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SSRN 2023年

作者： Yong, Jinjun Luo, Xianbing Sun, Shuyu School of Mathematics and Statistics Guizhou University Guiyang550025 China School of Mathematics And Big Data Guizhou Education University Guiyang550018 China Computational Transport Phenomena Laboratory Division of Physical Science and Engineering King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

Deep operator networks are widely used to solve optimal control problem due to the expressive capability of the approximate nonlinear operators. Generally, an optimal control problem is equivalent to a system of partial differential equations problem. Based on this, we propose a deep multi-input and multi-output operator neural network (MIMOONet) method. We apply the proposed MIMOONet and physically informed MIMOONet to solve the optimal control problems. It works successfully for solving the elliptic (linear and semi-linear) and parabolic optimal control problems. © 2023, The Authors. All rights reserved.

关键词： Mathematical operators

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JARVIS-Leaderboard:a large scale benchmark of materials design methods

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npj computational Materials 2024年第1期10卷 2280-2296页

作者： Kamal Choudhary Daniel Wines Kangming Li Kevin F.Garrity Vishu Gupta Aldo H.Romero Jaron T.Krogel Kayahan Saritas Addis Fuhr Panchapakesan Ganesh Paul R.C.Kent Keqiang Yan Yuchao Lin Shuiwang Ji Ben Blaiszik Patrick Reiser Pascal Friederich Ankit Agrawal Pratyush Tiwary Eric Beyerle Peter Minch Trevor David Rhone Ichiro Takeuchi Robert B.Wexler Arun Mannodi-Kanakkithodi Elif Ertekin Avanish Mishra Nithin Mathew Mitchell Wood Andrew Dale Rohskopf Jason Hattrick-Simpers Shih-Han Wang Luke E.K.Achenie Hongliang Xin Maureen Williams Adam J.Biacchi Francesca Tavazza Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD20899USA Department of Materials Science and Engineering University of Toronto27 King’s College CirTorontoONCanada Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Lewis-Sigler Institute for Integrative Genomics Princeton UniversityPrincetonNJ 08544USA Ludwig Institute for Cancer Research Princeton UniversityPrincetonNJ 08544USA Department of Physics and Astronomy West Virginia UniversityMorgantownWV 26506USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Globus University of ChicagoChicagoIL 60637USA Data Science and Learning Division Argonne National LabLemontIL 60439USA Institute of Nanotechnology Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Institute of Theoretical Informatics Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Department of Chemistry and Biochemistry and Institute for Physical Science and Technology University of MarylandCollege ParkMD 20742USA Department of Physics Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyNY12180USA Department of Materials Science and Engineering University of MarylandCollege ParkMD20742USA Department of Chemistry and Institute of Materials Science and Engineering Washington University in St.LouisSt.LouisMO63130USA School of Materials Engineering Purdue UniversityWest LafayetteIN47907USA Department of Mechanical Science and Engineering University of Illinois Urbana-ChampaignUrbanaIllinois 61801USA Materials Research Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Theore

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require careful *** efforts have been developed previously to mitigate these ***,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still *** work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances *** platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data *** cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).

关键词： rigorous perfect enable

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A Generative Adversarial Network Approach to Metastatic Cancer Cell Images 4

A Generative Adversarial Network Approach to Metastatic Canc...

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4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022

作者： Lee, Seohyun Kim, Hyuno Higuchi, Hideo Ishikawa, Masatoshi Natato, Ryuichiro The University of Tokyo Laboratory of Computational Genomics Institute for Quantitative Bioscience 1-1-1 Yayoi Bunkyo-ku Tokyo Japan The University of Tokyo Institute of Industrial Science Department of Mechanical and Biofunctional Systems 4-6-1 Komaba Meguro-ku Tokyo Japan The University of Tokyo Graduate School of Science Department of Physics Japan The University of Tokyo Data Science Research Division Information Technology Center 7-3-1 Hongo Bunkyo-ku Tokyo Japan

ISBN: (纸本)9781665458184

The shapes of metastatic cancer cells are considered to be relatively different from non-metastatic cancer cells, especially regarding the degree of development of lamellipodia or the pattern of internal organ arrangement. However, understanding the specific pattern of the metastatic cancer cell has just started to emerge. In this paper, based on the generative adversarial network approach, we attempted to generate metastatic cancer cell images using human breast cancer cells where the metastasis-promoting protein, PAR1, is expressed. © 2022 IEEE.

