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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Palici, Alexandra Maria Lu, Xiyuan Srinivasan, Kartik Lu, Xinda Labbe, Fabien Ding, Yunhong Laroque, Hugo Hamerly, Ryan Errando-Herranz, Carlos Pu, Minhao Englund, Dirk Heuck, Mikkel Department of Electrical and Photonics Engineering Technical University of Denmark Lyngby2800 Denmark Microsystems and Nanotechnology Division Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

We demonstrate a hybrid device consisting of a thin film lithium niobate membrane transfer-printed onto a silicon nitride ring resonator. We measure quality factors in the 105 range at telecom wavelengths. CLEO 2024 &... 详细信息

ISBN: (纸本)9781957171395

关键词： Silicon nitride

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An Adaptive Tensor-Train Decomposition Approach for Efficient Deep Neural Network Compression

arXiv

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arXiv 2024年

作者： Luo, Shiyi Liu, Mingshuo Sun, Pu Yu, Yifeng Ren, Shangping Bai, Yu Computational Science Research Center San Diego State University San Diego United States Computational Science Research Center University of California Irvine Irvine United States Department of Electrical and Computer Engineering University of California Davis Davis United States Department of Mathematics University of California Irvine Irvine United States Department of Computer Science San Diego State University United States Department of Electrical and Computer Engineering California State University Fullerton Fullerton United States

In the field of model compression, choosing an appropriate rank for tensor decomposition is pivotal for balancing model compression rate and efficiency. However, this selection, whether done manually or through optimization-based automatic methods, often increases computational complexity. Manual rank selection lacks efficiency and scalability, often requiring extensive trial-and-error, while optimization-based automatic methods significantly increase the computational burden. To address this, we introduce a novel, automatic, and budget-aware rank selection method for efficient model compression, which employs Layer-Wise Imprinting Quantitation (LWIQ). LWIQ quantifies each layer's significance within a neural network by integrating a proxy classifier. This classifier assesses the layer's impact on overall model performance, allowing for a more informed adjustment of tensor rank. Furthermore, our approach includes a scaling factor to cater to varying computational budget constraints. This budget awareness eliminates the need for repetitive rank recalculations for different budget scenarios. Experimental results on the CIFAR-10 dataset show that our LWIQ improved by 63.2% in rank search efficiency, and the accuracy only dropped by 0.86% with 3.2x less model size on the ResNet-56 model as compared to the state-of-the-art proxy-based automatic tensor rank selection *** Codes 68T10, 65K10 Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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Recognition of Complex Human Activities for Wellness Management from Smartwatch using Deep Residual Neural Network 7

Recognition of Complex Human Activities for Wellness Managem...

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7th International Conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, NCON 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665495103

Activity-based wellbeing administration integrating health apps on intelligent wearables is becoming increasingly prominent. The accessibility of multimodal sensors in everyday wearable devices makes it feasible to measure human behavior and provide wellness services that are aware of the user's environment. In smartphones, smartwatches, and many other wearables, integrated sensors provide a wealth of information that can be utilized to recognize human movement. This research shows how effective deep learning algorithms can extract abstract information for human movement categorization in daily life. We came up with an approach to deal with this issue is what we name one-dimensional deep residual neural network (1D-ResNet). With an overall accuracy of 94.15%, we observed that the proposed 1D-ResNet outperforms standard deep learning models in categorizing complicated human activities such as reading, writing, eating, and other hand-oriented tasks. © 2022 IEEE.

