Critérios compostos para delineamentos ótimos robustos

Pós-graduação em Biometria - IBB

A genetic algorithm for the one-dimensional cutting stock problem with setups

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

MR-Radix: a multi-relational data mining algorithm

Background: Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods: Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results: This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion: The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

Multiobjective Genetic Algorithm applied to dengue control

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Técnicas de otimização em alinhamentos múltiplos de sequência via Cadeias de Markov

Pós-graduação em Ciência da Computação - IBILCE

A Novel Cost-Efficient On-Line Intermediate Waveband-Switching Scheme in WDM Mesh Networks

Waveband switching (WBS) is an important technique to save switching and transmission cost in wavelength -division multiplexed (WDM) optical networks. A cost-efficient WBS scheme would enable network carriers to increase the network throughput (revenue) while achieving significant cost savings. We identify the critical factors that determine the WBS network throughput and switching cost and propose a novel intermediate waveband switching (IT-WBS) algorithm, called the minimizing-weighted-cost (MWC) algorithm. The MWC algorithm defines a cost for each candidate route of a call. By selecting the route with the smallest weighted cost, MWC balances between minimizing the call blocking probability and minimizing the network switching cost. Our simulations show that MWC outperforms other wavelength/waveband switching algorithms and can enhance the network throughput at a reduced cost.

Efficient Placement of Fault Indicators in an Actual Distribution System Using Evolutionary Computing

This paper proposes an evolutionary computing strategy to solve the problem of fault indicator (FI) placement in primary distribution feeders. More specifically, a genetic algorithm (GA) is employed to search for an efficient configuration of FIs, located at the best positions on the main feeder of a real-life distribution system. Thus, the problem is modeled as one of optimization, aimed at improving the distribution reliability indices, while, at the same time, finding the least expensive solution. Based on actual data, the results confirm the efficiency of the GA approach to the FI placement problem.

MR-Radix: a multi-relational data mining algorithm

Abstract Background Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

CID: an efficient complexity-invariant distance for time series

The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While dozens of classification algorithms have been applied to time series, recent empirical evidence strongly suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm is important, and depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping, and cardiology data requires invariance to the baseline (the mean value). Similarly, recent work suggests that for time series clustering, the choice of clustering algorithm is much less important than the choice of distance measure used.In this work we make a somewhat surprising claim. There is an invariance that the community seems to have missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where some complex objects may be incorrectly assigned to a simpler class. Similarly, for clustering this effect can introduce errors by “suggesting” to the clustering algorithm that subjectively similar, but complex objects belong in a sparser and larger diameter cluster than is truly warranted.We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification and clustering accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series mining experiments ever attempted in a single work, and show that complexity-invariant distance measures can produce improvements in classification and clustering in the vast majority of cases.

Multi-objective evolutionary algorithm for single and multiple fault service restoration in large-scale distribution systems

Network reconfiguration for service restoration (SR) in distribution systems is a complex optimization problem. For large-scale distribution systems, it is computationally hard to find adequate SR plans in real time since the problem is combinatorial and non-linear, involving several constraints and objectives. Two Multi-Objective Evolutionary Algorithms that use Node-Depth Encoding (NDE) have proved able to efficiently generate adequate SR plans for large distribution systems: (i) one of them is the hybridization of the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) with NDE, named NSGA-N; (ii) the other is a Multi-Objective Evolutionary Algorithm based on subpopulation tables that uses NDE, named MEAN. Further challenges are faced now, i.e. the design of SR plans for larger systems as good as those for relatively smaller ones and for multiple faults as good as those for one fault (single fault). In order to tackle both challenges, this paper proposes a method that results from the combination of NSGA-N, MEAN and a new heuristic. Such a heuristic focuses on the application of NDE operators to alarming network zones according to technical constraints. The method generates similar quality SR plans in distribution systems of significantly different sizes (from 3860 to 30,880 buses). Moreover, the number of switching operations required to implement the SR plans generated by the proposed method increases in a moderate way with the number of faults.

Enabling computationally intensive bioinformatics applications on the Grid platform

