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Noise reduction of add-on acoustic black holes with different topologies based on combined Reduced Multibody System Transfer Matrix Method and Elementary Radiators Method

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Thin-Walled Structures 2025年 214卷

作者： Liu, Jianghong Zhang, Yanni Jia, Huiliang Rui, Xiaoting Yan, Xiaowei Wang, Guoping Liu, Yang Institute of Launch Dynamics Nanjing University of Science and Technology Jiangsu Nanjing China National Key Laboratory of Complex Multibody System Dynamics China China State Shipbuilding Corporation Systems Engineering Research Institute Beijing 100094 China School of Energy and Power Jiangsu University of Science and Technology Zhenjiang212003 China

Recently, add-on acoustic black holes (ABHs) have received increasing attentions since they would not reduce the thickness/stiffness of the main structure and are flexible to install. Most of the studies, nevertheless, were conducted from the numerical simulation and experimental perspectives. Efficient theoretical computational methods are still lacking, especially for noise characteristics of a host structure with an add-on ABH as a dynamics vibration absorber (DVA), also known as an ABH-DVA. Herein, a theoretical method is proposed for calculating the noise radiation from a combined system formed by a host structure and an ABH-DVA via combining the Reduced Multibody System Transfer Matrix Method (RMSTMM) and the Elementary Radiators Method (ERM) or RMSTMM-ERM. The RMSTMM-ERM is applied to combined systems with ABH-DVAs of three topologies (unilateral, tree-shaped, spiral), and the results are compared with those from two finite element methods (FEMs). It is revealed that for combined systems with ABH-DVA of all three topologies, the RMSTMM-ERM reduces the computation time by >100 times compared with FEMs, while maintaining a high computational accuracy. Besides, remarkable and broadband low-frequency noise reductions are observed for all three ABH-DVAs, especially the spiral one with a deep-subwavelength dimension (λ/200). The vibration spatial distributions and wavenumber spectra with and without the ABH-DVAs are further investigated to disclose the noise reduction mechanisms. This paper provides an efficient and versatile theoretical computation method and design tool for noise studies of ABHs with complex geometries and topologies. © 2025 Elsevier Ltd

关键词： Finite element method

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Microcomb-driven photonic chip for solving partial differential equations

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Advanced Photonics 2025年第1期7卷 62-73页

作者： Hongyi Yuan Zhuochen Du Huixin Qi Guoxiang Si Cuicui Lu Yan Yang Ze Wang Bo Ni Yufei Wang Qi-Fan Yang Xiaoyong Hu Qihuang Gong Beijing Institute of Technology School of PhysicsCenter for Interdisciplinary Science of Optical Quantum and NEMS IntegrationBeijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic SystemsKey Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements of Ministry of EducationBeijingChina Beijing Academy of Quantum Information Sciences Peking UniversityNano-optoelectronics Frontier Center of Ministry of EducationCollaborative Innovation Center of Quantum MatterState Key Laboratory for Mesoscopic Physics&Department of PhysicsBeijingChina Chinese Academy of Sciences Institute of MicroelectronicsBeijingChina Peking University Yangtze Delta Institute of Optoelectronics NantongChina Shanxi University Collaborative Innovation Center of Extreme OpticsTaiyuanChina Hefei National Laboratory HefeiChina

With the development of the big data era,the need for computation power is dramatically growing,especially for solving partial differential equations(PDEs),because PDEs are often used to describe complex systems and phenomena in both science and ***,it is still a great challenge for on-chip photonic solving of time-evolving PDEs because of the difficulties in big coefficient matrix photonic computing,high accuracy,and error *** overcome these challenges by realizing a microcomb-driven photonic chip and introducing time-division multiplexing and matrix partition techniques into PDE photonic solving,which can solve PDEs with a large coefficient matrix on a photonic chip with a limited *** PDEs,including the heat equation with the first order of time derivative,the wave equation with the second order of time derivative,and the nonlinear Burgers equation,are solved with an accuracy of up to 97%.Furthermore,the parallel solving of the Poisson equation and Laplace's equation is demonstrated experimentally on a single chip,with an accuracy of 95.9%and 95.8%,*** offer a powerful photonic platform for solving PDEs,which takes a step forward in the application of photonic chips in mathematical problems and will promote the development of on-chip photonic computing.

