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Communications Biology 2023年第1期6卷 1110页

作者： Zelig, Aviv Kariti, Hagai Kaplan, Noam Data Science & Engineering Program Faculty of Industrial Engineering & Management Technion - Israel Institute of Technology Haifa Israel Department of Physiology Biophysics & Systems Biology Rappaport Faculty of Medicine Technion – Israel Institute of Technology Haifa Israel

The noisy and high-dimensional nature of biological data has spawned advanced clustering algorithms that are tailored for specific biological datatypes. However, the performance of such methods varies greatly between datasets and they require post hoc tuning of cryptic hyperparameters. We present k minimal distance (KMD) clustering, a general-purpose method based on a generalization of single and average linkage hierarchical clustering. We introduce a generalized silhouette-like function to eliminate the cryptic hyperparameter k, and use sampling to enable application to million-object datasets. Rigorous comparisons to general and specialized clustering methods on simulated, mass cytometry and scRNA-seq datasets show consistent high performance of KMD clustering across all datasets. © 2023, The Author(s).

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The KIND dataset: A Social Collaboration Approach for Nuanced Dialect data Collection 18

The KIND Dataset: A Social Collaboration Approach for Nuance...

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18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024 - Student Research Workshop, SRW 2024

作者： Yamani, Asma Z. Alziyady, Raghad AlYami, Reem Albelali, Salma A. Abouhagar, Leina Almulhim, Jawharah Alsulami, Amjad Alfarraj, Motaz Al-Zaidy, Rabeah Department of Information and Computer Science King Fahd University of Petroleum & Minerals Saudi Arabia Center for Integrative Petroleum Research CIPR King Fahd University of Petroleum & Minerals Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum & Minerals Saudi Arabia SDAIA-KFUPM Joint Research Center for AI King Fahd University of Petroleum & Minerals Saudi Arabia Preparatory Year Program King Fahd University of Petroleum & Minerals Saudi Arabia Department of Computer Science Imam Abdulrahman Bin Faisal University Saudi Arabia Saudi Data & AI Authority Saudi Arabia

ISBN: (纸本)9798891760905

Nuanced dialects are a linguistic variant that pose several challenges for NLP models and techniques. One of the main challenges is the limited amount of datasets to enable extensive research and experimentation. We propose an approach for efficiently collecting nuanced dialectal datasets that are not only of high quality, but are versatile enough to be multipurpose as well. To test our approach we collect the KIND corpus, which is a collection of fine-grained Arabic dialect data. The data is short texts, and unlike many nuanced dialectal datasets, it is curated manually through social collaboration efforts as opposed to being crawled from social media. The collaborative approach is incentivized through educational gamification and competitions for which the community itself benefits from the open source dataset. Our approach aims to achieve: (1) coverage of dialects from under-represented groups and fine-grained dialectal varieties, (2) provide aligned parallel corpora for translation between Modern Standard Arabic (MSA) and multiple dialects to enable translation and comparison studies, (3) promote innovative approaches for nuanced dialect data collection. We explain the steps for the competition as well as the resulting datasets and the competing data collection systems. The KIND dataset is shared with the research community. © 2024 Association for Computational Linguistics.

关键词： data acquisition

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Research on the Spillover Effects of Digital Transformation on the Sustainable Growth of Green Schools

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Proceedings of Business and Economic Studies 2023年第6期6卷 16-23页

作者： Yiting Qiu Qiuhua Chen Poh Soon Joseph Ng Zhejiang Technical Institute of Economics Hangzhou 3018Zhejiang ProvinceChina Innovation and Technology Program Engineering and Quantity SurveyingINTI International UniversityNilai 71800Negeri SembilanMalaysia Faculty of Data Science and Information Technology INTI International UniversityNilai 71800Negeri SembilanMalaysia

The development of society and the advancement of science and technology have led to the widespread integration of digital transformation in the field of ***,the current establishment of green schools faces various challenges,including non-environmental building facilities,high renovation costs,low organizational management efficiency,high energy consumption,outdated office tools,and insufficient environmental awareness among teachers and *** thorough research and analysis,it becomes evident that digital technology can play a pivotal role in addressing these challenges and contribute to all aspects of green school *** incorporation of digital thinking concepts is essential for the construction of ecologically civilized campuses and inclusive *** process of digital design and transformation proves instrumental in optimizing both software and hardware facilities within the campus,thereby reducing energy ***,comprehensive digital teaching management enhances overall efficiency in management and service *** digital teaching and learning models emerge as transformative tools,providing new avenues to create low-carbon,green classrooms for both teachers and *** exploring the application of digital transformation in establishing green schools and examining the resulting spillover effects,valuable insights can be *** insights,in turn,serve as reference points for building diversified digital technology paths on campus and fostering the creation of green schools.