Bioinformatics is a recent and emerging discipline which aims at studying biological problems through computational approaches. Most branches of bioinformatics such as Genomics, Proteomics and Molecular Dynamics are particularly computationally intensive, requiring huge amount of computational resources for running algorithms of everincreasing complexity over data of everincreasing size. In the search for computational power, the EGEE Grid platform, world's largest community of interconnected clusters load balanced as a whole, seems particularly promising and is considered the new hope for satisfying the everincreasing computational requirements of bioinformatics, as well as physics and other computational sciences. The EGEE platform, however, is rather new and not yet free of problems. In addition, specific requirements of bioinformatics need to be addressed in order to use this new platform effectively for bioinformatics tasks. In my three years' Ph.D. work I addressed numerous aspects of this Grid platform, with particular attention to those needed by the bioinformatics domain. I hence created three major frameworks, Vnas, GridDBManager and SETest, plus an additional smaller standalone solution, to enhance the support for bioinformatics applications in the Grid environment and to reduce the effort needed to create new applications, additionally addressing numerous existing Grid issues and performing a series of optimizations. The Vnas framework is an advanced system for the submission and monitoring of Grid jobs that provides an abstraction with reliability over the Grid platform. In addition, Vnas greatly simplifies the development of new Grid applications by providing a callback system to simplify the creation of arbitrarily complex multistage computational pipelines and provides an abstracted virtual sandbox which bypasses Grid limitations. Vnas also reduces the usage of Grid bandwidth and storage resources by transparently detecting equality of virtual sandbox files based on content, across different submissions, even when performed by different users. BGBlast, evolution of the earlier project GridBlast, now provides a Grid Database Manager (GridDBManager) component for managing and automatically updating biological flatfile databases in the Grid environment. GridDBManager sports very novel features such as an adaptive replication algorithm that constantly optimizes the number of replicas of the managed databases in the Grid environment, balancing between response times (performances) and storage costs according to a programmed cost formula. GridDBManager also provides a very optimized automated management for older versions of the databases based on reverse delta files, which reduces the storage costs required to keep such older versions available in the Grid environment by two orders of magnitude. The SETest framework provides a way to the user to test and regressiontest Python applications completely scattered with side effects (this is a common case with Grid computational pipelines), which could not easily be tested using the more standard methods of unit testing or test cases. The technique is based on a new concept of datasets containing invocations and results of filtered calls. The framework hence significantly accelerates the development of new applications and computational pipelines for the Grid environment, and the efforts required for maintenance. An analysis of the impact of these solutions will be provided in this thesis. This Ph.D. work originated various publications in journals and conference proceedings as reported in the Appendix. Also, I orally presented my work at numerous international conferences related to Grid and bioinformatics.

Efficient ultrasonic signal processing techniques for aided medical diagnostics

Ultrasound imaging is widely used in medical diagnostics as it is the fastest, least invasive, and least expensive imaging modality. However, ultrasound images are intrinsically difficult to be interpreted. In this scenario, Computer Aided Detection (CAD) systems can be used to support physicians during diagnosis providing them a second opinion. This thesis discusses efficient ultrasound processing techniques for computer aided medical diagnostics, focusing on two major topics: (i) Ultrasound Tissue Characterization (UTC), aimed at characterizing and differentiating between healthy and diseased tissue; (ii) Ultrasound Image Segmentation (UIS), aimed at detecting the boundaries of anatomical structures to automatically measure organ dimensions and compute clinically relevant functional indices. Research on UTC produced a CAD tool for Prostate Cancer detection to improve the biopsy protocol. In particular, this thesis contributes with: (i) the development of a robust classification system; (ii) the exploitation of parallel computing on GPU for real-time performance; (iii) the introduction of both an innovative Semi-Supervised Learning algorithm and a novel supervised/semi-supervised learning scheme for CAD system training that improve system performance reducing data collection effort and avoiding collected data wasting. The tool provides physicians a risk map highlighting suspect tissue areas, allowing them to perform a lesion-directed biopsy. Clinical validation demonstrated the system validity as a diagnostic support tool and its effectiveness at reducing the number of biopsy cores requested for an accurate diagnosis. For UIS the research developed a heart disease diagnostic tool based on Real-Time 3D Echocardiography. Thesis contributions to this application are: (i) the development of an automated GPU based level-set segmentation framework for 3D images; (ii) the application of this framework to the myocardium segmentation. Experimental results showed the high efficiency and flexibility of the proposed framework. Its effectiveness as a tool for quantitative analysis of 3D cardiac morphology and function was demonstrated through clinical validation.

Efficient calculation of gluon amplitudes with n legs and an implementation of parton showers using the dipole formalism

The conventional way to calculate hard scattering processes in perturbation theory using Feynman diagrams is not efficient enough to calculate all necessary processes - for example for the Large Hadron Collider - to a sufficient precision. Two alternatives to order-by-order calculations are studied in this thesis.rnrnIn the first part we compare the numerical implementations of four different recursive methods for the efficient computation of Born gluon amplitudes: Berends-Giele recurrence relations and recursive calculations with scalar diagrams, with maximal helicity violating vertices and with shifted momenta. From the four methods considered, the Berends-Giele method performs best, if the number of external partons is eight or bigger. However, for less than eight external partons, the recursion relation with shifted momenta offers the best performance. When investigating the numerical stability and accuracy, we found that all methods give satisfactory results.rnrnIn the second part of this thesis we present an implementation of a parton shower algorithm based on the dipole formalism. The formalism treats initial- and final-state partons on the same footing. The shower algorithm can be used for hadron colliders and electron-positron colliders. Also massive partons in the final state were included in the shower algorithm. Finally, we studied numerical results for an electron-positron collider, the Tevatron and the Large Hadron Collider.