关键词： photonic computing silicon photonics partial differential equations

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Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年

作者： Yu, Zhou Ouyang, Xuecheng Shao, Zhenwei Wang, Meng Yu, Jun Hangzhou Dianzi University School of Computer Science Key Laboratory of Complex Systems Modeling and Simulation China Hangzhou Dianzi University HDU-ITMO Joint Institute China Hefei University of Technology School of Computer Science and Information Engineering China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China

Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the question, hence restricting the performance of their models. Recent works have resorted to using a powerful large language model (LLM) as an implicit knowledge engine to acquire the necessary knowledge for answering. Despite the encouraging results achieved by these methods, we argue that they have not fully activated the capacity of the blind LLM as the provided textual input is insufficient to depict the required visual information to answer the question. In this paper, we present Prophet—a conceptually simple, flexible, and general framework designed to prompt LLM with answer heuristics for knowledge-based VQA. Specifically, we first train a vanilla VQA model on a specific knowledge-based VQA dataset without external knowledge. After that, we extract two types of complementary answer heuristics from the VQA model: answer candidates and answer-aware examples. The two types of answer heuristics are jointly encoded into a formatted prompt to facilitate the LLM's understanding of both the image and question, thus generating a more accurate answer. By incorporating the state-of-the-art LLM GPT-3 [1], Prophet significantly outperforms existing state-of-the-art methods on four challenging knowledge-based VQA datasets. Prophet is general that can be instantiated with the combinations of different VQA models (i.e., both discriminative and generative ones) and different LLMs (i.e., both commercial and open-source ones). Moreover, Prophet can also be integrated with modern large multimodal models in different stages, which is named Prophet++, to further improve the capabilities on knowledge-based VQA tasks. © 1979-2012 IEEE.

关键词： Question answering

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A Unified Modeling Framework for Automated Penetration Testing

arXiv

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arXiv 2025年

作者： Wang, Yunfei Liu, Shixuan Wang, Wenhao Zhou, Changling Zhang, Chao Jin, Jiandong Zhu, Cheng National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan China Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Hunan China National University of Defense Technology Hunan China Tsinghua University Beijing China Computer Center Peking University Beijing China

The integration of artificial intelligence into automated penetration testing (AutoPT) has highlighted the necessity of simulation modeling for the training of intelligent agents, due to its cost-efficiency and swift feedback capabilities. Despite the proliferation of AutoPT research, there is a recognized gap in the availability of a unified framework for simulation modeling methods. This paper presents a systematic review and synthesis of existing techniques, introducing MDCPM to categorize studies based on literature objectives, network simulation complexity, dependency of technical and tactical operations, and scenario feedback and variation. To bridge the gap in unified method for multi-dimensional and multi-level simulation modeling, dynamic environment modeling, and the scarcity of public datasets, we introduce AutoPT-Sim, a novel modeling framework that based on policy automation and encompasses the combination of all sub dimensions. AutoPT-Sim offers a comprehensive approach to modeling network environments, attackers, and defenders, transcending the constraints of static modeling and accommodating networks of diverse scales. We publicly release a generated standard network environment dataset and the code of Network Generator. By integrating publicly available datasets flexibly, support is offered for various simulation modeling levels focused on policy automation in MDCPM and the network generator help researchers output customized target network data by adjusting parameters or fine-tuning the network generator. Copyright © 2025, The Authors. All rights reserved.

关键词： Intelligent agents

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引用

science Bulletin 2025年第4期70卷 492-495页

作者： Mucong Li Chanyuan Wu Peng Yin Junyan Qian Jiuliang Zhao Qian Wang Dong Xu Jinmei Su Xiaomei Leng Wenjie Zheng Xinping Tian Yan Zhao AlíDuarte-García Mengtao Li Xiaofeng Zeng Maigeng Zhou Department of Rheumatology and Clinical Immunology Peking Union Medical College HospitalChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100730China National Clinical Research Center for Dermatologic and Immunologic Diseases(NCRC-DID) Ministry of Science&TechnologyBeijing 100730China State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College HospitalBeijing 100730China Key Laboratory of Rheumatology and Clinical Immunology Ministry of EducationBeijing 100730China National Center for Chronic and Noncommunicable Disease Control and Prevention Chinese Center for Disease Control and PreventionBeijing 100032China Division of Rheumatology Mayo ClinicRochester55905USA

Systemic autoimmune diseases(sAIDs)are a spectrum of diseases with multi-organ involvement that share similar clinical manifestations[1].The capacity for diagnosis and treatment of sAIDs is closely associated with the level of regional development,especially health care service delivery[2].With increasing incidence and prevalence[3],[4],[5],sAIDs have become global health concern as leading causes of death in young adults and substantial contributors to premature death[6].To date,increasing health resources have been invested in sAID related clinical settings and research.

