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Broadband miniaturized spectrometers with a van der Waals tunnel diode

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arXiv 2024年

作者： Uddin, Gius Das, Susobhan Shafi, Abde Mayeen Wang, Lei Cui, Xiaoqi Nigmatulin, Fedor Ahmed, Faisal Liapis, Andreas C. Cai, Weiwei Yang, Zongyin Lipsanen, Harri Hasan, Tawfique Yoon, Hoon Hahn Sun, Zhipei Department of Electronics and Nanoengineering Aalto University Espoo02150 Finland QTF Centre of Excellence Aalto University Aalto00076 Finland Key Lab of Education Ministry for Power Machinery and Engineering School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China College of Information Science and Electronic Engineering State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou310027 China Cambridge Graphene Centre University of Cambridge CambridgeCB3 0FA United Kingdom Department of Semiconductor Engineering School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology 123 Cheomdangwagi-ro Buk-gu Gwangju61005 Korea Republic of

Miniaturized spectrometers are of immense interest for various on-chip and implantable photonic and optoelectronic applications. State-of-the-art conventional spectrometer designs rely heavily on bulky dispersive components (such as gratings, photodetector arrays, and interferometric optics) to capture different input spectral components that increase their integration complexity. Here, we report a high-performance broadband spectrometer based on a simple and compact van der Waals heterostructure diode, leveraging a careful selection of active van der Waals materials-molybdenum disulfide and black phosphorus, their electrically tunable photoresponse, and advanced computational algorithms for spectral reconstruction. We achieve remarkably high peak wavelength accuracy of ~2 nanometers, and broad operation bandwidth spanning from ~500 to 1600 nanometers in a device with a ~30×20 μm2 footprint. This diode-based spectrometer scheme with broadband operation offers an attractive pathway for various applications, such as sensing, surveillance and spectral imaging. © 2024, CC BY.

关键词： Spectrometers

Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs

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arXiv 2021年

作者： Liang, Tianming Liu, Yang Liu, Xiaoyan Zhang, Hao Sharma, Gaurav Guo, Maozu The School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China The Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China The Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China

Label noise and long-tailed distributions are two major challenges in distantly supervised relation extraction. Recent studies have shown great progress on denoising, but paid little attention to the problem of long-tailed relations. In this paper, we introduce a constraint graph to model the dependencies between relation labels. On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously. CGRE employs graph convolution networks to propagate information from data-rich relation nodes to data-poor relation nodes, and thus boosts the representation learning of long-tailed relations. To further improve the noise immunity, a constraint-aware attention module is designed in CGRE to integrate the constraint information. Extensive experimental results indicate that CGRE achieves significant improvements over the previous methods for both denoising and long-tailed relation extraction. © 2021, CC BY.

关键词： Graphic methods

FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations

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arXiv 2022年

作者： Siew, Marie Sharma, Shikhar Li, Zekai Guo, Kun Xu, Chao Lorido-Botran, Tania Quek, Tony Q.S. Joe-Wong, Carlee The Information Systems and Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore The Electrical and Computer Engineering department Carnegie Mellon University PittsburghPA15213 United States The Shanghai Key Laboratory of Multidimensional Information Processing School of Communications and Electronics Engineering East China Normal University Shanghai200241 China The School of Information Engineering Northwest A&F University Yangling712100 China Roblox United States

In edge computing, users’ service profiles are migrated between edge servers due to user mobility. Reinforcement learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like AR/VR and real- time obstacle detection. These rare failures, being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. We introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose FIRE-ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove FIRE-ImRE’s boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (FIRE-ImDQL) and actor critic (FIRE-ImACRE) versions of our algorithm to enhance scalability. We extend our framework to accommodate users with varying risk tolerances of rare failure events. Through trace-driven experiments, we show that FIRE reduces edge computing costs compared to vanilla RL and the greedy baseline in the event of failures. © 2022, CC BY.

