121 results on '"Panos Pardalos"'
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52. Information Retrieval : 9th Russian Summer School, RuSSIR 2015, Saint Petersburg, Russia, August 24-28, 2015, Revised Selected Papers
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Pavel Braslavski, Ilya Markov, Panos Pardalos, Yana Volkovich, Dmitry I. Ignatov, Sergei Koltsov, Olessia Koltsova, Pavel Braslavski, Ilya Markov, Panos Pardalos, Yana Volkovich, Dmitry I. Ignatov, Sergei Koltsov, and Olessia Koltsova
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- Information storage and retrieval systems, Data mining, Natural language processing (Computer science), Artificial intelligence, Application software
- Abstract
This book constitutes the thoroughly refereed proceedings of the 9th Russian Summer School on Information Retrieval, RuSSIR 2015, held in Saint Petersburg, Russia, in August 2015.The volume includes 5 tutorial papers, summarizing lectures given at the event, and 6 revised papers from the school participants. The papers focus on various aspects of information retrieval.
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- 2016
53. Machine Learning, Optimization, and Big Data : First International Workshop, MOD 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers
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Panos Pardalos, Mario Pavone, Giovanni Maria Farinella, Vincenzo Cutello, Panos Pardalos, Mario Pavone, Giovanni Maria Farinella, and Vincenzo Cutello
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- Application software, Algorithms, Artificial intelligence, Computer science, Database management, Information storage and retrieval systems
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This book constitutes revised selected papers from the First International Workshop on Machine Learning, Optimization, and Big Data, MOD 2015, held in Taormina, Sicily, Italy, in July 2015.The 32 papers presented in this volume were carefully reviewed and selected from 73 submissions. They deal with the algorithms, methods and theories relevant in data science, optimization and machine learning.
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- 2016
54. Optimization in biomedical research
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O. Kundakcioglu and Panos Pardalos
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- 2009
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55. Connected dominating set in hypergraph
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Linxia Li, Xu Zhu, Ding-Zhu Du, Panos Pardalos, and Weili Wu
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- 2009
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56. Future Research on Multiobjective Coordinated Scheduling Problems for Discrete Manufacturing Enterprises in Supply Chain Environments
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Jun Pei, Xinbao Liu, Wenjuan Fan, Athanasios Migdalas, and Panos Pardalos
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- 2015
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57. Optimization and Security Challenges in Smart Power Grids
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Vijay Pappu, Marco Carvalho, Panos Pardalos, Vijay Pappu, Marco Carvalho, and Panos Pardalos
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- Smart power grids, Smart power grids--Security measures
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This book provides an overview of state-of-the-art research on “Systems and Optimization Aspects of Smart Grid Challenges.” The authors have compiled and integrated different aspects of applied systems optimization research to smart grids, and also describe some of its critical challenges and requirements.The promise of a smarter electricity grid could significantly change how consumers use and pay for their electrical power, and could fundamentally reshape the current Industry. Gaining increasing interest and acceptance, Smart Grid technologies combine power generation and delivery systems with advanced communication systems to help save energy, reduce energy costs and improve reliability. Taken together, these technologies support new approaches for load balancing and power distribution, allowing optimal runtime power routing and cost management. Such unprecedented capabilities, however, also present a set of new problems and challenges at the technical and regulatory levels that must be addressed by Industry and the Research Community.
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- 2013
58. Learning and Intelligent Optimization : 7th International Conference, LION 7, Catania, Italy, January 7-11, 2013, Revised Selected Papers
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Giuseppe Nicosia, Panos Pardalos, Giuseppe Nicosia, and Panos Pardalos
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- Algorithms, Numerical analysis, Artificial intelligence, Computer science—Mathematics, Discrete mathematics, Computer science
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This book constitutes the proceedings of the 7th International Conference on Learning and Optimization, LION 7, which was held in Catania, Italy, in January 2013. The 49 contributions presented in this volume were carefully reviewed and selected from 101 submissions. They explore the intersections and uncharted territories between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems.
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- 2013
59. Modeling and optimization in massive graphs
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Vladimir Boginski, Sergiy Butenko, and Panos Pardalos
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- 2003
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60. Network Design And Optimization For Smart Cities
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Konstantinos Gakis, Panos Pardalos
61. Biclustering EEG data from epileptic patients treated with vagus nerve stimulation
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Stanislav Busygin, Nikita Boyko, Panos M. Pardalos, Michael Bewernitz, Georges Ghacibeh, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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Biclustering ,medicine.diagnostic_test ,Eeg data ,Feature (computer vision) ,medicine.medical_treatment ,medicine ,Feature selection ,Electroencephalography ,Scalp eeg ,Vagus nerve stimulator ,Psychology ,Vagus nerve stimulation ,Biomedical engineering - Abstract
We present a pilot study of an application of consistent biclustering to analyze scalp EEG data obtained from epileptic patients undergoing treatment with a vagus nerve stimulator (VNS). The ultimate goal of this study is to develop a physiologic marker for optimal VNS parameters (e.g. output current, signal frequency, etc.) using measures of scalp EEG signals. A time series of STLmax values was computed for each scalp EEG channel recorded from two epileptic patients and used as a feature of the two datasets. The averaged samples from stimulation periods were then separated from averaged samples from non‐stimulation periods by feature selection performed within the consistent biclustering routine.The obtained biclustering results allow us to assume that signals from certain parts of the brain consistently change their characteristics when VNS is switched on and could provide a basis for desirable VNS stimulation parameters. A physiologic marker of optimal VNS effect could greatly reduce the cost, time, an...
