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arXiv 2021年

作者： Mondal, Chandana Moshe, Michael Procaccia, Itamar Roy, Saikat Shang, Jin Zhang, Jie Dept. of Chemical Physics The Weizmann Institute of Science Rehovot76100 Israel Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem9190 Israel Center for OPTical IMagery Analysis and Learning Northwestern Polytechnical University Xi’an710072 China Department of Chemical Engineering Indian Institute of Technology Punjab Ropar140001 India School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute of Natural Sciences School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China

The concept of mechanical screening is widely applied in solid-state systems. Examples include nucleation of defects in crystalline materials, scars and pleats in curved crystals, wrinkles in strongly confined thin sheets, and cell-rearrangements in biological tissue. Available theories of such screening usually contain a crucial ingredient, which is the existence of an ordered reference state, with respect to which screening elements nucleate to release stresses. In contradistinction, amorphous materials in which a unique reference state does not exist, nevertheless exhibits plastic events that act as screening geometric charges with significant implications on the mechanical response. In a recent paper [Phys. Rev. E 104, 024904] it was proposed that mechanical strains in amorphous solids can be either weakly or strongly screened by the formation of low or high density of plastic events. At low densities the screening effect is reminiscent of the role of dipoles in dielectrics, in only renormalizing the elastic moduli. The effect of high density screening has no immediate electrostatic analog and is expected to change qualitatively the mechanical response, as seen for example in the displacement field. On the basis of experiments and simulations, we show that in granular matter, strong screening results in significant deviation from elasticity theory. The theoretical analysis, which accounts for an emergent inherent length scale, the experimental measurements and the numerical simulations of frictional granular amorphous assemblies are in agreement with each other, and provide a strong support for the novel continuum theory. © 2021, CC BY.

关键词： Amorphous materials

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Inferring network structures via signal Lasso

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Physical Review Research 2021年第4期3卷 043210-043210页

作者： Lei Shi Chen Shen Libin Jin Qi Shi Zhen Wang Stefano Boccaletti School of Statistics and Mathematics Yunnan University of Finance and Economics Kunming 650221 China Interdisciplinary Research Institute of Data Science Shanghai Lixin University of Accounting and Finance Shanghai 201209 China Center for Optical Imagery Analysis and Learning Northwestern Polytechnical University Xi'an 710072 China Institute for Complex Systems CNR Via Madonna del Piano 10 50019 Florence Italy Unmanned Systems Research Institute Northwestern Polytechnical University Xi'an 710072 China Moscow Institute of Physics and Technology Institutskiy pereulok Dolgoprudny Moscow Region 141701 Russia

Inferring the connectivity structure of networked systems from data is an extremely important task in many areas of science. Most real-world networks exhibit sparsely connected topologies, with links between nodes that in some cases may even be associated with a binary state (0 or 1, denoting, respectively, the absence or presence of a connection). Such un-weighted topologies are elusive to classical reconstruction methods such as Lasso or compressed sensing techniques. We here introduce an approach called signal Lasso, in which the estimation of the signal parameter is subjected to 0 or 1 values. The theoretical properties and algorithm of the proposed method are studied in detail. Applications of the method are illustrated for an evolutionary game and synchronization dynamics in several synthetic and empirical networks, for which we show that our strategy is reliable and robust and outperforms the classical approaches in terms of accuracy and mean square errors.

关键词： Dynamics of networks Network structure Synchronization

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Emergence of nonlinear crossover under epidemic dynamics in heterogeneous networks

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Physical Review E 2020年第5期102卷 052311-052311页

作者： Zhen Su Chao Gao Jiming Liu Tao Jia Zhen Wang Jürgen Kurths The Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xian 710072 China The Potsdam Institute for Climate Impact Research (PIK) Potsdam 11473 Germany College of Computer and Information Science Southwest University Chongqing 400715 China Department of Computer Science Hong Kong Baptist University Kowloon Tong Hong Kong The Institute of Physics Humboldt University of Berlin Berlin 12489 Germany Nizhny Novgorod State University Nizhny Novgorod Russia

Potential diffusion processes of real-world systems are relevant to the underlying network structure and dynamical mechanisms. The vast majority of the existing work on spreading dynamics, in response to a large-scale network, is built on the condition of the infinite initial state, i.e., the extremely small seed size. The impact of an increasing seed size on the persistent diffusion has been less investigated. Based on classical epidemic models, this paper offers a framework for studying such impact through observing a crossover phenomenon in a two-diffusion-process dynamical system. The two diffusion processes are triggered by nodes with a high and low centrality, respectively. Specifically, given a finite initial state in networks with scale-free degree distributions, we demonstrate analytically and numerically that the diffusion process triggered by low centrality nodes pervades faster than that triggered by high centrality nodes from a certain point. The presence of the crossover phenomenon reveals that the dynamical process under the finite initial state is far more than the vertical scaling of the spreading curve under an infinite initial state. Further discussion emphasizes the persistent infection of individuals in epidemic dynamics as the essential reason rooted in the crossover, while the finite initial state is the catalyst directly leading to the emergence of this phenomenon. Our results provide valuable implications for studying the diversity of hidden dynamics on heterogeneous networks.

