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13th International Conference on the Simulation and Synthesis of Living systems: Artificial Life 13, ALIFE 2012

作者： Snyder, David Goudarzi, Alireza Teuscher, Christof Department of Computer Science Portland State University Portland OR 97201 United States Systems Science Graduate Program Portland State University Portland OR 97201 United States Department of Electrical and Computer Engineering Portland State University Portland OR 97201 United States

ISBN: (纸本)9780262310505

Reservoir Computing (RC) is a computational model in which a trained readout layer interprets the dynamics of a component called a reservoir that is excited by external input stimuli. The reservoir is often constructed using homogeneous neural networks in which a neuron's in-degree distributions as well as its functions are uniform. RC lends itself to computing with physical and biological systems. However, most such systems are not homogeneous. In this paper, we use Random Boolean Networks (RBN) to build the reservoir. We explore the computational capabilities of such a RC device using the temporal parity task and the temporal density classification. We study the sufficient dynamics of RBNs using kernel quality and generalization rank measures. We verify findings by Lizier et al. (2008) that the critical connectivity of RBNs optimizes the balance between the high memory capacity of RBNs with (K) 2. We show that in a RBN-based RC system, the optimal connectivity for the parity task, a processing intensive task, and the density classification task, a memory intensive task, agree with Lizier et al.'s theoretical results. Our findings may contribute to the development of optimal selfassembled nanoelectronic computer architectures and biologically-inspired computing paradigms. © 2012 Massachusetts Institute of Technology.

关键词： computer architecture

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COMPOOTIM: An approach to software processes composition and optimization

COMPOOTIM: An approach to software processes composition and...

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15th Ibero-American Conference on Software engineering, CIbSE 2012

作者： Magdaleno, Andréa Magalhães de Araujo, Renata Mendes Werner, Cláudia Maria Lima COPPE/UFRJ Systems Engineering and Computer Science Department P.O. Box 68511 Rio de Janeiro RJ Zip 21945-970 Brazil UNIRIO Brazil NP2Tec Research and Practice Group in Information Technology UNIRIO Brazil

Software process definition is a complex, time consuming and error prone activity. Such activity can be facilitated by a process reuse strategy. This strategy can be implemented through a context-based process line approach. Based on the data from this approach and following its systematic, this work presents a proposal to software processes composition and optimization (COMPOOTIM). The purpose is to support the project manager's decisions and automate the composition of a project specific process. COMPOOTIM optimizes the suggestion of processes to the context of a particular project. A usage scenario for process selection exemplifies the use of COMPOOTIM.

关键词： Optimization

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A robust gradient clock synchronization algorithm for wireless sensor networks

A robust gradient clock synchronization algorithm for wirele...

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International Communication systems and Networks and Workshops, COMSNETS

作者： André C. Pinho Daniel R. Figueiredo Felipe M. G. França System Engineering and Computer Science Program (PESC/COPPE) Federal University of Rio de Janeiro Rio de Janeiro Brazil

A variation of the classical clock synchronization problem is to synchronize clocks with an error that is proportinal to their distance in the network. Among several applications, gradient clock synchronization in wireless sensor networks can yield precise time coordination of packet transmissions, allowing for time-based collision free transmission scheduling. In this paper, we propose a simple and robust gradient clock synchronization (RGCS) algorithm designed for wireless sensor networks. The algorithm is fully distributed and operates independently of network topology, network routing and has no parameters to tune. It also does not require strict timing or reliability in clock synchronization messages or any global time reference. In particular, RGCS is based on a running average of logical clock rates and addresses specific aspects of gradient synchronization, such as estimating the logical clock of neighbors. We evaluate the algorithm through realistic simulations in various scenarios to illustrate the benefits of our approach. We also compare our results with GTSP a prominent gradient clock synchronization algorithm. Our results indicates that RCGS can operate adequately under high loss rates (50%) and high variability in the period of synchronization messages.

关键词： Synchronization Clocks Hardware Wireless sensor networks Delay Receivers Equations

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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Awareness and Comprehension in Software/systems engineering Practice and Education: Trends and Research Directions

Awareness and Comprehension in Software/Systems Engineering ...

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Brazilian Symposium on Software engineering (SBES)

作者： Marcelo Schots Cl'udia Werner Manoel Mendonça Systems Engineering and Computing Program COPPE Federal University of Rio de Janeiro Rio de Janeiro Brazil Computer Science Department Federal University of Bahia Rio de Janeiro Brazil

The creation of tools, techniques and methodologies to support the manipulation of large data sets has been receiving special attention of both scientific and industrial communities, in order to discover new ways of dealing with the underlying information, including learning purposes, identification of patterns, decision making support, amongst others. However, making use of computing resources to enhance awareness and understanding of software information and the software itself is still a challenge in software/systems engineering, since it involves the identification of suitable mechanisms, adequate abstractions, and studies on stimulation of the human perceptive and cognitive abilities. This paper presents some of the challenges in this context, based on current trends of software development lifecycle, program comprehension, and software engineering education. At the end, a special focus is given on ongoing research on using and improving current mechanisms for supporting software reuse practices and software comprehension in general.

