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
Canny edge detection consists of A convolution of the image with a blur kernel, Four convolutions of the image with edge detector kernels, Computation of the gradient direction, Non-maximum suppression, and Thresholding with hysteresis, Steps (1), (2), (3), and (4) are all implemented in terms of convolutions of the image with kernels of a fixed size. Using the FFT, it's possible to implement convolutions in time O(n log n), where n is the number of elements. If the image has dimensions m × n, the time complexity will be O(mn log mn) for these steps. The final step works by postprocessing the image to remove all the high and low values, then dropping all other pixels that aren't near other pixels. This can be done in time O(mn). Therefore, the overall time complexity is O(mn log mn). From: http://stackoverflow.com/questions/17458237/time-complexity-of-canny-edge-detecor
The proof of time complexity of the algorithm is not reliable Jiang Yongjiang Email: accsys@126.com The polynomial in the algorithm theory is P= , Where a i is the coefficient, k is the positive integer, and n is the integer variable.We know that any integer n can be expressed as n = , b j is decimal number.Thus we have P= . So, the highest order item a k n k becomes a k b m k 10 m k . For example , n =10 0 +3×10 m , P=5 n + n 2 , Thus P = 5(1+3 × 10 m )+ (1+3 × 10 m ) 2 = 5+15 × 10 m +1+6 × 10 m +9 × 10 2 m = 6+21 × 10 m +9 × 10 2m . The highest order n 2 becomes 9 × 10 2 m . Because Wikipedia says: The time complexity of an algorithm is commonly expressed using big O notation , which excludes coefficients and lower order terms. Therefore we have O ( n k ) = O ( 10 m k ), where n is variable and m is also. The time complexity of the algorithm represented by large O is a mistake. The above sentence is the wrong rule. This is the key to the error. The time complexity of the research algorithm must take all the time of the algorithm into account . 2015-6-16
The algorithm polynomial time complexity is a big joke ! Yongjiang Jiang Email: accsys@126.com Any natural number x can be expressed as x = , Where a i is 0 or 1. So that we have x k = . Thus O( x k ) = O(2 nk ), here n is a variable. The polynomial time becomes the exponential time! Is the algorithm polynomial time complexity is not a big joke? 2015-6-6
Group photo-1 Group photo-2 Hans Herrmann (ETHZ, Swiss Federal Institute of Technology Zurich) Prof. Yi-Cheng Zhang and Jianwei Zhang Prof. Zhang Jianwei talk Self introduction by Li Xin-Ye Self introduction by Liu Yi-min Self introduction by Yang Hui-Jie Sept 16 Afternoon Meeting a Sept 16 Afternoon Meeting b Lunch Sept 16 a Lunch Sept 16 b
Collective Picture 1 集体照-2 Professor Fred von Gunten ( ISC, Uni Fribourg )Talk: Complexity of systems with respect to the economy and society CCNU 辜姣 Dinner Sept.17 a Dinner Sept.17 b Dinner Sept.17 c Dinner Sept.17 d
2011 EU-China Workshop on Complexity Science Venue : University of Fribourg , Switzerland Time : Sept 14-19, 2011 Scientific Program Scientific Board Yi-Cheng Zhang (Chair) University of Fribourg, Switzerland Bing-Hong Wang (Chair) University of Science and Technology of China, Hefei, P.R. China Jeff Johnson Open University, London, UK Jian-Wei Zhang University of Hamburg, German Yan Gao University of Shanghai for Science and Technology , China Sai-Ping Li Institute of Physics, Academia Sinica, Taiwan Xu Cai Central China Normal University,Wuhan , P.R. China You-Gui Wang Beijing Normal University, Beijing, P.R. China Local Organizing Committee at University of Fribourg Miss. Pei Wu (吴培) , Mr. Ting Lei (雷庭) , Mr. Hao Liu (刘浩) , Mr. Yun Ye (叶云) , Mr. Cheng-jun Zhang (张成军) Dr. Matus Medo , Dr. Giulio Cimini, Dr. Stanislao GUALDI Invited Speakers Yan Gao (高岩) University of Shanghai for Science and Technology, Shanghai, China Guang-Le Yan (严广乐) University of Shanghai for Science and Technology, Shanghai, China Xing-Ye Li (李星野) University of Shanghai for Science and Technology, Shanghai, China Hui-Jie Yang (杨会杰) University of Shanghai for Science and Technology, Shanghai, China Sai-Ping Li (李世炳) Institute of Physics, Academia Sinica , Taiwan Bing-Hong Wang (汪秉宏) University of Science and Technology of China You-Gui Wang (王有贵) Beijing Normal University P.R.China Jiang Zhang (张江) Beijing Normal University P.R.China Qing-Hua Chen (陈清华) Beijing Normal University P.R.China Ding-Ding Han (韩定定) East China Normal University P.R.China