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39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024

作者： Li, Zhilin Ma, Xutong Hu, Mengze Yan, Jun State Key Lab. of Computer Science Ins. of Software CAS University of Chinese Academy of Sciences Beijing China State Key Lab. of Computer Science Ins. of Software CAS Beijing China State Key Lab. of Computer Science Ins. of Software CAS Tech. Center of Software Eng. Ins. of Software CAS University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Sequence Containers (SC) in the C++ Standard Template Library (STL), such as the vector, are widely used in large-scale projects for their maintainability and flexibility. However, accessing the elements in an SC is bug-prone, as such operations will not check their boundaries during compilation or execution, which can lead to memory errors, such as buffer overflow problems. And these bugs are difficult to detect with available static analyzers, since the size of SCs and the target of iterators cannot be precisely tracked without accurate analysis of the behavior of SCs and *** address this problem, we propose a combined model of SC sizes and iterator targets by tracking them simultaneously through a set of meta-operations extracted from corresponding method calls, and report improper operation usages according to three bug patterns. We implement the approach as a static analyzer, Scasa, on the top of the Clang Static Analyzer (CSA) framework, and evaluate its effectiveness and efficiency against CSA and other state-of-the-art static analyzers on a benchmark composed of 2,230 manually created code snippets and eight popular open-source C++ projects with a lot of SC usages. The experimental results reveal that Scasa effectively identifies nearly all inherent bugs within the manual code snippets and generates 125 reports for these projects (with a time loss of 5 - 85%) where 72 of them are marked as correct with a manual revision. And to further confirm these correct reports, we also select some important ones for developers. These results show that accessing elements of SCs is bug-prone, and cooperatively tracking SC sizes and iterator targets can accurately detect these bugs with acceptable overhead. Copyright held by the owner/author(s).

关键词： C++ (programming language)

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SinGRAV: Learning a Generative Radiance Volume from a Single Natural Scene

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Journal of computer science & Technology 2024年第2期39卷 305-319页

作者：王玉洁陈学霖陈宝权 School of Computer Science and Technology Shandong UniversityQingdao 266237China State Key Laboratory of General Artificial Intelligence Beijing 100871China School of Intelligence Science and Technology Peking UniversityBeijing 100871China Tencent AI Lab Tencent Holdings LimitedShenzhen 518057China

We present SinGRAV, an attempt to learn a generative radiance volume from multi-view observations of a single natural scene, in stark contrast to existing category-level 3D generative models that learn from images of many object-centric scenes. Inspired by SinGAN, we also learn the internal distribution of the input scene, which necessitates our key designs w.r.t. the scene representation and network architecture. Unlike popular multi-layer perceptrons (MLP)-based architectures, we particularly employ convolutional generators and discriminators, which inherently possess spatial locality bias, to operate over voxelized volumes for learning the internal distribution over a plethora of overlapping regions. On the other hand, localizing the adversarial generators and discriminators over confined areas with limited receptive fields easily leads to highly implausible geometric structures in the spatial. Our remedy is to use spatial inductive bias and joint discrimination on geometric clues in the form of 2D depth maps. This strategy is effective in improving spatial arrangement while incurring negligible additional computational cost. Experimental results demonstrate the ability of SinGRAV in generating plausible and diverse variations from a single scene, the merits of SinGRAV over state-of-the-art generative neural scene models, and the versatility of SinGRAV by its use in a variety of applications. Code and data will be released to facilitate further research.

关键词： generative model neural radiance field 3D scene generation

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Rendering discrete participating media using geometrical optics approximation

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Computational Visual Media 2022年第3期8卷 425-444页

作者： Jie Guo Bingyang Hu Yanjun Chen Yuanqi Li Yanwen Guo Ling-Qi Yan State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science UC Santa BarbaraUSA

We consider the scattering of light in participating media composed of sparsely and randomly distributed discrete *** particle size is expected to range from the scale of the wavelength to several orders of magnitude greater,resulting in an appearance with distinct graininess as opposed to the smooth appearance of continuous *** fundamental issue in the physically-based synthesis of such appearance is to determine the necessary optical properties in every local *** these properties vary spatially,we resort to geometrical optics approximation(GOA),a highly efficient alternative to rigorous Lorenz–Mie theory,to quantitatively represent the scattering of a single *** enables us to quickly compute bulk optical properties for any particle size *** then use a practical Monte Carlo rendering solution to solve energy transfer in the discrete participating *** proposed framework is the first to simulate a wide range of discrete participating media with different levels of graininess,converging to the continuous media case as the particle concentration increases.

