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Principles of high–fidelity, high–density 3–d neural recording

BMC Neuroscience 2014年第1期15卷 1-2页

作者： Caroline Moore-Kochlacs Nancy Kopell Justin P Kinney Ed S Boyden Jorg Scholvin Jacob G Bernstein Scott K Arfin Young Gyu Yoon Graduate Program for Neuroscience Boston University Boston MA 02215 USA McGovern Institute Massachusetts Institute of Technology MA 02139 USA Department of Mathematics and Statistics Boston University Boston MA 02215 USA Media Lab Massachusetts Institute of Technology MA 02139 USA Brain and Cognitive Science Massachusetts Institute of Technology MA 02139 USA Biological Engineering Massachusetts Institute of Technology MA 02139 USA Electrical Engineering and Computer Science Massachusetts Institute of Technology MA 02139 USA

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Prediction of carbon nanostructure mechanical properties and role of defects using machine learning

arXiv

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arXiv 2021年

作者： Zhao, Qi Winetrout, Jordan J. Xu, Yanxun Wang, Yusu Heinz, Hendrik Halıcıoglu Data Science Institute University of California San Diego San diegoCA92093 United States Department of Chemical and Biological Engineering University of Colorado Boulder BoulderCO80309 United States Materials Science and Engineering Program University of Colorado Boulder BoulderCO80309 United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD21218 United States

Carbon fiber and graphene-based nanostructures such as carbon nanotubes (CNTs) and defective structures have extraordinary potential as strong and lightweight materials. A longstanding bottleneck has been lack of understanding and implementation of atomic-scale engineering to harness the theoretical limits of modulus and tensile strength, of which only a fraction is routinely reached today. Here we demonstrate accurate and fast predictions of mechanical properties for CNTs and arbitrary 3D graphitic assemblies based on a training set of over 1000 stress-strain curves from cutting-edge reactive MD simulation and machine learning (ML). Several ML methods are compared and show that our newly proposed hierarchically structured graph neural networks with spatial information (HS-GNNs) achieve predictions in modulus and strength for any 3D nanostructure with only 5-10% error across a wide range of possible values. The reliability is sufficient for practical applications and a great improvement over off-the shelf ML methods with up to 50% deviation, as well as over earlier models for specific chemistry with 20% deviation. The algorithms allow more than 100 times faster mechanical property predictions than traditional molecular dynamics simulations, the identification of the role of defects and random 3D morphology, and high-throughput screening of 3D structures for enhanced mechanical properties. The algorithms can be scaled to morphologies up to 100 nm in size, expanded for chemically similar compounds, and trained to predict a broader range of properties. © 2021, CC BY.

关键词： Defects

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Two-dimensional Bose-Hubbard model for helium on graphene

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Physical Review B 2021年第23期103卷 235414-235414页

作者： Jiangyong Yu Ethan Lauricella Mohamed Elsayed Kenneth Shepherd, Jr. Nathan S. Nichols Todd Lombardi Sang Wook Kim Carlos Wexler Juan M. Vanegas Taras Lakoba Valeri N. Kotov Adrian Del Maestro Department of Physics University of Vermont Burlington Vermont 05405 USA Materials Science Program University of Vermont Burlington Vermont 05405 USA Department of Physics and Astronomy University of Missouri Columbia Missouri 65211 USA Department of Mathematics & Statistics University of Vermont Burlington Vermont 05405 USA Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee 37996 USA

An exciting development in the field of correlated systems is the possibility of realizing two-dimensional (2D) phases of quantum matter. For a system of bosons, an example of strong correlations manifesting themselves in a 2D environment is provided by helium adsorbed on graphene. We construct the effective Bose-Hubbard model for this system which involves hard-core bosons (U≈∞), repulsive nearest-neighbor (V>0) and small attractive (V′<0) next-nearest-neighbor interactions. The mapping onto the Bose-Hubbard model is accomplished by a variety of many-body techniques which take into account the strong He-He correlations on the scale of the graphene lattice spacing. Unlike the case of dilute ultracold atoms where interactions are effectively pointlike, the detailed microscopic form of the short-range electrostatic and long-range dispersion interactions in the helium-graphene system is crucial for the emergent Bose-Hubbard description. The result places the ground state of the first layer of He4 adsorbed on graphene deep in the commensurate solid phase with 1/3 of the sites on the dual triangular lattice occupied. Because the parameters of the effective Bose-Hubbard model are very sensitive to the exact lattice structure, this opens up an avenue to tune quantum phase transitions in this solid-state system.

