A Beam Search Method to Solve the Problem of Assignment Cells to Switches in a Cellular Mobile Network

Assigning cells to switches in a cellular mobile network is known as an NP-hard optimization problem. This means that the alternative for the solution of this type of problem is the use of heuristic methods, because they allow the discovery of a good solution in a very satisfactory computational time. This paper proposes a Beam Search method to solve the problem of assignment cell in cellular mobile networks. Some modifications in this algorithm are also presented, which allows its parallel application. Computational results obtained from several tests confirm the effectiveness of this approach and provide good solutions for large scale problems.

Beam Search Algorithms for Minimizing Tool Switches on a Flexible Manufacturing System

In the minimization of tool switches problem we seek a sequence to process a set of jobs so that the number of tool switches required is minimized. In this work different variations of a heuristic based on partial ordered job sequences are implemented and evaluated. All variations adopt a depth first strategy of the enumeration tree. The computational test results indicate that good results can be obtained by a variation which keeps the best three branches at each node of the enumeration tree, and randomly choose, among all active nodes, the next node to branch when backtracking.

Problem of assignment cells to switches in a cellular mobile network via beam search method

The problem of assigning cells to switches in a cellular mobile network is an NP-hard optimization problem. So, real size mobile networks could not be solved by using exact methods. The alternative is the use of the heuristic methods, because they allow us to find a good quality solution in a quite satisfactory computational time. This paper proposes a Beam Search method to solve the problem of assignment cell in cellular mobile networks. Some modifications in this algorithm are also presented, which allows its parallel application. Computational results obtained from several tests confirm the effectiveness of this approach to provide good solutions for medium- and large-sized cellular mobile network.

Resolução do problema de carregamento e descarregamento de contêineres em terminais portuários via Beam Search

One problem that has been happening frequently in port terminals is the poor planning of the loading and unloading of containers. The reason of this problem is the lack of an efficient method that provides the best means of these operations. The main goal of this work is, to implement a method that provides the best ways to perform the loading and unloading of containers, at each port and thus bring a great saving for these terminals, since the number of moves is directly proportional to cost. To carry out this program was used the idea that the containers are placed in vertical stacks, where the access can be done only by the top of the stack, so the ship was treated as an matrix and to fill it, two rules were created for loading and two for unloading. To obtain the best sequence of rules was used Beam Search method, which is an enumeration type implicit method that analyzes only the best solution of the tree generated. Thus, the program developed in the Java language, provides the best way to perform the loading and unloading ports and the way as the ship leaves each port using a graphical interface

Atribuição de turmas para professores via Beam Search em Java

In the universities, before the start of each school year, is held the distribution of classes among available teachers. Therefore, it is necessary to consider the maximum workweek for each teacher and their preferences for each discipline, to prevent a teacher to give lessons in two separate locations at the same time and to avoid some teachers to become overloaded while others with large clearance. This process, manually performed, is time consuming and does not allow the visualization of other combinations of assignment of teachers to classes, besides being liable to error. This work aims to develop a decision support tool for the problem of assigning teachers to classes in college. The project encompasses the development of a computer program using the concepts of object orientation and a tree search algorithm of a combinatorial nature called Beam Search. The programming language used is Java and the program has a graphical interface for entering and manipulating data of the problem. Once obtained the schedule data of classes and teachers is possible, by means of the tool, perform various simulations and manual adjustments to achieve the final result. It is an efficient method of class scheduling, considering the speed of task execution and the fact that it generates only feasible results

Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. Evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains.

Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks

Cette thèse étudie des modèles de séquences de haute dimension basés sur des réseaux de neurones récurrents (RNN) et leur application à la musique et à la parole. Bien qu'en principe les RNN puissent représenter les dépendances à long terme et la dynamique temporelle complexe propres aux séquences d'intérêt comme la vidéo, l'audio et la langue naturelle, ceux-ci n'ont pas été utilisés à leur plein potentiel depuis leur introduction par Rumelhart et al. (1986a) en raison de la difficulté de les entraîner efficacement par descente de gradient. Récemment, l'application fructueuse de l'optimisation Hessian-free et d'autres techniques d'entraînement avancées ont entraîné la recrudescence de leur utilisation dans plusieurs systèmes de l'état de l'art. Le travail de cette thèse prend part à ce développement. L'idée centrale consiste à exploiter la flexibilité des RNN pour apprendre une description probabiliste de séquences de symboles, c'est-à-dire une information de haut niveau associée aux signaux observés, qui en retour pourra servir d'à priori pour améliorer la précision de la recherche d'information. Par exemple, en modélisant l'évolution de groupes de notes dans la musique polyphonique, d'accords dans une progression harmonique, de phonèmes dans un énoncé oral ou encore de sources individuelles dans un mélange audio, nous pouvons améliorer significativement les méthodes de transcription polyphonique, de reconnaissance d'accords, de reconnaissance de la parole et de séparation de sources audio respectivement. L'application pratique de nos modèles à ces tâches est détaillée dans les quatre derniers articles présentés dans cette thèse. Dans le premier article, nous remplaçons la couche de sortie d'un RNN par des machines de Boltzmann restreintes conditionnelles pour décrire des distributions de sortie multimodales beaucoup plus riches. Dans le deuxième article, nous évaluons et proposons des méthodes avancées pour entraîner les RNN. Dans les quatre derniers articles, nous examinons différentes façons de combiner nos modèles symboliques à des réseaux profonds et à la factorisation matricielle non-négative, notamment par des produits d'experts, des architectures entrée/sortie et des cadres génératifs généralisant les modèles de Markov cachés. Nous proposons et analysons également des méthodes d'inférence efficaces pour ces modèles, telles la recherche vorace chronologique, la recherche en faisceau à haute dimension, la recherche en faisceau élagué et la descente de gradient. Finalement, nous abordons les questions de l'étiquette biaisée, du maître imposant, du lissage temporel, de la régularisation et du pré-entraînement.

