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5th International Conference on Image and Graphics Processing, ICIGP 2022

作者： Ge, Jing Wang, Qianxiang Tong, Jiahui Gao, Guangyu School of Computer Science and Technology Beijing Institute of Technology China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9781450395465

Vision Transformers constantly absorb the characteristics of convolutional neural networks to solve its shortcomings in translational invariance and scale invariance. However, dividing the image by a simple grid often destroys the position and scale features in the image at the beginning of the network. In this paper, we propose a vision transformer based on region proposal, which obtains the inductive bias in a simple way. Specifically, RPViT achieves locality and scale-invariance by extracting regions with locality using a traditional region proposal algorithm and deflating objects of different scales to the same scale by a bilinear interpolation algorithm. In addition, to enable the network to fully utilize and encode diverse candidate objects, a multi-class token approach based on orthogonalization is proposed and applied. Experiments on ImageNet demonstrate that RPViT outperforms baseline converters and related work. © 2022 ACM.

关键词： Convolutional neural networks

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Language-Queried Target Sound Extraction Without Parallel Training Data

Language-Queried Target Sound Extraction Without Parallel Tr...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ma, Hao Peng, Zhiyuan Li, Xu Li, Yukai Shao, Mingjie Kong, Qiuqiang Liu, Ju School of Information Science and Engineering Shandong University Qingdao China Department of Computer Science North Carolina State University NC United States ARC Lab Tencent PCG China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Language-queried target sound extraction (TSE) aims to extract specific sounds from mixtures based on language queries. Traditional fully-supervised training schemes require extensively annotated parallel audio-text data, which are labor-intensive. We introduce a parallel-data-free training scheme, requiring only unlabelled audio clips for TSE model training by utilizing the contrastive language-audio pre-trained model (CLAP). In a vanilla parallel-data-free training stage, target audio is encoded using the pre-trained CLAP audio encoder to form a condition embedding, while during testing, user language queries are encoded by CLAP text encoder as the condition embedding. This vanilla approach assumes perfect alignment between text and audio embeddings, which is unrealistic. Two major challenges arise from training-testing mismatch: the persistent modality gap between text and audio and the risk of overfitting due to the exposure of rich acoustic details in target audio embedding during training. To address this, we propose a retrieval-augmented strategy. Specifically, we create an embedding cache using audio captions generated by a large language model (LLM). During training, target audio embeddings retrieve text embeddings from this cache to use as condition embeddings, ensuring consistent modalities between training and testing and eliminating information leakage. Extensive experiment results show that our retrieval-augmented approach achieves consistent and notable performance improvements over existing state-of-the-art with better generalizability. © 2025 IEEE.

关键词： contrastive language-audio pre-training retrieval-augmented strategy target sound extraction uni-modal training for multi-modal tasks

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Mixed Structural Choice Operator: Enhancing Technology Mapping with Heterogeneous Representations

arXiv

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arXiv 2025年

作者： Hu, Zhang Pan, Hongyang Xia, Yinshui Wang, Lunyao Chu, Zhufei Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University Shanghai200433 China

—The independence of logic optimization and technology mapping poses a significant challenge in achieving high-quality synthesis results. Recent studies have improved optimization outcomes through collaborative optimization of multiple logic representations and have improved structural bias through structural choices. However, these methods still rely on technology-independent optimization and fail to truly resolve structural bias issues. This paper proposes a scalable and efficient framework based on Mixed Structural Choices (MCH). This is a novel heterogeneous mapping method that combines multiple logic representations with technology-aware optimization. MCH flexibly integrates different logic representations and stores candidates for various optimization strategies. By comprehensively evaluating the technology costs of these candidates, it enhances technology mapping and addresses structural bias issues in logic synthesis. Notably, the MCH-based lookup table (LUT) mapping algorithm set new records in the EPFL Best Results Challenge by combining the structural strengths of both And-Inverter Graph (AIG) and XOR-Majority Graph (XMG) logic representations. Additionally, MCH-based ASIC technology mapping achieves a 3.73% area and 8.94% delay reduction (balanced), 20.35% delay reduction (delay-oriented), and 21.02% area reduction (area-oriented), outperforming traditional structural choice methods. Furthermore, MCH-based logic optimization utilizes diverse structures to surpass local optima and achieve better results. Copyright © 2025, The Authors. All rights reserved.

关键词： Mapping

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A Method for Removing Complex Visible Watermarks of Electronic Map Based on Conditional Generative Adversarial Nets 29

A Method for Removing Complex Visible Watermarks of Electron...

