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General Theory for Longitudinal Nonreciprocal Charge Transport

Physical Review Letters 2024年第9期133卷 096802页

作者： Hong Jian Zhao Lingling Tao Yuhao Fu Laurent Bellaiche Yanming Ma Key Laboratory of Material Simulation Methods and Software of Ministry of Education College of Physics School of Physics Smart Ferroic Materials Center Physics Department and Institute for Nanoscience and Engineering Department of Materials Science and Engineering International Center of Future Science State Key Laboratory of Superhard Materials College of Physics

The longitudinal nonreciprocal charge transport (NCT) in crystalline materials is a highly nontrivial phenomenon, motivating the design of next generation two-terminal rectification devices (e.g., semiconductor diodes beyond PN junctions). The practical application of such devices is built upon crystalline materials whose longitudinal NCT occurs at room temperature and under low magnetic field. However, materials of this type are rather rare and elusive, and theory guiding the discovery of these materials is lacking. Here, we develop such a theory within the framework of semiclassical Boltzmann transport theory. By symmetry analysis, we classify the complete 122 magnetic point groups with respect to the longitudinal NCT phenomenon. The symmetry-adapted Hamiltonian analysis further uncovers a previously overlooked mechanism for this phenomenon. Our theory guides the first-principles prediction of longitudinal NCT in multiferroic ϵ−Fe2O3 semiconductor that possibly occurs at room temperature, without the application of external magnetic field. These findings advance our fundamental understandings of longitudinal NCT in crystalline materials, and aid the corresponding materials discoveries.

关键词： Antiferromagnets Multiferroics First-principles calculations k dot p method

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3D Building Generation in Minecraft via Large Language Models

arXiv

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arXiv 2024年

作者： Hu, Shiying Huang, Zengrong Hu, Chengpeng Liu, Jialin Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Recently, procedural content generation has exhibited considerable advancements in the domain of 2D game level generation such as Super Mario Bros. and Sokoban through large language models (LLMs). To further validate the capabilities of LLMs, this paper explores how LLMs contribute to the generation of 3D buildings in a sandbox game, Minecraft. We propose a Text to Building in Minecraft (T2BM) model, which involves refining prompts, decoding interlayer representation and repairing. Facade, indoor scene and functional blocks like doors are supported in the generation. Experiments are conducted to evaluate the completeness and satisfaction of buildings generated via LLMs. It shows that LLMs hold significant potential for 3D building generation. Given appropriate prompts, LLMs can generate correct buildings in Minecraft with complete structures and incorporate specific building blocks such as windows and beds, meeting the specified requirements of human users. Copyright © 2024, The Authors. All rights reserved.

关键词： Buildings

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Kinetics of Initial Diffusion Solidification During Continuous Heating in Term of IN718 Superalloy / 316LN Steel TLP Bonding System

SSRN

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SSRN 2024年

作者： Yang, Miao Ding, Ran Guo, Qianying Li, Chong Liu, Chenxi Liu, Yongchang State Key Laboratory of Hydraulic Engineering Simulation and Safety School of Materials Science & Engineering Tianjin University Tianjin300354 China

In this work, the effect of heating rate on the solidification kinetics of the dissimilar transient liquid phase (TLP) bonding joint of IN718 superalloy / 316LN steel was investigated. It was determined that the initial diffusion solidification (IDS) during continuous heating, accompanied by the melting of interlayer, plays an important role on the formation of TLP bonding joint. The lower heating rate results in the more sufficient IDS and thus the shorter time needed for isothermal solidification. This overturns the conventional view that the solidification behavior in TLP bonding joints mainly occurs at the stage of isothermal holding. Furthermore, the solidification kinetics during TLP bonding was modeled, which takes into account IDS as well as isothermal solidification for the first time. The proposed model is more applicable for the prediction of solidification time in TLP bonding joint than the classic models only on the consideration of isothermal solidification. © 2024, The Authors. All rights reserved.

关键词： Heating rate

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Spatiotemporal variation and hotspots of climate change in the Yangtze River Watershed during 1958-2017

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Journal of Geographical sciences 2022年第1期32卷 141-155页

作者： CHENG Guowei LIU Yong CHEN Yan GAO Wei Institute of International Rivers and Eco-security Yunnan Key Laboratory of International Rivers and Transboundary Eco-SecurityYunnan UniversityKunming 650091China College of Environmental Science and Engineering Key Laboratory of Water and Sediment Sciences(MOE)Peking UniversityBeijing 100871China State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation Chinese Academy for Environmental PlanningBeijing 100012China Institute of Environmental and Ecological Engineering Guangdong University of TechnologyGuangzhou 510006China

