Biologically Inspired Autonomous Rover Control

Robotic missions beyond 2013 will likely be precursors to a manned habitat deployment on Mars. Such missions require robust control systems for long duration activities. Current single rover missions will evolve into deployment of multiple, heterogeneous cooperating robotic colonies. This paper describes the map-making memory and action selection mechanism of BISMARC ( iologically nspired ystem for ap-based utonomous over ontrol) that is currently under development at the Jet Propulsion Laboratory in Pasadena, CA (Huntsberger and Rose, Neutral Networks, 11(7/8):1497–1510). BISMARC is an integrated control system for long duration missions involving robots performing cooperative tasks.     

Some Remarks on the Role of Minimal Length of Positive Maps in Constructing Entanglement Witnesses

The main objective of this paper is to discuss correspondence between the concept of entanglement witnesses (self-adjoint operators on a composite Hilbert space that are, in general, not positive, but are positive on separable states) and positive maps which are not completely positive. The notion of minimal length of linear positive map is introduced and the role of this quantity in the constructing of entanglement witnesses is explained.Presented at the 36th Symposium on Mathematical Physics, ‘Open Systems & Quantum Information’, Toruń, Poland, June 9-12, 2004.Supported by the Grant PBZ-MIN-008/P03/2003.     

Crash failure detection in asynchronous agent communication languages

Agent Communication Languages (ACLs) have been developed to provide a way for agents to communicate with each other supporting cooperation in Multi-Agent Systems (MAS). In the past few years many ACLs have been proposed for MAS and new standards are emerging such as the ACL developed by the Foundation for Intelligent Physical Agents (FIPA). Despite these efforts, an important issue in the research on ACLs is still open and concerns how these languages should deal with failures of agents in asynchronous MAS. The Fault Tolerant Agent Communication Language ( - ) presented in this paper addresses this issue dealing with crash failures of agents. - provides high-level communication primitives which support a fault-tolerant anonymous interaction protocol designed for open MAS. We present a formal semantics for - and a formal specification of the underlying agent architecture. This formal framework allows us to prove that the ACL satisfies a set of well defined knowledge-level programming requirements. To illustrate the language features we show how - can be effectively used to write high-level executable specifications of fault tolerant protocols, such as the Contract Net one.     

The complexity of constructing pseudorandom generators from hard functions

We study the complexity of constructing pseudorandom generators (PRGs) from hard functions, focussing on constant-depth circuits. We show that, starting from a function computable in alternating time O(l) with O(1) alternations that is hard on average (i.e. there is a constant such that every circuit of size fails to compute f on at least a 1/poly(l) fraction of inputs) we can construct a computable by DLOGTIME-uniform constant-depth circuits of size polynomial in n. Such a PRG implies under DLOGTIME-uniformity. On the negative side, we prove that starting from a worst-case hard function (i.e. there is a constant such that every circuit of size fails to compute f on some input) for every positive constant there is no black-box construction of a computable by constant-depth circuits of size polynomial in n. We also study worst-case hardness amplification, which is the related problem of producing an average-case hard function starting from a worst-case hard one. In particular, we deduce that there is no blackbox worst-case hardness amplification within the polynomial time hierarchy. These negative results are obtained by showing that polynomialsize constant-depth circuits cannot compute good extractors and listdecodable codes.     

Coupling and self-stabilization

A randomized self-stabilizing algorithm is an algorithm that, whatever the initial configuration is, reaches a set of М legal configurations} in finite time with probability 1. The proof of convergence towards is generally done by exhibiting a potential function , which measures the “vertical” distance of any configuration to , such that decreases with non-null probability at each step of . We propose here a method, based on the notion of coupling, which makes use of a “horizontal” distance between any pair of configurations, such that decreases in expectation at each step of . In contrast with classical methods, our coupling method does not require the knowledge of . In addition to the proof of convergence, the method allows us to assess the convergence rate according to two different measures. Proofs produced by the method are often simpler or give better upper bounds than their classical counterparts, as examplified here on Herman's mutual exclusion and Iterated Prisoner's Dilemma algorithms in the case of cyclic graphs.     

Exact Linear Thermodynamic Evolution Equations from the Robertson Formalism

A linear evolution equation for a thermodynamic variable F, odd under time-reversal, is obtained from the exact equation derived by Robertson from the Liouville equation for the information-theoretic phase-space distribution. One obtains an exact expression for , the relaxation time for F. For very short , is time-independent for t > if C(t) F{exp(-i t)}Fo, the equilibrium time correlation, decays exponentially for t > . is the Liouville operator. So long as C(t) is such that decays rapidly to a steady-state value, the t limit of agrees with the fluctuation-dissipation theorem in applications to fluid transport.     

Note on Multiple Additivity of Minimal Renyi Entropy Output of the Werner-Holevo Channels

We give an elementary self-contained proof that the minimal Renyi entropy output of arbitrary products of channels is additive.     

An algebraic approach for $${\mathcal{H}}$$-matrix preconditioners

Hierarchical matrices ( -matrices) approximate matrices in a data-sparse way, and the approximate arithmetic for -matrices is almost optimal. In this paper we present an algebraic approach for constructing -matrices which combines multilevel clustering methods with -matrix arithmetic to compute the -inverse, -LU, and the -Cholesky factors of a matrix. Then the -inverse, -LU or -Cholesky factors can be used as preconditioners in iterative methods to solve systems of linear equations. The numerical results show that this method is efficient and greatly speeds up convergence compared to other approaches, such as JOR or AMG, for solving some large, sparse linear systems, and is comparable to other -matrix constructions based on Nested Dissection.     

