检索结果-内蒙古大学图书馆

Journal of Reliable Intelligent Environments 2021年第4期7卷 277-278页

作者： Augusto, Juan Carlos Coronato, Antonio Research Group on Development of Intelligent Environments Department of Computer Science Middlesex University London United Kingdom Institute for High Performance Computing and Networking National Research Council Naples Italy

来源：评论

学校读者我要写书评

暂无评论

SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image

arXiv

引用

arXiv 2023年

作者： Deng, Guoyao Zou, Ke Ren, Kai Wang, Meng Yuan, Xuedong Ying, Sancong Fu, Huazhu National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Sichuan China College of Computer Science Sichuan University Sichuan China Institute of High Performance Computing A*STAR Singapore

Recently, Segmenting Anything Model has taken a significant step towards general artificial intelligence. Simultaneously, its reliability and fairness have garnered significant attention, particularly in the field of healthcare. In this study, we propose a multi-box prompt-triggered uncertainty estimation for SAM cues to demonstrate the reliability of segmented lesions or tissues. We estimate the distribution of SAM predictions using Monte Carlo with prior distribution parameters, employing different prompts as a formulation of test-time augmentation. Our experimental results demonstrate that multi-box prompts augmentation enhances SAM performance and provides uncertainty for each pixel. This presents a groundbreaking paradigm for a reliable SAM. Copyright © 2023, The Authors. All rights reserved.

关键词： Reliability

来源：评论

学校读者我要写书评

暂无评论

Impedance responses and size-dependent resonances in topolectrical circuits via the method of images

引用

Physical Review B 2023年第24期107卷 245114-245114页

作者： Haydar Sahin Zhuo Bin Siu S. M. Rafi-Ul-Islam Jian Feng Kong Mansoor B. A. Jalil Ching Hua Lee Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore 138632 Singapore Department of Physics National University of Singapore Singapore 117542 Singapore

Resonances in an electric circuit occur when capacitive and inductive components are present together. Such resonances appear in admittance measurements depending on the circuit's parameters and the driving AC frequency. In this study, we analyze the impedance characteristics of nontrivial topolectrical circuits such as one- and two-dimensional Su-Schrieffer-Heeger circuits and reveal that size-dependent anomalous impedance resonances inevitably arise in finite LC circuits. Through the method of images, we study how resonance modes in a multidimensional circuit array can be nontrivially modified by the reflection and interference of current from the structure and boundaries of the lattice. We derive analytic expressions for the impedance across two corner nodes of various lattice networks with homogeneous and heterogeneous circuit elements. We also derive the irregular dependency of the impedance resonance on the lattice size, and provide integral and dimensionally reduced expressions for the impedance in three dimensions and above.

关键词： Edge states Electrical conductivity Impedance Resistive switching Topological insulators Electronic structure Band structure methods Bloch wave theory Classical electromagnetism Tight-binding model

来源：评论

学校读者我要写书评

暂无评论

A Continuous Finite Element Method with Homotopy VanishingViscosity for Solving the Static Eikonal Equation

引用

Communications in Computational Physics 2022年第5期31卷 1402-1433页

作者： Yong Yang Wenrui Hao Yong-Tao Zhang College of Science Nanjing University of Aeronautics and AstronauticsNanjing 210016China Key Laboratory of Mathematical Modelling and High Performance Computing of Air Vehicles(NUAA) MIITNanjing 211106China Department of Mathematics Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Applied and Computational Mathematics and Statistics University of Notre DameNotre DameIN 46556USA

We develop a second-order continuousfinite element method for solving the static Eikonal *** is based on the vanishing viscosity approach with a homotopy method for solving the discretized nonlinear *** specifically,the homotopy method is utilized to decrease the viscosity coefficient gradually,while Newton’s method is applied to compute the solution for each viscosity ***’s method alone converges for just big enough viscosity coefficients on very coarse grids and for simple 1D examples,but the proposed method is much more robust and guarantees the convergence of the nonlinear solver for all viscosity coefficients and for all examples over all *** experiments from 1D to 3D are presented to confirm the second-order convergence and the effectiveness of the proposed method on both structured or unstructured meshes.