关键词： Adsorption Quantum phase transitions Graphene Helium-4 superfluids Bose-Hubbard model Density functional theory Extended Hubbard model Quantum Monte Carlo Wannier function methods

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Sensory Profile of Fermented Arabica Coffee in the Perception of American Cupping Tasters

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Agricultural Sciences 2019年第3期10卷 321-329页

作者： Lucas Louzada Pereira Rogério Carvalho Guarconi Tais Rizzo Moreira Dério Brioschi Jr. Joao Paulo Pereira Marcate Luiz Henrique Bozzi Pimenta de Sousa Aldemar Polonini Moreli Danieli Grancieri Debona Carla Schwengber Ten Caten Federal Institute of Education Science and Technology of Espírito Santo Venda Nova do Imigrante CampusFood Science and Technology ProgramRua Elizabeth Minete PerimVitóriaBrazil Capixaba Institute for Technical Assistance Research and ExtensionINCAPERDepartment of StatisticsDomingos MartinsBrazil Department of Forest Sciences and Wood Federal University of Espirito SantoCenter for Agrarian Sciences and EngineeringJeronimo MonteiroBrazil Department of Production and Transportation Engineering Federal University of Rio Grande do SulPorto AlegreBrazil

The consumption of specialty coffees has been growing year after year, indicating that several niches and market forms according to the sensorial orientation of the consumers, as well as the cupping tasters and agents are responsible for the composition of blends, and for the physical and sensory classification of the coffees in the industry. This study presented 5 methods of spontaneous and induced fermentation to different North American cupping tasters, in order to evaluate coffee quality potential, based on the Specialty Coffee Association (SCA) protocol. During the months of May to July of 2017, 150 coffee samples were evaluated. Initial results indicate that North American cupping tasters evaluated dry fermented coffees using yeast culture as superiors, in the sensorial aspect, than the more conventional methods in Brazil. Such indication opens new discussions on the acceptance of a determined product, followed by the potential for modification of the sensory route as of the fermentation that will affect the attributes.

关键词： Arabica Coffee Fermentation Acceptance Cupping Tasters

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Representing and extending ensembles of parsimonious evolutionary histories with a directed acyclic graph

arXiv

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arXiv 2023年

作者： Dumm, Will Barker, Mary Howard-Snyder, William DeWitt, William S. Matsen, Frederick A. Computational Biology Program Fred Hutchinson Cancer Research Center SeattleWA United States Howard Hughes Medical Institute Computational Biology Program Fred Hutchinson Cancer Research Center SeattleWA United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Department of Genome Sciences University of Washington SeattleWA United States Department of Statistics University of Washington SeattleWA United States

In many situations, it would be useful to know not just the best phylogenetic tree for a given data set, but the collection of high-quality trees. This goal is typically addressed using Bayesian techniques, however, current Bayesian methods do not scale to large data sets. Furthermore, for large data sets with relatively low signal one cannot even store every good tree individually, especially when the trees are required to be bifurcating. In this paper, we develop a novel object called the "history subpartition directed acyclic graph" (or "history sDAG" for short) that compactly represents an ensemble of trees with labels (e.g. ancestral sequences) mapped onto the internal nodes. The history sDAG can be built efficiently and can also be efficiently trimmed to only represent maximally parsimonious trees. We show that the history sDAG allows us to find many additional equally parsimonious trees, extending combinatorially beyond the ensemble used to construct it. We argue that this object could be useful as the "skeleton" of a more complete uncertainty *** Codes 92-08 (Primary) 92B10, 92-04 (Secondary) Copyright © 2023, The Authors. All rights reserved.

关键词： Phylogenetic

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Synchronization for Coupled Complex Networks with Mixed Couplings Via Aperiodically Event-Based Pinning Control

SSRN

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SSRN 2024年

作者： Gong, Shuqing Ci, Jingxuan Guo, Zhenyuan Wen, Shiping Huang, Tingwen School of Mathematics and Statistics Changsha University of Science and Technology Hunan Changsha410114 China School of Mathematics Hunan University Hunan Changsha410082 China Centre for Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia Science Program Texas A&M University at Qatar PO Box 23874 Doha Qatar

In this paper, the synchronization problem of complex coupled networks with mixed couplings is investigated, which encompass both non-delayed and delayed couplings. This model has wide-ranging applications in representing various real-world phenomena and systems, such as neural networks, power grids, and chemical reactions. In constrained real-world network environments, optimizing the utilization of communication resources becomes particularly crucial when achieving synchronization goals. First, a control strategy that combine pinning control with event-triggered control is designed. As it focuses its control operations on necessary nodes rather than all nodes, it reduces the frequency of network communications and the volume of data transmission. Second, under the designed control, the synchronization criteria are deduced and Zeno behavior does not occur in the controlled nodes, which ensures the feasibility of the pinning control and event-triggered control. Finally, the theoretical results are substantiated with a numerical example. © 2024, The Authors. All rights reserved.

