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Inverse Ising techniques to infer underlying mechanisms from data

Chinese Physics B 2020年第8期29卷 1-23页

作者： Hong-Li Zeng Erik Aurell School of Science New Energy Technology Engineering Laboratory of Jiangsu ProvinceNanjing University of Posts and TelecommunicationsNanjing 210023China Nordita Royal Institute of Technologyand Stockholm UniversitySE-10691 StockholmSweden KTH-Royal Institute of Technology AlbaNova University CenterSE-10691 StockholmSweden Faculty of Physics Astronomy and Applied Computer ScienceJagiellonian University30-348 KrakowPoland

As a problem in data science the inverse Ising(or Potts)problem is to infer the parameters of a Gibbs-Boltzmann distributions of an Ising(or Potts)model from samples drawn from that *** algorithmic and computational interest stems from the fact that this inference task cannot be carried out efficiently by the maximum likelihood criterion,since the normalizing constant of the distribution(the partition function)cannot be calculated exactly and *** practical interest on the other hand flows from several outstanding applications,of which the most well known has been predicting spatial contacts in protein structures from tables of homologous protein *** applications to date have been to data that has been produced by a dynamical process which,as far as it is known,cannot be expected to satisfy detailed *** is therefore no a priori reason to expect the distribution to be of the Gibbs-Boltzmann type,and no a priori reason to expect that inverse Ising(or Potts)techniques should yield useful *** this review we discuss two types of problems where progress nevertheless can be *** find that depending on model parameters there are phases where,in fact,the distribution is close to Gibbs-Boltzmann distribution,a non-equilibrium nature of the under-lying dynamics *** also discuss the relation between inferred Ising model parameters and parameters of the underlying dynamics.

关键词： inverse Ising problem kinetic Ising model statistical genetics fitness reconstruction

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CanVEC - The canberra Vietnamese-English code-switching natural speech corpus 12

CanVEC - The canberra Vietnamese-English code-switching natu...

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12th International Conference on Language Resources and Evaluation, LREC 2020

作者： Nguyen, Li Bryant, Christopher Theoretical and Applied Linguistics University of Cambridge Cambridge United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

ISBN: (纸本)9791095546344

This paper introduces the Canberra Vietnamese-English Code-switching corpus (CanVEC), an original corpus of natural mixed speech that we semi-automatically annotated with language information, part of speech (POS) tags and Vietnamese translations. The corpus, which was built to inform a sociolinguistic study on language variation and code-switching, consists of 10 hours of recorded speech (87k tokens) between 45 Vietnamese-English bilinguals living in Canberra, Australia. We describe how we collected and annotated the corpus by pipelining several monolingual toolkits to considerably speed up the annotation process. We also describe how we evaluated the automatic annotations to ensure corpus reliability. We make the corpus available for research purposes. © European Language Resources Association (ELRA), licensed under CC-BY-NC

关键词： Speech

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DP-Kmeans and Beyond: Optimal Clustering with a New Clustering Validity Index

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Journal of computers (Taiwan) 2022年第5期33卷 1-17页

作者： Ma, Zhu-Juan Wang, Zi-Han Chen, Xiang-Hua Liu, Feng School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei230088 China School of Computer Science and Technology Anhui University Hefei230601 China Computer Applied Research Institute of Traditional Chinese Medicine Anhui University of Traditional Chinese Medicine Hefei230038 China

The K-means clustering algorithm is widely used in many areas for its high efficiency. However, the performance of the traditional K-means algorithm is very sensitive to the selection of initial clustering centers. Furthermore, except the convex distributed datasets, the traditional K-means algorithm still cannot optimally process many non-convex distributed datasets and datasets with outliers. To this end, this paper proposes the DP-Kmeans, an improved K-means algorithm based on the Density Parameter and center replacement, which can be more accurate than the traditional K-means by dropping the random selection of the initial clustering centers and continuous updating of the new centers. Due to the unsupervised learning feature, the number of clusters and the quality of data partitions generated by the clustering algorithm cannot be guaranteed. In order to evaluate the results of the DP-Kmeans algorithm, this paper proposes the SII, a new clustering validity index based on the Sum of the Inner-cluster compactness and the Inter-cluster separateness. Based on the DP-Kmeans algorithm and the SII index, a new method is proposed to determine the optimal clustering numbers for different datasets. Experimental results on ten datasets with different distributions demonstrate that the proposed clustering method is more effective the existing ones. © 2022 Authors. All rights reserved.