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- 2007
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62. Numerical limitations in application of vector autoregressive modeling and Granger causality to analysis of EEG time series
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Alla Kammerdiner, Petros Xanthopoulos, Panos M. Pardalos, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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Nonlinear autoregressive exogenous model ,Autoregressive model ,Granger causality ,Econometrics ,SETAR ,Autoregressive integrated moving average ,Time series ,STAR model ,Mathematics ,Vector autoregression - Abstract
In this chapter a potential problem with application of the Granger‐causality based on the simple vector autoregressive (VAR) modeling to EEG data is investigated. Although some initial studies tested whether the data support the stationarity assumption of VAR, the stability of the estimated model is rarely (if ever) been verified. In fact, in cases when the stability condition is violated the process may exhibit a random walk like behavior or even be explosive. The problem is illustrated by an example.
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- 2007
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63. Quantitative analysis on electrooculography (EOG) for neurodegenerative disease
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Chang-Chia Liu, W. Art Chaovalitwongse, Panos M. Pardalos, Onur Seref, Petros Xanthopoulos, J. C. Sackellares, Frank M. Skidmore, O. Erhun Kundakcioglu, and Panos Pardalos
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medicine.medical_specialty ,Vog ,genetic structures ,medicine.diagnostic_test ,Eye movement ,Disease ,Electrooculography ,Audiology ,eye diseases ,Abnormal eye ,Alertness ,medicine ,sense organs ,Psychology ,Altered state ,Neuroscience ,Electroretinography - Abstract
Many studies have documented abnormal horizontal and vertical eye movements in human neurodegenerative disease as well as during altered states of consciousness (including drowsiness and intoxication) in healthy adults. Eye movement measurement may play an important role measuring the progress of neurodegenerative diseases and state of alertness in healthy individuals. There are several techniques for measuring eye movement, Infrared detection technique (IR). Video‐oculography (VOG), Scleral eye coil and EOG. Among those available recording techniques, EOG is a major source for monitoring the abnormal eye movement. In this real‐time quantitative analysis study, the methods which can capture the characteristic of the eye movement were proposed to accurately categorize the state of neurodegenerative subjects. The EOG recordings were taken while 5 tested subjects were watching a short (>120 s) animation clip. In response to the animated clip the participants executed a number of eye movements, including vert...
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- 2007
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64. Classification and disease prediction via mathematical programming
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Eva K. Lee, Tsung-Lin Wu, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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Physics ,Holographic principle ,General Relativity and Quantum Cosmology ,Vacuum energy ,Event horizon ,Horizon ,Quantum mechanics ,Dark energy ,Quantum entanglement ,Quantum information ,Quantum - Abstract
We suggest that dark energy has a quantum informational origin. Landauer's principle associated with the erasure of quantum information at a cosmic horizon implies the non-zero vacuum energy having effective negative pressure. Assuming the holographic principle, the minimum free energy condition, and the Gibbons-Hawking temperature for the cosmic event horizon we obtain the holographic dark energy with the parameter $d\simeq 1$, which is consistent with the current observational data. It is also shown that both the entanglement energy and the horizon energy can be related to Landauer's principle.
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- 2007
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65. Modeling and in vitro and in vivo characterization of a tissue engineered pancreatic substitute
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C. L. Stabler, C. Fraker, E. Pedraza, I. Constantinidis, A. Sambanis, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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Materials science ,Tissue engineered ,Cell ,In vitro ,Oxygen tension ,chemistry.chemical_compound ,medicine.anatomical_structure ,chemistry ,Cell culture ,In vivo ,medicine ,Agarose ,Viability assay ,Biomedical engineering - Abstract
This study investigated the model‐based design, fabrication and in vitro and in vivo experimental characterization of a pancreatic substitute consisting of mouse insulinoma cells encapsulated in agarose in a disk‐shaped construct. Two construct prototypes were examined: (i) a single disk construct comprised of agarose and β5TC3 cells; and (ii) a buffered disk construct, consisting of agarose and βTC3 cells, coated with an additional layer of pure agarose. Diffusional studies of glucose and insulin were performed to characterize the transport properties of the material. Three dimensional oxygen diffusion‐reaction models were used to predict the appropriate cell loadings for the two construct prototypes under varying external oxygen tensions. In vitro and in vivo experiments found the overall viable cell number for each construct prototype plateaued to the same value, regardless of the initial cell seeding number, when constructs were placed under identical environmental conditions. Furthermore, mathematical model calculations correlated well with experimental in vitro and in vivo results of cell viability, indicating oxygen tension to be the dominating factor in establishing total viable cell number in these constructs. These results indicate that modeling is useful for the development of tissue engineered constructs when permissive matrices and continuous cell lines are used. The applicability of this modeling and experimental methodology in the development of agarose‐based constructs for use as a bioartificial pancreas is discussed.
