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Multipartite high-dimensional quantum state engineering via discrete-time quantum walk

Physical Review A 2024年第3期109卷 032229-032229页

作者： Junhong Nie Meng Li Xiaoming Sun State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China and School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100049 China

Quantum state engineering, namely, the generation and control of arbitrary quantum states, is drawing more and more attention due to its wide applications in quantum information and computation. However, there is no general method in theory, and the existing schemes also depend heavily on the selected experimental platform. In this article, we give two schemes for the generating task of arbitrary quantum state in c-partite d-dimensional system, both of which are based on a discrete-time quantum walk with a 2c-dimensional time- and position-dependent coin. The first procedure is a d-step quantum walk where all the d coins are nonidentity, while the second procedure is an O(d)-step quantum walk where only O(logd) coins are nonidentity. A concrete example of preparing generalized Bell states is given to demonstrate the first scheme we proposed. Furthermore, the first scheme can be applied to give an alternative approach to the quantum state preparation problem which is one of the fundamental tasks of quantum information processing. We design circuits for quantum state preparation with the help of our quantum state engineering scheme that match the best current result in both size and depth of the circuit asymptotically.

关键词： Quantum circuits Quantum communication Quantum engineering Quantum state engineering Quantum walks

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Medical Image Fusion Based on Adaptive Pulse Coupled Neural Networks and Nonsubsampled Contourlet Transform 6

Medical Image Fusion Based on Adaptive Pulse Coupled Neural ...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Chen, Rushan Huang, Mengxing Zhang, Yu Wu, Yuanyuan Hainan University State Key Lab. of Mar. Rsrc. Utiliz. in S. China Sea Rsrc. Coll. of Info. Science and Technology Haikou China Hainan University College of Computer Science and Technology Haikou China

ISBN: (纸本)9798350325485

Multi-modal medical image fusion can effectively integrate imaging information from multiple sensors and has become a research hotspot in medical image processing in recent years. In this paper, a new multimodal medical image fusion method based on adaptive pulse-coupled neural network (PCNN) and nonsubsampled contour wave transform (NSCT) is proposed. In the proposed, the NSCT decomposition is first performed on medical source images to obtain high-frequency subbands and low-frequency subbands. Secondly, the fusion strategy of phase coherence model for feature extraction of low frequency sub-bands can effectively retain the energy information of the source image. In addition, a new high-frequency subband fusion rule is designed to customize the decomposed high-frequency sublayer, and further optimized for the different frequency band characteristics of the decomposed obtained sublayer. And a new input parameter adaption is used to improve the PCNN model as a strategy for high frequency coefficient fusion. Finally,the fused image is reconstructed by performing inverse NSCT on the fused high-frequency and low-frequency bands. Experiments are conducted by performing on a publicly accessible brain image dataset. The experimental results show that the algorithm achieves positive performance in terms of visual quality and the objective indicators of overall Cross entropy (OCE), structural similarity (SSIM), feature mutual information (FMI) and visual fidelity (VIFF) have reached the best level. © 2023 IEEE.

关键词： Contourlet transform

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A New Framework for Bounding Reachability Probabilities of Continuous-time Stochastic Systems

arXiv

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arXiv 2023年

作者： Xue, Bai State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

This manuscript presents an innovative framework for constructing barrier functions to bound reachability probabilities for continuous-time stochastic systems described by stochastic differential equations (SDEs). The reachability probabilities considered in this paper encompass two aspects: the probability of reaching a set of specified states within a predefined finite time horizon, and the probability of reaching a set of specified states at a particular time instant. The barrier functions presented in this manuscript are developed either by relaxing a parabolic partial differential equation that characterizes the exact reachability probability or by applying the Grönwall's inequality. In comparison to the prevailing construction method, which relies on Doob's non-negative supermartingale inequality (or Ville's inequality), the proposed barrier functions provide stronger alternatives, complement existing methods, or fill gaps. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Relectrode: A reconfigurable electrode for multi-purpose sensing based on microfluidics 21

Relectrode: A reconfigurable electrode for multi-purpose sen...

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2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Sun, Wei Chen, Yanjun Zhan, Simon Han, Teng State Key Laboratory of Computer Science Beijing Key Lab of Human-Computer Interaction Institute of SoftwareChinese Academy of Sciences Beijing China

ISBN: (纸本)9781450380966

In this paper, we propose a reconfgurable electrode, RElectrode, using a microfuidic technique that can change the geometry and material properties of the electrode to satisfy the needs for sensing a variety of diferent types of user input through touch/touchless gestures, pressure, temperature, and distinguish between diferent types of objects or liquids. Unlike the existing approaches, which depend on the specifc-shaped electrode for particular sensing (e.g., coil for inductive sensing), RElectrode enables capacity, inductance, resistance/pressure, temperature, pH sensings all in a single package. We demonstrate the design and fabrication of the microfuidic structure of our RElectrode, evaluate its sensing performance through several studies, and provide some unique applications. RElectrode demonstrates technical feasibility and application values of integrating physical and biochemical properties of microfuidics into novel sensing interfaces. © 2021 ACM.

