检索结果-内蒙古大学图书馆

Protein codes promote selective subcellular compartmentalization

science (New York, N.Y.) 2025年第6738期387卷 1095-1101页

作者： Kilgore, Henry R. Chinn, Itamar Mikhael, Peter G. Mitnikov, Ilan Van Dongen, Catherine Zylberberg, Guy Afeyan, Lena Banani, Salman F. Wilson-Hawken, Susana Lee, Tong Ihn Barzilay, Regina Young, Richard A. Whitehead Institute for Biomedical Research Cambridge MA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology (MIT) Cambridge MA United States Abdul Latif Jameel Clinic for Machine Learning in Health MIT Cambridge MA United States Department of Biology MIT Cambridge MA United States Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA United States Computational and Systems Biology Program MIT Cambridge MA United States

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must assemble. In this study, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. We developed a protein language model, ProtGPS, that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in the nucleolus. ProtGPS identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code but also a previously unrecognized code governing their distribution to diverse subcellular compartments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Image segmentation via two-step deep variational priors

引用

Pattern Recognition Letters 2025年 195卷 44-50页

作者： Tan, Lu Tai, Xue-Cheng Li, Ling Liu, Wan-Quan Chan, Raymond H. Hong, Dan-Feng School of Computer Science and Information Engineering Bengbu University Bengbu Anhui China Norwegian Research Center (NORCE) Bergen Norway School of Elec Eng Comp and Math Sci (EECMS) Curtin University Perth Australia School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Department of Operations and Risk Management and School of Data Science Lingnan University Tuen Mun Hong Kong Hong Kong Centre for Cerebro-Cardiovascular Health Engineering Hong Kong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

This paper proposes an iterative deep variational approach for image segmentation in a fusion manner: it is not only able to realize selective segmentation, but can also alleviate the issue of parameter/initialization dependency. Moreover, it possesses a refinement process designed to handle challenging scenarios, such as images containing obscured, damaged, or absent objects, or those with complex backgrounds. Our proposed approach consists of two main procedures, i.e., selective segmentation and shape transformation. The first procedure works as a stem in a totally unsupervised way. A convolutional neural network (CNN) based architecture is properly incorporated into the selective weighting constrained variational segmentation model. The second procedure is to further refine the outputs. This part can be achieved in two ways: one direction is to establish a joint model with the semantic shape constraint. The other technical direction is to make the shape descriptor separated from the joint model and work as an individual unit. In the proposed approach, the minimization problem is transformed from iterative minimization for each variable to automatically minimizing the loss function by learning the generator network parameters. This also leads to a good inductive bias associated with classic variational methods. Extensive experiments have demonstrated the significant advantages. © 2025 Elsevier B.V.

关键词： Flexible module Integration approach Iterative deep variational priors

来源：评论

学校读者我要写书评

暂无评论

Auto Digit Selection for Most Significant Digit First Multiplication

引用

IEEE Embedded systems Letters 2025年

作者： Gorgin, Saeid Jaberipur, Ghassem Lee, Jeong-A Shin, Seokjoo Kim, Jungrae Computer Engineering Iranian Research Organization for Science and Technology Tehran Iran Semiconductor Systems Engineering Department College of Information and Communication Engineering SungKyunKwan University Korea Republic of Brain Pool Program for Chosun University Gwangju Korea Republic of Shahid Beheshti University Tehran Iran Chosun University Department of Computer Engineering Gwangju Korea Republic of

Performance in modern embedded systems, particularly those executing computation-intensive signal/image processing and machine learning algorithms, is critically dependent on the efficiency of multiplication operations. Serial multiplication offers key advantages in these resource-constrained environments, including low memory usage and minimal interconnect complexity. The most significant digit first (MSDF) serial multiplication scheme further enhances efficiency by enabling early termination and supporting variable precision. It is ideal for embedded applications where power and performance are critical. This paper proposes an auto-selection scheme for MSDF multiplication, where product digits are generated as a byproduct of residual maintenance, leading to speed, cost, and power efficiency improvements. We demonstrate the implementation of serial-serial and serial-parallel multipliers, where the multiplier operand in the latter is fully available throughout the computation. Simulation and synthesis results, compared to the most relevant previous works, show reductions in delay (16% and 20%), area consumption (11% and 23%), and power dissipation (7% and 20%), making our approach well-suited for embedded systems requiring efficient, low-power multiplication hardware. © 2009-2012 IEEE.

