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arXiv 2022年

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao The Chien-Shiung Wu College Southeast University Nanjing210096 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Mathematical Science Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Systems Engineering National University of Defense Technology China The 52nd Research Institute of China Electronics Technology Group China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://***/Chongjie-Si/HOMI. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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Hamiltonian Hopping for Efficient Chiral Mode Switching in Encircling Exceptional Points

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Physical Review Letters 2020年第18期125卷 187403-187403页

作者： Aodong Li Jianji Dong Jian Wang Ziwei Cheng John S. Ho Dawei Zhang Jing Wen Xu-Lin Zhang C. T. Chan Andrea Alù Cheng-Wei Qiu Lin Chen Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Engineering Research Center of Optical Instrument and Systems Ministry of Education and Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology No. 516 Jungong Road Shanghai 200093 China State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun 130012 China Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Hong Kong 999077 China Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Dynamically encircling exceptional points (EPs) can lead to chiral mode switching as the system parameters are varied along a path that encircles EP. However, conventional encircling protocols result in low transmittance due to path-dependent losses. Here, we present a paradigm to encircle EPs that includes fast Hamiltonian variations on the parameter boundaries, termed Hamiltonian hopping, enabling ultrahigh-efficiency chiral mode switching. This protocol avoids path-dependent loss and allows us to experimentally demonstrate nearly 90% efficiency at 1550 nm in the clockwise direction, overcoming a long-standing challenge of non-Hermitian optical systems and powering up new opportunities for EP physics.

关键词： Open quantum systems & decoherence PT-symmetric quantum mechanics Photonics Waveguides

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P-122: High Quantum Yield Green and Red CdSe/CdS Dot-in-Rods and Their Electroluminescent Light Emitting Diodes

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SID Symposium Digest of Technical Papers 2019年第1期50卷

作者： Ziming Zhou Haochen Liu Junjie Hao Kai Wang Kie Leong Teo Xiao Wei Sun Department of Electrical and Electronic Engineering Southern University of Science and Technology Guangdong University Key Lab for Advanced Quantum Dot Display and Lighting Shenzhen Key Lab for Advanced Quantum Dot Display and Lighting Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Shenzhen Planck Innovation Technologies Co. Ltd. Shenzhen Guangdong China

We successfully synthesized CdSe/CdS dot-in-rods (DiRs) with a high quantum yield (QY, 70% and 65% for green and red). And these DiRs were further utilized to fabricate DiRs electroluminescent (EL) light emitting diodes (LEDs), as the first-time report for green LED and record-high performance for red LED. This kind of DiRs can be utilized for high efficiency and color gamut display technologies.

关键词： Dot-in-rods quantum yield electroluminescence light emitting diodes

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Hybridization gap approaching the two-dimensional limit of topological insulator BixSb1−x

arXiv

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arXiv 2024年

作者： Corbae, Paul Engel, Aaron N. Dong, Jason T. Yánez-Parreño, Wilson J. Lu, Donghui Hashimoto, Makoto Fedorov, Alexei Palmstrøm, Chris J. Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Quantum Foundry University of California Santa BarbaraCA93106 United States Department of Materials University of California Santa BarbaraCA93106 United States Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Advanced Light Source Lawrence Berkeley National Lab BerkeleyCA94702 United States

Bismuth antimony alloys (BixSb1−x) provide a tuneable materials platform to study topological transport and spin-polarized surface states resulting from the nontrivial bulk electronic structure. In the two-dimensional limit, it is a suitable system to study the quantum spin Hall effect. In this work we grow epitaxial, single orientation thin films of BixSb1−x on an InSb(111)B substrate down to two bilayers where hybridization effects should gap out the topological surface states. Supported by a tight-binding model, spin- and angle-resolved photoemission spectroscopy data shows pockets at the Fermi level from the topological surface states disappear as the bulk gap increases from confinement. Evidence for a gap opening in the topological surface states is shown in the ultrathin limit. Finally, we observe spin-polarization approaching unity from the topological surface states in 10 bilayer films. The growth and characterization of ultrathin BixSb1−x alloys suggest ultrathin films of this material system can be used to study two-dimensional topological physics as well as applications such as topological devices, low power electronics, and spintronics. Copyright © 2024, The Authors. All rights reserved.

