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15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2019

作者： Lin, Meng-Ju Zhao, Ming Chen, Shuo-Tsung Chen, Ching-Ju Department of Applied Mathematics Tunghai University No. 1727 Sec.4 Taiwan Boulevard Taichung Taiwan40704 Taiwan School of Computer Science Yangtze University Jingzhou CityHubei Province China College of Future Bachelor Program in Interdisciplinary Studies National Yunlin University of Science and Technology TaiwanYunlin64002 Taiwan National Yunlin University of Science and Technology TaiwanYunlin64002 Taiwan

ISBN: (纸本)9789811397134

This paper proposed an amplitude embedding system for digital audio watermarking in the wavelet domain. In this paper, the relationship between the watermarked coefficients and the original coefficients is first presented and then amplitude-based embedding rules are combined into an amplitude-based embedding system that incorporates a system state. For the embedding in this system, the information includes synchronization codes and the watermarks are embedded into the DWT lowest frequency sub-band coefficients which can be described by a matrix form. Moreover, the SNR is rewritten as a performance index which is a quadratic form. Then, a nonlinear function connecting the performance index and the embedding system is derived. Accordingly, an optimization-based embedding system is proposed. Finally, the Karush–Kuhn–Tucker (KKT) Theorem is used to obtain the optimal solution, which is an important formula for watermarking. In addition, the hidden information can be extracted without knowledge of the original audio. In the experiments, the performance of the proposed system is tested and the results verify the high SNR and strong robustness against multimedia signal processing or malicious attacks. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Embeddings

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Spatial atlas of the mouse central nervous system at molecular resolution (vol 622, pg 552, 2023)

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NATURE 2024年第7993期625卷 E6-E6页

作者： Shi, Hailing He, Yichun Zhou, Yiming Huang, Jiahao Maher, Kamal Wang, Brandon Tang, Zefang Luo, Shuchen Tan, Peng Wu, Morgan Lin, Zuwan Ren, Jingyi Thapa, Yaman Tang, Xin Chan, Ken Y. Deverman, Benjamin E. Shen, Hao Liu, Albert Liu, Jia Wang, Xiao Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA USA Computational and Systems Biology PhD Program Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Identifying viruses from metagenomic data using deep learning

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Quantitative Biology 2020年第1期8卷 64-77页

作者： Jie Ren Kai Song Chao Deng Nathan AAhlgren Jed AFuhrman Yi Li Xiaohui Xie Ryan Poplin Fengzhu Sun Quantitative and Computational Biology Program University of Southern CaliforniaLos AngelesCA 90089USA School of Mathematics and Statistics Qingdao UniversityQingdao 266071China Department of Biology Clark UniversityWorcesterMA 01610USA Department of Biological Sciences University of Southern CaliforniaLos AngelesCA 90089USA Department of Computer Science University of CaliforniaIrvineCA 92697USA Google Inc. Mountain ViewCA 94043USA

Background:The recent development of metagenomic sequencing makes it possible to massively sequence microbial genomes including viral genomes without the need for laboratory *** reference-based and gene homology-based methods are not efficient in identifying unknown viruses or short viral sequences from metagenomic ***:Here we developed a reference-free and alignment-free machine learning method,DeepVirFinder,for identifying viral sequences in metagenomic data using deep ***'.Trained based on sequences from viral RefSeq discovered before May 2015,and evaluated on those discovered after that date,DeepVirFinder outperformed the state-of-the-art method VirFinder at all contig lengths,achieving AUROC 0.93,0.95,0.97,and 0.98 for 300,500,1000,and 3000 bp sequences *** the training data with additional millions of purified viral sequences from metavirome samples further improved the accuracy for identifying virus groups that are *** DeepVirFinder to real human gut metagenomic samples,we identified 51,138 viral sequences belonging to 175 bins in patients with colorectal carcinoma(CRC).Ten bins were found associated with the cancer status,suggesting viruses may play important roles in ***:Powered by deep learning and high throughput sequencing metagenomic data,DeepVirFinder significantly improved the accuracy of viral identification and will assist the study of viruses in the era of metagenomics.

关键词： metagenome deep learning virus identification machine learning

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Digital Audio Watermarking by Quantization Embedding System 15th

Digital Audio Watermarking by Quantization Embedding System

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作者： Chen, Ching-Ju Zhao, Ming Chen, Shuo-Tsung Lin, Meng-Ju College of Future Bachelor Program in Interdisciplinary Studies National Yunlin University of Science and Technology DouliuYunlin Taiwan64002 Taiwan School of Computer Science Yangtze University Jingzhou CityHubei Province China Department of Applied Mathematics Tunghai University No. 1727 Sec. 4 Taiwan Boulevard TaichungTaiwan40704 Taiwan National Yunlin University of Science and Technology DouliuYunlin Taiwan64002 Taiwan

ISBN: (纸本)9789811397134

This paper designs a digital audio watermarking scheme using a quantization embedding system in the wavelet domain. In this paper, the relationship between the watermarked coefficients and the original coefficients is first presented and then the quantization-based embedding rules are combined into a quantization-based embedding system that incorporates a system state. For the embedding in this system, the information includes synchronization codes and the watermarks are embedded into the DWT lowest frequency sub-band coefficients, which can be described by a matrix form. Moreover, the SNR is rewritten as a performance index which is a quadratic form. Then, a nonlinear function connecting the performance index and the embedding system is derived. Accordingly, an optimization-based embedding system is proposed. Finally, the Lagrange Principle is used to obtain the optimal solution which is an important formula for watermarking. In addition, the hidden information can be extracted without knowledge of the original audio. In the experiments, the performance of the proposed system is tested and the results verify the high SNR and strong robustness against multimedia signal processing or malicious attacks. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Audio watermarking

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A Hybrid Method of FEBI and PO for Scattering Analysis of Inhomogeneous Structures with Large-Scale Platform 5

A Hybrid Method of FEBI and PO for Scattering Analysis of In...

