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Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning

Neuroscience Bulletin 2023年第6期39卷 893-910页

作者： Feng Su Yangzhen Wang Mengping Wei Chong Wang Shaoli Wang Lei Yang Jianmin Li Peijiang Yuan Dong-Gen Luo Chen Zhang Department of Neurobiology School of Basic Medical SciencesBeijing Key Laboratory of Neural Regeneration and RepairCapital Medical UniversityBeijing100069China Chinese Institute for Brain Research Beijing102206China State Key Laboratory of Translational Medicine and Innovative Drug Development Nanjing210000China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing100871China School of Life Sciences Tsinghua UniversityBeijing100084China School of Biological Science and Medical Engineering Beihang UniversityBeijing100191China The Key Laboratory of Developmental Genes and Human Disease Institute of Life SciencesSoutheast UniversityNanjing210096JiangsuChina Institute for Artificial Intelligence the State Key Laboratory of Intelligence Technology and SystemsBeijing National Research Center for Information Science and TechnologyDepartment of Computer Science and TechnologyTsinghua UniversityBeijing100084China School of Mechanical Engineering and Automation Beihang UniversityBeijing100191China

Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social *** tracking methods(e.g.,marking each animal with dye or surgically implanting microchips)can be invasive and may have an impact on the social behavior being *** overcome these shortcomings,video-based methods for tracking unmarked animals,such as fruit flies and zebrafish,have been ***,tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction *** this study,we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network(R-CNN)deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion *** system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing *** proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.

关键词： Noninvasive tracking Deep learning Multi-camera Mouse group Social interaction

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Hybrid surface waves in twisted anisotropic heterometasurfaces

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Physical Review Applied 2024年第6期21卷 064034-064034页

作者： Xinyan Zhang Chenxu Bian Zheng Gong Ruoxi Chen Tony Low Hongsheng Chen Xiao Lin Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation International Joint Innovation Center The Electromagnetics Academy at Zhejiang University Department of Electrical and Computer Engineering Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Shaoxing Institute of Zhejiang University

Recent advances in twisted photonic structures (e.g., twisted bilayer graphene and twisted anisotropic metasurfaces) have enabled many exotic phenomena of light-matter interactions, such as topological transition of the isofrequency contour of surface waves at the magic angle, photonic flatbands, and localization-to-delocalization transition of light. In general, the optical features of these twisted photonic structures are sensitive to the interlayer twist angle. Here we investigate the mode hybridization of surface waves supported by twisted bilayer anisotropic heterometasurfaces, whose two constituent metasurfaces are nonidentical and support surface waves with similar spatial confinements but distinct polarizations. Counterintuitively, we find that the shape of the isofrequency contour of these hybrid surface waves—eigenmodes with both transverse magnetic and transverse electric wave components—can be insensitive to the twist angle, due to the weak interlayer electromagnetic coupling. Moreover, the interlayer distance provides a unique route to reshape the dispersion of one specific kind of hybrid surface wave, while it has negligible influence on the other kinds. Our finding enriches the physics of light-matter interactions in twisted photonic structures and may open new possibilities to mold the flow of light at the subwavelength scale.

关键词： Classical optics Light-matter interaction Metamaterials Metasurfaces Near-field optics Photonics Plasmonics Van der Waals interaction

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LLM4WM: Adapting LLM for Wireless Multi-Tasking

arXiv

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arXiv 2025年

作者： Liu, Xuanyu Gao, Shijian Liu, Boxun Cheng, Xiang Yang, Liuqing State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Guangzhou511400 China Guangzhou511400 China Department of Electronic and Computer Engineering The Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong

The wireless channel is fundamental to communication, encompassing numerous tasks collectively referred to as channel-associated tasks. These tasks can leverage joint learning based on channel characteristics to share representations and enhance system design. To capitalize on this advantage, LLM4WM is proposed—a large language model (LLM) multi-task fine-tuning framework specifically tailored for channel-associated tasks. This framework utilizes a Mixture of Experts with Low-Rank Adaptation (MoE-LoRA) approach for multi-task fine-tuning, enabling the transfer of the pre-trained LLM’s general knowledge to these tasks. Given the unique characteristics of wireless channel data, preprocessing modules, adapter modules, and multi-task output layers are designed to align the channel data with the LLM’s semantic feature space. Experiments on a channel-associated multi-task dataset demonstrate that LLM4WM outperforms existing methodologies in both full-sample and few-shot evaluations, owing to its robust multi-task joint modeling and transfer learning capabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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DG-CNN:Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

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Journal of computer Science & Technology 2022年第2期37卷 277-294页

作者： Xiao-Zheng Xie Jian-Wei Niu Senior Member Xue-Feng Liu Qing-Feng Li Yong Wang Jie Han Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityHangzhou 310051China Department of Diagnostic Ultrasound National Cancer CenterChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100021China Naveen Jindal School of Management The University of Texas at DallasRichardsonTX 75080-3021U.S.A.

