Scientists reveal new super-fast form of computer that 'grows as it computes' March 1, 2017 DNA double helix. Credit: public domain Researchers from The University of Manchester have shown it is possible to build a new super-fast form of computer that grows as it computes. Professor Ross D King and his team have demonstrated for the first time the feasibility of engineering a nondeterministic universal Turing machine (NUTM), and their research is to be published in the prestigious Journal of the Royal Society Interface . The theoretical properties of such a computing machine, including its exponential boost in speed over electronic and quantum computers, have been well understood for many years – but the Manchester breakthrough demonstrates that it is actually possible to physically create a NUTM using DNA molecules. Imagine a computer is searching a maze and comes to a choice point, one path leading left, the other right, explained Professor King, from Manchester's School of Computer Science. Electronic computers need to choose which path to follow first. But our new computer doesn't need to choose, for it can replicate itself and follow both paths at the same time, thus finding the answer faster. This 'magical' property is possible because the computer's processors are made of DNA rather than silicon chips. All electronic computers have a fixed number of chips. Our computer's ability to grow as it computes makes it faster than any other form of computer, and enables the solution of many computational problems previously considered impossible. Quantum computers are an exciting other form of computer, and they can also follow both paths in a maze, but only if the maze has certain symmetries, which greatly limits their use. As DNA molecules are very small a desktop computer could potentially utilize more processors than all the electronic computers in the world combined - and therefore outperform the world's current fastest supercomputer, while consuming a tiny fraction of its energy. The University of Manchester is famous for its connection with Alan Turing - the founder of computer science - and for creating the first stored memory electronic computer. This new research builds on both these pioneering foundations, added Professor King. Alan Turing's greatest achievement was inventing the concept of a universal Turing machine (UTM) - a computer that can be programmed to compute anything any other computer can compute. Electronic computers are a form of UTM, but no quantum UTM has yet been built. DNA computing is the performing of computations using biological molecules rather than traditional silicon chips . In DNA computing, information is represented using the four-character genetic alphabet - A , G , C , and T - rather than the binary alphabet, which is a series of 1s and 0s used by traditional computers. Explore further: Researchers restore first ever computer music recording More information: Currin, A., Korovin, K., Ababi, M., Roper, K., Kell, D.B., Day, P.J., King, R.D. (2017) Computing exponentially faster: Implementing a nondeterministic universal Turing machine using DNA. Journal of the Royal Society Interface . (in press). On Arxiv : arxiv.org/abs/1607.08078 Journal reference: Journal of the Royal Society Interface ； arXiv Provided by: University of Manchester
A first course in computing with applications to biology Ran Libeskind-Hadas Ran Libeskind-Hadas is the R. Michael Shanahan Professor of Computer Science at Harvey Mudd College. He received the A.B. in applied mathematics from Harvard University and the PhD in computer science from the University of Illinois at Urbana-Champaign. His research is in the area of cophylogenetics. Eliot Bush Eliot Bush is Assistant Professor of Biology at Harvey Mudd College. He received the A.B. in biology from Harvard University and the PhD in biology from the California Institute of Technology. His research interests in computational biology have focused on the evolution of noncoding sequences in mammals. Corresponding author. Ran Libeskind-Hadas, Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, USA. Tel.: +909-621-8976 ; Fax: + 909-621-8465 ; E-mail: