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Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer engineering

作者： Xiaonan Liu Chenyan Zhao Haoshan Xie Zhengyu Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University China School of Computer and Artificial Intelligence ZhengZhou University China

ISBN: (纸本)9781450397148

At present, the scale of quantum computers in the real sense is still small, and quantum simulation has become one of the important ways of quantum theory research, grover quantum search algorithm is suitable for the search problem of disordered database. Firstly, according to the implementation principle of Grover algorithm and Boolean logic relationship, the design idea of multi-objective Oracle is analyzed, based on IBMQ quantum cloud platform, the quantum circuit of Grover algorithm with multi-objective items is simulated. Based on the characteristics of Grover algorithm and the simulation process of quantum gate, the action of multiple identical quantum gates is combined to reduce the update times of probability amplitude and improve the simulation efficiency. The libquantum quantum simulator is used for experiments and the target item is successfully searched, which proves the feasibility of the optimization method and provides reference for the simulation and optimization of other quantum algorithms.

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Preparation and Simulation of Quantum States of Matrices 22

Preparation and Simulation of Quantum States of Matrices

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Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer engineering

作者： Zhengyu Liu Xiaonan Liu Haoshan Xie Chenyan Zhao School of Computer and Artificial Intelligence of Zhengzhou University China State Key Laboratory of Mathematical Engineering and advanced computing Information Engineering University China

ISBN: (纸本)9781450397148

A basic problem of quantum computing is how to effectively express classical data in quantum systems. This problem is called state preparation problem, and the process of preparing quantum states is called coding. In this paper, the classical data of matrix is transformed into quantum states in the form of amplitude coding and simulated. This paper first introduces the detailed process of matrix transformation into the linear combination of quantum ground state, then designs a quantum circuit model to realize amplitude coding according to the idea of binary tree model and quantum random walk algorithm, and finally simulates the three matrices on IBM quantum cloud platform, and analyzes and introduces many situations. The experimental results verify the feasibility of converting the matrix into quantum states by amplitude coding, analyze the causes of errors in the experimental process, and put forward the corresponding solutions. This paper introduces in detail the whole process of matrix conversion to quantum states and the construction of quantum circuits for simulation. Compared with the existing experiments, the quantum circuits constructed in this paper are shorter in length, less in number of quantum gates, and more versatile.

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Bridge Past and Future: Overcoming Information Asymmetry in Incremental Object Detection

arXiv

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arXiv 2024年

作者： Mo, Qijie Gao, Yipeng Fu, Shenghao Yan, Junkai Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

In incremental object detection, knowledge distillation has been proven to be an effective way to alleviate catastrophic forgetting. However, previous works focused on preserving the knowledge of old models, ignoring that images could simultaneously contain categories from past, present, and future stages. The co-occurrence of objects makes the optimization objectives inconsistent across different stages since the definition for foreground objects differs across various stages, which limits the model's performance greatly. To overcome this problem, we propose a method called "Bridge Past and Future" (BPF), which aligns models across stages, ensuring consistent optimization directions. In addition, we propose a novel Distillation with Future (DwF) loss, fully leveraging the background probability to mitigate the forgetting of old classes while ensuring a high level of adaptability in learning new classes. Extensive experiments are conducted on both Pascal VOC and MS COCO benchmarks. Without memory, BPF outperforms current state-of-the-art methods under various settings. The code is available at https://***/iSEE-laboratory/BPF. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Joint Feature Fusion Approach to Human Pose Estimation Using mmWave Radar 5th

Joint Feature Fusion Approach to Human Pose Estimation Usin...