关键词： Deep neural networks

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Plant regeneration in the new era:from molecular mechanisms to biotechnology applications

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science China(Life sciences) 2024年第7期67卷 1338-1367页

作者： Chunli Chen Yuxin Hu Momoko Ikeuchi Yuling Jiao Kalika Prasad Ying Hua Su Jun Xiao Lin Xu Weibing Yang Zhong Zhao Wenkun Zhou Yun Zhou Jian Gao Jia-Wei Wang National Key Laboratory for Germplasm Innovation&Utilization of Horticultural Crops Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhan 430070China College of Life Science and Technology Huazhong Agricultural UniversityWuhan 430070China Key Laboratory of Plant Molecular Physiology Institute of BotanyChinese Academy of Sciences(CAS)China National Botanical GardenBeijing 100093China Division of Biological Sciences Graduate School of Science and TechnologyNara Institute of Science and TechnologyNara 630-0192Japan State Key Laboratory of Protein and Plant Gene Research School of Life SciencesPeking UniversityBeijing 100871China Peking-Tsinghua Center for Life Sciences Center for Quantitative BiologyAcademy for Advanced Interdisciplinary StudiesPeking UniversityBeijing 100871China Indian Institute of Science Education and Research Pune 411008IndiaThiruvananthapuram 695551India State Key Laboratory of Wheat Improvement College of Life SciencesShandong Agricultural UniversityTai’an 271018China Sino-German Joint Research Center on Agricultural Biology Shandong Agricultural UniversityTai’an 271018China Key Laboratory of Plant Cell and Chromosome Engineering Institute of Genetics and Developmental Biology(IGDB)CASBeijing 100101China CAS-JIC Centre of Excellence for Plant and Microbial Science(CEPAMS) IGDBCASBeijing 100101China National Key Laboratory of Plant Molecular Genetics CEMPSInstitute of Plant Physiology and Ecology(SIPPE)CASShanghai 200032China CEPAMS SIPPECASShanghai 200032China Hefei National Laboratory for Physical Sciences at the Microscale CEMPSSchool of Life SciencesDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei 230027China State Key Laboratory of Plant Environmental Resilience College of Biological SciencesChina Agricultural UniversityBeijing 100193China Department of Botany and Plant Pathology and Center for Plant Biology Purdue UniversityWest Lafayette 47907USA School of Life Science and Technology Shang

Plants or tissues can be regenerated through various *** animal regeneration,cell totipotency and pluripotency are the molecular basis of plant *** systematic studies on Arabidopsis thaliana gradually unravel the fundamental mechanisms and principles underlying plant ***,plant hormones,cell division,epigenetic remodeling,and transcription factors play crucial roles in reprogramming somatic cells and reestablishing meristematic *** research on basal non-vascular plants and monocot crops has revealed that plant regeneration differs among species,with various plant species using distinct mechanisms and displaying significant differences in regenerative *** multi-omics studies at the single-cell level,tracking plant regeneration processes in real-time,and deciphering the natural variation in regenerative capacity will ultimately help understand the essence of plant regeneration,improve crop regeneration efficiency,and contribute to future crop design.

关键词： apical meristem model species regeneration phytohormone plant cell reprogramming somatic embryogenesis

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Adaptive Block-Level Quality Parameter Adjustment Towards Low Video Bit-Rate Fluctuation

Adaptive Block-Level Quality Parameter Adjustment Towards Lo...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Longtao Feng Qian Yin Huiwen Ren Zhao Wang Siwei Ma Yuwen He National Engineering Research Center of Visual Technology School of Computer Science Peking University China Advanced Institute of Information Technology Peking University China Bytedance Inc. San Diego CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Existing quantization parameter (QP) adjustment methods in video coding often focus solely on coding efficiency and ignore the impact of bit-rate fluctuations on video transmission and bandwidth waste. This is mainly because intra pictures, in a hierarchical coding structure, are allocated smaller QP and thus consume more bits. To address this issue, we propose an adaptive block-level QP adjustment method. Specifically, intra picture importance (IPI) is first introduced to evaluate the adjustability of intra picture QP. For intra pictures whose QP can be adjusted, we further propose block importance (BI) to determine their optimal block-level QP adjustment. Experimental results show that our proposed method reduce the bit-rate fluctuations while basically maintaining the coding performance. Notably, significant improvements can be observed in high-resolution videos, with a reduction of approximately 11% in bit-rate fluctuations.

