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Parallel chaotic sources based on microcombs

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Parallel chaotic sources based on microcombs

Conference on Lasers and Electro-Optics (CLEO)

作者： Bitao Shen Haowen Shu Weiqiang Xie Ruixuan Chen Zhi Liu Xuguang Zhang Yimeng Wang Yunhao Zhang Buwen Cheng Shaohua Yu John E. Bowers Lin Chang Xingjun Wang State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA USA State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Beijing China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China

Massively parallel optical chaotic sources with large bandwidth are demonstrated based on chaotic microcombs in the AlGaAsOI platform. 30-channel parallel random bit generation with 30 Gbit/s per channel is realized b... 详细信息

ISBN: (纸本)9781957171258

关键词： Diode lasers Integrated photonics Kerr effect Nonlinear effects Optical microscopy Random number generation

Large-area SNSPDs for up to 7.4 μm wavelengths

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Large-area SNSPDs for up to 7.4 μm wavelengths

CLEO: QELS_Fundamental science, QELS 2022

作者： Colangelo, Marco Walter, Alexander B. Korzh, Boris Schmidt, Ekkehart Bumble, Bruce Lita, Adriana E. Beyer, Andrew D. Allmaras, Jason P. Briggs, Ryan M. Kozorezov, Alexander Wollman, Emma E. Shaw, Matthew D. Berggren, Karl K. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. PasadenaCA United States National Institute of Standards and Technology BoulderCO United States Department of Physics Lancaster University LancasterLA1 4YB United Kingdom

ISBN: (纸本)9781557528209

关键词： Particle beams

Local-to-Global Cross-Modal Attention-Aware Fusion for HSI-X Semantic Segmentation

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arXiv 2024年

作者： Zhang, Xuming Yokoya, Naoto Gu, Xingfa Tian, Qingjiu Bruzzone, Lorenzo The International Institute for Earth System Science Nanjing University Nanjing210023 China The Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo Chiba277-8561 Japan The RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan The Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The Department of Information Engineering and Computer Science University of Trento Trento38050 Italy

Hyperspectral image (HSI) classification has recently reached its performance bottleneck. Multimodal data fusion is emerging as a promising approach to overcome this bottleneck by providing rich complementary information from the supplementary modality (X-modality). However, achieving comprehensive cross-modal interaction and fusion that can be generalized across different sensing modalities is challenging due to the disparity in imaging sensors, resolution, and content of different modalities. In this study, we propose a Local-to-Global Cross-modal Attention-aware Fusion (LoGoCAF) framework for HSI-X classification that jointly considers efficiency, accuracy, and generalizability. LoGoCAF adopts a pixel-to-pixel two-branch semantic segmentation architecture to learn information from HSI and X modalities. The pipeline of LoGoCAF consists of a local-to-global encoder and a lightweight multilayer perceptron (MLP) decoder. In the encoder, convolutions are used to encode local and high-resolution fine details in shallow layers, while transformers are used to integrate global and low-resolution coarse features in deeper layers. The MLP decoder aggregates information from the encoder for feature fusion and prediction. In particular, two cross-modality modules, the feature enhancement module (FEM) and the feature interaction and fusion module (FIFM), are introduced in each encoder stage. The FEM is used to enhance complementary information by combining the feature from the other modality across direction-aware, position-sensitive, and channel-wise dimensions. With the enhanced features, the FIFM is designed to promote cross-modality information interaction and fusion for the final semantic prediction. Extensive experiments demonstrate that our LoGoCAF achieves superior performance and generalizes well on various multimodal fusion and classification tasks. The code will be made publicly available. Copyright © 2024, The Authors. All rights reserved.

关键词： Remote sensing

Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Waveform Data

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arXiv 2024年

作者： Nagaraj, Sujay Goodwin, Andrew J. Lopushanskyy, Dmytro Eytan, Danny Greer, Robert W. Goodfellow, Sebastian D. Assadi, Azadeh Jayarajan, Anand Goldenberg, Anna Mazwi, Mjaye L. Department of Computer Science University of Toronto Toronto Canada Department of Genetics & Genome Biology SickKids Toronto Canada Vector Institute Toronto Canada Department of Critical Care Medicine SickKids Toronto Canada School of Biomedical Engineering University of Sydney Sydney Australia Department of Computer Science University of Oxford Oxford United Kingdom Technion - Israel Institute of Technology Haifa Israel Department of Civil & Mineral Engineering University of Toronto Toronto Canada Canadian Institute for Advanced Research Toronto Canada Department of Laboratory Medicine & Pathobiology University of Toronto Toronto Canada Division of Critical Care Medicine Seattle Children's Hospital Seattle United States

