A saturated linear dynamical network for approximating maximum clique

Cataloged from PDF version of article.We use a saturated linear gradient dynamical network for finding an approximate solution to the maximum clique problem. We show that for almost all initial conditions, any solution of the network defined on a closed hypercube reaches one of the vertices of the hypercube, and any such vertex corresponds to a maximal clique. We examine the performance of the method on a set of random graphs and compare the results with those of some existing methods. The proposed model presents a simple continuous, yet powerful, solution in approximating maximum clique, which may outperform many relatively complex methods, e.g., Hopfield-type neural network based methods and conventional heuristics

A saturated linear dynamical network for approximating maximum clique

We use a saturated linear gradient dynamical network for finding an approximate solution to the maximum clique problem. We show that for almost all initial conditions, any solution of the network defined on a closed hypercube reaches one of the vertices of the hypercube, and any such vertex corresponds to a maximal clique. We examine the performance of the method on a set of random graphs and compare the results with those of some existing methods. The proposed model presents a simple continuous, yet powerful, solution in approximating maximum clique, which may outperform many relatively complex methods, e.g., Hopfield-type neural network based methods and conventional heuristics. © 1999 IEEE

Internet of Things in urban waste collection

Nowadays, the waste collection management has an important role in urban areas. This paper faces this issue and proposes the application of a metaheuristic for the optimization of a weekly schedule and routing of the waste collection activities in an urban area. Differently to several contributions in literature, fixed periodic routes are not imposed. The results significantly improve the performance of the company involved, both in terms of resources used and costs saving

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

Dynamic frequency assignment for mobile users in multibeam satellite constellations

Els nivells de flexibilitat i escalabilitat mai vistos de la propera generació de sistemes de comunicació per satèl·lit exigeixen nous algorismes de gestió de recursos que s'adaptin a contextos dinàmics. El futur entorn dels serveis de comunicació per satèl·lit estarà definit per un nombre més gran d'usuaris, una gran part dels quals correspondrà a usuaris mòbils com avions o vaixells. El repte addicional que introdueixen aquests usuaris és abordar la incertesa espai-temporal que es presenta en forma de retards, canvis en la seva trajectòria, o tots dos. Atès que els usuaris mòbils constituiran un segment important del mercat, els operadors de satèl·lits prioritzen l'aprofitament dels avançats sistemes digitals per desenvolupar estratègies flexibles d'assignació de recursos que siguin robustes davant de les bases d'usuaris dinàmiques. Un dels problemes clau en aquest context és com gestionar l'espectre de freqüències de manera eficient. Mentre que nombroses solucions aborden escenaris d'assignació de dinàmica freqüències, el nivell addicional de complexitat que presenten els usuaris mòbils no ha estat prou estudiat, i no és clar si els nous algorismes d'assignació de freqüències poden abordar la incertesa espai-temporal. Concretament, sostenim que els canvis inesperats en la posició dels usuaris introdueixen noves restriccions en l'assignació de freqüències que els algoritmes la literatura podrien no ser capaços de complir, especialment si les decisions s'han de prendre en temps real i a escala. Per solucionar aquesta limitació, proposem un algorisme de gestió dinàmica de freqüències basat en programació lineal entera que assigna recursos a escenaris amb usuaris tant fixos com mòbils, tenint en compte la incertesa espai-temporal d'aquests últims. El nostre mètode inclou tant la planificació a llarg termini com l'operació en temps real, una sinergia que no ha estat prou explorada per a les comunicacions per satèl·lit i que és crítica quan s'opera sota incertesa. PLos niveles de flexibilidad y escalabilidad nunca vistos de la próxima generación de sistemas de comunicación por satélite exigen nuevos algoritmos de gestión de recursos que se adapten a contextos dinámicos. El futuro entorno de los servicios de comunicación por satélite estará definido por un mayor número de usuarios, una gran parte de los cuales corresponderá a usuarios móviles como aviones o barcos. El reto adicional que introducen estos usuarios es abordar la incertidumbre espacio-temporal que se presenta en forma de retrasos, cambios en su trayectoria, o ambos. Dado que los usuarios móviles constituirán un segmento importante del mercado, los operadores de satélites dan prioridad al aprovechamiento de los avanzadas sistemas digitales para desarrollar estrategias flexibles de asignación de recursos que sean robustas frente a las bases de usuarios dinámicas. Uno de los problemas clave en este contexto es cómo gestionar el espectro de frecuencias de forma eficiente. Mientras que numerosas soluciones abordan escenarios de asignación dinámica de frecuencias, el nivel adicional de complejidad que presentan los usuarios móviles no ha sido suficientemente estudiado, y no está claro si los nuevos algoritmos de asignación de frecuencias pueden abordar la incertidumbre espacio-temporal. En concreto, sostenemos que los cambios inesperados en la posición de los usuarios introducen nuevas restricciones en la asignación de frecuencias que los algoritmos la literatura podrían no ser capaces de cumplir, especialmente si las decisiones deben tomarse en tiempo real y a escala. Para solventar esta limitación, proponemos un algoritmo de gestión dinámica de frecuencias basado en la programación lineal entera que asigna recursos en escenarios con usuarios tanto fijos como móviles, teniendo en cuenta la incertidumbre espacio-temporal de estos últimos. Nuestro método incluye tanto la planificación a largo plazo como la operación en tiempo real, una sinergia que no ha sido suficientThe unprecedented levels of flexibility and scalability of the next generation of communication satellite systems call for new resource management algorithms that adapt to dynamic environments. The upcoming landscape of satellite communication services will be defined by an increased number of unique users, a large portion of which will correspond to mobile users such as planes or ships. The additional challenge introduced by these users is addressing the spatiotemporal uncertainty that comes in the form of delays, changes in their trajectory, or both. Given that mobile users will constitute an important segment of the market, satellite operators prioritize leveraging modern digital payloads to develop flexible resource allocation strategies that are robust against dynamic user bases. One of the key problems in this context is how to manage the frequency spectrum efficiently. While numerous solutions address dynamic frequency assignment scenarios, the additional layer of complexity presented by mobile users has not been sufficiently studied, and it is unclear whether novel frequency assignment algorithms can address spatiotemporal uncertainty. Specifically, we argue that unexpected changes in the position of users introduce new restrictions into the frequency assignment, which previous algorithms in the literature might not be able to meet, especially if decisions need to be made in real-time and at scale. To address this gap, we propose a dynamic frequency management algorithm based on integer linear programming that assigns resources in scenarios with both fixed and mobile users, accounting for the spatiotemporal uncertainty of the latter. Our method includes both long-term planning and real-time operation, a synergy that has not been sufficiently explored for satellite communications and proves to be critical when operating under uncertainty. To fulfill the problem’s scope, we propose different strategies that extend a state-of-the-art frequency management algOutgoin