关键词： Chaos Epidemic Network diffusion

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Target localization intervention and prognosis evaluation for an individual with mild cognitive impairment

Brain‐X

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Brain‐X 2023年第3期1卷

作者： Weiping Wang Haiyan Zhao Chang He Yuanbo Cui Zhen Wang Alexander Hramov Ping Luan Xiong Luo Jipeng Ouyang Kurths Jürgen School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan Guangdong China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Contribution: Writing - review & editing Department of Neurology Peking University Third Hospital Beijing China Contribution: Resources Zhen Wang The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi 710072 China. Contribution: Writing - original draft The 6th Research Institute of China Electronics Corporation Beijing China The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi China Chang He School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China. Contribution: Investigation Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Saratov State Medical University Saratov Russia Contribution: Data curation Guangdong Second Provincial General Hospital & Health Science Center Shenzhen Guangdong China Department of Neurology Shunde Hospital Southern Medical University Foshan Guangdong China Institute of Physics Humboldt-University Berlin Germany Potsdam Institute for Climate Impact Research Potsdam Germany

Currently, no specific treatments are available for Alzheimer's disease (AD). Mild cognitive impairment (MCI), the preclinical stage of AD, has a high possibility of reversing symptoms through neural regulation. A state dynamics model for single brain regions was developed to simulate blood oxygen level-dependent signals in a patient with early mild cognitive impairment. Subsequently, the analysis of functional connections was used to comprehensively consider multiple complex network centralities to locate the intervention targets, and a multiple brain region collaborative control scheme was designed. Finally, the reliability and effectiveness of the intervention were verified at the brain region and subnetwork levels. This technique provides a basis for future clinical diagnosis and treatment of AD and MCI.

关键词： adaptive pinning control Alzheimer's disease BOLD signals computational modeling Sub_network

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Erratum to: Investigating the effects of updating rules on cooperation by incorporating interactive diversity

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The European Physical Journal B 2021年第4期94卷 1-1页

作者： Peican Zhu Xin Hou Yangming Guo Jiwei Xu Jinzhuo Liu School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi’an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China School of Software Yunnan University Kunming China

A Correction to this paper has been published https://***/10.1140/epjb/s10051-021-00059-1

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CROSS-MODAL MESSAGE PASSING FOR TWO-STREAM FUSION

CROSS-MODAL MESSAGE PASSING FOR TWO-STREAM FUSION

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Dong Wang Yuan Yuan Qi Wang School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL)

ISBN: (纸本)9781538646595

Processing and fusing information among multi-modal is a very useful technique to achieving high performance in many computer vision problem. In order to tackle multi-modal information more effectively, we introduce a novel framework for multi-modal fusion: Cross-modal Message Passing (CMMP). Specifically, we propose a cross-modal message passing mechanism to fuse two-stream network for action recognition, which composes of an appearance modal network (RGB image) and a motion modal (optical flow image) network. The objectives of individual networks in this framework are two-fold: a standard classification objective and a competing objective. The classification object ensures that each modal network predicts the true action category while the competing objective encourages each modal network to outperform the other one. We quantitatively show that the proposed CMMP fuse the traditional two-stream network more effectively, and outperforms all existing two-stream fusion method on UCF-101 and HMDB-51 datasets.

关键词： message passing action recognition message passing UCF 101 images Fusion reactions Fusion, biological FUSION fusion (melting) Cross-modal Network computer vision HIGH-PERFORMANCE Fuses

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Patch-based topic model for group detection

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science China(Information sciences) 2017年第11期60卷 235-241页

作者： Mulin CHEN Qi WANG Xuelong LI School of Computer Science and Center for Optical Imagery Analysis and Learning Northwestern Polytechnical University Unmanned System Research Institute Northwestern Polytechnical University Center for Optical Imagery Analysis and Learning Xi'an Institute of Optics and Precision MechanicsChinese Academy of Sciences

Pedestrians in crowd scenes tend to connect with each other and form coherent groups. In order to investigate the collective behaviors in crowds, plenty of studies have been conducted on group ***, most of the existing methods are limited to discover the underlying semantic priors of individuals. By segmenting the crowd image into patches, this paper proposes the Patch-based Topic Model(PTM) for group detection. The main contributions of this study are threefold:(1) the crowd dynamics are represented by patchlevel descriptor, which provides a macroscopic-level representation;(2) the semantic topic label of each patch are inferred by integrating the Latent Dirichlet Allocation(LDA) model and the Markov Random Fields(MRF);(3) the optimal group number is determined automatically with an intro-class distance evaluation *** results on real-world crowd videos demonstrate the superior performance of the proposed method over the state-of-the-arts.