Yu-Gang Ma (马余刚) Shanghai Institute of Applied Physics, Chinese Academy of Sciences , Shanghai , P.R.China Xu Cai (蔡勖) Central China Normal University , Wuhan , P.R.China Jian Jiang(江健) Central China Normal University , Wuhan , P.R.China Jiao Gu ( 辜姣) Central China Normal University , Wuhan , P.R.China Yi-Min Liu (刘益民) Shaoguan University, Guangdong, P.R.China Zike Zhang (张子柯) University of Fribourg, Switzerland Jian-Wei Zhang , University of Hamburg , Hamburg , German Jeff Johnson , Open University, London, UK Fred von Gunten, International Strategy and Competition, University of Fribourg , Switzerland Luciano Pietronero , University of Rome , Italy Paul Ormerod , Volterra consulting, Lond , United Kingdom Bridget Rosewell , Volterra consulting Andrzej Nowak , University of Warsaw , Poland David Hall , Open University, London, UK Hans Herrmann , Swiss Federal Institute of Technology Zurich , Switzerland Danial Stauffacher , ICT4Peace, Geneva , Switzerland PROGRAM Wednsday, Sept 14: Arriving at Zurich Thursday, Sept 15: Traveling to Bern(伯尔尼), Lu Saien ( 卢塞恩) , INTERLAKEN (因特拉肯,少女峰) Friday, Sept 16 Meeting Room: Pavillion Vert " Green Temple" (near the Department of Physics, University of Fribourg) Session 1, Chaired by Yi-Cheng Zhang 14:00 – 14:30, Yi-Cheng Zhang: Welcome 14:30 – 18:00, Plenary Talks Hans Herrmann (ETHZ, Swiss Federal Institute of Technology Zurich): Physics of Sand dunes and beyond Dott. Giulio Cimini ( Uni Fribourg and Univ. Rome, Italy ): Newsbag, an adaptive model for news recommendation Jian-Wei Zhang, (University of Hamburg), Potentials of sino-european cooperations in complexity sciences Sai-Ping Li, (Institute of Physics, Academia Sinica) TAIPEX------An Online Experimental Platform to Study Market Behavior 18:00-20:30, Welcoming Banquet Saturday, Sept 17 Meeting Room: Pavillion Vert " Green Temple" (near the Department of Physics, University of Fribourg) Session 2, Chaired by Jian-Wei Zhang 9:00 –10 :30. Plenary Talks Xu Cai ( CCNU ) : Conspectus on complexity science Researc Bing-Hong Wang (USTC): Perspectives of several directions in recent complex system research Yu-Gang Ma ( SIAP ): Critical point and critical cluster distribution of explosive site percolation in random network Jian Jiang( CCNU ): Long division unites - long union divides, A model for cultural evolution Jiao Gu ( Central China Normal University , Wuhan , P.R.China ) The spectral analysis for biology networks 10:30-11:00: Coffee Break 11:00-12:00: Plenary Talks: You-Gui Wang (BNU) : Self-organization and Preconditions of Efficient Markets Qinghua Chen ( BNU ): Statistics and Evolution of Donations for 2008 Wenchuan Earthquake Jiang Zhang ( BNU ): Accelerating growth and size-dependent distribution of human online activities 12:00-1 4:30: Lunch Session 3 Chaired by Bing-Hong Wang 14:30-16:00, Plenary Talks Yan Gao (USST) : Piecewise Smooth Lyapunov Function for a Nonlinear Dynamical System Xing-Ye Li ( USST ) : Global Compact Representation of Continuous Piecewise Linear Functions and Its Applicatio Hui-Jie Yang ( USST ): Hurst Exponents for Short Time Series Ding-Ding Han ( ECNU ): Fluctuation scaling in complex networks Zike Zhang (Fribourg U) : Statistical Mechanics of Social Tagging Networks: Structure, Dynamics and Function 16:00-16:30: Coffee Break 16:30-18:00: Plenary Talks: Dott. Stanislao Gualdi ( Uni Fribourg and Univ. Rome, Italy ): A genetic perspective on citation networks Jeff Johnson, (Open University, London, UK) European and China cooperation opportunities in Complexity Sciences Fred von Gunten ( ISC, Uni Fribourg ) Complexity of systems with respect to the economy and society Yi-Cheng Zhang ( Fribourg U ): Summary and Conclusion Remarks 18:00-19:30: Dinner Sunday, Sept 18 Travelling to Lausanne( 洛桑 ) and Geneva(日内瓦) Monday, Sept 19 l NESS Cooperation Session, Chaired by Yi-Cheng Zhang 9:00-10:30, Discussion and Talks: Yi-Cheng Zhang, (University of Fribourg) Yougui Wang ( BNU ) Paul Ormerod, (Volterra consulting) Bridget Rosewell, (Volterra consulting) Luciano Pietronero , ( University of Rome ) Andrzej Nowak , ( University of Warsaw ) David Hall ,( Open University ) Danial Stauffacher , President of The ICT For Peace Foundation 10:30-11:00: Coffee Break 11:00-11:30: Discussion and Closing Proceedings and post-event paperwork : Wei Han , UESTC, Chengdu, China Tang Yong , UESTC, Chengdu, China Li Chuncheng , UESTC, Chengdu, China
