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arXiv 2023年

作者： Ma, Jun He, Yuting Li, Feifei Han, Lin You, Chenyu Wang, Bo Peter Munk Cardiac Centre University Health Network Toronto Canada Department of Laboratory Medicine and Pathobiology University of Toronto Toronto Canada Vector Institute Toronto Canada Department of Computer Science Western University Canada Tandon School of Engineering New York University United States Department of Electrical Engineering Yale University United States Department of Computer Science University of Toronto Toronto Canada UHN AI Hub Toronto Canada

Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to specific modalities or disease types, lack generalizability across the diverse spectrum of medical image segmentation tasks. Here we present MedSAM, a foundation model designed for bridging this gap by enabling universal medical image segmentation. The model is developed on a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. We conduct a comprehensive evaluation on 86 internal validation tasks and 60 external validation tasks, demonstrating better accuracy and robustness than modality-wise specialist models. By delivering accurate and efficient segmentation across a wide spectrum of tasks, MedSAM holds significant potential to expedite the evolution of diagnostic tools and the personalization of treatment plans. © 2023, CC BY-NC-ND.

关键词： Diagnosis

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An Analytical Framework for Control Synthesis of Cyber-Physical Systems with Safety Guarantee

An Analytical Framework for Control Synthesis of Cyber-Physi...

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IEEE Conference on Decision and Control

作者： Luyao Niu Abdullah Al Maruf Andrew Clark J. Sukarno Mertoguno Radha Poovendran Department of Electrical and Computer Engineering Network Security Lab University of Washington Seattle WA Electrical and Systems Engineering Department McKelvey School of Engineering Washington University in St. Louis St. Louis MO School of Cybersecurity and Privacy Georgia Institute of Technology Atlanta GA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Cyber-physical systems (CPS) are required to operate safely under fault and malicious attacks. The simplex architecture and the recently proposed cyber resilient architectures, e.g., Byzantine fault tolerant++ (BFT++), provide safety for CPS under faults and malicious cyber attacks, respectively. However, these existing architectures make use of different timing parameters and implementations to provide safety, and are seemingly unrelated. In this paper, we propose an analytical framework to represent the simplex, BFT++ and other practical cyber resilient architectures (CRAs). We construct a hybrid system that models CPS adopting any of these architectures. We derive sufficient conditions via our proposed framework under which a control policy is guaranteed to be safe. We present an algorithm to synthesize the control policy. We validate the proposed framework using a case study on lateral control of a Boeing 747, and demonstrate that our proposed approach ensures safety of the system.

关键词： Sufficient conditions Fault tolerance Computational modeling Fault tolerant systems computer architecture Cyber-physical systems Safety

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SiamIRPN: Siamese Visual Tracking with Improved Region Proposal networks

SiamIRPN: Siamese Visual Tracking with Improved Region Propo...

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Frontiers of Robotics and Software engineering (FRSE), International Conference on

作者： Dun Cao Renhua Dai Tianyu Zhao Fayez Alqahtani Amr Tolba R. Simon Sherratt Min Zhu School of Computer and Communications Engineering Changsha University of Science and Technology Changsha Hunan China Key Lab of Broadcast Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Computer Science Department Community College King Saud University Riyadh Saudi Arabia School of Biomedical Engineering The University of Reading United Kingdom College of Information Science and Technology Zhejiang Shuren University Zhejiang China

Though introducing the Region Proposal network (RPN) from object detection enabled Siamese trackers’ success, RPN-based trackers still struggle in challenging scenarios. We posit that the reason comes from two major limitations of the introduced RPN, where one is that the external structure of the RPN is simple and straight, the other is that the internal components used in RPN cannot cope with complex scenes. In this paper, we propose an Improved RPN (IRPN) suitable for visual tracking. Externally, we place a Convolutional Block Attention Mechanism (CBAM) to the IRPN, and internally, we adopt the advanced component groups for the IPRN. Using multiple IRPN blocks and deep architecture, we propose a Siamese tracker (SiamIRPN) having a layer-wise structure. Comprehensive experiments and ablation studies on five benchmarks (VOT-2019, OTB100, UAV123, GOT-10k, and NFS) show our proposed SiamIRPN achieves competitive performance.

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CSCL-DTI: predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: predicting drug-target interaction through cross-v...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuan Lin Xi Zhang Zu-Guo Yu Yahui Long Xiangxiang Zeng Philip S. Yu College of Computer Science Xiangtan University China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China Singapore Immunology Network (SIgN) Agency for Science Technology and Research (A*STAR) Singapore College of Information Science and Engineering Hunan University China Department of Computer Science University of Illinois Chicago USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI 1 outperforms six state-of-the-art methods.