关键词： K-means clustering

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Sludge Age Prediction Using Machine Learning (Deep Neural Network vs Deep Bi-LSTM Model)

Sludge Age Prediction Using Machine Learning (Deep Neural Ne...

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International Workshop on Research and Education in Mechatronics (REM)

作者： Djeddou Messaoud Hellal Aouatef Loukam Imed Al Dallal Jehad Bachiri Aida A. Hameed Ibrahim Research Laboratory in Subterranean and Surface Hydraulics (LAHE) Faculty of Sciences and Technology Mohamed Khider University of Biskra Biskra Algeria Hydraulic Department Faculty of sciences and applied science Mohamed Larbi Ben M'Hidi University of Oum el-Bouaghi Algeria Research Laboratory in Subterranean and Surface Hydraulics (LARHYSS) Faculty of Sciences and Technology Mohamed Khider University of Biskra Biskra Algeria Civil Engineering Department Faculty of sciences and Technology Mohamed University of Souk Ahras Algeria Department of Computer Science Gulf University for Science and Technology Kuwait Department of ICT and Natural Sciences Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology (NTNU) Ålesund Norway

ISBN: (数字)9798331505974

ISBN: (纸本)9798331505981

The Sludge Age (SAGE) is a significant operational parameter in activated sludge processes. However, precisely estimating it can be difficult due to non-linear data and changeable operating conditions. The objective of this project is to create a bidirectional long short-term memory (Bi-LSTM) network to forecast sludge age (SAGE) using intricate time-series data obtained from a Wastewater Treatment Plant (WWTP). The Bi-LSTM architecture is recognized for efficiently handling time series and non-uniform data. The datasets used for training and evaluating the Bi-LSTM model was gathered from the Nine Springs Wastewater Treatment Plant located near Madison, Wisconsin, United States. Following the utilization of historical data for training, the suggested Bi-LSTM model outperform the DNN model and successfully forecasted SAGE, offering potential advantages to WWTP managers in enhancing operational performance, system management, and process stability.

关键词： Training Accuracy Computational modeling Time series analysis computer architecture Predictive models Data models Wastewater treatment Long short term memory Testing

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Macroscopic effects of an anisotropic Gaussian-type repulsive potential: nematic alignment and spatial effects

arXiv

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arXiv 2024年

作者： Merino-Aceituno, Sara Plunder, Steffen Wytrzens, Claudia Yoldaş, Havva Faculty of Mathematics University of Vienna Oskar-Morgenstern-Platz 1 Vienna1090 Austria KUIAS Kyoto University Faculty of Medicine Bldg. B Kyoto606-8303 Japan Delft Institute of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628CD Netherlands Okinawa Institute of Science and Technology Graduate University Onna904-0495 Japan

Elongated particles in dense systems often exhibit alignment due to volume exclusion interactions, leading to packing configurations. Traditional models of collective dynamics typically impose this alignment phenomenologically, neglecting the influence of volume exclusion on particle positions. In this paper, we derive nematic alignment from an anisotropic repulsive potential, focusing on a Gaussian-type potential and first-order dynamics for the particles. By analyzing larger particle systems and performing a hydrodynamic limit, we uncover the effects of anisotropy on both particle density and direction. Our findings reveal that while particle density evolves independently of direction, anisotropy slows down nonlinear diffusion. The direction dynamics are affected by the particles' position and involve complex transport and diffusion processes, with different behaviors for oblate and prolate particles. The key to obtaining these results lies in recent advancements in Generalized Collision Invariants offered by Degond, Frouvelle and Liu (KRM 2022) in [14].MSC Codes 35Q92, 82C22, 82D30, 82B40 Copyright © 2024, The Authors. All rights reserved.

