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Researcher profile

Xing Hu

Xing Hu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceMachine LearningComputer VisionCryptography and SecurityDistributed, Parallel, and Cluster ComputingHardware ArchitectureData Structures and AlgorithmsNeural and Evolutionary Computingeess.SPInformation Theorymath.CAmath.FAmath.ITMultiagent Systems

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Published work

16 published item(s)

preprint2026arXiv

Discrete Diffusion for Complex and Congested Multi-Agent Path Finding with Sparse Social Attention

Multi-Agent Path Finding (MAPF) is a coordination problem that requires computing globally consistent, collision-free trajectories from individual start positions to assigned goal positions under combinatorial planning complexity. In dense environments, suboptimal initial plans induce compound conflicts that hinder feasible repair. For repair-based solvers like LNS2, initial plan quality critically affects downstream repair, yet this factor remains underexplored. We propose DiffLNS, a hybrid framework that integrates a discrete denoising diffusion probabilistic model (D3PM) with LNS2. The D3PM serves as an initializer with sparse social attention that learns a spatiotemporal prior over coordinated multi-agent action trajectories from expert demonstrations and samples multiple joint plans. Operating directly on the categorical action space, our discrete diffusion preserves the MAPF action structure and samples from a multimodal joint-plan distribution to produce diverse drafts well suited for neighborhood repair. These drafts act as warm starts for downstream repair, which completes unfinished trajectories and resolves remaining conflicts under hard MAPF constraints. Experimental results show that despite being trained only on instances with at most 96 agents, the initializer generalizes to scenarios with up to 312 agents at inference time. Across 20 complex and congested settings, DiffLNS achieves an average success rate of 95.8%, outperforming the strongest tested baseline by 9.6 percentage points and matching or exceeding all baselines in all 20 settings. To the best of our knowledge, this is the first work to leverage discrete diffusion for warm-starting an LNS-based MAPF solver.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

LGN-Net: Local-Global Normality Network for Video Anomaly Detection

Video anomaly detection (VAD) has been intensively studied for years because of its potential applications in intelligent video systems. Existing unsupervised VAD methods tend to learn normality from training sets consisting of only normal videos and regard instances deviating from such normality as anomalies. However, they often consider only local or global normality in the temporal dimension. Some of them focus on learning local spatiotemporal representations from consecutive frames to enhance the representation for normal events. But powerful representation allows these methods to represent some anomalies and causes miss detection. In contrast, the other methods are devoted to memorizing prototypical normal patterns of whole training videos to weaken the generalization for anomalies, which also restricts them from representing diverse normal patterns and causes false alarm. To this end, we propose a two-branch model, Local-Global Normality Network (LGN-Net), to simultaneously learn local and global normality. Specifically, one branch learns the evolution regularities of appearance and motion from consecutive frames as local normality utilizing a spatiotemporal prediction network, while the other branch memorizes prototype features of the whole videos as global normality by a memory module. LGN-Net achieves a balance of representing normal and abnormal instances by fusing local and global normality. In addition, the fused normality enables LGN-Net to generalize to various scenes more than exploiting single normality. Experiments demonstrate the effectiveness and superior performance of our method. The code is available online: https://github.com/Myzhao1999/LGN-Net.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis

Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision. The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. However, the quality of completed point clouds is still needed to be further enhanced to meet the practical utilization. Therefore, this work aims to conduct a comprehensive survey on various methods, including point-based, convolution-based, graph-based, and generative model-based approaches, etc. And this survey summarizes the comparisons among these methods to provoke further research insights. Besides, this review sums up the commonly used datasets and illustrates the applications of point cloud completion. Eventually, we also discussed possible research trends in this promptly expanding field.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection

Autonomous driving faces great safety challenges for a lack of global perspective and the limitation of long-range perception capabilities. It has been widely agreed that vehicle-infrastructure cooperation is required to achieve Level 5 autonomy. However, there is still NO dataset from real scenarios available for computer vision researchers to work on vehicle-infrastructure cooperation-related problems. To accelerate computer vision research and innovation for Vehicle-Infrastructure Cooperative Autonomous Driving (VICAD), we release DAIR-V2X Dataset, which is the first large-scale, multi-modality, multi-view dataset from real scenarios for VICAD. DAIR-V2X comprises 71254 LiDAR frames and 71254 Camera frames, and all frames are captured from real scenes with 3D annotations. The Vehicle-Infrastructure Cooperative 3D Object Detection problem (VIC3D) is introduced, formulating the problem of collaboratively locating and identifying 3D objects using sensory inputs from both vehicle and infrastructure. In addition to solving traditional 3D object detection problems, the solution of VIC3D needs to consider the temporal asynchrony problem between vehicle and infrastructure sensors and the data transmission cost between them. Furthermore, we propose Time Compensation Late Fusion (TCLF), a late fusion framework for the VIC3D task as a benchmark based on DAIR-V2X. Find data, code, and more up-to-date information at https://thudair.baai.ac.cn/index and https://github.com/AIR-THU/DAIR-V2X.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Demystifying Arch-hints for Model Extraction: An Attack in Unified Memory System

