A new model for solution of complex distributed constrained problems

In this paper we describe an original computational model for solving different types of Distributed Constraint Satisfaction Problems (DCSP). The proposed model is called Controller-Agents for Constraints Solving (CACS). This model is intended to be used which is an emerged field from the integration between two paradigms of different nature: Multi-Agent Systems (MAS) and the Constraint Satisfaction Problem paradigm (CSP) where all constraints are treated in central manner as a black-box. This model allows grouping constraints to form a subset that will be treated together as a local problem inside the controller. Using this model allows also handling non-binary constraints easily and directly so that no translating of constraints into binary ones is needed. This paper presents the implementation outlines of a prototype of DCSP solver, its usage methodology and overview of the CACS application for timetabling problems

Neural Distributed Autoassociative Memories: A Survey

Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension. The purpose of this paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons). Scope. The survey is focused mainly on the networks of Hopfield, Willshaw and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections and networks with a bipartite graph structure for non-binary data with linear constraints. Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors.Comment: 31 page

Thermodynamics and statistical mechanics of frozen systems in inherent states

We discuss a Statistical Mechanics approach in the manner of Edwards to the ``inherent states'' (defined as the stable configurations in the potential energy landscape) of glassy systems and granular materials. We show that at stationarity the inherent states are distributed according a generalized Gibbs measure obtained assuming the validity of the principle of maximum entropy, under suitable constraints. In particular we consider three lattice models (a diluted Spin Glass, a monodisperse hard-sphere system under gravity and a hard-sphere binary mixture under gravity) undergoing a schematic ``tap dynamics'', showing via Monte Carlo calculations that the time average of macroscopic quantities over the tap dynamics and over such a generalized distribution coincide. We also discuss about the general validity of this approach to non thermal systems.Comment: 10 pages, 16 figure

High performance constraint satisfaction problem solving: State-recomputation versus state-copying.

Constraint Satisfaction Problems (CSPs) in Artificial Intelligence have been an important focus of research and have been a useful model for various applications such as scheduling, image processing and machine vision. CSPs are mathematical problems that try to search values for variables according to constraints. There are many approaches for searching solutions of non-binary CSPs. Traditionally, most CSP methods rely on a single processor. With the increasing popularization of multiple processors, parallel search methods are becoming alternatives to speed up the search process. Parallel search is a subfield of artificial intelligence in which the constraint satisfaction problem is centralized whereas the search processes are distributed among the different processors. In this thesis we present a forward checking algorithm solving non-binary CSPs by distributing different branches to different processors via message passing interface and execute it on a high performance distributed system called SHARCNET. However, the problem is how to efficiently communicate the state of the search among processors. Two communication models, namely, state-recomputation and state-copying via message passing, are implemented and evaluated. This thesis investigates the behaviour of communication from one process to another. The experimental results demonstrate that the state-recomputation model with tighter constraints obtains a better performance than the state-copying model, but when constraints become looser, the state-copying model is a better choice.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Y364. Source: Masters Abstracts International, Volume: 44-01, page: 0417. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Bypassing the Kochen-Specker theorem: an explicit non-contextual statistical model for the qutrit

We present an explicit non-contextual model of hidden variables for the qutrit. The model consists of an infinite set of possible hidden configurations uniformly distributed over a sphere, each one having a well-defined probability density to happen and a well-defined non-contextual binary outcome, either $+1$ or $-1$, for every properly formulated test. The model reproduces the predictions of quantum mechanics and, thus, it bypasses the constraints imposed by the Kochen-Specker theorem and its subsequent reformulations. The crux of the model is the observation that all these theorems crucially rely on an implicit assumption that is not actually required by fundamental physical principles, namely, the existence of an absolute frame of reference with respect to which the polarization properties of the qutrit as well as the orientation of the tests performed on it can be defined. We notice, on the other hand, that pairs of compatible tests defined in such an hypothetical absolute frame of reference that can be obtained from each other through a global rotation that leaves the state of the qutrit unchanged would by physically undistinguishable and, hence, equivalent under a gauge symmetry transformation. This spurious gauge degree of freedom must be properly fixed in order to build the statistical model for the qutrit. In two previous papers we have shown that the same implicit assumption is also required in order to prove both Bell's theorem and the Greenberger-Horne-Zeilinger theorem

On the Design and Analysis of Secure Inference Networks

Parallel-topology inference networks consist of spatially-distributed sensing agents that collect and transmit observations to a central node called the fusion center (FC), so that a global inference is made regarding the phenomenon-of-interest (PoI). In this dissertation, we address two types of statistical inference, namely binary-hypothesis testing and scalar parameter estimation in parallel-topology inference networks. We address three different types of security threats in parallel-topology inference networks, namely Eavesdropping (Data-Confidentiality), Byzantine (Data-Integrity) or Jamming (Data-Availability) attacks. In an attempt to alleviate information leakage to the eavesdropper, we present optimal/near-optimal binary quantizers under two different frameworks, namely differential secrecy where the difference in performances between the FC and Eve is maximized, and constrained secrecy where FC’s performance is maximized in the presence of tolerable secrecy constraints. We also propose near-optimal transmit diversity mechanisms at the sensing agents in detection networks in the presence of tolerable secrecy constraints. In the context of distributed inference networks with M-ary quantized sensing data, we propose a novel Byzantine attack model and find optimal attack strategies that minimize KL Divergence at the FC in the presence of both ideal and non-ideal channels. Furthermore, we also propose a novel deviation-based reputation scheme to detect Byzantine nodes in a distributed inference network. Finally, we investigate optimal jamming attacks in detection networks where the jammer distributes its power across the sensing and the communication channels. We also model the interaction between the jammer and a centralized detection network as a complete information zero-sum game. We find closed-form expressions for pure-strategy Nash equilibria and show that both the players converge to these equilibria in a repeated game. Finally, we show that the jammer finds no incentive to employ pure-strategy equilibria, and causes greater impact on the network performance by employing mixed strategies

Analysis of a Waveguide-Fed Metasurface Antenna

The metasurface concept has emerged as an advantageous reconfigurable antenna architecture for beam forming and wavefront shaping, with applications that include satellite and terrestrial communications, radar, imaging, and wireless power transfer. The metasurface antenna consists of an array of metamaterial elements distributed over an electrically large structure, each subwavelength in dimension and with subwavelength separation between elements. In the antenna configuration we consider here, the metasurface is excited by the fields from an attached waveguide. Each metamaterial element can be modeled as a polarizable dipole that couples the waveguide mode to radiation modes. Distinct from the phased array and electronically scanned antenna (ESA) architectures, a dynamic metasurface antenna does not require active phase shifters and amplifiers, but rather achieves reconfigurability by shifting the resonance frequency of each individual metamaterial element. Here we derive the basic properties of a one-dimensional waveguide-fed metasurface antenna in the approximation that the metamaterial elements do not perturb the waveguide mode and are non-interacting. We derive analytical approximations for the array factors of the 1D antenna, including the effective polarizabilities needed for amplitude-only, phase-only, and binary constraints. Using full-wave numerical simulations, we confirm the analysis, modeling waveguides with slots or complementary metamaterial elements patterned into one of the surfaces.Comment: Original manuscript as submitted to Physical Review Applied (2017). 14 pages, 14 figure