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Nearest-Better Network for Fitness Landscape Analysis of Continuous Optimization Problems

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IEEE Transactions on Evolutionary Computation 2024年

作者： Diao, Yiya Li, Changhe Zeng, Sanyou Yang, Shengxiang Coello, Carlos A. Coello China University of Geosciences School of Automation Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Anhui University of Sciences and Technology School of Artificial Intelligence Hefei China China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan China De Montfort University School of Computer Science and Informatics LeicesterLE1 9BH United Kingdom Department of Computer Science CINVESTAV-IPN 07360 Mexico Tecnologico de Monterrey Faculty of Excellence The School of Engineering and Sciences N.L. Monterrey Mexico

Fitness landscape analysis (FLA) is quite important in evolutionary computation. In this paper, we propose a novel FLA method, the nearest-better network (NBN), which uses the nearest-better relationship to simplify the original fitness landscape of continuous optimization problems. We introduce an efficient algorithm to calculate NBN for continuous problems. We also propose four numerical measurements and a 3D visualization method based on NBN. Experiments show that compared to the other main FLA methods, the four numerical measurements proposed here can effectively measure the four intended features: neutrality, ruggedness, modality, and basin of attraction, respectively, and common features of the fitness landscape can be maintained in 3D NBN visualization, regardless of the scale of the problem. NBN also provides a view of how algorithms search in high-dimensional problems with the help of the 3D NBN visualization. © 1997-2012 IEEE.

关键词： Optimization algorithms

CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron

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arXiv 2022年

作者： Liu, Wanli Li, Chen Xu, Ning Jiang, Tao Rahaman, Md Mamunur Sun, Hongzan Wu, Xiangchen Hu, Weiming Chen, Haoyuan Sun, Changhao Yao, Yudong Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Liaoning Petrochemical University Fushun China School of Control Engineering Chengdu University of Information Technology Chengdu China Shengjing Hospital China Medical University Shenyang China Suzhou Ruiqian Technology Company Ltd. Suzhou China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken United States Institute of Medical Informatics University of Luebeck Luebeck Germany

Cervical cancer is the seventh most common cancer among all the cancers worldwide and the fourth most common cancer among women. Cervical cytopathology image classification is an important method to diagnose cervical cancer. Manual screening of cytopathology images is time-consuming and error-prone. The emergence of the automatic computer-aided diagnosis system solves this problem. This paper proposes a framework called CVM-Cervix based on deep learning to perform cervical cell classification tasks. It can analyze pap slides quickly and accurately. CVM-Cervix first proposes a Convolutional Neural Network module and a Visual Transformer module for local and global feature extraction respectively, then a Multilayer Perceptron module is designed to fuse the local and global features for the final classification. Experimental results show the effectiveness and potential of the proposed CVM-Cervix in the field of cervical Pap smear image classification. In addition, according to the practical needs of clinical work, we perform a lightweight post-processing to compress the model. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification

Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

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arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

A Survey for Large Language Models in Biomedicine

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arXiv 2024年

作者： Wang, Chong Li, Mengyao He, Junjun Wang, Zhongruo Darzi, Erfan Chen, Zan Ye, Jin Li, Tianbin Su, Yanzhou Ke, Jing Qu, Kaili Li, Shuxin Yu, Yi Liò, Pietro Wang, Tianyun Wang, Yu Guang Shen, Yiqing School of Medical Engineering Xinxiang Medical University Xinxiang China Engineering Technology Research Center of Neurosense and Control of Henan Province Xinxiang China Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design Xinxiang China Shanghai AI Laboratory Shanghai China Amazon Palo AltoCA United States Boston Children's Hospital MA United States Harvard Medical School Harvard University MA United States Toursun Synbio Shanghai China Department of Data Science & AI Faculty of IT Monash University Melbourne Australia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom School of Basic Medical Sciences Xinxiang Medical University Xinxiang China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Department of Computer Science Johns Hopkins University MD United States

Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of Science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs. As this field of LLM rapidly evolves, continued research and development are essential to fully harness the capabilities of LLMs in biomedicine while ensuring their respon

关键词： Federated learning

fMRI-based Brain Disease Diagnosis: A Graph Network Approach

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TechRxiv

TechRxiv 2023年

作者： Chen, Qi-Qi Gao, Yubing Tong, Wei Li, Ping Wu, Edmond Q. The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Nanjing University of Science and Technology Jiangsu210008 China The School of Biological Science and Medical Engineering Beihang University Beijing100191 China The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

