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2021 Eurographics Workshop on 3D Object Retrieval, EG 3DOR 2021

作者： Gubins, Ilja Chaillet, Marten L. van der Schot, Gijs Trueba, M. Cristina Veltkamp, Remco C. Förster, Friedrich Wang, Xiao Kihara, Daisuke Moebel, Emmanuel Nguyen, Nguyen P. White, Tommi Bunyak, Filiz Papoulias, Giorgos Gerolymatos, Stavros Zacharaki, Evangelia I. Moustakas, Konstantinos Zeng, Xiangrui Liu, Sinuo Xu, Min Wang, Yaoyu Chen, Cheng Cui, Xuefeng Zhang, Fa Department of Information and Computing Sciences Utrecht University Netherlands Department of Chemistry Utrecht University Netherlands Department of Computer Science Purdue University United States Department of Biological Sciences Purdue University United States Inria Rennes Bretagne Atlantique France Department of Electrical Engineering and Computer Science University of Missouri United States Department of Biochemistry Electron Microscopy Core Facility University of Missouri United States Department of Electrical and Computer Engineering University of Patras Greece Computational Biology Department Carnegie Mellon University United States School of Computer Science and Technology Shandong University China Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9783038681373

Cryo-electron tomography (cryo-ET) is an imaging technique that allows three-dimensional visualization of macro-molecular assemblies under near-native conditions. Cryo-ET comes with a number of challenges, mainly low signal-to-noise and inability to obtain images from all angles. computational methods are key to analyze cryo-electron tomograms. To promote innovation in computational methods, we generate a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in tomograms. Our publicly available dataset contains ten tomographic reconstructions of simulated cell-like volumes. Each volume contains twelve different types of complexes, varying in size, function and structure. In this paper, we have evaluated seven different methods of finding and classifying proteins. Seven research groups present results obtained with learning-based methods and trained on the simulated dataset, as well as a baseline template matching (TM), a traditional method widely used in cryo-ET research. We show that learning-based approaches can achieve notably better localization and classification performance than TM. We also experimentally confirm that there is a negative relationship between particle size and performance for all methods. © 2021 The Author(s) Eurographics Proceedings © 2021 The Eurographics Association.

关键词： Template matching

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PowerNet: Efficient Representations of Polynomials and Smooth Functions by Deep Neural Networks with Rectified Power Units

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数学研究 2020年第2期53卷 159-191页

作者： Bo Li Shanshan Tang Haijun Yu NCMIS&LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China China Justice Big Data Institute Beijing 100043 China

Deep neural network with rectified linear units (ReLU) is getting more and more popular recently. However, the derivatives of the function represented by a ReLU network are not continuous, which limit the usage of ReLU network to situations only when smoothness is not required. In this paper, we construct deep neural networks with rectified power units (RePU), which can give better approximations for smooth functions. Optimal algorithms are proposed to explicitly build neural networks with sparsely connected RePUs, which we call PowerNets, to represent polynomials with no approximation error. For general smooth functions, we first project the function to their polynomial approximations, then use the proposed algorithms to construct cor-responding PowerNets. Thus, the error of best polynomial approximation provides an upper bound of the best RePU network approximation error. For smooth functions in higher dimensional Sobolev spaces, we use fast spectral transforms for tensor-product grid and sparse grid discretization to get polynomial approximations. Our construc-tive algorithms show clearly a close connection between spectral methods and deep neural networks: PowerNets with n hidden layers can exactly represent polynomials up to degree sn, where s is the power of RePUs. The proposed PowerNets have po-tential applications in the situations where high-accuracy is desired or smoothness is required.

关键词： Deep neural network rectified linear unit rectified power unit sparse grid Power-Net

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A monotone finite volume method for time fractional Fokker-Planck equations

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science China Mathematics 2019年第4期62卷 783-794页

作者： Yingjun Jiang Xuejun Xu School of Mathematics and Statistics Changsha University of Science and Technology Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences Tongji University

We develop a monotone finite volume method for the time fractional Fokker-Planck equations and theoretically prove its unconditional stability. We show that the convergence rate of this method is of order 1 in the space and if the space grid becomes sufficiently fine, the convergence rate can be improved to order *** results are given to support our theoretical findings. One characteristic of our method is that it has monotone property such that it keeps the nonnegativity of some physical variables such as density, concentration,etc.

关键词： time fractional Fokker-Planck equations finite volume methods monotone convergence

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Correction to: Certificate-less Aggregate Signature Authentication Scheme (CLASAS) for secure and efficient data transmission in Wireless Sensor Networks (WSNs)

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Peer-to-Peer Networking and Applications 2024年第5期17卷 3505-3505页

作者： Radhakrishnan, Pradeep Sugumar, Praveen Kumar Ponnan, Preethi Varadharajan, Gopirajan Punniyakotti Department of Computer Science and Engineering Saveetha Engineering College Thandalam India Department of Electronics and Communication Engineering Saveetha Engineering College Thandalam India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India

