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Elastic computational metasurfaces for subwavelength differentiations

Physical Review B 2024年第16期109卷 L161108-L161108页

作者： Guangyuan Su Zongliang Du Yongquan Liu State Key Laboratory for Strength and Vibration of Mechanical Structures School of Aerospace Engineering Xi'an Jiaotong University Xi'an 710049 China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Department of Engineering Mechanics Dalian University of Technology Dalian 116023 China

Analog computations enable information processing with negligible energy costs and massively parallel architectures, but currently are limited to process macroscale waveforms with characteristic lengths much larger than the operating wavelength λ0. We explore here, in contrast, the differentiation of subwavelength waveforms by using an elastic computational metasurface. We find that the numerical aperture of metasurface governs the threshold of the characteristic length of waveforms, below which the metasurface outputs an identical differentiated pattern. Remarkably, for a subwavelength waveform below the threshold, the metasurface can locate the source because the differentiated pattern is of cylindrical wavefronts centered at the source, which can be harnessed to detect single or multiple subwavelength-scaled scatterers. The detectability reaches a deep subwavelength of 0.12λ0, and the localization error stays smaller than λ0. Our work elucidates the physical image of subwavelength differentiations, which may promote promising applications in nondestructive testing, signal processing, and computational acoustics.

关键词： Acoustic metamaterials Metasurfaces Sound detection

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How far are we to GPT-4V?Closing the gap to commercial multimodal models with open-source suites

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Science China(Information Sciences) 2024年第12期67卷 5-22页

作者： Zhe CHEN Weiyun WANG Hao TIAN Shenglong YE Zhangwei GAO Erfei CUI Wenwen TONG Kongzhi HU Jiapeng LUO Zheng MA Ji MA Jiaqi WANG Xiaoyi DONG Hang YAN Hewei GUO Conghui HE Botian SHI Zhenjiang JIN Chao XU Bin WANG Xingjian WEI Wei LI Wenjian ZHANG Bo ZHANG Pinlong CAI Licheng WEN Xiangchao YAN Min DOU Lewei LU Xizhou ZHU Tong LU Dahua LIN Yu QIAO Jifeng DAI Wenhai WANG State Key Laboratory for Novel Software Technology Nanjing University Shanghai AI Laboratory School of Computer Science Fudan University SenseTime Research Department of Information Engineering The Chinese University of Hong Kong Department of Electronic Engineering Tsinghua University

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model(MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements.(1) Strong vision encoder: we explored a continuous learning strategy for the large-scale vision foundation model — InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.(2) Dynamic high-resolution: we divide images into tiles ranging from 1 to 40 of 448×448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input.(3) High-quality bilingual dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images,and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in optical character recognition(OCR) and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary commercial models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks. Code and models are available at https://***/OpenGVLab/InternVL.

关键词： multimodal model open-source vision encoder dynamic resolution bilingual dataset

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New analytical solutions for free vibration of embedded magneto-electro-elastic cylindrical shells with step-wise thickness variations

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Applied Mathematics and Mechanics(English Edition) 2025年第3期46卷 447-466页

作者： Jufang JIA Huilin YIN Qinyu YU Jiabin SUN Xinsheng XU Zhenhuan ZHOU School of Mechanical Engineering and Automation Dalian Polytechnic UniversityDalian 116034Liaoning ProvinceChina State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentSchool of Mechanics and Aerospace EngineeringDalian University of TechnologyDalian 116024Liaoning ProvinceChina State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentSchool of Chemical EngineeringOcean and Life SciencesDalian University of TechnologyPanjin 124221Liaoning ProvinceChina

In recent years,magneto-electro-elastic(MEE)cylindrical shells with step-wise thicknesses have shown significant potential in the field of vibration energy *** aid the design of such energy harvesting devices,an accurate free vibration analysis of embedded MEE cylindrical shells with step-wise thicknesses is performed within the framework of symplectic *** using the Legendre transformation,a new known vector is defined to transform the higher-order partial differential governing equations into a set of lower-order ordinary differential ***,the original vibration analysis is regarded as an eigen problem in the symplectic space,and analytical solutions can be represented by the symplectic *** numerical examples,the new analytical solutions are compared with the existing results,and good agreement is ***,the effects of critical design parameters on free vibration characteristics are thoroughly *** numerical results can serve as benchmarks for the development of other approximate or numerical methods.

