award. She is also the recipient of the 2015 Group Decision and Negotiation (GDN) Award of the Institute for Operations Research and the Management Sciences (INFORMS) GDN Section for her outstanding contributions to the field of group decision and negotiation. According to the citation of the award: Katia Sycara is widely acknowledged as one of the leading researchers in the field of autonomous software agents and in particular on problems related to joint decision making and negotiations of such agents. Her work is characterized by a unique combination of methods from Artificial Intelligence and research on human negotiations, and thus has contributed to significant advances in both fields. Sycara's robot teams have won multiple international awards. In the 2005 Robocup Urban Search and Rescue (US Open) held in Atlanta, her team won the First-in-Class Award for Autonomy, and the First-in-Class Award for Mobility. Two years later, again in Atlanta, she led another team that became a world champions in the 2007 International Robocup Search and Rescue Simulation League Competition. In 2008, her robotic team placed third in the Worldwide Robocup Championship Competition in the Urban Search and Rescue Virtual robots League held in Beijing, China. In 2005, she received the Outstanding Alumnus Award from the University of Wisconsin–Milwaukee. She was awarded an Honorary Doctorate from the University of the Aegean in 2004.
Scott Fahlman
Scott Elliott Fahlman (born March 21, 1948) is an American computer scientist and Professor Emeritus at Carnegie Mellon University's Language Technologies Institute and Computer Science Department. He is notable for early work on automated planning and scheduling in a blocks world, on semantic networks, on neural networks (especially the cascade correlation algorithm), on the programming languages Dylan, and Common Lisp (especially CMU Common Lisp), and he was one of the founders of Lucid Inc. During the period when it was standardized, he was recognized as "the leader of Common Lisp." From 2006 to 2015, Fahlman was engaged in developing a knowledge base named Scone, based in part on his thesis work on the NETL Semantic Network. He also is credited with coining the use of the emoticon. == Life and career == Fahlman was born in Medina, Ohio, the son of Lorna May (Dean) and John Emil Fahlman. He attended the Massachusetts Institute of Technology (MIT), where he received a Bachelor of Science (B.S.) and Master of Science (M.S.) degree in electrical engineering and computer science in 1973, and a Doctor of Philosophy (Ph.D.) in artificial intelligence in 1977. He has noted that his doctoral diploma says the degree was awarded for "original research as demonstrated by a thesis in the field of Artificial Intelligence" and suggested that it may be the first doctorate to use that term. He is a fellow of the American Association for Artificial Intelligence. Fahlman acted as thesis advisor for Donald Cohen, David B. McDonald, David S. Touretzky, Skef Wholey, Justin Boyan, Michael Witbrock, and Alicia Tribble Sagae. From May 1996 to July 2001, Fahlman directed the Justsystem Pittsburgh Research Center. === Boltzmann Machine (1983) === In 1983, Fahlman, Geoffrey Hinton, and Terry Sejnowski published a paper in Proceedings of the AAAI-83 Conference, Washington DC, August 1983. The paper was titled as "Massively Parallel Architectures for AI: NETL, Thistle and Boltzmann Machines". === Emoticons === Fahlman was not the first to suggest the concept of the emoticon – a similar concept for a marker appeared in an article of Reader's Digest in May 1967, although that idea was never put into practice. In an interview printed in The New York Times in 1969, Vladimir Nabokov noted: "I often think there should exist a special typographical sign for a smile – some sort of concave mark, a supine round bracket." Fahlman is credited with originating the first smiley emoticon, which he thought would help people on a message board at Carnegie Mellon to distinguish serious posts from jokes. He proposed the use of :-) and :-( for this purpose, and the symbols caught on. The original message from which these symbols originated was posted on 19 September 1982. The message was recovered by Jeff Baird on 10 September 2002 and read: 19-Sep-82 11:44 Scott E Fahlman :-) From: Scott E Fahlman
Hard sigmoid
In artificial intelligence, especially computer vision and artificial neural networks, a hard sigmoid is non-smooth function used in place of a sigmoid function. These retain the basic shape of a sigmoid, rising from 0 to 1, but using simpler functions, especially piecewise linear functions or piecewise constant functions. These are preferred where speed of computation is more important than precision. == Examples == The most extreme examples are the sign function or Heaviside step function, which go from −1 to 1 or 0 to 1 (which to use depends on normalization) at 0. Other examples include the Theano library, which provides two approximations: ultra_fast_sigmoid, which is a multi-part piecewise approximation and hard_sigmoid, which is a 3-part piecewise linear approximation (output 0, line with slope 0.2, output 1).