关键词： diseases autoimmune involvement

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EK-Net++: Real-time scene text detection with expand kernel distance and Epoch Adaptive Weight

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Expert systems with Applications 2025年 267卷

作者： Zhu, Boyuan Chen, Xi Tang, Quan Chen, C.L. Philip Liu, Fagui Computer Science and Engineering South China University of Technology Guangzhou510006 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518066 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Recently, scene text detection has received significant attention due to its wide applications. Accurate detection in complex scenes of multiple scales, orientations, and curvature remains a challenge. Component-based methods have emerged as the dominant approach for offering a more flexible way to achieve superior performance. However, Component-based methods employ the Vatti clipping algorithm for multi-instance training to handle the difficulty of arbitrary-shaped text. Yet we identify several bias results from these approaches called the "shrinked kernel" leading to decreased accuracy. Additionally, almost all text detection methods fail to consider the characteristics of different datasets and directly assign fixed loss weights to multi-instances. To address these limitations, we propose a new approach named Expand Kernel Network (EK-Net++), which presents the expand kernel distance map to compensate for the previous deficiency and incorporates three-stage regression to complete integral instance detection. Furthermore, we design the Epoch Adaptive Weight algorithm which dynamically distinguishes the characteristics of different datasets. EK-Net++ not only achieves precise positioning of arbitrary-shaped text but also strikes a balance between performance and speed. Evaluation results show that EK-Net++ attains state-of-the-art or competitive performance compared to other advanced methods, e.g., F-measure of 86.72% at 35.42 FPS on ICDAR 2015, F-measure of 85.83% at 40.13 FPS on CTW1500. © 2024 Elsevier Ltd

关键词： Epoch Adaptive Weight Expand kernel distance Real-time Scene text detection

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Suppression of heading errors in Bell-Bloom optically pumped free-induction-decay alkali-metal atomic magnetometers

arXiv

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arXiv 2025年

作者： Liu, Siqi Wang, Xueke Zhang, Xiangdong Xiao, Wei Sheng, Dong Department of Precision Machinery and Precision Instrumentation Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes University of Science and Technology of China Hefei230027 China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China Beijing Computational Science Research Center Beijing100193 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Center for Quantum Information Technology Peking University Beijing100871 China MIIT Key Laboratory of Complex-field Intelligent Sensing Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China

Heading errors of atomic magnetometers refer to the dependence of measurement results on the sensor orientation with respect to the external magnetic field. There are three main sources of such errors: the light shift effect, the linear nuclear-spin Zeeman effect, and the nonlinear Zeeman effect. In this work, we suppress the former two effects by using the Bell-Bloom optical pumping method and probe the atomic signals while the pumping beam is off, and focus on the heading error induced by nonlinear Zeeman effect while the sensor operates in the geomagnetic field range. We demonstrate several schemes to suppress this remaining heading error within 1 nT using a single magnetometer or a comagnetometer. In the magnetometer system, two schemes are developed to average out the horizontal atomic polarization in space or in time, respectively. In the comagnetometer system, we combine the simultaneously measured Larmor frequencies of two different kinds of alkali atoms to either suppress the heading error or extract the orientation of the pumping beam relative to the bias field. © 2025, CC BY.

关键词： Blooms (metal)

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Experimental study and molecular dynamics simulation:Thermal behaviors, volatile products and chlorine migration characteristics during pyrolysis and combustion of 2,4-dichlorophenoxyacetic acid waste salt

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Journal of Environmental Management 2025年 382卷 125277页

作者： Song, Yueyan Gu, Xiangyu He, Di Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education Institute of Environmental and Ecological Engineering Guangdong University of Technology Guangzhou510006 China Chinese Academy of Science Institute of Process Engineering State Key Lab Multiphase Complex Systems Beijing100190 China

