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Transition Waste Optimization for Coded Elastic Computing

IEEE Transactions on Information Theory 2023年第7期69卷 4442-4465页

作者： Dau, Son Hoang Gabrys, Ryan Huang, Yu-Chih Feng, Chen Luu, Quang-Hung Alzahrani, Eidah J. Tari, Zahir The School of Computing Technologies RMIT University MelbourneVIC3000 Australia The California Institute for Telecommunications and Information Technology University of California La Jolla San DiegoCA92093 United States The Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan KelownaBCV1V 1V7 Canada The Department of Computing Technologies School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia The Department of Civil Engineering Monash University ClaytonVIC3800 Australia The Department of Computer Science Al-Baha University Al Bahah65545 Saudi Arabia

Distributed computing, in which a resource-intensive task is divided into subtasks and distributed among different machines, plays a key role in solving large-scale problems. Coded computing is a recently emerging paradigm where redundancy for distributed computing is introduced to alleviate the impact of slow machines (stragglers) on the completion time. We investigate coded computing solutions over elastic resources, where the set of available machines may change in the middle of the computation. This is motivated by recently available services in the cloud computing industry (e.g., EC2 Spot, Azure Batch) where low-priority virtual machines are offered at a fraction of the price of the on-demand instances but can be preempted on short notice. Our contributions are three-fold. We first introduce a new concept called transition waste that quantifies the number of tasks existing machines must abandon or take over when a machine joins/leaves. We then develop an efficient method to minimize the transition waste for the cyclic task allocation scheme recently proposed in the literature (Yang et al. ISIT’19). Finally, we establish a novel solution based on finite geometry achieving zero transition wastes given that the number of active machines varies within a fixed range. © 2023 IEEE.

关键词： Toys

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Development of a Near-Field Sensor for Diagnosis of Liver Diseases Using 3D-EM Simulation of Mouse Model

Development of a Near-Field Sensor for Diagnosis of Liver Di...

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Microwaves Antennas and Propagation Conference (MAPCON)

作者： Poulami Samaddar Sunil Gaddam Md Mirazur Rahman Shuvashis Dey Sayan Roy Dipankar Mitra Shivaram P. Arunachalam Department of Medicine Mayo Clinic Division of Gastroenterology and Hepatology Microwave Engineering & Imaging Laboratory Rochester MN USA Department of Electrical and Computer Engineering North Dakota State University Fargo ND USA Division of Energy Matter and Systems School of Science and Engineering University of Missouri-Kansas City Kansas City MO USA Department of Computer Science & Computer Engineering University of Wisconsin-La Crosse La Crosse WI USA

In this work, we studied the response of an antipodal Vivaldi antenna in the presence of different types of mice liver models. The scope of the study was limited to two types of liver diseases, i.e., nonalcoholic steatohepatitis (NASH) and fibrosis. The complex dielectric properties of each liver type were measured using the open-ended coaxial probe dielectric spectroscopy method. A Vivaldi antenna for near-field sensing was designed and then simulated in CST Studio to determine the antenna characteristics. The antenna performance from the simulation matched the reflection coefficients (S11) measurements collected with the fabricated antenna in an anechoic chamber. This verified antenna was used for further measurements of different liver models placed near the antenna. The antenna response, S11 measurements, varied from one tissue type to another, demonstrating the potential of using the Vivaldi antenna as a near-field sensor to differentiate liver disease models from the healthy liver model.

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Masked Autoencoders in 3D Point Cloud Representation Learning

arXiv

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arXiv 2022年

作者： Jiang, Jincen Lu, Xuequan Zhao, Lizhi Dazaley, Richard Wang, Meili The College of Information Engineering Northwest A&F University China The Department of Computer Science and IT La Trobe University Australia The School of Information Technology Deakin University Australia

