119 research outputs found

    Intelligent detection of anomalies in telecommunications customer behaviour

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    Word processed copy.Includes bibliographical references (leaves 118-121).In this research, we present a modelling technique that can efficiently facilitate anomaly detection that will help call analysts and managers with adaptive decision-making. We developed and implemented a Data 'fransformation System (DTS), a new Hybrid Genetic Algorithm (HGA) and an Anomaly Detection System (ADS) to address this challenge

    Quantum Algorithms for Solving Hard Constrained Optimization Problems

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    En aquesta investigació, s'han examinat tècniques d'optimització per resoldre problemes de restriccions i s'ha fet un estudi de l'era quàntica i de les empreses líders del mercat, com ara IBM, D-Wave, Google, Xanadu, AWS-Braket i Microsoft. S'ha après sobre la comunitat, les plataformes, l'estat de les investigacions i s'han estudiat els postulats de la mecànica quàntica que serveixen per crear els sistemes i algorismes quàntics més eficients. Per tal de saber si és possible resoldre problemes de Problema de cerca de restriccions (CSP) de manera més eficient amb la computació quàntica, es va definir un escenari perquè tant la computació clàssica com la quàntica tinguessin un bon punt de referència. En primer lloc, la prova de concepte es centra en el problema de programació dels treballadors socials i més tard en el tema de la preparació per lots i la selecció de comandes com a generalització del Problema dels treballadors socials (SWP). El problema de programació dels treballadors socials és una mena de problema d'optimització combinatòria que, en el millor dels casos, es pot resoldre en temps exponencial; veient que el SWP és NP-Hard, proposa fer servir un altre enfoc més enllà de la computació clàssica per a la seva resolució. Avui dia, el focus a la computació quàntica ja no és només per la seva enorme capacitat informàtica sinó també, per l'ús de la seva imperfecció en aquesta era Noisy Intermediate-Scale Quantum (NISQ) per crear un poderós dispositiu d'aprenentatge automàtic que utilitza el principi variacional per resoldre problemes d'optimització en reduir la classe de complexitat. A la tesi es proposa una formulació (quadràtica) per resoldre el problema de l'horari dels treballadors socials de manera eficient utilitzant Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), Minimal Eigen Optimizer i ADMM optimizer. La viabilitat quàntica de l'algorisme s'ha modelat en forma QUBO, amb Docplex simulat Cirq, Or-Tools i provat a ordinadors IBMQ. Després d'analitzar els resultats de l'enfocament anterior, es va dissenyar un escenari per resoldre el SWP com a raonament basat en casos (qCBR), tant quànticament com clàssicament. I així poder contribuir amb un algorisme quàntic centrat en la intel·ligència artificial i l'aprenentatge automàtic. El qCBR és una tècnica d’aprenentatge automàtic basada en la resolució de nous problemes que utilitza l’experiència, com ho fan els humans. L'experiència es representa com una memòria de casos que conté qüestions prèviament resoltes i utilitza una tècnica de síntesi per adaptar millor l'experiència al problema nou. A la definició de SWP, si en lloc de pacients es tenen lots de comandes i en lloc de treballadors socials robots mòbils, es generalitza la funció objectiu i les restriccions. Per això, s'ha proposat una prova de concepte i una nova formulació per resoldre els problemes de picking i batching anomenat qRobot. Es va fer una prova de concepte en aquesta part del projecte mitjançant una Raspberry Pi 4 i es va provar la capacitat d'integració de la computació quàntica dins de la robòtica mòbil, amb un dels problemes més demandats en aquest sector industrial: problemes de picking i batching. Es va provar en diferents tecnologies i els resultats van ser prometedors. A més, en cas de necessitat computacional, el robot paral·lelitza part de les operacions en computació híbrida (quàntica + clàssica), accedint a CPU i QPU distribuïts en un núvol públic o privat. A més, s’ha desenvolupat un entorn estable (ARM64) dins del robot (Raspberry) per executar operacions de gradient i altres algorismes quàntics a IBMQ, Amazon Braket (D-Wave) i Pennylane de forma local o remota. Per millorar el temps d’execució dels algorismes variacionals en aquesta era NISQ i la següent, s’ha proposat EVA: un algorisme d’aproximació de Valor Exponencial quàntic. Fins ara, el VQE és el vaixell insígnia de la computació quàntica. Avui dia, a les plataformes líders del mercat de computació quàntica al núvol, el cost de l'experimentació dels circuits quàntics és proporcional al nombre de circuits que s'executen en aquestes plataformes. És a dir, amb més circuits més cost. Una de les