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- 2007
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66. Application of Bayesian networks and data mining to biomedical problems
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Alla R. Kammerdiner, Anatoliy M. Gupal, Panos M. Pardalos, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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Structure (mathematical logic) ,Artificial neural network ,Computer science ,business.industry ,Bayesian network ,Machine learning ,computer.software_genre ,Variable-order Bayesian network ,Data mining ,Artificial intelligence ,business ,Intelligent control ,computer ,Computer Science::Databases ,Biomedicine ,Dynamic Bayesian network - Abstract
During the last several decades, the Bayesian networks have turned into a dynamic area of research. This great interest is owning to the advantages offered by special structure of Bayesian networks, which allows them to be very efficient in modeling domains with inherent uncertainty. Bayesian networks techniques can be successfully applied to mining various types of biomedical data.This chapter demonstrates how various complex research problems in biology, biomedicine and other fields can be solved by means of the combination of methods from Bayesian networks and data mining.
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- 2007
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67. Monkey search: a novel metaheuristic search for global optimization
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Antonio Mucherino, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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Tree (data structure) ,Mathematical optimization ,Protein molecules ,Computer Science::Neural and Evolutionary Computation ,Geometric modeling ,Metaheuristic ,Global optimization ,Tabu search ,Selection (genetic algorithm) ,Parallel metaheuristic ,Mathematics - Abstract
We propose a novel metaheuristic search for global optimization inspired by the behavior of a monkey climbing trees looking for food. The tree branches are represented as perturbations between two neighboring feasible solutions of the considered global optimization problem. The monkey mark and update these branches leading to good solutions as it climbs up and down the tree. A wide selection of perturbations can be applied based on other metaheuristic methods for global optimization. We show that Monkey Search is competitive compared to the other metaheuristic methods for optimizing Lennard‐Jones and Morse clusters, and for simulating protein molecules based on a geometric model for protein folding.
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- 2007
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68. Presence of nonlinearity in intracranial EEG recordings: detected by Lyapunov exponents
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Chang-Chia Liu, Deng-Shan Shiau, W. Art Chaovalitwongse, Panos M. Pardalos, J. C. Sackellares, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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medicine.medical_specialty ,medicine.diagnostic_test ,Focus (geometry) ,Lyapunov exponent ,Audiology ,Electroencephalography ,medicine.disease ,Intracranial eeg ,Surrogate data ,Temporal lobe ,Nonlinear system ,Epilepsy ,symbols.namesake ,Statistics ,medicine ,symbols ,Psychology - Abstract
In this communication, we performed nonlinearity analysis in the EEG signals recorded from patients with temporal lobe epilepsy (TLE). The largest Lyapunov exponent (Lmax) and phase randomization surrogate data technique were employed to form the statistical test. EEG recordings were acquired invasively from three patients in six brain regions (left and right temporal depth, sub‐temporal and orbitofrontal) with 28–32 depth electrodes placed in depth and subdural of the brain. All three patients in this study have unilateral epileptic focus region on the right hippocampus(RH). Nonlinearity was detected by comparing the Lmax profiles of the EEG recordings to its surrogates. The nonlinearity was seen in all different states of the patient with the highest found in post‐ictal state. Further our results for all patients exhibited higher degree of differences, quantified by paired t‐test, in Lmax values between original and its surrogate from EEG signals recorded from epileptic focus regions. The results of this study demonstrated the Lmax is capable to capture spatio‐temporal dynamics that may not be able to detect by linear measurements in the intracranial EEG recordings.
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- 2007
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69. AntModeler analysis of mechanical stress driven transcription in three cell types
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Nan Lin, Andy Chen, Jennifer R. Mackley, Steven J. Winder, Hiroki Yokota, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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Cell type ,Microarray ,Biology ,ANT ,Cell biology ,chemistry.chemical_compound ,medicine.anatomical_structure ,chemistry ,Transcription (biology) ,Fetal mouse ,medicine ,Fibroblast ,Gene ,DNA - Abstract
Cellular stress activates transcription of various genes that mediate stress‐driven proliferation and differentiation in many cells including osteoblasts, endothelial cells, and fibroblasts. In response to mechanical stress, expression of some genes is altered regardless of cell types and that of others in specific cell types. Using the microarray‐based expression data for primary fibroblasts isolated from fetal mouse cornea, skin and tendon, we conducted a model‐based transcription analysis and predicted transcription‐factor binding motifs (TFBMs) responsible for the observed gene alteration. The computational procedure was formulated as a combinatorial optimization problem, and the AntModeler using an ant algorithm was employed to select TFBMs for each of the three fibroblast types. The results indicate that the stress responses are regulated mostly through cell type specific TFBMs together with a limited number of common TFBMs. The predicted role of those TFBMs should be evaluated experimentally.
