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arXiv 2023年

作者： Krueper, Gregory Cohen, Lior Gopinath, Juliet T. Department of Physics University of Colorado Boulder BoulderCO80309 United States Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO80309 United States Materials Science and Engineering Program University of Colorado Boulder BoulderCO80309 United States

We present an innovative, platform-independent concept for multiparameter sensing where the measurable parameters are in series, or cascaded, enabling measurements as a function of position. With temporally resolved detection, we show that squeezing can give a quantum enhancement in sensitivity over that of classical states by a factor of e2r, where r ≈ 1 is the squeezing parameter. As an example, we have modeled an interferometer that senses multiple phase shifts along the same path, demonstrating a maximal quantum advantage by combining a coherent state with squeezed vacuum. Further classical modeling with up to 100 phases shows linear scaling potential for adding nodes to the sensor. The approach can be applied to remote sensing, geophysical surveying, and infrastructure monitoring. © 2023, CC BY.

关键词： Interferometers

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A Comparative Study of Fishing Activity Detection Approaches in Maritime Surveillance

A Comparative Study of Fishing Activity Detection Approaches...

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computer Science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Vinicius D. Do Nascimento Tiago A. O. Alves Diego L. C. Dutra Sandip Kundu Systems Engineering and Computer Science Program (PESC) Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Mathematics and Statistics Institute State University of Rio de Janeiro (UERJ) Rio de Janeiro Brazil Electrical and Computer Engineering (ECE) Department University of Massachusetts Amherst USA

Maritime authorities (MA) must track fishing vessels to ensure that fishing activities are limited to permitted areas. If illegal fishing is suspected, resources must be allocated to intercept and inspect the vessels. Thus, a false flag by the MA is costly, so it is important to use accurate detection methods. We compare the accuracy and computational time of the main approaches for detecting fishing activities described in the literature, using the Global Fishing Watch (GFW) dataset. We find that Long Short-Term Memory (LSTM) neural networks achieves an optimal accuracy of 1.00, while the random forest approach comes second with an accuracy of 0.87. Given the high cost of mistakes for MA, we conclude that the LSTM's high computational cost is worthwhile.

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A Flexible Origami Opto-Electro Array For In Vivo Optogenetic Neurostimulation and Neurophysiology Recording

A Flexible Origami Opto-Electro Array For In Vivo Optogeneti...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Yan Gong Xiang Liu Yifan Liu Zhen Qiu Arthur Weber Wen Li Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA Institute for Quantitative Health Science and Engineering Michigan State University East Lansing Michigan USA Department of Physiology Neuroscience Program Michigan State University East Lansing MI USA Department of Biomedical Engineering Michigan State University East Lansing Michigan USA Department of Physiology Michigan State University East Lansing MI USA

This paper reports a thin-film, three-dimensional (3D) opto-electro array with four individually addressable microscale light-emitting diodes (μ-LEDs) capable of surface illumination of the cortex and nine penetrating electrodes for simultaneous recording of light-evoked neural activities. Inspired by the origami concept, a carefully designed "bridge + trench" structure facilitates the conversion of the array from two-dimensional (2D) to 3D while avoiding mechanical damage to thin film metal. Before device transformation, the shape and dimensions of the 2D array can be customized, making it versatile for a variety of applications. The array is packaged using polyimide (PI) and polydimethylsiloxane (PDMS) to ensure the device’s mechanical flexibility and biocompatibility. The efficacy of the device is characterized both in vitro and in vivo.

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Reducing information bottleneck for weakly supervised semantic segmentation 21

Reducing information bottleneck for weakly supervised semant...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Jungbeom Lee Jooyoung Choi Jisoo Mok Sungroh Yoon Department of Electrical and Computer Engineering Seoul National University Department of Electrical and Computer Engineering Seoul National University and Interdisciplinary Program in Artificial Intelligence Seoul National University

ISBN: (纸本)9781713845393

Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminative region of the target object. We interpret this phenomenon using the information bottleneck principle: the final layer of a deep neural network, activated by the sigmoid or softmax activation functions, causes an information bottleneck, and as a result, only a subset of the task-relevant information is passed on to the output. We first support this argument through a simulated toy experiment and then propose a method to reduce the information bottleneck by removing the last activation function. In addition, we introduce a new pooling method that further encourages the transmission of information from non-discriminative regions to the classification. Our experimental evaluations demonstrate that this simple modification significantly improves the quality of localization maps on both the PASCAL VOC 2012 and MS COCO 2014 datasets, exhibiting a new state-of-the-art performance for weakly supervised semantic segmentation.

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Comparative Analysis of Real-Time Multi-Person Pose Detection in electrical Industrial Safety Scenarios Using YOLOv8-Pose and OpenPose

Comparative Analysis of Real-Time Multi-Person Pose Detectio...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Shang-Te Tsai Zong-Rong Wu Pin-Hsuan Lin Chin-Hsiung Chen Wei Chien Yu-Cheng Chang School of Art and Design Sanming University Fujian China Department of Technical and Vocational Program in Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Business Administration National Central University Taoyuan Taiwan Department of Electrical Engineering New Taipei Municipal Tamsui Commercial Industrial Vocational Senior High School New Taipei Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electrical engineering. We focused on two leading pose detection technologies: YOLOv8 and OpenPose. To optimize performance, we integrated these two techniques into the LSTM model for training and investigated frame rates and accuracy.