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5th IEEE International Conference on computational Electromagnetics, ICCEM 2019

作者： Liu, Yang Zheng, Yu-Teng Zhou, Hai-Jing Chen, Xiao-Jie Guo, Xing-Yue Institute of Applied Physics and Computational Mathematics Beijing China CAEP Software Center for High Performance Numerical Simulation Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China

ISBN: (纸本)9781538671115

In the paper, a hybrid of finite element boundary integral (FEBI) method and physical optics (PO) is discussed for analyzing scattering problems of inhomogeneous structures with electrically large-scale platform. The hybrid of these two methods is realized by an iterative process. The hybrid method can efficiently reduce the number of unknowns and the computational complexity while preserving acceptable accuracy. Some numerical results are displayed to illuminate the efficiency of the method. © 2019 IEEE.

关键词： Physical optics

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Electron mobility of SnO2 from first principles

arXiv

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arXiv 2024年

作者： Wang, Amanda Bushick, Kyle Pant, Nick Lee, Woncheol Zhang, Xiao Leveillee, Joshua Giustino, Feliciano Poncé, Samuel Kioupakis, Emmanouil Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Electrical and Computer Engineering University of Michigan Ann ArborMI48109 United States Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX78712 United States Department of Physics The University of Texas at Austin AustinTX78712 United States European Theoretical Spectroscopy Facility Institute of Condensed Matter and Nanosciences Université catholique de Louvain Chemin des Étoiles 8 Louvain-la-NeuveB-1348 Belgium WEL Research Institute Avenue Pasteur 6 Wavre1300 Belgium

The transparent conducting oxide SnO2 is a wide bandgap semiconductor that is easily n-type doped and widely used in various electronic and optoelectronic applications. Experimental reports of the electron mobility of this material vary widely depending on the growth conditions and doping concentrations. In this work, we calculate the electron mobility of SnO2 from first principles to examine the temperature- and doping-concentration dependence, and to elucidate the scattering mechanisms that limit transport. We include both electron-phonon scattering and electron-ionized impurity scattering to accurately model scattering in a doped semiconductor. We find a strongly anisotropic mobility that favors transport in the direction parallel to the c-axis. At room temperature and intrinsic carrier concentrations, the low-energy polar-optical phonon modes dominate scattering, while ionized-impurity scattering dominates above 1018 cm-3 Copyright © 2024, The Authors. All rights reserved.

关键词： Electron mobility

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Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression Data 23rd

Pathway Activity Score Learning for Dimensionality Reduction...

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23rd International Conference on Discovery science, DS 2020

作者： Karagiannaki, Ioulia Pantazis, Yannis Chatzaki, Ekaterini Tsamardinos, Ioannis Department of Computer Science University of Crete Heraklion Greece Institute of Applied and Computational Mathematics Foundation for Research and Technology - Hellas Heraklion Greece Laboratory of Pharmacology Medical School Democritus University of Thrace Alexandroupoli Greece Institute of Agri-food and Life Sciences University Research Center Hellenic Mediterranean University HeraklionCrete71410 Greece Gnosis Data Analysis PC HeraklionCrete Greece

ISBN: (纸本)9783030615260

Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (e.g., high dimensional data). However, there exist lower-dimensional representations that retain the useful information. We present a novel algorithm for such dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features directly correspond to known molecular pathways and can be interpreted as pathway activity scores. Hence, unlike PCA and similar methods, PASL’s latent space has a relatively straight-forward biological interpretation. As a use-case, PASL is applied on two collections of breast cancer and leukemia gene expression datasets. We show that PASL does retain the predictive information for disease classification on new, unseen datasets, as well as outperforming PLIER, a recently proposed competitive method. We also show that differential activation pathway analysis provides complementary information to standard gene set enrichment analysis. The code is available at https://***/mensxmachina/PASL. © 2020, The Author(s).

关键词： Activation analysis

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers Machine learning Random Forest

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On the Efficient Evaluation of the Azimuthal Fourier Components of the Green's Function for Helmholtz's Equation in Cylindrical Coordinates

arXiv

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arXiv 2021年

作者： Garritano, James Kluger, Yuval Rokhlin, Vladimir Serkh, Kirill Program in Applied Mathematics Yale University New HavenCT06511 United States Yale School of Medicine New HavenCT06511 United States Dept. of Mathematics Yale University New HavenCT06511 United States Dept. of Math. and Computer Science University of Toronto TorontoONM5S 2E4 Canada

In this manuscript, we develop an efficient algorithm to evaluate the azimuthal Fourier components of the Green's function for the Helmholtz equation in cylindrical coordinates. A computationally efficient algorithm for this modal Green's function is essential for solvers for electromagnetic scattering from bodies of revolution (e.g., radar cross sections, antennas). Current algorithms to evaluate this modal Green's function become computationally intractable when the source and target are close or when the wavenumber is large. Furthermore, most state of the art methods cannot be easily parallelized. In this manuscript, we present an algorithm for evaluating the modal Green's function that has performance independent of both source-to-target proximity and wavenumber, and whose cost grows as $O(m)$, where $m$ is the Fourier mode. Furthermore, our algorithm is embarrassingly parallelizable. © 2021, CC BY.

关键词： Bodies of revolution

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