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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Jiang, Zite Zhang, Shuai Hou, Xingzhong Yuan, Mengting You, Haihang Institute of Computing Technology CAS State Key Lab of Processors Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijinz China Zhongguancun Laboratory Beijing China School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798350317152

The performance of the enumeration-based sub-graph matching, which searches all isomorphic subgraphs in the data graph, is crucial to various applications. The upper bound of the complexity for the enumeration-based method is exponential to the number of query graph vertices, denoted as n. We propose a novel subgraph matching algorithm called the Isolated Vertices Exploration (IVE). The IVE leverages isolated vertices during the reordering and enumeration phases, thereby significantly accelerating the subgraph matching process. During the enumeration, the isolated vertices can be matched by using a quick bipartite graph matching algorithm. Consequently, the complexity of matching the remaining non-isolated vertices is exponential to the number of non-isolated vertices, denoted as n'. For the reordering, we designed the Maximum Deleted Edges (MDE) to minimize n'. MDE iteratively selects the query vertex with the maximum edges. According to the experimental results, n' is less than 0.8n for 99.8% of arbitrary graphs. Moreover, IVE outperforms the state-of-the-art algorithms in various scenarios with different sizes, sparsities and fields, achieving a performance speedup of up to 80.3x. © 2024 IEEE.

关键词： Iterative methods

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Fast optical-writing recognition based on two-dimensional photothermoelectric effect assisted with deep learning

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InfoMat 2023年第3期5卷 33-44页

作者： Yujia Zhong Haoyu Fang Yutong Ran Hongwei Zhu State Key Lab of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijingthe People's Republic of China Department of Electrical and Computer Engineering New York UniversityNew YorkNew YorkUSA

The photothermoelectric(PTE)effect enables a simple structure to construct a noncontact and self-powered writing *** detectors based on active materials and one-dimensional(1D)position-sensitive PTE effect have attracted considerable ***,there is no research on applying the 1D position-sensitive PTE effect to a two-dimensional(2D)***,flexible writing devices based on the PTE effect have not been ***,we demonstrate a self-powered flexible optical writing device based on the 2D position-sensitive PTE effect of SnSe thin *** positioning,tracking,and distinguishing of optically written characters have been realized by only measuring the bi-directional output voltages during the writing process.A deep convolutional neural network has been introduced to recognize optically written numbers and characters with high recognition accuracy(92%)and fast processing speed(8.6 ms).The flexible optical-writing device with a simple structure could rapidly and accurately recognize characters in a noncontact fashion using a small amount of training data.

关键词： deep neural network optical writing photothermoelectric effect tin selenide

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Improving the Interpretability through Maximizing Mutual Information for EEG Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第02期16卷 744-757页

作者： Yang, Hua Philip Chen, C.L. Chen, Bianna Zhang, Tong North Carolina State University Department of Computer Science United States South China University of Technology Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition The School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center The Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Trustworthy Graph Neural Networks (GNNs) for EEG emotion recognition should identify emotions accurately and elucidate corresponding rationales. Current GNNs have achieved notable performance by dynamically modeling emotional connections between EEG channels. However, these GNNs lack interpretability due to the absence of explicit rationale behind their predictions. This paper conducts a comprehensive identification of important EEG channels to enhance the interpretability of EEG emotion recognition from the perspective of mutual information. Specifically, an Adjacency-Explainable Graph Neural Network (AEG) for ante-hoc interpretability is proposed to capture genuine EEG emotional connections, which gives a theoretical guarantee to remove spurious connections. Moreover, a Channel-wise Adaptive Class Activation Mapping Explainer (CACA) for post-hoc interpretability is developed to locate the EEG channels that contribute most to predictions. Experimental results on three datasets, i.e., SEED, SEED-IV, and DREAMER, prove that imbuing training processes with enhanced interpretability ensures significant performance improvements in emotion recognition. Quantitative comparisons of post-hoc interpretability also demonstrate the superiority of CACA. Furthermore, this paper illustrates two potential applications of the proposed methodologies, showing their broader utility and significance. © 2010-2012 IEEE.

关键词： Graph neural networks

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FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph Neural Network 27

FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph N...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Du, Boyu Zhou, Jingya Liu, Ling She, Xiaolong School of Computer Science and Technology Soochow University China Engineering Lab.of Big Data and Intelligence of Jiangsu Province Soochow University China State Key Lab.for Novel Software Technology Nanjing University China School of Computer Science Georgia Institute of Technology United States

ISBN: (纸本)9781643685489

Graph Neural Networks (GNNs) have become an important graph feature learning paradigm that is extensively applied to graph inference ***, GNNs still have limitations in some aspects such as representation capability, interpretability, *** this end, this paper introduces a novel hybrid Fuzzy-Logic Graph Neural Network (FL-GNN) that synergistically combines Fuzzy Neural Network (FNN) and GNN to effectively capture and aggregate local information flows within the ***-GNN exhibits three distinctive ***, we incorporate a specialized structure fuzzy rule to enhance FL-GNN's graph inference capability, surpassing representative GNN ***, we augment the interpretability of FL-GNN by integrating analytical exploration methods from two perspectives: Fuzzy Inference System and Message Passing Algorithm (MPA).Lastly, we refine the structure of FL-GNN based on MPA to optimize its calculation complexity and consequently improve learning *** results show that FL-GNN outperforms existing representative graph neural networks for graph inference tasks. © 2024 The Authors.