Although using convolutional neural networks(CNNs)for computer-aided diagnosis(CAD)has made tremendous progress in the last few years,the small medical datasets remain to be the major bottleneck in this *** address this problem,researchers start looking for information out of the medical *** efforts mainly leverage information from natural images via transfer *** recent research work focuses on integrating knowledge from medical practitioners,either letting networks resemble how practitioners are trained,how they view images,or using extra *** this paper,we propose a scheme named Domain Guided-CNN(DG-CNN)to incorporate the margin information,a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound(BUS)*** DG-CNN,attention maps that highlight margin areas of tumors are first generated,and then incorporated via different approaches into the *** have tested the performance of DG-CNN on our own dataset(including 1485 ultrasound images)and on a public *** results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their *** example,experimental results on our dataset show that with a certain integrating mode,the improvement of using DG-CNN over a baseline network structure ResNet 18 is 2.17%in accuracy,1.69%in sensitivity,2.64%in specificity and 2.57%in AUC(Area Under Curve).To the best of our knowledge,this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

关键词： medical consensus domain knowledge breast cancer diagnosis margin map deep neural network

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INNSE: Invertible neural network-based DNA image storage with self-correction encoding

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Computational and Structural Biotechnology Journal 2025年 27卷 2492-2502页

作者： Zheng, Yanfen Zhang, Xiaokang Sun, Lijun Cao, Ben Wang, Bin Zhang, Qiang Zheng, Pan School of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Liaoning Dalian116622 China Department of Accounting and Information Systems University of Canterbury Upper Riccarton Christchurch8140 New Zealand

DNA storage has become an alternative to silicon-based storage of media. However, when used to store multimodal data such as images, it fails to take full advantage of the characteristics of high correlation and variability present in such data, and currently remains inferior in terms of encoding density and time complexity. It also has the shortcoming of weak performance in terms of error correction methods to address insertion and deletion errors caused by error crosstalk. Therefore, we proposed an invertible neural network-based DNA image storage self-correction encoding method (INNSE) that uses the invertibility of network to perform up-down sampling processing on image and video data, thereby greatly reducing the number of DNA sequences required for data storage. A codebook generator was then designed to encode low-dimensional vectors into DNA sequences that meet various constraints. Finally, a self-correction method was constructed to achieve DNA sequence error correction at the base level without adding any redundant information. Our experimental results showed that, compared to the Yin-Yang algorithm, the encoding density of INNSE was increased by 3×, encoding time complexity was reduced by 95.26%. Under a simulated error rate of 2%, PSNR and SSIM were raised by 80.88% and 63.82%, respectively, proving that INNSE has significant advantages in encoding performance and error correction ability. © 2025

关键词： Network coding

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A Fast Beam Training Method with Adaptive Feedback for Holographic Communications

A Fast Beam Training Method with Adaptive Feedback for Holog...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Yutong Zhang Boya Di Hongliang Zhang Lingyang Song State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China

Holographic communication is recently envisioned to be a promising technology to handle the exponentially increasing data transmission demands, utilizing a large number of compact and tunable antenna elements. In this paper, we consider a holographic communication system where the beam-forming scheme is developed by the codebook design and beam training to avoid the high overhead of acquiring perfect channel state information. Given the large-scale antenna array, users are expected to be distributed in both the near and far fields of the base station, and thus, we design a near-far field codebook to apply to all users in unknown locations. However, the fine-grained beam training using narrow beams capable of enhancing received signal power at the expense of a high overhead which occupies significant time resources. To tackle such a conflict, we propose the adaptive beam training that leverages user feedback to train user-densely distributed regions at a fine-grained level and user-sparsely distributed ones in a coarse manner, thereby improving the throughput. Under a general setting including both the near-field and far-field users, simulation results show that the proposed scheme achieves a higher sum rate and throughput compared to the state-of-the-art schemes.

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Quantum-Dot-Based Optical Fiber Temperature Sensor Fabricated by Two-Photon Polymerization

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激光与光电子学进展 2024年第23期61卷 431-437页

作者： Wang Chao Sun Shengzhi Qian Bin Peng Sida Feng Zeqi Qiu Jianrong Laboratory of Infrared Materials and Devices Research Institute of Advanced TechnologiesNingbo UniversityNingbo 315211ZhejiangChina Institute of Light+X Science and Technology Faculty of Electrical Engineering and Computer ScienceNingbo UniversityNingbo 315211ZhejiangChina State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027ZhejiangChina State Key Laboratory of Modern Optical Instrumentation Zhejiang UniversityHangzhou 310027ZhejiangChina

Temperature is a common and significant physical quantity,particularly under harsh conditions such as a high voltage or strong electric/magnetic *** this study,we developed an all-fiber-optic temperature sensor based on a fiber-end microstructure doped with quantum dots(QDs).The microstructure was fabricated on a fiber tip using twophoton polymerization *** results show that the fluorescence intensity of the QD microstructure is reduced with an increase in temperature,and the peak position of photoluminescence exhibits a linear change in the temperature range of 26-70℃.The developed sensor exhibits a high-temperature sensitivity of 135 pm/℃.The proposed method has enormous potential for the development of fiber-end temperature sensors.

