Institution
National Technical University of Athens
Education•Athens, Attiki, Greece•
About: National Technical University of Athens is a education organization based out in Athens, Attiki, Greece. It is known for research contribution in the topics: Large Hadron Collider & Nonlinear system. The organization has 13445 authors who have published 31259 publications receiving 723504 citations. The organization is also known as: Athens Polytechnic & NTUA.
Topics: Large Hadron Collider, Nonlinear system, Boundary value problem, Finite element method, Higgs boson
Papers published on a yearly basis
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University of Manchester1, KEK2, CERN3, Complutense University of Madrid4, SLAC National Accelerator Laboratory5, Toyama College6, Lebedev Physical Institute7, Fermilab8, University of Paris-Sud9, Lawrence Livermore National Laboratory10, National Research Nuclear University MEPhI11, Queen's University Belfast12, Korea Institute of Science and Technology Information13, Istituto Nazionale di Fisica Nucleare14, Northeastern University15, University of Seville16, National University of Cordoba17, Saint Joseph University18, Joint Institute for Nuclear Research19, Illawarra Health & Medical Research Institute20, University of Wollongong21, Hampton University22, TRIUMF23, ETH Zurich24, University of Bordeaux25, Centre national de la recherche scientifique26, University of Helsinki27, National Technical University of Athens28, Johns Hopkins University School of Medicine29, University of Notre Dame30, Ashikaga Institute of Technology31, Kobe University32, Intelligence and National Security Alliance33, University of Trieste34, University of Warwick35, University of Belgrade36, Instituto Superior Técnico37, European Space Agency38, Varian Medical Systems39, George Washington University40, Ritsumeikan University41, Ton Duc Thang University42, Université Paris-Saclay43, Idaho State University44, Naruto University of Education45
01 Nov 2016-Nuclear Instruments & Methods in Physics Research Section A-accelerators Spectrometers Detectors and Associated Equipment
TL;DR: Geant4 as discussed by the authors is a software toolkit for the simulation of the passage of particles through matter, which is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection.
Abstract: Geant4 is a software toolkit for the simulation of the passage of particles through matter. It is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection. Over the past several years, major changes have been made to the toolkit in order to accommodate the needs of these user communities, and to efficiently exploit the growth of computing power made available by advances in technology. The adaptation of Geant4 to multithreading, advances in physics, detector modeling and visualization, extensions to the toolkit, including biasing and reverse Monte Carlo, and tools for physics and release validation are discussed here.
2,260 citations
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TL;DR: Basic issues in signal processing and analysis techniques for consolidating psychological and linguistic analyses of emotion are examined, motivated by the PKYSTA project, which aims to develop a hybrid system capable of using information from faces and voices to recognize people's emotions.
Abstract: Two channels have been distinguished in human interaction: one transmits explicit messages, which may be about anything or nothing; the other transmits implicit messages about the speakers themselves. Both linguistics and technology have invested enormous efforts in understanding the first, explicit channel, but the second is not as well understood. Understanding the other party's emotions is one of the key tasks associated with the second, implicit channel. To tackle that task, signal processing and analysis techniques have to be developed, while, at the same time, consolidating psychological and linguistic analyses of emotion. This article examines basic issues in those areas. It is motivated by the PKYSTA project, in which we aim to develop a hybrid system capable of using information from faces and voices to recognize people's emotions.
2,255 citations
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TL;DR: A brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders are provided.
Abstract: Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.
1,970 citations
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TL;DR: In this article, a brief discussion is presented regarding the operating temperature of one-sun commercial grade silicon-based solar cells/modules and its effect upon the electrical performance of photovoltaic installations.