coses que aconsegueix el VQE, el vaixell insígnia d'aquesta era de pocs qubits, és la poca profunditat en dividir el Hamiltonià en una llista de molts petits circuits (matrius de Pauli). Però aquest mateix fet, fa que simular amb el VQE sigui molt car al núvol. Per aquesta mateixa raó, es va dissenyar EVA per poder calcular el valor esperat amb un únic circuit. Tot i haver respost a la hipòtesi d'aquesta tesis amb tots els estudis realitzats, encara es pot continuar investigant per proposar nous algorismes quàntics per millorar problemes d'optimització.En esta investigación, se han examinado técnicas de optimización para resolver problemas de restricciones y se ha realizado un estudio de la era cuántica y de las empresas lideres del mercado, como IBM, D-Wave, Google, Xanadu, AWS-Braket y Microsoft. Se ha aprendido sobre su comunidad, sus plataformas, el estado de sus investigaciones y se han estudiado los postulados de la mecánica cuántica que sirven para crear los sistemas y algoritmos cuánticos más eficientes. Por tal de saber si es posible resolver problemas de Problema de búsqueda de restricciones (CSP) de manera más eficiente con la computación cuántica, se definió un escenario para que tanto la computación clásica como la cuántica tuvieran un buen punto de referencia. En primer lugar, la prueba de concepto se centra en el problema de programación de los trabajadores sociales y más tarde en el tema de la preparación por lotes y la selección de pedidos como una generalización del Problema de los trabajadores sociales (SWP). El problema de programación de los trabajadores sociales es una clase de problema de optimización combinatoria que, en el mejor de los casos, puede resolverse en tiempo exponencial; viendo que el SWP es NP-Hard, propone usar otro enfoque mas allá de la computación clásica para su resolución. Hoy en día, el foco en la computación cuántica ya no es sólo por su enorme capacidad informática sino también, por el uso de su imperfección en esta era Noisy Intermediate-Scale Quantum (NISQ) para crear un poderoso dispositivo de aprendizaje automático que usa el principio variacional para resolver problemas de optimización al reducir su clase de complejidad. En la tesis se propone una formulación (cuadrática) para resolver el problema del horario de los trabajadores sociales de manera eficiente usando Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), Minimal Eigen Optimizer y ADMM optimizer. La viabilidad cuántica del algoritmo se ha modelado en forma QUBO, con Docplex simulado Cirq, Or-Tools y probado en computadoras IBMQ. Después de analizar los resultados del enfoque anterior, se diseñó un escenario para resolver el SWP como razonamiento basado en casos (qCBR), tanto cuántica como clásicamente. Y así, poder contribuir con un algoritmo cuántico centrado en la inteligencia artificial y el aprendizaje automático. El qCBR es una técnica de aprendizaje automático basada en la resolución de nuevos problemas que utiliza la experiencia, como lo hacen los humanos. La experiencia se representa como una memoria de casos que contiene cuestiones previamente resueltas y usa una técnica de síntesis para adaptar mejor la experiencia al nuevo problema. En la definición de SWP, si en lugar de pacientes se tienen lotes de pedidos y en lugar de trabajadores sociales robots móviles, se generaliza la función objetivo y las restricciones. Para ello, se ha propuesto una prueba de concepto y una nueva formulación para resolver los problemas de picking y batching llamado qRobot. Se hizo una prueba de concepto en esta parte del proyecto a través de una Raspberry Pi 4 y se probó la capacidad de integración de la computación cuántica dentro de la robótica móvil, con uno de los problemas más demandados en este sector industrial: problemas de picking y batching. Se probó en distintas tecnologías y los resultados fueron prometedores. Además, en caso de necesidad computacional, el robot paraleliza parte de las operaciones en computación híbrida (cuántica + clásica), accediendo a CPU y QPU distribuidos en una nube pública o privada. Además, desarrollamos un entorno estable (ARM64) dentro del robot (Raspberry) para ejecutar operaciones de gradiente y otros algoritmos cuánticos en IBMQ, Amazon Braket (D-Wave) y Pennylane de forma local o remota. Para mejorar el tiempo de ejecución de los algoritmos variacionales en esta era NISQ y la siguiente, se ha propuesto EVA: un algoritmo de Aproximación de Valor Exponencial cuántico. Hasta la fecha, el VQE es el buque insignia de la computación cuántica. Hoy en día, en las plataformas de computación cuántica en la nube líderes de mercado, el coste de la experimentación de los circuitos cuánticos es proporcional al número de circuitos que se ejecutan en dichas plataformas. Es decir, con más circuitos mayor coste. Una de las cosas que consigue el VQE, el buque insignia de esta