关键词： Digital transformation Green schools Spillover effect

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Multilevel Monte Carlo for a class of Partially Observed Processes in Neuroscience

arXiv

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arXiv 2023年

作者： Maama, Mohamed Jasra, Ajay Kamatani, Kengo 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 School of Data Science The Chinese University of Hong Kong CN Shenzhen China Institute of Statistical Mathematics Tokyo190-0014 Japan

In this paper we consider Bayesian parameter inference associated to a class of partially observed stochastic differential equations (SDE) driven by jump processes. Such type of models can be routinely found in applications, of which we focus upon the case of neuroscience. The data are assumed to be observed regularly in time and driven by the SDE model with unknown parameters. In practice the SDE may not have an analytically tractable solution and this leads naturally to a time-discretization. We adapt the multilevel Markov chain Monte Carlo method of [15], which works with a hierarchy of time discretizations and show empirically and theoretically that this is preferable to using one single time discretization. The improvement is in terms of the computational cost needed to obtain a pre-specified numerical error. Our approach is illustrated on models applied to examples of Bayesian inference problems using both simulated and real observed data from neuroscience. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Accuracy of a one-dimensional reduction of dynamical systems on networks

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Physical Review E 2022年第2期105卷 024305-024305页

作者： Prosenjit Kundu Hiroshi Kori Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Department of Complexity Science and Engineering The University of Tokyo Chiba 277-8561 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Resilience is an ability of a system with which the system can adjust its activity to maintain its functionality when it is perturbed. To study resilience of dynamics on networks, Gao et al. [Nature (London) 530, 307 (2016)] proposed a theoretical framework to reduce dynamical systems on networks, which are high dimensional in general, to one-dimensional dynamical systems. The accuracy of this one-dimensional reduction relies on three approximations in addition to the assumption that the network has a negligible degree correlation. In the present study, we analyze the accuracy of the one-dimensional reduction assuming networks without degree correlation. We do so mainly through examining the validity of the individual assumptions underlying the method. Across five dynamical system models, we find that the accuracy of the one-dimensional reduction hinges on the spread of the equilibrium value of the state variable across the nodes in most cases. Specifically, the one-dimensional reduction tends to be accurate when the dispersion of the node's state is small. We also find that the correlation between the node's state and the node's degree, which is common for various dynamical systems on networks, is unrelated to the accuracy of the one-dimensional reduction.

关键词： Complex systems Dynamics of networks Network resilience

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Tmf-Gnn: Temporal Matrix Factorization-Based Graph Neural Network for Multivariate Time Series Forecasting with Missing Values

SSRN

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SSRN 2023年

作者： Kim, Suhyeon Lee, Taek-Ho Lee, Junghye Graduate School of Data Science Kyungpook National University Daegu41566 Korea Republic of Technology Management Economics and Policy Program & Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of Technology Management Economics and Policy Program & Graduate School of Engineering Practice Seoul National University Seoul08826 Korea Republic of

Missing data in multivariate time series (MTS) is a very common issue, often caused by unreliable sensors and data storage or transmission problems. Particularly, such missing data can cause some errors and biases in the MTS forecasting tasks of real-world applications, implying that proper handling of the missing data is essential. Therefore, in this study, we propose a new method for MTS forecasting with missing values, called a temporal matrix factorization-based graph neural network (TMF-GNN), to improve predictive performance outcomes. TMF-GNN basically uses the concept of TMF, which reconstructs partially observed MTS data. We newly present a data-adaptive regularization method for TMF based on graph-based and sequential deep learning algorithms to capture both the variable- and sample-wise information of MTS data affected by the concept known as missingness. We demonstrate the feasibility of the proposed method by conducting various experiments on three MTS datasets and show how it outperforms baseline methods. We believe that our study will have an impact on several MTS-related tasks and that it can be a useful alternative for handling missing values in MTS data. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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KNU-DTI: KNowledge United Drug-Target Interaction prediction

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Computers in Biology and Medicine 2025年 189卷 109927-109927页

作者： Heo, Ryong Lee, Dahyeon Kim, Byung Ju Seo, Sangmin Park, Sanghyun Park, Chihyun Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Gangwon-do Chuncheon-si24341 Korea Republic of Department of Data Science Kangwon National University Korea Republic of UBLBio Corporation Yeongtong-ro 237 Gyeonggi-do Suwon16679 Korea Republic of Department of Computer Science Yonsei University Yonsei-ro 50 Seodaemun-gu Seoul03722 Korea Republic of Department of Computer Science and Engineering Kangwon National University Korea Republic of

