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16th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2023

作者： Wang, Kun Ma, Danyue Lu, Jixi Zhang, Xu Wang, Shuying Lu, Chenning Wang, Jing School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China Hefei National Laboratory Hefei China Hangzhou Institute of National Extremely-weak Magnetic Field Infrastructure Hangzhou China Innovation Institute Beihang University Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology Hangzhou China Beijing Institute of Electronic System Engineering Beijing China

ISBN: (纸本)9798350327144

This research proposes a self-shielded saddle shim coil design technique, based on the magnetic field data of the target field points, to counterbalance the coupling effect of the magnetic shield on the coil in the utilization of atomic sensors. This coil is composed of two nested saddle coils: the main coil and the shielding coil. The optimization function, which characterizes the internal homogeneity of the magnetic field and attenuation of the external field, is determined by the magnitude of magnetic induction at both internal and external points of the coil. The NSGA-II optimization algorithm was then used to solve the geometrical parameters of the coil, with the optimization function serving as the optimization criterion to attain the parametric solution. Based on the proposed optimized method, a self-shielded coil was designed and tested in a cylindrical ferrite shield barrel. Results from both simulation and experimentation demonstrate a decrease in the coil constant due to the coupling effect of magnetic shield and coil from 58.96% to 0.54%, while the coil designed preserves the magnetic field uniformity in the central region in comparison to the classic saddle coil. This method is suitable for applications where the magnetic shield is combined with coils. © 2023 IEEE.

关键词： Magnetic fields

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DarkSAM: fooling segment anything model to segment nothing 24

DarkSAM: fooling segment anything model to segment nothing

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Ziqi Zhou Yufei Song Minghui Li Shengshan Hu Xianlong Wang Leo Yu Zhang Dezhong Yao Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (纸本)9798331314385

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM.

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Parallelism or Fairness? How to Be Friendly for SSDs in Cloud Environments

Parallelism or Fairness? How to Be Friendly for SSDs in Clou...

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IEEE International Conference on Cluster Computing

作者： Yang Zhou Fang Wang Zhan Shi Dan Feng Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of data storage systems and Technology Ministry of Education of China Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350358711

ISBN: (纸本)9798350358728

Modern SSDs achieve low latency and high throughput by utilizing multiple levels of SSD parallelism. Fairness is also a critical design consideration in cloud environments and has inspired great interest in recent years to study it. However, we find that excessive striving for SSD parallelism can inadvertently harm the SSD fairness due to the striped data layout. We observe from our experimental analysis that some common access patterns in cloud loads (e.g., high-intensity I/O) are not addressed by SSD parallelism, which can affect both SSD parallelism and fairness. In this paper, we analyze two address mapping policies with orthogonal characteristics from SSD parallelism and fairness perspectives. Based on extensive experiments and observations, we schedule a hybrid address mapping policy to achieve fairness of SSDs in cloud environments by distinguishing different I/O access modes and cold/hot data, while ensuring SSD performance. Experimental results on the latest block-level I/O traces show that our approach significantly improves performance and fairness by up to 77.3% and 62.8%, respectively, over the previous schemes with negligible cost. Meanwhile, it can also reduce the write amplification by 23.5% to 33.5%.

关键词： Schedules Layout Interference Parallel processing Predictive models Throughput data models Resource management Decision trees Low latency communication

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Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models 30

Step-by-Step: Controlling Arbitrary Style in Text with Large...

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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： Liu, Pusheng Wu, Lianwei Wang, Linyong Guo, Sensen Liu, Yang National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China Research & Development Institute of Northwestern Polytechnical University Shenzhen China Chongqing Science & Technology Innovation Center of Northwestern Polytechnical University China State Key Laboratory of Integrated Service Networks Xidian University China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University China

ISBN: (纸本)9782493814104

Recently, the autoregressive framework based on large language models (LLMs) has achieved excellent performance in controlling the generated text to adhere to the required style. These methods guide LLMs through prompt learning to generate target text in an autoregressive manner. However, this manner possesses lower controllability and suffers from the challenge of accumulating errors, where early prediction inaccuracies might influence subsequent word generation. Furthermore, existing prompt-based methods overlook specific region editing, resulting in a deficiency of localized control over input text. To overcome these challenges, we propose a novel three-stage prompt-based approach for specific region editing. To alleviate the issue of accumulating errors, we transform the text style transfer task into a text infilling task, guiding the LLMs to modify only a small portion of text within the editing region to achieve style transfer, thus reducing the number of autoregressive iterations. To achieve an effective specific editing region, we adopt both prompt-based and word frequency-based strategies for region selection, subsequently employing a discriminator to validate the efficacy of the selected region. Experiments conducted on several publicly competitive datasets for text style transfer task confirm that our proposed approach achieves state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.

