An LDPCC decoding algorithm based on Bowman-Levin approximation --Comparison with BP and CCCP--

Belief propagation (BP) and the concave convex procedure (CCCP) are both methods that utilize the Bethe free energy as a cost function and solve information processing tasks. We have developed a new algorithm that also uses the Bethe free energy, but changes the roles of the master variables and the slave variables. This is called the Bowman-Levin (BL) approximation in the domain of statistical physics. When we applied the BL algorithm to decode the Gallager ensemble of short-length regular low-density parity check codes (LDPCC) over an additive white Gaussian noise (AWGN) channel, its average performance was somewhat better than that of either BP or CCCP. This implies that the BL algorithm can also be successfully applied to other problems to which BP or CCCP has already been applied.Comment: 2005 IEEE International Symposium on Information Theor

On Computing Maximal Independent Sets of Hypergraphs in Parallel

Whether or not the problem of finding maximal independent sets (MIS) in hypergraphs is in (R)NC is one of the fundamental problems in the theory of parallel computing. Unlike the well-understood case of MIS in graphs, for the hypergraph problem, our knowledge is quite limited despite considerable work. It is known that the problem is in \emph{RNC} when the edges of the hypergraph have constant size. For general hypergraphs with $n$ vertices and $m$ edges, the fastest previously known algorithm works in time $O(\sqrt{n})$ with $\text{poly}(m,n)$ processors. In this paper we give an EREW PRAM algorithm that works in time $n^{o(1)}$ with $\text{poly}(m,n)$ processors on general hypergraphs satisfying $m \leq n^{\frac{\log^{(2)}n}{8(\log^{(3)}n)^2}}$, where $\log^{(2)}n = \log\log n$ and $\log^{(3)}n = \log\log\log n$. Our algorithm is based on a sampling idea that reduces the dimension of the hypergraph and employs the algorithm for constant dimension hypergraphs as a subroutine

CDDT: Fast Approximate 2D Ray Casting for Accelerated Localization

Localization is an essential component for autonomous robots. A well-established localization approach combines ray casting with a particle filter, leading to a computationally expensive algorithm that is difficult to run on resource-constrained mobile robots. We present a novel data structure called the Compressed Directional Distance Transform for accelerating ray casting in two dimensional occupancy grid maps. Our approach allows online map updates, and near constant time ray casting performance for a fixed size map, in contrast with other methods which exhibit poor worst case performance. Our experimental results show that the proposed algorithm approximates the performance characteristics of reading from a three dimensional lookup table of ray cast solutions while requiring two orders of magnitude less memory and precomputation. This results in a particle filter algorithm which can maintain 2500 particles with 61 ray casts per particle at 40Hz, using a single CPU thread onboard a mobile robot.Comment: 8 pages, 14 figures, ICRA versio

Content-boosted Matrix Factorization Techniques for Recommender Systems

Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. These content-boosted matrix factorization algorithms not only improve recommendation accuracy, but also provide useful insights about the contents, as well as make recommendations more easily interpretable

From Poincare to affine invariance: How does the Dirac equation generalize?

A generalization of the Dirac equation to the case of affine symmetry, with SL(4,R) replacing SO(1,3), is considered. A detailed analysis of a Dirac-type Poincare-covariant equation for any spin j is carried out, and the related general interlocking scheme fulfilling all physical requirements is established. Embedding of the corresponding Lorentz fields into infinite-component SL(4,R) fermionic fields, the constraints on the SL(4,R) vector-operator generalizing Dirac's gamma matrices, as well as the minimal coupling to (Metric-)Affine gravity are studied. Finally, a symmetry breaking scenario for SA(4,R) is presented which preserves the Poincare symmetry.Comment: 34 pages, LaTeX2e, 8 figures, revised introduction, typos correcte

A broadcast-based test scheme for reducing test size and application time

[[abstract]]We present efficient method for reducing test application time by broadcasting test configuration. We compare our method based on single, multiple, 1-1 in-order mapping, even distribution, nearest signal probability matching, and in-order pseudo-exhaustive method. The results of our experiments indicate that our method reducing the test pattern number and the test application time by running the ATPG tool provided by SIS.[[conferencedate]]20060521~20060524[[conferencelocation]]Island of Kos, Greec