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- 2007
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70. Optimization of epilepsy treatment with vagus nerve stimulation
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Basim Uthman, Michael Bewernitz, Chang-Chia Liu, Georges Ghacibeh, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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medicine.diagnostic_test ,medicine.medical_treatment ,Electroencephalography ,medicine.disease ,Epilepsy ,Anticonvulsant ,Anesthesia ,Adjunctive treatment ,medicine ,Ictal ,Psychology ,Neuroscience ,Neurostimulation ,Vagus nerve stimulation ,Ketogenic diet - Abstract
Epilepsy is one of the most common chronic neurological disorders that affects close to 50 million people worldwide. Antiepilepsy drugs (AEDs), the main stay of epilepsy treatment, control seizures in two thirds of patients only. Other therapies include the ketogenic diet, ablative surgery, hormonal treatments and neurostimulation. While other approaches to stimulation of the brain are currently in the experimental phase vagus nerve stimulation (VNS) has been approved by the FDA since July 1997 for the adjunctive treatment of intractable partial onset epilepsy with and without secondary generalization in patients twelve years of age or older. The safety and efficacy of VNS have been proven and duplicated in two subsequent double‐blinded controlled studies after two pilot studies demonstrated the feasibility of VNS in man. Long term observational studies confirmed the safety of VNS and that its effectiveness is sustained over time. While AEDs influence seizure thresholds via blockade or modulation of ionic channels, inhibit excitatory neurotransmitters or enhance inhibitory neurotransmitters the exact mechanism of action of VNS is not known. Neuroimaging studies revealed that VNS increases blood flow in certain regions of the brain such as the thalamus. Chemical lesions in the rat brains showed that norepinephrine is an important link in the anticonvulsant effect of VNS. Analysis of cerebrospinal fluid obtained from patients before and after treatment with VNS showed modest decreases in excitatory neurotransmitters. Although Hammond et al. reported no effect of VNS on scalp EEG by visual analysis and Salinsky et al. found no effect of VNS on scalp EEG by spectral analysis, Kuba et al. suggested that VNS reduces interictal epileptiform activity. Further, nonlinear dynamical analysis of the electroencephalogram in the rat and man have reportedly shown predictable changes (decrease in the short term Lyapunov exponent STLmax and T‐index) more than an hour prior to the clinical or electroencephalographic seizure onset. It is possible that intermittent VNS maintains chaoticity of brain activity in patients with epilepsy that respond to this therapy. The most optimal stimulation parameters of VNS are not known and further study of nonlinear dynamics of brain activity may shed some light on more effective interception or prevention of seizures. Online real time analysis may allow on‐demand stimulation rather than hit‐or‐miss approach
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- 2007
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71. A web server for mining Comparative Genomic Hybridization (CGH) data
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Jun Liu, Sanjay Ranka, Tamer Kahveci, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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Genetics ,medicine.medical_specialty ,Web server ,Chromosomal Alterations ,Cytogenetics ,Cancer ,Computational biology ,Biology ,medicine.disease ,computer.software_genre ,medicine ,Identification (biology) ,Cluster analysis ,computer ,Human cancer ,Comparative genomic hybridization - Abstract
Advances in cytogenetics and molecular biology has established that chromosomal alterations are critical in the pathogenesis of human cancer. Recurrent chromosomal alterations provide cytological and molecular markers for the diagnosis and prognosis of disease. They also facilitate the identification of genes that are important in carcinogenesis, which in the future may help in the development of targeted therapy.A large amount of publicly available cancer genetic data is now available and it is growing. There is a need for public domain tools that allow users to analyze their data and visualize the results. This chapter describes a web based software tool that will allow researchers to analyze and visualize Comparative Genomic Hybridization (CGH) datasets. It employs novel data mining methodologies for clustering and classification of CGH datasets as well as algorithms for identifying important markers (small set of genomic intervals with aberrations) that are potentially cancer signatures. The developed...
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- 2007
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72. A continuous GRASP to determine the relationship between drugs and adverse reactions
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Michael J. Hirsch, Claudio N. Meneses, Panos M. Pardalos, Michelle Ragle, Mauricio G. C. Resende, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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Continuous optimization problem ,Risk analysis (engineering) ,Heuristic (computer science) ,business.industry ,GRASP ,Econometrics ,Medicine ,Patient treatment ,Statistical analysis ,Set (psychology) ,business ,Global optimization - Abstract
Adverse drag reactions (ADRs) are estimated to be one of the leading causes of death. Many national and international agencies have set up databases of ADR reports for the express purpose of determining the relationship between drugs and adverse reactions that they cause. We formulate the drug‐reaction relationship problem as a continuous optimization problem and utilize C‐GRASP, a new continuous global optimization heuristic, to approximately determine the relationship between drugs and adverse reactions. Our approach is compared against others in the literature and is shown to find better solutions.
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- 2007
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73. Automated MR image processing and analysis of malignant brain tumors: enabling technology for data mining
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Shishir Dube, Jason J. Corso, Timothy F. Cloughesy, Suzie El-Saden, Alan L. Yuille, Usha Sinha, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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medicine.medical_specialty ,medicine.diagnostic_test ,business.industry ,Image processing ,Magnetic resonance imaging ,computer.software_genre ,medicine.disease ,Imaging data ,Clinical trial ,Tumor progression ,medicine ,Medical physics ,Segmentation ,Data mining ,Mr images ,business ,computer ,Glioblastoma - Abstract
Glioblastoma multiforme (GBM) is a malignant brain cancer with poor patient prognosis (i.e. time to survival, time to tumor progression). A number of clinical trials are underway evaluating novel therapeutic strategies and magnetic resonance imaging is the most routinely performed procedure for accurate serial monitoring of patients. The electronic availability of the comprehensive data collected as part of the clinical trials provides an unprecedented opportunity to discover new relationships in complex diseases such as GBM. However, imaging data, which is the most accurate non‐invasive assessment of GBMs, is not directly amenable for data mining. The focus of this chapter is on image analysis techniques including image spatial and intensity standardization, novel methods for robust tumor and edema segmentation, and quantification of tumor intensity, texture, and shape characteristics. The chapter concludes with an application of discovering the relationship between these quantitative image‐derived features and time to survival in GBM patients; the data is part of a comprehensive large electronically accessible archive at UCLA (UCLA Neuro‐oncology database).