era de pocos qubits, es la poca profundidad al dividir el Hamiltoniano en una lista de muchos pequeños circuitos (matrices de Pauli). Pero este mismo hecho, hace que simular con el VQE sea muy caro en la nube. Por esta misma razón, se diseñó EVA para poder calcular el valor esperado con un único circuito. Aún habiendo respuesto a la hipótesis de este trabajo con todos los estudios realizados, todavía se puede seguir investigando para proponer nuevos algoritmos cuánticos para mejorar problemas de optimización combinatoria.In this research, Combinatorial optimization techniques to solve constraint problems have been examined. A study of the quantum era and market leaders such as IBM, D-Wave, Google, Xanadu, AWS-Braket and Microsoft has been carried out. We have learned about their community, their platforms, the status of their research, and the postulates of quantum mechanics that create the most efficient quantum systems and algorithms. To know if it is possible to solve Constraint Search Problem (CSP) problems more efficiently with quantum computing, a scenario was defined so that both classical and quantum computing would have a good point of reference. First, the proof of concept focuses on the social worker scheduling problem and later on the issue of batch picking and order picking as a generalization of the Social Workers Problem (SWP). The social workers programming problem is a combinatorial optimization problem that can be solved exponentially at best; seeing that the SWP is NP-Hard, it claims using another approach beyond classical computation for its resolution. Today, the focus on quantum computing is no longer only on its enormous computing power but also on the use of its imperfection in this era Noisy Intermediate-Scale Quantum (NISQ) to create a powerful machine learning device that uses the variational principle to solve optimization problems by reducing their complexity class. In the thesis, a (quadratic) formulation is proposed to solve the problem of social workers' schedules efficiently using Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), Minimal Eigen Optimizer and ADMM optimizer. The quantum feasibility of the algorithm has been modelled in QUBO form, with Cirq simulated, Or-Tools and tested on IBMQ computers. After analyzing the results of the above approach, a scenario was designed to solve the SWP as quantum case-based reasoning (qCBR), both quantum and classically. And thus, to be able to contribute with a quantum algorithm focused on artificial intelligence and machine learning. The qCBR is a machine learning technique based on solving new problems that use experience, as humans do. The experience is represented as a memory of cases containing previously resolved questions and uses a synthesis technique to adapt the background to the new problem better. In the definition of SWP, if instead of patients there are batches of orders and instead of social workers mobile robots, the objective function and the restrictions are generalized. To do this, a proof of concept and a new formulation has been proposed to solve the problems of picking and batching called qRobot. A proof of concept was carried out in this part of the project through a Raspberry Pi 4 and the integration capacity of quantum computing within mobile robotics was tested, with one of the most demanded problems in this industrial sector: picking and batching problems. It was tested on different technologies, and the results were promising. Furthermore, in case of computational need, the robot parallelizes part of the operations in hybrid computing (quantum + classical), accessing CPU and QPU distributed in a public or private cloud. Furthermore, we developed a stable environment (ARM64) inside the robot (Raspberry) to run gradient operations and other quantum algorithms on IBMQ, Amazon Braket (D-Wave) and Pennylane locally or remotely. To improve the execution time of variational algorithms in this NISQ era and the next, EVA has been proposed: A quantum Exponential Value Approximation algorithm. To date, the VQE is the flagship of quantum computing. Today, in the market-leading quantum cloud computing platforms, the cost of experimenting with quantum circuits is proportional to the number of circuits running on those platforms. That is, with more circuits, higher cost. One of the things that the VQE, the flagship of this low-qubit era, achieves is shallow depth by dividing the Hamiltonian into a list of many small circuits (Pauli matrices). But this very fact makes simulating with VQE very expensive in the cloud. For this same reason, EVA was designed to calculate the expected value with a single circuit. Even having answered the hypothesis of this work with all the studies carried out, it is still possible to continue research to propose new quantum algorithms to improve combinatorial optimization

