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NuSegDG: Integration of heterogeneous space and Gaussian kernel for domain-generalized nuclei segmentation

Knowledge-Based systems 2025年 322卷

作者： Lou, Zhenye Xu, Qing Jiang, Zekun He, Xiangjian Li, Chenxin Chen, Zhen Wang, Yi He, Maggie M. Duan, Wenting Sichuan University Pittsburgh Institute Sichuan University Chengdu China School of Computer Science Computer Vision and Intellifent Perception Laboratory and Municipal Digital Port Technology Key Laboratory University of Nottingham Ningbo Zhejiang China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong School of Software Dalian University of Technology Dalian116600 China Department of Electronic Engineering The Chinese University of Hong Kong 999077 Hong Kong Department of Cardiology Gold Coast University Hospital QLD Australia School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom

Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image conditions, cell types, and stain strategies. Recently, the Segment Anything Model (SAM) has made great success in universal image segmentation by interactive prompt modes (e.g., point and box). Despite its strengths, the original SAM presents limited adaptation to medical images. Moreover, SAM requires providing manual bounding box prompts for each object to produce satisfactory segmentation masks, so it is laborious in nuclei segmentation scenarios. To address these limitations, we propose a domain-generalizable framework for nuclei image segmentation, abbreviated to NuSegDG. Specifically, we first devise a Heterogeneous Space Adapter (HS-Adapter) to learn multi-dimensional feature representations of different nuclei domains by injecting a small number of trainable parameters into the image encoder of SAM. To alleviate the labour-intensive requirement of manual prompts, we introduce a Gaussian-Kernel Prompt Encoder (GKP-Encoder) to generate density maps driven by a single point, which guides segmentation predictions by mixing position prompts and semantic prompts. Furthermore, we present a Two-Stage Mask Decoder (TSM-Decoder) to effectively convert semantic masks to instance maps without the manual demand for morphological shape refinement. Based on our experimental evaluations, the proposed NuSegDG demonstrates state-of-the-art performance in nuclei semantic and instance segmentation, exhibiting superior domain generalization capabilities. The source code is available at https://***/xq141839/NuSegDG. © 2025 Elsevier B.V.

关键词： Semantic Segmentation

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Companding techniques for SC-FDMA and sensor network applications

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International Journal of Electronics Letters 2020年第3期8卷 241-255页

作者： Abd El-Rahman, Amal F Ahmed, Rania A Abd Elnaby, Mustafa M. Khamis, Salah Napoleon, Sameh Abd-Elnaby, Mohammed Ahmed, Zeinab A El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Electronics and Communications Department Faculty of Engineering Tanta University Tanta Egypt Training Evaluating and Updating Expert Systems Central Laboratory for Agricultural Expert System Giza Giza Egypt Department of Electronics and Electrical Communications Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Computer Engineering College of Computers and Information Technology Taif University Al-Hawiya Saudi Arabia

The large Peak-to-Average Power Ratio (PAPR) is a serious problem in wireless communication systems for mobile and sensor network applications. This paper presents efficient non-linear techniques to solve the large PAPR problem in Single-Carrier Frequency Division Multiple Access (SC-FDMA) system and Wireless Sensor Network (WSN) systems. The performance of the proposed techniques is investigated over a realistic channel model. Simulation results reveal PAPR reduction while preserving low Bit Error Rate (BER) levels. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： companding DCT PAPR Power Amplifier SC-FDMA

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An Access Control Mechanism Based on Risk Prediction for the IoV

An Access Control Mechanism Based on Risk Prediction for the...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yuanni Liu Man Xiao Yanyan Zhou Di Zhang Jianhui Zhang Haris Gacanin Jianli Pan Chongqing University of Posts and Telecommunications Chongqing China Zhengzhou University Zhengzhou China National Digital Switching System Engineering & Technological R&D Center China Nokia Bell Labs Antwerp Belgium Department of Mathematics and Computer Science University of Missouri-St.Louis MO USA

