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Exact Quantum Sensing Limits for Bosonic Dephasing Channels

PRX Quantum 2024年第2期5卷 020354-020354页

作者： Zixin Huang Ludovico Lami Mark M. Wilde School of Mathematical and Physical Sciences Centre for Quantum Software and Information Faculty of Engineering and Information Technology Science Park 123 Amsterdam 1098 XG the Netherlands Korteweg–de Vries Institute for Mathematics Institute for Theoretical Physics School of Electrical and Computer Engineering

Dephasing is a prominent noise mechanism that afflicts quantum information carriers, and it is one of the main challenges toward realizing useful quantum computation, communication, and sensing. Here, we consider discrimination and estimation of bosonic dephasing channels, when using the most general adaptive strategies allowed by quantum mechanics. We reduce these difficult quantum problems to simple classical ones based on the probability densities defining the bosonic dephasing channels. By doing so, we rigorously establish the optimal performance of various distinguishability and estimation tasks and construct explicit strategies to achieve this performance. To the best of our knowledge, this is the first example of a non-Gaussian bosonic channel for which there are exact solutions for these tasks.

关键词： Non-gaussian quantum states Quantum channels Quantum fluctuations & noise Quantum information theory Quantum parameter estimation Quantum sensing

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DualVAE: Dual Disentangled Variational AutoEncoder for Recommendation

arXiv

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arXiv 2024年

作者： Guo, Zhiqiang Li, Guohui Li, Jianjun Wang, Chaoyang Shi, Si School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China Wuhan Digital Engineering Institute China China

Learning precise representations of users and items to fit observed interaction data is the fundamental task of collaborative filtering. Existing studies usually infer entangled representations to fit such interaction data, neglecting to model the diverse matching relationships between users and items behind their interactions, leading to limited performance and weak interpretability. To address this problem, we propose a Dual Disentangled Variational AutoEncoder (DualVAE) for collaborative recommendation, which combines disentangled representation learning with variational inference to facilitate the generation of implicit interaction data. Specifically, we first implement the disentangling concept by unifying an attention-aware dual disentanglement and disentangled variational autoencoder to infer the disentangled latent representations of users and items. Further, to encourage the correspondence and independence of disentangled representations of users and items, we design a neighborhood-enhanced representation constraint with a customized contrastive mechanism to improve the representation quality. Extensive experiments on three real-world benchmarks show that our proposed model significantly outperforms several recent state-of-the-art baselines. Further empirical experimental results also illustrate the interpretability of the disentangled representations learned by DualVAE. Copyright © 2024, The Authors. All rights reserved.

关键词： Collaborative filtering

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Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

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computer Systems Science & engineering 2023年第5期45卷 1695-1709页

作者： Xiaorui Zhang Feng Xu Wei Sun Yan Jiang Yi Cao Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorONN9B 3P4Canada

3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one *** existing methods reconstruct the mesh surface of complex objects,the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework;the 3D topology is easily limited by predefined templates and inflexible,and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology,thus destroying the surface details;the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices,and the training time of the reconstructed network is too *** this paper,we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network(GCN)and topology *** use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the ***,a feature fusion method is proposed to make full use of the features of each stage of the image *** use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training *** experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces,but also has better qualitative and quantitative results.

关键词： 3D surface reconstruction deep learning GCN topology modification end-to-end framework

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Probabilistic-Search and Neighbor-Density Based Doppler-Shift Acquisition in Space Communications

引用

IEEE Transactions on Communications 2025年

作者： Zhang, Zhaowei Du, Shuai Liu, Lin Liu, Hui Zuo, Jiakuo Miao, Wang Zhao, Haitao Nanjing University of Posts and Telecommunications School of Internet of Things China Nanjing University State Key Laboratory for Novel Software Technology China Jinling Institute of Technology School of Software Engineering China University of Exeter Department of Computer Science United Kingdom

In space communications, the signal's long-distance transmission between a flying transmitter-receiver pair encounters two obstacles: a huge path-loss and a high-speed movement. The huge path-loss results in a low signal-to-noise ratio (SNR) and the high-speed movement brings a dynamic Doppler-shift, thus posing a great challenge for Doppler-shift acquisition. Under the low SNR, the Doppler-shift acquisition has to accumulate many symbols in a long-time period. However, during this period, the dynamic Doppler-shift disperses all these symbols' total energy over a wide range, thus causing the energy dispersion problem. To address this problem, we propose a probabilistic-search and neighbor-density (PSND) scheme, where the probabilistic-search considers the element's amplitude and sacrifices some redundant signal elements for a narrower search-range, while the neighbordensity sums the signal element's all neighbors for a larger signal energy. The PSND scheme includes two algorithms: the Generic PSND and Iterative PSND. The Generic PSND algorithm first selects some search-elements with their probabilities to construct a probabilistic-search-range, then selects some density-elements with their neighbor-elements to derive the neighbor-densities, and finally searches the largest neighbor-density to obtain the acquisition result. Built upon the Generic PSND algorithm above, the Iterative PSND algorithm iteratively updates the searchelements to further strengthen the neighbor-density in a narrower probabilistic-search-range. Moreover, the simulations results have demonstrated our PSND scheme's higher acquisition probability, as compared with the existing schemes. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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Transforming Diabetes Care: A Review of IoT-Based Mobile Health Systems 10th

Transforming Diabetes Care: A Review of IoT-Based Mobile He...