Presentation Files of the Invited Talks For The 4 th China-Europe Summer School on Complexity Science Time: 12th-14th, August, 2010 Venue: Shanghai University for Science and Technology , Shanghai, China Joint Sponsors : University of Shanghai for Science and Technology (USST) Shanghai Academy of System Science Research Centor for Complex Systems, USST EU Seventh Framework Programme (FP7) University of Fribourg (UF) University of Science and Technology of China (USTC) University of Electronic Science and Technology of China (UESTC) Co-Chairs : Yi-Cheng Zhang ( University of Fribourg , Switzerland , and Complex Systems Research Centor, USST) Jianwei Zhang ( University of Hamburg , Germany ) Xiao-Ming Xu (President of USST, Shanghai ) Bing-Hong Wang (Complex Systems Research Centor, USST and USTC, China ) Tao Zhou (UESTC, China ) Local Organizing Committee : Yan Gao, Hong-An Che, Hengshan Wang, Jianguo Liu, Qiang Guo, Ji-Ming Li, Xiao-Qian Guo, Ze-Hui Ge Aug. 12, 2010 Session T0 Chair: Yi-Cheng Zhang Xiao-Ming Xu (President of USST): Conference Welcome Bridget Rosewell (Chief Economist to Mayor of London ) : Traffic Complexity, comparison of London and Shanghai Jeff Johnson ( President of ECSS ): EU Research China Abstracts_Jeffrey_Johnson_29-Juy-2010.doc Session T1 Chair: Bing-Hong Wang Paul Ormerod ( British Academy for the Social Sciences) : Challenges for Econophysics Paul Ormerod Challenges for Econophysics.pdf Andrzej Nowak ( University of Warsaw ): Cognitive Capabilities and information Technologies Leihan Tang ( Hong Kong Baptist University ) : Simplification of complex networks and linking structure to function: lessons learned from microbial metabolism Sino_Euro_shanghai2010_LHT Yan Gao (USST) : Viability for a Hybrid Control System Gao-Yan Viability for a Hybrid Control System .ppt Session T2 Chair: Yan Gao Hawoong Jeong ( Korea Advanced Institute of Science and Technology) : Structure and Dynamics of Complex Directed Networks HJong 2010_08China-EU.pdf Maurizio Marchese ( University of Trento ) : Leveraging Social Network Knowledge Sharing and Mining in Current ICT-Based Activities Maurizio Marchese Leveraging Social N-agentSystems Jinhu Lu ( Chinese Academy of Science) : Modeling, Analysis, and Control of Multi-agent Systems Jinhu L ModelingAnalysisControl Session T3 Chair: Jin-Qing Fang Haijun Zhou ( Chinese Academy of Sciences) : Solution S pace P hase T ransitions o f T he Random K- S atisfiability Problem 周海军 the random K-satisfiability problem.pdf Michael Small ( Hong Kong Polytechnic University ): Complex Network to Describe Complex Dynamics smaller2.pdf Tian-Ping Chen ( Fudan University ) : Cluster Synchronization 陈天平 Cluster synchronization in complex networks.pd f Jeff Johnson (Open University): From Networks to Hypernetworks for a Science of Complex Systems Johnson From Networks to Hypernetworks .ppt Aug. 13, 2010 Session F1 Chair: Wei-Xing Zhou Beom Jun Kim (Sung Kyun Kwan University ) : Scaling Laws between Population and Facility Densities bjkim Facility and PopulationBeomJun.pdf Andrea De Martino (Rome University) Generating Graphs with Prescribed Probability Measures Martino Generating graphs with prescribed probabil Bertrand Roehner ( University of Paris ) How Can One Use Population Pyramids to Explore The Past? Roehner Pyramide2.pdf Session F2 Chair: Tao Zhou Chin-Kun Hu (Academia Sinica , Taiwan ) Effects of noises in some complex nonlinear and biological systems ckhu Effects of Noises in Biological Systems.pdf Jin-Qing Fang (China Institute of Atomic Energy) Try to Discuss Complexity of The Brian Networks 方锦清-Try to Discuss Complexity of the Brain Network Jian-Guo Liu ( University of Shanghai for Science and Technology) Information Filtering on Dynamical Networks 刘建国 Information filtering Session F3 Chair: Jianwei Zhang Fabio Casati ( University of Trento ) Social and Pervasive Models for information Dissemination in Science Fabio Casati Social and Pervasive Models for infor Xiang Li ( Fudan University ) Evolutionary Game in Our Networking Life: More than Cooperation 李翔 EvolutionaryDynamics.pdf Linyuan Lv ( University of Fribourg ) Ranking Leadership in Social Networks Linyuan Lu Ranking Leadership in Social Networks.pdf Session F4 Chair: You-Gui Wang Ji-Ping Huang ( Fudan University ) Introduction to Three Econophysics Approaches: Statistical Analysis, Agent-based Modeling, and Controlled Experiments 黄吉平 Introduction to Three Approaches.pdf Jie Ren ( National University of Singapore ) Listen to The Noise: Bridge Dynamics and Topology of Complex Oscillator Networks. 