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Origin of minimal conductivity in Dirac materials: Momentum-dependent self-energy function from long-ranged disorder scattering

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Physical Review B 2023年第17期108卷 174205-174205页

作者： Weiwei Chen Yedi Shen Bo Fu Qinwei Shi W. Zhu Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of Science China Jiliang University Hangzhou 310018 China Hefei National Laboratory for Physical Sciences at the Microscale and Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui 230026 China School of Sciences Great Bay University Dongguan 523000 Guangdong Province China Department of Physics The University of Hong Kong Pokfulam Road Hong Kong China School of Science Westlake University Hangzhou 310024 China

We present a unified understanding of the experimentally observed minimal dc conductivity in Dirac materials. First, based on the linear response theory, we reveal that the momentum-dependent self-energy function induces a tunable minimal conductivity in Dirac electrons, the magnitude of which is directly related to the coefficient of the momentum-dependent part in the self-energy function. Taking the long-ranged Gaussian and Coulomb potentials as examples, the momentum-dependent self-energy function is perturbatively derived using the Born approximation and supplemented by the self-consistent Born approximation and renormalization group analysis. Moreover, we further validate our theory via numerical simulations using the large-scale Lanczos algorithm. The explicit momentum dependence of the self-energy on the intensity, concentration, and range of potential are critically addressed. Therefore, our theory provides a reasonable interpretation of the sample-dependent minimal conductivity observed in experiments.

关键词： Dirac fermions Quantum transport Disordered systems Diagrammatic methods

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Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Su, Yukun Lin, Guosheng Zhu, Jinhui Wu, Qingyao School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Beijing China Nanyang Technological University Singapore Singapore

ISBN: (数字)9783030585488

ISBN: (纸本)9783030585471

This paper introduces a new method for recognizing violent behavior by learning contextual relationships between related people from human skeleton points. Unlike previous work, we first formulate 3D skeleton point clouds from human skeleton sequences extracted from videos and then perform interaction learning on these 3D skeleton point clouds. A novel Skeleton Points Interaction Learning (SPIL) module, is proposed to model the interactions between skeleton points. Specifically, by constructing a specific weight distribution strategy between local regional points, SPIL aims to selectively focus on the most relevant parts of them based on their features and spatial-temporal position information. In order to capture diverse types of relation information, a multi-head mechanism is designed to aggregate different features from independent heads to jointly handle different types of relationships between points. Experimental results show that our model outperforms the existing networks and achieves new state-of-the-art performance on video violence datasets. © 2020, Springer Nature Switzerland AG.

关键词： Musculoskeletal system

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From Continuity to Editability: Inverting GANs with Consecutive Images

From Continuity to Editability: Inverting GANs with Consecut...

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International Conference on Computer Vision (ICCV)

作者： Yangyang Xu Yong Du Wenpeng Xiao Xuemiao Xu Shengfeng He South China University of Technology Ocean University of China Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information State Key Laboratory of Subtropical Building Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot

ISBN: (纸本)9781665428132

Existing GAN inversion methods are stuck in a paradox that the inverted codes can either achieve high-fidelity reconstruction, or retain the editing capability. Having only one of them clearly cannot realize real image editing. In this paper, we resolve this paradox by introducing consecutive images (e.g., video frames or the same person with different poses) into the inversion process. The rationale behind our solution is that the continuity of consecutive images leads to inherent editable directions. This inborn property is used for two unique purposes: 1) regularizing the joint inversion process, such that each of the inverted codes is semantically accessible from one of the other and fastened in an editable domain; 2) enforcing inter-image coherence, such that the fidelity of each inverted code can be maximized with the complement of other images. Extensive experiments demonstrate that our alternative significantly outperforms state-of-the-art methods in terms of reconstruction fidelity and editability on both the real image dataset and synthesis dataset. Furthermore, our method provides the first support of video-based GAN inversion and an interesting application of unsupervised semantic transfer from consecutive images. Source code can be found at: https://***/cnnlstm/InvertingGANs_with_ConsecutiveImgs.

关键词： Computer vision Codes Image resolution Semantics Coherence Image reconstruction

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Short-term Traffic Flow Prediction by Graph Deep Learning with Spatial Temporal Modeling

Short-term Traffic Flow Prediction by Graph Deep Learning wi...