关键词： Generative adversarial networks

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COVIDX: Computer-aided diagnosis of COVID-19 and its severity prediction with raw digital chest X-ray scans

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Quantitative Biology 2022年第2期10卷 208-220页

作者： Wajid Arshad Abbasi Syed Ali Abbas Saiqa Andleeb Maryum Bibi Fiaz Majeed Abdul Jaleel Muhammad Naveed Akhtar Computational Biology and Data Analysis Lab Department of Computer Sciences&Information TechnologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Biotechnology Lab Department of ZoologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Department of Software Engineering University of GujratGujrat 50700Pakistan Department of Computer Science (RCET)UETLahore 54000Pakistan Computational and Internet Services Division Pakistan Institute of Engineering and Applied Sciences(PIEAS)Islamabad 44000Pakistan

Background: Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the ***: In the absence or inadequate provision of therapeutic treatments of COVID-19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest X-ray (CXR) image can effortlessly be used as a substitute modality to diagnose the ***: In this study, we present an automatic COVID-19 diagnostic and severity prediction system (COVIDX) that uses deep feature maps of CXR images along with classical machine learning algorithms to identify COVID-19 and forecast its severity. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COVID-19 vs pneumonia, and COVID-19 severity) using different conventional supervised classification ***: We evaluated COVIDX through 10-fold cross-validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. In all the adopted evaluation settings, COVIDX showed strong generalization power and outperforms all the prevailing state-of-the-art methods designed for this ***: Our proposed method (COVIDX), with vivid performance in COVID-19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow-up studies of COVID-19 infected ***: We made COVIDX easily accessible through a cloud-based webserver and python code available at https://***/view/wajidarshad/software and https://***/wajidarshad/covidx, respectively.

关键词： coronavirus COVID-19 radiology machine learning chest X-ray contagious infection

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Actively Targeting Redox-Responsive Multifunctional Micelles for Synergistic Chemotherapy of Cancer

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ACS Omega 2024年第32期9卷 34268-34280页

作者： Darge, Haile Fentahun Addisu, Kefyalew Dagnew Tsai, Hsieh-Chih Birhan, Yihenew Simegniew Hanurry, Endris Yibru Mekonnen, Tefera Worku Gebrie, Hailemichael Tegenu Arunagiri, Vinothini Thankachan, Darieo Wu, Tsung-Yun Lai, Juin-Yih Chang, Hao-Ming Huang, Chun-Chiang Wu, Szu-Yuan Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology College of Medicine and Health Science Bahir Dar University Centre for Ocular Research & Education (CORE) School of Optometry and Vision Science University of Waterloo Institute of Technology Bahir Dar University Advanced Membrane Materials Center National Taiwan University of Science and Technology R&D Center for Membrane Technology Chung Yuan University Department of Chemistry College of Natural and Computational Sciences Debre Markos University School of Medicine Health Science College Addis Ababa University Division of General Surgery Tri-Service General Hospital National Defense Medical Center Taiwan Instrument Research Institute National Applied Research Laboratories Department of Food Nutrition and Health Biotechnology College of Medical and Health Science Asia University Big Data Center Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Division of Radiation Oncology Department of Medicine Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Department of Healthcare Administration College of Medical and Health Science Asia University Cancer Center Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Graduate Institute of Business Administration Fu Jen Catholic University Centers for Regional Anesthesia and Pain Medicine Taipei Municipal Wan Fang Hospital Taipei Medical University