关键词： Video coding Fluctuations Image coding Quantization (signal) Visual communication Bandwidth Encoding Motion compensation

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ResNet-based Deep Neural Network using Transfer Learning for Animal Activity Recognition 6

ResNet-based Deep Neural Network using Transfer Learning for...

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6th International Conference on Information Technology, InCIT 2022

ISBN: (纸本)9781665489126

Animal activity recognition (AAR) is a challenging and complex task for monitoring animal behavior to determine their health conditions and physical responses. To address this requirement, AAR research aims to develop an internet of things (IoT) platform that anticipates animal behavior based primarily on sensor data collected from wearable devices connected to animals. Because of its superior effectiveness on multi-dimensional sensor data, deep learning (DL) currently dominates sensor-based AAR applications. Nevertheless, DL techniques require significantly more training time than other learning-based strategies. This study employs transfer learning (TL) to generalize the DL model to overcome the issue of training time, indicating significant promise for future research on animal activity identification. Furthermore, we apply the TL to allow the reusability of a pre-trained DL model on entirely new data without the time-consuming operations of model training and data labeling. We conducted experiments on a publicly available AAR benchmark dataset (SAR) utilizing deep residual networks with automatically learned features. Utilizing source data, our technique achieved an overall accuracy of 96.46% (dataset collected by the first sensor) and 98.40% (dataset collected by the second sensor) based on evaluating findings. © 2022 IEEE.

关键词： Wearable sensors

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Boosting Thermal Conductivity by Surface Plasmon Polaritons Propagating along a Thin Ti Film

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Physical Review Letters 2023年第17期130卷 176302-176302页

作者： Dong-min Kim Sinwoo Choi Jungwan Cho Mikyung Lim Bong Jae Lee Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon 34141 South Korea Center for Extreme Thermal Physics and Manufacturing Korea Advanced Institute of Science and Technology Daejeon 34141 South Korea School of Mechanical Engineering Sungkyunkwan University Suwon 16419 South Korea Nano-Convergence Manufacturing Systems Research Division Korea Institute of Machinery and Materials Daejeon 34103 South Korea

We experimentally demonstrate boosted in-plane thermal conduction by surface plasmon polaritons (SPPs) propagating along a thin Ti film on a glass substrate. Due to the lossy nature of metal, SPPs can propagate over centimeter-scale distances even along a supported metal film, and the resulting ballistic heat conduction can be quantitatively validated. Further, for a 100-nm-thick Ti film on a glass substrate, a significant enhancement of in-plane thermal conductivity compared to bulk value (∼25%) is experimentally shown. This Letter will provide a new avenue to employ SPPs for heat dissipation along a supported thin film, which can be readily applied to mitigate hot-spot issues in microelectronics.

关键词： Plasmons Polaritons Thermal conductivity

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High-resolution simulations of nonlinear electromagnetic turbulence in tokamak devices

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The European Physical Journal Special Topics 2025年 1-14页

作者： Liu, Yanjun Liu, Zixi Liu, Jian Gao, Baofeng Zhang, Wei Xia, Tianyang Liu, Haiqing Zhuang, Ge Gao, Xiang Institute of Plasma Physics Chinese Academy of Sciences Anhui China KTX Laboratory and Department of Engineering and Applied Physics University of Science and Technology of China Anhui China Weihai Institute for Interdisciplinary Research Shandong University Shandong China SDU-ANU Joint Science College Shandong University Shandong China Shandong Computer Science Center (National Supercomputing Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong China Advanced Algorithm Joint Lab National Supercomputing Center in Jinan Shandong China

To achieve long-pulse steady operation, the physical mechanisms of boundary turbulence need further investigation. We employ the two-fluid model with flute reduction on BOUT++ to simulate the boundary plasma in Tokamaks. The space and time scales of turbulence reproduced by our simulations closely relate to the spatial mesh size and time step size, respectively. As an inherent time scale, the Alfven time is sufficient to resolve MHD instabilities. The spatial scale can be refined by increasing mesh resolutions, which necessitates larger scale parallel computing resources. We have conducted nonlinear simulations using more than 33 million spatial meshes with 16,384 CPU processors in parallel. The results indicate that while the decrease in parallel efficiency with an increase in core numbers does not necessarily lead to shorter runtimes, higher computational complexity improves parallel efficiency for the same number of cores. In addition, the mesh resolution required for convergence conditions differs between linear and nonlinear simulations, with nonlinear simulations demanding higher resolution. Besides finer structure obtained, the fluctuation characteristic of density similar to WCM, which is more consistent with the experimental observation, also shows the requirement for high-resolution meshes and large-scale computing in the future.

