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arXiv 2024年

作者： Nam, Juno Liu, Sulin Winter, Gavin Jun, KyuJung Yang, Soojung Gómez-Bombarelli, Rafael Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Computational and Systems Biology Program Massachusetts Institute of Technology CambridgeMA02139 United States

We introduce LiFlow, a generative framework to accelerate molecular dynamics (MD) simulations for crystalline materials that formulates the task as conditional generation of atomic displacements. The model uses flow matching, with a Propagator submodel to generate atomic displacements and a Corrector to locally correct unphysical geometries, and incorporates an adaptive prior based on the Maxwell–Boltzmann distribution to account for chemical and thermal conditions. We benchmark LiFlow on a dataset comprising 25-ps trajectories of lithium diffusion across 4,186 solid-state electrolyte (SSE) candidates at four temperatures. The model obtains a consistent Spearman rank correlation of 0.7–0.8 for lithium mean squared displacement (MSD) predictions on unseen compositions. Furthermore, LiFlow generalizes from short training trajectories to larger supercells and longer simulations while maintaining high accuracy. With speed-ups of up to 600,000× compared to first-principles methods, LiFlow enables scalable simulations at significantly larger length and time scales. Copyright © 2024, The Authors. All rights reserved.

关键词： Molecular dynamics

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3D scaffolds-specific cellular mechanoresponse as a pivotal regulating factor in tissue engineering

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JMST Advances 2024年第2期6卷 121-134页

作者： Ahn, Yejin Chang, Hyewon Baek, Jieung Department of Biological Sciences Pusan National University Busan Republic of Korea Division of Mechanical and Biomedical Engineering Ewha Womans University Seoul Republic of Korea Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Republic of Korea

In tissue engineering, the mechanical properties of the extracellular matrix (ECM) or scaffolds have increasingly been considered to impact therapeutic efficacy by regulating cell behaviors, including differentiation, proliferation, migration, and adhesion. However, the understanding of how cells sense, integrate, and convert the mechanical cues from the ECM cues into biochemical signals to control certain cell behaviors is still elusive, especially in 3D, which more closely mimics the natural microenvironment than 2D systems. This review highlights the key differences between 2 and 3D in the contexts of mechanoregulative cell behaviors such as cell adhesion, spreading, migration, and force transmission. Furthermore, critical designing factors that needs to be considered for the fabrication of 3D tissue engineering scaffolds is discussed: stiffness, viscoelasticity, degradability, and the immobilization of biomolecules. Although mechanotransduction in 3D is actively being studied, understanding cellular mechanotransduction in 3D and designing of mechanoregulative 3D scaffolds still presents several challenges, including varying mechanical properties depending on different tissues, dynamic mechanical environments, and integration of multimodal cues. Interdisciplinary methodologies encompassing material engineering, cell biology, and mechanical engineering would serve to mitigate these challenges and augment our understanding of mechanoregulation governing cellular behaviors, thus fostering advancements in biomedical applications in the future.

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Projection of climate change effects on flood inundation in the Gin River basin, Sri Lanka 9

Projection of climate change effects on flood inundation in ...

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9th International Conference on Flood Management, ICFM 2023

作者： Jayapadma, Jayapadma Mudalige Miyuru Uthpala Souma, Kazuyoshi Ishidaira, Hiroshi Magome, Jun Wickramaarachchi, Thushara Navodani Special Educational Program on River Basin Environmental Science Integrated Graduate School of Medicine Engineering and Agricultural Sciences University of Yamanashi 4-3-11 Takeda Yamanashi Kofu400-8511 Japan Interdisciplinary Centre for River Basin Environment University of Yamanashi 4-3-11 Takeda Yamanashi Kofu400-8511 Japan Department of Civil and Environmental Engineering Faculty of Engineering University of Ruhuna Galle Hapugala Sri Lanka

Human-induced climate change has altered the frequency and severity of heavy precipitation, droughts, and floods worldwide. Sri Lanka, a developing country in the Indian Ocean, has suffered frequent floods, affecting over 64 % of the population in the past decade. The Gin River, the primary drinking water source of the densely populated Galle district with over 1 million residents, faces recurrent flood disasters. Therefore, this study employed a multi-model ensemble to simultaneously simulate the impact of climate change on both river flow and inundation extent in the frequently flooded Gin River basin located in humid tropical monsoon region. Bias-corrected climate projections of an ensemble of five General Circulation Models (GCMs) from the Coupled Model Inter-comparison Project Phase 5 (CMIP5) under Representative Concentration Pathway (RCP) 4.5 emission scenario were used to project future river flow and flood inundation in the Gin River basin, Sri Lanka. The validated Rainfall-Runoff-Inundation (RRI) model projected a 5 % increase in high flows (flows exceeding a 5 % probability of exceedance ( Copyright © 2024 Jayapadma Mudalige Miyuru Uthpala Jayapadma et al.