Transformer-based Self-supervised Representation Learning methods learn generic features from unlabeled datasets for providing useful network initialization parameters for downstream tasks. Recently, methods based upon masking Autoencoders have been explored in the fields. The input can be intuitively masked due to regular content, like sequence words and 2D pixels. However, the extension to 3D point cloud is challenging due to irregularity. In this paper, we propose masked Autoencoders in 3D point cloud representation learning (abbreviated as MAE3D), a novel autoencoding paradigm for self-supervised learning. We first split the input point cloud into patches and mask a portion of them, then use our Patch Embedding Module to extract the features of unmasked patches. Secondly, we employ patch-wise MAE3D Transformers to learn both local features of point cloud patches and high-level contextual relationships between patches, then complete the latent representations of masked patches. We use our Point Cloud Reconstruction Module with multi-task loss to complete the incomplete point cloud as a result. We conduct self-supervised pre-training on ShapeNet55 with the point cloud completion pre-text task and fine-tune the pre-trained model on ModelNet40 and ScanObjectNN (PB_T50_RS, the hardest variant). Comprehensive experiments demonstrate that the local features extracted by our MAE3D from point cloud patches are beneficial for downstream classification tasks, soundly outperforming state-of-the-art methods (93.4% and 86.2% classification accuracy, respectively). Our source codes are available at: https://***/Jinec98/MAE3D. Copyright © 2022, The Authors. All rights reserved.

关键词： Supervised learning

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Angle Basis: a Generative Model and Decomposition for Functional Connectivity

arXiv

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arXiv 2023年

作者： Orlichenko, Anton Qu, Gang Zhou, Ziyu Ding, Zhengming Wang, Yu-Ping Department of Biomedical Engineering Tulane University New OrleansLA70118 United States Department of Computer Science Tulane University New OrleansLA70118 United States

Functional connectivity (FC) is one of the most common inputs to fMRI-based predictive models, due to a combination of its simplicity and robustness. However, there may be a lack of theoretical models for the generation of FC. In this work, we present a straightforward decomposition of FC into a set of basis states of sine waves with an additional jitter component. We show that the decomposition matches the predictive ability of FC after including 5-10 bases. We also find that both the decomposition and its residual have approximately equal predictive value, and when combined into an ensemble, exceed the AUC of FC-based prediction by up to 5%. Additionally, we find the residual can be used for subject fingerprinting, with 97.3% same-subject, different-scan identifiability, compared to 62.5% for FC. Unlike PCA or Factor Analysis methods, our method does not require knowledge of a population to perform its decomposition;a single subject is enough. Our decomposition of FC into two equally-predictive components may lead to a novel appreciation of group differences in patient populations. Additionally, we generate synthetic patient FC based on user-specified characteristics such as age, sex, and disease diagnosis. By creating synthetic datasets or augmentations we may reduce the high financial burden associated with fMRI data acquisition. © 2023, CC BY-SA.

关键词： Data acquisition

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An agent-based model for cell microenvironment simulation using FlaMEGPU2

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computers in Biology and Medicine 2024年 179卷 108831-108831页

作者： Borau, C. Chisholm, R. Richmond, P. Walker, D. Mechanical Engineering Dept University of Zaragoza Zaragoza Spain Centro Universitario de la Defensa Zaragoza Spain Department of Computer Science and Insigneo Institute of in Silico Medicine University of Sheffield Sheffield United Kingdom

This work presents an advanced agent-based model developed within the FlaMEGPU2 framework, aimed at simulating the intricate dynamics of cell microenvironments. Our primary objective is to showcase FlaMEGPU2's potential in modelling critical features such as cell-cell and cell-ECM interactions, species diffusion, vascularisation, cell migration, and/or cell cycling. By doing so, we provide a versatile template that serves as a foundational platform for researchers to model specific biological mechanisms or processes. We highlight the utility of our approach as a microscale component within multiscale frameworks. Through four example applications, we demonstrate the model's versatility in capturing phenomena such as strain-stiffening behaviour of hydrogels, cell migration patterns within hydrogels, spheroid formation and fibre reorientation, and the simulation of diffusion processes within a vascularised and deformable domain. This work aims to bridge the gap between computational efficiency and biological fidelity, offering a scalable and flexible platform to advance our understanding of tissue biology and engineering. © 2024 The Authors

关键词： Cells

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Boolean matrix compressed sensing

Boolean matrix compressed sensing

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qiang Soleymani, Mahdi Mahdavifar, Hessam National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Halıcıoğlu Data Science Institute University of California La Jolla San DiegoCA92093 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9798350368741

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements. © 2025 IEEE.