关键词： Antimony alloys

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An Explainable Deep Neural Network Model Based on Multi-Granularity Co-Attention to Improve Prediction of Drug-Target Binding Affinity

SSRN

引用

SSRN 2024年

作者： Lin, Yanmei Cao, Aiqing Deng, Lei Liao, Mingzhi Zhang, Ru Zhang, Hao Peng, Yuzhong Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530001 China Guangxi Academy of Sciences Nanning530007 China School of Computer Science and Engineering Central South University Changsha410083 China Center of Bioinformatics College of Life Sciences Northwest A&F University Shaanxi Yangling712100 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518000 China Center for Applied Mathematics of Guangxi Nanning Normal University Nanning530100 China

The prediction of drug-target interactions is pivotal in modern new drug development and drug repositioning. Nonetheless, current methods for predicting drug-target interactions often lose essential molecular structure information during the modeling process, leading to low prediction accuracy. This work proposes rMolGIN, a novel graph neural network with residual connectivity for molecular feature learning. rMolGIN not only takes into account the differences in chemical bonding properties and atomic neighborhood interactions, but also captures global and local structural information of drug molecules. Furthermore, we propose a novel drug-target affinity model based on rMolGIN and deep neural networks, coupled with a multi-granularity co-attention mechanism called HMCoDTA to capture comprehensive structural insights from both drugs and proteins. The proposed innovative model allows for comprehensive insight into the structural relationships between drugs and proteins, facilitating a more accurate prediction of binding affinities. To validate the effectiveness of our proposed methods, we conducted extensive experiments on three benchmark datasets of drug-target binding affinity, comparing HMCoDTA against 19 state-of-the-art models. Notably, the results show that HMCoDTA outperforms existing methods, demonstrating the effectiveness and advantages of our model in predicting drug-target binding affinity. © 2024, The Authors. All rights reserved.

关键词： Prediction models

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SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for prior-informed assessment of muscle function and pathology

arXiv

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arXiv 2022年

作者： Mühlberg, Alexander Ritter, Paul Langer, Simon Goossens, Chloë Nübler, Stefanie Schneidereit, Dominik Taubmann, Oliver Denzinger, Felix Nörenberg, Dominik Haug, Michael Goldmann, Wolfgang H. Maier, Andreas K. Friedrich, Oliver Kreiss, Lucas Institute of Medical Biotechnology Department of Chemical and Biological Engineering Friedrich-Alexander University Erlangen-Nuremberg Germany Erlangen Graduate School in Advanced Optical Technologies Germany Pattern Recognition Lab Department of Computer Science Friedrich-Alexander University Erlangen-Nuremberg Germany Clinical Division Laboratory of Intensive Care Medicine KU Leuven Belgium Department of Radiology and Nuclear Medicine University Medical Center Mannheim Medical Faculty Mannheim Germany Biophysics Group Department of Physics Friedrich-Alexander University Erlangen-Nuremberg Germany

Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as a black box, exclude biomedical experts, and need extensive data. We introduce the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI), that integrates hypothesis-driven priors in a data-driven DL approach for research on multiphoton microscopy (MPM) of muscle fibers. SEMPAI utilizes meta-learning to optimize prior integration, data representation, and neural network architecture simultaneously. This allows hypothesis testing and provides interpretable feedback about the origin of biological information in MPM images. SEMPAI performs joint learning of several tasks to enable prediction for small datasets. The method is applied on an extensive multi-study dataset resulting in the largest joint analysis of pathologies and function for single muscle fibers. SEMPAI outperforms state-of-the-art biomarkers in six of seven predictive tasks, including those with scarce data. SEMPAI’s DL models with integrated priors are superior to those without priors and to prior-only machine learning approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Muscle

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Deep learning for image and point cloud fusion in autonomous driving: A review

arXiv

引用

arXiv 2020年

作者： Cui, Yaodong Chen, Ren Chu, Wenbo Chen, Long Tian, Daxin Li, Ying Cao, Dongpu The Waterloo CogDrive Lab Department of Mechanical and Mechatronics Engineering University of Waterloo 200 University Ave West WaterlooONN2L3G1 Canada Research Institute Co. Ltd. Beijing100176 China School of Data and Computer Science Sun Yat-sen University Zhuhai519082 China Waytous Inc. Qingdao266109 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China

Autonomous vehicles were experiencing rapid development in the past few years. However, achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic driving environment. Therefore, autonomous vehicles are equipped with a suite of different sensors to ensure robust, accurate environmental perception. In particular, the camera-LiDAR fusion is becoming an emerging research theme. However, so far there has been no critical review that focuses on deep-learning-based camera-LiDAR fusion methods. To bridge this gap and motivate future research, this paper devotes to review recent deep-learning-based data fusion approaches that leverage both image and point cloud. This review gives a brief overview of deep learning on image and point cloud data processing. Followed by in-depth reviews of camera-LiDAR fusion methods in depth completion, object detection, semantic segmentation, tracking and online cross-sensor calibration, which are organized based on their respective fusion levels. Furthermore, we compare these methods on publicly available datasets. Finally, we identified gaps and over-looked challenges between current academic researches and real-world applications. Based on these observations, we provide our insights and point out promising research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Object detection

