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14th International Conference on Genetic and Evolutionary Computation Companion, GECCO'12 Companion

作者： De Barros, Márcio O. Costa, Hélio R. Figueiredo, Fábio V. Da Rocha, Ana Regina C. Post-graduate Information Systems Program - PPGI UNIRIO Av. Pasteur 458 Urca - Rio de Janeiro RJ Brazil COPPE UFRJ System Engineering and Computer Science Department Caixa Postal: 68511 CEP: 21945-970 - Rio de Janeiro Brazil CCA-RJ Brazilian Air Force Ponta do Galeão s/no Ilha do Governador CEP: 21941-510 - Rio de Janeiro Brazil

ISBN: (纸本)9781450311786

This paper proposes a multiobjective heuristic search approach to support a project portfolio selection technique on scenarios with a large number of candidate projects. The original formulation for the technique requires analyzing all combinations of candidate projects, which is unfeasible when more than a few alternatives are available. We have used a multiobjective genetic algorithm to partially explore the search space of project combinations and select the most effective ones. We present an experimental study based on four project selection problems that compares the results found by the genetic algorithm to those yielded by a non-systematic search procedure. Results show evidence that the project selection technique can be used in large-scale scenarios and that GA presents better results than simpler search strategy. Copyright is held by the author/owner(s).

关键词： Multiobjective optimization

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Building better interdisciplinary scientists: creating graduate level courses to address the communication gap in interdisciplinary research

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BMC Bioinformatics 2012年第12期13卷 1-2页

作者： Denise R Koessler Elizabeth G Johnson Jordan M Utley Harry A Richards Cynthia B Peterson Department of Electrical Engineering and Computer Science The University of Tennessee Knoxville TN 37996 USA Department of Microbiology The University of Tennessee Knoxville TN 37996 USA UT/ORNL Graduate School of Genome Science and Technology The University of Tennessee Knoxville TN 37996 USA SCALE-IT Program Administrator The University of Tennessee Knoxville TN 37996 USA Department of Biochemistry and Molecular Biology The University of Tennessee Knoxville TN 37996 USA

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Accelerating the Artificial Bee Colony Algorithm by Hardware Parallel Implementations

Accelerating the Artificial Bee Colony Algorithm by Hardware...

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IEEE Latin American Symposium on Circuits and Systems

作者： Daniel M. Mufioz Carlos H. Llanos Leandro dos S. Coelho Mauricio Ayala-Rincon Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Parana Department of Computer Science and Mathematics University of Brasilia

ISBN: (纸本)9781467312073

Artificial bee colony (ABC) is an optimization algorithm inspired on the intelligent behavior of honey bee swarms. It is suitable to be applied when mathematical techniques are impractical or provide suboptimal solutions. As a population-based algorithm, the ABC suffers on large execution times specifically for embedded optimization problems with computational limitations. For that we propose a hardware parallel architecture of the opposition-based ABC algorithm (HPOABC) that facilitates the implementation in Field programmable Gate Arrays (FPGAs). Numerical simulations using four well-known benchmark problems demonstrate that the opposition-based approach allows the algorithm to improve its functionality, preserving the swarm diversity. Additionally, synthesis results point outs that the HPOABC architecture is effectively mapped in hardware and is suitable for embedded applications.

关键词： Global optimization Swarm intelligence Artificial bee colony FPGAs Floating-point arithmetic

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Prevent distributed denial of service attacks based on statistical and fuzzy logic control

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Journal of Technology 2012年第2期27卷 97-108页

作者： Liu, Chung-Hsin Shao, I. Department of Computer Science and Information Engineering Chinese Culture University Taipei City 11114 Taiwan Graduate Institute of R and D Master Program in Information Security Industry Chinese Culture University Taipei City 11114 Taiwan

The research proposes monitoring network traffic to preventing malicious attacks. It uses statistical theory and fuzzy control architecture to create a single transmission behavior. It will establish a normal transmission behavior module in order to achieve network monitoring and an alert mechanism. The mechanism tries to improve the abnormal behavior detection rate. The study is divided into three parts. The first part uses tools to simulate detection and aggressive behavior. According to many defensive measure reports, we will build a detection mechanism. The third part uses statistical theory and fuzzy control to monitor each network flow. The mechanism will preprocess denial of service attacks for an intrusion detection system.

