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arXiv 2022年

作者： Shen, Tao Hu, Zhihang Sun, Siqi Liu, Di Wong, Felix Wang, Jiuming Chen, Jiayang Wang, Yixuan Hong, Liang Xiao, Jin Zheng, Liangzhen Krishnamoorthi, Tejas King, Irwin Wang, Sheng Yin, Peng Collins, James J. Li, Yu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Zelixir Biotech Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA United States Department of Systems Biology Harvard Medical School BostonMA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Synthetic Biology Center Massachusetts Institute of Technology CambridgeMA United States Integrated Biosciences Redwood CityCA United States OneAIM Ltd. Hong Kong Shanghai AI Laboratory Shanghai China The CUHK Shenzhen Research Institute Hi-Tech Park Shenzhen Nanshan China Shenzhen Institute of Advanced Technology Guangdong Shenzhen China School of Molecular Sciences Center for Molecular Design and Biomimetics the Biodesign Institute Arizona State University TempeAZ United States School of Computing and Augmented Intelligence Arizona State University TempeAZ United States

Accurate prediction of RNA three-dimensional (3D) structure remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding their functions and informing RNA-targeting drug development and synthetic biology design. The structural flexibility of RNA, which leads to scarcity of experimentally determined data, complicates computational prediction efforts. Here, we present RhoFold+, an RNA language model-based deep learning method that accurately predicts 3D structures of single-chain RNAs from sequences. By integrating an RNA language model pre-trained on ∼23.7 million RNA sequences and leveraging techniques to address data scarcity, RhoFold+ offers a fully automated end-to-end pipeline for RNA 3D structure prediction. Retrospective evaluations on RNA-Puzzles and CASP15 natural RNA targets demonstrate RhoFold+’s superiority over existing methods, including human expert groups. Its efficacy and generalizability are further validated through cross-family and cross-type assessments, as well as time-censored benchmarks. Additionally, RhoFold+ predicts RNA secondary structures and inter-helical angles, providing empirically verifiable features that broaden its applicability to RNA structure and function studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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CNN-based Broad Learning with Efficient Incremental Reconstruction Model for Facial Emotion Recognition ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10236-10241页

作者： Luefeng Chen Min Li Xuzhi Lai Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

Convolutional neural network-based broad learning with efficient incremental reconstruction model (CNNBL) is proposed to recognize emotions in human-robot interaction. It aims to extract deep and abstract features from facial emotional images, and reduce the influence of the complex structure and slow network updates on facial emotion recognition in deep learning. Feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotion recognition. When the network needs to expand, the network is dynamically updated by incremental learning algorithm. We verified the experimental performance through k -fold cross validation. According to the recognition results, the accuracy on JAFFE database of our proposal is greater than that of the state of the art, such as the Local Binary Patterns with Softmax and Deep Attentive Multi-path convolutional neural network.

关键词： Convolution Neural Networks Broad Learning Emotion Recognition Human-Robot Interaction

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A meta-inspired termite queen algorithm for global optimization and engineering design problems

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Engineering Applications of Artificial Intelligence 2022年 111卷

作者： Peng Chen Shihua Zhou Qiang Zhang Nikola Kasabov Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian 116622 China School of Computer Science and Technology Dalian University of Technology Dalian 116024 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland 1010 New Zealand Intelligent Systems Research Center Ulster University Londonderry BT52 1SA UK

This paper proposes a novel bio-inspired termite queen algorithm (TQA) to solve optimization problems by simulating the division of labor in termite populations under a queen’s rule. TQA is benchmarked on a set of 23 functions to test its performance at solving global optimization problems, and applied to six real-world engineering design problems to verify its reliability and effectiveness. Comparative simulation studies with other algorithms are conducted, from whose results it is observed that TQA satisfactorily solves global optimization problems and engineering design problems.

关键词： Termite queen algorithm Benchmarked functions Loss minimization Metaheuristic technique Engineering design problems