Motivation: Accurately predicting drug-target protein interactions (DTI) is a cornerstone of drug discovery, enabling the identification of potential therapeutic compounds. Sequence-based prediction models, despite their simplicity, hold great promise in extracting essential information directly from raw sequences. However, the focus in recent DTI studies has increasingly shifted toward enhancing algorithmic complexity, often at the expense of fully leveraging robust sequence representation learning methods. This shift has led to the underestimation and gradual neglect of methodologies aimed at effectively capturing discriminative features from sequences. Our work seeks to address this oversight by emphasizing the value of well-constructed sequence representation algorithms, demonstrating that even with simple interaction mapping algorithm techniques, accurate DTI models can be achieved. By prioritizing meaningful information extraction over excessive model complexity, we aim to advance the development of practical and generalizable DTI prediction frameworks. Results: We developed the KNowledge Uniting DTI model (KNU-DTI), which retrieves structural information and unites them. Protein structural properties were obtained using structural property sequence (SPS). Extended-connectivity fingerprint (ECFP) was used to estimate the structure-activity relationship in molecules. Including these two features, a total of five latent vectors were derived from protein and molecule via various neural networks and integrated by elemental-wise addition to predict binding interactions or affinity. Using four test concepts to evaluate the model, we show that the model outperforms recently published competitors. Finally, a case study indicated that our model has a competitive edge over existing docking simulations in some cases. © 2025

关键词： Prediction models

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning

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Procedia Computer science 2023年 216卷 57-66页

作者： Mira Hayati Kahlil Muchtar Roslidar Novi Maulina Irfan Syamsuddin Gregorius Natanael Elwirehardja Bens Pardamean Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh 23111 Indonesia Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh 23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar 90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-aided diagnosis of diabetic retinopathy.

关键词： Diabetic retinopathy Deep Learning CNN Image Enhancement CLAHE

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Theoretical and Experimental Studies on Characterisation and Collection of Particulate Matter in Single-Wire, Single-Stage Electrostatic Precipitator with Square Cross-Section

SSRN

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SSRN 2024年

作者： Kumar, Aiswarya A. Nawale, Prashant Rohidas Mayya, Y.S. Sahu, Manoranjan Aerosol and Nanoparticle Technology Laboratory Environmental Science and Engineering Department Indian Institute of Technology Bombay Mumbai400076 India Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai400076 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai400076 India Chemical Engineering Department Indian Institute of Technology Bombay Mumbai400076 India

Electrostatic precipitation (ESP) is a technology widely used to remove particulate matter (PM) from industrial gas streams. To adopt the same for varying scales as well as for different clean air delivery applications as in indoor and outdoor air pollution, there exists a requirement for the development of comprehensive, readily adaptable, reasonably good, comparable, rigorous, step-by-step analytical theory and experimental validation of same for design of modular units of ESP. In this regard, the current study conducted theoretical and experimental studies to investigate corona characterisation and PM collection efficiency in a modular unit of a single-wire, single-stage, wire-plate ESP with square cross-sectional geometry. The theoretical study integrated the corona discharge model with particle transport model while the experimental study was conducted using a laboratory scale ESP operated at different flow rates and various potentials. Results obtained from both theory and experimental studies for current-voltage (I-V) characteristics were consistent with the well-known quadratic behaviour beyond the critical potential. The best agreement between the I-V characteristics of theory and the experiment was obtained while adjusting the inception electric field ( to 12.35´105 Vm-1 as well as the ion diffusion coefficient value to 0.0647´10-4 m2s-1. Tuned theory predicted PM collection efficiency at three different flow rates of 30, 50 as well as 100 LPM and at various potentials 9 kV, 11 kV as well as 13 kV respectively. Comparing the predicted results from theory and experiment, it is understood that agreement between theory and experiment is acceptable in the case of varied flow rates and is good for potentials for varied size ranges from 13 nm to 800 nm. Relative error for local minimum efficiency between theory as well as experiment for three tested flow rates were 5.37%, 1.36%, 14.58% and was 1.36%, 7.52% and 6.23% for three potentials respectively. As accuracy

关键词： Electrostatic precipitators

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Fixation probability in evolutionary dynamics on switching temporal networks

arXiv

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arXiv 2023年

作者： Bhaumik, Jnanajyoti Masuda, Naoki Department of Mathematics State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Population structure has been known to substantially affect evolutionary dynamics. Networks that promote the spreading of fitter mutants are called amplifiers of selection, and those that suppress the spreading of fitter mutants are called suppressors of selection. Research in the past two decades has found various families of amplifiers while suppressors still remain somewhat elusive. It has also been discovered that most networks are amplifiers of selection under the birth-death updating combined with uniform initialization, which is a standard condition assumed widely in the literature. In the present study, we extend the birth-death processes to temporal (i.e., time-varying) networks. For the sake of tractability, we restrict ourselves to switching temporal networks, in which the network structure deterministically alternates between two static networks at constant time intervals or stochastically in a Markovian manner. We show that, in a majority of cases, switching networks are less amplifying than both of the two static networks constituting the switching networks. Furthermore, most small switching networks, i.e., networks on six nodes or less, are suppressors, which contrasts to the case of static networks. © 2023, CC BY.

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