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Distributed interior-point method for loosely coupled problems 19

Distributed interior-point method for loosely coupled proble...

引用

19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014

作者： Pakazad, Sina Khoshfetrat Hansson, Anders Andersen, Martin S. Division of Automatic Control Department of Electrical Engineering Linköping University Sweden Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark

ISBN: (纸本)9783902823625

In this paper, we put forth distributed algorithms for solving loosely coupled unconstrained and constrained optimization problems. Such problems are usually solved using algorithms that are based on a combination of decomposition and first order methods. These algorithms are commonly very slow and require many iterations to converge. In order to alleviate this issue, we propose algorithms that combine the Newton and interior-point methods with proximal splitting methods for solving such problems. Particularly, the algorithm for solving unconstrained loosely coupled problems, is based on Newton's method and utilizes proximal splitting to distribute the computations for calculating the Newton step at each iteration. A combination of this algorithm and the interior-point method is then used to introduce a distributed algorithm for solving constrained loosely coupled problems. We also provide guidelines on how to implement the proposed methods efficiently, and briefly discuss the properties of the resulting solutions. © IFAC.

关键词： Problem solving

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Regularized Neural-Congruency on Spoonerism: Toward exploring the neural underpinnings of reading disorders on phonological processing 36th

Regularized Neural-Congruency on Spoonerism: Toward explorin...

引用

36th International Florida Artificial Intelligence Research Society Conference, FLAIRS-36 2023

作者： Christoforou, Christoforos Papadopoulos, Timothy C. Theodorou, Maria Division of Computer Science Mathematics and Science St. John’s University NY United States Department of Psychology & Center for Applied Neuroscience University of Cyprus Nicosia Cyprus

Children’s performance on the spoonerism task, a behavioral test that measures phonological processing skills, predicts reading abilities and related disorders. However, this rela-tionship between phonological processing skills and dyslexia has been primarily examined based on behavioral responses to the spoonerism task. As a result, there is a growing interest in developmental neuroscience to explore the neural origins of this relationship and its relation to reading difficulties. Yet, traditional electroencephalography (EEG) analysis methods had little success identifying informative neural components that depict neural differences in children with reading disorders during spoonerism. The current study explores a novel computational approach to isolate informative neural signatures elicited during the spoonerism test. We apply our method to EEG data obtained from a group of children with dyslexia and controls during the execution of a spoonerism task. Our findings demonstrate that our method extracts components that characterize the neural origins of complex cognitive phonological processes, explains differences between children with dyslexia and controls, and generates novel insights into the neural underpinnings of dyslexia in children. © 2023 by the authors. All rights reserved.

关键词： Electroencephalography

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Exploring Contrastive Learning Neural-Congruency on EEG Recording of Children with Dyslexia 37

Exploring Contrastive Learning Neural-Congruency on EEG Reco...

引用

37th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2024

作者： Christoforou, Christoforos Torres, Jacqueline M. Papadopoulos, Timothy C. Theodorou, Maria Division of Computer Science Mathematics and Science St. John’s University NY United States Department of Psychology Center for Applied Neuroscience University of Cyprus Nicosia Cyprus

Electroencephalogram (EEG) recordings of children are often used to study the underlying neural basis of causal factors of reading disorders and dyslexia. However, the inter-subject variability in EEG and the unconstrained nature of reading experiments used to elicit these factors made it challenging for traditional EEG analysis methods to extract neural components of these factors. In this work, we aim to explore the use of novel deep neural network architectures and contrastive learning methods to overcome the methodological limitations of traditional techniques and enhance the extraction process of neural components during reading tasks. Notably, we formulate a neural network architecture to extract EEG embedding using contrastive loss that maximizes the neural congruency in non-dyslexic children compared to children with dyslexia. We plan to evaluate our approach on three EEG datasets involving children with dyslexia performing Rapid Automatized Naming (RAN) and Phonological Processing (PA) tasks. The proposed contrastive learning framework will provide an enhanced tool to facilitate studying the neural underpinnings of naming speed and their association with reading performance and related difficulties. Copyright © 2024 by the authors.

关键词： Electroencephalography

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Machine learning approach for studying the neural underpinnings of dyslexia on a phonological awareness task 35th

Machine learning approach for studying the neural underpinni...

引用

35th International Florida Artificial Intelligence Research Society Conference, FLAIRS-35 2022

Understanding the neural underpinnings of dyslexia is an open and fundamental question in developmental neuroscience. A widely agreed causal risk factor for dyslexia is phonological deficit (PD). However, the causal relationships between PD and dyslexia have been primarily investigated and theorized based on findings derived from behavioral measures. What is missing is evidence of the underlying neu-rophysiological origins of these relationships. The present study examined whether the performance on a phonological awareness task, namely phoneme elision (PE), differentiated children with dyslexia from their typically developing counterparts at a neural level. We proposed a novel machine-learning-based approach to extract neural activity from EEG to identify neural differences at the group level. Specifically, we formulated an optimization problem to first extract informative EEG components (termed phoneme-related neural-congruency components) by maximizing the congruency in neural activity among typically developing children during phoneme elision. Next, we utilized a machine-learning algorithm to optimally combine the resulting components to differentiate between children with dyslexia and controls. Results showed that the proposed phoneme-related neural-congruency components are predictive about the underlying neuronal differences amongst groups. These results provide empirical evidence towards the neural underpinnings of dyslexia and the potential neural origins of PD as a causal link to dyslexia. Notably, the proposed method could be used to study other behaviorally defined developmental disorders. © 2022, by the authors. All rights reserved.