关键词： Floods

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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Accessing Gender Bias in Speech Processing Using Machine Learning and Deep Learning with Gender Balanced Audio Deepfake Dataset

Accessing Gender Bias in Speech Processing Using Machine Lea...

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International Conference on Informatics and Computing (ICIC)

作者： Tricia Estella Amalia Zahra Wai-Keung Fung Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia Department of Engineering Cardiff School of Technologies EUREKA Robotics Centre Cardiff Metropolitan University United Kingdom

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The rapid advancement of AI has led to the rise of Audio Deepfakes (AD), which pose serious ethical and security concerns by accurately mimicking human speech. This research addresses the urgent need for effective AD detection, with a focus on gender bias that can reduce the effectiveness of detection models. We examined how gender affects the performance of both Machine Learning (Support Vector Machine, Random Forest, Logistic Regression, XGBoost) and Deep Learning (Deep Neural Networks, Convolutional Neural Networks) models using the GBAD dataset. Our findings show that models trained on female audio outperform those trained on male audio, likely due to the expressive nature of female voice features and high-pitched artifacts in FAKE audio. This highlights the need for more robust, gender-sensitive detection systems. Future work should focus on developing adaptive models to reduce gender bias, improving security, and creating lightweight models for wider public use.

关键词： Deep learning Support vector machines Deepfakes Adaptation models Logistic regression computational modeling Security Speech processing Informatics Random forests

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Diradical Characters of s-Indaceno[1,2,3-cd;5,6,7-c’d’]Diphenalene with and without Interaction with MgO(001)

Diradical Characters of s-Indaceno[1,2,3-cd;5,6,7-c’d’]Dip...

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作者： Tada, Kohei Ozaki, Hiroyuki Fujimaru, Koji Kitagawa, Yasutaka Kawakami, Takashi Okumura, Mitsutaka 1-8-31 Midorigaoka Ikeda563-8577 Japan Department of Chemistry Graduate School of Science Osaka University Toyonaka560-0043 Japan Department of Materials Engineering Science Graduate School of Engineering Science Osaka University Toyonaka560-8531 Japan RIKEN Center for Computational Science Hyogo Kobe650-0047 Japan Kyoto University 1-30 Goryo-Ohara Nishikyo-ku Kyoto615-8245 Japan Osaka University 1-1 Yamada Suita565-0871 Japan

Diradical character is important for investigating and predicting the functions of singlet biradical molecules. Singlet biradical molecules possess many unique properties, and their immobilisation by surfaces has been extensively studied for applications in devices, such as quantum computers, single-molecule magnets, single-atom catalysts, organic batteries, and organic superconductivity devices. Recently, an estimation scheme of diradical character from the calculated results of density functional theory with plane-wave basis (DFT/plane-wave), which is a general method for calculating the electronic structures of surfaces, was developed by our research group. This scheme enables us to investigate the diradical character of the open-shell structure stabilised by the surfaces. In this study, DFT/plane-wave calculations for s-indaceno[1,2,3-cd;5,6,7-c’d’]diphenalene molecules, whose diradical character was experimentally observed, were performed. Using the developed scheme, it was verified whether the diradical character was modulated by the interaction with the MgO(001) surface. The calculated results showed that the diradical character increased as the distance from the surface decreased, although the enhancement effect of the surface was inhibited by the steric hindrance of the substituents. Copyright: © 2022 The author(s).