    A Field Guide to Genetic Programming

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    xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

    Computational tools for modeling and measuring chromosome structure

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 99-112).DNA conformation within cells has many important biological implications, but there are challenges both in modeling DNA due to the need for specialized techniques, and experimentally since tracing out in vivo conformations is currently impossible. This thesis contributes two computational projects to these efforts. The first project is a set of online and offline calculators of conformational statistics using a variety of published and unpublished methods, addressing the current lack of DNA model-building tools intended for general use. The second project is a reconstructive analysis that could enable in vivo mapping of DNA conformation at high resolution with current experimental technology.by Brian Christopher Ross.Ph.D

    NASA Tech Briefs, June 1993

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    Topics include: Imaging Technology: Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

    Analysing functional genomics data using novel ensemble, consensus and data fusion techniques

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    Motivation: A rapid technological development in the biosciences and in computer science in the last decade has enabled the analysis of high-dimensional biological datasets on standard desktop computers. However, in spite of these technical advances, common properties of the new high-throughput experimental data, like small sample sizes in relation to the number of features, high noise levels and outliers, also pose novel challenges. Ensemble and consensus machine learning techniques and data integration methods can alleviate these issues, but often provide overly complex models which lack generalization capability and interpretability. The goal of this thesis was therefore to develop new approaches to combine algorithms and large-scale biological datasets, including novel approaches to integrate analysis types from different domains (e.g. statistics, topological network analysis, machine learning and text mining), to exploit their synergies in a manner that provides compact and interpretable models for inferring new biological knowledge. Main results: The main contributions of the doctoral project are new ensemble, consensus and cross-domain bioinformatics algorithms, and new analysis pipelines combining these techniques within a general framework. This framework is designed to enable the integrative analysis of both large- scale gene and protein expression data (including the tools ArrayMining, Top-scoring pathway pairs and RNAnalyze) and general gene and protein sets (including the tools TopoGSA , EnrichNet and PathExpand), by combining algorithms for different statistical learning tasks (feature selection, classification and clustering) in a modular fashion. Ensemble and consensus analysis techniques employed within the modules are redesigned such that the compactness and interpretability of the resulting models is optimized in addition to the predictive accuracy and robustness. The framework was applied to real-word biomedical problems, with a focus on cancer biology, providing the following main results: (1) The identification of a novel tumour marker gene in collaboration with the Nottingham Queens Medical Centre, facilitating the distinction between two clinically important breast cancer subtypes (framework tool: ArrayMining) (2) The prediction of novel candidate disease genes for Alzheimer’s disease and pancreatic cancer using an integrative analysis of cellular pathway definitions and protein interaction data (framework tool: PathExpand, collaboration with the Spanish National Cancer Centre) (3) The prioritization of associations between disease-related processes and other cellular pathways using a new rule-based classification method integrating gene expression data and pathway definitions (framework tool: Top-scoring pathway pairs) (4) The discovery of topological similarities between differentially expressed genes in cancers and cellular pathway definitions mapped to a molecular interaction network (framework tool: TopoGSA, collaboration with the Spanish National Cancer Centre) In summary, the framework combines the synergies of multiple cross-domain analysis techniques within a single easy-to-use software and has provided new biological insights in a wide variety of practical settings