关键词： Software Software engineering Education Visualization Conferences Industries Data visualization

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Modeling the breakage-fusion-bridge mechanism: Combinatorics and cancer genomics

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16th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2012

作者： Kinsella, Marcus Bafna, Vineet Bioinformatics and Systems Biology Program University of California San Diego San Diego CA 92093 United States Department of Computer Science and Engineering University of California San Diego San Diego CA 92093 United States

ISBN: (纸本)9783642296260

The breakage-fusion-bridge (BFB) mechanism was proposed over seven decades ago and is a source of genomic variability and gene amplification in cancer. Here we formally model and analyze the BFB mechanism, to our knowledge this first time this has been undertaken. We show that BFB can be modeled as successive inverted prefix duplications of a string. Using this model, we show that BFB can achieve a surprisingly broad range of amplification patterns. We find that a sequence of BFB operations can be found that nearly fits most patterns of copy number increases along a chromosome. We conclude that this limits the usefulness of methods like array CGH for detecting BFB and discuss other implications for understanding mechanisms of genomic instability. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Pattern matching

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Towards a formal model to reason about context-aware exception handling

Towards a formal model to reason about context-aware excepti...

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International Workshop on Exception Handling (WEH)

作者： Lincoln S. Rocha Rossana M. C. Andrade Group of Computer Networks Software Engineering and Systems Graduate Program in Computer Science Federal University of Ceará Fortaleza Ceara Brazil

The context-awareness is a central aspect in the design of pervasive systems, characterizing their ability to adapt its structure and behavior. The context-aware exception handling (CAEH) is an existing approach employed to design exception handling in pervasive systems. In this approach, the context is used to define, detect, propagate, and handle exceptions. CAEH is a complex and error prone activity, needing designers' insights and domain expertise to identify and characterize contextual exceptions. However, despite the existence of formal methods to analyze the adaptive behavior of pervasive systems, such methods lack specific support to specify the CAEH behavior. In this paper, we propose a formal model to reason about the CAEH behavior. It comprises an extension of the Kripke Structure to model the context evolution of a pervasive system and a transformation function that derivates the CAEH control flow from that proposed structure.

关键词： Context Context modeling Adaptation models Middleware Computational modeling Engines Cognition

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The Use of Support Vector Machine and Genetic Algorithms to Predict Protein Function

The Use of Support Vector Machine and Genetic Algorithms to ...

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IEEE International Conference on systems, Man, and Cybernetics

作者： Walkiria K. Resende Renato A. Nascimento Carolina R. Xavier Iara F. Lopes Cristianc N. Nobre Department of Computer Science Universidade Federal de Sao Joao del Rei - UFSJ Department of Computer Science Pontificia Universidade Catolica de Minas Gerais PUC/MG Civil Engineering Program COPPE - UFRJ

ISBN: (纸本)9781467317139

In Bio informatics, the prediction of protein function is considered a very important task but also difficult. Using a set of enzymes represented by Hydrolase, Isomerase, Ligase, Lyase, Transferase and Oxidoreductase classes, previously used by Dobson et ah, this paper proposes a self-learning process able to predict their classes, based on their primary and secondary structures, through a Support Vector Machine (SVM) classifier and genetic algorithm. An SVM can he characterized as a supervised machine learning algorithm capable of resolving linear and non-linear classification problems. During the learning process, both the training data and the corresponding output are presented to the SVM to allow its parameters to he adjusted. This study utilized genetic algorithms - optimization heuristics often used to estimate parameters - to adjust the main parameters of the classifier such as kernel function type and parameter C, which provides the relationship between the training error and the margin of separation between classes. In this specific prediction problem, the results indicate that the best function is an RBF where width is 6.1 and C is 6.9. Using these parameters, the classifier obtains an average accuracy of 79.74%.

关键词： Component: Proteins Prediction Support Vector Machine Genetic Algorithms protein structure function Support Vector Network Genetic Algorithm (GA) Classifiers Learning processes secondary structures

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A solution to the state estimation problem of systems with unknown inputs

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Recent Patents on Mechanical engineering 2012年第2期5卷 102-112页

作者： Bolandhemmat, Hamidreza Clark, Christopher Golnaraghi, Farid MicroPilot Inc 72067 Road 8E Sturgeon Rd. Stony Mountain Manitoba R0C 3A0 Canada Computer Science Program Cal Poly State University San Luis Obispo CA 93407 United States Mechatronics Systems Engineering Simon Fraser University Surrey BC Canada

A solution to the state estimation problem of systems with unmeasurable non-zero mean inputs/disturbances, which do not satisfy the disturbance decoupling conditions, is given using the Kalman filtering and Bayesian estimation theory. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called supervisory layer, numerically solves the measurement equations for the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter's last updated estimate. Then, the estimated measurement vector associated with each particle is compared to the actual measurement vector to identify the particle's probability to be a solution. Finally, a so-called resampling stage is implemented to refine the particles with higher likelihoods. The effectiveness of the SKF is demonstrated by comparing its estimation performance with that of the standard Kalman Filter and the particle filter for a vehicle state estimation problem. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, regardless of the unknown disturbances from the road. The filtering methodology offered in the article has a potential to improve performance of the systems presented in the patents WO2011115960, WO2010024751, US8073528, and US20110299730. © 2012 Bentham science Publishers.

关键词： State estimation

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Multiobjective optimization for project portfolio selection 12

Multiobjective optimization for project portfolio selection

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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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