Analog Photonics Computing for Information Processing, Inference and Optimisation

This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.Comment: Invited submission by Journal of Advanced Quantum Technologies; accepted version 5/06/202

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

Community detection in graphs

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.Comment: Review article. 103 pages, 42 figures, 2 tables. Two sections expanded + minor modifications. Three figures + one table + references added. Final version published in Physics Report

Integrated High-Resolution Modeling for Operational Hydrologic Forecasting

Current advances in Earth-sensing technologies, physically-based modeling, and computational processing, offer the promise of a major revolution in hydrologic forecasting—with profound implications for the management of water resources and protection from related disasters. However, access to the necessary capabilities for managing information from heterogeneous sources, and for its deployment in robust-enough modeling engines, remains the province of large governmental agencies. Moreover, even within this type of centralized operations, success is still challenged by the sheer computational complexity associated with overcoming uncertainty in the estimation of parameters and initial conditions in large-scale or high-resolution models. In this dissertation we seek to facilitate the access to hydrometeorological data products from various U.S. agencies and to advanced watershed modeling tools through the implementation of a lightweight GIS-based software package. Accessible data products currently include gauge, radar, and satellite precipitation; stream discharge; distributed soil moisture and snow cover; and multi-resolution weather forecasts. Additionally, we introduce a suite of open-source methods aimed at the efficient parameterization and initialization of complex geophysical models in contexts of high uncertainty, scarce information, and limited computational resources. The developed products in this suite include: 1) model calibration based on state of the art ensemble evolutionary Pareto optimization, 2) automatic parameter estimation boosted through the incorporation of expert criteria, 3) data assimilation that hybridizes particle smoothing and variational strategies, 4) model state compression by means of optimized clustering, 5) high-dimensional stochastic approximation of watershed conditions through a novel lightweight Gaussian graphical model, and 6) simultaneous estimation of model parameters and states for hydrologic forecasting applications. Each of these methods was tested using established distributed physically-based hydrologic modeling engines (VIC and the DHSVM) that were applied to watersheds in the U.S. of different sizes—from a small highly-instrumented catchment in Pennsylvania, to the basin of the Blue River in Oklahoma. A series of experiments was able to demonstrate statistically-significant improvements in the predictive accuracy of the proposed methods in contrast with traditional approaches. Taken together, these accessible and efficient tools can therefore be integrated within various model-based workflows for complex operational applications in water resources and beyond