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Electronic Information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Lilian Yuan Weilin Fang Hongbo Xiao Jianhua Xiao Yuanquan Shi Yujun Yang School of Computer Science and Engineering Huaihua University Huaihua Hunan P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua Hunan P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan P. R. China

ISBN: (纸本)9781665464024

An extensive amount of research applications on traffic flow prediction models taking into account spatio-temporal modeling have been undertaken in recent years. Relative to traditional spatio-temporal modeling approaches, good application results have been achieved in predicting traffic flow. However, in real life, traffic flow proves to be time- and space-dependent, which poses difficulties for accurate modeling of traffic flow prediction. To address these problems, we propose the ST-GNN-GRU model, which mainly contains S-GNN and T-GRU modules. The model uses a combination of spatio-temporal modeling and deep learning, which can not only cope with the spatio-temporal modeling problem, but also improve the traffic flow prediction accuracy. The experiments are conducted using the METR-LA dataset, and the outcomes show that the proposed method performs more effectively than other baseline approaches.

关键词： Deep learning Smart agriculture Predictive models Prediction algorithms Information technology Testing

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TomoSAR-ALISTA: Efficient TomoSAR imaging via deep unfolded network

TomoSAR-ALISTA: Efficient TomoSAR imaging via deep unfolded ...

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International Conference on Radar Systems (RADAR 2022)

作者： M. Wang Z. Zhang Y. Wang S. Gao X. Qiu Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 People's Republic of China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou 215123 People's Republic of China Electrical and Computer Engineering Department George Mason University Fairfax VA 22030 USA

ISBN: (数字)9781839537776

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving threedimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications.

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Design and Optimization of Evaluation Metrics in Object Detection and Tracking for Low-Altitude Aerial Video 1st

Design and Optimization of Evaluation Metrics in Object Dete...

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1st International Conference on big data and Security, ICBDS 2019

作者： Wang, Li Shu, Xin Zhang, Wei Chen, Yunfang Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China

ISBN: (纸本)9789811575297

The combination of Unmanned Aerial Vehicle (UAV) technology and computer vision has become popular in a wide range of applications, such as surveillance and reconnaissance, while popular evaluation measures are sometimes not applicable for specific tasks. In order to evaluate visual object detection and tracking algorithms of low-altitude aerial video properly, we first summarize the evaluation basis of computer vision tasks, including ground truth, prediction-to-ground truth assignment strategy and distance measures between prediction and ground truth. Then, we analyze the advantages and disadvantages of visual object detection and tracking performance measures, including average precision (AP), F-measure, and accuracy. Finally, for the low-altitude (nearly 100 m) surveillance mission of small unmanned aerial vehicles, we discuss the threshold optimization method of popular measures and the design strategy of application measures. Our work provides a reference in the aspect of performance measures design for researchers of UAV vision. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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A Recommendation for Flight Extra Fuel Based on Random Forest 2020

A Recommendation for Flight Extra Fuel Based on Random Fores...

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Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

作者： Yiming Wang Linkai Luo Qifeng Zhou Junsong Lin Zixuan Wu Department of Automation Xiamen University Xiamen P.R. China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Xiamen P.R. China Department of Automation Xiamen University Xiamen P.R. China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Xiamen P.R. China Department of Automation Xiamen University Xiamen P.R. China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Xiamen P.R. China Xiamen Airlines Xiamen P.R.China

ISBN: (纸本)9781450387835

It is critical for airlines to develop an effective fuel-saving strategy because the fuel cost accounts for a large proportion of the operating costs in air transport industry. This paper aims to optimize the flight extra fuel predicted by dispatchers via data mining techniques on the consideration of both economy and safety. We first establish a random forest (RF) model from history data to predict the consumption of trip fuel. Then to ensure safety index, we add a margin to the output of the model as the modified prediction value. Finally, the experimental results on real flight data show that the proposed method can make a more accurate prediction for the extra fuel adding amount under the condition of ensuring safety. It indicates that the proposed method is beneficial for airlines in economic cost saving.