引用

10th EAI International Conference on IoT Technologies for Health-Care, HealthyIoT 2023, and 4th EAI International Conference on Wearables in Healthcare, HealthWear 2023

作者： Ashfaq, Fiza Ahad, Abdul Hussain, Mudassar Madeira, Filipe Department of Computer Science University of Management and Technology Sialkot51040 Pakistan School of Software Northwestern Polytechnical University Shaanxi Xian China Istanbul34467 Turkey /Pole of Digital Literacy and Social Inclusion Polytechnic Institute of Santarem Santarem2001-904 Portugal Department of Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman

ISBN: (纸本)9783031719103

Diabetes, a chronic ailment requiring ongoing care and monitoring, has undergone a healthcare shift thanks to the Internet of Things (IoT). Real-time monitoring, individualized treatment plans, and improved patient-provider communication are all features of IoT-based mobile health solutions. This study explores the components, functionalities, benefits, and disadvantages of IoT-based mobile health systems for the management of diabetes. It investigates wearable sensors, smartphone apps, and linked devices like glucose meter and monitors, placing a focus on their precision, dependability, and usability. The study explores data analytics techniques that allow for customized advice and actions. Patients can effectively control their diabetes with the use of individualized treatments and real-time monitoring. The research also looks into the data analytical methods used in IoT-based mobile health systems for the treatment of diabetes. IoT-based mobile health system issues and implications are also covered. These include issues with data security and privacy, platform and device compatibility, standardization of data formats, and ensuring that all people with diabetes have equal access to technology. Healthcare workers, academics, and policymakers may learn more about the potential of IoT-based mobile health systems to revolutionize diabetes treatment through this thorough assessment. © ICST institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Electronic health record

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MCGMark: An Encodable and Robust Online Watermark for LLM-Generated Malicious Code

arXiv

引用

arXiv 2024年

作者： Ning, Kaiwen Chen, Jiachi Zhong, Qingyuan Zhang, Tao Wang, Yanlin Li, Wei Zhang, Yu Zhang, Weizhe Zheng, Zibin The School of Software Engineering Sun Yat-Sen University China Peng Cheng Laboratory China The School of Computer Science and Technology Harbin Institute of Technology China The School of Computer Science and Engineering Macau University of Science and Technology China

With the advent of large language models (LLMs), numerous software service providers (SSPs) are dedicated to developing LLMs customized for code generation tasks, such as CodeLlama and Copilot. However, these LLMs can be leveraged by attackers to create malicious software, which may pose potential threats to the software ecosystem. For example, they can automate the creation of advanced phishing malware. To address this issue, we first conduct an empirical study and design a prompt dataset, MCGTEST, which involves approximately 400 person-hours of work and consists of 406 malicious code generation tasks. Utilizing this dataset, we propose MCGMARK, the first robust, code structure-aware, and encodable watermarking approach to trace LLM-generated code. We embed encodable information by controlling the token selection and ensuring the output quality based on probabilistic outliers. Additionally, we enhance the robustness of the watermark by considering the structural features of malicious code, preventing the embedding of the watermark in easily modified positions, such as comments. We validate the effectiveness and robustness of MCGMARK on the DeepSeek-Coder. MCGMARK achieves an embedding success rate of 88.9% within a maximum output limit of 400 tokens. Furthermore, it also demonstrates strong robustness and has minimal impact on the quality of the output code. Our approach assists SSPs in tracing and holding responsible parties accountable for malicious code generated by LLMs. Copyright © 2024, The Authors. All rights reserved.

关键词： Watermarking

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FedPartial: Enabling Model-Heterogeneous Federated Learning via Partial Model Transmission and Aggregation

FedPartial: Enabling Model-Heterogeneous Federated Learning ...

引用

IEEE International Conference on Web Services (ICWS)

作者： Changkun Jiang Jiahao Chen Lin Gao Jianqiang Li College of Computer Science and Software Engineering Shenzhen University China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

Federated learning (FL) is emerging as a new privacy-preserving learning paradigm that allows multiple devices to collaborate in training a model without sharing their raw data, using a central server for coordination. However, device heterogeneity poses a challenge in FL, as participating devices often have different computing capacities. To address this issue, heterogeneous models need to be designed to accommodate different device computing capacities. The existing approach involves pre-designing multiple heterogeneous models and extracting sub-models from the server model. While such an approach effectively tackles device heterogeneity, it has several drawbacks such as high communication overhead and insufficient personalization. In each training round, the server distributes the entire model parameters to each device, and each device also transmits the entire model parameters to the server for aggregation. In this work, we propose FedPartial, a new framework that overcomes these challenges by introducing a partial model transmission and aggregation mechanism. The FedPartial framework eliminates the need for devices to transmit the entire model parameters in each training round while still benefiting from global model aggregation. Specifically, FedPartial divides the device model into two parts: the shallow part participates in the global aggregation of heterogeneous models, while the deep part remains on the device locally. By keeping the deep part of the model on the device, FedPartial reduces the communication overhead significantly and achieves a certain degree of personalization. Through extensive experiments, we demonstrate that FedPartial outperforms existing state-of-the-art methods, particularly in more complex and statistically heterogeneous scenarios.