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A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training

arXiv

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arXiv 2023年

作者： Wang, Hanwen Qi, Yu Yao, Lin Wang, Yueming Farina, Dario Pan, Gang The College of Computer Science and Technology Zhejiang University Hangzhou China The Affiliated Mental Health Center Hangzhou Seventh People's Hospital The MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University School of Medicine The State Key Lab of Brain-Machine Intelligence Hangzhou China The Department of Neurobiology Affiliated Mental Health Center Hangzhou Seventh People's Hospital Zhejiang University School of Medicine MOE Frontiers Science Center for Brain and Brain-Machine Integration Zhejiang University Department of Biomedical Engineering Zhejiang University College of Computer Science and Technology Hangzhou China The Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China The Department of Bioengineering Imperial College London London United Kingdom The State Key Lab of Brain-Machine Intelligence The College of Computer Science and Technology Zhejiang University Hangzhou China The First Affiliated Hospital Zhejiang University Hangzhou China

Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as motor imagery (MI) BCIs, can provide some level of control. However, mastering spontaneous BCI control requires the users to generate discriminative and stable brain signal patterns by imagery, which is challenging and is usually achieved over a very long training time (weeks/months). Here, we propose a human-machine joint learning framework to boost the learning process in endogenous BCIs, by guiding the user to generate brain signals towards an optimal distribution estimated by the decoder, given the historical brain signals of the user. To this end, we firstly model the human-machine joint learning process in a uniform formulation. Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel "copy/new" feedback paradigm to help shape the signal generation of the subject toward the optimal distribution;2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process. Specifically, the decoder reweighs the brain signals generated by the subject to focus more on "good" samples to cope with the learning process of the subject. Online and psuedo-online BCI experiments with 18 healthy subjects demonstrated the advantages of the proposed joint learning process over co-adaptive approaches in both learning efficiency and effectiveness. © 2023, CC BY.

关键词： Electroencephalography

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Diffusive topological transport in spatiotemporal thermal lattices

arXiv

引用

arXiv 2021年

作者： Xu, Guoqiang Yang, Yihao Zhou, Xue Chen, Hongsheng Alù, Andrea Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Kent Ridge117583 Singapore Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou310027 China International Joint Innovation Center ZJU-UIUC Institute The Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center of the City University of New York New YorkNY10016 United States

Topological insulating phases are usually found in periodic lattices stemming from collective resonant effects, and it may thus be expected that similar features may be prohibited in thermal diffusion, given its purely dissipative and largely incoherent nature. We report the diffusion-based topological states supported by spatiotemporally-modulated advections stacked over a fluidic surface, thereby imitating a periodic propagating potential in effective thermal lattices. We observe edge and bulk states within purely nontrivial and trivial lattices, respectively. At interfaces between these two types of lattices, the diffusive system exhibits interface states, manifesting inhomogeneous thermal properties on the fluidic surface. Our findings establish a framework for topological diffusion and thermal edge/bulk states, and it may empower a distinct mechanism for flexible manipulation of robust heat and mass transfer. © 2021, CC BY.

关键词： Interface states

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Cardiovascular disease identification using a hybrid CNN-LSTM model with explainable AI

引用

Informatics in Medicine Unlocked 2023年 42卷

作者： Hossain, Md Maruf Ali, Md Shahin Ahmed, Md Mahfuz Rakib, Md Rakibul Hasan Kona, Moutushi Akter Afrin, Sadia Islam, Md Khairul Ahsan, Md Manjurul Raj, Sheikh Md Razibul Hasan Rahman, Md Habibur Department of Biomedical Engineering Islamic University Kushtia 7003 Bangladesh Department of Food and Nutrition Government College of Applied Human Science Azimpur Dhaka 1205 Bangladesh Department of Radiology Northwestern University Machine and Hybrid Intelligence Lab Chicago 60611 IL United States Industrial & Production Engineering Shahjalal University of Science and Technology Sylhet 3114 Bangladesh Department of Information and Communication Technology Islamic University Kushtia 7003 Bangladesh Department of Computer Science and Engineering Islamic University Kushtia 7003 Bangladesh Center for Advanced Bioinformatics and Artificial Intelligent Research Islamic University Kushtia 7003 Bangladesh

Cardiovascular disease (CVD) is a leading cause of death worldwide, with millions dying each year. The identification and early diagnosis of CVD are critical in preventing adverse health outcomes. Hence, this study proposes a hybrid deep learning (DL) model that combines a convolutional neural network (CNN) and long short-term memory (LSTM) to identify CVD from the clinical data. This study utilizes CNN to extract the relevant features from the input data and the LSTM network to process sequential data and capture dependencies and patterns over time. This study provides insights into the potential of a hybrid DL model combined with feature engineering and explainable AI to improve the accuracy and interpretability of CVD prediction. We evaluated our model on a publicly available dataset where the proposed CNN-LSTM achieved a high accuracy of 73.52% and 74.15% with and without feature engineering, respectively, in identifying individuals with CVD, which is the best result compared to the current state-of-the-art model. The results of this study demonstrate the potential of DL models for the early diagnosis of CVD. Our proposed CNN-LSTM model also incorporates explainable AI to identify the top features responsible for CVD. They could be used to develop more eﬀective screening tools in clinical practice. © 2023 The Authors

关键词： Cardiovascular disease CNN-LSTM Deep learning Explainable AI Feature engineering

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