关键词： group detection collective behavior crowd analysis latent topic

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A generalized power iteration method for solving quadratic problem on the Stiefel manifold

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science China(Information sciences) 2017年第11期60卷 146-155页

作者： Feiping NIE Rui ZHANG Xuelong LI School of Computer Science and Center for OPTical IMagery Analysis and Learning(OPTIMAL) Northwestern Polytechnical University Center for OPTical IMagery Analysis and Learning (OPTIMAL) State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision MechanicsChinese Academy of Sciences

In this paper, we first propose a novel generalized power iteration(GPI) method to solve the quadratic problem on the Stiefel manifold(QPSM) as minWTW =ITr(WTAW-2WTB) along with the theoretical analysis. Accordingly, its special case known as the orthogonal least square regression(OLSR) is under further investigation. Based on the aforementioned studies, we then majorly focus on solving the unbalanced orthogonal procrustes problem(UOPP). As a result, not only a general convergent algorithm is derived theoretically but the efficiency of the proposed approach is verified empirically as well.

关键词： quadratic problem Stiefel manifold power iteration procrustes problem orthogonal least square regression

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Edge-centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment

Brain-X

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Brain-X 2023年第3期1卷

作者： Wang, Weiping Du, Ruiying Wang, Zhen Luo, Xiong Zhao, Haiyan Luan, Ping Ouyang, Jipeng Liu, Song School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School Beijing University of Science and Technology Guangdong Shunde China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Shaanxi Xi'an China Department of Neurology Peking University Third Hospital Beijing China Guangdong Second People's Hospital Guangdong Guangzhou China Department of Neurology Shunde Hospital Southern Medical University Guangdong Shunde China Southeast Asia Project Manager Department of China Petroleum Pipeline Engineering Co. Ltd. Hebei Langfang China

Most neuroimaging studies of the pathogenesis of early mild cognitive impairment (EMCI) rely on a node-centric network model, which only calculates correlations between brain regions. Considering the interaction of low-order correlations between pairs of brain regions, we use an edge-centric network model to study high-order functional network correlations. Here, we compute edge time series (eTS) to obtain overlapping communities and study the relationship between subnetworks and communities in space. Then, based on the overlapping communities, we calculate the normalized entropy to measure the diversity of each node. Next, we compute the high-amplitude co-fluctuation of the eTS to explore the pattern of brain activity with temporal precision. Our results show that the normal control and EMCI patients differ in brain regions, subnetworks, and the whole brain. In particular, entropy values show a gradual decrease, and brain network co-fluctuation increases with disease progression. Our study is the first to investigate the pathogenesis of EMCI from the perspective of spatiotemporal flexibility and cognitive diversity based on high-order edge connectivity, further characterizing brain dynamics and providing new insights into the search for biomarkers of EMCI. © 2023 The Authors. Brain-X published by John Wiley & Sons Australia, Ltd on behalf of Ainuohui Medical Technology.

关键词： co-fluctuation edge-centric functional flexibility overlapping community spatiotemporal

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MULTI-SCALE CROPPING MECHANISM FOR REMOTE SENSING IMAGE CAPTIONING

MULTI-SCALE CROPPING MECHANISM FOR REMOTE SENSING IMAGE CAPT...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Xueting Zhang Qi Wang Shangdong Chen Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning(OPTIMAL) Northwestern Polytechnical University Xi’an Shaanxi P. R. China School of Information Science and Technology Northwest University Xi’an Shaanxi P. R. China

With the rapid development of artificial satellite, a large number of high resolution remote sensing images can be easily obtained now. Recently, remote sensing image captioning, which aims to generate accurate and concise descriptive sentences for remote sensing images, has been promoted by template-based model and encoder-decoder model with several related datasets released. Based on an encoder-decoder model, we propose a training mechanism of multi-scale cropping for remote sensing image captioning in this paper, which can extract more fine-grained information from remote sensing images and enhance the generalization performance of the base model. The experimental results on two datasets UCM-captions and Sydney-captions demonstrate that the proposed approach availably improves the performances in describing high resolution remote sensing images.

关键词： Remote sensing Feature extraction Training Semantics Logic gates Task analysis Image resolution

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