检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Sun, Zhiyao Wen, Yu-Hui Lv, Tian Sun, Yanan Zhang, Ziyang Wang, Yaoyuan Liu, Yong-Jin BNRist the Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University Beijing100084 China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Advanced Computing and Storage Lab Huawei Technologies Company Ltd. Shenzhen518129 China

Recently audio-driven talking face video generation has attracted considerable attention. However, very few researches address the issue of emotional editing of these talking face videos with continuously controllable expressions, which is a strong demand in the industry. The challenge is that speech-related expressions and emotion-related expressions are often highly coupled. Meanwhile, traditional image-to-image translation methods cannot work well in our application due to the coupling of expressions with other attributes such as poses, i.e., translating the expression of the character in each frame may simultaneously change the head pose due to the bias of the training data distribution. In this paper, we propose a high-quality facial expression editing method for talking face videos, allowing the user to control the target emotion in the edited video continuously. We present a new perspective for this task as a special case of motion information editing, where we use a 3DMM to capture major facial movements and an associated texture map modeled by a StyleGAN to capture appearance details. Both representations (3DMM and texture map) contain emotional information and can be continuously modified by neural networks and easily smoothed by averaging in coefficient/latent spaces, making our method simple yet effective. We also introduce a mouth shape preservation loss to control the trade-off between lip synchronization and the degree of exaggeration of the edited expression. Extensive experiments and a user study show that our method achieves state-of-the-art performance across various evaluation criteria. Copyright © 2022, The Authors. All rights reserved.

关键词： Quality control

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Network comparison via encoding, decoding, and causality

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Physical Review Research 2023年第3期5卷 033129-033129页

作者： Yang Tian Hedong Hou Guangzheng Xu Ziyang Zhang Pei Sun Department of Psychology & Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing 100084 China and Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing 100084 China Institut de Mathématiques d'Orsay 14 Rue Joliot-Curie 91190 Gif-sur-Yvette France Department of Computer Science University College London London WC1E 6AE United Kingdom Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing 100084 China Department of Psychology & Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing 100084 China

Quantifying the relations (e.g., similarity) between complex networks paves the way for studying the latent information shared across networks. However, fundamental relation metrics are not well-defined between networks. As a compromise, prevalent techniques measure network relations in data-driven manners, which are inapplicable to analytic derivations in physics. To resolve this issue, we present a theory for obtaining an optimal characterization of network topological properties. We show that a network can be fully represented by a Gaussian variable defined by a function of the Laplacian, which simultaneously satisfies network-topology-dependent smoothness and maximum entropy properties. Based on it, we can analytically measure diverse relations between complex networks. As illustrations, we define encoding (e.g., information divergence and mutual information), decoding (e.g., Fisher information), and causality (e.g., Granger causality and conditional mutual information) between networks. We validate our framework on representative networks (e.g., random networks, protein structures, and chemical compounds) to demonstrate that a series of science and engineering challenges (e.g., network evolution, embedding, and query) can be tackled from a new perspective. An implementation of our theory is released as a multiplatform toolbox.

关键词： Networks & random structures Random graphs Graph theory Information theory

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Laplacian dynamics of convergent and divergent swarm behaviors

arXiv

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arXiv 2023年

作者： Tian, Yang Xu, Yunhui Sun, Pei Department of Psychology Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing100084 China Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing100084 China Department of Physics Tsinghua University Beijing100084 China

Swarming phenomena are ubiquitous in various physical, biological, and social systems, where simple local interactions between individual units lead to complex global patterns. A common feature of diverse swarming phenomena is that the units exhibit either convergent or divergent evolution in their behaviors, i.e., becoming increasingly similar or distinct, respectively. The associated dynamics changes across time, leading to complex consequences on a global scale. In this study, we propose a generalized Laplacian dynamics model to describe both convergent and divergent swarm behaviors, where the trends of convergence and divergence compete with each other and jointly determine the evolution of global patterns. We empirically observe non-trivial phase-transition-like phenomena between the convergent and divergent evolution phases, which are controlled by local interaction properties. We also propose a conjecture regarding the underlying phase transition mechanisms and outline the main theoretical difficulties for testing this conjecture. Overall, our framework may serve as a minimal model of swarm behaviors and their intricate phase transition dynamics. © 2023, CC BY.