Decomposition Methods and Network Design Problems

Decomposition based approaches are recalled from primal and dual point of view. The possibility of building partially disaggregated reduced master problems is investigated. This extends the idea of aggregated-versus-disaggregated formulation to a gradual choice of alternative level of aggregation. Partial aggregation is applied to the linear multicommodity minimum cost flow problem. The possibility of having only partially aggregated bundles opens a wide range of alternatives with different trade-offs between the number of iterations and the required computation for solving it. This trade-off is explored for several sets of instances and the results are compared with the ones obtained by directly solving the natural node-arc formulation. An iterative solution process to the route assignment problem is proposed, based on the well-known Frank Wolfe algorithm. In order to provide a first feasible solution to the Frank Wolfe algorithm, a linear multicommodity min-cost flow problem is solved to optimality by using the decomposition techniques mentioned above. Solutions of this problem are useful for network orientation and design, especially in relation with public transportation systems as the Personal Rapid Transit. A single-commodity robust network design problem is addressed. In this, an undirected graph with edge costs is given together with a discrete set of balance matrices, representing different supply/demand scenarios. The goal is to determine the minimum cost installation of capacities on the edges such that the flow exchange is feasible for every scenario. A set of new instances that are computationally hard for the natural flow formulation are solved by means of a new heuristic algorithm. Finally, an efficient decomposition-based heuristic approach for a large scale stochastic unit commitment problem is presented. The addressed real-world stochastic problem employs at its core a deterministic unit commitment planning model developed by the California Independent System Operator (ISO).

Scale bridging concepts in molecular simulation : coarse-graining and thermodynamic coupling

In dieser Arbeit wurden Simulation von Flüssigkeiten auf molekularer Ebene durchgeführt, wobei unterschiedliche Multi-Skalen Techniken verwendet wurden. Diese erlauben eine effektive Beschreibung der Flüssigkeit, die weniger Rechenzeit im Computer benötigt und somit Phänomene auf längeren Zeit- und Längenskalen beschreiben kann.rnrnEin wesentlicher Aspekt ist dabei ein vereinfachtes (“coarse-grained”) Modell, welches in einem systematischen Verfahren aus Simulationen des detaillierten Modells gewonnen wird. Dabei werden ausgewählte Eigenschaften des detaillierten Modells (z.B. Paar-Korrelationsfunktion, Druck, etc) reproduziert.rnrnEs wurden Algorithmen untersucht, die eine gleichzeitige Kopplung von detaillierten und vereinfachten Modell erlauben (“Adaptive Resolution Scheme”, AdResS). Dabei wird das detaillierte Modell in einem vordefinierten Teilvolumen der Flüssigkeit (z.B. nahe einer Oberfläche) verwendet, während der Rest mithilfe des vereinfachten Modells beschrieben wird.rnrnHierzu wurde eine Methode (“Thermodynamische Kraft”) entwickelt um die Kopplung auch dann zu ermöglichen, wenn die Modelle in verschiedenen thermodynamischen Zuständen befinden. Zudem wurde ein neuartiger Algorithmus der Kopplung beschrieben (H-AdResS) der die Kopplung mittels einer Hamilton-Funktion beschreibt. In diesem Algorithmus ist eine zur Thermodynamischen Kraft analoge Korrektur mit weniger Rechenaufwand möglich.rnrnAls Anwendung dieser grundlegenden Techniken wurden Pfadintegral Molekulardynamik (MD) Simulationen von Wasser untersucht. Mithilfe dieser Methode ist es möglich, quantenmechanische Effekte der Kerne (Delokalisation, Nullpunktsenergie) in die Simulation einzubeziehen. Hierbei wurde zuerst eine Multi-Skalen Technik (“Force-matching”) verwendet um eine effektive Wechselwirkung aus einer detaillierten Simulation auf Basis der Dichtefunktionaltheorie zu extrahieren. Die Pfadintegral MD Simulation verbessert die Beschreibung der intra-molekularen Struktur im Vergleich mit experimentellen Daten. Das Modell eignet sich auch zur gleichzeitigen Kopplung in einer Simulation, wobei ein Wassermolekül (beschrieben durch 48 Punktteilchen im Pfadintegral-MD Modell) mit einem vereinfachten Modell (ein Punktteilchen) gekoppelt wird. Auf diese Weise konnte eine Wasser-Vakuum Grenzfläche simuliert werden, wobei nur die Oberfläche im Pfadintegral Modell und der Rest im vereinfachten Modell beschrieben wird.