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SSRN 2023年

作者： Li, Jia Wang, Chenxu Wang, Xiaochun Wang, Xiali School of Software Engineering Xi’an Jiaotong University Xi’an710049 China MOE Key Lab of Intelligent Network and Network Security Xi’an Jiaotong University China Department of Computer Science Chang’an University Xi’an China

Outlier detection is a critical method in data mining as it has a wide range of practical use in various fields. Spectral clustering has attracted much attention for its advantage of coping with the curse of dimensionality in high-dimensional spaces. This paper presents a new spectral analysis-based outlier detection (SAOD) framework. We first construct the $k$ nearest neighbors ($k$NN) graphs of each sample and compute the eigenvalues of the Laplacian matrices of these graphs. Our findings indicate that the distribution of eigenvalues for outliers differs from that of inliers. Based on this observation, we employ the multivariate Gaussian kernel density estimation to empirically estimate the eigenvalues distribution of a given dataset. We then introduce a new outlier factor, based on the eigenvalue probabilities of samples to assess their outlierness degree. The experimental results, obtained from both synthetic and real-world datasets, show that our spectral analysis-based outlier detection method outperforms the state-of-the-art approaches. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need

arXiv

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arXiv 2024年

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC Copyright © 2024, The Authors. All rights reserved.

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Adaptive Sparse Deep Neural Network Inference on Resource-Constrained Cost-Efficient GPUs

Adaptive Sparse Deep Neural Network Inference on Resource-Co...

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Ming Dun Xu Zhang Huawei Cao Yuan Zhang Junying Huang Xiaochun Ye State Key Lab of Processors Institute of Computing Technology CAS Beijing China School of Computer and Control Engineering UCAS Beijing China Nanjing Institute of InforSuperbahn Nanjing China

Sparse Deep Neural Networks (SpDNNs) has gained great popularity and been widely applied in various machine learning area. Compared to traditional dense DNNs, the unpre-dictable irregularity and sparsity in the sparse weight matrices of SpDNNs make them difficult to be efficiently parallelled. Moreover, most of the recent advanced efforts to optimize SpDNNs are based on high-end GPUs like NVIDIA V100, which may not be affordable to individuals and smaller re-search groups. However, migrating the SpDNNs to those cost-efficient but resource-constrained GPUs confronts enormous challenges, including limitations in both memory and computing resources, as well as the tiresome hyper-parameter tuning in batch parallelism. In this paper, we accelerate SpDNNs on GPUs with more restricted resources through exploiting the memory and computing resources. On one hand, we design the adaptive memory-aware data partition scheme to reduce memory consumption automatically. On the other hand, we propose the Tensor core/CUDA core fusion mechanism to efficiently utilize the hetergeneous computing resources on modern GPU architecture. To the best of our knowledge, we are the first to improve the performance on SpDNNs through adaptive memory tuning and utilizing hetergeneous computing core concurrency. We compare our implementation with the state-of-art previous champions, and the results demonstrate that our work achieves the highest speedup of 1.35 x and 1.39x compared to 2022 champion S&Z on single and multiple NVIDIA Tesla T4 GPUs, respectively. What's more, our work can reach similar or even better throughput compared to 2020 champion H&P on 6 V100 GPUs with only 4 T4 GPUs.

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Efficient Model-Based OPC via Graph Neural Network

Efficient Model-Based OPC via Graph Neural Network

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Electronics Design Automation (ISEDA), International Symposium of

作者： Shuyuan Sun Xuelian Chen Fan Yang Bei Yu Shang Li Xuan Zeng State Key Lab of ASIC & System School of Microelectronics Fudan University China Cogenda Inc China Department of Computer Science and Engineering The Chinese University of Hong Kong China

As feature size continues to shrink and light source wavelengths remain unchanged, the optical diffraction effects seriously degrade chip yield. Optical proximity correction (OPC) has become an essential step for chip manufacturability. However, OPC cannot achieve satisfactory mask correction results in an affordable number of iterations on some layouts, delaying the chip design cycle. In this paper, we propose to use the Graph Neural Network (GNN) to predict the initial mask correction and speed up model-based OPC. We design a graph model to represent the chip layout, with segments represented as graph nodes and the diffraction effects between them represented as graph edges. The GNN is used to obtain the embedding of the segment, and we predict the shift value based on its output. The proposed GNN-based predictor enhances the original OPC to find a good correction solution for full-chip-size masks in fewer iterations. compared with the original OPC method, experimental results on 55nm and 32nm full chip designs show that the proposed GNN-based OPC method reduces the number of iterations up to 75% and the acquired mask of comparable quality.

