检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Islam, Khayrul Forelli, Ryan F. Han, Jianzhong Bhadane, Deven Huang, Jian Agar, Joshua C. Tran, Nhan Ogrenci, Seda Liu, Yaling Department of Mechanical Engineering Lehigh University BethlehemPA18015 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Coriell Institute for Medical Research CamdenNJ United States Department of Computer Science Lehigh University BethlehemPA18015 United States Cooper Medical School of Rowan University CamdenNJ08103 United States Center for Metabolic Disease Research Temple University Lewis Katz School of Medicine PhiladelphiaPA19122 United States Department of Mechanical Engineering and Mechanics Drexel University PhiladelphiaPA19104 United States Real-time Processing Systems Division Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Bioengineering Lehigh University BethlehemPA18015 United States

Precise cell classification is essential in biomedical diagnostics and therapeutic monitoring, particularly for identifying diverse cell types involved in various diseases. Traditional cell classification methods such as flow cytometry depend on molecular labeling which is often costly, time-intensive, and can alter cell integrity. To overcome these limitations, we present a label-free machine learning framework for cell classification, designed for real-time sorting applications using bright-field microscopy images. This approach leverages a teacher-student model architecture enhanced by knowledge distillation, achieving high efficiency and scalability across different cell types. Demonstrated through a use case of classifying lymphocyte subsets, our framework accurately classifies T4, T8, and B cell types with a dataset of 80,000 preprocessed images, accessible via an open-source Python package for easy adaptation. Our teacher model attained 98% accuracy in differentiating T4 cells from B cells and 93% accuracy in zero-shot classification between T8 and B cells. Remarkably, our student model operates with only 0.02% of the teacher model’s parameters, enabling field-programmable gate array (FPGA) deployment. Our FPGA-accelerated student model achieves an ultra-low inference latency of just 14.5 µs and a complete cell detection-to-sorting trigger time of 24.7 µs, delivering 12x and 40x improvements over the previous state-of-the-art real-time cell analysis algorithm in inference and total latency, respectively, while preserving accuracy comparable to the teacher model. This framework provides a scalable, cost-effective solution for lymphocyte classification, as well as a new SOTA real-time cell sorting implementation for rapid identification of subsets using in situ deep learning on off-the-shelf computing hardware. © 2025, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Target Localization Using Adaptive Radar Processing and Convolutional Neural Networks

arXiv

引用

arXiv 2022年

作者： Venkatasubramanian, Shyam Gogineni, Sandeep Kang, Bosung Pezeshki, Ali Rangaswamy, Muralidhar Tarokh, Vahid The Department of Electrical and Computer Engineering Duke University DurhamNC27705 United States Information Systems Laboratories Inc. DaytonOH45431 United States The University of Dayton Research Institute DaytonOH45469 United States The Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO80523 United States The United States Air Force Research Laboratory Wright-Patterson Air Force Base OH45433 United States

Leveraging the advanced functionalities of modern radio frequency (RF) modeling and simulation tools, specifically designed for adaptive radar processing applications, this paper presents a data-driven approach to improve accuracy in radar target localization post adaptive radar detection. To this end, we generate a large number of radar returns by randomly placing targets of variable strengths in a predefined area, using RFView®, a high-fidelity, site-specific, RF modeling & simulation tool. We produce heatmap tensors from the radar returns, in range, azimuth [and Doppler], of the normalized adaptive matched filter (NAMF) test statistic. We then train a regression convolutional neural network (CNN) to estimate target locations from these heatmap tensors, and we compare the target localization accuracy of this approach with that of peak-finding and local search methods. This empirical study shows that our regression CNN achieves a considerable improvement in target location estimation accuracy. The regression CNN offers significant gains and reasonable accuracy even at signal-to-clutter-plus-noise ratio (SCNR) regimes that are close to the breakdown threshold SCNR of the NAMF. We also study the robustness of our trained CNN to mismatches in the radar data, where the CNN is tested on heatmap tensors collected from areas that it was not trained on. We show that our CNN can be made robust to mismatches in the radar data through few-shot learning, using a relatively small number of new training samples. © 2022, CC BY.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Label-free Imaging with Photonic Crystal Surface for Hematopoietic Stem Cell Differentiation

Label-free Imaging with Photonic Crystal Surface for Hematop...

引用

Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Zhuo, Yue Choi, Ji Sun Marin, Thibault Yu, Hojeong Harley, Brendan A. Cunningham, Brian T. Gordon Center for Medical Imaging Massachusetts General Hospital BostonMA02114 United States Center for Systems Biology Massachusetts General Hospital BostonMA02114 United States Department of Radiology Harvard Medical School BostonMA02114 United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Chemical and Biomolecular Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Micro and Nanotechnology Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States

With the Photonic Resonator Outcoupler Microscopy (PROM), it is possible to detect and monitor weak-adhesive HSC adhesion without labeling. These findings indicate that PROM can be used to quantitatively and dynamical... 详细信息

