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arXiv 2024年

作者： Wu, Kaiyi Cohen, Lucas M. Myilswamy, Karthik V. Lingaraju, Navin B. Lu, Hsuan-Hao Lukens, Joseph M. Weiner, Andrew M. Elmore Family School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States The Johns Hopkins University Applied Physics Laboratory LaurelMD20723 United States Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Research Technology Office and Quantum Collaborative Arizona State University TempeAZ85287 United States

We demonstrate spectral shaping of entangled photons with a six-channel microring-resonatorbased silicon photonic pulse shaper. Through precise calibration of thermal phase shifters in a microresonator-based pulse shaper, we demonstrate line-by-line phase control on a 3 GHz grid for two frequency-bin-entangled qudits, corresponding to Hilbert spaces of up to 6×6 (3×3) dimensions for shared (independent) signal-idler filters. The pulse shaper's fine spectral resolution enables control of nanosecond-scale temporal features, which are observed by direct coincidence detection of biphoton correlation functions that show excellent agreement with theory. This work marks, to our knowledge, the first demonstration of biphoton pulse shaping using an integrated spectral shaper and holds significant promise for applications in quantum information processing. Copyright © 2024, The Authors. All rights reserved.

关键词： Hilbert spaces

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User-experience oriented POI recommendation with pseudo rating

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Multimedia Tools and Applications 2024年第16期84卷 1-21页

作者： Yi, Kun Yamagishi, Ryu Li, Jiyi Li, Taishan Bai, Zhengyang Ma, Qiang Kyoto Institute of Economic Research Kyoto University Yoshida-honmachi Sakyo-ku Kyoto Kyoto606-8501 Japan Graduate School of Informatics Kyoto University Yoshida-honmachi Sakyo-ku Kyoto Kyoto606-8501 Japan Department of Computer Science and Engineering University of Yamanashi Takeda 4-3-11 Kofu 400- 8511 Yamanashi Japan Research Institute of Non-Timber Forestry Chinese Academy of Forestry Key Laboratory of Non-Timber Forest Germplasm Enhancement & ampUtilization of State Administration of Forestry and Grassland No. 3 Weiwu Road Jinshui District Zhengzhou450003 China RIKEN Center for Computational Science RIKEN Nihonbashi 1-chome Mitsubishi Bld. 15th floor 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Department of Information Science Kyoto Institute of Technology Matsugasaki Sakyo-ku Kyoto Kyoto606-8585 Japan

Point-of-interest (POI) recommendation faces challenges in managing data sparsity and cold-start problems, particularly in terms of personalized recommendation. Firstly, most existing users only visit a small number of POIs, which results in severe data sparsity and makes most conventional methods unsuitable because they require adequate historical data from each user to learn the model. In addition, new users have little or no historical record. Existing ask-to-rate methods for cold start user scenarios treat user answers as historical data, which is inappropriate and causes difficulty in capturing user preferences. Therefore, in this paper, to tackle the data sparsity problem, we propose a user-experience model (UEM) to uncover user behaviors from several aspects. The proposed UEM involves four factors: Who (does it), What (the user does), Where (the user does it), and When (the user does it). In contrast to other methods that model user behavior from individual records, our model introduces a latent variable of user experience to extract similarity among users. This feature makes our model suitable for mitigating the severe sparse-data issue in POI recommendations. Furthermore, to handle the cold-start problem for new users, we propose a pseudo-rating mechanism (PRM) to capture new users’ preferences and recommend POIs. For new users, photos are provided for rating, and these ratings are then used to capture their preferences and estimate the probability of their visits. Extensive experimental results demonstrate that the proposed method achieves better recommendation performance and fairness than the state-of-the-art methods. Moreover, user study results validates that the users’ preferences captured via PRM are satisfactory, and UEM provides satisfactory POI recommendations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Behavioral research

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Raman response, piezoelectricity, and transport properties of the two-dimensional Janus HfSiX3H (X=N/P/As) semiconductors: A first-principles study

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Physical Review B 2024年第23期110卷 235403-235403页

作者： Tuan V. Vu Nguyen T. Hiep Huynh V. Phuc Bui D. Hoi A. I. Kartamyshev Nguyen N. Hieu Laboratory for Computational Physics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City 70000 Vietnam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City 70000 Vietnam Institute of Research and Development Duy Tan University Da Nang 550000 Vietnam Faculty of Natural Sciences Duy Tan University Da Nang 550000 Vietnam Division of Physics School of Education Dong Thap University Cao Lanh 870000 Vietnam Faculty of Physics University of Education Hue University Hue 530000 Vietnam