Central Venous Lines (C-Lines) and Arterial Lines (A-Lines) are routinely used in the Critical Care Unit (CCU) for blood sampling, medication administration, and high-frequency blood pressure measurement. Judiciously accessing these lines is important, as over-utilization is associated with significant in-hospital morbidity and mortality. Documenting the frequency of line-access is an important step in reducing these adverse outcomes. Unfortunately, the current gold-standard for documentation is manual and subject to error, omission, and bias. The high-frequency blood pressure waveform data from sensors in these lines are often noisy and full of artifacts. Standard approaches in signal processing remove noise artifacts before meaningful analysis. However, from bedside observations, we characterized a distinct artifact that occurs during each instance of C-Line or A-Line use. These artifacts are buried amongst physiological waveform and extraneous noise. We focus on Machine Learning (ML) models that can detect these artifacts from waveform data in real-time - finding needles in needle stacks, in order to automate the documentation of line-access. We built and evaluated ML classifiers running in real-time at a major children's hospital to achieve this goal. We demonstrate the utility of these tools for reducing documentation burden, increasing available information for bedside clinicians, and informing unit-level initiatives to improve patient safety. © 2024, CC BY.

关键词： Blood pressure

Mid-Infrared Photoluminescence from Tellurium Thin Films

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Nano letters 2025年第23期25卷 2025 Jun 2页

作者： Shu Wang Naoki Higashitarumizu Moniruzzaman Jamal Finn Babbe Daryl C Chrzan Mary C Scott Ali Javey Department of Materials Science and Engineering University of California Berkeley California 94720 United States. Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 United States. Electrical Engineering and Computer Sciences University of California Berkeley California 94720 United States. The National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley California 94720 United States. Chemical Science Division Lawrence Berkeley National Laboratory Berkely California 94720 United States. Kavli Energy NanoSciences Institute University of California Berkeley and Lawrence Berkeley National Laboratory Berkeley California 94720 United States.

Tellurium is an elemental semiconductor with a 0.34 eV optical band gap, showing promise for mid-infrared (MIR) optoelectronics including light-emitting diodes. However, quantitative measurement of the tellurium luminescence efficiency has not yet been investigated, and growing optically active films remain challenging. Here, we demonstrate the low-temperature growth of bright tellurium thin films by using physical vapor transport. The sample morphology is controlled by varying the growth temperature, yielding continuous thin films, microparticles, or nanowires. Additionally, we demonstrated patterned growth by using a seed layer for selective nucleation. Quantitative photoluminescence measurements at room temperature reveal an internal quantum yield of 2.0% at 0.34 eV for the as-grown tellurium films, comparable to the best-reported efficiencies of III-V and II-VI compound semiconductors with similar band gaps. Furthermore, we demonstrate band-gap tunability with Te-Se alloys, where up to 20% selenium incorporation gradually blue-shifts the band gap to 0.55 eV. The work demonstrates the potential use of tellurium for efficient MIR devices.

关键词： Te−Se alloys mid-infrared photoluminescence physical vapor transport tellurium

Deficient Excitation in Parameter Learning

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arXiv 2025年

作者： Cao, Ganghui Wang, Shimin Guay, Martin Wang, Jinzhi Duan, Zhisheng Polycarpou, Marios M. State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Massachusetts Institute of Technology CambridgeMA02139 United States Queen’s University KingstonONK7L 3N6 Canada KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

This paper investigates parameter learning problems under deficient excitation (DE). The DE condition is a rank-deficient, and therefore, a more general evolution of the well-known persistent excitation condition. Under the DE condition, a proposed online algorithm is able to calculate the identifiable and non-identifiable subspaces, and finally give an optimal parameter estimate in the sense of least squares. In particular, the learning error within the identifiable subspace exponentially converges to zero in the noise-free case, even without persistent excitation. The DE condition also provides a new perspective for solving distributed parameter learning problems, where the challenge is posed by local regressors that are often insufficiently excited. To improve knowledge of the unknown parameters, a cooperative learning protocol is proposed for a group of estimators that collect measured information under complementary DE conditions. This protocol allows each local estimator to operate locally in its identifiable subspace, and reach a consensus with neighbours in its non-identifiable subspace. As a result, the task of estimating unknown parameters can be achieved in a distributed way using cooperative local estimators. Application examples in system identification are given to demonstrate the effectiveness of the theoretical results developed in this paper. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