Li Sheng (computer scientist)
Li Sheng (Chinese: 李生; born 1943), is a professor at the School of Computer Science and Engineering, Harbin Institute of Technology (HIT), China. He began his research on Chinese-English machine translation in 1985, making himself one of the earliest Chinese scholars in this field. After that, he pursued in vast topics of natural language processing, including machine translation, information retrieval, question answering and applied artificial intelligence. He was the final review committee member for computer area in NSF China. Born and raised in Heilongjiang province, he graduated in 1965 from the computer specialty of HIT, which is one of the earliest computer specialties in Chinese universities. Then he started to work as a staff in the Computer specialty of HIT, which was finally granted as a department in 1985. Also from 1985, he was appointed to undertake a series administrative positions in HIT, e.g. Dean of Computer Department(1987–1988), Director of R&D Division (1988–1990), Chief R&D Officer and several other key leading positions in HIT. Resigned all his administrative positions in 2004, Li devoted himself as the director of MOE-Microsoft Join Key Lab of NLP& Speech (HIT), making it a leading NLP research group with more than 100 staffs and students working on various aspects of NLP. So far, the lab has already been granted for dozens of technology awards by the ministries of central government and local provincial government of China. Its research progresses are reported annually in top tier conferences including ACL, IJCAI, SIGIR etc. As one of the pioneers in NLP research in China, he contributes NLP in China not only in technology innovations but also in talents education. So far, his research group has graduated more than 60 Ph.D. and almost 200 M.E with NLP major. Most of them are now working as the chief researcher in various NLP groups of universities and companies in China, including several world-known NLP scholars, such as Wang Haifeng of Baidu, Zhou Ming of Microsoft Research, Zhang Min (张民) of Soochow University (China), and Zhao Tiejun (赵铁军) and Liu Ting (刘挺) of HIT. Owing to his contributions in Chinese language processing, Li was elected as the President of Chinese Information Processing Society of China (CIPSC) in 2011. He scaled this top level academic organization in China up to more than 3000 registered members, and promoted NLP into several national projects for research or industry development. In addition, the CIPSC is now enhancing its co-operations with world NLP organizations including ACL. == Machine Intelligence & Translation Laboratory (MI&TLAB) == Originates from Machine Translation Research Group of Computer Science Department, Harbin Institute of Technology, which was started Li in 1985. It is one of the earliest institutions engaged in MT research in China, featured by its investigations into Chinese-English machine translation. It is now running under the Research Center on Language Technology, School of Computer Science and Technology, HIT. Details for staffs and publications can be found at https://mitlab.hit.edu.cn. == MOE-MS Joint Key Lab of Natural Language Processing and Speech (HIT) == In June, 2000, the Joint HIT-Microsoft Machine Translation Lab was founded by MI&T Lab and Microsoft Research (China). It was the third joint lab established by Microsoft Research (China) with Chinese universities, and the only one focusing on Machine Translation. Based on this jointly lab, the cooperation between HIT and Microsoft gradually extended to the areas of machine translation, information retrieval, speech recognition and processing, natural language understanding. In Oct, 2004, the joint key lab was granted as one of the 10 joint key labs supported by the Microsoft Research of Asia and Ministry of Education in China. In July 2006, the Shenzhen extension of the lab was launched. More than 200 staff and students have undertaken research projects, including some sponsored by the National Natural Science Foundation of China and the National 863 program of China. Since 2005, the lab has also been organizing a summer camp in Harbin Institute of Technology, and approximately 150 faculty members and students from universities in China have participated. This summer workshop was organized annually until 2014, when it was organized formally as the summer school series by Chinese Information Processing Society, China. Through the lab, a Microsoft Research of Asia-HIT joint PhD program was implemented in 2012. == CEMT-I MT System == In May 1989, CEMT-I passed the formal project appraisal in Harbin, China. Capable of translating technical paper titles from Chinese to English, it is not only the first MT system completed by Li and his group, but also the first Chinese-English Translation system that passed the technical appraisal by Chinese government according to the public reports. It was then awarded the Second Prize of Ministry Level Technology Innovation by the former National Aerospace Industry Corporation in 1990. == Daya Translation Workstation == Owing to the technical achievements by Li's group in Chinese-English machine translation, the former National Aerospace Industry Corporation of China sponsored a commercial system development of "Daya Translation Station (MT)" in 1993. Designed as a comprehensive English composition aid for Chinese users, this system was finished and put into the market in 1995. And in 1997, this system was awarded the Second Prize of Ministry Level Technology Innovation by the former National Aerospace Industry Corporation. == BT863 MT System == From 1994, the researches in Li's lab were supported by National 863 Hi-tech Research and Development Program. During this period, the BT863 system was explored to employ one engine for both Chinese-English and English-Chinese translation. This system was proved to be the best performance among Chinese-English MT systems in the formal technical evaluation of National 863 program, yielding the Third Prize of Ministry Level Technology Innovation by the former National Aerospace Industry Corporation in 1997. == Next Generation IR == This is a key project granted by NSF China (with a joint sponsorship from MSRA) started form 2008. In contrast to his previous NSF grants for different NLP issues, Li explored in his last PI project on key technologies in personalized IR, together with researchers from Tsinghua University and Institute of Software, Chinese Academy of Science. With impressive publications in top tier journals and conferences (including breakthrough publications in SIGIR of his own group), this projected was approved "A-level" achievements by the NSF China office in 2012.