关键词： Denial-of-service attack

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A Study of NetCDF as an Approach for High Performance Medical Image Storage

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Journal of Physics: Conference Series 2012年第1期341卷

作者： Marcone Magnus Thiago Coelho Prado Aldo von Wangenhein Douglas D J de Macedo M A R Dantas Post-graduate Program in Computer Science Department of Informatics and Statistic Federal University of Santa Catarina Florianópolis SC - Brasil Post-graduate Program in Knowledge Engineering and Managemet Department of Knowledge Engineering Federal University of Santa Catarina Florianópolis SC - Brasil

The spread of telemedicine systems increases every day. The systems and PACS based on DICOM images has become common. This rise reflects the need to develop new storage systems, more efficient and with lower computational costs. With this in mind, this article discusses a study for application in NetCDF data format as the basic platform for storage of DICOM images. The study case comparison adopts an ordinary database, the HDF5 and the NetCDF to storage the medical images. Empirical results, using a real set of images, indicate that the time to retrieve images from the NetCDF for large scale images has a higher latency compared to the other two methods. In addition, the latency is proportional to the file size, which represents a drawback to a telemedicine system that is characterized by a large amount of large image files.

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Accelerated oxidation of silicon due to x-ray irradiation

Accelerated oxidation of silicon due to x-ray irradiation

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12th European Conference on Radiation and Its Effects on Component and Systems, RADECS 2011

作者： Bhandaru, Shweta Zhang, En Xia Fleetwood, Daniel M. Reed, Robert A. Weller, Robert A. Weiss, Sharon M. Interdisciplinary Graduate Program in Material Science Vanderbilt University Nashville TN 37235 United States Department of Electrical Engineering and Computer Science Vanderbilt University Nashville TN 37235 United States

Enhanced rates of oxide growth have been observed on silicon when exposed to high-energy x-ray irradiation. This observed effect could potentially be utilized for remote total ionizing dose-sensing applications. ©... 详细信息

ISBN: (纸本)9781457705878

关键词： Silicon

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Modularity detection in protein-protein interaction networks

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BMC Research Notes 2011年第1期4卷 1-6页

作者： Narayanan, Tejaswini Gersten, Merril Subramaniam, Shankar Grama, Ananth Department of Electrical and Computer Engineering University of California San Diego United States Graduate Program in Bioinformatics and Systems Biology University of California San Diego United States Department of Bioengineering University of California San Diego United States Department of Computer Science Purdue University West Lafayette IN United States

Background: Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG) algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the betweenness of that edge. The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated. Results: We validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of Yeast, *** and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient. Conclusions: We have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the underlying network, and the applic

关键词： Biological Network Jaccard Index Input Network High Betweenness Jaccard Similarity Coefficient

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282

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Cytokine 2013年第3期63卷 310-310页

作者： Elia D. Tait Wojno Mario Noti Brian S. Kim Mark C. Siracusa Paul R. Giacomin Meera G. Nair Alain J. Benitez Kathryn R. Ruymann Amanda B. Muir David A. Hill Kudakwashe R. Chikwava Amin E. Moghaddam Quentin J. Sattentau Aneesh Alex Chao Zhou Paul Menard-Katcher Masato Kubo Kazushige Obata-Ninomiya Hajime Karasuyama Michael R. Comeau David Artis Department of Microbiology University of Pennsylvania Philadelphia PA USA Institute for Immunology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Queensland Tropical Health Alliance and School of Public Health and Tropical Medicine James Cook University Cairns Australia Division of Gastroenterology Hepatology and Nutrition Children’s Hospital of Philadelphia Philadelphia PA USA Department of Pediatrics Division of Allergy and Immunology Children’s Hospital of Philadelphia Philadelphia PA USA Department of Pathology and Laboratory Medicine Children’s Hospital of Philadelphia Philadelphia PA USA The Sir William Dunn School of Pathology The University of Oxford Oxford UK Department of Electrical and Computer Engineering Lehigh University Bethlehem PA USA Center for Photonics and Nanoelectronics Lehigh University BethlehemPA USA Bioengineering Program Lehigh University Bethlehem PA USA Division of Gastroenterology Hospital of the University of Pennsylvania Philadelphia PA USA Laboratory for Signal Network Research Center for Allergy and Immunology RIKEN Yokohama Institute Yokohama Japan Division of Molecular Pathology Research Institute for Biological Science Tokyo University of Science Chiba Japan Department of Immune Regulation Tokyo Medical and Dental University Graduate School Tokyo Japan JST CREST Tokyo Medical and Dental University Graduate School Tokyo Japan Inflammation Research Amgen Inc. Seattle WA USA Department of Pathobiology School of Veterinary Medicine University of Pennsylvania Philadelphia PA USA