The Yangtze River Watershed in China is a climate change hotspot featuring strong spatial and temporal variability;hence, it poses a certain threat to social development. Identifying the characteristics of and regions vulnerable to climate change is significantly important for formulating adaptive countermeasures. However, with regard to the Yangtze River Watershed, there is currently a lack of research on these aspects from the perspective of natural and anthropogenic factors. To address this issue, in this study, based on the temperature and precipitation records from 717 meteorological stations, the RClim Dex and random forest models were used to assess the spatiotemporal characteristics of climate change and identify mainly the natural and anthropogenic factors influencing climate change hotspots in the Yangtze River Watershed for the period 1958-2017. The results indicated a significant increasing trend in temperature, a trend of wet and dry polarization in the annual precipitation, and that the number of temperature indices with significant variations was 2.8 times greater than that of precipitation indices. Significant differences were also noted in the responses of the climate change characteristics of the sub-basins to anthropogenic and natural factors;the delta plain of the Yangtze River estuary exhibited the most significant climate changes, where 88.89% of the extreme climate indices varied considerably. Furthermore, the characteristics that were similar among the identified hotpots, including human activities(higher Gross Domestic Product and construction land proportions) and natural factors(high altitudes and large proportions of grassland and water bodies), were positively correlated with the rapid climate warming.

关键词： Yangtze River Watershed climate change RClim Dex Random Forest anthropogenic and natural factors hotspots

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Structure-Based Uncertainty Estimation for Source-Free Active Domain Adaptation

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IET Computer Vision 2025年第1期19卷

作者： Ouyang, Jihong Zhang, Zhengjie Meng, Qingyi Chi, Jinjin College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Active domain adaptation (active DA) provides an effective solution by selectively labelling a limited number of target samples to significantly enhance adaptation performance. However, existing active DA methods often struggle in real-world scenarios where, due to data privacy concerns, only a pre-trained source model is available, rather than the source samples. To address this issue, we propose a novel method called the structure-based uncertainty estimation model (SUEM) for source-free active domain adaptation (SFADA). To be specific, we introduce an innovative active sample selection strategy that combines both uncertainty and diversity sampling to identify the most informative samples. We assess the uncertainty in target samples using structure-wise probabilities and implement a diversity selection method to minimise redundancy. For the selected samples, we not only apply standard-supervised loss but also conduct interpolation consistency training to further explore the structural information of the target domain. Extensive experiments across four widely used datasets demonstrate that our method is comparable to or outperforms current UDA and active DA methods. © 2025 The Author(s). IET Computer Vision published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： computer vision convolutional neural nets image classification

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Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models 39

Expensive Multi-Objective Bayesian Optimization Based on Dif...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Bingdong Di, Zixiang Lu, Yongfan Qian, Hong Wang, Feng Yang, Peng Tang, Ke Zhou, Aimin Shanghai Frontiers Science Center of Molecule Intelligent Syntheses Shanghai Institute of AI for Education School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Computer Science Wuhan University Wuhan430072 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)157735897X

Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective optimization problems (EMOPs). However, effectively modeling complex distributions of the Pareto optimal solutions is difficult with limited function evaluations. Existing Pareto set learning algorithms may exhibit considerable instability in such expensive scenarios, leading to significant deviations between the obtained solution set and the Pareto set (PS). In this paper, we propose a novel Composite Diffusion Model based Pareto Set Learning algorithm (CDM-PSL) for expensive MOBO. CDM-PSL includes both unconditional and conditional diffusion model for generating high-quality samples efficiently. Besides, we introduce a weighting method based on information entropy to balance different objectives. This method is integrated with a guiding strategy to appropriately balancing different objectives during the optimization process. Experimental results on both synthetic and real-world problems demonstrates that CDM-PSL attains superior performance compared with state-of-the-art MOBO algorithms. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multiobjective optimization

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SSRMamba: Efficient Visual State Space Model for Spectral Super-Resolution

SSRMamba: Efficient Visual State Space Model for Spectral Su...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Baisong Li Xingwang Wang Haixiao Xu College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Krowledge Engineering of Ministry of Education Jilin University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spectral super-resolution, which reconstructs hyperspectral images (HSI) from a single RGB image, has garnered increasing attention. Due to the limitations of CNN structures in spectral modeling and the high computational cost of Transformer structures, existing deep learning (DL)-based methods struggle to balance spectral reconstruction quality and computational efficiency. Recently, Mamba methods base on state-space models (SSM) show great potential in modeling long-range dependencies with linear complexity. Therefore, we introduce the Mamba model into spectral super-resolution (SSR) task. Specifically, we propose a three-stage SSR network base on Mamba, called SSRMamba. We design SpaMamba, SSMamba, and SpeMamba modules for shallow spatial information extraction, mixed information encoding, and spectral information reconstruction, respectively. Extensive experimental results demonstrate that SSRMamba not only surpasses existing methods in terms of quantification and quality, achieving state-of-the-art (SOTA) performance, but also significantly reduces model size and computational cost. The source code of SSRMamba is available at: https://***/Baisonm-Li/SSRMamba.

关键词： Visualization computational modeling Superresolution Information retrieval Transformers computational efficiency State-space methods Speech processing Image reconstruction Hyperspectral imaging

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BinaryAI: Binary Software Composition Analysis via Intelligent Binary Source Code Matching 24

BinaryAI: Binary Software Composition Analysis via Intellige...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Jiang, Ling An, Junwen Huang, Huihui Tang, Qiyi Nie, Sen Wu, Shi Zhang, Yuqun Southern University of Science and Technology Shenzhen China Tencent Security Keen Lab Shanghai China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technol-ogy Department of Computer Science and Engineering Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation China

ISBN: (纸本)9798400702174

While third-party libraries (TPLs) are extensively reused to enhance productivity during software development, they can also introduce potential security risks such as vulnerability propagation. Software composition analysis (SCA), proposed to identify reused TPLs for reducing such risks, has become an essential procedure within modern DevSecOps. As one of the mainstream SCA techniques, binary-to-source SCA identifies the third-party source projects contained in binary files via binary source code matching, which is a major challenge in reverse engineering since binary and source code exhibit substantial disparities after compilation. The existing binary-to-source SCA techniques leverage basic syntactic features that suffer from redundancy and lack robustness in the large-scale TPL dataset, leading to inevitable false positives and compromised recall. To mitigate these limitations, we introduce BinaryAI, a novel binary-to-source SCA technique with two-phase binary source code matching to capture both syntactic and semantic code features. First, BinaryAI trains a transformer-based model to produce function-level embeddings and obtain similar source functions for each binary function accordingly. Then by applying the link-time locality to facilitate function matching, BinaryAI detects the reused TPLs based on the ratio of matched source functions. Our experimental results demonstrate the superior performance of BinaryAI in terms of binary source code matching and the downstream SCA task. Specifically, our embedding model outperforms the state-of-the-art model CodeCMR, i.e., achieving 22.54% recall@l and 0.34 MRR compared with 10.75% and 0.17 respectively. Additionally, BinaryAI outperforms all existing binary-to-source SCA tools in TPL detection, increasing the precision from 73.36% to 85.84% and recall from 59.81% to 64.98% compared with the well-recognized commercial SCA product Black Duck. E-https://*** © 2024 ACM.

关键词： Syntactics

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Improving Local Search for Pseudo Boolean Optimization by Fragile Scoring Function and Deep Optimization 29

Improving Local Search for Pseudo Boolean Optimization by Fr...

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29th International Conference on Principles and Practice of Constraint Programming, CP 2023

作者： Zhou, Wenbo Zhao, Yujiao Wang, Yiyuan Cai, Shaowei Wang, Shimao Wang, Xinyu Yin, Minghao School of Information Science and Technology Northeast Normal University Changchun China Key Laboratory of Applied Statistics of MOE Northeast Normal University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE Jilin University Changchun China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783959773003

Pseudo-Boolean optimization (PBO) is usually used to model combinatorial optimization problems, especially for some real-world applications. Despite its significant importance in both theory and applications, there are few works on using local search to solve PBO. This paper develops a novel local search framework for PBO, which has three main ideas. First, we design a two-level selection strategy to evaluate all candidate variables. Second, we propose a novel deep optimization strategy to disturb some search spaces. Third, a sampling flipping method is applied to help the algorithm jump out of local optimum. Experimental results show that the proposed algorithms outperform three state-of-the-art PBO algorithms on most instances. © Wenbo Zhou, Yujiao Zhao, Yiyuan Wang, Shaowei Cai, Shimao Wang, Xinyu Wang, and Minghao Yin.

关键词： Combinatorial optimization

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An Interval Fractional Electric Power System Planning Model for CO2 Emission Intensity Reduction: A Case Study of Fujian, China 14th

An Interval Fractional Electric Power System Planning Model ...

引用

14th International Conference on Environmental science and Technology, ICEST 2023

作者： Zhao, S.H. Li, J.W. Liu, J. Li, Y.P. Fujian Engineering and Research Center of Rural Sewage Treatment and Water Safety School of Environmental Science and Engineering Xiamen University of Technology Xiamen361024 China State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Beijing Normal University Beijing100875 China

ISBN: (纸本)9783031546839

Using low-carbon/zero-carbon energy sources and negative emission technologies, and economic policies have proven to be effective measures for electric power plants’ CO2 emission mitigation. This study constructs an interval fractional electric power system planning (IFES) model for minimizing CO2 emission intensity considering the interactions between the measures and other constraints under uncertainty. The model is applied to Fujian Province as a real-case study, which contains electricity mix improvement, three types of CO2 emission reduction technologies, one policy. It was found that: (i) System carbon emission intensity is [170, 213] g/RMB;(ii) The proportion of clean energy power generation in Fujian Province will reach [92.32, 93.13]% in the 2055–2060, with nuclear energy, wind power and photovoltaic power generation ranking the top three;(iii) The electricity mix improvement has made the greatest contribution to the carbon neutrality of the power system (74.62%), and the contributions of CCS, DAC, and ESC are (9.11, 5.25, 11.02%) respectively. The research results provide scientific planning solutions for carbon reduction in the power industry in Fujian Province. The developed IF-ESP model can also be used in other regions around the world. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electric power system planning

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