Stimuli-responsive polymeric micelles decorated with cancer biomarkers represent an optimal choice for drug delivery applications due to their ability to enhance therapeutic efficacy while mitigating adverse side effects. Accordingly, we synthesized a digoxin-modified novel multifunctional redox-responsive disulfide-linked poly(ethylene glycol-b-poly(lactic-co-glycolic acid) copolymer (Bi(Dig-PEG-PLGA)-S₂) for the targeted and controlled release of doxorubicin (DOX) in cancer cells. Within the micellar aggregate, the disulfide bond confers redox responsiveness, while the presence of the digoxin moiety acts as a targeting agent and chemosensitizer for DOX. Upon self-assembly in aqueous solution, Bi(Dig-PEG-PLGA)-S₂ formed uniformly distributed spherical micelles with a hydrodynamic diameter (D_h) of 58.36 ± 0.78 nm and a zeta potential of −24.71 ± 1.01 mV. The micelles exhibited desirable serum and colloidal stability with a substantial drug loading capacity (DLC) of 6.26% and an encapsulation efficiency (EE) of 83.23%. In addition, the release of DOX demonstrated the redox-responsive behavior of the micelles, with approximately 89.41 ± 6.09 and 79.64 ± 6.68% of DOX diffusing from DOX@Bi(Dig-PEG-PLGA)-S₂ in the presence of 10 mM GSH and 0.1 mM H₂O₂, respectively, over 96 h. Therefore, in HeLa cell lines, DOX@Bi(Dig-PEG-PLGA)-S₂ showed enhanced intracellular accumulation and subsequent apoptotic effects, attributed to the targeting ability and chemosensitization potential of digoxin. Hence, these findings underscore the promising characteristics of Bi(Dig-PEG-PLGA)-S₂ as a multifunctional drug delivery vehicle for cancer treatment. © 2024 The Authors. Published by American Chemical Society

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Citrus black spot caused by Phyllosticta citricarpa found on Valencia oranges in Botswana

New Disease Reports

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New Disease Reports 2024年第1期49卷

作者： Mawere, P. Coetzee, B. Ntlogelang, K. Mmapetla, Z. Sebua, K. van Niekerk, J. Carstens, E. Department of Crop Production Plant Protection Division Gaborone Botswana Department of Plant Pathology Stellenbosch University Stellenbosch South Africa School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Citrus Research International Nelspruit South Africa

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SACB-Net: Spatial-awareness Convolutions for Medical Image Registration

arXiv

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arXiv 2025年

作者： Cheng, Xinxing Zhang, Tianyang Lu, Wenqi Meng, Qingjie Frangi, Alejandro F. Duan, Jinming School of Computer Science University of Birmingham United Kingdom Department of Computing and Mathematics Manchester Metropolitan University United Kingdom Division of Informatics Imaging and Data Sciences University of Manchester United Kingdom Centre for Computational Imaging and Modelling in Medicine University of Manchester United Kingdom Department of Computing Impeiral College London United Kingdom

Deep learning-based image registration methods have shown state-of-the-art performance and rapid inference speeds. Despite these advances, many existing approaches fall short in capturing spatially varying information in non-local regions of feature maps due to the reliance on spatially-shared convolution kernels. This limitation leads to suboptimal estimation of deformation fields. In this paper, we propose a 3D Spatial-Awareness Convolution Block (SACB) to enhance the spatial information within feature representations. Our SACB estimates the spatial clusters within feature maps by leveraging feature similarity and subsequently parameterizes the adaptive convolution kernels across diverse regions. This adaptive mechanism generates the convolution kernels (weights and biases) tailored to spatial variations, thereby enabling the network to effectively capture spatially varying information. Building on SACB, we introduce a pyramid flow estimator (named SACB-Net) that integrates SACBs to facilitate multi-scale flow composition, particularly addressing large deformations. Experimental results on the brain IXI and LPBA datasets as well as Abdomen CT datasets demonstrate the effectiveness of SACB and the superiority of SACB-Net over the state-of-the-art learning-based registration methods. The code is available at https://***/x-xc/SACB_Net. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

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Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance

arXiv

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arXiv 2023年

作者： Chen, Dangxing Zhang, Luyao Zu Chongzhi Center for Mathematics and Computational Sciences Duke Kunshan University Duke Avenue No.8 Kunshan Jiangsu Suzhou215316 China Data Science Research Center Social Science Division Duke Kunshan University Duke Avenue No.8 Kunshan Jiangsu Suzhou215316 China