关键词： Electrodes

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Affinity Propagation Hierarchical Memetic Algorithm for Multimodal Multi-Objective Flexible Job Shop Scheduling With Variable Speed

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IEEE Transactions on Evolutionary Computation 2024年

作者： Luo, Cong Li, Xinyu Gong, Wenyin Gao, Liang Huazhong University of Science and Technology State Key Lab of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Wuhan430074 China China University of Geosciences School of Computer Science Wuhan430074 China

The flexible job shop scheduling, as the most typical production mode in industrial manufacturing, aims to improve production efficiency. However, the proposal of energy-saving and emission-reduction policy implies that it is impossible to increase the processing speed to improve productivity, and energy consumption is also becoming another important optimization objective. For the multi-objective flexible job shop scheduling problem, the optimization process tends to converge faster in some regions. This is because different scheduling sequences obtain the same objective values, i.e. there is a multimodal characteristic, which is still hardly investigated. Therefore, optimizing the decision space and the objective space simultaneously has become an urgent challenge that needs to be solved. To overcome the above challenges, we model the multimodal multi-objective flexible job shop scheduling problem with variable speed (MMFJSP-S) and propose an affinity propagation hierarchical memetic algorithm (APHMA) to minimize makespan and total energy consumption. Firstly, four problem-specific neighborhood structures are employed to enhance the convergence;Then, an affinity propagation clustering combined with the random forests strategy is proposed to classify the global and local Pareto sets;Finally, a hierarchical environmental selection strategy is designed to ensure the convergence and diversity in the decision and objective spaces. Evaluations against seven advanced algorithms on MK and DP benchmarks demonstrate the competitive performance of APHMA in solving MMFJSP-S. © 1997-2012 IEEE.

关键词： Scheduling algorithms

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Retrospection on the Performance Analysis Tools for Large-Scale HPC Programs

Retrospection on the Performance Analysis Tools for Large-Sc...

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International Conference on High Performance Computing

作者： Zhibo Xuan Xin You Hailong Yang Mingzhen Li Zhongzhi Luan Yi Liu Depei Qian School of Computer Science and Engineering Beihang University China State Key Lab of Processors Institute of Computing Technology Chinese Academy China

ISBN: (数字)9798331509095

ISBN: (纸本)9798331509101

As the performance gap between hardware and software widens, performance analysis tools are essential for understanding the behavior of large-scale High-Performance Computing (HPC) programs. These tools provide insights into the performance bottlenecks and help in optimizing the performance of the programs. In this paper, we present a comprehensive study of performance analysis tools for large-scale HPC systems including both sampling-based and instrumentation-based tools that are commonly adopted in the HPC community. We investigate the abundance and overheads of data collection as well as the analysis capabilities of HPCToolkit, TAU, and Scalasca with representative programs at scale. Our study shows that different performance analysis tools have distinct strengths and weaknesses, and the choice of a performance analysis tool depends on the specific requirements of the user. We also discuss the challenges and future directions in the field of performance analysis tools for large-scale HPC systems.

关键词： High performance computing Instruments Data collection Software Hardware Performance analysis

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An Iterative Method for Computing Controlled Reach-Avoid Sets

An Iterative Method for Computing Controlled Reach-Avoid Set...

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American Control Conference (ACC)

作者： Dejin Ren Taoran Wu Bai Xue State Key Lab. of Computer Science CAS Institute of Software Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper focuses on addressing the problem of computing controlled reach-avoid sets for continuous-time systems modeled by polynomial ordinary differential equations with control inputs in order to ensure the safety and reachability of safety-critical systems. In a controlled reach-avoid set (CRAS), there exists a feedback controller capable of driving the closed-loop system into a target set of desired states while avoiding hazardous regions. The computation of a controlled reach-avoid set can be transformed into a search for a guidance-barrier function in existing literature. However, existing guidance-barrier functions tend to produce conservative CRASs due to certain flaws. To overcome this issue, a new guidance-barrier function is introduced, which generates less conservative CRASs compared to existing ones. Given the inherent nonlinearity associated with searching for guidance-barrier functions in the control setting, an iterative computational approach is proposed. This approach modifies controllers from previous iterations to find these functions and expand CRASs by solving convex optimizations. Finally, numerical results demonstrate the efficiency of our method in generating less conservative CRASs.