关键词： light scattering geometrical optics approximation(GOA) discrete participating media volume rendering

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m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt 30

m3P: Towards Multimodal Multilingual Translation with Multim...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Yang, Jian Guo, Hongcheng Yin, Yuwei Bai, Jiaqi Wang, Bing Liu, Jiaheng Liang, Xinnian Chai, Linzheng Yang, Liqun Li, Zhoujun State Key Lab of Software Development Environment Beihang University China Department of Computer Science University of British Columbia Canada

ISBN: (纸本)9782493814104

Multilingual translation supports multiple translation directions by projecting all languages in a shared space, but the translation quality is undermined by the difference between languages in the text-only modality, especially when the number of languages is large. To bridge this gap, we introduce visual context as the universal language-independent representation to facilitate multilingual translation. In this paper, we propose a framework to leverage the multimodal prompt to guide the Multimodal Multilingual neural Machine Translation (m3P), which aligns the representations of different languages with the same meaning and generates the conditional vision-language memory for translation. We construct a multilingual multimodal instruction dataset (InstrMulti102) to support 102 languages Our method aims to minimize the representation distance of different languages by regarding the image as a central language. Experimental results show that m3P outperforms previous text-only baselines and multilingual multimodal methods by a large margin. Furthermore, the probing experiments validate the effectiveness of our method in enhancing translation under the low-resource and massively multilingual scenario. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Visual languages

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HHLPar: Automated Theorem Prover for Parallel Hybrid Communicating Sequential Processes

arXiv

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arXiv 2024年

作者： Jin, Xiangyu Zhan, Bohua Wang, Shuling Zhan, Naijun Key Laboratory of System Software State Key Lab. of Computer Science ISCAS China University of Chinese Academy of Sciences China Huawei Technologies Co. Ltd. China National Key Laboratory of Space Integrated Information System ISCAS China School of Computer Science Peking University China

We present a tool called HHLPar for verifying hybrid systems modelled in Hybrid Communicating Sequential Processes (HCSP). HHLPar is built upon a Hybrid Hoare Logic for HCSP, which is able to reason about continuous-time properties of differential equations, as well as communication and parallel composition of parallel HCSP processes with the help of parameterised trace assertions and their synchronization. The logic was formalised and proved to be sound in Isabelle/HOL, which constitutes a trustworthy foundation for the verification conducted by HHLPar. HHLPar implements the Hybrid Hoare Logic in Python and supports automated verification: On one hand, it provides functions for symbolically decomposing HCSP processes, generating specifications for separate sequential processes and then composing them via synchronization to obtain the final specification for the whole parallel HCSP processes;On the other hand, it is integrated with external solvers for handling differential equations and real arithmetic properties. We have conducted experiments on a simplified cruise control system to validate the performance of the tool. Copyright © 2024, The Authors. All rights reserved.

关键词： Hybrid systems

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Joint Optimization of Compression, Transmission and Computation for Cooperative Perception Aided Intelligent Vehicular Networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8201-8214页

作者： Lu, Binbin Huang, Xumin Wu, Yuan Qian, Liping Zhou, Sheng Niyato, Dusit University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science Macau China Guangdong University of Technology School of Automation Guangzhou510006 China University of Macau State Key Lab of Internet of Things for Smart City Macau China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer Information Science Macau China Zhuhai UM Science and Technology AQ2 Research Institute Zhuhai519301 China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Nanyang Technological University School of Computer Science and Engineering Singapore

Cooperative perception is a promising paradigm to tackle the perception limitations of a single intelligent vehicle (IV) to enhance the driving safety and efficiency in intelligent vehicular networks. However, the real-time transmission and computation-intensive fusion of raw sensing data raise new challenges for satisfying the stringent delay requirement of delay-sensitive applications. In this paper, we formulate a joint optimization problem of the cooperative IV selection, compression ratio selection, transmit power control, task offloading decision and computation allocation to minimize the end-to-end delay consisting of compression, transmission and computation. In particular, to meet the perception probability requirement, the quality and quantity-aware matching algorithm is proposed to optimize the cooperative IV selection. To guarantee the queue stability of offloading tasks, we develop the Lyapunov optimization algorithm to determine the upper bound of the offloading tasks and the corresponding optimal computation allocation. The Lyapunov aided deep reinforcement learning algorithm is further proposed to dynamically adjust the task offloading decision and compression ratio selection to minimize the end-to-end delay with the transmit power control being optimized by the successive convex approximation algorithm. Simulation results demonstrate that, compared to several benchmark algorithms, our proposed algorithm achieves the lowest end-to-end delay while guaranteeing the requirements on perception probability and queue stability effectively. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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Retrospection on the Performance Analysis Tools for Large-Scale HPC Programs 31

Retrospection on the Performance Analysis Tools for Large-Sc...

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31st Annual IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2024

作者： Xuan, Zhibo You, Xin Yang, Hailong Li, Mingzhen Luan, Zhongzhi Liu, Yi Qian, Depei School of Computer Science and Engineering Beihang University China State Key Lab of Processors Institute of Computing Technology Chinese Academy China

ISBN: (纸本)9798331509095

As the performance gap between hardware and software widens, performance analysis tools are essential for understanding the behavior of large-scale High-Performance Computing (HPC) programs. These tools provide insights into the performance bottlenecks and help in optimizing the performance of the programs. In this paper, we present a comprehensive study of performance analysis tools for large-scale HPC systems including both sampling-based and instrumentation-based tools that are commonly adopted in the HPC community. We investigate the abundance and overheads of data collection as well as the analysis capabilities of HPCToolkit, TAU, and Scalasca with representative programs at scale. Our study shows that different performance analysis tools have distinct strengths and weaknesses, and the choice of a performance analysis tool depends on the specific requirements of the user. We also discuss the challenges and future directions in the field of performance analysis tools for large-scale HPC systems. © 2024 IEEE.