关键词： Anisotropy

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Effective Surrogate Models for Docking Scores Prediction of Candidate Drug Molecules on SARS-CoV-2 Protein Targets

Effective Surrogate Models for Docking Scores Prediction of ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenqi Shi Mio Murakoso Xiaoyan Guo Linxi Xiong Matthew Chen May D. Wang Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Department of Applied Life Science Kyoto University Kyoto Japan College of Computing Georgia Institute of Technology Atlanta USA Tanwei College Tsinghua University Beijing China Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta USA

Emerging infectious diseases, such as coronavirus disease 2019 (COVID-19), pose a major threat to public health and present a critical challenge for drug discovery. Due to the cost- and time-consuming process of new drug development, virtual pre-screening methods such as protein-ligand docking prediction have become essential tools in enhancing drug refurbishment and repurposing. In this study, we propose a machine learning-based surrogate model for docking score prediction of drug candidates on SARS-CoV-2 protein targets via deep feature concatenation. We investigate 14 different combinations of rule-based and data-driven fingerprinting methods to identify the optimal representation of candidate drug molecules. Extensive experiments on docking scores of 270,000 molecules across 18 different SARS-CoV-2 protein targets demonstrate the effectiveness of the proposed surrogate models. In addition to unseen drugs, we further investigate the generalization of the proposed framework for unseen protein targets. This study may provide an instrumental and generalizable framework for exploring ligand-protein interaction, serving as a useful tool to facilitate rapid drug pre-screening during emerging public health crises.

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High Isolation Dual Port MIMO Antenna for Vehicle-to-Vehicle Communication

High Isolation Dual Port MIMO Antenna for Vehicle-to-Vehicle...

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Communications, Information, Electronic and Energy Systems (CIEES), International Conference on

作者： Preeti Sharma Prabhakar Singh Rakesh N. Tiwari Pradeep Kumar Physics Division School of Basic Sciences Galgotias University Greater Noida India Dept. of Applied Science Greater Noida Institute of Technology Greater Noida India Dept. of Electronics and Communication Engineering Madanapalle Institute of Technology & Science Madanapalle India Dept. of Electrical Electronic and Computer Engineering University of KwaZulu-Natal Durban South Africa

This research paper reports a very thin 2-port multiple-input multiple-output (MIMO) antenna with a small footprint of $14\times 10\times 0.58\mathrm{m}\mathrm{m}^{3}$. The proposed design works between the frequencies of 8.73 to 15.86 GHz. A modified hexagonal patch is used along with the defected ground plane to obtain wide bandwidth. A conventional decoupling strip technique is utilized to improve the isolation between the ports and it is found >19dB across the whole operating band. In addition to that, 'H'-shaped slot has been incorporated into the grounding plane to enhance impedance matching. The realized gain and radiation patterns of the designed antenna are calculated and found quite acceptable. Furthermore, the diversity parameters of the antenna are computed with envelope correlation coefficient (ECC) <0.025 and diversity gain (DG) is found >9.97dB across the entire operating frequency range. Finally, the results of MIMO antenna are compared with the calculated data for the purpose of practical validation.

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WELL-POSEDNESS OF THE STOKES EQUATIONS ON A WEDGE WITH NAVIER-SLIP BOUNDARY CONDITIONS

arXiv

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arXiv 2024年

作者： Bravin, Marco Gnann, Manuel V. Knüpfer, Hans Masmoudi, Nader Roodenburg, Floris B. Sauer, Jonas Departmento de Matemática Aplicada y Ciencias de la Computacíon E.T.S.I. Industriales y de Telecomunicacíon Universidad de Cantabria Santander39005 Spain Delft Institute of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628CD Netherlands Institute for Applied Mathematics and IWR Heidelberg University INF 205 Heidelberg69120 Germany NYUAD Research Institute New York University Abu Dhabi Saadiyat Island P.O. Box Abu Dhabi129188 United Arab Emirates Courant Institute of Mathematical Sciences New York University 251 Mercer Street New YorkNY10012 United States Delft Institute of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628CD Netherlands Institute for Mathematics Faculty of Mathematics and Computer Science University of Jena Ernst-Abbe-Platz 2 Jena07737 Germany