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Large Language Model for Science: A Study on P vs. NP

arXiv

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arXiv 2023年

作者： Dong, Qingxiu Dong, Li Xu, Ke Zhou, Guangyan Hao, Yaru Sui, Zhifang Wei, Furu Microsoft Research United States School of Computer Science Peking University China State Key Lab of Software Development Environment Beihang University China Department of Mathematics and Statistics Beijing Technology and Business University China

In this work, we use large language models (LLMs) to augment and accelerate research on the P versus NP problem, one of the most important open problems in theoretical computer science and mathematics. Specifically, we propose Socratic reasoning, a general framework that promotes in-depth thinking with LLMs for complex problem-solving. Socratic reasoning encourages LLMs to recursively discover, solve, and integrate problems while facilitating self-evaluation and refinement. Our pilot study on the P vs. NP problem shows that GPT-4 successfully produces a proof schema and engages in rigorous reasoning throughout 97 dialogue turns, concluding "P ≠ NP", which is in alignment with (Xu and Zhou, 2023). The investigation uncovers novel insights within the extensive solution space of LLMs, shedding light on LLM for Science. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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Robust Quantum Feature Selection With Sparse Optimization Circuit

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Li, Jiaye Song, Jiagang Shi, Jinjing Xu, Hang Yu, Hao Chen, Gang Zhang, Shichao Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou310027 China Institute of Blockchain and Data Security China Central South University School of Computer Science and Engineering Changsha410083 China Central South University School of Electronic Information Changsha410083 China Xiangtan University School of Computer Science and School of Cyberspace Science Hunan Xiangtan411105 China Guangxi Normal University Guangxi Key Lab of Multisource Information Mining and Security Guilin541004 China

High-dimensional data has long been a notoriously challenging issue. Existing quantum dimension reduction technology primarily focuses on quantum principal component analysis. However, there are only a few studies on quantum feature selection (QFS) algorithms, and these algorithms are often not robust. Additionally, there are limited quantum circuits specifically designed for feature selection, and they still cannot address the objective function based on sparse learning. To address these issues, this paper proposes a robust QFS algorithm by designing a novel sparse optimization circuit. Specifically, we first apply sparse regularization and least squares loss to construct the proposed objective function. Then, six types of quantum registers and their initial states are prepared. Furthermore, quantum techniques such as quantum phase estimation and controlled rotation are employed to construct a sparse optimization circuit, which is used to obtain the final quantum state of the feature selection ***, a series of experiments are conducted to verify the accuracy of the feature selection and the robustness of the proposed algorithm. © 1982-2012 IEEE.

关键词： Quantum electronics

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Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing

Enabling Communication-Efficient Federated Learning via Dist...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yixuan Guan Xuefeng Liu Tao Ren Jianwei Niu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

Federated learning (FL) trains a shared global model by periodically aggregating gradients from local devices. Communication overhead becomes a principal bottleneck in FL since participating devices usually suffer from limited bandwidth and unreliable connections in uplink transmission. To address this problem, the gradient compression methods based on compressed sensing (CS) theory have been put forward recently. However, most existing CS-based works compress gradients independently, ignoring the gradient correlations between participants or adjacent communication rounds, which constrains the achievement of higher compression rates. In view of the above observation, we propose a novel gradient compression scheme named FedDCS, guided by distributed compressed sensing (DCS) theory. Following the design philosophy of separate encoding and joint decoding in DCS, FedDCS compresses gradients for participants in each round separately while reconstructing them at the central server jointly via fully exploiting correlated gradients from the previous round, which are known as side information (SI). Benefiting from this design, reconstruction performance is significantly improved with fewer decoding errors also iterations under the identical compression rate, and the total uploading bits to achieve model convergence are considerably reduced. Theoretical analysis and extensive experiments conducted on MNIST and Fashion-MNIST both verify the effectiveness of our approach.