关键词： Neurons

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Differential and Difference Equations through computer Experiments 2

引用

1000年

作者： Hüseyin Koçak

ISBN: (数字)9781461236108

ISBN: (纸本)9780387969183

Phaser is a sophisticated program for IBM personal com- puters, developed atBrown University by the author and some of his students, which enables usersto experiment with differential and difference equations and dynamical systems in an interactive environment using graphics. This book begins with a brief discussion of the geometric inter- pretation of differential equations and numerical methods, and proceeds to guide the student through the use of the program. To run Phaser, you need an IBM PC, XT, AT, or PS/2 with an IBM Color GRaphics Board (CGB), Enhanced Graphics Adapter (VGA). A math coprocessor is supported; however, one is not required for Phaser to run on the above hardware.

关键词： Numerical Analysis Analysis

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Twice-Ramanujan sparsifiers

Twice-Ramanujan sparsifiers

引用

作者： Batson, Joshua Spielman, Daniel A. Srivastava, Nikhil Department of Mathematics MIT Cambridge MA 02139 United States Program in Applied Mathematics Department of Computer Science Yale University New Haven CT 06520 United States Department of Computer Science Yale University New Haven CT 06520 United States

We prove that every graph has a spectral sparsifier with a number of edges linear in its number of vertices. As linear-sized spectral sparsifiers of complete graphs are expanders, our sparsifiers of arbitrary graphs can be viewed as generalizations of expander graphs. In particular, we prove that for every d > 1 and every undirected, weighted graph G = (V, E,w) on n vertices, there exists a weighted graph H = (V, F, w˜) with at most d(n - 1)] edges such that for every x ∈ v, xT LGx ≤ xT LHx ≤ (d+1+2√d/d+1+2√d)· x T Lgx, where LG and LH are the Laplacian matrices of G and H, respectively. Thus, H approximates G spectrally at least as well as a Ramanujan expander with dn/2 edges approximates the complete graph. We give an elementary deterministic polynomial time algorithm for constructing H. Copyright © by SIAM.

关键词： Polynomial approximation

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Shape from shading: Provably convergent algorithms and uniqueness results 3rd

引用

3rd European Conference on computer Vision, ECCV 1994

作者： Dupuis, Paul Oliensis, John Division of Applied Mathematics Brown University ProvidenceRI United States Department of Computer Science University of Massachusetts at Amherst AmherstMA01003 United States

ISBN: (纸本)9783540579571

An explicit representation for the surface corresponding to a shaded image is presented and proven to be correct (under standard conditions). Uniqueness of the surface is an immediate consequence. Using this representation, various iterative algorithms for shape reconstruction are derived. It has been proven that all these algorithms converge monotonically to the correct surface reconstruction, and they have been shown experimentally to be fast and robust. Some of the results of this paper extend previous ones to the case of illumination from a general direction. © Springer-Verlag Berlin Heidelberg 1994.

关键词： Iterative methods

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Data analysis, mathematical statistics, machine learning, data mining: Similarities and differences

Data analysis, mathematical statistics, machine learning, da...

引用

2011 International Conference on Advanced computer science and Information Systems, ICACSIS 2011

作者： Mirkin, Boris Division of Applied Mathematics and Informatics National Research University Higher School of Economics Moscow Russia Department of Computer Science Birkbeck University of London United Kingdom

ISBN: (纸本)9789791421119

An attempt is made to bring some structure in the meanings of the title subjects based on the perspectives at which they view the data. A rigid framework for Data Analysis is proposed pointing out its role in augmenting the knowledge of a phenomenon the data relate to. In particular, the task of summarization as a general problem is discussed. The ingredients of a data analysis problem are described as a base for positioning of various approaches in data analysis and mathematical statistics. © 2011 Universitas Indonesia.

关键词： Data mining

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Generalized scans and tri-diagonal systems 12th

引用

12th Annual Symposium on Theoretical Aspects of computer science, STACS 1995

作者： Fischer, Paul F. Preparata, Franco P. Savage, John E. Division of Applied Mathematics Brown University ProvidenceRI02912 United States Department of Computer Science Brown University ProvidenceRI02912 United States

ISBN: (纸本)3540590420

The classical problem of solving tridiagonal linear systems of equations is reconsidered. An extremely simple factorization of the system's matrix - implied by but not explicit in the known techniques - is identified and shown to belong to a class of transformations termed generalized scans. This class has an associative property which is the key to the complete parallelization of the technique. Due to the very weak constraints upon which it is based, the method extends naturally to arbitrary banded systems. © Springer-Verlag Berlin Heidelberg 1995.

关键词： Linear systems

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

引用

2022 Saudi Arabia Smart Grid Conference, SASG 2022

作者： Kamarianakis, Yiannis Pantazis, Yannis Kalligiannaki, Evangelia Katsaounis, Theodoros D. Kotsovos, Konstantinos Gereige, Issam Abdullah, Marwan Jamal, Aqil Tzavaras, Athanasios Foundation for Research and Technology-Hellas Institute of Applied and Computational Mathematics Data Science Group Greece University of Crete Dept. of Mathematics and Applied Mathematics Greece Saudi Aramco RandD Center Carbon Management Division Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9781665475891

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average. © 2022 IEEE.

关键词： Forecasting

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