检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Belyaeva, Anastasiya Kubjas, Kaie Sun, Lawrence J. Uhler, Caroline Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics and Systems Analysis Aalto University Espoo Finland

The spatial organization of the DNA in the cell nucleus plays an important role for gene regulation, DNA replication, and genomic integrity. Through the development of chromosome conformation capture experiments (such as 3C, 4C, Hi-C) it is now possible to obtain the contact frequencies of the DNA at the whole-genome level. In this paper, we study the problem of reconstructing the 3D organization of the genome from such whole-genome contact frequencies. A standard approach is to transform the contact frequencies into noisy distance measurements and then apply semidefinite programming (SDP) formulations to obtain the 3D configuration. However, neglected in such reconstructions is the fact that most eukaryotes including humans are diploid and therefore contain two copies of each genomic locus. We prove that the 3D organization of the DNA is not identifiable from distance measurements derived from contact frequencies in diploid organisms. In fact, there are infinitely many solutions even in the noise-free setting. We then discuss various additional biologically relevant and experimentally measurable constraints (including distances between neighboring genomic loci and higher-order interactions) and prove identifiability under these conditions. Furthermore, we provide SDP formulations for computing the 3D embedding of the DNA with these additional constraints and show that we can recover the true 3D embedding with high accuracy from both noiseless and noisy measurements. Finally, we apply our algorithm to real pairwise and higher-order contact frequency data and show that we can recover known genome organization patterns. © 2021, CC BY.

关键词： DNA

来源：评论

学校读者我要写书评

暂无评论

Extending a conjecture of Graham and Lovász on the distance characteristic polynomial

arXiv

引用

arXiv 2022年

作者： Abiad, Aida Brimkov, Boris Hayat, Sakander Khramova, Antonina P. Koolen, Jack H. Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Mathematics: Analysis Logic and Discrete Mathematics Ghent University Belgium Department of Mathematics and Data Science Vrije Universiteit Brussel Belgium Department of Mathematics and Statistics Slippery Rock University United States Faculty of Engineering Sciences GIK Institute of Engineering Sciences and Technology Topi Swabi Pakistan School of Mathematical Sciences University of Science and Technology of China China

Graham and Lovász conjectured in 1978 that the sequence of normalized coefficients of the distance characteristic polynomial of a tree of order n is unimodal with the maximum value occurring at ⌊ n2 ⌋. In this paper we investigate this problem for block graphs. In particular, we prove the unimodality part and we establish the peak for several extremal cases of uniform block graphs with small diameter. © 2022, CC BY.

关键词： Polynomials

来源：评论

学校读者我要写书评

暂无评论

Calling Cards: A Customizable Platform to Longitudinally Record Protein-DNA Interactions Over Time in Cells and Tissues (vol 3, e883, 2023)

CURRENT PROTOCOLS

引用

CURRENT PROTOCOLS 2023年第10期3卷 e883页

作者： Yen, Allen Mateusiak, Chase Sarafinovska, Simona Gachechiladze, Mariam A. Guo, Juanru Chen, Xuhua Moudgil, Arnav Cammack, Alexander J. Hoisington-Lopez, Jessica Crosby, MariaLynn Brent, Michael R. Mitra, Robi D. Dougherty, Joseph D. Department of Genetics Washington University School of Medicine Saint Louis Missouri Department of Psychiatry Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Software Supervision Validation Visualization Writing - original draft Writing - review & editing Edison Family Center for Genome Sciences and Systems Biology Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Data curation Formal analysis Methodology Software Visualization Writing - original draft Contribution: Formal analysis Funding acquisition Investigation Visualization Writing - original draft Writing - review & editing Contribution: Formal analysis Funding acquisition Investigation Visualization Writing - review & editing Contribution: Data curation Methodology Software Writing - review & editing Contribution: Methodology Writing - review & editing Current address: Department of Ophthalmology Stanford University School of Medicine Stanford California Contribution: Conceptualization Methodology Software Validation Department of Neurology Washington University School of Medicine Saint Louis Missouri Current address: Department of Neurodegenerative Diseases University College London London United Kingdom Contribution: Conceptualization Methodology Validation Contribution: Investigation Writing - review & editing Department of Computer Science and Engineering Washington University Saint Louis Missouri Contribution: Funding acquisition Contribution: Conceptualization Funding acquisition Project administration Resources Software Supervision Writing - review & editing Intellectual and Developmental Disabilities Research Center Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Funding acquisition Project administration Resources Supervision Writing - origin

Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions is recovered by next-generation sequencing. Compared with other genomic assays, readouts of which provide a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon “Calling Cards” into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile-specific transcription factor (TF) binding with custom TF-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Cards reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high-performance computing environment and to conduct downstream analyses. © 2023 Wiley Periodicals LLC. This article was corrected on 16 October 2023. See the end of the full text for details. Basic Protocol 1 : Preparation and delivery of Calling Cards reagents Support Protocol 1 : Next-generation sequencing quantification of barcode

关键词： calling cards epigenetics enhancer usage gene regulation transcription factor binding

