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The digital computer Z4 which was installed in 1950 at the Institute for Applied Mathematics of the ETH Zurich was a remarkable instrument in many respects.


Quantum information theory is a new interdisciplinary research field related to quantum mechanics, computer science, information theory, and applied mathematics.


The Wiberg algorithm is a numerical algorithm developed for the problem in the community of applied mathematics.


To allow direct comparisons with algorithms from the applied mathematics and computer vision communities, we consider both inhomogeneous and homogeneous systems.


Supercomputing applications to the numerical modeling of industrial and applied mathematics problems

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 >=There have been numerous and various complex networks with the development of science, technology and human society, such as the Internet, the World Wide Web, the network of air lines, largescale electric power networks, the structure of a piece of Very LargeScale Integration (VLSI), the human social relationships, the neural networks, and the spreading path net of an infectious disease, etc. Empirical studies and theoretical modeling of networks have been the subject of a large body of recent research in... >=There have been numerous and various complex networks with the development of science, technology and human society, such as the Internet, the World Wide Web, the network of air lines, largescale electric power networks, the structure of a piece of Very LargeScale Integration (VLSI), the human social relationships, the neural networks, and the spreading path net of an infectious disease, etc. Empirical studies and theoretical modeling of networks have been the subject of a large body of recent research in statistical physics and applied mathematics. A property that seems to be common to many networks is community structure, the division of network nodes into groups within which the network connections are dense, but between which they are sparser. Community detection in large networks is potentially very useful. Nodes belonging to a tightknit community are more than likely to have other properties in common. For instance, in the worldwideweb, community analysis has uncovered thematic clusters. In biochemical or neural networks, communities may be functional groups, and separating the network into such groups could simplify functional analysis considerably. The problem of community detection is quite challenging and has been the subject of discussion in various disciplines, particularly in computer science and sociology. It is an NP complete problem and the fact that we often have no idea how many communities we wish to discover makes the process all the more costly. Many attempts to tackle the problem have been proposed, such as the KernighanLin algorithm, spectral algorithm, GN algorithm, Radicchi algorithm and so forth. However, these methods are more or less inaccurate, computationally demanding or specific to particular applications. In this paper, therefore, we propose another approach for modularity identification based on the evolutionary computation (EC). We demonstrate that our algorithms are highly effective at discovering community structure in both computergenerated and realworld network data.  现实世界中复杂网络无处不在,从因特网到万维网,从航空路线图到大型电力网格, 从超大规模集成电路图到人际关系网,从细胞神经网络到传染病传播过程等等。随着近年来对复杂网络性质的物理意义和数学特性的深入研究,人们发现许多实际网络都具有一个共同的性质, 即社区结构。整个网络是由若干个“社区”构成的,每个社区内部的节点之间的连接相对非常紧密,但是各个社区之间的连接却比较稀疏。  
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