关键词： Dynamics

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TimeReplayer: Unlocking the Potential of Event Cameras for Video Interpolation

arXiv

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arXiv 2022年

作者： He, Weihua You, Kaichao Qiao, Zhendong Jia, Xu Zhang, Ziyang Wang, Wenhui Lu, Huchuan Wang, Yaoyuan Liao, Jianxing Department of Precision Instrument Tsinghua University China Advanced Computing and Storage Lab. Huawei Technologies Co. Ltd. China School of Artificial Intelligence Dalian University of Technology China School of Software Tsinghua University China Peng Cheng Laboratory China

Recording fast motion in a high FPS (frame-per-second) requires expensive high-speed cameras. As an alternative, interpolating low-FPS videos from commodity cameras has attracted significant attention. If only low-FPS videos are available, motion assumptions (linear or quadratic) are necessary to infer intermediate frames, which fail to model complex motions. Event camera, a new camera with pixels producing events of brightness change at the temporal resolution of µs (10-6 second), is a game-changing device to enable video interpolation at the presence of arbitrarily complex motion. Since event camera is a novel sensor, its potential has not been fulfilled due to the lack of processing algorithms. The pioneering work Time Lens introduced event cameras to video interpolation by designing optical devices to collect a large amount of paired training data of high-speed frames and events, which is too costly to scale. To fully unlock the potential of event cameras, this paper proposes a novel TimeReplayer algorithm to interpolate videos captured by commodity cameras with events. It is trained in an unsupervised cycle-consistent style, canceling the necessity of high-speed training data and bringing the additional ability of video extrapolation. Its state-of-the-art results and demo videos in supplementary reveal the promising future of event-based vision. © 2022, CC BY.

关键词： High speed cameras

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Large contributions from optical phonons to thermal transport in hexagonal carbon-boron-nitrogen monolayers

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Physical Review B 2023年第23期108卷 235420-235420页

作者： Xue-Kun Chen (陈学坤) Yue Zhang (张越) Qing-Qing Luo (罗青清) Xue Chen (陈雪) Pin-Zhen Jia (贾聘真) Wu-Xing Zhou (周五星) School of Mathematics and Physics and Hunan Key Laboratory of Mathematical Modeling and Scientific Computing University of South China Hengyang 421001 China Department of Mathematics and Physics Hunan Institute of Technology Hengyang 421002 China School of Materials Science and Engineering and Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion Hunan University of Science and Technology Xiangtan 411201 People's Republic of China

As representative semiconducting hexagonal carbon-boron-nitride lattices, C6BN and C2BN are experimentally realized two-dimensional (2D) plane materials and have recently become the focus of research. Herein, combining first-principles calculations with the Boltzmann transport equation, we performed a comprehensive study on the phonon interaction and thermal conductivity in C6BN and C2BN monolayers. It is found that the thermal conductivities of C6BN and C2BN monolayers at room temperature are reduced by 79% and 73%, respectively, due to four-phonon scattering, compared with the results including three-phonon scattering only. We can attribute this phenomenon to giant four-phonon scattering exclusive for the heat-carrying out-of-plane acoustic (ZA) phonons, because the reflection symmetry allows four-ZA processes much higher than three-ZA processes, and the quasiparallel behavior between the ZA and low-lying out-of-plane optical (ZO) branches contribute to a broad phase space for four-phonon scattering as well. Moreover, C6BN monolayer exhibits unusual behavior that optical phonons contribute about ∼60% to the overall thermal conductivity under the four-phonon picture, which differs from the traditional case that acoustic phonons dominate thermal conductivity. Unexpectedly, two low-lying ZO modes have as high as 38% contributions to the thermal transport at 300 K under the four-phonon picture, causing 60% contribution of optical phonon modes, apparently larger than that of the three-phonon case (15%) and many other 2D materials, also indicating the four-phonon scattering has a more significant effect on acoustic phonons than on optical phonons. This finding not only highlights insight into the nature of phonon transport, but also provides a promising strategy for manipulation of heat transport based on optical phonon modes.

