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PDS-UKAN: Subdivision hopping connected to the U-KAN network for medical image segmentation

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 2025年 124卷 102568页

作者： Liwei Deng Wenbo Wang Songyu Chen Xin Yang Sijuan Huang Jing Wang Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration School of Automation Harbin University of Science and Technology Harbin Heilongjiang 150080 China School of Computer Science and Technology Harbin University of Science and Technology Harbin Heilongjiang 150080 China. Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration School of Automation Harbin University of Science and Technology Harbin Heilongjiang 150080 China. State Key Laboratory of Oncology in South China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangdong Provincial Clinical Research Center for Cancer Sun Yat-sen University Cancer Center Guangzhou Guangdong 510060 China Guangdong Esophageal Cancer Institute Guangzhou Guangdong 510060 China. Guangdong Esophageal Cancer Institute Guangzhou Guangdong 510060 China. Electronic address: huangsj@***. Institute for Brain Research and Rehabilitation South China Normal University Guangzhou 510631 China. Electronic address: wangjing_2022010@***.

Accurate and efficient segmentation of medical images plays a vital role in clinical tasks, such as diagnostic procedures and planning treatments. Traditional U-shaped encoder-decoder architectures, built on convolutional and transformer-based networks, have shown strong performance in medical image processing. However, the simple skip connections commonly used in these networks face limitations, such as insufficient nonlinear modeling capacity, weak global multiscale context modeling, and limited interpretability. To address these challenges, this study proposes the PDS-UKAN network, an innovative subdivision-based U-KAN architecture, designed to improve segmentation accuracy. The PDS-UKAN incorporates a PKAN module-comprising partial convolutions and Kolmogorov - Arnold network layers-into the encoder bottleneck, enhancing the network's nonlinear modeling and interpretability. Additionally, the proposed Dual-Branch Convolutional Boundary Enhancement Module (DBE) focuses on pixel-level boundary refinement, improving edge detail preservation in shallow skip connections. Meanwhile, the Skip Connection Channel Spatial Attention Module (SCCSA) mechanism is applied in the deeper skip connections to strengthen cross-dimensional interactions between channels and spatial features, mitigating the loss of spatial information due to downsampling. Extensive experiments across multiple medical imaging datasets demonstrate that PDS-UKAN consistently achieves superior performance compared to state-of-the-art (SOTA) methods.

关键词： Medical image segmentation Skip Connections U-KAN U-Net

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Design and polarization scattering characteristics of L-shape reflector

TechRxiv

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TechRxiv 2020年

作者： Bai, Xiaokai Wang, ZhanLing Huang, Jiankai Pang, Chen Li, Jianbing Li, Yongzhen State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Engineering National University of Defense Technology Hunan Changsha410073 China

This paper presents a new L-shape reflector to enhance the cross-polarization radar cross section (RCS). Cross-polarization component can increase the jamming effect against the polarization radars. Such L-shape reflectors can also be used as radar tags in cars, books and so on for radar target detection. By bending the structure of traditional dipole reflector to L shape, the direction of surface current is changed, which produces the cross-polarization component. We have simulated L-shape reflector with different lengths of branches in X-band. Compared with the scattering characteristics of traditional dipole reflector, the linear depolarization ratio (LDRvh ) of the L-shape reflector can be easily controlled by changing the length of two branches. © 2020, CC BY.

关键词： Radar cross section

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A GMTI algorithm based on novel channel equalization method for multi-channel imaging radar

A GMTI algorithm based on novel channel equalization method ...

引用

IET International Radar Conference (IET IRC 2020)

作者： B. Pang S. Xing D. Dai Y. Li X. Wang National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Changsha China National University of Defense Technology College of Electronic Science Changsha China

For multi-channel SAR-GMTI system, the consistency between the channels is the indispensable condition for location and velocity measurement of ground moving targets. Therefore, particular attention should be paid to the study of channel equalization. However, traditional channel equalization methods often have the problems of low operating efficiency, large computational burden and poor adaptability, making them less effective. Aiming at this problem, the signal model for multi-channel SAR-GMTI processing is established. Based on this, a novel channel equalization method is proposed in this paper. The experimental results based on the measured data of airborne 7-channel SAR-GMTI system show that the proposed method can effectively eliminate the amplitude and phase error between channels without the need for iterative operation, thus improves the calculation efficiency and GMTI processing effect simultaneously.