We consider the incompressible and stationary Stokes equations on an infinite two-dimensional wedge with non-scaling invariant Navier-slip boundary conditions. We prove well-posedness and higher regularity of the Stokes problem in a certain class of weighted Sobolev spaces. The novelty of this work is the occurrence of two different scalings in the boundary condition, which is not treated so far for the Stokes system in unbounded wedge-type domains. These difficulties are overcome by first constructing a variational solution in a second order weighted Sobolev space and subsequently proving higher regularity up to the tip of the wedge by employing an iterative scheme. We believe that this method can be used for other problems with variational structure and multiple scales. Copyright © 2024, The Authors. All rights reserved.

关键词： Boundary conditions

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ONLINE POSTING EFFECTS: UNVEILING THE NON-LINEAR JOURNEYS OF USERS IN DEPRESSION COMMUNITIES ON REDDIT

arXiv

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arXiv 2023年

作者： Morini, Virginia Citraro, Salvatore Sajno, Elena Sansoni, Maria Riva, Giuseppe Stella, Massimo Rossetti, Giulio Department of Computer Science University of Pisa Pisa Italy Italy Humane Technology Lab Catholic University of Sacred Heart Milano Italy Department of Psychology Catholic University of Sacred Heart Milano Italy Applied Technology for Neuro-Psychology Lab IRCCS Istituto Auxologico Italiano Italy CogNosco Lab Department of Psychology and Cognitive Science University of Trento Italy

Social media platforms have become pivotal as self-help forums, enabling individuals to share personal experiences and seek support. However, on topics as sensitive as depression, what are the consequences of online self-disclosure? Here, we delve into the dynamics of mental health discourse on various Reddit boards focused on depression. To this aim, we introduce a data-informed framework reconstructing online dynamics from 303k users interacting over two years. Through user-generated content, we identify 4 distinct psychological states resembling the stages of the Patient Health Engagement model. Our longitudinal analysis unveils online posting effects: a user can transition to another psychological state after online exposure to peers’ emotional/semantic content. As described by conditional Markov chains and different levels of social exposure, users’ transitions reveal non-linear journeys with individuals navigating through both positive and negative phases in a spiral rather than a linear progression. Interpreted in light of psychological literature, our findings provide evidence that the type and layout of online social interactions can deeply influence users’ journeys when posting about depression. © 2023, CC BY-NC-ND.

关键词： Tweets

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DAFD: Domain Adaptation via Feature Disentanglement for Image Classification

arXiv

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arXiv 2023年

作者： Wu, Zhize Du, Changjiang Zou, Le Tan, Ming Xu, Tong Cheng, Fan Nian, Fudong Weise, Thomas The Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei230601 China The Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei230601 China The School of Advanced Manufacturing Engingeering Hefei University Hefei230601 China The School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

A good feature representation is the key to image classification. In practice, image classifiers may be applied in scenarios different from what they have been trained on. This so-called domain shift leads to a significant performance drop in image classification. Unsupervised domain adaptation (UDA) reduces the domain shift by transferring the knowledge learned from a labeled source domain to an unlabeled target domain. We perform feature disentanglement for UDA by distilling category-relevant features and excluding category-irrelevant features from the global feature maps. This disentanglement prevents the network from overfitting to category-irrelevant information and makes it focus on information useful for classification. This reduces the difficulty of domain alignment and improves the classification accuracy on the target domain. We propose a coarse-to-fine domain adaptation method called Domain Adaptation via Feature Disentanglement (DAFD), which has two components: (1) the Category-Relevant Feature Selection (CRFS) module, which disentangles the category-relevant features from the category-irrelevant features, and (2) the Dynamic Local Maximum Mean Discrepancy (DLMMD) module, which achieves fine-grained alignment by reducing the discrepancy within the category-relevant features from different domains. Combined with the CRFS, the DLMMD module can align the category-relevant features properly. We conduct comprehensive experiment on four standard datasets. Our results clearly demonstrate the robustness and effectiveness of our approach in domain adaptive image classification tasks and its competitiveness to the state of the art. © 2023, CC BY-NC-SA.

关键词： Image classification

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