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arXiv 2024年

作者： Xing, Tianyi Liu, Junxuan Zhang, Likang Wang, Min-Yan Li, Yu-Huai Liu, Ruiyin Peng, Qingquan Wang, Dongyang Wang, Yaxuan Liu, Hongwei Li, Wei Cao, Yuan Huang, Anqi Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Hefei National Research Center for Physical Sciences The Microscale and School of Physical Sciences University of Science and Technology of China Hefei230026 China Shanghai Research Center for Quantum Science CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China China Information Technology Security Evaluation Center Beijing100085 China

One of the most significant vulnerabilities in the source unit of quantum key distribution (QKD) is the correlation between quantum states after modulation, which shall be characterized and evaluated for its practical security performance. In this work, we propose a methodology to characterize the intensity correlation according to the single-photon detection results in the measurement unit without modifying the configuration of the QKD system. In contrast to the previous research that employs extra classical optical detector to measure the correlation, our method can directly analyse the detection data generated during the raw key exchange, enabling to characterize the feature of correlation in real-time system operation. The basic method is applied to a BB84 QKD system and the characterized correlation significantly decreases the secure key rate shown by the security proof. Furthermore, the method is extended and applied to characterize the correlation from the result of Bell-state measurement, which demonstrates its applicability to a running full-scheme MDI QKD system. This study provides an approach for standard certification of a QKD system. Copyright © 2024, The Authors. All rights reserved.

关键词： Photons

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Maximal coin-walker entanglement in a ballistic quantum walk

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Physical Review A 2022年第4期105卷 042216-042216页

作者： Rong Zhang Ran Yang Jian Guo Chang-Wei Sun Jia-Chen Duan Heng Zhou Zhenda Xie Ping Xu Yan-Xiao Gong Shi-Ning Zhu National Laboratory of Solid State Microstructure School of Physics School of Electronic Science and Engineering and Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunication Nanjing 210023 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computing National University of Defense Technology Changsha 410073 China

We report that the position-inhomogeneous quantum walk (IQW) can be utilized to produce the maximal high-dimensional entanglement while maintaining the quadratic speedup spread of the wave function. Our calculations show that the maximal coin-walker entanglement can be generated in any odd steps or asymptotically in even steps, and the nearly maximal entanglement can be obtained in even steps after 2. We implement the IQW by a stable resource-saving time-bin optical network, in which a polarization Sagnac loop is employed to realize the precisely tunable phase shift. Our approach opens up an efficient way for high-dimensional entanglement engineering as well as promotes investigations on the role of coin-walker interactions in QW-based applications.

关键词： Entanglement manipulation Optical quantum information processing Quantum optics Quantum walks

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SMART: towards pre-trained missing-aware model for patient health status prediction 24

SMART: towards pre-trained missing-aware model for patient h...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhihao Yu Xu Chu Yujie Jin Yasha Wang Junfeng Zhao School of Computer Science Peking University and National Research and Engineering Center of Software Engineering Peking University School of Computer Science Peking University and Center on Frontiers of Computing Studies Peking University Beijing China and Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China and Peking University Information Technology Institute (Tianjin Binhai) National Research and Engineering Center of Software Engineering Peking University and Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China and Peking University Information Technology Institute (Tianjin Binhai) School of Computer Science Peking University and National Research and Engineering Center of Software Engineering Peking University and Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China and Nanhu Laboratory Jiaxing China

ISBN: (纸本)9798331314385

Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status. However, the prevalence of missing data in EHR poses significant challenges to existing methods, leading to spurious correlations and suboptimal predictions. While various imputation techniques have been developed to address this issue, they often obsess difficult-to-interpolate details and may introduce additional noise when making clinical predictions. To tackle this problem, we propose SMART, a Self-Supervised Missing-Aware RepresenTation Learning approach for patient health status prediction, which encodes missing information via missing-aware temporal and variable attentions and learns to impute missing values through a novel self-supervised pre-training approach which reconstructs missing data representations in the latent space rather than in input space as usual. By adopting elaborated attentions and focusing on learning higher-order representations, SMART promotes better generalization and robustness to missing data. We validate the effectiveness of SMART through extensive experiments on six EHR tasks, demonstrating its superiority over state-of-the-art methods. Our code is available at https://***/yzhHoward/SMART.

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A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking

A Novel Deep Learning Approach Featuring Graph-Based Algorit...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Keliang Zhao Jovial Niyogisubizo Linxia Xiao Yi Pan Dongqing Wei Didi Rosiyadi Yanjie Wei Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China Research Center for Artificial Intelligence and Cybersecurity National Research and Innovation Agency Bandung Indonesia

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs.