关键词： radar imaging synthetic aperture radar equalisers iterative methods

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α [email protected] carrier modulated platinum single atoms for high-efficient solar cells

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Applied Surface science 2023年 611卷

作者： Jixin Yao Jie Wang Wen Wang Ying Meng Haibo Wang Shibin Lu Feng Du Li Yang Xueqin Zuo Guang Li Anhui Province Key Laboratory of simulation and design for Electronic information system Universities Joint Key Laboratory of photoelectric detection Science and Technology in Anhui Province Hefei Normal University Hefei 230601 China School of Materials Science and Engineering Anhui Key Laboratory of Information Materials and Devices Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of Education Institute of Physical Science and Information Technology Anhui University Hefei 230601 China Institute of Materials Science and Devices School of Materials Science and Engineering Suzhou University of Science and Technology Suzhou 215011 China

Single-atom electrocatalysts have been proven to be the great promising catalysts for energy-related fields yet their active site structure remains elusive, especially in dye-sensitized solar cell (DSSC). Herein, atomically dispersed Pt is anchored into α [email protected] carrier (Pt/α [email protected] ), which vigorously stimulates the activity of Pt single atoms under the strong metal interaction. As expected, the Pt/α [email protected] as the cathode in DSSC exhibits high power conversion efficiency (PCE) of 9.67%, exceeding Pt electrode (7.61%). X-ray photoelectron spectroscopy combined with density functional theory calculations reveals that the high activity is mainly derived from charge re-distribution of Pt/α [email protected] caused by the interaction between Pt atoms and α-MoC. This interaction not only achieves the optimized adsorption/desorption energy of reactants but also brings synergic advantages to expedite the reaction kinetics of the overall system. This work paves further research prospects for charge re-distribution, stimulating electrocatalysts to effectively enhance their catalytic properties by an atomic-level engineering strategy.

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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PARDA: A dataset for scholarly PDF document metadata extraction evaluation 14th

PARDA: A dataset for scholarly PDF document metadata extract...

引用

14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Fan, Tiantian Liu, Junming Qiu, Yeliang Jiang, Congfeng Zhang, Jilin Zhang, Wei Wan, Jian School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou310018 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9783030129804

Metadata extraction from scholarly PDF documents is the fundamental work of publishing, archiving, digital library construction, bibliometrics, and scientific competitiveness analysis and evaluations. However, different scholarly PDF documents have different layout and document elements, which make it impossible to compare different extract approaches since testers use different source of test documents even if the documents are from the same journal or conference. Therefore, standard datasets based performance evaluation of various extraction approaches can setup a fair and reproducible comparison. In this paper we present a dataset, namely, PARDA(Pdf Analysis and Recognition DAtaset), for performance evaluation and analysis of scholarly documents, especially on metadata extraction, such as title, authors, affiliation, author-affiliation-email matching, year, date, etc. The dataset covers computer science, physics, life science, management, mathematics, and humanities from various publishers including ACM, IEEE, Springer, Elsevier, arXiv, etc. And each document has distinct layouts and appearance in terms of formatting of metadata. We also construct the ground truth metadata in Dublin Core XML format and BibTex format file associated this dataset. © 2019, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Metadata

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Approaching the Fundamental Limit of Orbital Angular Momentum Multiplexing Through a Hologram Metasurface

arXiv

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arXiv 2021年

作者： Yuan, Shuai S.A. Wu, Jie Chen, Menglin L.N. Lan, Zhihao Zhang, Liang Sun, Sheng Huang, Zhixiang Chen, Xiaoming Zheng, Shilie Jiang, Li Jun Zhang, Xianmin Sha, Wei E.I. College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Ministry of Education The Key Laboratory of Electromagnetic Environmental Sensing Department of Education of Anhui Province Anhui University Hefei230039 China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Electronic and Electrical Engineering University College London Torrington Place LondonWC1E 7JE United Kingdom Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Hefei230601 China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Communications Engineering Xi’an Jiaotong University Xi’an710049 China

Establishing and approaching the fundamental limit of orbital angular momentum (OAM) multiplexing are necessary and increasingly urgent for current multiple-input multiple-output research. In this work, we elaborate the fundamental limit in terms of independent scattering channels (or the degrees of freedom of scattered fields) through angular-spectral analysis, in conjunction with a rigorous Green’s function method. The scattering-channel limit is universal for arbitrary spatial-mode multiplexing, which is launched by a planar electromagnetic device, such as antenna, metasurface, etc., with a predefined physical size. As a proof of concept, we demonstrate both theoretically and experimentally the limit by a phase-only metasurface hologram that transforms orthogonal OAM modes to plane-wave modes scattered at critically separated angular-spectral regions. Particularly, a minimax optimization algorithm is applied to suppress angular-spectrum aliasing, achieving good performances in both full-wave simulation and experimental measurement at microwave frequencies. This work offers a theoretical upper bound and corresponding approach route for engineering designs of OAM multiplexing. Copyright © 2021, The Authors. All rights reserved.