关键词： analytical solution free vibration magneto-electro-elastic(MEE)material cylindrical shell with variable thickness symplectic method

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swSpAMM:optimizing large-scale sparse approximate matrix multiplication on Sunway Taihulight

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Frontiers of Computer Science 2023年第4期17卷 29-41页

作者： Xiaoyan LIU Yi LIU Bohong YIN Hailong YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China

Although matrix multiplication plays an essential role in a wide range of applications,previous works only focus on optimizing dense or sparse matrix *** Sparse Approximate Matrix Multiply(SpAMM)is an algorithm to accelerate the multiplication of decay matrices,the sparsity of which is between dense and sparse *** addition,large-scale decay matrix multiplication is performed in scientific applications to solve cutting-edge *** optimize large-scale decay matrix multiplication using SpAMM on supercomputers such as Sunway Taihulight,we present swSpAMM,an optimized SpAMM algorithm by adapting the computation characteristics to the architecture features of Sunway ***,we propose both intra-node and inter-node optimizations to accelerate swSpAMM for large-scale *** intra-node optimizations,we explore algorithm parallelization and block-major data layout that are tailored to better utilize the architecture advantage of Sunway *** inter-node optimizations,we propose a matrix organization strategy for better distributing sub-matrices across nodes and a dynamic scheduling strategy for improving load balance across *** compare swSpAMM with the existing GEMM library on a single node as well as large-scale matrix multiplication methods on multiple *** experiment results show that swSpAMM achieves a speedup up to 14.5×and 2.2×when compared to xMath library on a single node and 2D GEMM method on multiple nodes,respectively.

关键词： approximate calculation sunway processor performance optimization

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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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Science China(Information Sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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Organoids revealed:morphological analysis of the profound next generation in-vitro model with artificial intelligence

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Bio-Design and Manufacturing 2023年第3期6卷 319-339页

作者： Xuan Du Zaozao Chen Qiwei Li Sheng Yang Lincao Jiang Yi Yang Yanhui Li Zhongze Gu State Key Laboratory of Bioelectronics School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210008China Key Laboratory of Environmental Medicine Engineering Ministry of EducationSchool of Public HealthSoutheast UniversityNanjing 210009China

In modern terminology,“organoids”refer to cells that grow in a specific three-dimensional(3D)environment in vitro,sharing similar structures with their source organs or *** themorphology or growth characteristics of organoids through a microscope is a commonly used method of organoid ***,it is difficult,time-consuming,and inaccurate to screen and analyze organoids only manually,a problem which cannot be easily solved with traditional *** intelligence(AI)technology has proven to be effective in many biological and medical research fields,especially in the analysis of single-cell or hematoxylin/eosin stained tissue *** used to analyze organoids,AI should also provide more efficient,quantitative,accurate,and fast *** this review,we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis ***,we will summarize the development from machine learning to deep learning and the advantages of the latter,and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and ***,we will discuss the limitations of current AI used in organoid research,as well as opportunities and future research directions.

关键词： Artificial intelligence Organoids Morphology Growth characteristics Deep learning Convolutional neural network

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(Information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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Simultaneous Bulk-and Surface-initiated Living Polymerization Studied with a Heterogeneous Stochastic Reaction Model

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Chinese Journal of Polymer Science 2024年第3期42卷 364-372,I0008页

作者： Jia-Shu Ma Zhi-Ning Huang Jia-Hao Li Bang-Ping Jiang Yan-Da Liao Shi-Chen Ji Xing-Can Shen State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources(Ministry of Education of China)School of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilin 541004China School of Computer Science and Engineering&School of Software Guangxi Normal UniversityGuilin 541004China

To better characterize the properties of surface-initiated polymers, simultaneous bulk-and surface-initiated polymerizations are usually carried out by assuming that the properties of the surface-initiated polymers resemble those of the bulk-initiated polymers. Through a Monte Carlo simulation using a heterogeneous stochastic reaction model, it was discovered that the bulk-initiated polymers exhibit a higher molecular weight and a lower dispersity than the corresponding surface-initiated polymers, which indicates that the equivalent assumption is invalid. Furthermore, the molecular weight distributions of the two types of polymers are also different, suggesting different polymerization mechanisms. The results can be simply explained by the heterogeneous distributions of reactants in the system. This study is helpful to better understand surface-initiated polymerization.