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- 2007
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74. Approximation algorithms of non-unique probes selection for biological target identification
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My T. Thai, Ping Deng, Weili Wu, Taieb Znati, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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Discrete mathematics ,Set (abstract data type) ,Matrix (mathematics) ,Approximation theory ,Identification (information) ,Biological target ,Approximation algorithm ,Time complexity ,Algorithm ,Decoding methods ,Mathematics - Abstract
Non‐unique probes are used to identify the targets, i.e., viruses, present in a given sample. Since the number of selected non‐unique probes is equal to the number of hybridization experiments, it is important to find a minimum set of non‐unique probes, which is NP‐complete. Using d‐disjunct matrix, we present two (1+(d+1)logn)‐approximation algorithms to identify at most d targets. Based on our selected non‐unique probes, we also present the decoding algorithms with linear time complexity. In addition, our solutions are fault tolerant. The proposed algorithms can identify at most d targets in the presence of experimental errors.
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- 2007
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75. Quantification of the impact of vagus nerve stimulation parameters on electroencephalographic measures
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Michael Bewernitz, Georges Ghacibeh, Onur Seref, Panos M. Pardalos, Chang-Chia Liu, Basim Uthman, O. Erhun Kundakcioglu, and Panos Pardalos
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medicine.diagnostic_test ,medicine.medical_treatment ,Electroencephalography ,Scalp eeg ,EEG-fMRI ,medicine.disease ,Eeg patterns ,Epilepsy ,medicine.anatomical_structure ,Anesthesia ,Scalp ,medicine ,Psychology ,Vagus nerve stimulation ,Time segment - Abstract
This study presents an application of support vector machines (SVMs) to the analysis of electroencephalograms (EEG) obtained from the scalp of patients with epilepsy implanted with the vagus nerve stimulator (VNS) used in VNS Therapy®. The purpose of this study is to devise a physiologic marker using scalp EEG for determining optimal VNS parameters. Scalp EEG recordings were obtained from six patients with history of intractable partial onset epilepsy treated with VNS as adjunctive therapy to medicines. Averaged scalp EEG samples were used as features for separation. SVM classification accuracy was used as a measure of EEG similarity to separate a time segment during the beginning of stimulation from all the successive non‐overlapping time segments within a full VNS on/off cycle. This analysis was performed for all the automated VNS cycles occurring during approximately twenty‐four hours of 25 channels of scalp EEG. The patient that resulted in the lowest degree of EEG pattern similarity had the highest V...
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- 2007
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76. Early seizure detection in an animal model of temporal lobe epilepsy
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Sachin S. Talathi, Dong-Uk Hwang, William Ditto, Paul R. Carney, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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medicine.diagnostic_test ,business.industry ,Autocorrelation ,Statistical parameter ,Pattern recognition ,Electroencephalography ,Machine learning ,computer.software_genre ,medicine.disease ,Measure (mathematics) ,Temporal lobe ,Epilepsy ,Wavelet ,medicine ,Artificial intelligence ,Sensitivity (control systems) ,business ,Psychology ,computer - Abstract
The performance of five seizure detection schemes, i.e., Nonlinear embedding delay, Hurst scaling, Wavelet Scale, autocorrelation and gradient of accumulated energy, in their ability to detect EEG seizures close to the seizure onset time were evaluated to determine the feasibility of their application in the development of a real time closed loop seizure intervention program (RCLSIP). The criteria chosen for the performance evaluation were, high statistical robustness as determined through the predictability index, the sensitivity and the specificity of a given measure to detect an EEG seizure, the lag in seizure detection with respect to the EEG seizure onset time, as determined through visual inspection and the computational efficiency for each detection measure. An optimality function was designed to evaluate the overall performance of each measure dependent on the criteria chosen. While each of the above measures analyzed for seizure detection performed very well in terms of the statistical parameters, the nonlinear embedding delay measure was found to have the highest optimality index due to its ability to detect seizure very close to the EEG seizure onset time, thereby making it the most suitable dynamical measure in the development of RCLSIP in rat model with chronic limbic epilepsy.
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- 2007
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77. Inverse source localization for EEG using system identification approach
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Petros Xanthopoulos, Vitaliy Yatsenko, Alla Kammerdiner, Panos M. Pardalos, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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Mathematical optimization ,Computational neuroscience ,Quantitative Biology::Neurons and Cognition ,Underdetermined system ,medicine.diagnostic_test ,System identification ,Inverse ,Electroencephalography ,Inverse problem ,medicine ,Segmentation ,Boundary element method ,Algorithm ,Mathematics - Abstract
The reconstruction of the brain current sources from scalp electric recordings (Electroen‐cephalogram) also known as the inverse source localization problem is a highly underdetermined problem in the field of computational neuroscience, and this problem still remains open . In this chapter we propose an alternative formulation for the inverse electroencephalography (EEG) problem based on optimization theory. For simulation purposes, a three shell realistic head model based on an averaged magnetic resonance imaging (MRI) segmentation and Boundary Element method (BEM) is constructed. System identification methodology is employed in order to determine the parameters of the system. In the last stage the inverse problem is solved using the computed forward model.
