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On-the-Fly Improving Segment Anything for Medical Image Segmentation using Auxiliary Online Learning

IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Huang, Tianyu Zhou, Tao Xie, Weidi Wang, Shuo Dou, Qi Zhang, Yizhe Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Shanghai Jiaotong University Shanghai China School of Basic Medical Sciences Fudan University Digital Medical Research Center China Shanghai Key Laboratory of Miccai Shanghai China Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong

The current variants of the Segment Anything Model (SAM), which include the original SAM and Medical SAM, still lack the capability to produce sufficiently accurate segmentation for medical images. In medical imaging contexts, it is not uncommon for human experts to rectify segmentations of specific test samples after SAM generates its segmentation predictions. These rectifications typically entail manual or semi-manual corrections employing state-of-the-art annotation tools. Motivated by this process, we introduce a novel approach that leverages the advantages of online machine learning to enhance Segment Anything (SA) during test time. We employ rectified annotations to perform online learning, with the aim of improving the segmentation quality of SA on medical images. To ensure the effectiveness and efficiency of online learning when integrated with large-scale vision models like SAM, we propose a new method called Auxiliary Online Learning (AuxOL), which entails adaptive online-batch and adaptive segmentation fusion. Experiments conducted on eight datasets covering four medical imaging modalities validate the effectiveness of the proposed method. Our work proposes and validates a new, practical, and effective approach for enhancing SA on downstream segmentation tasks (e.g., medical image segmentation). The code is publicly available at https://***/AuxOL/. © 2025 IEEE.

关键词： Contrastive Learning

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Design of Impulsive Feedback Controller for Dosing 32

Design of Impulsive Feedback Controller for Dosing

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Medvedev, Alexander Proskurnikov, Anton V. Zhusubaliyev, Zhanybai T. Uppsala University Department of Information Technology UppsalaSE-752 37 Sweden Politecnico di Torino Department of Electronics and Telecommunications Turin10 129 Italy Southwest State University International Scientific Laboratory for Dynamics of Non-Smooth Systems Department of Computer Science Kursk Russia Osh State University Faculty of Mathematics and Information Technology Osh723500 Kyrgyzstan

ISBN: (纸本)9798350395440

This paper proposes a pulse-modulated controller that generates, under stationary conditions, a desired sequence of uniform and equidistant impulsive control actions from continuous measurements of the output of a smooth nonlinear time-invariant positive single-input single-output plant of Wiener structure. The proposed controller is applied to the dosing of the drug atracurium in closed-loop neuromuscular blockade and its performance is studied on a database of patient-specific pharmacokinetic-pharmacodynamic models previously estimated from clinical data. © 2024 IEEE.

关键词： Controllers

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Quantum Recursive Programming with Quantum Case Statements

arXiv

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arXiv 2023年

作者： Ying, Mingsheng Zhang, Zhicheng The State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences The Department of Computer Science and Technology Tsinghua University China The Centre for Quantum Software and Information University of Technology Sydney Australia

We introduce a novel scheme of quantum recursive programming, in which large unitary transformations, i.e. quantum gates, can be recursively defined using quantum case statements, which are quantum counterparts of conditionals and case statements extensively used in classical programming. A simple programming language for supporting this kind of quantum recursion is defined, and its semantics is formally described. A series of examples are presented to show that some quantum algorithms can be elegantly written as quantum recursive programs. © 2023, CC BY.

关键词： Semantics

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FaceCat: Enhancing Face Recognition Security with a Unified Diffusion Model

arXiv

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arXiv 2024年

作者： Chen, Jiawei Yang, Xiao Dong, Yinpeng Su, Hang Yin, Zhaoxia Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China Department of Computer Science & Technology Tsinghua University China