ISBN: (纸本)9781957171340

关键词： Stem cells

来源：评论

学校读者我要写书评

暂无评论

Identifying 3D genome organization in diploid organisms via euclidean distance geometry

arXiv

引用

arXiv 2021年

作者： Belyaeva, Anastasiya Kubjas, Kaie Sun, Lawrence J. Uhler, Caroline Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics and Systems Analysis Aalto University Espoo Finland

The spatial organization of the DNA in the cell nucleus plays an important role for gene regulation, DNA replication, and genomic integrity. Through the development of chromosome conformation capture experiments (such as 3C, 4C, Hi-C) it is now possible to obtain the contact frequencies of the DNA at the whole-genome level. In this paper, we study the problem of reconstructing the 3D organization of the genome from such whole-genome contact frequencies. A standard approach is to transform the contact frequencies into noisy distance measurements and then apply semidefinite programming (SDP) formulations to obtain the 3D configuration. However, neglected in such reconstructions is the fact that most eukaryotes including humans are diploid and therefore contain two copies of each genomic locus. We prove that the 3D organization of the DNA is not identifiable from distance measurements derived from contact frequencies in diploid organisms. In fact, there are infinitely many solutions even in the noise-free setting. We then discuss various additional biologically relevant and experimentally measurable constraints (including distances between neighboring genomic loci and higher-order interactions) and prove identifiability under these conditions. Furthermore, we provide SDP formulations for computing the 3D embedding of the DNA with these additional constraints and show that we can recover the true 3D embedding with high accuracy from both noiseless and noisy measurements. Finally, we apply our algorithm to real pairwise and higher-order contact frequency data and show that we can recover known genome organization patterns. © 2021, CC BY.

关键词： DNA

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Model Using Snapshot Ensemble Approach for Soil Respiration Prediction for an Experimental Oak Forest

SSRN

引用

SSRN 2024年

作者： Ferdous, Syeda Nyma Ahire, Jayendra Pandit Bergman, Richard Li, Xin Blanc-Betes, Elena Zhang, Zhou Wang, Jinxin Lane Department of Computer Science and Electrical Engineering West Virginia University MorgantownWV26505 United States Biological Systems Engineering Department University of Wisconsin MadisonWI53706 United States Forest Products Laboratory USDA Forest Service MadisonWI53726 United States Department of Computer Science University at Albany New YorkNY12222 United States Institute for Sustainability Energy and Environment University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Advanced Bioenergy and Bioproducts Innovation University of Illinois at Urbana-Champaign UrbanaIL61801 United States Biological Systems Engineering University of Wisconsin MadisonWI53706 United States Davis Michael Professor of Forestry and Natural Resources West Virginia University MorgantownWV26506 United States

Soil respiration plays a key role in regulating ecosystem CO2 emissions and atmospheric concentrations yet, subject to a strong sensitivity to the environment and large temporal and spatial variability, its contribution to the global carbon cycle remains highly uncertain. Recent advances in ecosystem process-based models have provided unique means to constrain soil respiration rates largely based on learnings from the tracing of ecosystem CO2 losses with radio and stable isotope approaches. However, the computational requirements of process-based models at large scales are costly, limiting their application and predicting capability. With high computational power for the assimilation of high-throughput data to identify patterns and trends in soil respiration and guide in deployment of effective strategies to reduce GHG emission, the application of ML models can help reduce requirement of intensive computational capacity and time for model development and improve the accuracy of predictions. Here, we developed a novel machine learning model that integrated hybrid Prophet-ANN and snapshot ensemble approach. The developed model can be employed as a surrogate of the ForCent model for the Oak Ridge National laboratory deciduous forest. Our proposed model accurately predicted soil CO2 flux with a higher coefficient of determination (R2 > 0.99) for the studied site. We show the potential of our proposed ML model to significantly improve soil CO2 flux predictions which in turn can help us develop more effective strategies for managing soil carbon. © 2024, The Authors. All rights reserved.

关键词： Ecosystems

来源：评论

学校读者我要写书评

暂无评论

The Challenges of Music Deep Learning for Traditional Music

The Challenges of Music Deep Learning for Traditional Music

引用

International Conference on Modern Circuits and systems Technologies (MOCAST)

作者： Lazaros Moysis Lazaros Alexios Iliadis Sotirios P. Sotiroudis Kostas Kokkinidis Panagiotis Sarigiannidis Spiridon Nikolaidis Christos Volos Achilles D. Boursianis Dimitrios Babas Maria S. Papadopoulou Sotirios K. Goudos Laboratory of Nonlinear Systems - Circuits & Complexity School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Mechanical Engineering University of Western Macedonia Kozani Greece ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Applied Informatics University of Macedonia Thessaloniki Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Information and Electronic Engineering International Hellenic University Sindos Greece