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- 2007
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78. Automated image processing and analysis of cartilage MRI: enabling technology for data mining applied to osteoarthritis
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Hussain Z. Tameem, Usha S. Sinha, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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education.field_of_study ,medicine.diagnostic_test ,business.industry ,Cartilage ,Population ,Image processing ,Magnetic resonance imaging ,Osteoarthritis ,computer.software_genre ,medicine.disease ,Imaging data ,Article ,medicine.anatomical_structure ,Voxel ,Medicine ,Data mining ,business ,education ,Cartilage damage ,computer ,Biomedical engineering - Abstract
Osteoarthritis (OA) is a heterogeneous and multi‐factorial disease characterized by the progressive loss of articular cartilage. Magnetic Resonance Imaging has been established as an accurate technique to assess cartilage damage through both cartilage morphology (volume and thickness) and cartilage water mobility (Spin‐lattice relaxation, T2). The Osteoarthritis Initiative, OAI, is a large scale serial assessment of subjects at different stages of OA including those with pre‐clinical symptoms. The electronic availability of the comprehensive data collected as part of the initiative provides an unprecedented opportunity to discover new relationships in complex diseases such as OA. However, imaging data, which provides the most accurate non‐invasive assessment of OA, is not directly amenable for data mining. Changes in morphometry and relaxivity with OA disease are both complex and subtle, making manual methods extremely difficult. This chapter focuses on the image analysis techniques to automatically localize the differences in morphometry and relaxivity changes in different population sub‐groups (normal and OA subjects segregated by age, gender, and race). The image analysis infrastructure will enable automatic extraction of cartilage features at the voxel level; the ultimate goal is to integrate this infrastructure to discover relationships between the image findings and other clinical features.
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- 2007
79. Phase-rotated MR spectroscopy using dual-PRESS: theory and application in human brain
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Saadallah Ramadan, M. Albert Thomas, Carolyn E. Mountford, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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Sequence ,Quality (physics) ,Data acquisition ,Nuclear magnetic resonance ,Computer science ,Echo (computing) ,Phase (waves) ,Constant (mathematics) ,Rotation (mathematics) ,Algorithm ,Imaging phantom - Abstract
Phase‐rotation spectroscopic acquisition is inherently different from the popular signal‐averaging method. Phase‐rotation will be described theoretically and experimentally in this article. Traditionally, a single echo is acquired in a PRESS or STEAM sequence at a particular TE. If a long‐TE spectrum is desired, then another echo is usually acquired at a longer echo time. We here propose a method by which a pair echoes, at short‐TE and a long‐TE, are acquired in one experiment, thus saving 50% of total acquisition time without significant sacrifice spectral quality. The phase‐rotation approach has been implemented with the proposed method. An additional benefit the proposed Dual‐PRESS method, is that it gives an insight into the transverse relaxation time constant, T2, for the various metabolites. The Dual‐PRESS method is applied in phantom and in‐vivo.
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- 2007
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80. Nonparametric smoothing and its applications in biomedical imaging
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John D. Carew, Ming Yuan, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos
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Mathematical optimization ,Optimization problem ,media_common.quotation_subject ,Physics::Medical Physics ,Nonparametric statistics ,Hilbert space ,Fidelity ,Smoothing spline ,symbols.namesake ,Medical imaging ,symbols ,Algorithm ,Smoothing ,media_common ,Mathematics ,Reproducing kernel Hilbert space - Abstract
This chapter provides a brief review of smoothing splines, a powerful nonparametric smoothing tool that have found applications in a variety of biomedical imaging problems. It involves an optimization problem in a reproducing kernel Hilbert space that balances the tradeoff between the fidelity to the data and the smoothness of the estimate. After reviewing the basic theory, we then provide two examples where these methods have been applied to magnetic resonance imaging (MRI) data. The first example involves smoothing a functional MRI (fMRI) time series to induce a known correlation structure. The second example shows how to model arterial blood flow and shear.
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- 2007
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81. Robust Decision Making: Addressing Uncertainties in Distributions
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Pavlo Krokhmal, Robert Murphey, Panos Pardalos, Stanislav Uryasev, and Grigory Zrazhevski
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Expected shortfall ,Mathematical optimization ,Linear programming ,CVAR ,business.industry ,Function (mathematics) ,business ,Random variable ,Stochastic programming ,Risk management ,Mathematics ,Robust decision-making - Abstract
This paper develops a general approach to risk management in military applications involving uncertainties in information and distributions. The risk of loss, damage, or failure is measured by the Conditional Value-at-Risk (CVaR) measure. Loosely speaking, CVaR with the confidence level α estimates the risk of loss by averaging the possible losses over the (1 - α) · 100% worst cases (e.g., 10%). As a function of decision variables, CVaR is convex and therefore can be efficiently controlled/optimized using convex or (under quite general assumptions) linear programming. The general methodology was tested on two Weapon-Target Assignment (WTA) problems. It is assumed that the distributions of random variables in the WTA formulations are not known with certainty. The total cost of a mission (including weapon attrition) was minimized, while satisfying operational constraints and ensuring destruction of all targets with high probabilities. The risk of failure of the mission (e.g., targets are not destroyed) is controlled by CVaR constraints. The case studies conducted show that there are significant qualitative and quantitative differences in solutions of deterministic WTA and stochastic WTA problems.