An organochlorine pesticide, 2,4-Dichlorophenoxyacetic acid (24D), produces pesticide by-product waste salts that pose a potential risk of secondary contamination, creating significant challenges in disposal management. While thermal treatment has emerged as a viable method for managing 24D waste salt, reducing the thermal temperature and controlling the release of chlorine-containing organic compounds from volatile products remains a significant obstacle. In this work, the effects of pyrolysis and combustion on the 24D thermal treatment were explored by TGA-FTIR-GCMS coupling techniques and molecular dynamics calculations (ReaxFF). Combustion achieved a total organic carbon (TOC) removal rate of 93.17 % at 873K, which was higher than that achieved by pyrolysis. In terms of volatile products, the release of small molecule gases such as CO2, CO, CH4, and HCl is significantly enhanced during combustion, the HCl emission in the combustion process is 2.7 times that in the pyrolysis process. However, as the temperature of the combustion process increased, 24D synthesis reaction to form substances such as heterocyclic-Cl, the amount of which increased to 7.01 % at 873K. ReaxFF-MD simulations investigated the thermal treatment of 24D, revealing a complex pyrolysis and combustion mechanism, including multiple reaction pathways for dechlorination, dehydrogenation, dehydroxylation, and generation of chlorinated products HCl and CH3Cl. The combustion process can increase the free radicals and provide more degradation pathways in the reaction system, which promotes the migratory conversion of organic-Cl to inorganic-Cl in 24D. Therefore, combustion treatment with low decomposition temperature and easy degradation of chlorine-containing intermediate products is a feasible method to purify 24D WS. © 2025 Elsevier Ltd

关键词： Dechlorination

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Generating Targeted Universal Adversarial Perturbation against Automatic Speech Recognition via Phoneme Tailoring

Generating Targeted Universal Adversarial Perturbation again...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yujun Zhang Yanqu Chen Jiakai Wang Jin Hu Renshuai Tao Xianglong Liu State Key Laboratory of Complex & Critical Software Environment Beihang University China School of Computer Science and Engineering Beihang University China College of Computer Science Beijing University of Technology China Zhongguancun Laboratory China School of Computer and Information Technology Beijing Jiaotong University China Institute of Data Space Hefei Comprehensive National Science Center China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

There is a growing concern about adversarial attacks against automatic speech recognition (ASR) systems. Although research into targeted universal adversarial examples (AEs) has progressed, current methods are constrained by inefficient exploitation of audio features, demonstrating insufficient attack ability and robustness in the physical world. To solve this problem, we propose a phoneme-tailored attack (PTA) to improve the quality of the generated AEs. Specifically, to improve attack ability, we propose a Diverse Audio Composition Enrichment method, which enhances the utilization of audio features through phoneme-level slicing and recombination. To adapt AEs to complex environments, we propose a Natural Noise Pattern Guidance method to align AEs with natural noise patterns to improve their robustness. Experiments show that our method achieves an average accuracy of more than 72.34% and 98% with and without a norm constraint, and also demonstrates excellent performance in terms of generalization across datasets and resilience to MP3 compression.

关键词： Accuracy Target recognition Perturbation methods Noise Robustness Acoustics Speech processing Digital audio players Automatic speech recognition Resilience

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Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model

arXiv

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arXiv 2025年

作者： Liu, Chenyang Chen, keyan Zhao, Rui Zou, Zhengxia Shi, Zhenwei The Department of Aerospace Intelligent Science and Technology School of Astronautics The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shen Yuan Honors College of Beihang University Beijing100191 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Recently, generative foundation models have significantly advanced large-scale text-driven natural image generation and have become a prominent research trend across various vertical domains. However, in the remote sensing field, there is still a lack of research on large-scale text-to-image (text2image) generation technology. Existing remote sensing image-text datasets are small in scale and confined to specific geographic areas and scene types. Besides, existing text2image methods have struggled to achieve global-scale, multi-resolution controllable, and unbounded image generation. To address these challenges, this paper presents two key contributions: the Git-10M dataset and the Text2Earth foundation model. Git-10M is a global-scale image-text dataset comprising 10.5 million image-text pairs, 5 times larger than the previous largest one. The dataset contains essential resolution information and covers a wide range of geographic scenes and contains essential geospatial metadata, significantly surpassing existing datasets in both size and diversity. Building on Git-10M, we propose Text2Earth, a 1.3 billion parameter generative foundation model based on the diffusion framework to model global-scale remote sensing scenes. Text2Earth integrates a resolution guidance mechanism, enabling users to specify image resolutions. A dynamic condition adaptation strategy is proposed for training and inference to improve image generation quality. Text2Earth not only excels in zero-shot text2image generation but also demonstrates robust generalization and flexibility across multiple tasks, including unbounded scene construction, image editing, and cross-modal image generation. This robust capability surpasses previous models restricted to the basic fixed size and limited scene types. On the previous text2image benchmark dataset, Text2Earth outperfoms previous models with a significant improvement of +26.23 FID and +20.95% Zero-shot Cls-OA metric. Our project page is https://chen-y

关键词： Optical remote sensing

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