关键词： Training Federated learning Web services Computational modeling Data models Servers

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Design of a Lower Limb Exoskeleton Rehabilitation Robot System Based on Forward Dynamics Simulation

Design of a Lower Limb Exoskeleton Rehabilitation Robot Syst...

引用

Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Yudong Fang Yuefei Wang Liucun Zhu Zhen Liu Lei Shi Muxin Du Jianchu Lin School of Mechanical and Marine Engineering Beibu Gulf University Qinzhou China Sentinel Institute of Science and Technology Beibu Gulf University Qinzhou China Nagasaki Institute of Applied Science Nagasaki Japan Guangzhou Shiyuan Electronic Technology Company Limited Guangzhou China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Lower limb exoskeleton rehabilitation robots can effectively improve the rehabilitation of stroke patients and can substantially reduce the workload of rehabilitation therapists and improve work efficiency. In this paper, we propose a method to investigate the influence of muscle groups on the hip joint during the gait cycle through kinetic modeling simulation. The method is validated using electromyographic signals (EMG). The patients are treated according to their gait characteristics. Simulation with OpenSim; optimisation by Scone controller; joint motion change curves were analysed by Matlab simulation to verify the correctness of the mathematical model, and the simulation showed excellent following effect. The feasibility of the theory is verified, and a new scheme is proposed for the treatment of stroke patients.

关键词： Limbs Exoskeletons Stroke (medical condition) Muscles Assistive robots Mathematical models Kinetic theory MATLAB Robots Optimization

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Conceptual Clustering Based on Linguistic-Valued Layered Concept Lattice 1

引用

3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Pang, Kuo Kang, Ning Zou, Li Lu, Mingyu Information Science and Technology College Dalian Maritime University Dalian116026 China Software Engineering Institute East China Normal University Shanghai200062 China School of Computer Science and Technology Shandong Jianzhu University Jinan250102 China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Formal concept analysis (FCA) is well suited to explain the meaning of clusters. However, most of the existing FCA-based concept clustering algorithms cannot handle fuzzy linguistic information. In this work, we propose a conceptual clustering based on linguistic-valued layered concept lattice is generated by the fuzzy linguistic coupling relationship between attributes. To obtain bi-clustering results for linguistic concepts, attribute-induced and object-induced matrices are first proposed. Second, the problem of overlapping clustering results is handled by calculating the stability of linguistic-valued layered concepts. In addition, the conceptual clustering results can be obtained at different granularities by varying the fuzzy linguistic-valued trust degrees. Finally, illustration example is used to demonstrate the effectiveness of our proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Linguistics

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LV-Mamba: Integrating Denoising Mechanism with Mamba for Improved Segmentation of the Pediatric Echocardiographic Left Ventricle 5th

LV-Mamba: Integrating Denoising Mechanism with Mamba for Imp...

引用

5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Chen, Tianxiang Chang, Zeyu Wang, Fangyijie Wang, Ziyang Ye, Zi University of Science and Technology of China Hefei China School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom School of Medicine University College Dublin Dublin Ireland Department of Computer Science University of Oxford Oxford United Kingdom Institute of Intelligent Software Guangzhou China

ISBN: (纸本)9789819638628

In pediatric cardiology, the accurate and immediate assessment of cardiac function through echocardiography is crucial since it can determine whether urgent intervention is required in many emergencies. However, echocardiography is characterized by ambiguity and heavy background noise interference, causing more difficulty in accurate segmentation. Present methods lack efficiency and are prone to mistakenly segmenting some background noise areas, such as the left ventricular area, due to noise disturbance. We introduce LV-Mamba for efficient pediatric echocardiographic left ventricular segmentation to relieve the two issues. Specifically, we turn to the recently proposed vision mamba layers in our vision mamba encoder branch to improve our model’s computing and memory efficiency while modeling global dependencies. In the other DWT-based PMD encoder branch, we devise DWT-based Perona-Malik Diffusion (PMD) Blocks that utilize PMD for noise suppression while preserving the left ventricle’s local shape cues. Leveraging the strengths of both encoder branches, LV-Mamba achieves superior accuracy and efficiency to established models, such as vision transformers with quadratic and linear computational complexity. This innovative approach promises significant advancements in pediatric cardiac imaging and beyond. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Pediatrics

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