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29th International Conference on Geoinformatics, Geoinformatics 2022

作者： Baode, Jiang Shaofen, Xu Liufeng, Tao Zhong, Xie National Engineering Research Center of Geographic Information System China University of Geosciences Wuhan430074 China School of Computer Science China University of Geosciences Wuhan430074 China State Key Laboratory of Geo-information Engineering Xi'an710054 China

ISBN: (数字)9798350309881

ISBN: (纸本)9798350309881

With the rapid development and extensive application of the electronic map technology, complex visible watermarks have been widely used in e-maps and map tiles delivered by map service providers to protect copyright. To evaluate the robustness and effectiveness of visible watermarks, many researches on watermark removal have attracted much attention in recent years. However, most of the existing watermark removal methods have the problems of watermark residue or repaired area being indistinct and unreal. To address these difficulties, this paper presented a deep neural network model based on conditional generative adversarial networks (cGANs) to remove complex visible watermark for electronic map tiles. The model is mainly consisted of a generator and a discriminator. The generator adopts a U-Net structure with jump connections to generate watermark-free maps based on watermarked maps, and the discriminator uses a full convolutional network based on region discrimination model to distinguish the differences between the generated watermark-free maps and real watermark-free ones. Specifically, a perceptual loss function was introduced to enhance the structural details of the generated maps. The experiments show that the proposed method can remove not only simple textual watermarks, but also colorful graphic watermarks with complex structures as well, and it does not need manual intervention and can achieve fast batch removal of a wide variety of watermarks for electronic maps. In addition, the detailed information contained in map tiles can be effectively preserved while acquiring high-quality watermark-free electronic map tiles. © 2022 IEEE.

关键词： Deep neural networks

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Optical spatiotemporal vortices

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eLight 2023年第1期3卷 156-168页

作者： Chenhao Wan Andy Chong Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Optical and Electronic Information and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074HubeiChina Department of Physics Pusan National UniversityGeumjeong-GuBusan 46241South Korea Zhangjiang Laboratory 100 Haike RoadShanghai 201204China Shanghai Key Lab of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China

Spatiotemporal vortices of light,featuring transverse orbital angular momentum(OAM)and energy circulation in the spatiotemporal domain,have received increasing attention *** experimental realization of the controllable generation of spatiotemporal vortices triggers a series of research in this *** review article covers the latest developments of spatiotemporal vortices of light ranging from theoretical physics,experimental generation schemes,and characterization methods,to applications and future *** new degree of freedom in photonic OAM endowed by spatiotemporal vortices paves the way to the discovery of novel physical mechanisms and photonic applications in light science.

关键词： Spatiotemporal optical vortex Orbital angular momentum Spin-orbit interaction Linear momentum density Toroidal vortex Topological charge

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Using Lock-Free Design for Throughput-Optimized Cache Eviction 25

Using Lock-Free Design for Throughput-Optimized Cache Evicti...

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Abstracts of the 2025 ACM SIGMETRICS International Conference on Measurement and Modeling of computer systems

作者： Chao Dong Fang Wang Hong Jiang Dan Feng Key Laboratory of Information Storage System (Wuhan National Laboratory for Optoelectronics) Engineering Research Center of data storage systems and Technology (School of Computer Science and Technology) Ministry of Education of China Huazhong University of Science and Techonolgy Wuhan Hubei China Department of Computer Science and Engineering University of Texas at Arlington Arlington Texas USA

ISBN: (纸本)9798400715938

This paper presents a practical approach to cache eviction algorithm design, called Mobius, that optimizes the concurrent throughput of caches and reduces cache operation latency by utilizing lock-free data structures, while maintaining high cache hit ratios. Mobius includes two key designs. First, Mobius employs two lock-free FIFO queues to manage cache items, ensuring that all cache operations are executed efficiently in concurrency. Second, Mobius integrates a consecutive detection mechanism that merges multiple modifications during eviction into a single operation, thereby reducing data races. The implementation of Mobius in CacheLib and RocksDB highlights its high concurrency in both synthetic and real-world workloads.

关键词： cache eviction policy

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Jointgraph: A DAG-based efficient consensus algorithm for consortium blockchains

Jointgraph: A DAG-based efficient consensus algorithm for co...