关键词： Three-dimensional displays Computational modeling Ordinary differential equations Polynomials Mathematical models Convex functions Safety

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Constructing A Theoretical Model to Bridge Neural Transition with a state Switch in Bipolar Disorder

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Neuroscience Bulletin 2025年第1期41卷 181-185页

作者： Xiaonan Guo Lizichen Chen Jianbo Lai Shaohua Hu Department of Psychiatry the First Affiliated HospitalZhejiang University School of MedicineHangzhou310003China Department of Engineering Mechanics Zhejiang UniversityHangzhou310027China Nanhu Brain-computer Interface Institute Hangzhou311100China The Zhejiang Key Laboratory of Precision Psychiatry Hangzhou310003China MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University School of MedicineHangzhou310003China Brain Research Institute of Zhejiang University Hangzhou310003China The State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhouChina Zhejiang Engineering Center for Mathematical Mental Health Hangzhou310003China

Reward or stress,which exists extensively,causes resilient emotional fluctuations under common ***,reward or stress is a typical trigger for manic or depressive episodes of bipolar disorder(BD),which is corroborated by psychological theory,biological findings,and psychosocial treatment approaches[1,2].During an episode of BD,the affective aberration can be persistent and switchable,accompanied by opposite constellations of cognitive and psychomotor *** by uncontrollable mood ranging in severity,duration,and polarity,to disentangle the pathophysiology mechanism of BD is to delineate the mystery of affective fluctuations driven by reward or stress.

关键词： bipolar Transition finding

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Decoding Respiratory Signals from LFP in Olfactory Bulb based on ConvLSTM Framework 7

Decoding Respiratory Signals from LFP in Olfactory Bulb base...

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7th International Conference on Biological Information and Biomedical Engineering, BIBE 2024

作者： Xu, Haoze Yu, Bowen Yu, Chaonan Xu, Kedi Qiushi Academy for Advanced Studies Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Key Laboratory for Biomedical Engineering of Ministry of Education School of Biomedical Engineering and Instrument Science Zhejiang University China Nanhu Brain-computer interface institute Hangzhou311100 China State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

ISBN: (纸本)9783800762910

Respiratory signals are crucial for decoding olfactory neural circuits and understanding respiration-entrained local field potential (LFP) rhythms. Acquiring high-precision respiratory signals often requires additional equipment, increasing the experimental burden, particularly in free-moving paradigms. However, within the current neuroscience paradigm, we have not yet found a non-invasive method for obtaining the respiratory signals of laboratory animals. To minimize surgical trauma to experimental animals, this paper introduces a ConvLSTM-based decoding method, which uses the LFP from the olfactory bulb (OB) region of animals to accurately decode respiratory signals. After validating the effectiveness of the ConvLSTM method through comparative experiments, we further investigated the factors influencing the model's decoding performance. The Gamma frequency band exhibited the best decoding results, with a mean absolute error (MAE) of 0.16±0.09, which may be related to the causal direction from respiratory signals to Gamma rhythms. Additionally, the ConvLSTM model demonstrated strong trend prediction capabilities in multi-sequence point predictions and forward predictions, despite a slight decline in single-point prediction accuracy. Extensive validation on publicly available datasets indicates that the ConvLSTM-based respiratory decoding approach is highly applicable, offering a non-invasive alternative for respiratory signal acquisition in animal studies. © VDE VERLAG GMBH.

关键词： Noninvasive medical procedures

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Knowledge-aware Multi-view Cross Learning for Edge-based Collaborative Recommendation 29

Knowledge-aware Multi-view Cross Learning for Edge-based Col...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Dai, Yang Meng, Shunmei Gu, Huanhuan Liu, Nan Tu, Longchuan NanJing University of Science and Technology School of Cyberspace Security Nanjing China NanJing University State Key Lab. for Novel Software Technology Nanjing China NanJing University of Science and Technology Department of Computer Science and Engineering Nanjing China Nanjing Sinovatio Technology Co. Ltd. Nanjing China

ISBN: (纸本)9798350330717

In order to effectively overcome the sparsity and cold start challenges of collaborative filtering in actual scenarios, and provide more accurate suggestions, knowledge graphs are commonly utilized in recommender systems. Graph neural networks are increasingly used in knowledge graph-based recommender systems. Nevertheless, conventional recommender systems overlook the relationship information between items and entities as well as the sparse supervision signal, potentially leading to a decline in the actual recommendation performance. In this investigation, we propose KMCL, a Knowledge-aware Multi-view Cross-learning framework for edge-based collaborative recommendation. In contrast to the typical manner of dealing with knowledge graphs, our approach investigates three different graph views to improve users' and items' embedding representations, respectively. Firstly, the merged user-item KG graph is utilized to integrate the multi-hop neighbor information of each user, with the Graph Attention Network assigning various weights to different neighbors. Secondly, Graph Neural Network collects the top-x neighbor nodes for learning in the collaborative view of user-item graph and item-entity graph. Finally, a cross module is developed to share high-level information between embeddings with the same features. In addition, we also introduce the idea of edge computing, which preprocesses and calculates the locally obtained user preferences first, and then inputs them into different views to improve the recommendation efficiency. Experimental results demonstrate that KMCL delivers better suggestions than state-of-the-art baseline models in delivering superior recommendations. Furthermore, our approach maintains higher accuracy in predicting outcomes in less familiar interaction scenarios. © 2023 IEEE.

关键词： Edge Computing Graph Attention Network Knowledge Graph Multi-view Cross Learning Recommender System

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