关键词： Eikonal equation finite element method homotopy method

来源：评论

学校读者我要写书评

暂无评论

MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining

arXiv

引用

arXiv 2025年

作者： Wu, Ruiqi Su, Na Zhang, Chenran Ma, Tengfei Zhou, Tao Cui, Zhiting Tang, Nianfeng Mao, Tianyu Zhou, Yi Fan, Wen Wu, Tianxing Jing, Shenqi Fu, Huazhu School of Computer Science and Engineering Southeast University Nanjing China Department of Ophthalmology The First Affiliated Hospital of Nanjing Medical University Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of High-Performance Computing Agency for Science Technology and Research Singapore

Vision-language pretraining (VLP) has been investigated to generalize across diverse downstream tasks for fundus image analysis. Although recent methods showcase promising achievements, they significantly rely on large-scale private image-text data but pay less attention to the pretraining manner, which limits their further advancements. In this work, we introduce MM-Retinal V2, a high-quality image-text paired dataset comprising CFP, FFA, and OCT image modalities. Then, we propose a novel fundus vision-language pretraining model, namely KeepFIT V2, which is pretrained by integrating knowledge from the elite data spark into categorical public datasets. Specifically, a preliminary textual pretraining is adopted to equip the text encoder with primarily ophthalmic textual knowledge. Moreover, a hybrid image-text knowledge injection module is designed for knowledge transfer, which is essentially based on a combination of global semantic concepts from contrastive learning and local appearance details from generative learning. Extensive experiments across zero-shot, few-shot, and linear probing settings highlight the generalization and transferability of KeepFIT V2, delivering performance competitive to state-of-the-art fundus VLP models trained on large-scale private image-text datasets. Our dataset and model are publicly available via https://***/lxirich/MM-Retinal. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Scalable parallel photonic processing unit for various neural network accelerations

引用

Photonics Research 2024年第11期12卷 2691-2702页

作者： SHIYIN DU JUN ZHANG HAO OUYANG ZILONG TAO QIUQUAN YAN HAO HAO JUNHU ZHOU JIE YOU YUHUA TANG TIAN JIANG State Key Laboratory of High-Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China National Innovation Institute of Defense Technology Academy of Military Sciences PLA ChinaBeijing 100071China College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangsha 410073China Institute for Quantum Science and Technology College of ScienceNational University of Defense TechnologyChangsha 410073China

In recent years,integrated optical processing units(IOPUs)have demonstrated advantages in energy efficiency and computational speed for neural network inference ***,limited by optical integration technology,the practicality and versatility of IOPU face serious *** this work,a scalable parallel photonic processing unit(SPPU)for various neural network accelerations based on high-speed phase modulation is proposed and implemented on a silicon-on-insulator platform,which supports parallel processing and can switch between multiple computational paradigms simply and without latency to infer different neural network structures,enabling to maximize the utility of on-chip *** SPPU adopts a scalable and process-friendly architecture design,with a preeminent photonic-core energy efficiency of 0.83 TOPS/W,two to ten times higher than existing integrated *** the proof-of-concept experiment,a convolutional neural network(CNN),a residual CNN,and a recurrent neural network(RNN)are all implemented on our photonic processor to handle multiple tasks of handwritten digit classification,signal modulation format recognition,and review emotion *** SPPU achieves multi-task parallel processing capability,serving as a promising and attractive research route to maximize the utility of on-chip components under the constraints of integrated technology,which helps to make IOPU more practical and universal.

关键词： network acceleration attractive

来源：评论

学校读者我要写书评

暂无评论

Online Demand Peak Shaving with Machine-Learned Advice in Cyber-Physical Energy Systems

Online Demand Peak Shaving with Machine-Learned Advice in Cy...

引用

IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Minxi Fengl Wei Li Boyu Qin Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Electrical Engineering State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University China

With the deep integration of cyber tools, control algorithms are increasingly employed in cyber-physical energy systems to enhance management, cost efficiency, and robustness. Effective demand load management is crucial in cyber-physical energy systems as it directly impacts operational costs. Failure to adequately manage spiky or seasonal demand loads can lead to significant expenses on monthly utility bills. In this study, we propose AMPAMOD, a randomized online algorithm with machine-learned advice, to optimize battery operations and mitigate highly dynamic peak loads. AMPAMOD utilizes limited advice from machine learning algorithms to guide our online algorithm and ensure cost-effective peak load management. The theoretical analysis shows that our solution has minimal advice complexity, a linear computational cost, and an improved competitive ratio. Finally, we conduct extensive trace-driven experiments on real-world datasets. AMPAMOD achieves a peak shaving success rate of over 90%, outperforming baselines by at least 50%. These experimental results confirm theoretical findings and demonstrate the potential of AMPAMOD.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Generalized upwind summation-by-parts operators and their application to nodal discontinuous Galerkin methods