On the complexity of computing determinants

We present new baby steps/giant steps algorithms of asymptotically fast running time for dense matrix problems. Our algorithms compute the determinant, characteristic polynomial, Frobenius normal form and Smith normal form of a dense n × n matrix A with integer entries in and bit operations; here denotes the largest entry in absolute value and the exponent adjustment by +o(1) captures additional factors for positive real constants C₁, C₂, C₃. The bit complexity results from using the classical cubic matrix multiplication algorithm. Our algorithms are randomized, and we can certify that the output is the determinant of A in a Las Vegas fashion. The second category of problems deals with the setting where the matrix A has elements from an abstract commutative ring, that is, when no divisions in the domain of entries are possible. We present algorithms that deterministically compute the determinant, characteristic polynomial and adjoint of A with n^3.2+o(1) and O(n^2.697263) ring additions, subtractions and multiplications.To B. David Saunders on the occasion of his 60th birthday     

Complexity of some arithmetic problems for binary polynomials

We study various combinatorial complexity measures of Boolean functions related to some natural arithmetic problems about binary polynomials, that is, polynomials over . In particular, we consider the Boolean function deciding whether a given polynomial over is squarefree. We obtain an exponential lower bound on the size of a decision tree for this function, and derive an asymptotic formula, having a linear main term, for its average sensitivity. This allows us to estimate other complexity characteristics such as the formula size, the average decision tree depth and the degrees of exact and approximative polynomial representations of this function. Finally, using a different method, we show that testing squarefreeness and irreducibility of polynomials over cannot be done in for any odd prime p. Similar results are obtained for deciding coprimality of two polynomials over as well.     

Synchronous condition-based consensus

The condition-based approach studies restrictions on the inputs to a distributed problem, called conditions, that facilitate its solution. Previous work considered mostly the asynchronous model of computation. This paper studies conditions for consensus in a synchronous system where processes can fail by crashing. It describes a full classification of conditions for consensus, establishing a continuum between the asynchronous and synchronous models, with the following hierarchy where includes all conditions (and in particular the trivial one made up of all possible input vectors). For a condition , we have:

Image Analysis and Reconstruction using a Wavelet Transform Constructed from a Reducible Representation of the Euclidean Motion Group

Inspired by the early visual system of many mammalians we consider the construction of-and reconstruction from- an orientation score as a local orientation representation of an image, . The mapping is a wavelet transform corresponding to a reducible representation of the Euclidean motion group onto and oriented wavelet . This wavelet transform is a special case of a recently developed generalization of the standard wavelet theory and has the practical advantage over the usual wavelet approaches in image analysis (constructed by irreducible representations of the similitude group) that it allows a stable reconstruction from one (single scale) orientation score. Since our wavelet transform is a unitary mapping with stable inverse, we directly relate operations on orientation scores to operations on images in a robust manner. Furthermore, by geometrical examination of the Euclidean motion group , which is the domain of our orientation scores, we deduce that an operator Φ on orientation scores must be left invariant to ensure that the corresponding operator on images is Euclidean invariant. As an example we consider all linear second order left invariant evolutions on orientation scores corresponding to stochastic processes on G. As an application we detect elongated structures in (medical) images and automatically close the gaps between them. Finally, we consider robust orientation estimates by means of channel representations, where we combine robust orientation estimation and learning of wavelets resulting in an auto-associative processing of orientation features. Here linear averaging of the channel representation is equivalent to robust orientation estimation and an adaptation of the wavelet to the statistics of the considered image class leads to an auto-associative behavior of the system. The Netherlands Organization for Scientific Research is gratefully acknowledged for financial support. This work has been supported by EC Grant IST-2003-004176 COSPAL.     

Linear Interval Equations: Midpoint Preconditioning May Produce a 100% Overestimation for Arbitrarily Narrow Data Even in Case <Emphasis Type="Italic">n</Emphasis> = 4

We construct a linear interval system Ax = b with a 4 × 4 interval matrix whose all proper interval coefficients (there are also some noninterval ones) are of the form [–, ]. It is proved that for each > 0, the interval hull and interval hull of the midpoint preconditioned system satisfy and , hence midpoint preconditioning produces a 100% overestimation of independently of in this case. The example was obtained as a result of an extensive MATLAB search.     

A Time-Splitting Spectral Method for the Generalized Zakharov System in Multi-Dimensions

The generalized Zakharov system (ZS) couples a dispersive field E (scalar or vectorial) and nondispersive fields with a propagating speed of . In this paper, we extend our one-dimensional time-splitting spectral method (TSSP) for the generalized ZS into higher dimension. A main new idea is to reformulate the multi-dimensional wave equations for the nondispersive fields into a first-order system using a change of variable defined in the Fourier space. The proposed scheme TSSP is unconditionally stable, second-order in time and spectrally accurate in space. Moreover, in the subsonic regime, it allows numerical capturing of the subsonic limit without resolving the small parameters . Numerical examples confirm these properties of this method     

R-Tiles

The square root of the Golden Section, , is at times not given its due in discussions regarding the Golden Section. This is an unfortunate situation because its geometric properties provide us with a unique and rarely applied design tool. In this column, geometer Mark Reynolds focuses on the rectangle and its abilities to create an exciting tiling, R-Tiles.