关键词： Fuel consumption prediction extra fuel optimization random forest

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Highly sensitive ethylene glycol gas sensor based on SnO 2 /ZnO heterojunction composites

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Vacuum 2025年 240卷

作者： JunTang Dong Te Duan TingTing Shao NingJuan Li JiaKe Guo Fuchun Zhang School of Physics and Electronic Information Yan'an University Yan'an 716000 China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Yan'an 716000 China

Ethylene glycol (EG), widely used in industry, poses severe health and environmental risks even at low concentrations, necessitating the development of highly sensitive gas sensors. Although ZnO and SnO 2 are promising semiconductor metal oxides for gas detection, their single-component limitations hinder optimal performance. This work focuses on SnO 2 -decorated ZnO nanosheets, where a heterojunction structure is constructed to overcome individual material drawbacks. The aim is to enhance EG detection performance through compositional tuning and interface engineering. SnO 2 /ZnO composites with varying SnO 2 contents were synthesized via a one-step hydrothermal method and systematically evaluated. The SnO 2 /ZnO-1 sample (1:1 M ratio) exhibited the best performance, achieving a response of 169.89 to 100 ppm EG at 240 °C, a detection limit as low as 158 ppb, high linearity (R 2 = 0.99593), and strong repeatability and selectivity. Structural characterizations confirmed the successful formation of an n–n heterojunction and increased surface area and mesoporosity, enhancing gas adsorption and electron transfer. XPS revealed a higher proportion of adsorbed oxygen (30.21 %) in the composite versus pure ZnO (27.28 %), contributing to better reactivity. This research demonstrates that tailoring heterostructure interfaces and material morphology significantly improves gas sensing performance, offering a promising strategy for reliable and real-time EG detection in environmental and safety monitoring.

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Community Detection Based on DeepWalk in Large Scale Networks 1st

Community Detection Based on DeepWalk in Large Scale Network...

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1st International Conference on big data and Security, ICBDS 2019

作者： Chen, Yunfang Wang, Li Qi, Dehao Zhang, Wei Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China

ISBN: (纸本)9789811575297

The large scale and complex structure of real networks bring enormous challenges to traditional community detection methods. In order to detect community structure in large scale networks more accurately and efficiently, we propose a community detection algorithm based on network embedding representation method. This algorithm first selects the DeepWalk network embedding method to represent the vertices in the network graph as low-dimensional vector representations which reflect the topology in the network. Then this algorithm treats each vertex as a sample, and each dimension of the vector representation as a feature. Finally, the sample data are input into the variational Gaussian mixture clustering model to obtain the clusters in the network. Experimental results on the DBLP dataset show that the model method of this paper can more effectively discover the communities in large scale networks. Through further analysis of the excavated community structure, the organizational characteristics within the community are better revealed. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Network embeddings

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How to train your agent to read and write

arXiv

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arXiv 2021年

作者： Liu, Li He, Mengge Xu, Guanghui Tan, Mingkui Wu, Qi School of Software Engineering South China University of Technology China Pazhou Laboratory China University of Adelaide Australia Key Laboratory of Big Data and Intelligent Robot Ministry of Education United Kingdom

Reading and writing research papers is one of the most privileged abilities that a qualified researcher should master. However, it is difficult for new researchers (e.g., students) to fully grasp this ability. It would be fascinating if we could train an intelligent agent to help people read and summarize papers, and perhaps even discover and exploit the potential knowledge clues to write novel papers. Although there have been existing works focusing on summarizing (i.e., reading) the knowledge in a given text or generating (i.e., writing) a text based on the given knowledge, the ability of simultaneously reading and writing is still under development. Typically, this requires an agent to fully understand the knowledge from the given text materials and generate correct and fluent novel paragraphs, which is very challenging in practice. In this paper, we propose a Deep ReAder-Writer (DRAW) network, which consists of a Reader that can extract knowledge graphs (KGs) from input paragraphs and discover potential knowledge, a graph-to-text Writer that generates a novel paragraph, and a Reviewer that reviews the generated paragraph from three different aspects. Extensive experiments show that our DRAW network outperforms considered baselines and several state-of-the-art methods on AGENDA and M-AGENDA datasets. Our code and supplementary are released at https://***/menggehe/DRAW. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

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