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Roadmap on Neuromorphic Photonics

arXiv

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arXiv 2025年

作者： Brunner, Daniel Shastri, Bhavin J. Al-Qadasi, Mohammed A. Ballani, H. Barbay, Sylvain Biasi, Stefano Bienstman, Peter Bilodeau, Simon Bogaerts, Wim Böhm, Fabian Brennan, G. Buckley, Sonia Cai, Xinlun Strinati, Marcello Calvanese Canakci, B. Charbonnier, Benoit Chemnitz, Mario Chen, Yitong Cheung, Stanley Chiles, Jeff Choi, Suyeon Christodoulides, Demetrios N. Chrostowski, Lukas Chu, J. Clegg, J.H. Cletheroe, D. Conti, Claudio Dai, Qionghai Di Lauro, Luigi Diamantopoulos, Nikolaos-Panteleimon Dinc, Niyazi Ulas Ewaniuk, Jacob Fan, Shanhui Fang, Lu Franchi, Riccardo Freire, Pedro Gentilini, Silvia Gigan, Sylvain Giorgi, Gian Luca Gkantsidis, C. Gladrow, J. Goi, Elena Goldmann, M. Grabulosa, A. Gu, Min Guo, Xianxin Hejda, Matěj Horst, F. Hsieh, Jih-Liang Hu, Jianqi Hu, Juejun Huang, Chaoran Hurtado, Antonio Jaurigue, Lina Kalinin, K.P. Kamalian-Kopae, Morteza Kelly, D.J. Khajavikhan, Mercedeh Kremer, H. Laydevant, Jeremie Lederman, Joshua C. Lee, Jongheon Lenstra, Daan Li, Gordon H.Y. Li, Mo Li, Yuhang Lin, Xing Lin, Zhongjin Lis, Mieszko Lüdge, Kathy Lugnan, Alessio Lupo, Alessandro Lvovsky, A.I. Manuylovich, Egor Marandi, Alireza Marchesin, Federico Massar, Serge McCaughan, Adam N. McMahon, Peter L. Moralis-Pegios, Miltiadis Morandotti, Roberto Moser, Christophe Moss, David J. Mukherjee, Avilash Nikdast, Mahdi Offrein, B.J. Oguz, Ilker Oripov, Bakhrom O'Shea, G. Ozcan, Aydogan Parmigiani, F. Pasricha, Sudeep Pavanello, Fabio Pavesi, Lorenzo Peserico, Nicola Pickup, L. Pierangeli, Davide Pleros, Nikos Porte, Xavier Primavera, Bryce A. Prucnal, Paul Psaltis, Demetri Puts, Lukas Qiao, Fei Rahmani, B. Raineri, Fabrice Ríos Ocampo, Carlos A. Robertson, Joshua Romeira, Bruno Roques-Carmes, Charles Rotenberg, Nir Rowstron, A. Schoenhardt, Steffen Schwartz, Russell L.T. Shainline, Jeffrey M. Shekhar, Sudip Skalli, A. Sohoni, Mandar M. Sorger, Volker J. Soriano, Miguel C. Spall, James Stabile, Ripalta Stiller, Birgit Sunada, Satoshi Tefas, Anastasios Tossoun, Bassem Tsakyridis, Apostolos Turitsyn, Sergei K. Van der Sande, G Université Marie et Louis Pasteur CNRS UMR 6174 Institut FEMTO-ST Besançon25000 France Centre for Nanophotonics Department of Physics Engineering Physics & Astronomy Queen's University Canada Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Microsoft Research Cambridge United Kingdom Université Paris-Saclay CNRS Centre de Nanosciences et de Nanotechnologies France Nanoscience Laboratory Department of Physics University of Trento Italy Photonics Research Group Department of Information Technology Ghent University imec Belgium Princeton University NJ United States Hewlett Packard Labs Hewlett Packard Enterprise Böblingen Germany National Institute of Standards and Technology BoulderCO United States State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-sen University China Enrico Fermi Research Center Rome Italy Université Grenoble-Alpes CEA Leti Grenoble France Leibniz-Institute of Photonic Technology Jena Germany Institute of Applied Optics and Biophysics Jena Germany Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering North Carolina State University NC United States Department of Electrical Engineering Stanford University CA United States University of Southern California Los AngelesCA United States Department of Physics Sapienza University Rome Italy Institute for Complex Systems National Research Council Rome Italy Varennes Canada NTT Device Technology Labs NTT Corporation Kanagawa Atsugi Japan Institute of Electrical and Microengineering School of Engineering École Polytechnique Fédérale de Lausanne Switzerland Edward L. Ginzton Laboratory Stanford University StanfordCA United States Department of Electronic Engineering Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China Aston Univ