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VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GROUNDING IN VIDEO-LANGUAGE MODELS

arXiv

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arXiv 2023年

作者： Kesen, Ilker Pedrotti, Andrea Dogan, Mustafa Cafagna, Michele Acikgoz, Emre Can Parcalabescu, Letitia Calixto, Iacer Frank, Anette Gatt, Albert Erdem, Aykut Erdem, Erkut Koç University KUIS AI Center 2 Koç University Department of Computer Engineering Turkey University of Pisa Department of Computer Science Italy Institute of Information Science and Technologies "Alessandro Faedo" Italy Hacettepe University Department of Computer Engineering 6 Aselsan Research Turkey University of Malta Institute of Linguistics and Language Technology Malta Heidelberg University Department of Computational Linguistics Germany Amsterdam UMC University of Amsterdam Department of Medical Informatics Netherlands Amsterdam Public Health Methodology & Mental Health Amsterdam Netherlands Utrecht University Department of Information and Computing Sciences Netherlands

With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present VILMA1 (Video Language Model Assessment), a task-agnostic benchmark that places the assessment of fine-grained capabilities of these models on a firm footing. Task-based evaluations, while valuable, fail to capture the complexities and specific temporal aspects of moving images that VidLMs need to process. Through carefully curated counterfactuals, VILMA offers a controlled evaluation suite that sheds light on the true potential of these models, as well as their performance gaps compared to human-level understanding. VILMA also includes proficiency tests, which assess basic capabilities deemed essential to solving the main counterfactual tests. We show that current VidLMs’ grounding abilities are no better than those of vision-language models which use static images. This is especially striking once the performance on proficiency tests is factored in. Our benchmark serves as a catalyst for future research on VidLMs, helping to highlight areas that still need to be explored. © 2023, CC BY.

关键词： computational linguistics

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Symmetrized two-scale finite element discretizations for partial differential equations with symmetric solutions

arXiv

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arXiv 2022年

作者： Hou, Pengyu Liu, Fang Zhou, Aihui School of Mathematical Sciences Beijing Normal University Beijing100875 China School of Statistics and Mathematics Central University of Finance and Economics Beijing102206 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

In this paper, a symmetrized two-scale finite element method is proposed for a class of partial differential equations with symmetric solutions. With this method, the finite element approximation on a fine tensor product grid is reduced to the finite element approximations on a much coarse grid and a univariant fine grid. It is shown by both theory and numerics including electronic structure calculations that the resulting approximation still maintains an asymptotically optimal accuracy. Consequently the symmetrized two-scale finite element method reduces computational cost *** Codes 65N15, 65N25, 65N30, 65N50 Copyright © 2022, The Authors. All rights reserved.

关键词： Partial differential equations

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Large Language Models for Disease Diagnosis: A Scoping Review

arXiv

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arXiv 2024年

作者： Zhou, Shuang Xu, Zidu Zhang, Mian Xu, Chunpu Guo, Yawen Zhan, Zaifu Ding, Sirui Wang, Jiashuo Xu, Kaishuai Fang, Yi Xia, Liqiao Yeung, Jeremy Zha, Daochen Lin, Mingquan Zhang, Rui Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States School of Nursing Columbia University New YorkNY United States Erik Jonsson School of Engineering and Computer Science University of Texas at Dallas RichardsonTX United States Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Informatics University of California Irvine IrvineCA United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN United States Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States Shanghai China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong Department of Computer Science Rice University HoustonTX United States

Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the growing attention in this field, many critical research questions remain under-explored. For instance, what diseases and LLM techniques have been investigated for diagnostic tasks? How can suitable LLM techniques and evaluation methods be selected for clinical decision-making? To answer these questions, we performed a comprehensive analysis of LLM-based methods for disease diagnosis. This scoping review examined the types of diseases, associated organ systems, relevant clinical data, LLM techniques, and evaluation methods reported in existing studies. Furthermore, we offered guidelines for data preprocessing and the selection of appropriate LLM techniques and evaluation strategies for diagnostic tasks. We also assessed the limitations of current research and delineated the challenges and future directions in this research field. In summary, our review outlined a blueprint for LLM-based disease diagnosis, helping to streamline and guide future research endeavors. Copyright © 2024, The Authors. All rights reserved.

关键词： Diseases

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Accurately Simulating the Time Evolution of an Ising Model with Echo Verified Clifford Data Regression on a Superconducting Quantum Computer

arXiv

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arXiv 2024年

作者： Weaving, Tim Ralli, Alexis Love, Peter J. Succi, Sauro Coveney, Peter V. Centre for Computational Science Department of Chemistry University College London WC1H 0AJ United Kingdom Department of Physics and Astronomy Tufts University MedfordMA02155 United States Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Center for Life Nano-Neuro Science @ La Sapienza Italian Institute of Technology Roma00161 Italy Department of Mechanical Engineering University College London WC1E 7JE United Kingdom Department of Physics Harvard University CambridgeMA02138 United States Advanced Research Computing Centre University College London WC1H 0AJ United Kingdom Informatics Institute University of Amsterdam Amsterdam1098 XH Netherlands