As a non-invasive brain imaging technology, functional magnetic resonance imaging (fMRI) provides a basic tool for brain functional network modeling and brain disease diagnosis. Problems, such as large number of parameters, low training efficiency, and poor interpretability, are encountered in mainstream models because of the high complexity of fMRI and brain networks. To solve these problems, a novel structure feature combined graph neural network (SFC-GNN) with a low number of parameters is proposed. In particular, SFC-GNN is composed of 1) the graph convolution layer of the brain region perception and 2) the node pooling layer of the graph structure feature (GSF). It also receives the sparse brain graph modeled by each subject’s fMRI as input. Especially, the GSF layer can select brain regions that are important for classification, thereby localizing all active regions related to brain disease. Moreover, a group network is constructed according to the correlation among subjects, and SFC-GNN can be extended further to a node classification model to achieve better diagnosis performance. The proposed method has been validated on the ABIDE and ADNI datasets, thereby showing the effectiveness of our proposed method in various experiments. © 2023, CC BY.

关键词： Magnetic resonance imaging

GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration

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arXiv 2023年

作者： Dou, Haoran Bi, Ning Han, Luyi Huang, Yuhao Mann, Ritse Yang, Xin Ni, Dong Ravikumar, Nishant Frangi, Alejandro F. Huang, Yunzhi University of Leeds Leeds United Kingdom Department of Radiology and Nuclear Medicine Radboud University Medical Centre Nijmegen Netherlands Department of Radiology Netherlands Cancer Institute Amsterdam Netherlands National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Health Science Center Shenzhen University China Lab Shenzhen University China Marshall Laboratory of Biomedical Engineering Shenzhen University China Shenzhen RayShape Medical Technology Co. Ltd China Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester United Kingdom Electrical Engineering and Cardiovascular Sciences Departments KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China

Deep learning-based deformable registration methods have been widely investigated in diverse medical applications. Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the deformation field. Therefore, they inevitably require tuning the hyperparameter for optimal registration performance. Tuning the hyperparameters is highly computationally expensive and introduces undesired dependencies on domain knowledge. In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses. In GSMorph, we reformulate the optimization procedure by projecting the gradient of similarity loss orthogonally to the plane associated with the smoothness constraint, rather than additionally introducing a hyperparameter to balance these two competing terms. Furthermore, our method is model-agnostic and can be merged into any deep registration network without introducing extra parameters or slowing down inference. In this study, We compared our method with state-of-the-art (SOTA) deformable registration approaches over two publicly available cardiac MRI datasets. GSMorph proves superior to five SOTA learning-based registration models and two conventional registration techniques, SyN and Demons, on both registration accuracy and smoothness. © 2023, CC BY.

关键词： medical applications

Realization of the all-optical phase modulator, filter, splitter, and self-consistent logic gates based on assembled magneto-optical heterostructures

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arXiv 2023年

作者： Xu, Jie You, Yun Kang, Fengwen Xiao, Sanshui Hong, Lujun Shen, Yun Luo, Yamei Tsakmakidis, Kosmas L. School of Medical Information and Engineering Southwest Medical University Luzhou646000 China Medical Engineering & Medical Informatics Integration and Transformational Medicine of Luzhou Key Laboratory Luzhou646000 China School of Science East China Jiaotong University Nanchang330013 China College of Materials Science and Engineering Sichuan University Chengdu610065 China DTU Fotonik Department of Photonics Engineering Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Institute of Space Science and Technology Nanchang University Nanchang330031 China Section of Condensed Matter Physics Department of Physics National and Kapodistrian University of Athens Panepistimioupolis AthensGR-157 84 Greece

All-optical computing has recently emerged as a vibrant research field in response to the energy crisis and the growing demand for information processing. However, the efficiency of subwavelength-scale all-optical devices remains relatively low due to challenges such as back-scattering reflections and strict surface roughness. Furthermore, achieving multifunctionality through the reassembly of all-optical structures has thus far been rarely accomplished. One promising approach to address these issues is the utilization of one-way edge modes. In this study, we propose four types of deep-subwavelength (∼ 10−2λ0, where λ0 is the wavelength in vacuum) all-optical functional devices: a phase modulator, a filter, a splitter, and logic gates. These devices are based on robust one-way modes but do not require an external magnetic field, which can allow for flexible assembly. In particular, we investigate a phase modulation range spanning from −π to π, a perfect filter that divides the input port’s one-way region into two output one-way regions with equal bandwidth, a multi-frequency splitter with an equal splitting ratio (e.g., 50/50), and self-consistent logic gates. We validate these theoretical findings through comprehensive full-wave numerical simulations. Our findings may find applications in minimal optical calculations and integrated optical circuits. © 2023, CC BY.