Act in such a way that you treat humanity, whether in your own person or in the person of any other, never merely as a means to an end, but always at the same time as an end. —Immanuel Kant, Grounding for the Metaphysics of Morals Algorithm fairness in the application of artificial intelligence (AI) is essential for a better society. As the foundational axiom of social mechanisms, fairness consists of multiple facets. Although the machine learning (ML) community has focused on intersectionality as a matter of statistical parity, especially in discrimination issues, an emerging body of literature addresses another facet—monotonicity. Based on domain expertise, monotonicity plays a vital role in numerous fairness-related areas, where violations could misguide human decisions and lead to disastrous consequences. In this paper, we first systematically evaluate the significance of applying monotonic neural additive models (MNAMs), which use a fairness-aware ML algorithm to enforce both individual and pairwise monotonicity principles, for the fairness of AI ethics and society. We have found, through a hybrid method of theoretical reasoning, simulation, and extensive empirical analysis, that considering monotonicity axioms is essential in all areas of fairness, including criminology, education, health care, and finance. Our research contributes to the interdisciplinary research at the interface of AI ethics, explainable AI (XAI), and human-computer interactions (HCIs). By evidencing the catastrophic consequences if monotonicity is not met, we address the significance of monotonicity requirements in AI applications. Furthermore, we demonstrate that MNAMs are an effective fairness-aware ML approach by imposing monotonicity restrictions integrating human intelligence. Copyright © 2023, The Authors. All rights reserved.

关键词： Finance

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Deep Mixed Residual Method for Solving Pde-Constrained Optimization Problems

SSRN

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SSRN 2024年

作者： Yong, Jinjun Luo, Xianbing Sun, Shuyu Ye, Changlun School of Mathematics and Statistics Guizhou University Guiyang550025 China School of Mathematics And Big Data Guizhou Education University Guiyang550018 China Computational Transport Phenomena Laboratory Division of Physical Science and Engineering King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia School of Mathematical Sciences Guizhou Normal University Guiyang550025 China

The deep mixed residual method (DeepMRM) is a technique to solve partial differential equation. It is applied to tackle PDE-constrained optimization problems (PDE-COPs) in this paper. For a PDE-COP, we transform it into an optimality system, and then employ mixed residual method (MRM) on the optimality system. By implementing the DeepMRM with three different network structures (fully connected neural network, residual network, and attention fully connected neural network), we successfully solve PDE-COPs including elliptic, semi-linear elliptic, and Navier-Stokes (NS) equation constrained optimization problems. Compared with the exact or high-fidelity solutions, the DeepMRM provides an effective approach for solving PDE-COPs using the three different network structures. © 2024, The Authors. All rights reserved.

关键词： Navier Stokes equations

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Finite temperature effects on the structural stability of Si-doped HfO2 using first-principles calculations

arXiv

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arXiv 2023年

作者： Harashima, Yosuke Koga, Hiroaki Ni, Zeyuan Yonehara, Takehiro Katouda, Michio Notake, Akira Matsui, Hidefumi Moriya, Tsuyoshi Si, Mrinal Kanti Hasunuma, Ryu Uedono, Akira Shigeta, Yasuteru Division of Materials Science Nara Institute of Science and Technology Ikoma630-0192 Japan Center for Computational Sciences University of Tsukuba Tsukuba305-8577 Japan Department of Computational Science and Technology Research Organization for Information Science and Technology Tokyo105-0013 Japan S-Technology Development Center Tokyo Electron Technology Solutions Ltd. Japan Advanced Data Planning Department Tokyo Electron Ltd. Japan Faculty of Pure and Applied Science University of Tsukuba Tsukuba305-8573 Japan

The structural stabilities of the monoclinic and tetragonal phases of Si-doped HfO2 at finite temperatures were analyzed using a computational scheme to assess the effects of impurity doping. The finite temperature effects considered in this work represented lattice vibration and impurity configuration effects. The results show that 6% Si doping stabilizes the tetragonal phase at room temperature, although a higher concentration of Si is required to stabilize the tetragonal phase at zero temperature. These data indicate that lattice vibration and impurity configuration effects are important factors determining structural stability at finite temperatures. © 2023, CC BY.

关键词： Lattice vibrations

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