In addressing the challenge of exponential scaling with the number of agents we adopt a cluster-based representation to approximately solve asymmetric games of very many players. A cluster groups together agents with a similar “strategic view” of the game. We learn the clustered approximation from data consisting of strategy profiles and payoffs, which may be obtained from observations of play ...

We study a novel clustering problem in which the pairwise relations between objects are categorical. This problem can be viewed as clustering the vertices of a graph whose edges are of different types (colors). We introduce an objective function that ensures the edges within each cluster have, as much as possible, the same color. We show that the problem is NP-hard and propose a randomized algo...

We discuss some recent progress in constructing analytic approximations to the galaxy clustering. We show that successful models can be constructed for the clustering of both dark matter and dark matter haloes. Our understanding of galaxy clustering and galaxy biasing can be greatly enhanced by these models.

We investigate two important questions about the use of the nonextensive thermo-statistics (NETS) formalism in the context of nonlinear galaxy clustering in the Universe. Firstly, we define a quantitative criterion for justifying nonextensivity at different physical scales. Then, we discuss the physics behind the ansatz of the entropic parameter q(r). Our results suggest the approximate range w...

In some respects, a cluster consisting of many atoms may be regarded as a single large atom. Knowing the dielectric properties of such a cluster permits one to evaluate the form of the van der Waals ~dispersion! interactions between two clusters or between one cluster and a surface. In this paper, we derive these interactions in two extreme opposite regimes of separation: fully retarded and non...

High-accuracy optimization is the key component of time-sensitive applications in computer sciences such as machine learning, and we develop single-GPU Iterative Discrete Approximation Monte Carlo Optimization (IDAMCS) and multi-GPU IDA-MCS in our previous research. However, because of the memory capability constrain of GPUs in a workstation, single-GPU IDA-MCS and multiGPU IDA-MCS may be in lo...

We present a general perturbative method for correcting a singles and doubles coupled-cluster energy. The coupled-cluster wave function is used to define a similarity-transformed Hamiltonian, which is partitioned into a zeroth-order part that the reference problem solves exactly plus a first-order perturbation. Standard perturbation theory through second-order provides the leading correction. A...

This report provides a comprehensive overview of 1990 and 2000 neighborhood dissimilarity indices measured for Blacks and Whites, Asians and Whites, and Hispanics and Whites among the nation’s 318 metropolitan areas, as well as 1220 places with populations exceeding 25,000 in 2000. Unlike most earlier studies which measure segregation on the basis of census tracts, this study measures segregati...

In ad hoc networks, the performance is significantly degraded as the size of the network grows. The network clustering is a method by which the nodes are hierarchically organized on the basis of the proximity and thus the scalability problem is alleviated. Finding the weakly connected dominating set (WCDS) is a well-known approach, proposed for clustering the wireless ad hoc networks. Finding t...