关键词： Phonons Thermal conductivity Boron nitride Graphene First-principles calculations Lattice-Boltzmann methods

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Koopman Neural Operator as a Mesh-Free Solver of Non-Linear Partial Differential Equations

SSRN

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SSRN 2023年

作者： Xiong, Wei Huang, Xiaomeng Zhang, Ziyang Deng, Ruixuan Sun, Pei Tian, Yang Department of Earth System Science Tsinghua University Beijing100084 China Department of Psychology Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing100084 China Department of Biomedical Engineering Tsinghua University Beijing100084 China Laboratory of Advanced Computing and Storage Central Research Institute Laboratories Huawei Technologies Co. Ltd 2012 China

The lacking of analytic solutions of diverse partial differential equations (PDEs) gives birth to a series of computational techniques for numerical solutions. Although numerous latest advances are accomplished in developing neural operators, a kind of neural-network-based PDE solver, these solvers become less accurate and explainable while learning long-term behaviors of non-linear PDE families. In this paper, we propose Koopman neural operator (KNO), a new neural operator, to overcome these challenges. With the same objective of learning an infinite-dimensional mapping between Banach spaces that serves as the solution operator of the target PDE family, our approach differs from existing models by formulating a non-linear dynamic system of equation solution. By approximating the Koopman operator, an infinite-dimensional operator governing all possible observations of the dynamic system, to act on the flow mapping of the dynamic system, we can equivalently learn the solution of a non-linear PDE family by solving simple linear prediction problems. In zero-shot prediction and long-term prediction experiments on representative PDEs (e.g., the Navier-Stokes equation), KNO exhibits notable advantages in breaking the tradeoff between accuracy and efficiency (e.g., model size) while previous state-of-the-art models are limited. These results suggest the potential of KNO in supporting diverse science and engineering applications. © 2023, The Authors. All rights reserved.

关键词： Partial differential equations

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Information Evolution in Complex Networks

arXiv

引用

arXiv 2021年

作者： Tian, Yang Li, Guoqi Sun, Pei Department of Psychology Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing100084 China Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing100084 China Institute of Automation Chinese Academy of Science Beijing100190 China University of Chinese Academy of Science Beijing100049 China

Many biological phenomena or social events critically depend on how information evolves in complex networks. However, a general theory to characterize information evolution is yet absent. Consequently, numerous unknowns remain about the mechanisms underlying information evolution. Among these unknowns, a fundamental problem, being a seeming paradox, lies in the coexistence of local randomness, manifested as the stochastic distortion of information content during individual-individual diffusion, and global regularity, illustrated by specific non-random patterns of information content on the network scale. Here, we attempt to formalize information evolution and explain the coexistence of randomness and regularity in complex networks. Applying network dynamics and information theory, we discover that a certain amount of information, determined by the selectivity of networks to the input information, frequently survives from random distortion. Other information will inevitably experience distortion or dissipation, whose speeds are shaped by the diversity of information selectivity in networks. The discovered laws exist irrespective of noise, but the noise accounts for the intensification. We further demonstrate the ubiquity of our discovered laws by analyzing the emergence of neural tuning properties in the primary visual and medial temporal cortices of animal brains and the emergence of extreme opinions in social networks. © 2021, CC BY.

关键词： Complex networks

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KoopmanLab: machine learning for solving complex physics equations

arXiv

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arXiv 2023年

作者： Xiong, Wei Ma, Muyuan Huang, Xiaomeng Zhang, Ziyang Sun, Pei Tian, Yang Department of Earth System Science Tsinghua University Beijing100084 China Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing100084 China Department of Psychology & Tsinghua Brain and Intelligence Lab Tsinghua University Beijing100084 China