关键词： Belief Propagation Boolean Matrix Compressed Sensing Matrix Completion

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Stochastic configuration networks with CPU-GPU implementation for large-scale data analytics

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Information sciences 2024年 667卷

作者： Li, Junqi Wang, Dianhui Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou221116 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

Stochastic configuration networks (SCNs) are a class of randomized learner models that have garnered increasing attention in data analytics. In the original implementation of SCN, the pseudo-inverse of the hidden layer output matrix is used to evaluate output weights. However, it is quite challenging to compute the pseudo-inverse of the hidden layer output matrices for large-scale datasets with high complexity, which occurs in many real-world applications. This paper aims to accelerate the learning process of SCNs by employing the well-known alternating direction method of multipliers (ADMM) solver and CPU-GPU heterogeneous computing technique. Empirical results on two benchmark datasets demonstrate the efficiency and effectiveness of the proposed method for large-scale data analytics. © 2024 The Author(s)

关键词： Data Analytics

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Goat Feeding Behavior Modeling and Monitoring Utilizing Livestock Wearable Device and Machine Learning Algorithm

Goat Feeding Behavior Modeling and Monitoring Utilizing Live...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Melchizedek Alipio Gianne Marcus Obusan Julianne Abbe Ong Samuel Perez Anton Luis Garces Miroslav Bures Department of Computer Science System Testing Intelligent Laboratory FEE Czech Technical University in Prague Prague 2 Czech Republic Department of Electronics and Computer Engineering De La Salle University Manila Philippines

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Goat farming shows potential as one of the so-lutions for providing quality livestock products in developing countries. Goat health is essential in determining the quality of goods obtained from the goat. Smart livestock farming has been a reliable solution to achieve high-yield and high-quality goat products. However, current smart livestock monitoring systems do not consider the feeding pattern and behavior of the animal as a factor in its overall health. Hence, this study aims to develop a system that effectively monitors the feeding behavior of the goat using wearable devices, machine learning, and a visualization interface. The wearable device consists of an accelerometer, gyrometer, and temperature sensors to monitor the goat's feeding behavior and movement. The data collected by the wearable device are wirelessly transmitted to a base station to build a sensor-based dataset. This dataset is used to generate a k-means clustering model to classify the feeding behavior of the goats. Results show that the system was able to classify the feeding behavior of the goat with an accuracy of 98.02 % and a reliability of 100% in comparison with the actual goat behavior on the farm. lastly, a graphical user interface is developed to provide monitoring and other relevant information to help the farmers in grazing the goats.

关键词： Temperature sensors Temperature measurement Temperature distribution Animals Agriculture Reliability Wearable devices

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On the Prospects of Stereolithography Method for 3D Printing of Future Microwave Devices with Low-Dk

On the Prospects of Stereolithography Method for 3D Printing...

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Antennas and Propagation Society International Symposium

作者： Dipankar Mitra Ryan Striker Tasin Nusrat Venkataramana Gadhamshetty Benjamin D. Braaten Sayan Roy Department of Computer Science University of Wisconsin-La Crosse La Crosse WI USA College of Engineering and Mines University of North Dakota Grand Forks ND USA Department of Electrical Engineering and Computer Science South Dakota Mines Rapid City SD USA Department of Civil and Environmental Engineering South Dakota Mines Rapid City SD USA Department of Electrical and Computer Engineering North Dakota State University Fargo ND USA

The stereolithography (Sla) method is known for its high-quality surface finish and details, hence, gaining its popularity with the realization of 3D-printed parts and structures for microwave applications. On the other hand, low-dielectric $(\boldsymbol{Dk})$ materials have an utmost importance in high-frequency PCB design and microwave applications. Here, the authors present the prospects of the Sla method in 3D-printing of $\mathbf{Low}-\boldsymbol{Dk}$ materials for future microwave devices. The Sla method is a relatively new manufacturing process, so it is critical to explore a range of dielectric constants of a few prospective commercially available materials. Here, eleven different resin-based materials were 3D-printed as cubes using the Sla method and characterized in terms of dielectric constant $(\boldsymbol{\varepsilon}_{\boldsymbol{r}})$ and loss tangent $(\mathbf{tan}\boldsymbol{\delta})$ using broadband coaxial probe method. The measured dielectric constant values were within the range of 2 - 4 for all the tested materials which thus proved to be among promising candidates for realizing microwave devices in the frequency bandwidth of 1 to 20 GHz. A few observed failures while manufacturing certain other shapes were also reported as a limitation of the Sla method.

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