    Analysing functional genomics data using novel ensemble, consensus and data fusion techniques

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    Motivation: A rapid technological development in the biosciences and in computer science in the last decade has enabled the analysis of high-dimensional biological datasets on standard desktop computers. However, in spite of these technical advances, common properties of the new high-throughput experimental data, like small sample sizes in relation to the number of features, high noise levels and outliers, also pose novel challenges. Ensemble and consensus machine learning techniques and data integration methods can alleviate these issues, but often provide overly complex models which lack generalization capability and interpretability. The goal of this thesis was therefore to develop new approaches to combine algorithms and large-scale biological datasets, including novel approaches to integrate analysis types from different domains (e.g. statistics, topological network analysis, machine learning and text mining), to exploit their synergies in a manner that provides compact and interpretable models for inferring new biological knowledge. Main results: The main contributions of the doctoral project are new ensemble, consensus and cross-domain bioinformatics algorithms, and new analysis pipelines combining these techniques within a general framework. This framework is designed to enable the integrative analysis of both large- scale gene and protein expression data (including the tools ArrayMining, Top-scoring pathway pairs and RNAnalyze) and general gene and protein sets (including the tools TopoGSA , EnrichNet and PathExpand), by combining algorithms for different statistical learning tasks (feature selection, classification and clustering) in a modular fashion. Ensemble and consensus analysis techniques employed within the modules are redesigned such that the compactness and interpretability of the resulting models is optimized in addition to the predictive accuracy and robustness. The framework was applied to real-word biomedical problems, with a focus on cancer biology, providing the following main results: (1) The identification of a novel tumour marker gene in collaboration with the Nottingham Queens Medical Centre, facilitating the distinction between two clinically important breast cancer subtypes (framework tool: ArrayMining) (2) The prediction of novel candidate disease genes for Alzheimer’s disease and pancreatic cancer using an integrative analysis of cellular pathway definitions and protein interaction data (framework tool: PathExpand, collaboration with the Spanish National Cancer Centre) (3) The prioritization of associations between disease-related processes and other cellular pathways using a new rule-based classification method integrating gene expression data and pathway definitions (framework tool: Top-scoring pathway pairs) (4) The discovery of topological similarities between differentially expressed genes in cancers and cellular pathway definitions mapped to a molecular interaction network (framework tool: TopoGSA, collaboration with the Spanish National Cancer Centre) In summary, the framework combines the synergies of multiple cross-domain analysis techniques within a single easy-to-use software and has provided new biological insights in a wide variety of practical settings

    Optimal Partitioning of a Surveillance Space for Persistent Coverage Using Multiple Autonomous Unmanned Aerial Vehicles: An Integer Programming Approach

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    Unmanned aerial vehicles (UAVs) are an essential tool for the battle eld commander in part because they represent an attractive intelligence gathering platform that can quickly identify targets and track movements of individuals within areas of interest. In order to provide meaningful intelligence in near-real time during a mission, it makes sense to operate multiple UAVs with some measure of autonomy to survey the entire area persistently over the mission timeline. This research considers a space where intelligence has identi ed a number of locations and their surroundings that need to be monitored for a period of time. An integer program is formulated and solved to partition this surveillance space into the minimum number of subregions such that these locations fall outside of each partitioned subregion for e cient, persistent surveillance of the locations and their surroundings. Partitioning is followed by a UAV-to-partitioned subspace matching algorithm so that each subregion of the partitioned surveillance space is assigned exactly one UAV. Because the size of the partition is minimized, the number of UAVs used is also minimized

    Seventh Biennial Report : June 2003 - March 2005

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    NASA Tech Briefs, July 2002

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    Topics include: a technology focus sensors, software, electronic components and systems, materials, mechanics, machinery/automation, manufacturing, bio-medical, physical sciences, information sciences, book and reports, and a special section of Photonics Tech Briefs
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