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Time delay reservoir computing based on mutually coupled add-drop microring resonators

Chaos, Solitons & Fractals 2025年 199卷

作者： Lili Li Yiyuan Xie Xiao Jiang Ye Su Yichen Ye Zelin Li Yuhan Tang School of Electronic and Information Engineering Southwest University Chongqing 400715 China The Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing Chongqing 400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing 400715 China

Add-drop silicon microring resonator (MRR) has notable advantages in low power consumption, light weight and scalability, making it one of the research hotspots in optical reservoir computing (ORC) and optical chip. In this paper, for the first time, we propose a novel nonlinear dynamic system using mutually coupled (MC) add-drop MRRs with clockwise and counter-clockwise optical injection. Utilizing the proposed dynamical model, which is grounded in modified nonlinear dynamic equations incorporating coupled mode theory (CMT), we further construct an ORC system. Different dynamic behaviors and internal physical mechanisms, affected by key parameters, are analyzed in detail through bifurcation diagrams. Based on this, the effects of key parameters including injection strength, pump power, and injection delay time on the performance of ORC are detailedly analyzed in results. Through comprehensive analysis and optimization, the proposed ORC can achieve the normalized mean square error (NMSE) of 0.4% for the prediction task and the symbol error rate (SER) of 0.2% with SNR of 24 dB for nonlinear channel equalization. By analyzing the effects of the system output state, the number of virtual node, and scaling factor on the above tasks, we achieve remarkable recognition accuracies, attaining 99% on the MNIST dataset and 86.8% on the Fashion-MNIST dataset. The results and analysis underscore the importance of mastering the dynamic mechanism of the proposed model to achieve optimal application performance for constructed ORC systems. An in-depth understanding of the proposed model offers valuable insights and inspiration for the subsequent development of integrated topologies.

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Revisiting adversarial robustness distillation: Robust soft labels make student better

arXiv

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arXiv 2021年

作者： Zi, Bojia Zhao, Shihao Ma, Xingjun Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan Univeristy Shanghai Collaborative Innovation Center on Intelligent Visual Computing School of Information Technology Deakin University Geelong Australia

Adversarial training is one effective approach for training robust deep neural networks against adversarial attacks. While being able to bring reliable robustness, adversarial training (AT) methods in general favor high capacity models, i.e., the larger the model the better the robustness. This tends to limit their effectiveness on small models, which are more preferable in scenarios where storage or computing resources are very limited (e.g., mobile devices). In this paper, we leverage the concept of knowledge distillation to improve the robustness of small models by distilling from adversarially trained large models. We first revisit several state-of-the-art AT methods from a distillation perspective and identify one common technique that can lead to improved robustness: the use of robust soft labels - predictions of a robust model. Following this observation, we propose a novel adversarial robustness distillation method called Robust Soft label Adversarial Distillation (RSLAD) to train robust small student models. RSLAD fully exploits the robust soft labels produced by a robust (adversarially-trained) large teacher model to guide the student's learning on both natural and adversarial examples in all loss terms. We empirically demonstrate the effectiveness of our RSLAD approach over existing adversarial training and distillation methods in improving the robustness of small models against state-of-the-art attacks including the AutoAttack. We also provide a set of understandings on our RSLAD and the importance of robust soft labels for adversarial robustness distillation. Code: https://***/zibojia/RSLAD. © 2021, CC BY.

关键词： Distillation

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Taming Stable Diffusion for MRI Cross-Modality Translation

Taming Stable Diffusion for MRI Cross-Modality Translation

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yingtai Li Shuo Yang Xiaoyan Wu Shan He S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine USTC Hefei P.R. China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research USTC P.R. China iFLYTEK Research iFLYTEK Co. Ltd Hefei P.R. China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing P.R. China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In this study, we explore using Stable Diffusion (SD) for unsupervised medical image-to-image translation. SD has shown remarkable performances in generating high-quality images and can be easily applied to generate custom contents by injecting standard plug-ins like LoRA, offering a promising solution to tackle the complexity caused by variations in imaging modalities, acquisition parameters, and body parts in medical imaging. However, We empirically find that existing pipelines designed for natural images fail to translate directly to medical images due to weak structural control and inappropriate color preservation. To address these issues, we propose a novel two-branch image translation pipeline. This pipeline decouples the generation of target image along the time axis and employs ControlNet to ensure precise structural preservation. Additionally, we customize SD to generate images of extreme brightness, a common feature in medical imaging. Our results on the BraTS dataset demonstrate that SD with task-specific plug-ins can generate high-quality medical images comparable to those generated by task-specific models. Since the development of these standard plug-ins can be easily done by clinicians without much knowledge of the underlying algorithm, such a mode holds the potential to significantly extend the use of medical image computing algorithms in the clinical environment.