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Meta Actor-Critic Framework for Multi-Agent Reinforcement Learning 21

Meta Actor-Critic Framework for Multi-Agent Reinforcement Le...

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Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition

作者： Jiateng Huang Wanrong Huang Dan Wu Long Lan Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology China Artifcial Intelligence Research Center National Innovation Institute of Defense Technology China College of Computer & Hefei Interdisciplinary Center National University of Defense Technology China

ISBN: (纸本)9781450384087

In recent years, multi-agent reinforcement learning has received sustained attention in the last few years. The typical Actor-Critic methods learn mappings directly from observation to action without understanding the tasks themselves. In this paper, we present a meta actor-critic framework for meta-actor critique based on observational learning processes and the additional loss of meta-learning actors to accelerate and improve multi-agent learning across agents' experiences. Within our framework, all agents are deliberately designed to share the same meta-critic loss to achieve the optimum actor learning progress. Meanwhile, by minimizing the loss of meta-actors, the meta actor learns the features of the meta-observation, leading to better actions. We implemented the MADDPG and MATD3 algorithms in our proposed framework and empirically demonstrated the superiority of our framework on two kinds of multi-agent tasks. In addition, the framework can be flexibly incorporated into various contemporary multi-agent Actor-Critic methods.

关键词： Reinforcement learning meta-learning multi-agent system

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A Segmentation-aware Synergy Network for Single Particle Recognition in Cryo-EM

A Segmentation-aware Synergy Network for Single Particle Rec...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuo Li Hongjia Li Chi Zhang Fa Zhang Xiaohua Wan High Performance Computer Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of CAD&CG Zhejiang University Hangzhou China Beijing Institute of Technology Beijing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Cryo-electron microscopy (cryo-EM) single particle analysis (SPA) has been an indispensable technology to reconstruct three-dimensional (3D) structures of biomolecules at near-atomic resolution. Tens of thousands of particles are required to obtain high-resolution 3D reconstructions, nevertheless, it is rather challenging due to the extremely noisy microscopy images and the diversity of particles. Recently, while deep learning-based methods have been devoted into the improvement of particle feature extraction and location estimation, most of them are plagued with vulnerable feature representation, inexact supervised ground truth. Furthermore, these DL-methods usually adopt denoising and particle picking as two-stage operations in the existing pipeline, which is inadequate to achieve accurate estimation for location. In this paper, we propose a segmentation-aware synergy framework to automatically select particles in which two tightly-coupled networks are designed including a multiple output convolution subnet for denoise to jointly learn strong object representation and pixel representation simultaneously and a deep convolution subnet for particle location. Furthermore, joint learning of the two networks can effectively enhance the synergy relationship between denoising and downstream recognition, thus leading to accurate and reliable location estimations for SPA. When applied with various EMPAIR real-world datasets, our model improves the performance of particle detection and exaction, especially intersection over union metric, and this strength has important implications for the next 2D alignment, 2D classification averaging, and high-resolution 3D refinement steps in SPA.

关键词： Solid modeling Three-dimensional displays Convolution Microscopy Noise reduction Pipelines Estimation

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Large Tunneling Magnetoresistance in Nonvolatile 2D Hybrid Spin Filters

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Physical Review Letters 2025年第7期134卷 077001-077001页

作者： Xiaoyu Wang Lihao Zhang Miao He Qi Li Wenqin Song Kunlin Yang Shuxi Wang Takashi Taniguchi Kenji Watanabe MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter Shaanxi Province Key Laboratory of Advanced Materials and Mesoscopic Physics School of Physics Xi’an Jiaotong University Xi’an 710049 China Anhui Key Laboratory of Low-Energy Quantum Materials and Devices High Magnetic Field Laboratory Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei 230031 China Science Island Branch of Graduate School University of Science and Technology of China Hefei 230026 China Key Laboratory of Flexible Electronics and Institute of Advanced Materials Nanjing Tech University Nanjing 211816 China State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China Research Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Research Center for Electronic and Optical Materials National Institute for Materials Science Tsukuba 305-0044 Japan

Ferromagnetic semiconductors offer an efficient way to achieve high spin polarization via spin filtering effect. Large tunneling magnetoresistance (TMR) can then be realized when multiple spin filters are put in series, as recently demonstrated in van der Waals 2D A-type antiferromagnets such as CrI3 and CrSBr. However, the interlayer antiferromagnetic ground state of these magnets inherently results in a high resistance state at zero field, and this volatile behavior limits potential applications. Here we fabricate hybrid spin filters using 2D ferromagnetic metal Fe3GeTe2 and semiconductor CrBr3, which are nonvolatile as two magnets are magnetically decoupled. We achieve large TMR of around 100%, with its temperature dependence well fitted by the extended Jullière model. Additionally, the devices allow spin injection tuned through bias voltage, and TMR polarity reversals are observed. Our work opens a new route to develop 2D magnetic semiconductor based spintronics.