来源：评论

学校读者我要写书评

暂无评论

When to Update Your Model: Constrained Model-based Reinforcement Learning

arXiv

引用

arXiv 2022年

作者： Ji, Tianying Luo, Yu Sun, Fuchun Jing, Mingxuan He, Fengxiang Huang, Wenbing Department of Computer Science and Technology Tsinghua University China Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences China JD Explore Academy *** Inc China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization and model learning. Existing discrepancy bounds generally ignore the impacts of model shifts, and their corresponding algorithms are prone to degrade performance by drastic model updating. In this work, we first propose a novel and general theoretical scheme for a non-decreasing performance guarantee of MBRL. Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. These discoveries encourage us to formulate a constrained lower-bound optimization problem to permit the monotonicity of MBRL. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns. Motivated by these analyses, we design a simple but effective algorithm CMLO1 (Constrained Model-shift Lower-bound Optimization), by introducing an event-triggered mechanism that flexibly determines when to update the model. Experiments show that CMLO surpasses other state-of-the-art methods and produces a boost when various policy optimization methods are employed. © 2022, CC BY-NC-SA.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

Joint Covert and Secure Communication for SWIPT Assisted CNOMA Systems

引用

IEEE Internet of Things Journal 2025年

作者： Huang, Gaojian Lei, Yuxin Li, Xingwang Khan, Wali Ullah Wang, Gongpu Nallanathan, Arumugam Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China City University of Macau Faculty of Data Science 999078 China Henan Polytechnic University School of Surveying and Land Information Engineering Jiaozuo454003 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Luxembourg City1855 Luxembourg Beijing Jiaotong University Beijing Key Laboratory of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing100044 China Institute of Internet of Things Wuxi214111 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

With the rapid advancement of physical-layer security technology, the covert and secure communication has become crucial in safeguarding wireless communication systems. In this paper, we propose a joint covert and secure transmission scheme for simultaneous wireless information and power transfer (SWIPT) assisted cooperative non-orthogonal multiple access (CNOMA) systems. In the CNOMA system, a greedy relay transmits the confidential information to the far user (Carol), with the assistance of the near user (Bob). Meanwhile, as a SWIPT node, Bob is self-sustained by harvesting energy from relay. What's more, a warden (Alice) and non-colluding eavesdroppers (Eves) always attempt to detect and capture the confidential information, respectively. To counteract the attacks from Alice and Eves, a jamming-assisted scheme is employed. For the proposed system model, we derive closed-form expressions for the detection error probability (DEP) and the average minimum detection error probability (AMDEP) of Alice. Additionally, closed-form expressions for the outage probability (OP) of users and the intercept probability (IP) of Eves are obtained. Furthermore, to maximize the effective covert rate (ECR) of Carol, an optimization problem is formulated, subject to covertness and security constraints. Numerical results are provided to demonstrate the impact of the system parameters on covert and secure performance, with the results showing perfect agreement with the theoretical analysis. © 2014 IEEE.

关键词： Inductive power transmission

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models

arXiv

引用

arXiv 2024年

作者： Ahmad, Muhammad Distefano, Salvatore Khan, Adil Mehmood Mazzara, Manuel Li, Chenyu Li, Hao Aryal, Jagannath Ding, Yao Vivone, Gemine Hong, Danfeng Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy School of Computer Science University of Hull HullHU6 7RX United Kingdom Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Big Geospatial Data Management Technical University of Munich Munich85521 Germany Department of Infrastructure Engineering University of Melbourne ParkvilleVIC Australia Intelligent Control Laboratory PLA Rocket Force University of Engineering Xi’an710025 China Institute of Methodologies for Environmental Analysis National Research Council Tito85050 Italy School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven design, inherent biases, and methodological limitations. Specifically, TML suffers from the curse of dimensionality, difficulties in feature selection and extraction, insufficient consideration of spatial information, limited robustness against noise, scalability issues, and inadequate adaptability to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. This survey offers a comprehensive overview of current trends and future prospects in HSC, emphasizing advancements from DL models to the increasing adoption of Transformer and Mamba Model architectures. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSC. Furthermore, we investigate the potential of Transformer-based models and the Mamba Model in HSC, detailing their advantages and challenges. Emerging trends in HSC are explored, including in-depth discussions on Explainable AI and Interoperability concepts, alongside Diffusion Models for image denoising, feature extraction, and image fusion. To substantiate the efficacy of various conventional DL models, Transformers, and the Mamba Model, comprehensive experimental results were conducted on three HS datasets, yielding notable accuracies: 99.94% on Pavia University, 99.41% on Indian Pines, and 99.97% on the University of Houston dataset. Additionally, we identify several open challenges and pertinent research questions in the field of HSC. Finally, we outline future research directions and potential applications aimed at enhancing the accuracy and effic

