Representations and algorithms for finite-state bisimulations of linear discrete-time control systems

While a large amount of research over the past two decades has focused on discrete abstractions of infinite-state dynamical systems, many structural and algorithmic details of these abstractions remain unknown. To clarify the computational resources needed to perform discrete abstractions, this paper examines the algorithmic properties of an existing method for deriving finite-state systems that are bisimilar to linear discrete-time control systems. We explicitly find the structure of the finite-state system, show that it can be enormous compared to the original linear system, and give conditions to guarantee that the finite-state system is reasonably sized and efficiently computable. Though constructing the finite-state system is generally impractical, we see that special cases could be amenable to satisfiability based verification techniques. ©2009 IEEE.

Brillouin scattering of cluster-assembled carbon films

The low frequency vibrational spectrum of cluster beam deposited carbon films was studied by Brillouin light scattering. In thin films the values of both bulk modulus and shear modulus has been estimated from the shifts of surface phonon peaks. The values found indicate a mainly sp2 coordinated random network with low density. In thick films a bulk longitudinal phonon peak was detected in a spectral range compatible with the value of the index of refraction and of the elastic constants of thin films. High surface roughness, combined with a rather strong bulk central peak, prevented the observation of surface phonon features. © 1998 Elsevier Science Ltd. All rights reserved.

Distributed flow algorithms for scalable similarity visualization

We describe simple yet scalable and distributed algorithms for solving the maximum flow problem and its minimum cost flow variant, motivated by problems of interest in objects similarity visualization. We formulate the fundamental problem as a convex-concave saddle point problem. We then show that this problem can be efficiently solved by a first order method or by exploiting faster quasi-Newton steps. Our proposed approach costs at most O(|ε|) per iteration for a graph with |ε| edges. Further, the number of required iterations can be shown to be independent of number of edges for the first order approximation method. We present experimental results in two applications: mosaic generation and color similarity based image layouting. © 2010 IEEE.

Convex relaxation of mixture regression with efficient algorithms

We develop a convex relaxation of maximum a posteriori estimation of a mixture of regression models. Although our relaxation involves a semidefinite matrix variable, we reformulate the problem to eliminate the need for general semidefinite programming. In particular, we provide two reformulations that admit fast algorithms. The first is a max-min spectral reformulation exploiting quasi-Newton descent. The second is a min-min reformulation consisting of fast alternating steps of closed-form updates. We evaluate the methods against Expectation-Maximization in a real problem of motion segmentation from video data.

A dependent partition-valued process for multitask clustering and time evolving network modelling

The fundamental aim of clustering algorithms is to partition data points. We consider tasks where the discovered partition is allowed to vary with some covariate such as space or time. One approach would be to use fragmentation-coagulation processes, but these, being Markov processes, are restricted to linear or tree structured covariate spaces. We define a partition-valued process on an arbitrary covariate space using Gaussian processes. We use the process to construct a multitask clustering model which partitions datapoints in a similar way across multiple data sources, and a time series model of network data which allows cluster assignments to vary over time. We describe sampling algorithms for inference and apply our method to defining cancer subtypes based on different types of cellular characteristics, finding regulatory modules from gene expression data from multiple human populations, and discovering time varying community structure in a social network.

Spin-cluster effect and lattice-deformation-induced Kondo effect, spin-glass freezing, and strong phonon scattering in La 0.7 Ca 0.3Mn 1-xCr xO 3

Besides the Kondo effect observed in dilute magnetic alloys, the Cr-doped perovskite manganate compounds La0.7 Ca0.3 Mn1-x Crx O3 also exhibit Kondo effect and spin-glass freezing in a certain composition range. An extensive investigation for the La0.7 Ca0.3 Mn1-x Crx O3 (x=0.01, 0.05, 0.10, 0.3, 0.6, and 1.0) system on the magnetization and ac susceptibility, the resistivity and magnetoresistance, as well as the thermal conductivity is done at low temperature. The spin-glass behavior has been confirmed for these compounds with x=0.05, 0.1, and 0.3. For temperatures above Tf (the spin-glass freezing temperature) a Curie-Weiss law is obeyed. The paramagnetic Curie temperature θ is dependent on Cr doping. Below Tf there exists a Kondo minimum in the resistivity. Colossal magnetoresistance has been observed in this system with Cr concentration up to x=0.6. We suppose that the substitution of Mn with Cr dilutes Mn ions and changes the long-range ferromagnetic order of La0.7 Ca0.3 MnO3. These behaviors demonstrate that short-range ferromagnetic correlation and fluctuation exist among Mn spins far above Tf. Furthermore, these interactions are a precursor of the cooperative freezing at Tf. The "double bumps" feature in the resistivity-temperature curve is observed in compounds with x=0.05 and 0.1. The phonon scattering is enhanced at low temperatures, where the second peak of double bumps comes out. The results indicate that the spin-cluster effect and lattice deformation induce Kondo effect, spin-glass freezing, and strong phonon scattering in mixed perovskite La0.7 Ca0.3 Mn1-x Crx O3. © 2005 American Institute of Physics.