关键词： Left-to-Right Arithmetic Machine Learning Algorithms MSDF Multiplication Online Arithmetic

来源：评论

学校读者我要写书评

暂无评论

Accuracy-Improved Privacy-Preserving Asynchronous Federated Learning in IoT

引用

IEEE Internet of Things Journal 2025年第9期12卷 11733-11743页

作者： Liu, Shuying Zhu, Lingbo Zhang, Weibin Miao, Yinbin Leng, Tao Raymond Choo, Kim-Kwang Xian Yang Normal University Department of Computer Xianyang712000 China Northwest University School of Information Science and Technology Xi’an710119 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Xidian University School of Cyber Engineering Xi’an710071 China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province Luzhou646000 China Institute of Information Engineering Chinese Academy of Sciences Beijing100085 China University of Chinese Academy of Sciences School of Cyber Security Beijing100049 China The University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

To address the asynchronous challenges stemming from user resource constraints and intermittent network connections in distributed Internet of Things (IoT) systems, asynchronous federated learning (AFL) has been extensively explored in both academic and industrial domains. However, existing AFL approaches still struggle to effectively tackle the issue of model aggregation information loss due to delayed users, which leads to degraded model accuracy. To alleviate the impact of delayed or unavailable model updates on model aggregation, we propose a novel model update enhancement method to compensate for the loss of model aggregation information. Specifically, we utilize delayed model updates as an update agent for this user and correct the weights of these updates within the delayed rounds threshold to ensure the delayed user’s contribution to the model aggregation. Additionally, we integrate symmetric homomorphic encryption (SHE) into AFL to ensure user privacy while simultaneously minimizing computational overhead. Lastly, we conduct extensive experiments to demonstrate that our scheme improves model accuracy by 6.53% compared to state-of-the-art solutions. © 2014 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A Secure NFC Cardless Cash Withdrawal System 5th

A Secure NFC Cardless Cash Withdrawal System

引用

5th International Conference on Financial Technology, ICFT 2024

作者： Hwang, Min-Shiang Lin, Cheng-Ying Chang, Chun-Hsien Yang, Cheng-Ying Department of Computer Science and Information Engineering Asia University Taichung Taiwan Fintech and Blockchain Research Center Asia University Taichung Taiwan The Ph.D. Program in Artificial intelligence Asia University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science University of Taipei Taipei Taiwan

ISBN: (纸本)9789819638109

As IoT technology grows, several different payment methods are available now. NFC (Near Field Communication) as a short-range communication technology has been applied in many fields, such as contactless payment and electronic tickets. ATM (Auto Teller Machine) also benefits from NFC technology as one way of cardless cash withdrawal. However, there are many security problems, and convenience problems exist. In security, certain kinds of attacks, such as man-in-the-middle, replay, and brute-force attacks, must be considered. These attacks may influence user privacy. Furthermore, considering user experience, existing methods may not make users feel enough convenience when using. To enhance security and user experience, we proposed a secure NFC cardless cash withdrawal system using time stamps and nonce to reduce the risk of attack and design more effortless operations for users to enhance user experience. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automatic teller machines

来源：评论

学校读者我要写书评

暂无评论

Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

引用

IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

来源：评论

学校读者我要写书评

暂无评论

Uncertainty Quantification and Quality Control for Heatmap-based Landmark Detection Models

引用

IEEE Transactions on Medical Imaging 2025年

作者： Feng, Yong Yang, Jinzhu Tang, Lingzhi Sun, Song Wang, Yonghuai Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China Northeastern University School of Computer Science and Engineering Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China The First Hospital of China Medical University Department of Cardiovascular Ultrasound Shenyang China

Uncertainty quantification is a vital aspect of explainable artificial intelligence that fosters clinician trust in medical applications and facilitates timely interventions, leading to safer and more reliable outcomes. Although deep learning models have reached clinically acceptable accuracy in anatomical landmark detection, their predictions remain susceptible to contextual noise due to the small size of the target structures, making uncertainty quantification more challenging than in classification and segmentation tasks. This paper presents an end-to-end uncertainty quantification method tailored for heatmapbased anatomical landmark detection models, designed to improve both interpretability and controllability in clinical applications. Leveraging Dempster-Shafer Theory and Subjective Logic Theory, we implement probability assignment and uncertainty quantification through a single forward pass to ensure computational efficiency. We introduce an evidence map that captures the strength of landmark evidence, alongside an uncertainty map that calibrates predicted probabilities within the Subjective Logic framework. The interaction between these two components, facilitated by a cross-attention mechanism, further improves landmark detection accuracy and enhances the effectiveness of uncertainty quantification. Experimental results demonstrate that the proposed method maintains detection accuracy, even in noisy environments, while outperforming state-of-the-art methods in terms of uncertainty quantification and quality control. Furthermore, the model effectively identifies out-of-distribution data solely through calibrated probabilities when encountering inconsistencies in multi-center data and novel data, underscoring its potential for clinical applications. © 1982-2012 IEEE.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuning the BERT Model to Predict Depression and Anxiety Using Multi-Labeled Twitter Data

Fine-Tuning the BERT Model to Predict Depression and Anxiety...