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5th China Conference on Intelligent Networked Things, CINT 2022

作者： Zhang, Jianxiong Cao, Zhongping Ding, Wen Chen, Rihui Guo, Xuemei Wang, Guoli School of Artificial Intelligence Sun-Yat-Sen University Zhuhai519000 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Beijing China School of Computer Science and Engineering Sun-Yat-Sen University Guangzhou510006 China

ISBN: (纸本)9789811989148

This paper mainly studies the use of millimeter wave (mmWave) radar for 3D human pose estimation. Although pioneering works have achieved remarkable success, the lack of stability of continuous data acquisition by mmWave radar is still an intractable problem in pose capturing. To mitigate this problem, a joint feature fusion method is proposed, which is based on the assumption that human pose in a motion sequence is domained by bone connection and specific motion cooperation mechanism. Specifically, we introduce a forget gate to suppress error features extracted due to the loss of information, and a select gate to select the most reasonable joint characteristics. Outputs of the two gates are fused to form an anti distortion mechanism of human pose estimation. Experiments show the effectiveness of our method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data acquisition

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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Non-Markovian effect enhanced quantum noises in a coherent Ising machine

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Optics Express 2025年第11期33卷 22208-22221页

作者： Chen-Rui Fan Bo Lu Yi-Xuan Yao Qing Ai Chuan Wang School of Artificial Intelligence Beijing Normal University Beijing 100875 China Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou 450001 China lubo@*** School of Physics and Astronomy Applied Optics Beijing Area Major Laboratory Beijing 100875 China Key Laboratory of Multiscale Spin Physics Ministry of Education Beijing Normal University Beijing 100875 China wangchuan@***

Combinatorial optimization problems (COPs) constitute a fundamental class of computational challenges with extensive applications across scientific and industrial domains. The emergence of the coherent Ising machine (CIM) as a computational paradigm has demonstrated exceptional capabilities in rapidly generating arbitrary spin configurations and effectively solving large-scale COPs. Despite its promising potential, the CIM framework encounters an inherent limitation related to amplitude heterogeneity. Current approaches to this limitation primarily rely on Gaussian approximations to model quantum noise in the system’s amplitude dynamics within the original CIM framework. However, these methods generally fail to account for the temporal and periodic non-Markovian characteristics of pulses in practical environments. In this study, we introduce temporally correlated non-Markovian noise into the dynamics of both conventional and spiking neural network-based CIM systems, investigating their performance through comprehensive hyperparameter analysis. Our method incorporates a detailed examination of hyperparameter selection and its influence on algorithmic efficiency. Through extensive numerical simulations, we demonstrate that the integration of non-Markovian noise significantly accelerates variance evolution in both canonical coordinates and amplitude dynamics over time. The proposed approach not only exhibits enhanced computational performance but also shows superior scalability for large-scale problem instances. These findings suggest that our approach offers distinct advantages in addressing complex COPs, potentially opening new avenues for efficient optimization in various applications.

关键词： Neural networks Nonlinear optical fibers Numerical simulation Optical parametric oscillators Optical systems Quantum noise

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Friend or Foe? Mining Suspicious Behavior via Graph Capsule Infomax Detector against Fraudsters 24

Friend or Foe? Mining Suspicious Behavior via Graph Capsule ...

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33rd ACM Web Conference, WWW 2024

作者： Zheng, Xiangping Wu, Bo Liang, Xun Li, Wei College of Computer Science and Technology Harbin Engineering University Modeling and Emulation in E-Government National Engineering Laboratory Heilongjiang Harbin China Qingdao Innovation and Development Base of Harbin Engineering University China R and D and External Relations Department Xiangjiang Laboratory School of Artificial Intelligence and Advanced Computing Hunan University of Technology and Business Hunan Changsha China School of Information Renmin University of China Beijing China College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China

ISBN: (纸本)9798400701719

Anomaly detection on graphs has recently attracted considerable attention due to its broad range of high-impact applications, including cybersecurity, financial transactions, and recommendation systems. Although many efforts have thus far been made, how to effectively handle the high inconsistency between users' behavior and labels, a fundamental issue in anomaly detection, has not yet received sufficient concern. Moreover, the inconsistency problem is hard to investigate and even deteriorates the performance of anomaly detectors. To this end, we propose a novel graph self-supervised learning framework, Capsule Graph Infomax (termed CapsGI), to overcome the inconsistency of anomaly detection. Inspired by the recent advances of capsules on images, we explore another possibility of reforming the node embedding by capsule ideas to represent the unique node's properties. Concretely, by disentangling heterogeneous factors underlying each node representation, we can establish node capsules such that their representation can reflect intrinsic node properties. To strengthen the connection among normal nodes, CapsGI further represents the part-whole contrastive learning between lower-level capsules (part) and higher-level capsules (whole) by explicitly considering the context graph relations. Extensive experiments on multiple real-world datasets demonstrate that our model significantly outperforms state-of-the-art models. © 2024 ACM.