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

The information sharing among vehicles provides intelligent transport applications in the Internet of Vehicles (IoV), such as self-driving and traffic awareness. However, due to the openness of the wireless communication (e.g., DSRC), the integrity, confidentiality and availability of information resources are easy to be hacked by illegal access, which threatens the security of the related IoV applications. In this paper, we propose a novel Risk Prediction-Based Access Control model, named RPBAC, which assigns the access rights to a node by predicting the risk level. Considering the impact of limited training datasets on prediction accuracy, we first introduce the Generative Adversarial Network (GAN) in our risk prediction module. The GAN increases the items of training sets to train the Neural Network, which is used to predict the risk level of vehicles. In addition, focusing on the problem of pattern collapse and gradient disappearance in the traditional GAN, we develop a combined GAN based on Wasserstein distance, named WCGAN, to improve the convergence time of the training model. The simulation results show that the WCGAN has a faster convergence speed than the traditional GAN, and the datasets generated by WCGAN have a higher similarity with real datasets. Moreover, the Neural Network (NN) trained with the datasets generated by WCGAN and real datasets (NN-WCGAN) performs a faster speed of training, a higher prediction accuracy and a lower false negative rate than the Neural Network trained with the datasets generated by GAN and real datasets (NN-GAN), and the Neural Network trained with the real datasets (NN). Additionally, the RPBAC model can improve the accuracy of access control to a great extent.

关键词： Gallium nitride Generative adversarial networks Generators Access control Training Predictive models Artificial neural networks

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Hybrid LD and LED-based underwater optical communication: state-of-the-art, opportunities,challenges, and trends [Invited]

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Chinese Optics Letters 2019年第10期17卷 2-10页

作者： Xuan Huang Fang Yang Jian Song Department of Electronic Engineering Tsinghua University&Beijing National Research Center for Information Science and Technology(BNRist)Beijing 100084China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in ShenzhenShenzhen 518057China

In this paper,the current research of an underwater optical wireless communication(UWOC)network is reviewed first.A hybrid laser diode(LD)and light-emitting diode(LED)-based UWOC system is then proposed and investigated,in which hybrid cluster-based networking with mobility restricted nodes is utilized to improve both the life cycle and throughput of the UWOC ***,the LEDs are utilized for the coarse alignment,while the LDs are used for high-precision positioning to reduce the difficulty of optical ***,challenges and trends for UWOC are pointed out to provide some insight for potential future work of researchers.

关键词： communication optical underwater

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Why botnets work: Distributed brute-force attacks need no synchronization

Why botnets work: Distributed brute-force attacks need no sy...

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作者： Salamatian, Salman Huleihel, Wasim Beirami, Ahmad Cohen, Asaf Medard, Muriel Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering-Systems Tel-Aviv University Tel-Aviv6997801 Israel EA Digital Platform-Data and AI Electronic Arts Redwood CityCA94065 United States Department of Electrical Engineering Ben-Gurion University of the Negev Beersheba8410501 Israel

In September 2017, the McAfee Labs quarterly report estimated that brute-force attacks represent 20% of total network attacks, making them the most prevalent type of attack ex-aequo with browser-based vulnerabilities. These attacks have sometimes catastrophic consequences, and understanding their fundamental limits may play an important role in the risk assessment of password-secured systems and in the design of better security protocols. While some solutions exist to prevent online brute-force attacks that arise from one single IP address, attacks performed by botnets are more challenging. In this paper, we analyze these distributed attacks by using a simplified model. Our aim is to understand the impact of distribution and asynchronization on the overall computational effort necessary to breach a system. Our result is based on guesswork, a measure of the number of queries (guesses) before a correct sequence, such as a password, is found in an optimal attack. Guesswork is a direct surrogate for time and computational effort of guessing a sequence from a set of sequences with the associated likelihoods. We model the lack of synchronization by a worst-case optimization in which the queries made by multiple adversarial agents are received in the worst possible order for the adversary, resulting in a min-max formulation. We show that, even without synchronization, and for sequences of growing length, the asymptotic optimal performance is achievable by using randomized guesses drawn from an appropriate distribution. Therefore, randomization is key for the distributed asynchronous attacks. In other words, asynchronous guessers can asymptotically perform brute-force attacks as efficiently as synchronized guessers. © 2019 IEEE.