Face anti-spoofing (FAS) and adversarial detection (FAD) have been regarded as critical technologies to ensure the safety of face recognition systems. However, due to limited practicality, complex deployment, and the additional computational overhead, it is necessary to implement both detection techniques within a unified framework. This paper aims to achieve this goal by breaking through two primary obstacles: 1) the suboptimal face feature representation and 2) the scarcity of training data. To address the limited performance caused by existing feature representations, motivated by the rich structural and detailed features of face diffusion models, we propose FaceCat, the first approach leveraging the diffusion model to simultaneously enhance the performance of FAS and FAD. Specifically, FaceCat elaborately designs a hierarchical fusion mechanism to capture rich face semantic features of the diffusion model. These features then serve as a robust foundation for a lightweight head, designed to execute FAS and FAD simultaneously. Due to the limitations in feature representation that arise from relying solely on single-modality image data, we further propose a novel text-guided multi-modal alignment strategy that utilizes text prompts to enrich feature representation, thereby enhancing performance. To combat data scarcity, we build a comprehensive dataset with a wide range of 28 attack types, offering greater potential for a unified framework in facial security. Extensive experiments validate the effectiveness of FaceCat generalizes significantly better and obtains excellent robustness against common input transformations. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Optimizing Forest Fire Prevention: Intelligent Scheduling Algorithms for Drone-Based Surveillance System 27

Optimizing Forest Fire Prevention: Intelligent Scheduling Al...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Jemmali, Mahdi Melhim, Loai Kayed B. Boulila, Wadii Amdouni, Hajer Alharbi, Mafawez T. College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates MARS Laboratory University of Sousse Sousse Tunisia Department of Computer Science Higher Institute of Computer Science and Mathematics Monastir University Monastir5000 Tunisia Department of Health Information Management and Technology College of Applied Medical Sciences University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Ecole Supérieure des Sciences Economiques et Commerciales de Tunis Université de Tunis Tunisia Unit of Scientific Research Applied College Qassim University Buraydah51452 Saudi Arabia

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones' capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones' assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm RID achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： algorithms drone fire Forest monitoring

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Balancing Usability and Security Innovative Approaches to Privacy in Digital Environments

Balancing Usability and Security Innovative Approaches to Pr...

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Communication, computer sciences and Engineering (IC3SE), International Conference on

作者： Swathi Baswaraju M Geeta Yadav Ginni Nijhawan Ajay Rana T K. Pushpa Rani Taqi Mohammed Khattab Al-Rubaye Department of Computer Science – Data Science New Horizon College of Engineering Bangalore India Department of Computer Science And Engineering Institute Of Aeronautical Engineering Hyderabad Telangana India Lovely Professional University Phagwara India Amity School of Engineering and Technology Amity University Greater Noida India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of Medical Laboratory Technology College Of Medical Technologies The Islamic University Najaf Iraq

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Data analysis, private protection, detecting unusual things, identifying people, and transferring data are part of the study's unique security design. The major system method is AUA. Your authentication level depends on your activity, device, and location. Context-aware Privacy uses AUA to tailor user privacy options. Encrypting AES data transfers with Secure Data Transfer (SDT) is offered. Machine learning anomaly detection (ADML) analyzes user behavior and implements security measures to uncover vulnerabilities rapidly. DPDA employs controlled noise to make it statistically tougher to identify a person while processing data to safeguard privacy. Ablation study indicates adaptive security design strategies function better together. The recommended design may be safer and easier to employ with visual models and performance testing. With this knowledge, managers may be able to create business-friendly security solutions. Finally, the framework's extensive and adaptable security models boost digital safety. It offers new privacy protection in the internet age.

关键词： Privacy Data privacy Adaptation models Visualization Data analysis Machine learning Data transfer

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Staggered Microstrip Grating Structure for Sheet-Beam TWTs

Staggered Microstrip Grating Structure for Sheet-Beam TWTs

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International Conference on Vacuum Electronics

作者： Robert Marosi Muhammed Zuboraj Filippo Capolino Department of Electrical Engineering and Computer Science University of California Irvine CA USA Los Alamos National Laboratory Los Alamos NM USA

ISBN: (数字)9798350348705

ISBN: (纸本)9798350348712

We introduce a simple geometry for slow-wave structures (SWSs) in sheet-beam traveling-wave tubes (TWTs). The staggered microstrip grating SWS is a space harmonic structure with geometry and bandwidth with analogous to the staggered vane SWS commonly used for sheet beam TWTs. The staggered microstrip grating TWT is designed to operate in V-band with a center frequency of 74 GHz and electron beam voltage of 20 kV.

关键词： Geometry Microstrip antenna arrays Electron beams Voltage Harmonic analysis Space exploration Microstrip Gratings Vacuum electronics Electron sources

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Privacy-Preserving Quantum Annealing for Quadratic Unconstrained Binary Optimization (QUBO) Problems

Privacy-Preserving Quantum Annealing for Quadratic Unconstra...