arXiv

引用

arXiv 2024年

作者： Glaubitz, Jan Ranocha, Hendrik Winters, Andrew R. Schlottke-Lakemper, Michael Öffner, Philipp Gassner, Gregor Department of Aeronautics and Astronautics Massachusetts Institute of Technology United States Department of Mathematics Linköping University Sweden Institute of Mathematics Johannes Gutenberg University Mainz Germany High-Performance Scientific Computing University of Augsburg Germany High-Performance Computing Center Stuttgart Germany Institute of Mathematics TU Clausthal Germany Department of Mathematics and Computer Science Center for Data and Simulation Science University of Cologne Germany

high-order numerical methods for conservation laws are highly sought after due to their potential efficiency. However, it is challenging to ensure their robustness, particularly for under-resolved flows. Baseline high-order methods often incorporate stabilization techniques that must be applied judiciously—sufficient to ensure simulation stability but restrained enough to prevent excessive dissipation and loss of resolution. Recent studies have demonstrated that combining upwind summation-by-parts (USBP) operators with flux vector splitting can increase the robustness of finite difference (FD) schemes without introducing excessive artificial dissipation. This work investigates whether the same approach can be applied to nodal discontinuous Galerkin (DG) methods. To this end, we demonstrate the existence of USBP operators on arbitrary grid points and provide a straightforward procedure for their construction. Our discussion encompasses a broad class of USBP operators, not limited to equidistant grid points, and enables the development of novel USBP operators on Legendre–Gauss–Lobatto (LGL) points that are well-suited for nodal DG methods. We then examine the robustness properties of the resulting DG-USBP methods for challenging examples of the compressible Euler equations, such as the Kelvin–Helmholtz instability. Similar to high-order FD-USBP schemes, we find that combining flux vector splitting techniques with DG-USBP operators does not lead to excessive artificial dissipation. Furthermore, we find that combining lower-order DG-USBP operators on three LGL points with flux vector splitting indeed increases the robustness of nodal DG methods. However, we also observe that higher-order USBP operators offer less improvement in robustness for DG methods compared to FD schemes. We provide evidence that this can be attributed to USBP methods adding dissipation only to unresolved modes, as FD schemes typically have more unresolved modes than nodal DG *** Codes 65M1

关键词： Vectors

来源：评论

学校读者我要写书评

暂无评论

DynamicNet: Efficient Federated Learning for Mobile Edge computing With Dynamic Privacy Budget and Aggregation Weights

引用

IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Li, Zheng Duan, Mingxing Yu, Siyang Yang, Wenjing School of Information Science and Engineering Hunan University Changsha China School of Computer Science and Electronic Engineering Hunan University Hunan China Hunan University of Finance and Economics Changsha China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

Mobile Edge computing (MEC) has been successfully applied to smart homes, mobile devices, and other electronic products, offering reduced communication latency and alleviating pressure on cloud services. The integration of Federated Learning (FL) and MEC provides customized services for enhancing user experiences. However, there are still gaps in combining these technologies: FL is susceptible to membership inference attacks, risking user privacy;MEC faces limitations in energy and computational resources, restricting FL performance. In order to address these issues, we propose a novel approach, named DynamicNet, which incorporates Privacy Budget Regulation (PBR) and Dynamic Aggregation Weights (DAW). PBR addresses privacy leaks in edge devices by assessing local model privacy, dynamically adjusting privacy budget for subsequent use, ensuring desired level of privacy protection while minimizing noise impact on global model. DAW tackles MEC-related performance limitations by generating synthetic data, computing optimal weights for edge devices using this synthetic data, and aggregating parameters accordingly. Experimental results on two mainstream datasets demonstrate: 1) PBR preserves participant data privacy while enhancing global model convergence speed and accuracy;2) DAW adapts well to heterogeneous federated environments, ensuring high global model accuracy even when some edge device datasets are of inferior quality. IEEE

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends

arXiv

引用

arXiv 2023年

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical 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：