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Distributed adaptive online learning for convex optimization with weight decay

Distributed adaptive online learning for convex optimization...

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作者： Shen, Xiuyu Li, Dequan Fang, Runyue Zhou, Yuejin Wu, Xiongjun School of Mathematics and Big Data Anhui University of Science and Technology Huainan China National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering The 802 Institute of Shanghai Academy of Space Flight Technology The Eighth Academy of China Aerospace Science and Technology Corporation Shanghai China

This paper investigates an adaptive gradient-based online convex optimization problem over decentralized networks. The nodes of a network aim to track the minimizer of a global time-varying convex function, and the communication pattern among nodes is captured as a connected undirected graph. To tackle such optimization problems in a collaborative and distributed manner, a weight decay distributed adaptive online gradient algorithm, called WDDAOG, is firstly proposed, which incorporates distributed optimization methods with adaptive strategies. Then, our theoretical analysis clearly illustrates the difference between weight decay and L2 regularization for distributed adaptive gradient algorithms. The dynamic regret bound for the proposed algorithm is further analyzed. It is shown that the dynamic regret bound for convex functions grows with order of (Formula presented.), where T and n represent the time horizon and the number of nodes associated with the network, respectively. Numerical experiments demonstrate that WDDAOG works well in practice and compares favorably to existing distributed online optimization schemes. © 2020 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd.

关键词： Convex optimization

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Highly stable and wide-range flexible pressure sensors with a gradient modulus and selective foaming layer

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Chemical Engineering Journal 2025年 515卷

作者： Wang, Meng Li, Wendong Lv, Yadong Li, Guangxian Zhao, Zhijun Wang, Yaobing Zhou, Qinghua Pu, Wei Kong, Miqiu School of Aeronautics and Astronautics Key Laboratory of Advanced Spatial Mechanism and Intelligent Spacecraft Ministry of Education National Key Laboratory of Advanced Polymer Materials Sichuan University Chengdu610065 China College of Polymer Science and Engineering National Key Laboratory of Advanced Polymer Materials Sichuan University Chengdu610065 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing100094 China

The service life and stability of flexible pressure sensors vary with the interfacial toughness of these multilayered structures, which are principally fabricated by adhesive bonding, hot melting, and interconnection. However, balancing the trade-off between interface toughness and sensing performance under complex stresses remains challenging. To address this issue, a foam sandwich structure with gradient modulus is constructed by integrating a thermoplastic polyurethane (TPU)/carbon nanotube electrode with a selectively foamed TPU/ionic liquid dielectric. The synthesis is fundamentally inspired by the molecular chain entanglements and hydrogen bonds formed at the electrode/dielectric interface owing to solvent diffusion due to secondary solubilization and differential foaming temperatures. The synthesized sensing structure exhibits ultra-high toughness (3000 J/m2) and dynamic impact resistance (1 J);meanwhile, the sensor shows an ultra-wide sensing range (0.02–2000 kPa), high sensitivity (1.7 nF/kPa), and excellent service stability up to 50,000 cycles under compressive stress (100 kPa), shear stress (30 kPa), bending stress (bending radius of 1.6 mm), and complex stress of 120 kPa (compression stress of 116 kPa and shear stress of 31 kPa). Additionally, on integration into robot grippers, the sensor provides accurate feedback on multi-stage object gripping and can resist shear stress while gripping high-weight objects. This study provides a new strategy for fabricating flexible pressure sensors that can be effectively used under complex stresses. © 2025 Elsevier B.V.

关键词： Shear bands

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3D Reconstruction for Scenes with Noticeable Differences in Elevation based on Few-view Satellite Images

3D Reconstruction for Scenes with Noticeable Differences in ...