关键词： Anomaly detection

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Quantum Multi-Path Communication Protocol Based on Maximum Flow Theory

Quantum Multi-Path Communication Protocol Based on Maximum F...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jihao Fan Liang Huang Jun Li Long Shi Yuwen Qian School of Cyber Science and Engineering Nanjing University of Science and Technology Jiangyin China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Quantum networks are an actively researched and promising field, aiming to achieve efficient quantum information transmission by interconnecting quantum nodes. In large-scale quantum networks, end-to-end throughput is a critical factor that affects the overall performance of the network. The maximum flow problem, extensively studied in classical network theory, identifies a set of paths between the source and destination nodes that maximizes the total flow. This study extends the maximum flow problem to quantum networks, focusing on coordinating multiple paths for multi-path quantum communication. We propose a Quantum Multi-Path Communication Protocol (QMCP) that employs maximum flow theory to allocate transmission resources across multiple nodes efficiently, thus maximizing the total transmission capacity from the source to the destination. Our evaluation demonstrates that QMCP significantly enhances end-to-end throughput in quantum networks.

关键词： Quantum entanglement Simulation Signal processing algorithms Focusing Information processing Signal processing Throughput Routing protocols Quantum networks Speech processing

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TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection

arXiv

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arXiv 2024年

作者： Chen, Jiankang Zhang, Tong Zheng, Wei-Shi Wang, Ruixuan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China

Out-of-distribution (OOD) detection is crucial in many real-world applications. However, intelligent models are often trained solely on in-distribution (ID) data, leading to overconfidence when misclassifying OOD data as ID classes. In this study, we propose a new learning framework which leverage simple Jigsaw-based fake OOD data and rich semantic embeddings (‘anchors’) from the ChatGPT description of ID knowledge to help guide the training of the image encoder. The learning framework can be flexibly combined with existing post-hoc approaches to OOD detection, and extensive empirical evaluations on multiple OOD detection benchmarks demonstrate that rich textual representation of ID knowledge and fake OOD knowledge can well help train a visual encoder for OOD detection. With the learning framework, new state-of-the-art performance was achieved on all the benchmarks. The code is available at https://***/Cverchen/TagFog. Copyright © 2024, The Authors. All rights reserved.

关键词： Jigs

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TexCIL: Text-Guided Continual Learning of Disease with Vision-Language Model

TexCIL: Text-Guided Continual Learning of Disease with Visio...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wentao Zhang Defeng Zhao Wei-Shi Zheng Ruixuan Wang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Current intelligent diagnostic systems often catastrophically forget old knowledge when learning new diseases only from the training dataset of the new diseases. Inspired by human learning of visual classes with the effective help of language, we propose a continual learning framework based on a pre-trained visual-language model (VLM) without storing any image of previously learned diseases. In this framework, textual prior knowledge of each new disease can be obtained by utilizing the frozen VLM’s text encoder, and then used to guide the visual learning of the new disease. This framework innovatively utilizes the textual prior knowledge of all previously learned diseases as out-of-distribution (OOD) information to help differentiate currently being-learned diseases from others. Extensive empirical evaluations on both medical and natural image datasets confirm the superiority of the proposed method over existing state-of-the-art methods in continual learning of new visual classes. The source code is available at https://***/OpenMedIA/TexCIL.

关键词： Continuing education Training Visualization Source coding Biological system modeling Benchmark testing Bioinformatics Medical diagnostic imaging Diseases

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