关键词： Translation Image color analysis Computational modeling Biological system modeling Magnetic resonance imaging Pipelines Brightness Complexity theory Standards Biomedical imaging

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zijian Zhu Yichi Zhang Hai Chen Yinpeng Dong Shu Zhao Wenbo Ding Jiachen Zhong Shibao Zheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Dept. of Comp. Sci. and Tech. THBI Lab Institute for AI Tsinghua University BNRist Center Zhongguancun Laboratory Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University SAIC Motor AI Lab

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.

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A Blockchain-Based Multi-cloud Storage Data Consistency Verification Scheme

A Blockchain-Based Multi-Cloud Storage Data Consistency Veri...

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IEEE International Conference on Big Data and cloud computing (Bdcloud)

作者： Feiyu Wang Jian-tao Zhou Hao Wang Xu Guo College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migrated and replicated between multi-cloud data centers, it is not easy to guarantee data con-sistency. This paper proposes an efficient, secure, and new data consistency verification scheme using blockchain technology. In order to reduce the computation and communication overhead in the verification process, our scheme uses encrypted tags to build Merkle hash tree to generate unique and lightweight verification proofs and does not use third-party auditors. The final theoretical and experimental analysis shows that our scheme has higher security and a faster verification process in multi-cloud storage.

关键词： cloud computing Data centers Costs Blockchains Behavioral sciences

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Hard-instance learning for quantum adiabatic prime factorization

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Physical Review A 2022年第6期105卷 062455-062455页

作者： Jian Lin Zhengfeng Zhang Junping Zhang Xiaopeng Li State Key Laboratory of Surface Physics Institute of Nanoelectronics and Quantum Computing and Department of Physics Fudan University Shanghai 200433 China Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University Shanghai 200433 China Shanghai Qi Zhi Institute Xuhui District Shanghai 200032 China Shanghai Research Center for Quantum Sciences Shanghai 201315 China

Prime factorization is a difficult problem with classical computing, whose exponential hardness is the foundation of Rivest-Shamir-Adleman cryptography. With programable quantum devices, adiabatic quantum computing has been proposed as a plausible approach to solve prime factorization, having promising advantage over classical computing. Here, we find there are certain hard instances that are consistently intractable for both classical simulated annealing and unconfigured adiabatic quantum computing (AQC). Aiming at an automated architecture for optimal configuration of quantum adiabatic factorization, we apply a deep reinforcement learning (RL) method to configure the AQC algorithm. By setting the success probability of the worst-case problem instances as the reward to RL, we show the AQC performance on the hard instances is dramatically improved by RL configuration. The success probability also becomes more evenly distributed over different problem instances, meaning the configured AQC is more stable as compared to the unconfigured case. Through a technique of transfer learning, we find prominent evidence that the framework of AQC configuration is scalable—the configured AQC as trained on five qubits remains working efficiently on nine qubits with a minimal amount of additional training cost.

关键词： Machine learning Quantum algorithms Quantum computation Quantum simulation

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Learning Music-Dance Representations through Explicit-Implicit Rhythm Synchronization

arXiv

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arXiv 2022年

作者： Yu, Jiashuo Pu, Junfu Cheng, Ying Feng, Rui Shan, Ying The School of Computer Science Shanghai Key Lab of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai200438 China The Academy for Engineering and Technology Fudan University Shanghai200438 China The Applied Research Center PCG Tencent Shenzhen518000 China

Although audio-visual representation has been proven to be applicable in many downstream tasks, the representation of dancing videos, which is more specific and always accompanied by music with complex auditory contents, remains challenging and uninvestigated. Considering the intrinsic alignment between the cadent movement of the dancer and music rhythm, we introduce MuDaR, a novel Music-Dance Representation learning framework to perform the synchronization of music and dance rhythms both in explicit and implicit ways. Specifically, we derive the dance rhythms based on visual appearance and motion cues inspired by the music rhythm analysis. Then the visual rhythms are temporally aligned with the music counterparts, which are extracted by the amplitude of sound intensity. Meanwhile, we exploit the implicit coherence of rhythms implied in audio and visual streams by contrastive learning. The model learns the joint embedding by predicting the temporal consistency between audio-visual pairs. The music-dance representation, together with the capability of detecting audio and visual rhythms, can further be applied to three downstream tasks: (a) dance classification, (b) music-dance retrieval, and (c) music-dance retargeting. Extensive experiments demonstrate that our proposed framework outperforms other self-supervised methods by a large margin. © 2022, CC BY.