关键词： Van der Waals forces

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Circuit area optimization of multi-output MPRM based on ERWOA algorithm

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Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics 2023年第5期49卷 1193-1200页

作者： He, Juncai He, Zhenxue Wang, Fushun Huo, Zhisheng Xiao, Limin Intelligent Agricultural Equipment Research Institute Hebei Agricultural University Baoding071001 China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University Baoding071001 China High Performance Computing Platform Beihang University Beijing100191 China School of Software Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

Mixed polarity Reed-Muller (MPRM) circuit area optimization has become a research hotspot in the field of integrated circuit design. It is a combinatorial optimization, aiming at finding the MPRM expression with the least number of terms among many MPRM expressions. A explosion strategy and restart strategy based whale optimization algorithm (ERWOA) is proposed. In addition, a multi-output MPRM circuit area optimization method is proposed, which uses the improved whale algorithm and the improved polarity conversion algorithm to search for the MPRM circuit with the least number of AND terms. Results on the MCNC Benchmark circuits show that the proposed algorithm increases the conversion efficiency by 99.93% and 99.96% at most, compared with the mixed polarity and inter-polarity conversion algorithms based on the list technology, respectively. Compared with the genetic algorithm and the artificial bee colony algorithm, the improved whale optimization algorithm saves the circuit area up to 18.32% with an average of 5.54%, and 14.41% with an average of 5.00%, respectively. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.

关键词： Genetic algorithms

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Association Pattern-enhanced Molecular Representation Learning 39

Association Pattern-enhanced Molecular Representation Learni...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jia, Lingxiang Ying, Yuchen Qiu, Tian Yao, Shaolun Xue, Liang Lei, Jie Song, Jie Song, Mingli Feng, Zunlei State Key Laboratory of Blockchain and Data Security Zhejiang University China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security China Computing Science and Artificial Intelligence College Suzhou City University China College of Computer Science Zhejiang University of Technology China

ISBN: (纸本)157735897X

The applicability of drug molecules in various clinical scenarios is significantly influenced by a diverse range of molecular properties. By leveraging self-supervised conditions such as atom attributes and interatomic bonds, existing advanced molecular foundation models can generate expressive representations of these molecules. However, such models often overlook the fixed association patterns within molecules that influence physiological or chemical properties. In this paper, we introduce a novel association pattern-aware message passing method, which can serve as an effective yet general plug- and-play plugin, thereby enhancing the atom representations generated by molecular foundation models without requiring additional pretraining. Additionally, molecular property-specific pattern libraries are constructed to collect the generated interpretable common patterns that bind to these properties. Extensive experiments conducted on 11 benchmark molecular property prediction tasks across 8 advanced molecular foundation models demonstrate significant superiority of the proposed method, with performance improvements of up to approximately 20%. Furthermore, a property-specific pattern library is tailored for blood-brain barrier penetration, which has undergone corresponding mechanistic validation. Copyright © 2025, Association for the Advancement of Artificial Intelligence.

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Graph adversarial self-supervised learning 21

Graph adversarial self-supervised learning

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

作者： Longqi Yang Liangliang Zhang Wenjing Yang Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China and Defense Innovation Institute Beijing China Institute of Systems Engineering AMS Beijing China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9781713845393

This paper studies a long-standing problem of learning the representations of a whole graph without human supervision. The recent self-supervised learning methods train models to be invariant to the transformations (views) of the inputs. However, designing these views requires the experience of human experts. Inspired by adversarial training, we propose an adversarial self-supervised learning (GASSL) framework for learning unsupervised representations of graph data without any handcrafted views. GASSL automatically generates challenging views by adding perturbations to the input and are adversarially trained with respect to the encoder. Our method optimizes the min-max problem and utilizes a gradient accumulation strategy to accelerate the training process. Experimental on ten graph classification datasets show that the proposed approach is superior to state-of-the-art self-supervised learning baselines, which are competitive with supervised models.

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