关键词： Drugs Proteins Representation learning Protein engineering Adaptation models Contrastive learning Predictive models Transformers Amino acids Diffusion tensor imaging

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Verifiable and Privacy-Preserving Cooperative Federated Learning in UAV-Assisted Vehicular networks

Verifiable and Privacy-Preserving Cooperative Federated Lear...

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IEEE International Conference on Communications (ICC)

作者： Qichao Xu Yulin Lan Zhou Su Dongfeng Fang Hongbing Zhang School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China Department of Computer Science California Polytechnic State University San Luis Obispo USA Shanghai DaoCloud Network Technology Co. Ltd Shanghai China

Federated learning (FL) is a promising distributed learning paradigm, which enables devices to collaboratively train an AI model without exposing participants' private data. However, FL is vulnerable to various attacks and thus remains exposed to privacy issues. For example, malicious parties can launch attacks to recover sensitive and private training data from the shared parameters. Leakage of privacy data can cause serious damage to data providers. Furthermore, user anonymity and data verification in FL also need to be considered. To tackle these problems, in this paper, a verifiable and privacy-preserving cooperative FL (VPPFL) scheme is proposed in UAV-assisted vehicular networks (UVNs). Specifically, to preserve the identity privacy of vehicles, elliptic curve cryptosystem (ECC) is used to generate pseudonyms for vehicles. To preserve the data privacy, Paillier homomorphic encryption algorithm is utilized to encrypt the updates of vehicles, whereby UAVs directly perform global aggregations on encrypted updates instead of raw ones. Additionally, pseudonym-based signature mechanism is presented for vehicles to generate verifiable signatures, so as to ensure the authenticity and validity of uploaded local model updates. Besides, to sufficiently use the multi-source data, multiple UAVs share the local updates packets with each other to execute global aggregation. Finally, simulations are carried out to demonstrate that the proposed scheme can achieve high accuracy and verification with providing strict privacy protection.

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Dynamic hidden-variable network models

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Physical Review E 2021年第5期103卷 052307-052307页

作者： Harrison Hartle Fragkiskos Papadopoulos Dmitri Krioukov Network Science Institute Northeastern University Boston 02115 Massachusetts USA Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 3036 Limassol Cyprus Northeastern University Departments of Physics Mathematics and Electrical & Computer Engineering Boston 02115 Massachusetts USA

Models of complex networks often incorporate node-intrinsic properties abstracted as hidden variables. The probability of connections in the network is then a function of these variables. Real-world networks evolve over time and many exhibit dynamics of node characteristics as well as of linking structure. Here we introduce and study natural temporal extensions of static hidden-variable network models with stochastic dynamics of hidden variables and links. The dynamics is controlled by two parameters: one that tunes the rate of change of hidden variables and another that tunes the rate at which node pairs reevaluate their connections given the current values of hidden variables. Snapshots of networks in the dynamic models are equivalent to networks generated by the static models only if the link reevaluation rate is sufficiently larger than the rate of hidden-variable dynamics or if an additional mechanism is added whereby links actively respond to changes in hidden variables. Otherwise, links are out of equilibrium with respect to hidden variables and network snapshots exhibit structural deviations from the static models. We examine the level of structural persistence in the considered models and quantify deviations from staticlike behavior. We explore temporal versions of popular static models with community structure, latent geometry, and degree heterogeneity. While we do not attempt to directly model real networks, we comment on interesting qualitative resemblances to real systems. In particular, we speculate that links in some real networks are out of equilibrium with respect to hidden variables, partially explaining the presence of long-ranged links in geometrically embedded systems and intergroup connectivity in modular systems. We also discuss possible extensions, generalizations, and applications of the introduced class of dynamic network models.

关键词： Complex systems Evolving networks network structure Block models Evolving network models Fitness models

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Differential evolution variants for Searching D- and A-optimal designs

arXiv

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arXiv 2022年

作者： Tong, Lyuyang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China Department of Computer Science City University of Hong Kong Hong Kong

Optimal experimental design is an essential subfield of statistics that maximizes the chances of experimental success. The D- and A-optimal design is a very challenging problem in the field of optimal design, namely minimizing the determinant and trace of the inverse Fisher information matrix. Due to the flexibility and ease of implementation, traditional evolutionary algorithms (EAs) are applied to deal with a small part of experimental optimization design problems without mathematical derivation and assumption. However, the current EAs remain the issues of determining the support point number, handling the infeasible weight solution, and the insufficient experiment. To address the above issues, this paper investigates differential evolution (DE) variants for finding D- and A-optimal designs on several different statistical models. The repair operation is proposed to automatically determine the support point by combining similar support points with their corresponding weights based on Euclidean distance and deleting the support point with less weight. Furthermore, the repair operation fixes the infeasible weight solution into the feasible weight solution. To enrich our optimal design experiments, we utilize the proposed DE variants to test the D- and A-optimal design problems on 12 statistical models. Compared with other competitor algorithms, simulation experiments show that LSHADE can achieve better performance on the D- and A-optimal design problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Quantum key distribution based on single-particle and EPR entanglement