hadas@cs.hmc.edu Received December 6, 2012. Accepted January 16, 2013. Abstract We believe that undergraduate biology students must acquire a foundational background in computing including how to formulate a computational problem; develop an algorithmic solution; implement their solution in software and then test, document and use their code to explore biological phenomena. Moreover, by learning these skills in the first year, students acquire a powerful tool set that they can use and build on throughout their studies. To address this need, we have developed a first-year undergraduate course that teaches students the foundations of computational thinking and programming in the context of problems in biology. This article describes the structure and content of the course and summarizes assessment data on both affective and learning outcomes. 文章网址：http://bib.oxfordjournals.org/content/early/2013/02/28/bib.bbt005.abstract
2013 Nobel Prize in Chemistry The Nobel Prize in Chemistry 2013 was awarded jointly to Martin Karplus, Michael Levitt and Arieh Warshel for the development of multiscale models for complex chemical systems . Press Release The computer — your Virgil in the world of atoms Chemists used to create models of molecules using plastic balls and sticks. Today, the modelling is carried out in computers. In the 1970s, Martin Karplus, Michael Levitt and Arieh Warshel laid the foundation for the powerful programs that are used to understand and predict chemical processes. Computer models mirroring real life have become crucial for most advances made in chemistry today. Chemical reactions occur at lightning speed. In a fraction of a millisecond, electrons jump from one atomic nucleus to the other. Classical chemistry has a hard time keeping up; it is virtually impossible to experimentally map every little step in a chemical process. Aided by the methods now awarded with the Nobel Prize in Chemistry, scientists let computers unveil chemical processes, such as a catalyst’s purification of exhaust fumes or the photosynthesis in green leaves. The work of Karplus, Levitt and Warshel is ground-breaking in that they managed to make Newton’s classical physics work side-by-side with the fundamentally different quantum physics. Previously, chemists had to choose to use either or. The strength of classical physics was that calculations were simple and could be used to model really large molecules. Its weakness, it offered no way to simulate chemical reactions. For that purpose, chemists instead had to use quantum physics. But such calculations required enormous computing power and could therefore only be carried out for small molecules. This year’s Nobel Laureates in chemistry took the best from both worlds and devised methods that use both classical and quantum physics. For instance, in simulations of how a drug couples to its target protein in the body, the computer performs quantum theoretical calculations on those atoms in the target protein that interact with the drug. The rest of the large protein is simulated using less demanding classical physics. Today the computer is just as important a tool for chemists as the test tube. Simulations are so realistic that they predict the outcome of traditional experiments. Martin Karplus, U.S. and Austrian citizen. Born 1930 in Vienna, Austria. Ph.D. 1953 from California Institute of Technology, CA, USA. Professeur Conventionné, Université de Strasbourg, France and Theodore William Richards Professor of Chemistry, Emeritus, Harvard University, Cambridge, MA, USA. http://chemistry.harvard.edu/people/martin-karplus http://www-isis.u-strasbg.fr/biop/start Michael Levitt, U.S. and Brittish citizen. Born 1947 in Pretoria, South Africa. Ph.D. 1971 from University of Cambridge, UK. Robert W. and Vivian K. Cahill Professor in Cancer Research, Stanford University School of Medicine, Stanford, CA, USA. http://med.stanford.edu/profiles/Michael_Levitt Arieh Warshel, U.S. and Israeli citizen. Born 1940 in Kibbutz Sde-Nahum, Israel. Ph.D. 1969 from Weizmann Institute of Science, Rehovot, Israel. Distinguished Professor, University of Southern California, Los Angeles, CA, USA.