任捷 Listen to the noise Session F5 Chair: Jian-Guo Liu Contributed Talks and Posters Tetsuya Takaishi ( Hiroshima University of Economics) Intraday Realized Volatility Distributions in Japanese Stock Market Ren Fei: Analysis of trading packages in Chinese stock market Huanmei Qin : The Chaos Analysis of The Unbalanced international Trade at The Later Stage of The Global Financial Crisis Wenwu Yu: Consensus in Multi-agent Systems with Second-order Dynamics Lin-Yun He : Multifractal Properties in China Agricultural Futures markets? Facts or Fictions? 何凌云 Multifractal Properties.ppt Tian Qiu : Financial Networks with Static and Dynamic Thresholds 邱天 Financial Networks Aug. 14, 2010 Session S1 Chair: Tao Zhou You-Gui Wang ( Beijing Normal University ) : Firm Size Mobility and Validity of The Gibrat Model 王有贵 mobility_in_firms_distribution.ppt Pak-Ming Hui (The Chinese University of Hong Kong ): Disconnected-connected network transitions and phase separation driven by Pak-Ming Hui Disconnected-connected network transi Bing-Hong Wang (Research Center for Complex Systems , USST and USTC) : Scaling in The Global Spreading Patterns of Pandemic Influenza A and The Role of Control: Empirical Statistics bhWang Scaling in global spreading patterns Session S2 Chair: Bing-Hong Wang Tao Zhou (University of Electronic Science and Technology of China ) : Wikipedia Networks 周涛 Wikipedia from Complexity Science.pdf Wei-Xing Zhou ( East China University of Science and Technology): Universal Price Impact Functions of Individual Trades in An Order-Driven Market WX Zhou Universal price impact functions .ppt Zhihai Rong ( Donghua University ) : Emergence of Cooperation through Coevolving Time Scale in Spatial Prisoners Dilemma 荣智海 Emergence of cooperation.ppt Yi-Cheng Zhang ( University of Fribourg ) and Jianwei Zhang ( University of Hamburg ) : Conclusion Remarks
From http://www.ontospace.net/ The name of this project is used to focus attention on source of problem before proposing a solution. The big-O notation is widely used as measure of software complexity in terms of required computing resources (memory and cycles). At the most basic level - writing software always involves reduction of quadratic complexity as close to linear as possible . However, the problem of reducing algorithmic complexity is only an example of general O(n 2 ) problem . General O(n 2 ) problem is simple to explain using search engine software as an example: if each page on the Web needs to be related to another page, in order to discover possible relevance (however small it may be), then it will require calculating n*(n-1) relations, where n is total number of pages. The simple (but not perfect) solution is an index , representing each page by set of keys so that all other pages represented by the same key are automatically related. Since the number of keys is much smaller than the number of pages - the problem is reduced to kn , where k is proportional to the number of keys. However, the O(n 2 ) problem would not go away easily. Set of keys in the example of Web search needs to be constantly updated as pages change, and the number of keys can not be large. Therefore, the entire content of the Web would have to be approximated by a limited number of keys. And even though there are many better ways to approximate the Web content (such as ranking pages and employing sophisticated heuristics to improve precision of search algorithms), - it is simply not enough to keep up with changing content. Another good example of O(n 2 ) problem in software development comes from distributed computing - when multiplicity of different software agents has to communicate with each other over network. In this case every message has to be translated between each pair of agents, with n*(n-1) translations required to keep all agents on the same page. There is also a good solution if the number of agents kept fixed or limited - Hub and Spoke toplogy . And again the problem would not go away easily. Finally, there is a problem of maintaining truth - as required, for example, to support multiple commitments between parties in all unforeseeable events. Each new event could have implications that would require re-evaluating each commitment with respect to the event and all other commitments, taking O(n 2 ) revisions in case of n commitments. It can be argued that the only thing in common between the examples above - is merely a mathematical expression, and that there is no such thing as O(n 2 ) problem - instead there are many separate problems. There is, however, onecommon approach to solving these problems. It is based on understanding of combinatorial explosion as a phenomenon that is characterized by its large spatial extension , much like explosion in a physical space. The extension of logical statement consists of all situations or 'states' that it covers. Software application developers usually avoid complete enumeration ofall possibilities. They do itbyfocusing on a much smallerextension (a.k.a. 