关键词： Botnet

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system of Agentic AI for the Discovery of Metal-Organic Frameworks

arXiv

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arXiv 2025年

作者： Inizan, Théo Jaffrelot Yang, Sherry Kaplan, Aaron Lin, Yen-Hsu Yin, Jian Mirzaei, Saber Abdelgaid, Mona Alawadhi, Ali H. Cho, KwangHwan Zheng, Zhiling Cubuk, Ekin Dogus Borgs, Christian Chayes, Jennifer T. Persson, Kristin A. Yaghi, Omar M. Department of Chemistry University of California BerkeleyCA United States Bakar Institute of Digital Materials for the Planet University of California BerkeleyCA United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Google DeepMind Mountain ViewCA United States Kavli Energy NanoScience Institute BerkeleyCA United States Department of Mathematics University of California BerkeleyCA United States Department of Statistics University of California BerkeleyCA United States School of Information University of California BerkeleyCA United States Department of Materials Science and Engineering University of California BerkeleyCA United States

Generative models and machine learning promise accelerated material discovery in MOFs for CO2 capture and water harvesting but face significant challenges navigating vast chemical spaces while ensuring synthetizability. Here, we present MOFGen, a system of Agentic AI comprising interconnected agents: a large language model that proposes novel MOF compositions, a diffusion model that generates crystal structures, quantum mechanical agents that optimize and filter candidates, and synthetic-feasibility agents guided by expert rules and machine learning. Trained on all experimentally reported MOFs and computational databases, MOFGen generated hundreds of thousands of novel MOF structures and synthesizable organic linkers. Our methodology was validated through high-throughput experiments and the successful synthesis of five"AI-dreamt" MOFs, representing a major step toward automated synthesizable material discovery. © 2025, CC BY.

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Apple Vision Pro for Healthcare: "The Ultimate Display"? Entering the Wonderland of Precision Medicine

arXiv

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arXiv 2023年

作者： Egger, Jan Gsaxner, Christina Chen, Xiaojun Bian, Jiang Kleesiek, Jens Puladi, Behrus Girardetstraße 2 Essen45131 Germany Hufelandstraße 55 Essen45147 Germany Hufelandstraße 55 Essen45147 Germany Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Pauwelsstraße 30 Aachen52074 Germany Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/II Graz8010 Austria Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Dongchuan Road 800 Minhang District Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Dongchuan Road 800 Minhang District Shanghai200240 China Health Outcomes & Biomedical Informatics College of Medicine University of Florida GainesvilleFL32610 United States Partner Site Essen Hufelandstraße 55 Essen45147 Germany Department of Physics TU Dortmund University August-Schmidt-Str. 4 Dortmund44227 Germany Institute of Medical Informatics University Hospital RWTH Aachen Pauwelsstraße 30 Aachen52074 Germany

At the Worldwide Developers Conference (WWDC) in June 2023, Apple introduced the Vision Pro. The Vision Pro is a Mixed Reality (MR) headset, more specifically it is a Virtual Reality (VR) device with an additional Video See-Through (VST) capability. The VST capability turns the Vision Pro also into an Augmented Reality (AR) device. The AR feature is enabled by streaming the real world via cameras to the (VR) screens in front of the user’s eyes. This is of course not unique and similar to other devices, like the Varjo XR-3. Nevertheless, the Vision Pro has some interesting features, like an inside-out screen that can show the headset wearers’ eyes to "outsiders" or a button on the top, called "digital Crown", that allows you to seamlessly blend digital content with your physical space by turning it. In addition, it is untethered, except for the cable to the battery, which makes the headset more agile, compared to the Varjo XR-3. This could actually come closer to the "Ultimate Display", which Ivan Sutherland had already sketched in 1965. Not available to the public yet, like the Ultimate Display, we want to take a look into the crystal ball in this perspective to see if it can overcome some clinical challenges that - especially - AR still faces in the medical domain, but also go beyond and discuss if the Vision Pro could support clinicians in essential tasks to spend more time with their patients. © 2023, CC BY.