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- 2003
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82. Multicriteria optimization for frequency assignment
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Robert Murphey, Panos Pardalos, and Eduardo Pasiliao
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- 2000
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83. On maximum clique problems in very large graphs
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J. Abello, Panos Pardalos, and M. Resende
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- 1999
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84. Preface
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Josef Kallrath, Panos Pardalos, and Steffen Rebennack
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Information Systems ,Management Information Systems - Published
- 2007
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85. On conflict-free channel set assignments for optical cluster-based hypercube networks
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Dongsoo Kim, Ding-Zhu Du, and Panos Pardalos
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- 1998
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86. A GRASP algorithm for the single source uncapacitated minimum concave-cost network flow problem
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Kristina Holmqvist, Athanasios Migdalas, and Panos Pardalos
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- 1998
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87. A greedy randomized adaptive search procedure for the multitarget multisensor tracking problem
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Robert Murphey, Panos Pardalos, and Leonidas Pitsoulis
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- 1998
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88. Approximate solution of weighted MAX-SAT problems using GRASP
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Mauricio Resende, Leonidas Pitsoulis, and Panos Pardalos
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- 1997
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89. Complexity issues in hierarchical optimization
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Panos Pardalos and Xinyu Deng
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- 1997
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90. A continuous based heuristic for the maximum clique problem
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Luana Gibbons, Donald Hearn, and Panos Pardalos
- Published
- 1996
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91. Cooperative Systems : Control and Optimization
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Don Grundel, Robert Murphey, Panos Pardalos, Oleg Prokopyev, Don Grundel, Robert Murphey, Panos Pardalos, and Oleg Prokopyev
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- Operations research, Mathematical optimization, Calculus of variations, Management science, Engineering
- Abstract
Cooperative systems are pervasive in a multitude of environments and at all levels. We find them at the microscopic biological level up to complex ecological structures. They are found in single organisms and they exist in large sociological organizations. Cooperative systems can be found in machine applications and in situations involving man and machine working together. They have some common elements: 1) more than one entity, 2) the entities have behaviors that influence the decision space, 3) entities share at least one common objective, and 4) entities share information whether actively or passively. Because of the clearly important role cooperative systems play in areas such as military sciences, biology, communications, robotics, and economics, just to name a few, the study of cooperative systems has intensified. This book provides an insight in the basic understanding of cooperative systems as well as in theory, modeling, and applications of cooperative control, optimization and related problems.
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- 2007
92. A greedy randomized adaptive search procedure for the quadratic assignment problem
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Yong Li, Panos Pardalos, and Mauricio Resende
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- 1994
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93. The quadratic assignment problem: A survey and recent developments
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Panos Pardalos, Franz Rendl, and Henry Wolkowicz
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- 1994
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94. Cell Death Discrimination with Raman Spectroscopy and Support Vector Machines.
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Georgios Pyrgiotakis, O. Kundakcioglu, Panos Pardalos, and Kevin Powers
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Abstract In the present study, Raman spectroscopy is employed to assess the potential toxicity of chemical substances. Having several advantages compared to other traditional methods, Raman spectroscopy is an ideal solution for investigating cells in their natural environment. In the present work, we combine the power of spectral resolution of Raman with one of the most widely used machine learning techniques. Support vector machines (SVMs) are used in the context of classification on a well established database. The database is constructed on three different classes: healthy cells, Triton X-100 (necrotic death), and etoposide (apoptotic death). SVM classifiers successfully assess the potential effect of the test toxins (Triton X-100, etoposide). The cells that are exposed to heat (45 °C) are tested using the classification rules obtained. It is shown that the heat effect results in apoptotic death, which is in agreement with existing literature. [ABSTRACT FROM AUTHOR]
- Published
- 2009
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95. Multiple phase neighborhood Search—GRASP based on Lagrangean relaxation, random backtracking Lin–Kernighan and path relinking for the TSP.
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Yannis Marinakis, Athanasios Migdalas, and Panos Pardalos
- Abstract
Abstract In this paper, a new modified version of Greedy Randomized Adaptive Search Procedure (GRASP), called Multiple Phase Neighborhood Search—GRASP (MPNS-GRASP), is proposed for the solution of the Traveling Salesman Problem. In this method, some procedures have been included to the classical GRASP algorithm in order to improve its performance and to cope with the major disadvantage of GRASP which is that it does not have a stopping criterion that will prevent the algorithm from spending time in iterations that give minor, if any, improvement in the solution. Thus, in MPNS-GRASP a stopping criterion based on Lagrangean Relaxation and Subgradient Optimization is proposed. Also, a different way for expanding the neighborhood search is used based on a new strategy, the Circle Restricted Local Search Moves strategy. A new variant of the Lin-Kernighan algorithm, called Random Backtracking Lin-Kernighan that helps the algorithm to diversify the search in non-promising regions of the search space is used in the Expanding Neighborhood Search phase of the algorithm. Finally, a Path Relinking Strategy is used in order to explore trajectories between elite solutions. The proposed algorithm is tested on numerous benchmark problems from TSPLIB with very satisfactory results. [ABSTRACT FROM AUTHOR]
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- 2009
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96. Selective support vector machines.