Scheduling the factory pick-up of new cars

Car manufacturers increasingly offer delivery programs for the factory pick-up of new cars. Such a program consists of a broad range of event-marketing activities. In this paper we investigate the problem of scheduling the delivery program activities of one day such that the sum of the customers’ waiting times is minimized. We show how to model this problem as a resource-constrained project scheduling problem with nonregular objective function, and we present a relaxation-based beam-search solution heuristic. The relaxations are solved by exploiting a duality relationship between temporal scheduling and min-cost network flow problems. This approach has been developed in cooperation with a German automaker. The performance of the heuristic has been evaluated based on practical and randomly generated test instances.

Scheduling the production of rolling ingots: Industrial context, model, and solution method

We study a real-world scheduling problem arising in the context of a rolling ingots production. First we review the production process and discuss peculiarities that have to be observed when scheduling a given set of production orders on the production facilities. We then show how to model this scheduling problem using prescribed time lags between operations, different kinds of resources, and sequence-dependent changeovers. A branch-and-bound solution procedure is presented in the second part. The basic principle is to relax the resource constraints by assuming infinite resource availability. Resulting resource conflicts are then stepwise resolved by introducing precedence relationships among operations competing for the same resources. The algorithm has been implemented as a beam search heuristic enumerating alternative sets of precedence relationships.

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

Integrazione di reti neurali con approcci simbolici per problemi di ragionamento deduttivo in linguaggio naturale

Uno degli obiettivi più ambizioni e interessanti dell'informatica, specialmente nel campo dell'intelligenza artificiale, consiste nel raggiungere la capacità di far ragionare un computer in modo simile a come farebbe un essere umano. I più recenti successi nell'ambito delle reti neurali profonde, specialmente nel campo dell'elaborazione del testo in linguaggio naturale, hanno incentivato lo studio di nuove tecniche per affrontare tale problema, a cominciare dal ragionamento deduttivo, la forma più semplice e lineare di ragionamento logico. La domanda fondamentale alla base di questa tesi è infatti la seguente: in che modo una rete neurale basata sull'architettura Transformer può essere impiegata per avanzare lo stato dell'arte nell'ambito del ragionamento deduttivo in linguaggio naturale? Nella prima parte di questo lavoro presento uno studio approfondito di alcune tecnologie recenti che hanno affrontato questo problema con intuizioni vincenti. Da questa analisi emerge come particolarmente efficace l'integrazione delle reti neurali con tecniche simboliche più tradizionali. Nella seconda parte propongo un focus sull'architettura ProofWriter, che ha il pregio di essere relativamente semplice e intuitiva pur presentando prestazioni in linea con quelle dei concorrenti. Questo approfondimento mette in luce la capacità dei modelli T5, con il supporto del framework HuggingFace, di produrre più risposte alternative, tra cui è poi possibile cercare esternamente quella corretta. Nella terza e ultima parte fornisco un prototipo che mostra come si può impiegare tale tecnica per arricchire i sistemi tipo ProofWriter con approcci simbolici basati su nozioni linguistiche, conoscenze specifiche sul dominio applicativo o semplice buonsenso. Ciò che ne risulta è un significativo miglioramento dell'accuratezza rispetto al ProofWriter originale, ma soprattutto la dimostrazione che è possibile sfruttare tale capacità dei modelli T5 per migliorarne le prestazioni.

Autonomous pagination for newspaper from a predefined plan

Description of the development of a product able to deliver an autonomous page construction from a predefined plan. The processes involve Machine Learning techniques for text fitting on shapes, Beam Search for associations and Deep Learning for autonomous cropping of images.

Addendum: search for ν μ → ν e oscillations with the OPERA experiment in the CNGS beam

A first result of the search for ν ( )μ( ) → ν ( )e( ) oscillations in the OPERA experiment, located at the Gran Sasso Underground Laboratory, is presented. The experiment looked for the appearance of ν ( )e( ) in the CNGS neutrino beam using the data collected in 2008 and 2009. Data are compatible with the non-oscillation hypothesis in the three-flavour mixing model. A further analysis of the same data constrains the non-standard oscillation parameters θ (new) and suggested by the LSND and MiniBooNE experiments. For large values (>0.1 eV(2)), the OPERA 90% C.L. upper limit on sin(2)(2θ (new)) based on a Bayesian statistical method reaches the value 7.2 × 10(−3).

Search for νμ→νe oscillations with the OPERA experiment in the CNGS beam

A first result of the search for nu(mu)->nu(e) oscillations in the OPERA experiment, located at the Gran Sasso Underground Laboratory, is presented. The experiment looked for the appearance of nu(e) in the CNGS neutrino beam using the data collected in 2008 and 2009. Data are compatible with the non-oscillation hypothesis in the three-flavour mixing model. A further analysis of the same data constrains the non-standard oscillation parameters theta(new) and Delta m(new)(2) suggested by the LSND and MiniBooNE experiments. For large Delta m(new)(2) values (>0.1 eV(2)), the OPERA 90% C.L. upper limit on sin(2)(2 theta(new)) based on a Bayesian statistical method reaches the value 7.2 x 10(-3).