Bidirectional cascaded superfluorescent lasing in air enabled by resonant third harmonic photon exchange from nitrogen to argon

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arXiv 2024年

作者： Nie, Zan Nambu, Noa Marsh, Kenneth A. Matteo, Daniel Patel, C. Kumar Zhang, Chaojie Wu, Yipeng Carlström, Stefanos Morales, Felipe Patchkovskii, Serguei Smirnova, Olga Ivanov, Misha Joshi, Chan Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA90095 United States Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Max Born Institute Max-Born-Str. 2A BerlinD-12489 Germany

Cavity-free lasing in atmospheric air has stimulated intense research towards fundamental understanding of underlying physical mechanisms. In this Letter, we identify a new mechanism – third harmonic photon mediated resonant energy transfer pathway leading to population inversion in argon via initial three-photon excitation of nitrogen molecules irradiated by intense 261 nm pulses – that enables bidirectional two-color cascaded lasing in atmospheric air. By making pump-probe measurements, we conclusively show that such cascaded lasing results from superfluorescence (SF) rather than amplified spontaneous emission (ASE). Such cascaded lasing with the capability of producing bidirectional multicolor coherent pulses opens additional possibilities for remote sensing applications. © 2024, CC BY.

关键词： Argon

Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition

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arXiv 2022年

作者： Chen, Zhan Li, Sicheng Yang, Bing Li, Qinghan Liu, Hong Key Laboratory of Machine Perception Shenzhen Graduate School Peking University China Institute of Information and Communication Engineering Zhejiang University China Department of Electrical Engineering and Computer Science Syracuse University United States

Graph convolutional networks have been widely used for skeleton-based action recognition due to their excellent modeling ability of non-Euclidean data. As the graph convolution is a local operation, it can only utilize the short-range joint dependencies and short-term trajectory but fails to directly model the distant joints relations and long-range temporal information that are vital to distinguishing various actions. To solve this problem, we present a multi-scale spatial graph convolution (MS-GC) module and a multi-scale temporal graph convolution (MT-GC) module to enrich the receptive field of the model in spatial and temporal dimensions. Concretely, the MS-GC and MT-GC modules decompose the corresponding local graph convolution into a set of subgraph convolution, forming a hierarchical residual architecture. Without introducing additional parameters, the features will be processed with a series of sub-graph convolutions, and each node could complete multiple spatial and temporal aggregations with its neighborhoods. The final equivalent receptive field is accordingly enlarged, which is capable of capturing both short- and long-range dependencies in spatial and temporal domains. By coupling these two modules as a basic block, we further propose a multi-scale spatial temporal graph convolutional network (MST-GCN), which stacks multiple blocks to learn effective motion representations for action recognition. The proposed MST-GCN achieves remarkable performance on three challenging benchmark datasets, NTU RGB+D, NTU-120 RGB+D and Kinetics-Skeleton, for skeleton-based action recognition. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

Contrastive Bayesian Analysis for Deep Metric Learning

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arXiv 2022年

作者： Kan, Shichao He, Zhiquan Cen, Yigang Li, Yang Mladenovic, Vladimir He, Zhihai School of Computer Science and Engineering Central South University Hunan Changsha410083 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Guangdong Multimedia Information Service Engineering Technology Research Center Shenzhen University 518060 China Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO65211 United States Faculty of Technical Sciences University of Kragujevac Cacak Serbia Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Pengcheng Lab Shenzhen518066 China

Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same class closer and push negative samples from different classes away from each other. In this work, we recognize that there is a significant semantic gap between features at the intermediate feature layer and class labels at the final output layer. To bridge this gap, we develop a contrastive Bayesian analysis to characterize and model the posterior probabilities of image labels conditioned by their features similarity in a contrastive learning setting. This contrastive Bayesian analysis leads to a new loss function for deep metric learning. To improve the generalization capability of the proposed method onto new classes, we further extend the contrastive Bayesian loss with a metric variance constraint. Our experimental results and ablation studies demonstrate that the proposed contrastive Bayesian metric learning method significantly improves the performance of deep metric learning in both supervised and pseudo-supervised scenarios, outperforming existing methods by a large margin. © 2022, CC0.

关键词： Distance education