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作者： Xiang, Fu Huaimin, Wang Peichang, Shi Xue, Ouyang Xunhui, Zhang School of Computer National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex System Changsha China

The blockchain is a distributed ledger that records all transactions and operations in a shared manner. Public blockchains such as Bitcoin realize decentralization at the cost of mining overhead, which is not suitable for real-life scenarios requiring high throughput. Techniques such as the consortium blockchain improve efficiency through partial decentralization. However, the consensus algorithms used in the existing state-of-the-art consortium blockchains face many challenges when dealing with commercial applications. For example, the high communication overhead hinders the scalability of PBFT-based consensus algorithms even though they are efficient at small scale. Hashgraph, one of the most popular Directed Acyclic Graph-based (DAG-based) consensus algorithms, achieves good performance in scalability;however, it does not allow users' dynamic participation. To deal with these challenges, we propose Jointgraph, a Byzantine fault-tolerance consensus algorithm for consortium blockchains based on DAG. In Jointgraph, transactions are packed into events and validated by no less than 2/3 of all members. A supervisor is introduced in our design, who monitors member behaviors and improves consensus efficiency. Simulation results demonstrate that Jointgraph outperforms Hashgraph in both throughput and latency. © 2019 John Wiley & Sons, Ltd.

关键词： Blockchain

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Sal-Guide Diffusion: Saliency Maps Guide Emotional Image Generation through Adapter

Sal-Guide Diffusion: Saliency Maps Guide Emotional Image Gen...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiangru Lin Shenghua Zhong Yan Liu Gong Chen College of Computer Science Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science Shenzhen University Shenzhen China Department of Computer Science The Hong Kong Polytechnic University HK China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

The existing text-to-image generation methods based on stable diffusion yield better results in low-semantic prompt but often neglect the generation quality of high-semantic prompt such as emotional vocabulary, resulting in poor emotional image generation. In order to address this issue, we propose a novel approach called Sal-Guide Diffusion, which leverages saliency maps to guide emotional image generation with the goal of producing superior emotionally expressive images. In order to let the saliency maps guide the diffusion process, we introduce a lightweight adapter to extract emotional information from saliency maps and incorporate it into the diffusion process. Experimental results demonstrate that our proposed method generates higher-quality images across eight emotional dimensions, excelling in both generalization, emotional congruence, and subjective preference compared to stable diffusion or similar methods.

关键词： Vocabulary Image synthesis Buildings Diffusion processes Text to image Data mining

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A Stability Condition for Asynchronous and Aperiodic Sampled-Data Cyber-Physical systems Under DoS Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 397-407页

作者： Chen, Liuyu Zhang, Xiaodan Xiao, Feng North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China

This paper is concerned with the stability problem of cyber-physical systems (CPSs) exposed to denial-of-service (DoS) attacks. In-depth analysis and discussion are made under practical constraints of asynchronous and aperiodic sampling with measurement errors. DoS attacks, which are supposed to be aperiodic, interrupt communication on control channels. A sufficient condition in terms of DoS attack duration and frequency is derived to guarantee the asymptotic stability of the system by combining the Lyapunov's method, inequality techniques and an iterative method. The stability result is applicable to the estimation of the maximum allowable sampling intervals which are closely related to the convergence speed, and characterizes the trade-off between communication resources and stability performance. Finally, the effectiveness of the theoretical analysis is verified by two simulation examples. © 2023 IEEE.

关键词： Asymptotic stability

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BIG-MOE: BYPASS ISOLATED GATING MOE FOR GENERALIZED MULTIMODAL FACE ANTI-SPOOFING

arXiv

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arXiv 2024年

作者： Ma, Yingjie Yu, Zitong Lin, Xun Xie, Weicheng Shen, Linlin Great Bay University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address these issues effectively. We identified three limitations in traditional MoE approaches to multimodal FAS: (1) Coarse-grained experts’ inability to capture nuanced spoofing indicators;(2) Gated networks’ susceptibility to input noise affecting decision-making;(3) MoE’s sensitivity to prompt tokens leading to overfitting with conventional learning methods. To mitigate these, we propose the Bypass Isolated Gating MoE (BIG-MoE) framework, featuring: (1) Fine-grained experts for enhanced detection of subtle spoofing cues;(2) An isolation gating mechanism to counteract input noise;(3) A novel differential convolutional prompt bypass enriching the gating network with critical local features, thereby improving perceptual capabilities. Extensive experiments on four benchmark datasets demonstrate significant generalization performance improvement in multimodal FAS task. The code is released at https://***/murInJ/BIGMoE. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

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