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Recognition of Human Activity from ECG and IMU Signals Using Deep Learning Networks

Recognition of Human Activity from ECG and IMU Signals Using...

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2022 IEEE Region 10 Symposium, TENSYMP 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (数字)9781665466585

ISBN: (纸本)9781665466585

Human activity recognition (HAR) is becoming more significant in various industrial applications, including medical consideration and rehabilitation monitoring. With the rapid development of information and communication technologies, wearable technologies have inspired a new variety of human-computer interaction. Wearable inertial sensors are typically operated in the area of HAR because they deliver the most critical data on motion signals. researchers in HAR continuously investigate alternative methodologies and signal sources to enhance HAR approaches. This study aims to determine the effect of combining biosignals with a publicly available dataset derived from wearable sensors on detecting everyday human activities. We utilized the MHEALTH dataset, which contains electrocardiogram (ECG), accelerometer, gyro-scope, and magnetometer data gathered from ten individuals engaged in twelve everyday actions. We proposed a unique deep learning technique for automatically extracting features and constructing a recognition model in several circumstances, including integrated sensor data. Our findings indicate that combining the ECG and IMU signals increases the F1-score of the classifier by 11.53%, from 86.83% to 98.36%. © 2022 IEEE.

关键词： Electrocardiograms

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Multi-modal Long-Short Distance Attention-based Transformer-GAN for PET Reconstruction with Auxiliary MRI

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zeng, Pinxian Zeng, Xinyi Wang, Yan Zhou, Luping Zu, Chen Wu, Xi Zhou, Jiliu Shen, Dinggang Sichuan University School of Computer Science China University of Sydney School of Electrical and Information Engineering Australia Department of Risk Controlling Research *** China Chengdu University of Information Technology School of Computer Science China ShanghaiTech University School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd Shanghai200232 China Shanghai Clinical Research and Trial Center Shanghai201210 China

To obtain high-quality PET scans while minimizing potential radiation hazards for patients, various GAN-based methods have been developed to reconstruct high-quality standard-count PET (SPET) images from low-count PET (LPET) ones. While recent efforts try to integrate MRI or CT to enhance reconstruction in a multi-modal way, current architectures mainly face two limitations: (1) CNN backbones or simple Transformer bottleneck layers are insufficient for robust semantic understanding, and (2) the identical strategies for multi-modal feature extraction and fusion overlook each modality's respective importance for the reconstruction task. In this work, we propose the Multi-modal Long-Short Distance Attention-based Transformer-GAN (MLSDA-GAN), a novel network combining 3D transformer and CNN architecture for PET image reconstruction. Specifically, to extract fine-grained features with a small number of parameters, our MLSDA-GAN integrates multi-scale convolution into the embedding part of the transformer. As for our multi-modal design, given the strong correlation between LPET and SPET in structural characteristics, we treat MRI as an auxiliary modality to LPET and achieve effective multi-modal extraction and fusion strategies. These strategies include (1) a PET-specific Self-attention Extraction (PSE) block for comprehensive feature extraction of the primary LPET and (2) a Multi-modality Cross-attention Fusion (MCF) block for effective multi-modal interaction and fusion, enabling us to more efficiently model both long- and short-range relationships in the corresponding feature extraction and fusion processes. Experiments demonstrate superiority of our method quantitatively and qualitatively. Code is available at https://***/Aru321/MLSDA-GAN. © 1991-2012 IEEE.

关键词： Radiation hazards

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