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Onur Seref, O. Kundakcioglu, Oleg Prokopyev, and Panos Pardalos
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Abstract In this study we introduce a generalized support vector classification problem: Let X i , i=1,…,n be mutually exclusive sets of pattern vectors such that all pattern vectors x i,k , k=1,…,|X i | have the same class label y i . Select only one pattern vector from each set X i such that the margin between the set of selected positive and negative pattern vectors are maximized. This problem is formulated as a quadratic mixed 0-1 programming problem, which is a generalization of the standard support vector classifiers. The quadratic mixed 0-1 formulation is shown to be -hard. An alternative approach is proposed with the free slack concept. Primal and dual formulations are introduced for linear and nonlinear classification. These formulations provide flexibility to the separating hyperplane to identify the pattern vectors with large margin. Iterative elimination and direct selection methods are developed to select such pattern vectors using the alternative formulations. These methods are compared with a naïve method on simulated data. The iterative elimination method is also applied to neural data from a visuomotor categorical discrimination task to classify highly cognitive brain activities. [ABSTRACT FROM AUTHOR]
- Published
- 2009
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97. Measuring resetting of brain dynamics at epileptic seizures: application of global optimization and spatial synchronization techniques.
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Shivkumar Sabesan, Niranjan Chakravarthy, Kostas Tsakalis, Panos Pardalos, and Leon Iasemidis
- Abstract
Abstract Epileptic seizures are manifestations of intermittent spatiotemporal transitions of the human brain from chaos to order. Measures of chaos, namely maximum Lyapunov exponents (STL max ), from dynamical analysis of the electroencephalograms (EEGs) at critical sites of the epileptic brain, progressively converge (diverge) before (after) epileptic seizures, a phenomenon that has been called dynamical synchronization (desynchronization). This dynamical synchronization/desynchronization has already constituted the basis for the design and development of systems for long-term (tens of minutes), on-line, prospective prediction of epileptic seizures. Also, the criterion for the changes in the time constants of the observed synchronization/desynchronization at seizure points has been used to show resetting of the epileptic brain in patients with temporal lobe epilepsy (TLE), a phenomenon that implicates a possible homeostatic role for the seizures themselves to restore normal brain activity. In this paper, we introduce a new criterion to measure this resetting that utilizes changes in the level of observed synchronization/desynchronization. We compare this criterion’s sensitivity of resetting with the old one based on the time constants of the observed synchronization/desynchronization. Next, we test the robustness of the resetting phenomena in terms of the utilized measures of EEG dynamics by a comparative study involving STL max , a measure of phase (φ max ) and a measure of energy (E) using both criteria (i.e. the level and time constants of the observed synchronization/desynchronization). The measures are estimated from intracranial electroencephalographic (iEEG) recordings with subdural and depth electrodes from two patients with focal temporal lobe epilepsy and a total of 43 seizures. Techniques from optimization theory, in particular quadratic bivalent programming, are applied to optimize the performance of the three measures in detecting preictal entrainment. It is shown that using either of the two resetting criteria, and for all three dynamical measures, dynamical resetting at seizures occurs with a significantly higher probability (α=0.05) than resetting at randomly selected non-seizure points in days of EEG recordings per patient. It is also shown that dynamical resetting at seizures using time constants of STL max synchronization/desynchronization occurs with a higher probability than using the other synchronization measures, whereas dynamical resetting at seizures using the level of synchronization/desynchronization criterion is detected with similar probability using any of the three measures of synchronization. These findings show the robustness of seizure resetting with respect to measures of EEG dynamics and criteria of resetting utilized, and the critical role it might play in further elucidation of ictogenesis, as well as in the development of novel treatments for epilepsy. [ABSTRACT FROM AUTHOR]
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- 2009
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98. OMEGa: an optimistic most energy gain method for minimum energy multicasting in wireless ad hoc networks.
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Manki Min and Panos Pardalos
- Abstract
Abstract In wireless ad hoc networks where every device runs on its own battery, the energy consumption is critical to lengthen the network lifetime. The communication among devices in the network can be categorized as unicasting and multicasting (including broadcasting). For the case of unicasting, computing the energy optimal path between the two communicating nodes is polynomially solvable by computing the shortest path. But for the case of multicasting, shortest path or minimum spanning tree does not guarantee an energy optimal communication. In this paper, we present our novel approach, Optimistic Most Energy Gain (OMEGa) method, for the minimum energy multicasting in wireless ad hoc networks. OMEGa aims at maximum utilization of Wireless Multicast Advantage (WMA), which essentially means covering more nodes by using larger energy. Both theoretical and experimental analysis shows OMEGa method performs very well. [ABSTRACT FROM AUTHOR]
- Published
- 2008
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99. Localization of minimax points.
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Pando Georgiev, Panos Pardalos, and Altannar Chinchuluun
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CHEBYSHEV approximation ,LOCALIZATION theory ,STEINER systems ,SET functions - Abstract
Abstract In their proof of Gilbert–Pollak conjecture on Steiner ratio, Du and Hwang (Proceedings 31th FOCS, pp. 76–85 (1990); Algorithmica 7:121–135, 1992) used a result about localization of the minimum points of functions of the type max y∈Y f(·, y). In this paper, we present a generalization of such a localization in terms of generalized vertices, when we minimize over a compact polyhedron, and Y is a compact set. This is also a strengthening of a result of Du and Pardalos (J. Global Optim. 5:127–129, 1994). We give also a random version of our generalization. [ABSTRACT FROM AUTHOR]
- Published
- 2008
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100. Novel Approaches to Hard Discrete Optimization
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Panos Pardalos, Henry Wolkowicz, Panos Pardalos, and Henry Wolkowicz
- Abstract
This volume contains the papers presented at the workshop on “Novel Approaches to Hard Discrete Optimization”. The articles cover a spectrum of issues regarding computationally hard discrete problems. The volume is suitable for graduate students and research mathematicians interested in theoretical and computational aspects of optimization.
- Published
- 2003
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