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Science China(Information Sciences) 2020年第6期63卷 256-258页

作者： Leilei LI Jian LI Yan CHANG Yuguang YANG Xiubo CHEN School of Computer Science Beijing University of Posts and Telecommunications Department of Network Engineering Chengdu University of Information Technology College of Computer Science and Technology Beijing University of Technology School of Cyberspace Security Beijing University of Posts and Telecommunications

Dear editor,The theoretical security of the quantum secure communication protocols can be proved with the law of quantum mechanics, when the classical communication protocols’ security can only depend on the computational complexity [1, 2]. Based on the quantum BB84 protocol [3] and the quantum MEQKD protocol [4], we present a QKD (quantum key distribution) protocol based on a singleparticle and an EPR (Einstein-Podolsky-Rosen)entanglement pair, which is called the quantum TEQKD protocol. Compared with MEQKD,

关键词： Quantum key distribution based on single-particle and EPR entanglement EPR

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Master Stability Functions in Complex networks

arXiv

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arXiv 2024年

作者： Acharyya, Suman Pradhan, Priodyuti Meena, Chandrakala Complex Network Dynamics Lab Department of Mathematics Bar-Ilan University Ramat Gan5290002 Israel networks.ai Lab Department of Computer Science & Engineering Indian Institute of Information Technology Karnataka Raichur584135 India Department of Physics Indian Institute of Science Education and Research Thiruvananthapuram Kerala 695551 India

Synchronization is an emergent phenomenon in coupled dynamical networks. The Master Stability Function (MSF) is a highly elegant and powerful tool for characterizing the stability of synchronization states. However, a significant challenge lies in determining the MSF for complex dynamical networks driven by nonlinear interaction mechanisms. These mechanisms introduce additional complexity through the intricate connectivity of interacting elements within the network and the intrinsic dynamics, which are governed by nonlinear processes with diverse parameters and higher dimensionality of systems. Over the past 25 years, extensive research has focused on determining the MSF for pairwise coupled identical systems with diffusive coupling. Our literature survey highlights two significant advancements in recent years: the consideration of multilayer networks instead of single-layer networks and the extension of MSF analysis to incorporate higher-order interactions alongside pairwise interactions. In this review article, we revisit the analysis of the MSF for diffusively pairwise coupled dynamical systems and extend this framework to more general coupling schemes. Furthermore, we systematically derive the MSF for multilayer dynamical networks and single-layer coupled systems by incorporating higher-order interactions alongside pairwise interactions. The primary focus of our review is on the analytical derivation and numerical computation of the MSF for complex dynamical networks. Finally, we demonstrate the application of the MSF in data science, emphasizing its relevance and potential in this rapidly evolving field. © 2024, CC BY.

关键词： Jacobian matrices

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Structure-guided Diffusion Transformer for Low-Light Image Enhancement

arXiv

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arXiv 2025年

作者： Yin, Xiangchen Yu, Zhenda Jiang, Longtao Gao, Xin Sun, Xiao Liu, Zhi Yang, Xun University of Science and Technology of China China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Anhui University China School of Vehicle and Mobility Tsinghua University China State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing China School of Computer Science and Information Engineering Hefei University of Technology China Department of Computer and Network Engineering The University of Electro-Communications Chofu-shi Tokyo1828585 Japan

While the diffusion transformer (DiT) has become a focal point of interest in recent years, its application in low-light image enhancement remains a blank area for exploration. Current methods recover the details from low-light images while inevitably amplifying the noise in images, resulting in poor visual quality. In this paper, we firstly introduce DiT into the low-light enhancement task and design a novel Structure-guided Diffusion Transformer based Low-light image enhancement (SDTL) framework. We compress the feature through wavelet transform to improve the inference efficiency of the model and capture the multi-directional frequency band. Then we propose a Structure Enhancement Module (SEM) that uses structural prior to enhance the texture and leverages an adaptive fusion strategy to achieve more accurate enhancement effect. In Addition, we propose a Structure-guided Attention Block (SAB) to pay more attention to texture-riched tokens and avoid interference from noisy areas in noise prediction. Extensive qualitative and quantitative experiments demonstrate that our method achieves SOTA performance on several popular datasets, validating the effectiveness of SDTL in improving image quality and the potential of DiT in low-light enhancement tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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