关键词： Training Accuracy Microcontrollers Neural networks Real-time systems Safety Internet of Things

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Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

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Measuring 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1394-1395页

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Cho, Min Gee Qian, Yiwen Gu, Junsi Rozeveld, Steve Ercius, Peter Fessler, Jeffrey A. Xu, Ting Scott, Mary Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann Arbor MI United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL United States Advanced Photon Source Argonne National Laboratory Lemont IL United States Department of Materials Science and Engineering University of California at Berkeley Berkeley CA United States National Center for Electron Microscopy Lawrence Berkeley National Laboratory Berkeley CA United States Dow Chemical Co. Midland MI United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States Materials Science Division Lawrence Berkeley National Laboratory Berkeley CA United States Applied Physics Program University of Michigan Ann Arbor MI United States

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Reset & Distill: A Recipe for Overcoming Negative Transfer in Continual Reinforcement Learning

arXiv

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arXiv 2024年

作者： Ahn, Hongjoon Hyeon, Jinu Oh, Youngmin Hwang, Bosun Moon, Taesup Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Samsung Advanced Institute of Technology Korea Republic of ASRI INMC IPAI/AIIS Seoul National University Korea Republic of

We argue that one of the main obstacles for developing effective Continual Reinforcement Learning (CRL) algorithms is the negative transfer issue occurring when the new task to learn arrives. Through comprehensive experimental validation, we demonstrate that such issue frequently exists in CRL and cannot be effectively addressed by several recent work on mitigating plasticity loss of RL agents. To that end, we develop Reset & Distill (R&D), a simple yet highly effective method, to overcome the negative transfer problem in CRL. R&D combines a strategy of resetting the agent’s online actor and critic networks to learn a new task and an offline learning step for distilling the knowledge from the online actor and previous expert’s action probabilities. We carried out extensive experiments on long sequence of Meta-World tasks and show that our method consistently outperforms recent baselines, achieving significantly higher success rates across a range of tasks. Our findings highlight the importance of considering negative transfer in CRL and emphasize the need for robust strategies like R&D to mitigate its detrimental effects. © 2024, CC BY.

关键词： Reinforcement learning

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Design and exploration of vertically stacked complementary tunneling field-effect transistors

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Applied Physics Express 2024年第1期17卷 014001-014001页

作者： Thoti, Narasimhulu Li, Yiming Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

The purpose of this letter is to study the design and explore vertically stacked complementary tunneling field-effect transistors (CTFETs) using CFET technology for emerging technology nodes. As a prior work, the CTFET’s device-level simulations are implemented and deliberated in strict compliance with the experimental settings. This work comprises the study of physical and DC analyses by scaling the p- to n-CTFET separation (D pn), being a significant factor in CFET/CTFET design for its process difficulty. By utilizing the 50% benefit in footprint, the work is further extended to CTFET static random access memory implementation and characterization with hold/read noise margin analysis. © 2023 The Author(s). Published on behalf of The Japan Society of Applied Physics by IOP Publishing Ltd

关键词： Si-Ge alloys

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Quasi-Nondestructive Read Out of Ferroelectric Capacitor Polarization by Exploiting a 2TnC Cell to Relax the Endurance Requirement

TechRxiv

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TechRxiv 2023年

作者： Xiao, Yi Deng, Shan Zhao, Zijian Faris, Zubair Xu, Yixin Huang, Tzu-Jung Narayanan, Vijaykrishnan Ni, Kai Microsystems Engineering Ph.D. program Rochester Institute of Technology RochesterNY14623 United States Department of Computer Science and Engineering Penn State University University ParkPA16802 United States Department of Electrical and Microelectronic Engineering Rochester Institute of Technology RochesterNY14623 United States

In this work, we propose a 2TnC ferroelectric random access memory (FeRAM) cell design to realize the quasi-nondestructive readout (QNRO) of ferroelectric polarization (PFE) in a capacitor, which can relax the endurance requirement of the ferroelectric thin film and exploits the benefits of both FeRAM and ferroelectric FET (FeFET). We demonstrate that: i) QNRO sensing of PFE is conducted successfully in experiment with a ON/OFF ratio (ION/IOFF)>103, ION>10 µA, and read endurance>106 cycles, which can relax the FeRAM endurance requirement by 106x;ii) optimization of the cell performance can be realized by tuning the metal-ferroelectric-metal capacitor (MFM) capacitor to read transistor area ratio and read transistor threshold voltage (VTH);iii) the proposed cell structure is 3D-compatible, enabling integration of highly dense memory solution;iv) the proposed cell structure also enables compute-in-memory (CIM) applications of FeRAM, which has not been widely explored. With this technology, storage and memory-centric computing can be enabled. © 2023, CC BY.

关键词： Cytology

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