Neuromorphic photonics are processors inspired by the human brain and enabled by light (photons) instead of traditional electronics. Neuromorphic photonics and its associated concepts are experiencing a significant resurgence, building on foundational research from the 1980s and 1990s. This renewed momentum is driven by breakthroughs in photonic integration, nonlinear optics, and advanced materials, alongside the growing necessity of neuro-inspired computing in numerous applications of economic and societal relevance. The increasing demand for energy-efficient artificial intelligence (AI) solutions underscores the need for innovation and a cohesive vision to address key challenges, including scalability, energy efficiency, precision, and standardized performance benchmarks. Together, these efforts present an opportunity to establish a unique photonic advantage with practical, real-world applications. This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. The roadmap introduces various approaches to embedding the high-complexity transformations central to neuromorphic computing, focusing on frequency, delay, and spectral embeddings. This is followed by a discussion of architectures of photonic neural networks (PNNs) and an in-depth analysis of methods for implementing these architectures in photonic hardware. Dedicated sections delve into integrated photonic hardware, the realization of photonic weights and memories, and the optimization of training processes for photonic neuromorphic architectures. The roadmap concludes by exploring numerous potential applications, highlighting the challenges and advances necessary to transition neuromorphic photonic computing from a primarily academic pursuit to a technolog

关键词： Philosophical aspects

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Non-Hermitian chiral degeneracy of gated graphene metasurfaces

arXiv

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arXiv 2022年

作者： Baek, Soojeong Park, Sang Hyun Oh, Donghak Lee, Kanghee Lee, Sang-Ha Lim, Hosub Ha, Taewoo Park, Hyun-Sung Zhang, Shuang Yang, Lan Min, Bumki Kim, Teun-Teun Daejeon34141 Korea Republic of Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Harvard Institute of Medicine Harvard Medical School Harvard University Brigham and Women’s Hospital BostonMA02215 United States Sungkyunkwan University Suwon16419 Korea Republic of Samsung Advanced Institute of Technology Samsung Electronics Suwon16678 Korea Republic of Department of Physics University of Hong Kong 999077 Hong Kong Department of Electrical and Systems Engineering Washington University Saint LouisMO63130 United States Daejeon34141 Korea Republic of Department of Physics University of Ulsan Ulsan44610 Korea Republic of

Non-Hermitian degeneracies, also known as exceptional points (EPs), have been the focus of much attention due to their singular eigenvalue surface structure. Nevertheless, as pertaining to a non-Hermitian metasurface platform, the reduction of an eigenspace dimensionality at the EP has been investigated mostly in a passive repetitive manner. Here, we propose an electrical and spectral way of resolving chiral EPs and clarifying the consequences of chiral mode collapsing of a non-Hermitian gated graphene metasurface. More specifically, the measured non-Hermitian Jones matrix in parameter space enables the quantification of nonorthogonality of polarisation eigenstates and half-integer topological charges associated with a chiral EP. Interestingly, the output polarisation state can be made orthogonal to the coalesced polarisation eigenstate of the metasurface, revealing the missing dimension at the chiral EP. In addition, the maximal nonorthogonality at the chiral EP leads to a blocking of one of the cross-polarised transmission pathways and, consequently, the observation of enhanced asymmetric polarisation conversion. We anticipate that electrically controllable non-Hermitian metasurface platforms can serve as an interesting framework for the investigation of rich non-Hermitian polarisation dynamics around chiral EPs. © 2022, CC BY.

关键词： Graphene

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Long-Distance Continuous-Variable Quantum Key Distribution over 202.81 km of Fiber

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Physical Review Letters 2020年第1期125卷 010502-010502页

作者： Yichen Zhang Ziyang Chen Stefano Pirandola Xiangyu Wang Chao Zhou Binjie Chu Yijia Zhao Bingjie Xu Song Yu Hong Guo State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing 100876 China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics and Center for Quantum Information Technology Peking University Beijing 100871 China Department of Computer Science University of York York YO10 5GH United Kingdom Science and Technology on Security Communication Laboratory Institute of Southwestern Communication Chengdu 610041 China

Quantum key distribution provides secure keys resistant to code-breaking quantum computers. The continuous-variable version of quantum key distribution offers the advantages of higher secret key rates in metropolitan areas, as well as the use of standard telecom components that can operate at room temperature. However, the transmission distance of these systems (compared with discrete-variable systems) are currently limited and considered unsuitable for long-distance distribution. Herein, we report the experimental results of long distance continuous-variable quantum key distribution over 202.81 km of ultralow-loss optical fiber by suitably controlling the excess noise and employing highly efficient reconciliation procedures. This record-breaking implementation of the continuous-variable quantum key distribution doubles the previous distance record and shows the road for long-distance and large-scale secure quantum key distribution using room-temperature standard telecom components.