1,914 citations
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01 Jan 2003TL;DR: In this paper, the authors proposed a sampling-based approach for estimating Elasticities in time series regression models, which can be used to estimate a single Beta Parameter for m - 1 of the m Levels of a Variable Checking Regression Assumptions Regression Outliers Regression Model GOF Measures Multicollinearity in the Regression Regression model-Building Strategies Estimating Elasticities Censored Dependent Variables-Tobit Model Box-Cox Regression Violations of Regression this paper
Abstract: FUNDAMENTALS Statistical Inference I: Descriptive Statistics Measures of Relative Standing Measures of Central Tendency Measures of Variability Skewness and Kurtosis Measures of Association Properties of Estimators Methods of Displaying Data Statistical Inference II: Interval Estimation, Hypothesis Testing, and Population Comparisons Confidence Intervals Hypothesis Testing Inferences Regarding a Single Population Comparing Two Populations Nonparametric Methods CONTINUOUS DEPENDENT VARIABLE MODELS Linear Regression Assumptions of the Linear Regression Model Regression Fundamentals Manipulating Variables in Regression Estimate a Single Beta Parameter Estimate Beta Parameter for Ranges of a Variable Estimate a Single Beta Parameter for m - 1 of the m Levels of a Variable Checking Regression Assumptions Regression Outliers Regression Model GOF Measures Multicollinearity in the Regression Regression Model-Building Strategies Estimating Elasticities Censored Dependent Variables-Tobit Model Box-Cox Regression Violations of Regression Assumptions Zero Mean of the Disturbances Assumption Normality of the Disturbances Assumption Uncorrelatedness of Regressors and Disturbances Assumption Homoscedasticity of the Disturbances Assumption No Serial Correlation in the Disturbances Assumption Model Specification Errors Simultaneous-Equation Models Overview of the Simultaneous-Equations Problem Reduced Form and the Identification Problem Simultaneous-Equation Estimation Seemingly Unrelated Equations Applications of Simultaneous Equations to Transportation Data Panel Data Analysis Issues in Panel Data Analysis One-Way Error Component Models Two-Way Error Component Models Variable-Parameter Models Additional Topics and Extensions Background and Exploration in Time Series Exploring a Time Series Basic Concepts: Stationarity and Dependence Time Series in Regression Forecasting in Time Series: Autoregressive Integrated Moving Average (ARIMA) Models and Extensions Autoregressive Integrated Moving Average Models The Box-Jenkins Approach Autoregressive Integrated Moving Average Model Extensions Multivariate Models Nonlinear Models Latent Variable Models Principal Components Analysis Factor Analysis Structural Equation Modeling Duration Models Hazard-Based Duration Models Characteristics of Duration Data Nonparametric Models Semiparametric Models Fully Parametric Models Comparisons of Nonparametric, Semiparametric, and Fully Parametric Models Heterogeneity State Dependence Time-Varying Covariates Discrete-Time Hazard Models Competing Risk Models COUNT AND DISCRETE DEPENDENT VARIABLE MODELS Count Data Models Poisson Regression Model Interpretation of Variables in the Poisson Regression Model Poisson Regression Model Goodness-of-Fit Measures Truncated Poisson Regression Model Negative Binomial Regression Model Zero-Inflated Poisson and Negative Binomial Regression Models Random-Effects Count Models Logistic Regression Principles of Logistic Regression The Logistic Regression Model Discrete Outcome Models Models of Discrete Data Binary and Multinomial Probit Models Multinomial Logit Model Discrete Data and Utility Theory Properties and Estimation of MNL Models The Nested Logit Model (Generalized Extreme Value Models) Special Properties of Logit Models Ordered Probability Models Models for Ordered Discrete Data Ordered Probability Models with Random Effects Limitations of Ordered Probability Models Discrete/Continuous Models Overview of the Discrete/Continuous Modeling Problem Econometric Corrections: Instrumental Variables and Expected Value Method Econometric Corrections: Selectivity-Bias Correction Term Discrete/Continuous Model Structures Transportation Application of Discrete/Continuous Model Structures OTHER STATISTICAL METHODS Random-Parameter Models Random-Parameters Multinomial Logit Model (Mixed Logit Model) Random-Parameter Count Models Random-Parameter Duration Models Bayesian Models Bayes' Theorem MCMC Sampling-Based Estimation Flexibility of Bayesian Statistical Models via MCMC Sampling-Based Estimation Convergence and Identifi ability Issues with MCMC Bayesian Models Goodness-of-Fit, Sensitivity Analysis, and Model Selection Criterion using MCMC Bayesian Models Appendix A: Statistical Fundamentals Appendix B: Glossary of Terms Appendix C: Statistical Tables Appendix D: Variable Transformations References Index
1,843 citations
Authors
Showing all 13584 results
Name | H-index | Papers | Citations |
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J. S. Lange | 160 | 2083 | 145919 |
Nicholas A. Peppas | 141 | 825 | 90533 |
Claude Amsler | 138 | 1454 | 135063 |
Y. B. Hsiung | 138 | 1258 | 94278 |
M. I. Martínez | 134 | 1251 | 79885 |
Elliott Cheu | 133 | 1219 | 91305 |
Evangelos Gazis | 131 | 1147 | 84159 |
Stavros Maltezos | 129 | 943 | 79654 |
Serkant Ali Cetin | 129 | 1369 | 85175 |
Matteo Cavalli-Sforza | 129 | 1273 | 89442 |
Stefano Colafranceschi | 129 | 1103 | 79174 |
Konstantinos Nikolopoulos | 128 | 931 | 75907 |
Ilya Korolkov | 128 | 884 | 75312 |
Martine Bosman | 128 | 942 | 73848 |
Sotirios Vlachos | 128 | 789 | 77317 |