关键词： Mobile edge computing

Deepgt: Deep Learning-Based Quantification of Nanosized Bioparticles in Bright-Field Micrographs of Gires-Tournois Biosensor

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SSRN

SSRN 2023年

作者： Kang, Jiwon Yoo, Young Jin Park, Jin-Hwi Ko, Joo Hwan Kim, Seungtaek Stanciu, Stefan G. Stenmark, Harald A. Lee, Jinah Mahmud, Abdullah Al Jeon, Hae-Gon Song, Young Min School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States AI Graduate School Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Zoonotic Virus Laboratory Institut Pasteur Korea Seongnam13488 Korea Republic of Center for Microscopy-Microanalysis and Information Processing Politehnica University of Bucharest Bucharest060042 Romania Department of Molecular Cell Biology Institute for Cancer Research Oslo University Hospital Oslo0379 Norway Anti-Viral Research Center Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of

Rapid and decentralized quantification of viral load profiles in infected patients is vital for assessing clinical severity and tailoring appropriate therapeutic strategies. Although microscopic imaging offers potential for label-free and amplification-free quantitative diagnostics, the small size (~100 nm in diameter) and low refractive index (~1.5) of bioparticles present challenges in achieving accurate estimations, consequently increasing the limit of detection (LoD). In this study, we present a novel synergistic biosensing approach, DeepGT, combining Gires-Tournois (GT) sensing platforms with deep learning algorithms to enhance nanoscale bioparticle counting accuracy. The GT sensing platform serves as a photonic resonator, increasing bioparticle visibility in bright-field microscopy and maximizing chromatic contrast. By employing a back-end with a dilated convolutional neural network architecture, DeepGT effectively refines artifacts and color deviations, significantly improving particle estimation accuracy (MAE ~ 2.37 across 1,596 images) compared to rule-based algorithms (MAE ~ 13.47). Notably, the enhanced accuracy in detecting invisible particles (e.g., two- or three-particles) enables an LoD of 138 pg ml-1, facilitating a dynamic linear correlation at low viral concentration ranges within the clinical spectrum of infection, from asymptomatic to severe cases. Leveraging transfer learning, DeepGT, which relies on a chromatometry-based strategy instead of a spatial resolution approach, exhibits exceptional precision when analyzing particles of diverse dimensions smaller than the microscopy system’s minimum diffraction limit in visible light ( © 2023, The Authors. All rights reserved.

关键词： Biosensors

δ-Calculus:A New Approach to Quantifying Location Privacy

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computers, Materials & Continua 2020年第6期63卷 1323-1342页

作者： Lihua Yin Ran Li Jingquan Ding Xiao Li Yunchuan Guo Huibing Zhang Ang Li Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Institute of Information Engineering CASBeijing100093China Xinjiang Technical Institute of Physics&Chemistry Chinese Academy ScienceUrumqi830011China Key laboratory of Speech Language Information Processing of Xinjiang Urumqi830046China University of Chinese Academy of Sciences Beijing100049China Guilin University of Electronic Technology Guilin541004China Department of Electrical and Computer Engineering Duke UniversityDurham27708USA

With the rapid development of mobile wireless Internet and high-precision localization devices,location-based services(LBS)bring more convenience for people over recent *** LBS,if the original location data are directly provided,serious privacy problems *** a response to these problems,a large number of location-privacy protection mechanisms(LPPMs)(including formal LPPMs,FLPPMs,etc.)and their evaluation metrics have been proposed to prevent personal location information from being leakage and quantify privacy ***,existing schemes independently consider FLPPMs and evaluation metrics,without synergizing them into a unifying *** this paper,a unified model is proposed to synergize FLPPMs and evaluation *** detail,the probabilistic process calculus(calledδ-calculus)is proposed to characterize obfuscation schemes(which is a LPPM)and integrateα-entropy toδ-calculus to evaluate its privacy ***,we use two calculus moving and probabilistic choice to model nodes’mobility and compute its probability distribution of nodes’locations,and a renaming function to model privacy *** formally defining the attacker’s ability and extending relative entropy,an evaluation algorithm is proposed to quantify the leakage of location ***,a series of examples are designed to demonstrate the efficiency of our proposed approach.

关键词： Location privacy δ-calculus relative entropy

Deep Learning Methods for Calibrated Photometric Stereo and Beyond

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arXiv 2022年

作者： Ju, Yakun Lam, Kin-Man Xie, Wuyuan Zhou, Huiyu Dong, Junyu Shi, Boxin School of Electrical and Electronic Engineering Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong The Research Institute for Future Media Computing Shenzhen University Shenzhen China The Department of Informatics University of Leicester Leicester United Kingdom The Faculty of Information Science and Engineering The Institute for Advanced Ocean Study Ocean University of China Qingdao China The National Key Laboratory for Multimedia Information Processing National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models. Copyright © 2022, The Authors. All rights reserved.