Exploring advanced materials with extraordinary properties is necessary for developing high-performance devices. In this work we construct two-dimensional Janus HfSiX3H (X=N/P/As) monolayers and systematically examine their physical properties by utilizing first-principles calculations. Specifically, we focus on the stabilities, Raman spectra, electronic structures, piezoelectricity, and transport properties of the monolayers. As a result, the studied HfSiN3H, HfSiP3H, and HfSiAs3H monolayers are found as semiconductors with good structural, mechanical, dynamic, and thermodynamic stabilities. The piezoelectric coefficients (e11 and e31) are calculated to examine the piezoelectricity of the HfSiX3H monolayers. It is noted that besides the in-plane piezoelectricity, the three monolayers show additional out-of-plane piezoelectric effects because of their noncentrosymmetric structures. We also include fully anisotropic acoustic deformation potential, ionized impurity, piezoelectric, and polar electron–phonon scattering to calculate the total carrier mobilities of Janus HfSiX3H. It is demonstrated that Janus HfSiX3H monolayers exhibit low carrier mobilities at room temperature (<100cm2V−1s−1). Scattering from polar optical phonons is found to dominate the total mobilities of both electrons and holes.

关键词： Electronic structure Piezoelectricity Transport phenomena Vibrational states Layered semiconductors First-principles calculations

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Unsupervised learning for anticipating critical transitions

arXiv

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arXiv 2025年

作者： Panahi, Shirin Kong, Ling-Wei Glaz, Bryan Haile, Mulugeta Lai, Ying-Cheng School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Computational Biology Cornell University IthacaNY14850 United States Vehicle Technology Directorate CCDC Army Research Laboratory 2800 Powder Mill Road AdelphiMD20783-1138 United States Vehicle Technology Directorate CCDC Army Research Laboratory 6340 Rodman Road Proving Ground AberdeenMD21005-5069 United States Department of Physics Arizona State University TempeAZ85287 United States

For anticipating critical transitions in complex dynamical systems, the recent approach of parameter-driven reservoir computing requires explicit knowledge of the bifurcation parameter. We articulate a framework combining a variational autoencoder (VAE) and reservoir computing to address this challenge. In particular, the driving factor is detected from time series using the VAE in an unsupervised-learning fashion and the extracted information is then used as the parameter input to the reservoir computer for anticipating the critical transition. We demonstrate the power of the unsupervised learning scheme using prototypical dynamical systems including the spatiotemporal Kuramoto–Sivashinsky system. The scheme can also be extended to scenarios where the target system is driven by several independent parameters or with partial state observations. © 2025, CC BY-NC-ND.

关键词： Adversarial machine learning

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Short-Time Variational Mode Decomposition

arXiv

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arXiv 2025年

作者： Jia, Hao Cao, Pengfei Liang, Tong Caiafa, Cesar F. Sun, Zhe Kushihashi, Yasuhiro Grau, Antoni Bolea, Yolanda Duan, Feng Solé-Casals, Jordi School of Medicine Nankai University Tianjin China Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Tianjin China Universitat Politècnica de Catalunya Barcelona Spain Mechanical Systems Engineering Nippon Institute of Technology Saitama Japan Villa Elisa Argentina Computational Bioengineering Laboratory Faculty of Health Data Science Juntendo University Tokyo Japan Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain

Variational mode decomposition (VMD) and its extensions like Multivariate VMD (MVMD) decompose signals into ensembles of band-limited modes with narrow central frequencies. These methods utilize Fourier transformations to shift signals between time and frequency domains. However, since Fourier transformations span the entire time-domain signal, they are suboptimal for non-stationary time series. We introduce Short-Time Variational Mode Decomposition (STVMD), an innovative extension of the VMD algorithm that incorporates the Short-Time Fourier transform (STFT) to minimize the impact of local disturbances. STVMD segments signals into short time windows, converting these segments into the frequency domain. It then formulates a variational optimization problem to extract band-limited modes representing the windowed data. The optimization aims to minimize the sum of the bandwidths of these modes across the windowed data, extending the cost functions used in VMD and MVMD. Solutions are derived using the alternating direction method of multipliers, ensuring the extraction of modes with narrow bandwidths. STVMD is divided into dynamic and non-dynamic types, depending on whether the central frequencies vary with time. Our experiments show that non-dynamic STVMD is comparable to VMD with properly sized time windows, while dynamic STVMD better accommodates non-stationary signals, evidenced by reduced mode function errors and tracking of dynamic central frequencies. This effectiveness is validated by steady-state visual-evoked potentials in electroencephalogram signals. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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RSAFormer: A method of polyp segmentation with region self-attention transformer

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computers in Biology and Medicine 2024年 172卷 108268-108268页