关键词： Augmented reality

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Study on Subsynchronous Resonance Damping Control for Series-Compensated DFIG-Based Wind Farm

Study on Subsynchronous Resonance Damping Control for Series...

引用

2019 IEEE Sustainable Power and Energy Conference, iSPEC 2019

作者： Luo, Chao Guo, Qi Xiao, Xiangning Gole, A.M. Liao, Mengjun Electric Power Research Institute State Key Laboratory of HVDC China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China North China Electric Power University State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources Beijing China University of Manitoba Electrical and Computer Engineering Department Winnipeg Canada

ISBN: (纸本)9781728149301

Subsynchronous Resonance (SSR) is a potential threat for long distance power transmission from doubly fed induction generator (DFIG) based wind farms with fixed series compensation (FSC). In this paper, subsynchronous resonance damping control (SSRDC) strategy based on shunt voltage source converter is proposed. Taking account of different series compensation levels and wind speeds, several potential inputs to the SSRDC are investigated. SSRDC control parameters are determined via root locus techniques to ensure the system stability of subsynchronous modes. Considering implementation feasibility in practical project and stability of supersynchronous modes, an enhanced SSRDC is proposed. Impact of transmission line and DFIG parameters and the reactive power compensation to the mitigation effect is investigated. Time domain simulation using PSCAD/EMTDC are carried out to validate the effectiveness of the SSRDC. © 2019 IEEE.

关键词： Wind farm

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The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Phyllotaxis-inspired nanosieves with multiplexed orbital angular momentum

arXiv

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arXiv 2021年

作者： Jin, Zhongwei Janoschka, David Deng, Junhong Ge, Lin Dreher, Pascal Frank, Bettina Hu, Guangwei Ni, Jincheng Yang, Yuanjie Li, Jing Yu, Changyuan Lei, Dangyuan Li, Guixin Xiao, Shumin Mei, Shengtao Giessen, Harald zu Heringdorf, Frank Meyer Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore College of Optical and Electronic Technology China Jiliang University Hangzhou310018 China University of Duisburg–Essen Lotharstrasse 1-21 Duisburg47057 Germany Department of Materials Science and Engineering Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Beijing Qianjunyide Technology Co. Ltd. Beijing100031 China University of Stuttgart StuttgartD-70569 Germany School of Physics University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Optoelectronic Materials Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing100190 China Department of Electronic and Information Engineering Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Hong Kong Department of Materials Science and Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong Hong Kong State Key Laboratory on Tunable Laser Technology Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Shenzhen Graduate School Harbin Institute of Technology Shenzhen518055 China

Nanophotonic platforms such as metasurfaces, achieving arbitrary phase profiles within ultrathin thickness, emerge as miniaturized, ultracompact and kaleidoscopic optical vortex generators. However, it is often required to segment or interleave independent sub-array metasurfaces to multiplex optical vortices in a single nano-device, which in turn affects the device’s compactness and channel capacity. Here, inspired by phyllotaxis patterns in pine cones and sunflowers, we theoretically prove and experimentally report that multiple optical vortices can be produced in a single compact phyllotaxis nanosieve, both in free space and on a chip, where one meta-atom may contribute to many vortices simultaneously. The time-resolved dynamics of on-chip interference wavefronts between multiple plasmonic vortices was revealed by ultrafast time-resolved photoemission electron microscopy. Our nature-inspired optical vortex generator would facilitate various vortex-related optical applications, including structured wavefront shaping, free-space and plasmonic vortices, and high-capacity information metaphotonics. Copyright © 2021, The Authors. All rights reserved.

关键词： Plasmonics

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