关键词： two-photon polymerization femtosecond laser temperature sensors quantum dots optical fiber

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Split Fine-Tuning for Large Language Models in Wireless Networks

arXiv

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arXiv 2025年

作者： Zhang, Songge Cheng, Guoliang Huang, Xinyu Li, Zuguang Wu, Wen Song, Lingyang Shen, Xuemin School of Electronic and Computer Engineering Peking University Shenzhen518000 China Frontier Research Center Pengcheng Laboratory Shenzhen518055 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Department of Electrical and Computer Engineering University of Waterloo WaterlooN2L 3G1 Canada

Fine-tuning is the process of adapting the pre-trained large language models (LLMs) for downstream tasks. Due to substantial parameters, fine-tuning LLMs on mobile devices demands considerable memory resources, and suffers from high communication overhead and long fine-tuning delay. In this paper, we propose an efficient LLM fine-tuning scheme in wireless networks, named Split Fine-Tuning (SFT), which can accommodate LLM fine-tuning on mobile devices. Specifically, an LLM is split into a server-side part on the edge server and a device-side part on the mobile device to satisfy the device-side memory constraint. All devices share a server-side model and perform parallel fine-tuning to reduce fine-tuning delay. In addition, to reduce significant communication overhead incurred by data exchange between devices and the edge server, we propose a data compression scheme by jointly leveraging sparsification, stochastic quantization, and lossless encoding methods. Furthermore, we formulate a fine-tuning delay minimization problem under accuracy and memory constraints, taking device heterogeneity and channel dynamics into account. To solve the problem, the nonlinear mixed-integer problem is decoupled into two subproblems in different timescales. The two-timescale resource management algorithm is proposed to jointly optimize the compression rate and transformer block allocation in the large timescale using the augmented Lagrangian method, and determine spectrum resource allocation in the small timescale via sequential quadratic programming. Extensive simulation results demonstrate that the proposed scheme can reduce the fine-tuning delay by up to 80.2% and communication overhead by 93.6% compared to state-of-the-art benchmarks, while satisfying device-side memory and model accuracy constraints. Copyright © 2025, The Authors. All rights reserved.

关键词： Quadratic programming

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Multi-Layer Perceptron Regression Model for Material Characterization in the Microwave Frequency Range with Cross-Dataset Validation

Multi-Layer Perceptron Regression Model for Material Charact...

引用

International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Hanima Kannan C H Deepa Gupta Parul Mathur Dhanesh G Kurup Tilesh Sanjay Chaudhari Priyanka Mathur Department of Electronics and Communication Engineering RF and Wireless Systems Laboratory Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Chemical Engineering Creative and Advanced Research Based on Nanomaterials Laboratory Indian Institute of Technology Hyderabad School of Engineering and Applied Science Bennett University Greater Noida U.P. India

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an $R^{2}$ of 99.991% and MSE of $6.65 \times 10^{-5}$ , with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions.

关键词： Power engineering Dielectric constant Predictive models Mathematical models Software Scattering parameters Probes Permittivity Standards Load modeling

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Energy Efficiency Optimization for Full-Duplex D2D Communications Underlaying Distributed Antenna systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Liu, Zhan Liao, Zhiyuan Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. Hunan University of Humanities Science and Technology School of Information Loudi417000 China Nanchang University School of Information Engineering Nanchang330031 China Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang330031 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In this paper, we investigate the total system energy efficiency (EE) of full-duplex (FD) device-to-device (D2D) communications underlaying distributed antenna systems (DAS), where remote access units (RAUs), D2D users (DUs), and cellular users (CUs) are all capable of FD operation. Specifically, we jointly optimize subcarrier assignment and power allocation under the quality of service (QoS) requirements of CUs and DUs and the maximum power constraints of RAUs, CUs, and DUs. In addition, we propose a novel spectrum sharing strategy that allows each subcarrier to be assigned to multiple CUs and/or multiple D2D pairs (DPs) for flexibility. To solve the formulated non-convex optimization problem, we first employ fractional programming to transform the objective function in the optimization problem from the fractional form into the equivalent subtractive form. Then, an efficient iterative resource allocation algorithm is proposed, which needs to solve an inner problem in each iteration. After relaxing the variables and introducing penalty factors, the non-convex inner problem is transformed into a convex problem through the successive convex approximation (SCA) method and solved by iterative algorithm. Simulation results demonstrate that the proposed algorithm can considerably improve the system EE compared to other benchmark schemes. Furthermore, the proposed spectrum sharing strategy is superior to the existing sharing strategies. © 2017 IEEE.

关键词： Convex optimization

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