引用

Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Moyang Xie Yuan Zhang Sheng Zhong Qun Li State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science William & Mary Williamsburg VA USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum annealers offer a promising approach to solve Quadratic Unconstrained Binary Optimization (QUBO) problems, which have a wide range of applications. However, when a user submits its QUBO problem to a third-party quantum annealer, the problem itself may disclose the user's private information to the quantum annealing service provider. To mitigate this risk, we introduce a privacy-preserving QUBO framework and propose a novel solution method. Our approach employs a combination of digit-wise splitting and matrix permutation to obfuscate the QUBO problem's model matrix Q, effectively concealing the matrix elements. In addition, based on the solution to the obfuscated version of the QUBO problem, we can reconstruct the solution to the original problem with high accuracy. Theoretical analysis and empirical tests confirm the efficacy and efficiency of our proposed technique, demonstrating its potential for preserving user privacy in quantum annealing services.

关键词： Privacy Annealing Accuracy Quantum mechanics Quantum annealing Optimization

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Revealing Performance Issues in Server-side WebAssembly Runtimes via Differential Testing

arXiv

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arXiv 2023年

作者： Jiang, Shuyao Zeng, Ruiying Rao, Zihao Gu, Jiazhen Zhou, Yangfan Lyu, Michael R. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

WebAssembly (Wasm) is a bytecode format originally serving as a compilation target for Web applications. It has recently been used increasingly on the server side, e.g., providing a safer, faster, and more portable alternative to Linux containers. With the popularity of server-side Wasm applications, it is essential to study performance issues (i.e., abnormal latency) in Wasm runtimes, as they may cause a significant impact on server-side applications. However, there is still a lack of attention to performance issues in server-side Wasm runtimes. In this paper, we design a novel differential testing approach WarpDiff to identify performance issues in server-side Wasm runtimes. The key insight is that in normal cases, the execution time of the same test case on different Wasm runtimes should follow an oracle ratio. We identify abnormal cases where the execution time ratio significantly deviates from the oracle ratio and subsequently locate the Wasm runtimes that cause the performance issues. We apply WarpDiff to test five popular server-side Wasm runtimes using 123 test cases from the LLVM test suite and demonstrate the top 10 abnormal cases we identified. We further conduct an in-depth analysis of these abnormal cases and summarize seven performance issues, all of which have been confirmed by the developers. We hope our work can inspire future investigation on improving Wasm runtime implementation and thus promoting the development of server-side Wasm applications. Copyright © 2023, The Authors. All rights reserved.

关键词： computer operating systems

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Finding Cross-rule Optimization Bugs in Datalog Engines

arXiv

引用

arXiv 2024年

作者： Zhang, Chi Wang, Linzhang Rigger, Manuel State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China National University of Singapore Singapore

Datalog is a popular and widely-used declarative logic programming language. Datalog engines apply many cross-rule optimizations;bugs in them can cause incorrect results. To detect such optimization bugs, we propose an automated testing approach called Incremental Rule Evaluation (IRE), which synergistically tackles the test oracle and test case generation problem. The core idea behind the test oracle is to compare the results of an optimized program and a program without cross-rule optimization;any difference indicates a bug in the Datalog engine. Our core insight is that, for an optimized, incrementally-generated Datalog program, we can evaluate all rules individually by constructing a reference program to disable the optimizations that are performed among multiple rules. Incrementally generating test cases not only allows us to apply the test oracle for every new rule generated—we also can ensure that every newly added rule generates a non-empty result with a given probability and eschew recomputing already-known facts. We implemented IRE as a tool named Deopt, and evaluated Deopt on four mature Datalog engines, namely Soufflé, CozoDB, µZ, and DDlog, and discovered a total of 30 bugs. Of these, 13 were logic bugs, while the remaining were crash and error bugs. Deopt can detect all bugs found by queryFuzz, a state-of-the-art approach. Out of the bugs identified by Deopt, queryFuzz might be unable to detect 5. Our incremental test case generation approach is efficient;for example, for test cases containing 60 rules, our incremental approach can produce 1.17× (for DDlog) to 31.02× (for Soufflé) as many valid test cases with non-empty results as the naive random method. We believe that the simplicity and the generality of the approach will lead to its wide adoption in practice. Copyright © 2024, The Authors. All rights reserved.

关键词： Engines

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