We present an error mitigation strategy composed of Echo Verification (EV) and Clifford Data Regression (CDR), the combination of which allows one to learn the effect of the quantum noise channel to extract error mitigated estimates for the expectation value of Pauli observables. We analyse the behaviour of the method under the depolarizing channel and derive an estimator for the depolarization rate in terms of the ancilla purity and postselection probability. We also highlight the sensitivity of this probability to noise, a potential bottleneck for the technique. We subsequently consider a more general noise channel consisting of arbitrary Pauli errors, which reveals a linear relationship between the error rates and the estimation of expectation values, suggesting the learnability of noise in EV by regression techniques. Finally, we present a practical demonstration of Echo Verified Clifford Data Regression (EVCDR) on a superconducting quantum computer and observe accurate results for the time evolution of an Ising model over spin-lattices consisting of up to 35 sites and circuit depths up to 173 entangling layers. © 2024, CC BY.

关键词： Qubits

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Applications of nanogenerators for biomedical engineering and healthcare systems

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InfoMat 2022年第2期4卷 53-109页

作者： Wanli Wang Jinbo Pang Jie Su Fujiang Li Qiang Li Xiaoxiong Wang Jingang Wang Bergoi Ibarlucea Xiaoyan Liu Yufen Li Weijia Zhou Kai Wang Qingfang Han Lei Liu Ruohan Zang Mark HRümmeli Yang Li Hong Liu Han Hu Gianaurelio Cuniberti College of Electrical Engineering Qingdao UniversityQingdaoChina Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of Shandong Institute for Advanced Interdisciplinary Research(iAIR)University of JinanJinanChina College of Electronic Information Qingdao UniversityQingdaoChina Department of Pediatric Surgery The Affiliated Hospital of Qingdao UniversityQingdaoChina College of Physics Qingdao UniversityQingdaoChina Institute for Materials Science and Max Bergmann Center of Biomaterials Technische Universität DresdenDresdenGermany Center for Advancing Electronics Dresden Technische Universität DresdenDresdenGermany College of Biological Science and Technology University of JinanJinanChina School of Chemistry and Chemical Engineering University of JinanShandongJinanChina College of Energy Soochow Institute for Energy and Materials InnovationsSoochow UniversitySuzhouChina Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province Soochow UniversitySuzhouChina Centre of Polymer and Carbon Materials Polish Academy of SciencesZabrzePoland Institute for Complex Materials IFW DresdenDresdenGermany Institute of Environmental Technology VŠB-Technical University of OstravaOstravaCzech Republic School of Information Science and Engineering University of JinanJinanChina Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinanChina State Key Laboratory of Crystal Materials Center of Bio&Micro/Nano Functional MaterialsShandong UniversityJinanChina College of Chemical Engineering China University of Petroleum(East China)QingdaoChina Dresden Center for Computational Materials Science Technische Universität DresdenDresdenGermany The Dresden Center for Intelligent Materials(DCIM) Technische Universität DresdenDresdenGermany

The dream of human beings for long living has stimulated the rapid development of biomedical and healthcare ***,conventional biomedical and healthcare devices have shortcomings such as short service life,large equipment size,and high potential safety ***,the power supply for conventional implantable device remains predominantly *** emerging nanogenerators,which harvest micro/nanomechanical energy and thermal energy from human beings and convert into electrical energy,provide an ideal solution for self-powering of biomedical *** combination of nanogenerators and biomedicine has been accelerating the development of self-powered biomedical *** article first introduces the operating principle of nanogenerators and then reviews the progress of nanogenerators in biomedical applications,including power supply,smart sensing,and effective ***,the microbial disinfection and biodegradation performances of nanogenerators have been ***,the protection devices have been discussed such as face mask with air filtering function together with real-time monitoring of human health from the respiration and heat ***,the nanogenerator devices have been categorized by the types of mechanical energy from human beings,such as the body movement,tissue and organ activities,energy from chemical reactions,and gravitational potential ***,the challenges and future opportunities in the applications of nanogenerators are delivered in the conclusive remarks.

关键词： biomedical engineering healthcare implantable devices nanogenerators self-powered devices sensors

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Architected Materials for Mechanical Compression: Design via Simulation, Deep Learning, and Experimentation

arXiv

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arXiv 2022年

作者： Lew, Andrew J. Jin, Kai Buehler, Markus J. Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States Department of Chemistry Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States Center for Computational Science and Engineering Schwarzman College of Computing Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States

Architected materials can achieve enhanced properties compared to their plain counterparts. Specific architecting serves as a powerful design lever to achieve targeted behavior without changing the base material. Thus, the connection between architected structure and resultant properties remains an open field of great interest to many fields, from aerospace to civil to automotive applications. Here, we focus on properties related to mechanical compression, and design hierarchical honeycomb structures to meet specific values of stiffness and compressive stress. To do so, we employ a combination of techniques in a singular workflow, starting with molecular dynamics simulation of the forward design problem, augmenting with data-driven artificial intelligence models to address the inverse design problem, and verifying the behavior of de novo structures with experimentation of additively manufactured samples. We thereby demonstrate an approach for architected design that is generalizable to multiple material properties and agnostic to the identity of the base material. © 2022, CC BY-NC-ND.

关键词： Honeycomb structures

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