作者： Yin, Xuehui Zeng, Jun Hou, Tianxiao Tang, Chao Gan, Chenquan Jain, Deepak Kumar García, Salvador School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China Symbiosis Institute of Technology Symbiosis International University Pune412115 India Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada18071 Spain

Colonoscopy has attached great importance to early screening and clinical diagnosis of colon cancer. It remains a challenging task to achieve fine segmentation of polyps. However, existing State-of-the-art models still have limited segmentation ability due to the lack of clear and highly similar boundaries between normal tissue and polyps. To deal with this problem, we propose a region self-attention enhancement network (RSAFormer) with a transformer encoder to capture more robust features. Different from other excellent methods, RSAFormer uniquely employs a dual decoder structure to generate various feature maps. Contrasting with traditional methods that typically employ a single decoder, it offers more flexibility and detail in feature extraction. RSAFormer also introduces a region self-attention enhancement module (RSA) to acquire more accurate feature information and foster a stronger interplay between low-level and high-level features. This module enhances uncertain areas to extract more precise boundary information, these areas being signified by regional context. Extensive experiments were conducted on five prevalent polyp datasets to demonstrate RSAFormer's proficiency. It achieves 92.2% and 83.5% mean Dice on Kvasir and ETIS, respectively, which outperformed most of the state-of-the-art models. © 2024 Elsevier Ltd

关键词： Decoding

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Modern deep learning in bioinformatics

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Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

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Cracking the quantum scaling limit with machine learned electron densities

arXiv

引用

arXiv 2022年

作者： Rackers, Joshua A. Tecot, Lucas Geiger, Mario Smidt, Tess E. Center For Computing Research Sandia National Laboratories Department Of Computer Science University Of California Los Angeles United States Institute Of Physics École Polytechnique Fédérale De Lausanne Computational Research Division Lawrence Berkeley National Laboratory Department Of Electrical Engineering And Computer Science Massachusetts Institute Of Technology United States

A long-standing goal of science is to accurately solve the Schrödinger equation for large molecular systems. The poor scaling of current quantum chemistry algorithms on classical computers imposes an effective limit of about a few dozen atoms for which we can calculate molecular electronic structure. We present a machine learning (ML) method to break through this scaling limit and make quantum chemistry calculations of very large systems possible. We show that Euclidean Neural Networks can be trained to predict the electron density with high fidelity from limited data. Learning the electron density allows us to train a machine learning model on small systems and make accurate predictions on large ones. We show that this ML electron density model can break through the quantum scaling limit and calculate the electron density of systems of thousands of atoms with quantum accuracy. © 2022, CC BY.

关键词： Electrons

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COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

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computers, Materials & Continua 2021年第7期68卷 391-407页

作者： Aqib Ali Wali Khan Mashwani Samreen Naeem Muhammad Irfan Uddin Wiyada Kumam Poom Kumam Hussam Alrabaiah Farrukh Jamal Christophe Chesneau Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Program in Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiThanyaburi12110Thailand Departments of Mathematics Faculty of ScienceCenter of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research LaboratoryRoom SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand Department of Medical Research China Medical University HospitalTaichung40402Taiwan College of Engineering Al Ain UniversityAl Ain64141United Arab Emirates Department of Mathematics Tafila Technical UniversityTafila66110Jordan Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan 11Department of MathematicsUniversitéde CaenLMNOCaen14032France

The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning *** dataset contains data from patients who are prone to the *** contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as *** the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ***,12 optimized statistical features are selected via the chi-square feature reduction *** the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features *** is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed *** a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.

关键词： COVID-19 machine learning fuzzy c-mean deep learning J4

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A knowledge-guided framework for frame identification 59

A knowledge-guided framework for frame identification

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Joint Conference of the 59th Annual Meeting of the Association for computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Su, Xuefeng Li, Ru Li, Xiaoli Pan, Jeff Z. Zhang, Hu Chai, Qinghua Han, Xiaoqi School of Computer and Information Technology Shanxi University Taiyuan China School of E-Commerce and Logistics Shanxi Vocational University of Engineering Technology Taiyuan China Key Laboratory of Computational Intelligence Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China Institute for Infocomm Research A*Star Singapore ILCC School of Informatics University of Edinburgh United Kingdom

ISBN: (纸本)9781954085527

Frame Identification (FI) is a fundamental and challenging task in frame semantic parsing. The task aims to find the exact frame evoked by a target word in a given sentence. It is generally regarded as a classification task in existing work, where frames are treated as discrete labels or represented using one-hot embeddings. However, the valuable knowledge about frames is neglected. In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frame-to frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings. The extensive experimental results demonstrate KGFI significantly outperforms the state-of-the-art methods on two benchmark datasets. © 2021 Association for computational Linguistics

关键词： Embeddings

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