关键词： Graph neural networks

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SRender: Boosting Neural Radiance Field Efficiency via Sensitivity-Aware Dynamic Precision Rendering 57

SRender: Boosting Neural Radiance Field Efficiency via Sensi...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Song, Zhuoran He, Houshu Liu, Fangxin Hao, Yifan Song, Xinkai Jiang, Li Liang, Xiaoyao Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Institute of Computing Technology Chinese Academy of Sciences State Key Lab of Processors Beijing China

ISBN: (纸本)9798350350579

Neural Radiance Field (NeRF) holds immense promise for generating photo-realistic images and videos. How-ever, the computation and memory demands significantly impede its applicability in real-Time tasks such as virtual reality and augmented reality. While accelerators dedicatedly designed for NeRF have emerged, they may not be the optimal solution as they only consider the optimization of full-precision NeRF models, leading to unsatisfactory memory and computation saving. This paper proposes Sensitivity-Aware Dynamic Precision Rendering, abbreviated as SRender, an algorithm-hardware co-design framework that borrows the idea of Adaptive Rendering commonly used in conventional graphics rendering to accelerate the rendering process of NeRF exploiting the fine-grained per-ray and per-point levels sensitivity. The fundamental idea behind SRender is that rays and points with high sensitivity require high-precision data types, while the insensitive rays and points do not, allowing them to be sacrificed for greater acceleration. Accordingly, the algorithm part of SRender dynamically configures the precision of the hash table and the multilayer perceptron (MLP), and the hardware part features a specialized accelerator that exploits such mixed-precision memory access and computation patterns. Specifically, we predict the sensitive rays and points based on low-precision hash encoding and feature computation. Then, we trigger a high-precision rendering again on the sensitive ones to guarantee the rendering quality. The hardware design of SRender includes a sensitivity prediction engine, coarse-Then-fine-grained encoding engines, and a recovery engine, which translates the computational saving in the algorithm to real speedup. Using the coarse-Then-fine-grained encoding engine, we maximize the data reuse on-chip, thereby reducing the stress of off-chip memory bandwidth. Experiments show that the edge-level SRender can achieve 3.94x and 34.06x speedup over state-of-The-Art NeRF

关键词： Encoding (symbols)

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Quantum Byzantine Agreement Against Full-Information Adversary 38

Quantum Byzantine Agreement Against Full-Information Adversa...

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38th International Symposium on Distributed Computing, DISC 2024

作者： Li, Longcheng Sun, Xiaoming Zhu, Jiadong State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783959773522

We exhibit that, when given a classical Byzantine agreement protocol designed in the private-channel model, it is feasible to construct a quantum agreement protocol that can effectively handle a full-information adversary. Notably, both protocols have equivalent levels of resilience, round complexity, and communication complexity. In the classical private-channel scenario, participating players are limited to exchanging classical bits, with the adversary lacking knowledge of the exchanged messages. In contrast, in the quantum full-information setting, participating players can exchange qubits, while the adversary possesses comprehensive and accurate visibility into the system’s state and messages. By showcasing the reduction from quantum to classical frameworks, this paper demonstrates the strength and flexibility of quantum protocols in addressing security challenges posed by adversaries with increased visibility. It underscores the potential of leveraging quantum principles to improve security measures without compromising on efficiency or resilience. By applying our reduction, we demonstrate quantum advantages in the round complexity of asynchronous Byzantine agreement protocols in the full-information model. It is well known that in the full-information model, any classical protocol requires Ω(n) rounds to solve Byzantine agreement with probability one even against Fail-stop adversary when resilience t = Θ(n) [2]. We show that quantum protocols can achieve O(1) rounds (i) with resilience t 0, therefore surpassing the classical lower bound. © Longcheng Li, Xiaoming Sun, and Jiadong Zhu.