关键词： Phase modulation

Three-dimensional solitons in Rydberg-Dressed cold atomic gases with spin-orbit coupling

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arXiv 2023年

作者： Zhao, Yuan Hu, Heng-Jie Zhou, Qian-Qian Qiu, Zhang-Cai Xue, Li Xu, Si-Liu Zhou, Qin Malomed, Boris A. Laboratory of Optoelectronic Information and Intelligent Control Hubei University of Science and Technology Xianning437100 China School of Biomedical Engineering and Medical Imaging Xianning Medical College Hubei University of Science and Technology Xianning437100 China School of Electronic and Information Engineering Hubei University of Science and Technology Xianning437100 China Research Center of Nonlinear Science School of Mathematical and Physical Sciences Wuhan Textile University Wuhan430200 China Department of Physical Electronics School of Electrical Engineering Faculty of Engineering Tel Aviv Univesity P.O.B. 39040 Ramat Aviv Tel Aviv Israel Instituto de Alta Investigación Universidad de Tarapacá Casilla 7D Arica Chile

We present numerical results for three-dimensional (3D) solitons with symmetries of the semi-vortex (SV) and mixed-mode (MM) types, which can be created in spinor Bose-Einstein condensates of Rydberg atoms under the action of the spin-orbit coupling (SOC). By means of systematic numerical computations, we demonstrate that the interplay of SOC and long-range spherically symmetric Rydberg interactions stabilize the 3D solitons, improving their resistance to collapse. We find how the stability range depends on the strengths of the SOC and Rydberg interactions and the soft-core atomic radius. Copyright © 2023, The Authors. All rights reserved.

关键词： Solitons

Development of a Detection Method for Fecal Microbiota Suspension Concentration Based on Multidimensional Vis-Nir Spectroscopy

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SSRN

SSRN 2024年

作者： Zhao, Zhe Yang, Yuyan Dong, Xinming Wu, Lei Zhao, Wei Zhang, Haoyang Dong, Jiashu He, Xingxiang Wang, Huiquan School of Electronics and Information Engineering Tiangong University Tianjin300387 China Department of Gastroenterology Research Center for Engineering Techniques of Microbiota-Targeted Therapies of Guangdong Province The First Affiliated Hospital of Guangdong Pharmaceutical University Guangzhou510080 China School of Life Sciences Tiangong University Tianjin300387 China Tianjin Key Laboratory of Quality Control and Evaluation Technology for Medical Devices Tianjin300387 China Tianjin Rehabilitation Convalescent Center Tianjin300191 China Hebei University of Technology Tianjin300401 China Guangdong Institute of Microbiology China

In fecal microbiota transplantation (FMT) or washed microbiota transplantation (WMT), accurately determining the concentration of fecal microbiota suspensions is essential for safety control and provides a quantitative basis for assessing therapeutic efficacy. To enable rapid, efficient, and non-destructive concentration detection, this study introduces a novel, non-unsealing detection method based on multidimensional spectroscopy. Transmission spectra were captured from multiple positions within a centrifuge tube, and a multi-angle, multi-path model was constructed using rotational measurements to enhance data accuracy. This approach captures scattering information from the bacterial suspension as reflected in spectral variations across different positions. Finally, a calibration model was developed using partial least squares regression (PLSR) under multidimensional visible and near-infrared (VIS-NIR) spectroscopy. Compared with traditional single-dimensional spectral methods, this approach achieved a 17.39% increase in concentration detection accuracy, with a correlation coefficient (R) of 0.9323. Thus, this study provides a robust, multi-path detection model based on multi-position spectroscopy within the visible and near-infrared range, presenting an effective new method for improving clinical accuracy in bacterial suspension concentration detection. This method offers a promising tool for concentration analysis and quality control in microbiota transplantation. © 2024, The Authors. All rights reserved.

关键词： Near infrared spectroscopy