关键词： Light sources

Dynamic models of spherical parallel robots for model-based control schemes

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arXiv 2021年

作者： Hassani, Ali Bataleblu, Abbas Khalilpour, S.A. Taghirad, Hamid D. Cardou, Philippe Faculty of Electrical and Computer Engineering K.N. Toosi University of Technology Tehran Iran Department of Mechanical Engineering Robotics Laboratory Laval University Quebec CityQCG1V 0A6 Canada

In this paper, derivation of different forms of dynamic formulation of spherical parallel robots (SPRs) is investigated. These formulations include the explicit dynamic forms, linear regressor, and Slotine-Li (SL) regressor, which are required for the design and implementation of the vast majority of model-based controllers and dynamic parameters identification schemes. To this end, the implicit dynamic of SPRs is first formulated using the principle of virtual work in task-space, and then by using an extension, their explicit dynamic formulation is derived. The dynamic equation is then analytically reformulated into linear and S-L regression form with respect to the inertial parameters, and by using the Gauss-Jordan procedure, it is reduced to a unique and closed-form structure. Finally, to illustrate the effectiveness of the proposed method, two different SPRs, namely, the ARAS-Diamond, and the 3-RRR, are examined as the case studies. The obtained results are verified by using the MSC-ADAMS® software, and are shared to interested audience for public access. © 2021, CC BY.

关键词： Spheres

engineering Fe‑N_(4)Electronic Structure with Adjacent Co‑N_(2)C_(2)and Co Nanoclusters on Carbon Nanotubes for Efficient Oxygen Electrocatalysis

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Nano-Micro Letters 2023年第12期15卷 283-297页

作者： Mingjie Wu Xiaohua Yang Xun Cui Ning Chen Lei Du Mohamed Cherif Fu‑Kuo Chiang Yuren Wen Amir Hassanpour Francois Vidal Sasha Omanovic Yingkui Yang Shuhui Sun Gaixia Zhang State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile UniversityWuhan 430200People’s Republic of China Institut National de la Recherche Scientifique(INRS) Center Energie Materiaux TelecommunicationsVarennesQC J3X 1P7Canada Department of Chemical Engineering McGill University3610 University StreetMontrealQC H3A 0C5Canada Department of Electrical Engineering Ecole de Technologie Superieure(ETS)MontrealQC H3C 1K3Canada Canadian Light Source(CLS) 44 Innovation BoulevardSaskatoonSK S7N 2V3Canada National Institute of Low-Carbon-and-Clean-Energy Beijing 102211People’s Republic of China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083People’s Republic of China

Regulating the local configuration of atomically dispersed transition-metal atom catalysts is the key to oxygen electrocatalysis performance *** the previously reported singleatom or dual-atom configurations,we designed a new type of binary-atom catalyst,through engineering Fe-N_(4)electronic structure with adjacent Co-N_(2)C_(2)and nitrogen-coordinated Co nanoclusters,as oxygen *** resultant optimized electronic structure of the Fe-N_(4)active center favors the binding capability of intermediates and enhances oxygen reduction reaction(ORR)activity in both alkaline and acid *** addition,anchoring M-N-C atomic sites on highly graphitized carbon supports guarantees of efficient charge-and mass-transports,and escorts the high bifunctional catalytic activity of the entire ***,through the combination of electrochemical studies and in-situ X-ray absorption spectroscopy analyses,the ORR degradation mechanisms under highly oxidative conditions during oxygen evolution reaction processes were *** work developed a new binary-atom catalyst and systematically investigates the effect of highly oxidative environments on ORR electrochemical *** demonstrates the strategy for facilitating oxygen electrocatalytic activity and stability of the atomically dispersed M-N-C catalysts.

关键词： Atomically dispersed Fe-N-C catalysts ORR/OER Rechargeable zinc-air battery Fuel cells

Overview of the Tenth Dialog System technology Challenge: DSTC10

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IEEE/ACM Transactions on Audio Speech and Language processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis