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Observation of quantum oscillations near the Mott-Ioffe-Regel limit in CaAs3

National Science Review 2024年第12期11卷 122-133页

作者： Yuxiang Wang Minhao Zhao Jinglei Zhang Wenbin Wu Shichao Li Yong Zhang Wenxiang Jiang Nesta Benno Joseph Liangcai Xu Yicheng Mou Yunkun Yang Pengliang Leng Yong Zhang Li Pi Alexey Suslov Mykhaylo Ozerov Jan Wyzula Milan Orlita Fengfeng Zhu Yi Zhang Xufeng Kou Zengwei Zhu Awadhesh Narayan Dong Qian Jinsheng Wen Xiang Yuan Faxian Xiu Cheng Zhang State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University State Key Laboratory of Surface Physics and Department of Physics Fudan University Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory of the Chinese Academy of Sciences State Key Laboratory of Precision Spectroscopy East China Normal University National Laboratory of Solid State Microstructures and Department of Physics Nanjing University Key Laboratory of Artificial Structures and Quantum Control(Ministry of Education) School of Physics and Astronomy Shanghai Jiao Tong University Solid State and Structural Chemistry Unit Indian Institute of Science Wuhan National High Magnetic Field Center and School of Physics Huazhong University of Science and Technology School of Information Science and Technology Shanghai Tech University National High Magnetic Field Laboratory LNCMI-CNRS UPR3228 Université Grenoble Alpes International Center for Quantum Materials School of Physics Peking University School of Physics and Electronic Science East China Normal University Shanghai Center of Brain-Inspired Intelligent Materials and Devices East China Normal University Shanghai Qi Zhi Institute Zhangjiang Fudan International Innovation Center Fudan University Shanghai Research Center for Quantum Sciences

The Mott-Ioffe-Regel limit sets the lower bound of the carrier mean free path for coherent quasiparticle transport. Metallicity beyond this limit is of great interest because it is often closely related to quantum criticality and unconventional superconductivity. Progress along this direction mainly focuses on the strange-metal behaviors originating from the evolution of the quasiparticle scattering rate, such as linear-in-temperature resistivity, while the quasiparticle coherence phenomena in this regime are much less explored due to the short mean free path at the diffusive bound. Here we report the observation of quantum oscillations from Landau quantization near the Mott-Ioffe-Regel limit in CaAs3. Despite the insulator-like temperature dependence of resistivity, CaAs3presents giant magnetoresistance and prominent Shubnikov–de Haas oscillations from Fermi surfaces, indicating highly coherent band transport. In contrast,quantum oscillation is absent in the magnetic torque. The quasiparticle effective mass increases systematically with magnetic fields, manifesting a much larger value than what is expected based on magneto-infrared spectroscopy. This suggests a strong many-body renormalization effect near the Fermi surface. We find that these unconventional behaviors may be explained by the interplay between the mobility edge and the van Hove singularity, which results in the formation of coherent cyclotron orbits emerging at the diffusive bound. Our results call for further study on the electron correlation effect of the van Hove singularity.

关键词： Mott-Ioffe-Regel limit quantum oscillations van Hove singularity mobility edge

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An Autoenconder and Self-Attention Network Model for Dynamic Network Link Prediction 6

An Autoenconder and Self-Attention Network Model for Dynamic...

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6th International Symposium on Computer and Information Processing technology, ISCIPT 2021

作者： Tang, Shun Xiao, Xiaoqiang Li, Baicai Computer School National University of Defense Technology State Key Laboratory of High Performance Computing ChangSha China

ISBN: (纸本)9781665441377

Link prediction is a basic and essential problem in complex network field. There are lots of dynamic networks in reality, nodes and links of which change with time such as online society network, e-commerce network et al. Effective and accurate link prediction for dynamic network is a challenging task because of the potential non-linear characteristics and massive dynamic nature. We propose an end-to-end model named AESAN (Autoencoder Self-Attention Network) that combines autoencoder and self-attention mechanism for dynamic network link prediction task. This model can integrate the structural and temporal evolution features of the dynamic network to achieve high-precision prediction, while effectively reducing the training time. Extensive experiments on real-world dynamic network datasets verified the validity of our model. © 2021 IEEE.

关键词： Complex networks

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Joint Spatial-Spectral Encoder for Hyperspectral Image Denoising 3

Joint Spatial-Spectral Encoder for Hyperspectral Image Denoi...

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3rd IEEE International Conference on Communications, Information System and Computer Engineering, CISCE 2021

作者： Xu, Yunpeng Peng, Yuanxi State Key Laboratory of High-Performance Computing College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9780738112152

Hyperspectral image(HSI) has a wide range of application prospects because it contains rich image information. However, in a large amount of information, it usually contains a lot of noise, which will have a great impact on image processing and application. The biggest difficulty of hyperspectral image denoising lies in how to combine the information of many bands instead of denoising only one band. In this paper, a joint band hyperspectral image denoising model (HSI-UBDAE) based on the encoder is proposed, which makes full use of the correlation between bands and combines the spatial domain with the spectral domain to denoise HIS image effectively. Experiments on several real data sets show that the proposed method is superior to some mainstream denoising methods. © 2021 IEEE.

关键词： Image denoising

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Thermal transport in lithium-ion battery: A micro perspective for thermal management

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Frontiers of physics 2022年第1期17卷 143-153页

作者： Changqing Xiang Cheng-Wei Wu Wu-Xing Zhou Guofeng Xie Gang Zhang College of Information Science and Engineering Jishou UniversityJishou416000China School of Materials Science and Engineering&Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion Hunan University of Science and TechnologyXiangtan411201China Institute of High Performance Computing A*STAR Singapore138632Singapore

In recent years, lithium ion (Li-ion) batteries have served as significant power sources in portable electronic devices and electric vehicles because of their high energy density and rate capability. There are growing concerns towards the safety of Li-ion batteries, in which thermal conductivities of anodes, cathodes, electrolytes and separator play key roles for determining the thermal energy transport in Li-ion battery. In this review, we summarize the state-of-the-art studies on the thermal conductivities of commonly used anodes, cathodes, electrolytes and separator in Li-ion batteries, including both theoretical and experimental reports. First, the thermal conductivities of anodes and cathodes are discussed, and the effects of delithiation degree and temperature of materials are also discussed. Then, we review the thermal conductivities of commonly used electrolytes, especially on solid electrolytes. Finally, the basic concept of interfacial thermal conductance and simulation methods are presented, as well as the interfacial thermal conductance between separator and cathodes. This perspective review would provide atomic perspective knowledge to understand thermal transport in Li-ion battery, which will be beneficial to the thermal management and temperature control in electrochemical energy storage devices.

关键词： lithium ion batteries thermal management phonon transport interfacial thermal transport amorphous materials

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Consistent Multi-Threaded Mesh Refinement with Adaptive Length Scale Estimation for Moving Boundary Problems 21

Consistent Multi-Threaded Mesh Refinement with Adaptive Leng...

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5th International Conference on Computer Science and Application Engineering, CSAE 2021

作者： Li, Chao Zhao, Ran Zhao, Ran State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781450389853

Computational fluid dynamic simulations with moving boundaries are widely involved in high performance computing applications. For problems with large-displacement or large-deformation boundaries, mesh cells near the boundaries are often excessively stretched or compressed, thus it's hard to maintain a high-quality mesh. To deal with the distorted cells, this paper adopts the mesh refinement method based on the open source software OpenFOAM. In order to achieve the desired effects of localization and adaptation, we propose an adaptive length scale estimation algorithm based on the specified growth factor and current edge lengths. Considering the inconsistency problems for the original implementation of parallelization, an optimized multi-threaded master/worker model is developed for the process of edge checking. Experiments show that our adaptive length scale estimation algorithm works well for moving boundary problems. Compared to the original mesh deformation, using the adaptive mesh refinement could greatly improve the mesh quality. In parallel testing, all the results are consistent and a maximum speedup of 3.8 is achieved on a computing node of 24 cores.. © 2021 Association for computing Machinery. All rights reserved.

关键词： Open source software

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Magnetic field-induced non-linear transport in HfTe5

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National Science Review 2022年第10期9卷 98-104页

作者： Cheng Zhang Jinshan Yang Zhongbo Yan Xiang Yuan Yanwen Liu Minhao Zhao Alexey Suslov Jinglei Zhang Li Pi Zhong Wang Faxian Xiu State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University State Key Laboratory of Surface Physics and Department of Physics Fudan University Zhangjiang Fudan International Innovation Center Fudan University State Key Laboratory of High-Performance Ceramics and Superfine Microstructure Shanghai Institute of Ceramics Chinese Academy of Sciences School of Physics Sun Yat-Sen University State Key Laboratory of Precision Spectroscopy East China Normal University National High Magnetic Field Laboratory Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory of the Chinese Academy of Sciences Institute for Advanced Study Tsinghua University Collaborative Innovation Center of Quantum Matter Shanghai Qi Zhi Institute Shanghai Research Center for Quantum Sciences

The interplay of electron correlations and topological phases gives rise to various exotic phenomena including fractionalization,excitonic instability and axionic *** discovered transition-metal pentatellurides can reach the ultra-quantum limit in low magnetic fields and serve as good candidates for achieving such a ***,we report evidence of density wave and metal-insulator transition in HfTe5induced by intense magnetic *** the non-linear transport technique,we detect a distinct non-linear conduction behavior in the longitudinal resistivity within the a-c plane,corresponding to the formation of a density wave induced by magnetic *** high fields,the onset of non-linear conduction in the Hall resistivity indicates an impurity-pinned magnetic freeze-out as the possible origin of the insulating *** frozen electrons can be gradually reactivated into mobile states above a threshold of electric *** experimental evidence calls for further investigation into the underlying mechanism of the bulk quantum Hall effect and field-induced phase transitions in pentatellurides.

关键词： topological state quantum transport electron correlation density wave non-linear transport

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Simultaneously Learning Syntactic Dependency and Semantics Reasonability for Relation Extraction

Simultaneously Learning Syntactic Dependency and Semantics R...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Wang, Xin Yin, Nan Zhang, Xiang Bai, Xinyi Luo, Zhigang College of Computer National University of Defense Technology Changsha China Institute for Quantum and State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789811669620

Relation extraction as an important Natural Language Processing (NLP) task is to identify relations between named entities in text. Recently, graph convolutional networks over dependency trees have been widely used to capture syntactic features and achieved attractive performance. However, most existing dependency-based approaches ignore the positive influence of the words outside the dependency trees, sometimes conveying rich and useful information on relation extraction. In this paper, we propose a novel model, Entity-aware Self-attention Contextualized GCN (ESC-GCN), which efficiently incorporates syntactic structure of input sentences and semantic context of sequences. To be specific, relative position self-attention obtains the overall semantic pairwise correlation related to word position, and contextualized graph convolutional networks capture rich intra-sentence dependencies between words by adequately pruning operations. In this way, our proposed model not only reduces the noisy impact from dependency trees but also obtains easily-ignored entity-related semantic representation. Extensive experiments demonstrate that our model achieves encouraging performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qiu, Yanlong Wang, Siqi Yang, Xi Qiu, Xinyuan Wu, Chengkun Cui, Yingbo Yang, Canqun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science Hunan Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Hunan Changsha410073 China National University of Defense Technology Department of Biology and Chemistry College of Science Hunan Changsha410073 China

ISBN: (纸本)9798350337488

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

关键词： Drug-Protein Interaction Knowledge Graph Embedding Triple Embedding Weakly Supervised Learning

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Efficient and Accurate Numerical Methods Using the Accelerated Spectral Deferred Correction for Solving Fractional Differential Equations

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Numerical Mathematics(Theory,Methods and Applications) 2022年第4期15卷 876-902页

作者： Xuejuan Chen Zhiping Mao George Em Karniadakis School of Science Jimei UniversityXiamen 361021China School of Mathematical Sciences Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific ComputingXiamen UniversityXiamenChina Division of Applied Mathematics Brown UniversityProvidenceRI 02912USA

We develop an efficient and accurate spectral deferred correction(SDC)method for fractional differential equations(FDEs)by extending the algorithm in[14]for classical ordinary differential equations(ODEs).Specifically,we discretize the resulted Picard integral equation by the SDC method and accelerate the convergence of the SDC iteration by using the generalized minimal residual algorithm(GMRES).We first derive the correction matrix of the SDC method for FDEs and analyze the convergence region of the SDC *** then present several numerical examples for stiff and non-stiff FDEs including fractional linear and nonlinear ODEs as well as fractional phase field models,demonstrating that the accelerated SDC method is much more efficient than the original SDC method,especially for stiff ***,we resolve the issue of low accuracy arising from the singularity of the solutions by using a geometric mesh,leading to highly accurate solutions compared to uniform mesh solutions at almost the same computational ***,for long-time integration of FDEs,using the geometric mesh leads to great computational savings as the total number of degrees of freedom required is relatively small.

关键词： Stiff problem generalized minimal residual geometric mesh refinement long time evolution fractional phase field models

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A Faster Algorithm for the 4-Coloring Problem 32

A Faster Algorithm for the 4-Coloring Problem

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32nd Annual European Symposium on Algorithms, ESA 2024

作者： Wu, Pu Gu, Huanyu Jiang, Huiqin Shao, Zehui Xu, Jin School of Computer Science Peking University Beijing China Institute of Computing Science and Technology Guangzhou University China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education Beijing China

ISBN: (纸本)9783959773386

We explore the 4-coloring problem, a fundamental combinatorial NP-hard problem. Given a graph G, the 4-coloring problem asks whether there exists a function f from the vertex set of G to {1, 2, 3, 4} such that f(u) ≠ f(v) for each edge uv of G. Such function f is referred to as a 4-coloring of G. The fastest known algorithm for the 4-coloring problem, introduced by Fomin, Gaspers, and Saurabh (COCOON 2007), exhibits a time complexity of O(1.7272n) and exponential space. In this paper, we propose an enhanced algorithm for the 4-coloring problem with a time complexity of O(1.7159n) and polynomial space. Our algorithm is deterministic and built upon a novel method. Specifically, inspired by previous algorithmic approaches for the 4-coloring problem, such as the aforementioned O(1.7272n) time algorithm, we consider the instance (G, I, S), where G is a graph and I, S are subsets of its vertex set representing vertices colored with 1 and vertices unable to be colored with 1, respectively. For a given instance (G, I, S), we aim to determine the existence of a 4-coloring f of G such that f(v) = 1 for v ∈ I and f(v) ≠ 1 for v ∈ S. Our key innovation lies in recognizing that, leveraging certain combinatorial properties, the instance (G, I, S) can be efficiently solved when G − I − S is a union of K3's and K4's (where K3 and K4 denote complete graphs with 3 and 4 vertices, respectively). The ability to efficiently solve instances (G, I, S), where G − I − S is comprised solely of K3's and K4's, enables us to devise a branching algorithm capable of efficiently addressing instances (G, I, S), where G − I − S is not a union of K3's and K4's (the other case). Based on this innovative method, we derive our final enhanced algorithm. © Pu Wu, Huanyu Gu, Huiqin Jiang, Zehui Shao, and Jin Xu;licensed under Creative Commons License CC-BY 4.0.

关键词： Graph algorithms

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