关键词： Complex networks

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Integrated Vocal Deviation Index (IVDI): A Machine Learning Model to Classifier of the General Grade of Vocal Deviation

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Journal of Voice 2024年

作者： Lima-Filho, Luiz Medeiros Araujo Lopes, Leonardo Wanderley Filho, Telmo de Menezes e Silva Department of Statistics Member of the Graduate Program in Decision Models and Health Universidade Federal da Paraíba - UFPB Paraíba João Pessoa Brazil Department of Communication Science Disorders Member of the Graduate Program in Decision Models and Health Universidade Federal da Paraíba - UFPB Paraíba João Pessoa Brazil School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom

Objective: To develop a multiparametric index based on machine learning (ML) to predict and classify the overall degree of vocal deviation (GG). Method: The sample consisted of 300 dysphonic and non-dysphonic participants of both sexes. Two speech tasks were sustained vowel [a] and connected speech (counting numbers from 1 to 10). Five speech-language pathologists performed the auditory-perceptual judgment (APJ) of the GG and the degrees of roughness (GR), breathiness (GB), instability (GI), and strain (GS). We extracted 47 acoustic measurements from these tasks. The APJ result and the acoustic measurements were used to develop the multiparametric index. We used mean absolute error, root mean square error, and coefficient of determination (R²) to select the best model of ML to predict GG and feature importance to select the best set of variables for the index. After classifying the GG between nondysphonic, mild, moderate, and severe, the final model was validated using accuracy, sensitivity, specificity, predictive values, likelihood ratios, F1-Score, and weighted kappa. Results: The gradient boost model showed the best performance among the ML models. Eight features were selected in the model, including four acoustic measures (jitterLoc, smoothed cepstral peak prominenc, mean harmonic-to-noise ratio (HNRmean), and correlation) and four APJ measures (GR, GB, GS, and GI). The final model correctly classified 93.75% of participants and obtained a weighted kappa index of 0.9374, demonstrating the model's excellent performance. Conclusion: The Integrated Vocal Deviation Index includes four acoustic measures and four auditory-perceptual measures and showed excellent performance in classifying voices according to GG. © 2024 The Voice Foundation

关键词： Machine learning—Acoustic—Voice disorders—Dysphonia—Automatic diagnosis.

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Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders

arXiv

引用

arXiv 2021年

作者： Gajamannage, Kelum Park, Yonggi Paffenroth, Randy Jayasumana, Anura P. Department of Mathematics and Statistics Texas A&M University–Corpus Christi Corpus ChristiTX78412 United States Department of Mathematical Sciences Department of Computer Science Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO80525 United States

Learning dynamics of collectively moving agents such as fish or humans is an active field in research. Due to natural phenomena such as occlusion and change of illumination, the multi-object methods tracking such dynamics might lose track of the agents where that might result fragmentation in the constructed trajectories. Here, we present an extended deep autoencoder (DA) that we train only on fully observed segments of the trajectories by defining its loss function as the Hadamard product of a binary indicator matrix with the absolute difference between the outputs and the labels. The trajectories of the agents practicing collective motion is low-rank due to mutual interactions and dependencies between the agents that we utilize as the underlying pattern that our Hadamard deep autoencoder (HDA) codes during its training. The performance of our HDA is compared with that of a low-rank matrix completion scheme in the context of fragmented trajectory reconstruction. © 2021, CC BY.

关键词： Learning systems

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Parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications

arXiv

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arXiv 2024年

作者： Zhang, Xiran Abdulah, Sameh Cao, Jian Ltaief, Hatem Sun, Ying Genton, Marc G. Keyes, David E. Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Statistics Program King Abdullah University of Science and Technology Saudi Arabia Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematics The University of Houston HoustonTX United States

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of- Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n3) and space complexity of O(n2), mainly due to a Cholesky factorization operation for an n × n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on largescale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificing the

关键词： statistics

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On Time Uniform Wong-Zakai Approximation Theorems

arXiv

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arXiv 2023年

作者： Del Moral, Pierre Hu, Shulan Jasra, Ajay Ruzayqat, Hamza Wang, Xinyu Institut de Mathematiques de Bordeaux Bordeaux33405 France School of Statistics and Mathematics Zhongnan University of Economics and Law China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Wenlan School of Business Zhongnan University of Economics and Law China

We consider the long time behavior of Wong-Zakai approximations of stochastic differential equations. These piecewise smooth diffusion approximations are of great importance in many areas, such as those with ordinary differential equations associated to random smooth fluctuations;e.g. robust filtering problems. In many examples, the mean error estimate bounds that have been derived in the literature can grow exponentially with respect to the time horizon. We show in a simple example that indeed mean error estimates do explode exponentially in the time parameter, i.e. in that case a Wong-Zakai approximation is only useful for extremely short time intervals. Under spectral conditions, we present some quantitative time-uniform convergence theorems, i.e. time-uniform mean error bounds, yielding what seems to be the first results of this type for Wong-Zakai diffusion *** Codes 60H35(Primary), 65C30(Secondary) Copyright © 2023, The Authors. All rights reserved.

关键词： Brownian movement

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