关键词： Quantum computers

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Self-Distillation Hashing for Efficient Hamming Space Retrieval 48

Self-Distillation Hashing for Efficient Hamming Space Retrie...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhai, Hongjia Li, Hai Zhang, Hanzhi Bao, Hujun Zhang, Guofeng Zhejiang University State Key Lab of Cad&cg Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9781728163277

Deep hashing-based approaches have become the optimal solutions for large-scale image retrieval task due to their high computational efficiency and low storage burden. Some methods leverage a large teacher network to improve the retrieval performance of the small student network through knowledge distillation, which incurs high computational and time costs. In this paper, we propose Self-Distillation Hashing (SeDH), which improves the image retrieval performance without introducing a complex teacher model and significantly reduces the overall computation costs. Specifically, we generate the soft targets via ensembling the logits of other similar images among the mini-batch. The ensembled soft targets can model the relations between different image samples, which can act as additional supervision for classification. Besides, to learn more compact features and accurate inter-sample similarities, we propose a similarity-preserving loss on the learned hashing features, which aligns the softened similarity distribution with the pairwise soft similarity. Extensive experiments demonstrate that our approach can yield state-of-the-art performance on deep supervised hashing retrieval. © 2023 IEEE.

关键词： Deep supervised hashing Image retrieval Pairwise similarity Self-distillation

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Alleviating the Semantic Gap for Generalized fMRI-to-Image Reconstruction 37

Alleviating the Semantic Gap for Generalized fMRI-to-Image R...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Fang, Tao Zheng, Qian Pan, Gang College of Computer Science and Technology Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

ISBN: (纸本)9781713899921

Although existing fMRI-to-image reconstruction methods could predict high-quality images, they do not explicitly consider the semantic gap between training and testing data, resulting in reconstruction with unstable and uncertain semantics. This paper addresses the problem of generalized fMRI-to-image reconstruction by explicitly alleviates the semantic gap. Specifically, we leverage the pre-trained CLIP model to map the training data to a compact feature representation, which essentially extends the sparse semantics of training data to dense ones, thus alleviating the semantic gap of the instances nearby known concepts (i.e., inside the training super-classes). Inspired by the robust low-level representation in fMRI data, which could help alleviate the semantic gap for instances that far from the known concepts (i.e., outside the training super-classes), we leverage structural information as a general cue to guide image reconstruction. Further, we quantify the semantic uncertainty based on probability density estimation and achieve Generalized fMRI-to-image reconstruction by adaptively integrating Expanded Semantics and Structural information (GESS) within a diffusion process. Experimental results demonstrate that the proposed GESS model outperforms state-of-the-art methods, and we propose a generalized scenario split strategy to evaluate the advantage of GESS in closing the semantic gap. Our codes are available at https://***/duolala1/GESS. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Image reconstruction

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Efficient quantum circuits for machine learning activation functions including constant T-depth ReLU

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Physical Review Research 2024年第4期6卷 043048页

作者： Wei Zi Siyi Wang Hyunji Kim Xiaoming Sun Anupam Chattopadhyay Patrick Rebentrost State Key Lab of Processors School of Computer Science and Technology School of Computer Science and Engineering Division of IT Convergence Engineering School of Computing

In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers. Among the components in this domain, activation functions hold a fundamental and indispensable role. Our research focuses on the development of activation functions quantum circuits for integration into fault-tolerant quantum computing architectures, with an emphasis on minimizing T-depth. Specifically, we present novel implementations of ReLU and leaky ReLU activation functions, achieving constant T-depths of 4 and 8, respectively. Leveraging quantum lookup tables, we extend our exploration to other activation functions such as the sigmoid. This approach enables us to customize precision and T-depth by adjusting the number of qubits, making our results more adaptable to various application scenarios. This study represents a significant advancement towards enhancing the practicality and application of quantum machine learning.

关键词： Machine learning Quantum algorithms Quantum algorithms & computation Quantum circuits Quantum computation

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Spatio-Temporal Interactive Learning for Efficient Image Reconstruction of Spiking Cameras 38

Spatio-Temporal Interactive Learning for Efficient Image Rec...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Fan, Bin Yin, Jiaoyang Dai, Yuchao Xu, Chao Huang, Tiejun Shi, Boxin Nat'l Key Lab of General AI School of Intelligence Science and Technology Peking University China State Key Lab of Multimedia Info. Processing School of Computer Science Peking University China Nat'l Eng. Research Ctr. of Visual Technology School of Computer Science Peking University China School of Electronics and Information Northwestern Polytechnical University China

The spiking camera is an emerging neuromorphic vision sensor that records high-speed motion scenes by asynchronously firing continuous binary spike streams. Prevailing image reconstruction methods, generating intermediate frames from these spike streams, often rely on complex step-by-step network architectures that overlook the intrinsic collaboration of spatio-temporal complementary information. In this paper, we propose an efficient spatio-temporal interactive reconstruction network to jointly perform inter-frame feature alignment and intra-frame feature filtering in a coarse-to-fine manner. Specifically, it starts by extracting hierarchical features from a concise hybrid spike representation, then refines the motion fields and target frames scale-by-scale, ultimately obtaining a full-resolution output. Meanwhile, we introduce a symmetric interactive attention block and a multi-motion field estimation block to further enhance the interaction capability of the overall network. Experiments on synthetic and real-captured data show that our approach exhibits excellent performance while maintaining low model complexity. The code is available at https://***/GitCVfb/STIR. © 2024 Neural information processing systems foundation. All rights reserved.

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