'use cases') and treating all other situations as exceptions, thus hiding a large stretch of the state-space behind it. O(n 2 ) problem arises when the original point of viewis later shifted as application evolves- exposing hidden dimension. Conceptually the problem can be avoided if extension is not frozen butinstead isprojectedvia some spatial transformation that includes given point of view, or in logical terms - intension . O(n 2 ) Space isa conceptual modeling and development framework that allows a two-way mapping between given application intensionand extension by means of meta-modeling . To that end O(n 2 ) Space defines container architecture that automatically translates models betweendata(extension) and program (intension).Each container performs as a kind of spatial projector that reflects component's extension as a proper image of its intension. For example, in database applications the intensional information, present in a form of data integrity constraints, can be mapped to its use cases by O(n 2 ) Space container. A prototype tool developed at Next Generation Software, OntoBase demonstrates how it is possible to define and maintain a single set of constraints for both: data and programs
Singular systems (differential-algebraic systems, descriptor systems, degenerate systems, constrained systems, etc.), which have been investigated over the last decades, are rather general kind of equations. They are established according to relationships among the variables. Naturally, it is usually differential or algebraic equations that form the mathematical model of the system, or the descriptor equation. Differential-algebraic systems are suitable for describing systems which evolve over time. Especially, nonlinear differential-algebraic equations are the natural outcome of component-based modeling of complex dynamic systems. Compared with the ordinary differential systems, the advantage they offer over the more often used ordinary differential equations is that they are generally easier to formulate. The price paid is that they are more difficult to deal with. In general, differential-algebraic model systems exhibit more complicated dynamics than ordinary differential models. The differential-algebraic systems have been applied widely in power systems, aerospace engineering, chemical processes, social economic systems, biological systems, network analysis, etc. With the help of the differential-algebraic model for the power systems and bifurcation theory, complex dynamical behaviors of the power systems, especially the bifurcation phenomena which can reveal the instability mechanism of power systems have been extensively studied. However, as far as the biological systems are concerned, the related research results are few. Furthermore, some applications of differential-algebraic models in the filed of economics. However, as far as the biological systems are concerned, the related research results are few. Complex systems are complete units or entities which have many components interacting in a complicated way. There are many species and a large amount of life on earth. Thus, it is very complicated to study the phenomenon of life. Biological complex systems are regarded as the most complex systems, with complexity higher than that of other systems. From the point of view of methodology, the traditional 'breaking up to pieces' and 'putting pieces together' method is not suitable for most complex systems. Therefore, there is a strong need to resort to more advanced methods to study the original system as a whole. However, most of developed theory for complex system is still not up to a quantitative level because of the complexity of individuals interacting between each other in the system. Additionally, few reports on the quantitative study of biological complex systems have been published.