The deep neural network (DNN) models are deemed confidential due to their unique value in expensive training efforts, privacy-sensitive training data, and proprietary network characteristics. Consequently, the model value raises incentive for adversary to steal the model for profits, such as the representative model extraction attack. Emerging attack can leverage timing-sensitive architecture-level events (i.e., Arch-hints) disclosed in hardware platforms to extract DNN model layer information accurately. In this paper, we take the first step to uncover the root cause of such Arch-hints and summarize the principles to identify them. We then apply these principles to emerging Unified Memory (UM) management system and identify three new Arch-hints caused by UM's unique data movement patterns. We then develop a new extraction attack, UMProbe. We also create the first DNN benchmark suite in UM and utilize the benchmark suite to evaluate UMProbe. Our evaluation shows that UMProbe can extract the layer sequence with an accuracy of 95% for almost all victim test models, which thus calls for more attention to the DNN security in UM system.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Information retrieval for label noise document ranking by bag sampling and group-wise loss

Long Document retrieval (DR) has always been a tremendous challenge for reading comprehension and information retrieval. The pre-training model has achieved good results in the retrieval stage and Ranking for long documents in recent years. However, there is still some crucial problem in long document ranking, such as data label noises, long document representations, negative data Unbalanced sampling, etc. To eliminate the noise of labeled data and to be able to sample the long documents in the search reasonably negatively, we propose the bag sampling method and the group-wise Localized Contrastive Estimation(LCE) method. We use the head middle tail passage for the long document to encode the long document, and in the retrieval, stage Use dense retrieval to generate the candidate's data. The retrieval data is divided into multiple bags at the ranking stage, and negative samples are selected in each bag. After sampling, two losses are combined. The first loss is LCE. To fit bag sampling well, after query and document are encoded, the global features of each group are extracted by convolutional layer and max-pooling to improve the model's resistance to the impact of labeling noise, finally, calculate the LCE group-wise loss. Notably, our model shows excellent performance on the MS MARCO Long document ranking leaderboard.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Left fractional Sobolev space via Riemann$-$Liouville derivatives on time scales and its application to a fractional boundary value problem on time scales

We first prove the equivalence of two definitions of Riemann-Liouville fractional integral on time scales, then by the concept of fractional derivative of Riemann-Liouville on time scales, we introduce fractional Sobolev spaces, characterize them, define weak fractional derivatives, and show that they coincide with the Riemann-Liouville ones on time scales. Next, we prove equivalence of some norms in the introduced spaces and derive their completeness, reflexivity, separability and some imbeddings. Finally, as an application, by constructing an appropriate variational setting, using the mountain pass theorem and the genus properties, the existence of weak solutions for a class of Kirchhoff-type fractional p-Laplacian systems on time scales with boundary condition is studied, and three results of the existence of weak solutions for this problem is obtained.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Neural Program Synthesis with Query

Aiming to find a program satisfying the user intent given input-output examples, program synthesis has attracted increasing interest in the area of machine learning. Despite the promising performance of existing methods, most of their success comes from the privileged information of well-designed input-output examples. However, providing such input-output examples is unrealistic because it requires the users to have the ability to describe the underlying program with a few input-output examples under the training distribution. In this work, we propose a query-based framework that trains a query neural network to generate informative input-output examples automatically and interactively from a large query space. The quality of the query depends on the amount of the mutual information between the query and the corresponding program, which can guide the optimization of the query framework. To estimate the mutual information more accurately, we introduce the functional space (F-space) which models the relevance between the input-output examples and the programs in a differentiable way. We evaluate the effectiveness and generalization of the proposed query-based framework on the Karel task and the list processing task. Experimental results show that the query-based framework can generate informative input-output examples which achieve and even outperform well-designed input-output examples.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks

DNN-based video object detection (VOD) powers autonomous driving and video surveillance industries with rising importance and promising opportunities. However, adversarial patch attack yields huge concern in live vision tasks because of its practicality, feasibility, and powerful attack effectiveness. This work proposes Themis, a software/hardware system to defend against adversarial patches for real-time robust video object detection. We observe that adversarial patches exhibit extremely localized superficial feature importance in a small region with non-robust predictions, and thus propose the adversarial region detection algorithm for adversarial effect elimination. Themis also proposes a systematic design to efficiently support the algorithm by eliminating redundant computations and memory traffics. Experimental results show that the proposed methodology can effectively recover the system from the adversarial attack with negligible hardware overhead.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Toward Robust Spiking Neural Network Against Adversarial Perturbation

As spiking neural networks (SNNs) are deployed increasingly in real-world efficiency critical applications, the security concerns in SNNs attract more attention. Currently, researchers have already demonstrated an SNN can be attacked with adversarial examples. How to build a robust SNN becomes an urgent issue. Recently, many studies apply certified training in artificial neural networks (ANNs), which can improve the robustness of an NN model promisely. However, existing certifications cannot transfer to SNNs directly because of the distinct neuron behavior and input formats for SNNs. In this work, we first design S-IBP and S-CROWN that tackle the non-linear functions in SNNs' neuron modeling. Then, we formalize the boundaries for both digital and spike inputs. Finally, we demonstrate the efficiency of our proposed robust training method in different datasets and model architectures. Based on our experiment, we can achieve a maximum $37.7\%$ attack error reduction with $3.7\%$ original accuracy loss. To the best of our knowledge, this is the first analysis on robust training of SNNs.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

On Register Linearizability and Termination

In a seminal work, Golab et al. showed that a randomized algorithm that works with atomic objects may lose some of its properties if we replace the atomic objects that it uses with linearizable objects. It was not known whether the properties that can be lost include the important property of termination (with probability 1). In this paper, we first show that, for randomized algorithms, termination can indeed be lost. Golab et al. also introduced strong linearizability, and proved that strongly linearizable objects can be used as if they were atomic objects, even for randomized algorithms: they preserve the algorithm's correctness properties, including termination. Unfortunately, there are important cases where strong linearizability is impossible to achieve. In particular, Helmi et al. MWMR registers do not have strongly linearizable implementations from SWMR registers. So we propose a new type of register linearizability, called write strong-linearizability, that is strictly stronger than linearizability but strictly weaker than strong linearizability. We prove that some randomized algorithms that fail to terminate with linearizable registers, work with write strongly-linearizable ones. In other words, there are cases where linearizability is not sufficient but write strong-linearizability is. In contrast to the impossibility result mentioned above, we prove that write strongly-linearizable MWMR registers are implementable from SWMR registers. Achieving write strong-linearizability, however, is harder than achieving just linearizability: we give a simple implementation of MWMR registers from SWMR registers and we prove that this implementation is linearizable but not write strongly-linearizable. Finally, we prove that any linearizable implementation of SWMR registers is necessarily write strongly-linearizable; this holds for shared-memory, message-passing, and hybrid systems.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization

As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with high efficiency. Model compression has been proposed as a promising technique to improve the running efficiency via parameter and operation reduction. Whereas, this technique is mainly practiced in ANNs rather than SNNs. It is interesting to answer how much an SNN model can be compressed without compromising its functionality, where two challenges should be addressed: i) the accuracy of SNNs is usually sensitive to model compression, which requires an accurate compression methodology; ii) the computation of SNNs is event-driven rather than static, which produces an extra compression dimension on dynamic spikes. To this end, we realize a comprehensive SNN compression through three steps. First, we formulate the connection pruning and weight quantization as a constrained optimization problem. Second, we combine spatio-temporal backpropagation (STBP) and alternating direction method of multipliers (ADMM) to solve the problem with minimum accuracy loss. Third, we further propose activity regularization to reduce the spike events for fewer active operations. These methods can be applied in either a single way for moderate compression or a joint way for aggressive compression. We define several quantitative metrics to evaluation the compression performance for SNNs. Our methodology is validated in pattern recognition tasks over MNIST, N-MNIST, CIFAR10, and CIFAR100 datasets, where extensive comparisons, analyses, and insights are provided. To our best knowledge, this is the first work that studies SNN compression in a comprehensive manner by exploiting all compressible components and achieves better results.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