Numerous applications for music listeners, educators, DJs, and musicians have been created over the past decade as the field of Music Deep Learning has expanded. Evidently, the majority of works rely on training their architectures using well-established databases, which consist primarily of Western popular music genres. This creates a deficiency in applications focusing on traditional and regional music, which are vital to preserving culture. As an increasing number of research groups begin to develop works centered on traditional music, several practical challenges for applying deep learning arise. This work discusses these issues and the future of deep learning in the study of traditional music.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization

arXiv

引用

arXiv 2024年

作者： Deng, Xiumei Li, Jun Wei, Kang Shi, Long Xiong, Zeihui Ding, Ming Chen, Wen Jin, Shi Poor, H. Vincent School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing Hong Kong Polytechnic University 999077 Hong Kong The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Data61 CSIRO SydneyNSW2015 Australia Department of Electronics Engineering Shanghai Jiao Tong University Shanghai200240 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Electrical and Computer Engineering Princeton University NJ08544 United States

Adaptive moment estimation (Adam), as a Stochastic Gradient Descent (SGD) variant, has gained widespread popularity in federated learning (FL) due to its fast convergence. However, federated Adam (FedAdam) algorithms suffer from a threefold increase in uplink communication overhead compared to federated SGD (FedSGD) algorithms, which arises from the necessity to transmit both local model updates and first and second moment estimates from distributed devices to the centralized server for aggregation. Driven by this issue, we propose a novel sparse FedAdam algorithm called FedAdam-SSM, wherein distributed devices sparsify the updates of local model parameters and moment estimates and subsequently upload the sparse representations to the centralized server. To further reduce the communication overhead, the updates of local model parameters and moment estimates incorporate a shared sparse mask (SSM) into the sparsification process, eliminating the need for three separate sparse masks. Theoretically, we develop an upper bound on the divergence between the local model trained by FedAdam-SSM and the desired model trained by centralized Adam, which is related to sparsification error and imbalanced data distribution. By minimizing the divergence bound between the model trained by FedAdam-SSM and centralized Adam, we optimize the SSM to mitigate the learning performance degradation caused by sparsification error. Additionally, we provide convergence bounds for FedAdam-SSM in both convex and non-convex objective function settings, and investigate the impact of local epoch, learning rate and sparsification ratio on the convergence rate of FedAdam-SSM. Experimental results show that FedAdam-SSM outperforms baselines in terms of convergence rate (over 1.1× faster than the sparse FedAdam baselines) and test accuracy (over 14.5% ahead of the quantized FedAdam baselines). Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

arXiv

引用

arXiv 2024年

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing314001 China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight (LoS) dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom (DoFs) to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent (BCD) algorithm to iteratively optimize the antenna position vector (APV), array rotation vector (ARV), receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Multi-step Greedy Reinforcement Learning Based on Model Predictive Control

引用

IFAC-PapersOnLine 2021年第3期54卷 699-705页

作者： Yucheng Yang Sergio Lucia Department of Electrical Engineering and Computer Science TU Berlin Einsteinufer 17 10587 Berlin Germany Laboratory of Process Automation Systems TU Dortmund University Emil-Figge-Str. 70 44227 Dortmund Germany

Reinforcement learning aims to compute optimal control policies with the help of data from closed-loop trajectories. Traditional model-free approaches need huge number of data points to achieve an acceptable performance, rendering them not applicable in most real situations, even if the data can be obtained from a detailed simulator. Model-based reinforcement learning approaches try to leverage model knowledge to drastically reduce the amount of data needed or to enforce important constraints to the closed-loop operation, which is another important drawback of model-free approaches. This paper proposes a novel model-based reinforcement learning approach. The main novelty is the fact that we exploit all the information of a model predictive control (MPC) computing step, and not only the first input that is actually applied to the plant, to efficiently learn a good approximation of the state value function. This approximation can be included into a model predictive control formulation as a terminal cost with a short prediction horizon, achieving a similar performance to an MPC with a very long prediction horizon. Simulation results of a discretized batch bioreactor illustrate the potential of the proposed methodology.

关键词： Reinforcement learning Model predictive control Nonlinear system Batch reactor

来源：评论

学校读者我要写书评

暂无评论

Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming

引用

IFAC-PapersOnLine 2021年第7期54卷 379-384页

作者： Ştefan-Cristian Nechita Roland Tóth Dhruv Khandelwal Maarten Schoukens Department of Electrical Engineering Eindhoven University of Technology Eindhoven University of Technology Eindhoven The Netherlands Systems and Control Laboratory Institute for Computer Science and Control Kende u. 13-17 H-1111 Budapest Hungary

Data-driven modeling of nonlinear dynamical systems often requires an expert user to take critical decisions a priori to the identification procedure. Recently, an automated strategy for data driven modeling of single-input single-output (SISO) nonlinear dynamical systems based on genetic programming (GP) and tree adjoining grammars (TAG) was introduced. The current paper extends these latest findings by proposing a multi-input multi-output (MIMO) TAG modeling framework for polynomial NARMAX models. Moreover, we introduce a TAG identification toolbox in Matlab that provides implementation of the proposed methodology to solve multi-input multi-output identification problems under NARMAX noise assumption. The capabilities of the toolbox and the modeling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models.

关键词： Nonlinear system identification Equation discovery Tree Adjoining Grammar Genetic Programming Data-driven system modeling

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：