关键词： Surface-initiated polymerization Polymer brush Stochastic reaction model Heterogeneous polymerization Simultaneous polymerization

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Ultrasensitive electrospinning fibrous strain sensor with synergistic conductive network for human motion monitoring and human-computer interaction

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材料科学技术（英文版） 2025年第10期213卷 213-222页

作者： Jingwen Wang Shun Liu Zhaoyang Chen Taoyu Shen Yalong Wang Rui Yin Hu Liu Chuntai Liu Changyu Shen State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China China Astronaut Research and Training Center Beijing 100094China

With the rapid development of wearable electronic skin technology,flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection,medical diagnostics,and human-computer interaction owing to their outstanding sensing *** pa-per reports a strain sensor with synergistic conductive network,consisting of stable carbon nanotube dispersion(CNT)layer and brittle MXene layer by dip-coating and electrostatic self-assembly method,and breathable three-dimensional(3D)flexible substrate of thermoplastic polyurethane(TPU)fibrous membrane prepared through electrospinning *** MXene/CNT@PDA-TPU(MC@p-TPU)flexible strain sensor had excellent air permeability,wide operating range(0-450％),high sensitivity(Gauge Fac-tor,GF max=8089.7),ultra-low detection limit(0.05％),rapid response and recovery times(40 ms/60 ms),and excellent cycle stability and durability(10,000 cycles).Given its superior strain sensing capabilities,this sensor can be applied in physiological signals detection,human motion pattern recognition,and driv-ing exoskeleton *** addition,MC@p-TPU fibrous membrane also exhibited excellent photothermal conversion performance and can be used as a wearable photo-heater,which has far-reaching application potential in the photothermal therapy of human joint diseases.

关键词： Flexible strain sensors Synergistic conductive network Electrospinning fibrous membrane Motion monitoring Human-machine interface

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Alloy-induced reduction and anisotropy change of lattice thermal conductivity in Ruddlesden–Popper phase halide perovskites

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Frontiers of physics 2023年第6期18卷 183-191页

作者： Huimin Mu Kun Zhou Fuyu Tian Yansong Zhou Guoqi Zhao Yuhao Fu Lijun Zhang State Key Laboratory of Superhard Materials International Center of Computational Method and SoftwareCollege of PhysicsJilin UniversityChangchun 130012China State Key Laboratory of Integrated Optoelectronics Key Laboratory of Automobile Materials of MOEInternational Center of Computational Method and SoftwareCollege of Materials Science and EngineeringJilin UniversityChangchun 130012China

The effective modulation of the thermal conductivity of halide perovskites is of great importance in optimizing their optoelectronic device *** on first-principles lattice dynamics calculations,we found that alloying at the B and X sites can significantly modulate the thermal transport properties of 2D Ruddlesden−Popper(RP)phase halide perovskites,achieving a range of lattice thermal conductivity values from the lowest(κ_(c)=0.05 W·m^(−1)·K^(−1)@Cs_(4)AgBiI_(8))to the highest(κ_(a/b)=0.95 W·m^(−1)·K^(−1)@Cs4NaBiCl_(4)I_(4)).Compared with the pure RP-phase halide perovskites and three-dimensional halide perovskite alloys,the two-dimensional halide perovskite introduces more phonon branches through alloying,resulting in stronger phonon branch coupling,which effectively scatters phonons and reduces thermal *** can also dramatically regulate the thermal transport anisotropy of RP-phase halide perovskites,with the anisotropy ratio ranging from 1.22 to ***,analysis of the phonon transport modes in these structures revealed that the lower phonon velocity and shorter phonon lifetime were the main reasons for their low thermal *** work further reduces the lattice thermal conductivity of 2D pure RP-phase halide perovskites by alloying methods and provides a strong support for theoretical guidance by gaining insight into the interesting phonon transport phenomena in these compounds.

关键词： first-principles lattice dynamics calculations Boltzmann transport all-inorganic RP-phase halide perovskite alloys thermal conductivity

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