关键词： Supervised learning

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OpenAUC: towards AUC-oriented open-set recognition 22

OpenAUC: towards AUC-oriented open-set recognition

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Proceedings of the 36th International Conference on Neural information processing Systems

作者： Zitai Wang Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang SKLOIS Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University and SKLOIS Institute of Information Engineering CAS School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS and BDKM University of Chinese Academy of Sciences and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Traditional machine learning follows a close-set assumption that the training and test set share the same label space. While in many practical scenarios, it is inevitable that some test samples belong to unknown classes (open-set). To fix this issue, Open-Set Recognition (OSR), whose goal is to make correct predictions on both close-set samples and open-set samples, has attracted rising attention. In this direction, the vast majority of literature focuses on the pattern of open-set samples. However, how to evaluate model performance in this challenging task is still unsolved. In this paper, a systematic analysis reveals that most existing metrics are essentially inconsistent with the aforementioned goal of OSR: (1) For metrics extended from close-set classification, such as Open-set F-score, Youden's index, and Normalized Accuracy, a poor open-set prediction can escape from a low performance score with a superior close-set prediction. (2) Novelty detection AUC, which measures the ranking performance between close-set and open-set samples, ignores the close-set performance. To fix these issues, we propose a novel metric named OpenAUC. Compared with existing metrics, OpenAUC enjoys a concise pairwise formulation that evaluates open-set performance and close-set performance in a coupling manner. Further analysis shows that OpenAUC is free from the aforementioned inconsistency properties. Finally, an end-to-end learning method is proposed to minimize the OpenAUC risk, and the experimental results on popular benchmark datasets speak to its effectiveness.

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OTKGE: multi-modal knowledge graph embeddings via optimal transport 22

OTKGE: multi-modal knowledge graph embeddings via optimal tr...

引用

Proceedings of the 36th International Conference on Neural information processing Systems

作者： Zongsheng Cao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang SKLOIS Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University and SKLOIS Institute of Information Engineering CAS School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS and BDKM University of Chinese Academy of Sciences and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Multi-modal knowledge graph embeddings (KGE) have caught more and more attention in learning representations of entities and relations for link prediction tasks. Different from previous uni-modal KGE approaches, multi-modal KGE can leverage expressive knowledge from a wealth of modalities (image, text, etc.), leading to more comprehensive representations of real-world entities. However, the critical challenge along this course lies in that the multi-modal embedding spaces are usually heterogeneous. In this sense, direct fusion will destroy the inherent spatial structure of different modal embeddings. To overcome this challenge, we revisit multi-modal KGE from a distributional alignment perspective and propose optimal transport knowledge graph embeddings (OTKGE). Specifically, we model the multi-modal fusion procedure as a transport plan moving different modal embeddings to a unified space by minimizing the Wasserstein distance between multi-modal distributions. Theoretically, we show that by minimizing the Wasserstein distance between the individual modalities and the unified embedding space, the final results are guaranteed to maintain consistency and comprehensiveness. Moreover, experimental results on well-established multi-modal knowledge graph completion benchmarks show that our OTKGE achieves state-of-the-art performance.

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Direct Participant Recruitment Strategy in Sparse Mobile Crowdsensing

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Jisuanji Xuebao/Chinese Journal of Computers 2022年第7期45卷 1539-1556页

作者： Tu, Chun-Yu Yu, Zhi-Yong Han, Lei Zhu, Wei-Ping Huang, Fang-Wan Guo, Wen-Zhong Wang, Le-Ye College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Department of Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China Key Lab of High Confidence Software Technologies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China

Sparse Mobile Crowdsensing (Sparse MCS) selects a small part of sub-areas for data collection and infers the data of other sub-areas from the collected data. Compared with Mobile Crowdsensing (MCS) that does not use data inference methods, Sparse MCS saves sensing costs while ensuring the quality of global data. However, the existing research works on Sparse MCS only focus on selecting a small part of sub-areas with higher value. It does not consider whether the recruited participants can collect the data of the required sub-areas, and also ignores the value of other data collected by the participants. In order to solve the limitations of traditional methods in sub-areas selection, this paper starts from the perspective of participants and concentrates on the contribution of the data collected by each participant to the entire collection task. All the data contributions collected by each participant will become the basis for decision-making for the participant's choice. And correspondingly, a new idea to deal with the problem of participant selection under Sparse MCS is proposed. In view of the fact that each person's daily movement trajectory is basically stable, and the data collected by different people on their respective trajectories have different values, this paper uses this regularity and difference to study how to directly recruit participants who can collect high-value data. Furthermore, the participant selection problem considered in this paper is not limited to the data collection in the next cycle, but directly recruits some participants to continue the data collection task in the next multiple cycles. The participant selection problem that spans multiple cycles can be modeled as a dynamic decision-making problem. Since heuristic strategies may fall into a local optimal solution, this paper uses reinforcement learning to solve the participant selection problem: We use the participant selection system as an agent of reinforcement learning, and design the

关键词： Compressed sensing

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