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Information systems and Computing technology (ISCTech), International Conference on

作者： Xinqi Liu Maosheng Wei Luyun Tian Yiming Yan Key Laboratory of Advanced Marine Communication and Information Technology College of Information and Communication Engineering Harbin Engineering University HEU Harbin China Harbin Space Star Data System Science and Technology Company Ltd. Harbin China State Key Laboratory of Space-Ground Integrated Information Technology Beijing Institute of Satellite Information Engineering Beijing China

ISBN: (数字)9798350379860

ISBN: (纸本)9798350379877

Due to the difficulty of acquiring a sufficient amount of satellite images in a short period of time, three-dimensional (3D) reconstruction with few-view satellite images has a wide range of application areas. However, based on few-view satellite images, most existing algorithms, including dense matching and the neural radiation fields, lose their stability when facing scenes with noticeable differences in elevation which is abbreviated as NDE-scenes. The greater the elevation difference, the more challenging the reconstruction becomes. To solve the problem of poor reconstruction in NDE-scenes, we propose a new framework based on neural radiation fields in this paper, along with two improvement methods: the signed distance function (SDF) field generation network (SDFGN) optimization strategy and the shadow prior module. It enables accurate and robust reconstruction of NDE-scenes with fewer views. Comparison and ablation experiments on the DFC2019 dataset verify the advancements of the proposed methods and the effectiveness of each module.

关键词： Point cloud compression Computers Three-dimensional displays Accuracy Lighting Satellite images Image reconstruction Optimization Information systems

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Using Monotonic Neural Networks for Accurate and Efficient Passive Localization Performance Modeling

Using Monotonic Neural Networks for Accurate and Efficient P...

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IEEE Conference on Wireless Communications and Networking

作者： Hanyue Guo Rui Zhou Wenqiang Put Ming-Yi You JunKun Yan Hangzhou Institute of Technology Xidian University Hangzhou China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Shenzhen China National Key Laboratory of Electromagnetic Space Security Jiaxing China National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Integrated Sensing and Communication (ISAC) systems are at the forefront of next-generation wireless technologies, enhancing high-precision target localization. Accurate prediction of localization performance is crucial for the design and optimization of these systems. Traditionally, the Cramer- Rao Lower Bound (CRLB) has been used as a theoretical benchmark for estimating localization errors, but it often does not reflect actual errors encountered in practice. The Monte Carlo simulation method, while accurate, is computationally intensive and less adaptable to varying parameters. To bridge this gap, we introduce LocNet-Mono, a novel approach based on monotonic neural networks, designed specifically for predicting localization errors. This approach maintains a consistent, monotonic relationship between input and output features, addressing the shortcomings of traditional methods. Our numerical experiments validate the high accuracy and efficiency of LocNet-Mono, confirming its potential as a superior tool for performance prediction in ISAC systems.

关键词： Location awareness Wireless communication Wireless sensor networks Monte Carlo methods Accuracy Neural networks Predictive models Integrated sensing and communication Numerical models Optimization

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Prompt Alchemy: Automatic Prompt Refinement for Enhancing Code Generation

arXiv

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arXiv 2025年

作者： Ye, Sixiang Sun, Zeyu Wang, Guoqing Guo, Liwei Liang, Qingyuan Li, Zheng Liu, Yong Beijing University of Chemical Technology Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Peking University Beijing China

Code generation has gained increasing attention as a task to automate software development by transforming high-level descriptions into executable code. While large language models (LLMs) are effective in generating code, their performance heavily relies on the quality of input prompts. Current prompt engineering methods involve manual effort in designing prompts, which can be time-consuming and yield inconsistent results, potentially constraining the efficacy of LLMs in practical applications. This paper introduces Prochemy, a novel approach for automatically refining prompts iteratively to enhance code generation. Prochemy addresses the limitations of manual prompt engineering by automating the optimization process, ensuring prompt consistency during inference, and aligning with multi-agent systems. It iteratively refines prompts based on model performance, using an optimized final prompt to improve consistency and reliability across tasks. We evaluate Prochemy on both natural language-based code generation and code translation tasks using three series of LLMs. Results show that when combining Prochemy with existing approaches, it outperforms baseline prompting methods. It achieves improvements of 5.0% (GPT-3.5-Turbo) and 1.9% (GPT-4o) over zero-shot baselines on HumanEval. For the state-of-the-art LDB, Prochemy + LDB outperforms standalone methods by 1.2–1.8%. For code translation, Prochemy elevates GPT-4o’s performance on Java-to-Python (AVATAR) from 74.5 to 84.1 (+12.9%) and Python-to-Java from 66.8 to 78.2 (+17.1%). Furthermore, considering that the o1-mini model integrates prompt engineering techniques, Prochemy can continue to show good performance among it, further validating its effectiveness in code generation and translation tasks. Additionally, Prochemy is designed to be plug-and-play, optimizing prompts with minimal human intervention and seamlessly bridging the gap between simple prompts and complex frameworks. Copyright © 2025, The Authors. All right

关键词： Software design

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