摘录自 “ What’s at Risk as We Get Smarter?” by Shawkat Ali http://smartgrid.ieee.org/april-2012/542-what-s-at-risk-as-we-get-smarter Computational Intelligence (CI) is a powerful and smart method that has the potential to identify and mitigate unknown threats in the smart grid. CI is a set of computational methodologies, which help to solve any complex issues in a smart manner using real-world data. Initially, the IT security community was not quick to adopt CI security techniques due to the unavailability of security related data and a lack of awareness about the techniques. However, data availability is no longer an issue in the contemporary IT world. Nowadays a huge number of free and open source software packages, commercial tools, and easy-to-use scripting languages are available to process network data for the purposes of a better security. Neural networks, support vector machines and decision trees are the most popular CI algorithms. In CI domain, there are still some debates about which algorithm is the best for a specific security problem. The consensus seems to be that there is no straightforward answer to this question. Different algorithms perform better in different situations and their relative performance can be unpredictable across a set of problems. Neural networks and support vector machines belong to the same group of CI algorithms, which is called the function estimation CI group. A solid mathematical theory underpins both types of algorithms. Support vector machines are more popular than neural networks in the many domains they apply, including cyber security. Two significant attributes account for their greater popularity. The first is computational complexity: Support vector machines are markedly faster. The other one is scalability: The machines can consider infinite data points to generate a model, and performance does not depend on the dimensionality in the data-training phase. As a result, support vector machines can potentially learn a larger dataset to make an effective security decision than solutions based on neural networks can manage. Basically, support vector machines first plot the data in a high-dimensional feature space and then start learning data points to construct a model; in particular, they learn some vectors corresponding their class values. The model generation in the learning phase is an optimisation process. At the same time that is done, the support vector machines construct an optimal hyper plane to classify the types of hacker. After finalizing the model with the optimal hyper plane, the machine uses some testing instances to evaluate model performance. Support vector machines have a wonderful ingredient to fit the optimal hyper plane in the learning phase, which is called kernel function. Linear, polynomial and radial basis function kernels are the classical kernels. Many other kernels that also are effective in the learning phase have recently been discovered. Some additional parameter tuning has been required to achieve the optimal model for a low expected probability of generalization errors. An excellent open-source support vector machine tool is available . Also, decision trees, members of the rule-based CI group, are a fine tool. Compared to many other CI algorithms, decision trees are consistent in their ability to generate a set of rules during the model construction that are transparent, easy to translate and also easily incorporated to solve the real life problems, such as instance intrusion detection of smart grid. Like natural trees, decision trees have three nodes: root, internal and leaf node. The tree always starts from the root node, which has no incoming branch. On the other hand, internal nodes have exactly one incoming branch and two or more outgoing branches. Leaf nodes have just one incoming branch to hold a decision, say "cyber attacker" or alternative "no cyber attacker." The advantage of such trees is that smart grid security team members with less experience handling and analysing grid security can still implement the decision tree technique and gain insight easily during the grid protection. An open source decision tree tool is also available.
The 8th Asian Computational Fluid Dynamics Conference January 10-14, 2010 Organized by: The Department of Mechanical Engineering The Hong Kong University of Science Technology Hong Kong （ http://www.me.ust.hk/~acfd8/ ） Call for Papers Original papers which deal with all aspects of CFD either qualitative and/or quantitative in nature are solicited. Topics include but not limited to: Navier-Stokes and Boltzmann Solvers New Algorithms and Numerical Schemes Grid Generation and Grid Adaptation Grid Free and Cartesian Methods Convergence Acceleration Schemes High Performance and Parallel Computing Micro and Low Reynolds Number Flows Turbomachinery and Internal Flows Supersonic and Hypersonic Flows Combustion and Reacting Flows Electrochemical Flows Bio Fluid Mechanics Multiphase Flows and Flows in Porous Media Turbulent Flows: Modelling and LES/DNS Multidisciplinary and Multi-objective Optimization Industrial Applications of CFD Important Dates Abstract: Submission deadline: 31 August 2009 Notice of acceptance: 30 September 2009 Full paper: Submission deadline: 30 November 2009 Early registration: 30 November 2009