关键词： Spectrum analysis

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Energy aware edge computing: a survey 2nd

Energy aware edge computing: a survey

引用

2nd International Workshop on High Performance Computing for Advanced Modeling and simulation in Nuclear Energy and Environmental science, HPCMS 2018, and 1st International Workshop on HPC Supported Data Analytics for Edge Computing, HiDEC 2018 held at the 32nd ACM International Conference on Supercomputing, ACM ICS 2018

作者： Fan, Tiantian Qiu, Yeliang Jiang, Congfeng Wan, Jian Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou310018 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9789813299863

Edge computing is an emerging paradigm to meet the ever-increasing computation demands from pervasive devices such as sensors, actuators, and smart things. Though the edge devices can execute complex applications, it is necessary for some applications to migrate to centralized servers. By offloading the computation from the edge nodes to the edge servers or cloud servers, the quality of computation experience could be greatly improved. However, it may cause delay and increase network overheads, and energy consumption eventually. Therefore, an optimal offloading strategy should take into account what task should be offloaded, when to offload and where to offload to avoid the overheads. Thus, it is important to tradeoff between energy consumption, computation delay and throughput when the system makes the computation offloading to achieve high energy efficiency. In this paper, we conduct a survey of energy aware edge computing, including the existing work on computation offloading frameworks and strategies in edge computing. Specifically, we describe the strategies from the perspective of energy aware offloading, energy optimization offloading and offloading algorithms. © Springer Nature Singapore Pte Ltd 2019.

关键词： Energy efficiency

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An edge computing platform for intelligent internet data center operational monitoring 2nd

An edge computing platform for intelligent internet data cen...

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作者： Qiu, Yeliang Jiang, Congfeng Fan, Tiantian Wan, Jian Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou310018 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9789813299863

The explosive growth in cloud-based services, big data analytics, and artificial intelligence related services provisioning leads to the rapid growth of construction of large scale Internet data centers (IDCs). Modern IDCs are equipped with various sensors to monitor its healthy operation and maintenance states, such as temperature, thermal distribution, air flow, etc. However, fine-grained, single-node level and even mainboard level monitoring including server resource consumption and power consumption is still needed for more progressive resource multiplexing and job scheduling in IDCs. In this paper, we propose an edge computing platform for intelligent Internet data center operational monitoring, which integrates wireless sensors and on-board built-in sensors to sense and collect the data of data center operation and maintenance. The edge computing based solution reduces the latency for data transportation to central clouds and reduces the amount of data and enhances the real time resource capping decisions in data centers. © Springer Nature Singapore Pte Ltd 2019.

关键词： Energy efficiency

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Towards computation offloading in edge computing: A survey 2nd

Towards computation offloading in edge computing: A survey

引用

作者： Cheng, Xiaolan Zhou, Xin Jiang, Congfeng Wan, Jian Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou310018 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9789813299863

The explosive growth of massive data generation from Internet of Things in industrial, agricultural and scientific communities has led to a rapid increase in cloud data centers for data analytics. The ubiquitous and pervasive demand for near-data processing urges the edge computing paradigm in recent years. Edge computing is promising for less network backbone bandwidth usage and thus less data center side processing, as well as enhanced service responsiveness and data privacy protection. Computation offloading plays a crucial role in network packets transmission and system responsiveness through dynamic task partitioning between cloud data centers and edge servers and edge devices. In this paper a thorough literature review is conduct to reveal the state-of-the-art of computation offloading in edge computing. Various aspects of computation offloading, including energy consumption minimization, Quality of Services (QoS), and Quality of Experiences (QoE) are surveyed. Resource scheduling approaches, gaming and tradeoffing among system performance and system overheads for offloading decision making are also reviewed. © Springer Nature Singapore Pte Ltd 2019.

关键词： computation offloading

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