Eosinophilic esophagitis (EoE) is a food allergy-associated disease characterized by esophageal eosinophilia and associated inflammation. EoE has become increasingly common in industrialized countries. However, current management strategies, such as treatment with swallowed steroids and dietary restrictions, are nonspecific or negatively impact the quality of life of EoE patients. In addition, use of more specific therapies that target immunoglobulin E (IgE) and interleukin (IL)-5 have been largely unsuccessful in ameliorating EoE. Thus, there is an urgent need to identify novel, specific immunological pathways that underlie the pathogenesis of EoE that could be targeted to treat this disease. Recently, a genome-wide association study identified that EoE is associated with a gain-of-function polymorphism in the gene that encodes thymic stromal lymphopoietin (TSLP). TSLP is an IL-7 family member cytokine that promotes allergic inflammation by eliciting T helper type 2 cytokine responses, IgE production, and the expansion of a distinct population of basophils . However, whether TSLP directly promotes allergic inflammation in the esophagus and the mechanisms by which TSLP might contribute to the pathogenesis of EoE remain unknown. Here, we describe a new murine model of experimental EoE-like disease that was used to investigate the role of TSLP in allergic inflammation of the esophagus. Murine experimental EoE-like disease developed independently of IgE but was dependent on TSLP-elicited basophils. Critically, therapeutic antibody-mediated neutralization of TSLP or depletion of basophil populations ameliorated established EoE-like disease in mice. Finally, elevated TSLP levels and exaggerated basophil responses were observed in esophageal biopsies from EoE patients, and a gain-of-function polymorphism in TSLP correlated with increased basophil responses in patients with EoE. Together, these data indicate that TSLP-elicited basophil responses may play a key role in medi

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An Approach Using Parallel Architecture to Storage DICOM Images in Distributed File System

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Journal of Physics: Conference Series 2012年第1期341卷

作者： Tiago S Soares Thiago C Prado M A R Dantas Douglas D J de Macedo Michael A Bauer Informatics and Statistic Department Federal University of Santa Catarina Florianpolis Brazil Post-Graduate Program of Knowledge Engineering and Management Federal University of Santa Catarina Florianpolis Brazil Department of Computer Science University of Western Ontario UWO London Canada

Telemedicine is a very important area in medical field that is expanding daily motivated by many researchers interested in improving medical applications. In Brazil was started in 2005, in the State of Santa Catarina has a developed server called the CyclopsDCMServer, which the purpose to embrace the HDF for the manipulation of medical images (DICOM) using a distributed file system. Since then, many researches were initiated in order to seek better performance. Our approach for this server represents an additional parallel implementation in I/O operations since HDF version 5 has an essential feature for our work which supports parallel I/O, based upon the MPI paradigm. Early experiments using four parallel nodes, provide good performance when compare to the serial HDF implemented in the CyclopsDCMServer.

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A Radial Basis Function Spike Model for Indirect Learning via Integrate-and-Fire Sampling and Reconstruction Techniques

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Advances in Artificial Neural Systems 2012年第1期2012卷

作者： X. Zhang G. Foderaro C. Henriquez A. M. J. VanDongen S. Ferrari Laboratory for Intelligent Systems and Control (LISC) Department of Mechanical Engineering and Materials Science Duke University Durham NC 27708 USAduke.edu Department of Biomedical Engineering and Department of Computer Science Duke University Durham NC 27708 USAduke.edu Program in Neuroscience & Behavioral Disorders Duke-NUS Graduate Medical School Singapore Singapore

This paper presents a deterministic and adaptive spike model derived from radial basis functions and a leaky integrate-and-fire sampler developed for training spiking neural networks without direct weight manipulation. Several algorithms have been proposed for training spiking neural networks through biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity and Hebbian plasticity. These algorithms typically rely on the ability to update the synaptic strengths, or weights, directly, through a weight update rule in which the weight increment can be decided and implemented based on the training equations. However, in several potential applications of adaptive spiking neural networks, including neuroprosthetic devices and CMOS/memristor nanoscale neuromorphic chips, the weights cannot be manipulated directly and, instead, tend to change over time by virtue of the pre- and postsynaptic neural activity. This paper presents an indirect learning method that induces changes in the synaptic weights by modulating spike-timing-dependent plasticity by means of controlled input spike trains. In place of the weights, the algorithm manipulates the input spike trains used to stimulate the input neurons by determining a sequence of spike timings that minimize a desired objective function and, indirectly, induce the desired synaptic plasticity in the network.

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