信息来自密歇根大学CSE Distinguished Lecture in Computer Science and Engineering Computational Thinking and Thinking about Computing Jeannette Wing(周以真) President's Professor of Computer Science and Assistant Director for CISE Carnegie Mellon University and National Science Foundation Wednesday, April 15, 2009 4:00 pm - 5:00 pm 1670 CSE Reception in Tishman Hall (CSE atrium) immediately following lecture. Abstract My vision for the 21st Century: Computational thinking will be a fundamental skill used by everyone in the world. To reading, writing,and arithmetic, let's add computational thinking to every child's analytical ability. Computational thinking is an approach to solving problems, building systems, and understanding human behavior that draws on the power and limits of computing. In this talk I will argue that computational thinking has already begun to influence many disciplines, from the sciences to the humanities, but that the best is yet to come. Looking to the future, we can anticipate even more profound impact of computational thinking on science, technology, and society: on the ways new discoveries will be made, innovation will occur, and cultures will evolve. The new NSF Cyber-enabled Discovery and Innovation initiative in a nutshell is computational thinking for science and engineering. Realizing this vision gives the field of computing both exciting research opportunities and novel educational challenges. The field of computing is driven by technology innovation, societal demands, and scientific questions. We are often too easily swept up with the rapid progress in technology and the surprising uses by society of our technology, that we forget about the science that underlies our field. In thinking about computing, I have started a list of Deep Questions in Computing, with the hope of encouraging the community to think about the scientific drivers of our field. Biography Dr. Jeannette M. Wing is the President's Professor of Computer Science in the Computer Science Department at Carnegie Mellon University. She received her S.B. and S.M. degrees in Electrical Engineering and Computer Science in 1979 and her Ph.D. degree in Computer Science in 1983, all from the Massachusetts Institute of Technology. From 2004-2007, she was Head of the Computer Science Department at Carnegie Mellon. Currently on leave from CMU, she is the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation. Professor Wing's general research interests are in the areas of specification and verification, concurrent and distributed systems, programming languages, and software engineering. Her current focus is on the foundations of trustworthy computing. Professor Wing was or is on the editorial board of eleven journals. She has been a member of many advisory boards, including: the Networking and Information Technology (NITRD) Technical Advisory Group to the President's Council of Advisors on Science and Technology (PCAST), the National Academies of Sciencess Computer Science and Telecommunications Board, ACM Council, the DARPA Information Science and Technology (ISAT) Board, NSF's CISE Advisory Committee, Microsoft's Trustworthy Computing Academic Advisory Board, and the Intel Research Pittsburgh's Advisory Board. She is a member of the Sloan Research Fellowships Program Committee. She is a member of AAAS, ACM, IEEE, Sigma Xi, Phi Beta Kappa, Tau Beta Pi, and Eta Kappa Nu. Professor Wing is an AAAS Fellow, ACM Fellow, and IEEE Fellow. Additional Details Sponsor: CSE Contact Name: Maureen Elkins Contact Phone: 4-8504 Contact Email: mdelkins@eecs.umich.edu Open To: Public Video: http://inst-tech.engin.umich.edu/media/index.php?sk=cse-dls-08id=5288
The Campus Berlin-Dahlem with the Free University of Berlin (FU), the Zuse Institute Berlin (ZIB) and the Max Planck Institute for Molecular Genetics (MPIMG) harbors a significant number of groups working at the interface of life sciences (molecular biology, genome research) and formal sciences (mathematics, computer science). The mathematical and computational approaches to study biological questions are manifold, covering mathematics, statistics, and computer science. At the same time any of these approaches can be applied in diverse application areas which, taken together, makes the field hard to master for a student. The unique concentration at FU and MPIMG offers an exceptional opportunity to familiarize students with the breadth of formal methods in computational biology and scientific computing and to expose them to the scope of applied questions one can study using these tools. Now you can only apply for PhD in 2010. http://www.imprs-cbsc.mpg.de/ Requirements Candidates who have or who will soon obtain a Master's degree or diploma in bioinformatics, scientific computing, mathematics, physics, computer science, or biology are eligible to apply for the PhD Program . Students with a degree in mathematics, computer science and physics are expected to have the relevant biological background, whereas students with a degree in biology are expected to show profound knowledge in mathematics and computer science. Students with a Bachelor degree who have the necessary background in Bioinformatics or Scientific Computing can apply for a 2-semester grant for the Preparatory Program . Prior to registering and applying online, you have to prepare the following documents in PDF formats:
标签: Computational