关键词： Optical quantum information processing Quantum cryptography Quantum information processing with continuous variables Quantum optics

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MBLLEn: Low-light image/video enhancement using CNNs 29

MBLLEn: Low-light image/video enhancement using CNNs

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29th British Machine Vision Conference, BMVC 2018

作者： Lv, Feifan Lu, Feng Wu, Jianhua Lim, Chongsoon State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China Panasonic R and D Center Singapore Singapore City Singapore

We present a deep learning based method for low-light image enhancement. This problem is challenging due to the difficulty in handling various factors simultaneously including brightness, contrast, artifacts and noise. To address this task, we propose the multi-branch low-light enhancement network (MBLLEN). The key idea is to extract rich features up to different levels, so that we can apply enhancement via multiple subnets and finally produce the output image via multi-branch fusion. In this manner, image quality is improved from different aspects. Through extensive experiments, our proposed MBLLEN is found to outperform the state-of-art techniques by a large margin. We additionally show that our method can be directly extended to handle low-light videos. © 2018. The copyright of this document resides with its authors.

关键词： Image enhancement

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Impact of environmental changes on the dynamics of temporal networks

arXiv

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arXiv 2020年

作者： Kim, Hyewon Jo, Hang-Hyun Jeong, Hawoong Asia Pacific Center for Theoretical Physics Pohang37673 Korea People's Democratic Rep Department of Physics Pohang University of Science and Technology Pohang37673 Korea People's Democratic Rep Department of Computer Science Aalto University EspooFI-00076 Finland Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea People's Democratic Rep Center for Complex Systems Korea Advanced Institute of Science and Technology Daejeon34141 Korea People's Democratic Rep

Dynamics of complex social systems has often been described in the framework of temporal networks, where links are considered to exist only at the moment of interaction between nodes. Such interaction patterns are not only driven by internal interaction mechanisms, but also affected by environmental changes. To investigate the impact of the environmental changes on the dynamics of temporal networks, we analyze several face-to-face interaction datasets using the multiscale entropy (MSE) method to find that the observed temporal correlations can be categorized according to the environmental similarity of datasets such as classes and break times in schools. By devising and studying a temporal network model considering a periodically changing environment as well as a preferential activation mechanism, we numerically show that our model could successfully reproduce various empirical results by the MSE method in terms of multiscale temporal correlations. Our results demonstrate that the environmental changes can play an important role in shaping the dynamics of temporal networks when the interactions between nodes are influenced by the environment of the systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamics

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Source-independent quantum random number generation with finite measurement range

Source-independent quantum random number generation with fin...

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Frontiers in Optics, FiO_2019

作者： Zheng, Ziyong Zhang, Yichen Zhao, Yijia Huang, Weinan Yu, Song Guo, Hong State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Center for Quantum Information Technology Peking University Beijing100871 China

We present a source-independent quantum random number generator based on the measurement of quadrature of quantum optical field by applying a modified entropic uncertainty relation to circumvent the measurement range ... 详细信息

ISBN: (纸本)9781943580675

关键词： Random number generation

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A 120 dB Dynamic Range Logarithmic Multispectral Imager for Near-Infrared Fluorescence Image-Guided Surgery

A 120 dB Dynamic Range Logarithmic Multispectral Imager for ...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Steven Blair Nan Cui Missael Garcia Viktor Gruev Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA Department of Electrical and Systems Engineering Washington University in St. Louis St. Louis MO USA Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Despite tremendous developments in preoperative imaging that have enabled physicians to identify cancers with remarkable precision, limited options for intraoperative imaging have left many surgeons ill-equipped to locate tumors in the operating room. Near-infrared fluorescence image-guided surgery has offered to use near-infrared fluorescent dyes and near-infrared sensitive cameras to highlight diseased tissue, but state-of-the-art imaging systems have been unable to provide both the single sensor architecture and the high dynamic range required to facilitate precise surgical procedures under operating room illumination. In seeking a solution, we have monolithically integrated an array of forward-biased photodiodes and an array of interference filters to provide a logarithmic photoresponse across four spectral channels. The resulting single-chip snapshot multispectral imaging system provides an instantaneous dynamic range of >120 dB and a maximum signal-to-noise ratio of ∼56 dB across three color channels and one near-infrared channel. Capable of detecting less than 50 nM of the clinically-relevant fluorescent dye indocyanine green, this image sensor offers the high performance required for real-world surgical application.

关键词： Photodiodes Optical filters Dynamic range Surgery Image sensors Interference Fluorescence

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