关键词： Molecules

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Geospatial Intelligence for Individual Crop Detection and Anomaly Monitoring

Geospatial Intelligence for Individual Crop Detection and An...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Freda Elikem Dorbu Leila Hashemi-Beni Department of Computational Data Science and Engineering North Carolina A&T State University Department of Built Environment Geomatics Program North Carolina A&T State University

Acquisition of geospatial data by UAV has been acknowledged as an effective method of attaining reliable and quick high-resolution remote sensing data for analysis and decision-making in different applications such as agriculture as it produces timely results, and it is affordable. UAVs coupled with other technological applications such as robotics, computing and deep learning facilitate the execution of precision agriculture. The execution of individual tree identification enables vivid description of the crop peak height and canopy for accurate estimation of issues related to crop growth such as biomass reduction, lodging, and stunted growth. A geospatial intelligence algorithm was developed to determine the lodging state of the crops. First, the optical imagery (RGB) was automatically annotated using maximum likelihood classification algorithm for faster acquisition of ground truth and to verify with the manually annotated image data for deep learning training. The acquired images were split for training of fine-tuned three deep learning methods including U-net, U-net++ and attention-residual Unet. The results show that each model has the tendency to learn patterns in data and predict lodged crops. Results from the analysis were validated by visual assessment of the time-series aerial images and validation data acquired from the ground truth data using k-fold cross validation approach.

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Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

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A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks

arXiv

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arXiv 2023年

作者： Moniri, Behrad Lee, Donghwan Hassani, Hamed Dobriban, Edgar Department of Electrical and Systems Engineering University of Pennsylvania United States Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania United States Department of Statistics and Data Science University of Pennsylvania United States

Feature learning is thought to be one of the fundamental reasons for the success of deep neural networks. It is rigorously known that in two-layer fully-connected neural networks under certain conditions, one step of gradient descent on the first layer can lead to feature learning;characterized by the appearance of a separated rank-one component—spike—in the spectrum of the feature matrix. However, with a constant gradient descent step size, this spike only carries information from the linear component of the target function and therefore learning non-linear components is impossible. We show that with a learning rate that grows with the sample size, such training in fact introduces multiple rank-one components, each corresponding to a specific polynomial feature. We further prove that the limiting large-dimensional and large sample training and test errors of the updated neural networks are fully characterized by these spikes. By precisely analyzing the improvement in the training and test errors, we demonstrate that these non-linear features can enhance learning. © 2023, CC BY.

关键词： Deep neural networks

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Building A Face Database Of Arab Faces Toward Evaluating Bias In Facial Analysis Systems 17

Building A Face Database Of Arab Faces Toward Evaluating Bia...

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17th International Conference on Interfaces and Human Computer Interaction 2023, IHCI 2023, the 17th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2023, CGVCVIP 2023 and 16th International Conference on Game and Entertainment Technologies 2023, GET 2023

作者： Khalil, Ashraf Glal, Suha Ahmed, Khider Khan, Sana Zeb Abdulgani, Aysha College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Mechatronics Engineering Graduate Program American university of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering American university of Sharjah Sharjah United Arab Emirates United Arab Emirates University Al Ain United Arab Emirates

ISBN: (纸本)9789898704498

Machine learning algorithms are fundamentally driven by the data provided by humans;consequently, the decisions made by those algorithms are not free from human bias. This is particularly evident in the case of facial analysis systems that employ machine learning algorithms. Recent studies have shown that the decisions made by many of the commercially available facial analysis systems are prejudiced against certain groups of race, ethnicity, age, gender and culture. Further studies have identified that the underlying reason for such biased decisions is that the open source material available for facial image databases which are used in commerce and academia to train the algorithms has meager diversity in these categories. To compound this issue, facial analysis technology is promoted by influential companies and artificial intelligence service providers without affirming the fairness and accuracy of the decisions given by these systems. To minimize bias and ensure representation of the Middle Eastern population in the imminent growth of this technology, we propose the development of two Arab face databases along with an algorithmic audit involving seven commercially available facial analysis systems. Of the databases, the first, Arab-LEANA, will include 300 Arab subjects' face images with variation in lighting, expression, accessory, nationality and age (LEANA). The second, Arab Public Figures Faces (APFF), will contain images and videos of 300 Arab public figures captured "in the wild". Faces for APFF will be selected manually from the internet since manual selection of faces will result in a high degree of variability in scale, pose, expression, illumination, age, occlusion and make-up. These databases will provide the worldwide community of face recognition researchers with a large-scale, diverse collection of Arab face images for training and evaluating algorithms toward developing a more representative, and therefore more robust, capacity for facial analysis. T

关键词： Machine learning

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