HyGCN: A GCN Accelerator with Hybrid Architecture

In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU. Guided by the characterization, we design a GCN accelerator, HyGCN, using a hybrid architecture to efficiently perform GCNs. Specifically, first, we build a new programming model to exploit the fine-grained parallelism for our hardware design. Second, we propose a hardware design with two efficient processing engines to alleviate the irregularity of Aggregation phase and leverage the regularity of Combination phase. Besides, these engines can exploit various parallelism and reuse highly reusable data efficiently. Third, we optimize the overall system via inter-engine pipeline for inter-phase fusion and priority-based off-chip memory access coordination to improve off-chip bandwidth utilization. Compared to the state-of-the-art software framework running on Intel Xeon CPU and NVIDIA V100 GPU, our work achieves on average 1509$\times$ speedup with 2500$\times$ energy reduction and average 6.5$\times$ speedup with 10$\times$ energy reduction, respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Randomized Consensus with Regular Registers

The well-known randomized consensus algorithm by Aspnes and Herlihy for asynchronous shared-memory systems was proved to work, even against a strong adversary, under the assumption that the registers that it uses are atomic registers. With atomic registers, every read or write operation is instantaneous (and thus indivisible). As pointed out by Golab et al. (2011), however, a randomized algorithm that works with atomic registers does not necessarily work if we replace the atomic registers that it uses with linearizable implementations of registers. This raises the following question: does the randomized consensus algorithm by Aspnes and Herlihy still work against a strong adversary if we replace its atomic registers with linearizable registers? We show that the answer is affirmative, in fact, we show that even linearizable registers are not necessary. More precisely, we prove that the algorithm by Aspnes and Herlihy works against a strong adversary even if the algorithm uses only regular registers.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

SEALing Neural Network Models in Secure Deep Learning Accelerators

Deep learning (DL) accelerators are increasingly deployed on edge devices to support fast local inferences. However, they suffer from a new security problem, i.e., being vulnerable to physical access based attacks. An adversary can easily obtain the entire neural network (NN) model by physically snooping the GDDR memory bus that connects the accelerator chip with DRAM memory. Therefore, memory encryption becomes important for DL accelerators on edge devices to improve the security of NN models. Nevertheless, we observe that traditional memory encryption solutions that have been efficiently used in CPU systems cause significant performance degradation when directly used in DL accelerators. The main reason comes from the big bandwidth gap between the GDDR memory bus and the encryption engine. To address this problem, our paper proposes SEAL, a Secure and Efficient Accelerator scheme for deep Learning. SEAL enhances the performance of the encrypted DL accelerator from two aspects, i.e., improving the data access bandwidth and the efficiency of memory encryption. Specifically, to improve the data access bandwidth, SEAL leverages a criticality-aware smart encryption scheme which identifies partial data that have no impact on the security of NN models and allows them to bypass the encryption engine, thus reducing the amount of data to be encrypted. To improve the efficiency of memory encryption, SEAL leverages a colocation mode encryption scheme to eliminate memory accesses from counters used for encryption by co-locating data and their counters. Our experimental results demonstrate that, compared with traditional memory encryption solutions, SEAL achieves 1.4 ~ 1.6 times IPC improvement and reduces the inference latency by 39% ~ 60%. Compared with a baseline accelerator without memory encryption, SEAL compromises only 5% ~ 7% IPC for significant security improvement.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints

As neural networks continue their reach into nearly every aspect of software operations, the details of those networks become an increasingly sensitive subject. Even those that deploy neural networks embedded in physical devices may wish to keep the inner working of their designs hidden -- either to protect their intellectual property or as a form of protection from adversarial inputs. The specific problem we address is how, through heavy system stack, given noisy and imperfect memory traces, one might reconstruct the neural network architecture including the set of layers employed, their connectivity, and their respective dimension sizes. Considering both the intra-layer architecture features and the inter-layer temporal association information introduced by the DNN design empirical experience, we draw upon ideas from speech recognition to solve this problem. We show that off-chip memory address traces and PCIe events provide ample information to reconstruct such neural network architectures accurately. We are the first to propose such accurate model extraction techniques and demonstrate an end-to-end attack experimentally in the context of an off-the-shelf Nvidia GPU platform with full system stack. Results show that the proposed techniques achieve a high reverse engineering accuracy and improve the one's ability to conduct targeted adversarial attack with success rate from 14.6\%$\sim$25.5\% (without network architecture knowledge) to 75.9\% (with extracted network architecture).

0 citations0 reviewsNo code linkedOpen access