引用

Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Lamia Bendebane Zakaria Laboudi Asma Saighi Seif Eddine Bouziane Research Laboratory on Computer Science's Complex Systems (ReLa(CS)2) University of Oum El Bouaghi Algeria Laboratory of Artificial Intelligence and Autonomous Things (LIAOA) University of Oum El Bouaghi Algeria Department of Intelligent Systems Engineering National School of Artificial Intelligence Algiers Algeria

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Studying mental health through social media data has become an emerging area of research, notably for the detection of depression and anxiety. In this regard, many researches have been conducted, yielding very satisfactory results (e.g., [1]–[3]). However, most of these studies have addressed each of these two mental disorders separately. This is due to the overlap of symptoms associated with depression and anxiety that makes distinguishing between them significantly challenging. Based on this context, this work leverages pretrained large language models (LLMs) to develop efficient multi-class models for predicting both depression and anxiety. For this purpose, a small, multi-labeled Twitter dataset is first constructed. Then, a pre-trained BERT-based uncased model is fine-tuned for six epochs on the dataset, resulting in a model referred to as DAC-BERT. Finally, the DAC-BERT model is evaluated and compared against both hybrid deep learning models and other LLM-based models. The obtained results show that DAC-BERT model outperforms existing approaches, achieving accuracies of up to 97.20% on a multi-labeled dataset containing normal, depressive, and anxious tweets, and up to 96.50% on a dataset with only normal and depressive tweets. These findings highlight the promising potential of fine-tuning LLMs for predicting mental health disorders, particularly depression and anxiety.

关键词： Deep learning Sentiment analysis Social networking (online) Large language models Anxiety disorders Blogs Mental health Predictive models Depression Data models

来源：评论

学校读者我要写书评

暂无评论

HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning

arXiv

引用

arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Wang, Xianbin Cheng, Zhipeng Hosseinalipour, Seyyedali Dai, Huaiyu Jiao, Zhenzhen School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Japan Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Suzhou China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering NC State University RaleighNC United States iF-Labs Beijing Teleinfo Technology Company Ltd. CAICT Beijing China

The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient machine learning (ML) solutions that can handle high vehicular mobility and decentralized data. This has motivated the emergence of Hierarchical Federated Learning over vehicle-edge-cloud architectures (VEC-HFL). Nevertheless, one aspect which is underexplored in the literature on VEC-HFL is that vehicles often need to execute multiple ML tasks simultaneously, where this multi-model training environment introduces crucial challenges. First, improper aggregation rules can lead to model obsolescence and prolonged training times. Second, vehicular mobility may result in inefficient data utilization by preventing the vehicles from returning their models to the network edge. Third, achieving a balanced resource allocation across diverse tasks becomes of paramount importance as it majorly affects the effectiveness of collaborative training. We take one of the first steps towards addressing these challenges via proposing a framework for multi-model training in dynamic VEC-HFL with the goal of minimizing global training latency while ensuring balanced training across various tasks—a problem that turns out to be NP-hard. To facilitate timely model training, we introduce a hybrid synchronous-asynchronous aggregation rule. Building on this, we present a novel method called Hybrid Evolutionary And gReedy allocaTion (HEART). The framework operates in two stages: first, it achieves balanced task scheduling through a hybrid heuristic approach that combines improved Particle Swarm Optimization (PSO) and Genetic Algorithms (GA);second, it employs a low-complexity greedy algorithm to determine the training priority of assigned tasks on vehicles. Experiments on real-world datasets demonstrate the superiority of HEART over existing methods. © 2025, CC BY-NC-ND.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Structural uncertainty estimation for medical image segmentation

引用

Medical Image Analysis 2025年 103卷 103602页

作者： Yang, Bing Zhang, Xiaoqing Zhang, Huihong Li, Sanqian Higashita, Risa Liu, Jiang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China TOMEY Corporation Nagoya4510051 Japan Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore

Precise segmentation and uncertainty estimation are crucial for error identification and correction in medical diagnostic assistance. Existing methods mainly rely on pixel-wise uncertainty estimations. They (1) neglect the global context, leading to erroneous uncertainty indications, and (2) bring attention interference, resulting in the waste of extensive details and potential understanding confusion. In this paper, we propose a novel structural uncertainty estimation method, based on Convolutional Neural Networks (CNN) and Active Shape Models (ASM), named SU-ASM, which incorporates global shape information for providing precise segmentation and uncertainty estimation. The SU-ASM consists of three components. Firstly, multi-task generation provides multiple outcomes to assist ASM initialization and shape optimization via a multi-task learning module. Secondly, information fusion involves the creation of a Combined Boundary Probability (CBP) and along with a rapid shape initialization algorithm, Key Landmark Template Matching (KLTM), to enhance boundary reliability and select appropriate shape templates. Finally, shape model fitting where multiple shape templates are matched to the CBP while maintaining their intrinsic shape characteristics. Fitted shapes generate segmentation results and structural uncertainty estimations. The SU-ASM has been validated on cardiac ultrasound dataset, ciliary muscle dataset of the anterior eye segment, and the chest X-ray dataset. It outperforms state-of-the-art methods in terms of segmentation and uncertainty estimation. © 2025 Elsevier B.V.

关键词： Image segmentation

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：