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

GL-BKGNN: Graphlet-based Bi-Kernel Interpretable Graph Neural Networks

引用

Information Fusion 2025年 123卷

作者： Xu, Lixiang Jiang, Kang Niu, Xin Chen, Enhong Luo, Bin Yu, Philip S. School of Artificial Intelligence Hefei Institute of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Hefei China Department of Computer Science University of Illinois at Chicago ChicagoIL United States

While graph neural networks (GNNs) have successfully applied generalized convolution operations to the graph domain, providing a direct method to explain the dependency between the output and the presence of certain features and structural patterns in the input graph remains challenging. Inspired by image filters in standard convolutional neural networks (CNN), we propose a neural framework that connects bi-kernel with GNNs, incorporating predefined rules and focusing on the interpretability of graph filters during training. We address graph kernels based on their differentiability, using backpropagation of differentiable graph kernels for end-to-end training to generate gradient information for model optimization. Simultaneously, we leverage the stronger stability of non-differentiable graph kernels to capture local critical subgraphs, achieving deep fusion of structural features for node updates. Extensive experiments demonstrate that our model achieves competitive performance on graph classification datasets while providing additional benefits in interpretability. © 2025

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Deep neural network approximation theory for high-dimensional functions

arXiv

引用

arXiv 2021年

作者： Beneventano, Pierfrancesco Cheridito, Patrick Graeber, Robin Jentzen, Arnulf Kuckuck, Benno Department of Mathematics ETH Zurich Switzerland Department of Operations Research and Financial Engineering Princeton University NJ United States Applied Mathematics: Institute for Analysis and Numerics Faculty of Mathematics and Computer Science University of Münster Germany School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

The purpose of this article is to develop machinery to study the capacity of deep neural networks (DNNs) to approximate high-dimensional functions. In particular, we show that DNNs have the expressive power to overcome the curse of dimensionality in the approximation of a large class of functions. More precisely, we prove that these functions can be approximated by DNNs on compact sets such that the number of parameters necessary to represent the approximating DNNs grows at most polynomially in the reciprocal 1/Ε of the approximation accuracy Ε > 0 and in the input dimension d ∈ N = {1, 2, 3, . . .}. To this end, we introduce certain approximation spaces, consisting of sequences of functions that can be efficiently approximated by DNNs. We then establish closure properties which we combine with known and new bounds on the number of parameters necessary to approximate locally Lipschitz continuous functions, maximum functions, and product functions by DNNs. The main result of this article demonstrates that DNNs have sufficient expressiveness to approximate certain sequences of functions which can be constructed by means of a finite number of compositions using locally Lipschitz continuous functions, maxima, and products without the curse of dimensionality. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

arXiv

引用

arXiv 2024年

作者： Handa, Palak Mahbod, Amirreza Schwarzhans, Florian Woitek, Ramona Goel, Nidhi Dhir, Manas Chhabra, Deepti Jha, Shreshtha Sharma, Pallavi Thakur, Vijay Chawla, Simarpreet Singh Gunjan, Deepak Kakarla, Jagadeesh Raman, Balasubramanian Research Center for Medical Image Analysis and Artificial Intelligence Department of Medicine Danube Private University Krems Austria Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Machine Learning University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India Department of Electronics and Communication Engineering Delhi Technological University Delhi India Columbia University New YorkNY United States Department of Gastroenterology and HNU All India Institute of Medical Sciences Delhi India Chennai Kancheepuram India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research Center for Medical Image analysis and Artificial Intelligence (MIAAI), department of Medicine, Danube Private University, Krems, Austria in collaboration with the 9th International Conference on computer Vision & Image Processing (CVIP 2024) being organized by the Indian institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document provides an overview of the challenge, including the registration process, rules, submission format, description of the datasets used, qualified team rankings, all team descriptions, and the benchmarking results reported by the organizers. © 2024, CC BY.

关键词： Endoscopy

来源：评论

学校读者我要写书评

暂无评论

ERNAS: An Evolutionary Neural Architecture Search for Magnetic Resonance Image Reconstructions

arXiv

引用

arXiv 2022年

作者： Eslahi, Samira Vafay Tao, Jian Ji, Jim Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Visualization Texas A&M University College StationTX United States Texas A&M Institute of Data Science Texas A&M University College StationTX United States Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar

Magnetic resonance imaging (MRI) is one of the noninvasive imaging modalities that can produce high-quality images. However, the scan procedure is relatively slow, which causes patient discomfort and motion artifacts in images. Accelerating MRI hardware is constrained by physical and physiological limitations. A popular alternative approach to accelerated MRI is to undersample the k-space data. While undersampling speeds up the scan procedure, it generates artifacts in the images, and advanced reconstruction algorithms are needed to produce artifact-free images. Recently deep learning has emerged as a promising MRI reconstruction method to address this problem. However, straightforward adoption of the existing deep learning neural network architectures in MRI reconstructions is not usually optimal in terms of efficiency and reconstruction quality. In this work, MRI reconstruction from undersampled data was carried out using an optimized neural network using a novel evolutionary neural architecture search algorithm. Brain and knee MRI datasets show that the proposed algorithm outperforms manually designed neural network-based MR reconstruction models. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：