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Spk2ImgMamba: Spiking Camera Image Reconstruction with Multi-Scale state Space Models

Spk2ImgMamba: Spiking Camera Image Reconstruction with Multi...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

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

ISBN: (纸本)9798331510831

As a bio-inspired vision sensor, the spiking camera has showcased remarkable capability in high-speed imaging with a sampling rate of 40,000 Hz. Reconstructing clear images from continuous spike streams, which is obtained by each photosensor continuously detecting photons and firing them asynchronously, has garnered significant attention. Despite promising results, existing spike-to-image reconstruction methods face challenges in balancing global receptive fields and efficient computation due to the inherent limitations of their backbones. Recently, due to powerful long-range modeling and linear complexity, the state space model (SSM) has emerged as a competitive alternative to CNNs and Transformers. In this paper, we propose a lightweight spike-to-image reconstruction network that harnesses Mamba as the backbone. Our approach sequentially executes three core modules: temporal information integration, spatial feature enhancement, and progressive image reconstruction. The former accumulates cues across diverse temporal windows to explore both long-term and short-term contexts. Subsequently, to model global dependencies while heightening local detail perception, we develop a multi-scale SSM block characterized by multi-scale multi-direction scanning, which effectively boosts spatial feature representations. Finally, intensity images are decoded progressively from the enhanced light-intensity features. Extensive experiments on both synthetic and real-captured data demonstrate that our approach achieves state-of-the-art performance, with only 10% of the network parameters and nearly two orders of magnitude less computational effort. The code will be available at https://***/interstellarH/Spk2ImgMamba. © 2025 IEEE.

关键词： Bioimaging

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FAN: A Lightweight Authenticated Cryptographic Algorithm 1

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Cryptographer's Track at the RSA Conference, CT-RSA 2021

作者： Jiao, Lin Feng, Dengguo Hao, Yonglin Gong, Xinxin Du, Shaoyu State Key Laboratory of Cryptology Beijing100878 China State Key Laboratory of Computer Science ISCAS Beijing China

ISBN: (数字)9783030755393

ISBN: (纸本)9783030755386

The wide application of the low-end embedded devices has largely stimulated the development of lightweight ciphers. In this paper, we propose a new lightweight authenticated encryption with additional data (AEAD) algorithm, named as Fan, which is based on a first non-Grain-like small-state stream cipher that adopts a novel block-wise structure, inspired by the 4-blade daily electric fan. It takes a 128-bit key, a 64-bit initial vector (IV), and a 192-bit state, promising 128-bit security and up to 72-bit authentication tag with the IV-respecting restriction. It consists of a nonlinear spindle, four linear blades and an accumulator, and updates by constant mutual feedbacks between the linear and nonlinear parts, which rapidly provides highly confused level by parallel diffusing the fastest-changing state of spindle. The key is used both in the initialization and generation phases as part of input and state respectively, making Fan suitable for resource-constrained scenarios with internal state diminishment but no security loss. A thorough security evaluation of the entire AEAD mode is provided, which shows that Fan can achieve enough security margin against known attacks. Furthermore, Fan can be implemented efficiently not only in hardware environments but also in software platforms, whose operations are carefully chosen for bit-slice technique, especially the S-box is newly designed efficiently implemented by logic circuit. The hardware implementation requires about 2327 GE on 90 nm technology with a throughput of 9.6 Gbps. The software implementation runs about 8.0 cycle/byte. © 2021, Springer Nature Switzerland AG.

关键词： Authentication

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ParameterNet: Parameters Are All You Need

arXiv

引用

arXiv 2023年

作者： Han, Kai Wang, Yunhe Guo, Jianyuan Wu, Enhua Huawei Noah’s Ark Lab Canada The University of Sydney Australia State Key Lab of Computer Science ISCAS University of Macau China

The large-scale visual pretraining has significantly improve the performance of large vision models. However, we observe the low FLOPs pitfall that the existing low-FLOPs models cannot benefit from large-scale pretraining. In this paper, we introduce a novel design principle, termed ParameterNet, aimed at augmenting the number of parameters in large-scale visual pretraining models while minimizing the increase in FLOPs. We leverage dynamic convolutions to incorporate additional parameters into the networks with only a marginal rise in FLOPs. The ParameterNet approach allows low-FLOPs networks to take advantage of large-scale visual pretraining. Furthermore, we extend the ParameterNet concept to the language domain to enhance inference results while preserving inference speed. Experiments on the large-scale ImageNet-22K have shown the superiority of our ParameterNet scheme. For example, ParameterNet-600M can achieve higher accuracy on ImageNet than the widely-used Swin Transformer (81.6% vs. 80.9%) and has much lower FLOPs (0.6G vs. 4.5G). In the language domain, LLaMA-1B enhanced with ParameterNet achieves 2% higher accuracy over vanilla LLaMA. The code will be released at https://***/. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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