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A Phase Estimation Algorithm for Quantum Speed-Up Multi-Party Computing

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computers, Materials & Continua 2021年第4期67卷 241-252页

作者： Wenbin Yu Hao Feng Yinsong Xu Na Yin Yadang Chen Zhi-Xin Yang Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Jiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering Michigan State UniversityEast Lansing48824MIUSA State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering University of Macao999078Macao

Security and privacy issues have attracted the attention of researchers in the field of IoT as the information processing scale grows in sensor *** computing,theoretically known as an absolutely secure way to store and transmit information as well as a speed-up way to accelerate local or distributed classical algorithms that are hard to solve with polynomial complexity in computation or *** this paper,we focus on the phase estimation method that is crucial to the realization of a general multi-party computing model,which is able to be accelerated by quantum algorithms.A novel multi-party phase estimation algorithm and the related quantum circuit are proposed by using a distributed Oracle operator with *** proved theoretical communication complexity of this algorithm shows it can give the phase estimation before applying multi-party computing efficiently without increasing any additional ***,a practical problem of multi-party dating investigated shows it can make a successful estimation of the number of solution in advance with zero communication complexity by utilizing its special statistic *** simulations present the correctness,validity and efficiency of the proposed estimation method.

关键词： Edge computing security multi-party computing quantum algorithm phase estimation communication complexity

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TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning Distillation

arXiv

引用

arXiv 2024年

作者： Xu, Shaoqing Li, Fang Huang, Peixiang Song, Ziying Yang, Zhi-Xin State Key Laboratory of Internet of Things for Smart City Department of Electromechanical Engineering University of Macau 999078 China School of Mechanical Engineering Beijing Institute of Technology China College of Engineering Peking University Beijing China School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China

Accurate multi-view 3D object detection is essential for applications such as autonomous driving. Researchers have consistently aimed to leverage LiDAR’s precise spatial information to enhance camera-based detectors through methods like depth supervision and bird-eye-view (BEV) feature distillation. However, existing approaches often face challenges due to the inherent differences between LiDAR and camera data representations. In this paper, we introduce the TiGDistill-BEV, a novel approach that effectively bridges this gap by leveraging the strengths of both sensors. Our method distills knowledge from diverse modalities(e.g., LiDAR) as the teacher model to a camera-based student detector, utilizing the Target Inner-Geometry learning scheme to enhance camera-based BEV detectors through both depth and BEV features by leveraging diverse modalities. Specially, we propose two key modules: an inner-depth supervision module to learn the low-level relative depth relations within objects which equips detectors with a deeper understanding of object-level spatial structures, and an inner-feature BEV distillation module to transfer high-level semantics of different keypoints within foreground targets. To further alleviate the domain gap, we incorporate both inter-channel and inter-keypoint distillation to model feature similarity. Extensive experiments on the nuScenes benchmark demonstrate that TiGDistill-BEV significantly boosts camera-based only detectors achieving a state-of-the-art with 62.8% NDS and surpassing previous methods by a significant margin. The codes is available at: https://***/Public-BOTs/*** Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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A Contrastive Learning Based Unsupervised Multi-View Stereo with Multi-Stage Self-Training Strategy

SSRN

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SSRN 2023年

作者： Wang, Zihang Luo, Haonan Wang, Xiang Zheng, Jin Ning, Xin Bai, Xiao School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing100191 China School of Computer and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China

Recent years, unsupervised multi-view stereo methods have achieved excellent success that can produce comparable results to earlier supervised ***, as unsupervised MVS uses image reconstruction as pretext task, it faces two vital drawbacks: RGB value, which is the the measurement of image, is not robust enough across views due to complicated environment like lighting conditions and reconstruction itself cannot reflect quality of depth estimation *** problems cause the actual optimization goal to diverge from the expected optimization goal, thus could impair the training *** enhance robustness of pretext task, we propose a contrastive learning based *** constraint adds featuremetric consistency across views by forcing the features between matching points similar and the features between unmatched points *** add linear properties to overall training procedure, we propose a multi-stage training strategy that uses pseudo label as supervision after unsupervised training at the *** the other hand, we adopt an iterative optimizer that proven to be quite effective in supervised MVS to accelerate ***, we conduct a series of experiments on the DTU dataset and Tanks and Temples dataset that demonstrate the efficiency and robustness of our method compared with the state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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