Numerous physics theories are rooted in partial differential equations (PDEs). However, the increasingly intricate physics equations, especially those that lack analytic solutions or closed forms, have impeded the further development of physics. Computationally solving PDEs by classic numerical approaches suffers from the trade-off between accuracy and efficiency and is not applicable to the empirical data generated by unknown latent PDEs. To overcome this challenge, we present KoopmanLab, an efficient module of the Koopman neural operator family, for learning PDEs without analytic solutions or closed forms. Our module consists of multiple variants of the Koopman neural operator (KNO), a kind of mesh-independent neural-network-based PDE solvers developed following dynamic system theory. The compact variants of KNO can accurately solve PDEs with small model sizes while the large variants of KNO are more competitive in predicting highly complicated dynamic systems govern by unknown, high-dimensional, and non-linear PDEs. All variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman–Burgers equation in fluid mechanics) and ERA5 (i.e., one of the largest high-resolution global-scale climate data sets in earth physics). These demonstrations suggest the potential of KoopmanLab to be a fundamental tool in diverse physics studies related to equations or dynamic systems. © 2023, CC BY.

关键词： Dynamics

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Inherent Redundancy in Spiking Neural Networks

arXiv

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arXiv 2023年

作者： Yao, Man Hu, Jiakui Zhao, Guangshe Wang, Yaoyuan Zhang, Ziyang Xu, Bo Li, Guoqi School of Automation Science and Engineering Xi'an Jiaotong University Xi’an China Institute of Automation Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China Peking University Health Science Center Peking University Beijing China Advanced Computing and Storage Lab Huawei Technologies Co Ltd China

Spiking Neural Networks (SNNs) are well known as a promising energy-efficient alternative to conventional artificial neural networks. Subject to the preconceived impression that SNNs are sparse firing, the analysis and optimization of inherent redundancy in SNNs have been largely overlooked, thus the potential advantages of spike-based neuromorphic computing in accuracy and energy efficiency are interfered. In this work, we pose and focus on three key questions regarding the inherent redundancy in SNNs. We argue that the redundancy is induced by the spatio-temporal invariance of SNNs, which enhances the efficiency of parameter utilization but also invites lots of noise spikes. Further, we analyze the effect of spatio-temporal invariance on the spatio-temporal dynamics and spike firing of SNNs. Then, motivated by these analyses, we propose an Advance Spatial Attention (ASA) module to harness SNNs' redundancy, which can adaptively optimize their membrane potential distribution by a pair of individual spatial attention sub-modules. In this way, noise spike features are accurately regulated. Experimental results demonstrate that the proposed method can significantly drop the spike firing with better performance than state-of-the-art SNN baselines. Our code is available in https://***/BICLab/ASA-SNN. Copyright © 2023, The Authors. All rights reserved.

关键词： Neural networks

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Inherent Redundancy in Spiking Neural Networks

Inherent Redundancy in Spiking Neural Networks

引用

International Conference on Computer Vision (ICCV)

作者： Man Yao Jiakui Hu Guangshe Zhao Yaoyuan Wang Ziyang Zhang Bo Xu Guoqi Li School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China Institute of Automation Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China Peking University Health Science Center Peking University Beijing China Advanced Computing and Storage Lab Huawei Technologies Co. Ltd.

Spiking Neural Networks (SNNs) are well known as a promising energy-efficient alternative to conventional artificial neural networks. Subject to the preconceived impression that SNNs are sparse firing, the analysis and optimization of inherent redundancy in SNNs have been largely overlooked, thus the potential advantages of spike-based neuromorphic computing in accuracy and energy efficiency are interfered. In this work, we pose and focus on three key questions regarding the inherent redundancy in SNNs. We argue that the redundancy is induced by the spatio-temporal invariance of SNNs, which enhances the efficiency of parameter utilization but also invites lots of noise spikes. Further, we analyze the effect of spatio-temporal invariance on the spatio-temporal dynamics and spike firing of SNNs. Then, motivated by these analyses, we propose an Advance Spatial Attention (ASA) module to harness SNNs’ redundancy, which can adaptively optimize their membrane potential distribution by a pair of individual spatial attention sub-modules. In this way, noise spike features are accurately regulated. Experimental results demonstrate that the proposed method can significantly drop the spike firing with better performance than state-of-the-art SNN baselines. Our code is available in https://***/BICLab/ASA-SNN.

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