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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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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 ...

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

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

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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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3D Building Generation in Minecraft via Large Language Models

3D Building Generation in Minecraft via Large Language Model...

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IEEE Symposium on computational Intelligence and Games, CIG

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

ISBN: (数字)9798350350678

ISBN: (纸本)9798350350685

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.

关键词： Three-dimensional displays Procedural generation Large language models Buildings Refining Windows Decoding

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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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Hamiltonian Boundary Value Methods (HBVMs) for functional differential equations with piecewise continuous arguments

arXiv

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arXiv 2024年

作者： Gurioli, Gianmarco Wang, Weijie Yan, Xiaoqiang Università degli Studi di Firenze Florence Italy School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan411105 China

In this paper, a class of high-order methods to numerically solve Functional Differential Equations with Piecewise Continuous Arguments (FDEPCAs) is discussed. The framework stems from the expansion of the vector field associated with the reference differential equation along the shifted and scaled Legendre polynomial orthonormal basis, working on a suitable extension of Hamiltonian Boundary Value Methods. Within the design of the methods, a proper generalization of the perturbation results coming from the field of ordinary differential equations is considered, with the aim of handling the case of FDEPCAs. The error analysis of the devised family of methods is performed, while a few numerical tests on Hamiltonian FDEPCAs are provided, to give evidence to the theoretical findings and show the effectiveness of the obtained resolution *** Codes 65L05, 65L03, 65L06, 65P10 Copyright © 2024, The Authors. All rights reserved.

关键词： Ordinary differential equations

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Quasiperiodic Symmetric Tilt FCC Grain Boundaries

arXiv

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arXiv 2024年

作者： Zou, Wenwen Zhang, Juan Xu, Jie Jiang, Kai Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China LSEC NCMIS Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

In this work, we investigate [110] symmetric tilt FCC grain boundaries (GBs) by a recently developed approach for quasiperiodic interfaces using the Landau-Brazovskii model. On special tilt angles associated with quadratic algebraic numbers, quasiperiodic GBs exhibit generalized Fibonacci substitution rules. The transition mechanism from quasiperiodic GBs to periodic GBs is explored through sphere offsets and spectral coalescence. We also propose an accurate method to calculate the GB energy for arbitrary tilt angle and analyze the factors affecting the GB energy. The revealing GB energy change is continue along with tilt angle except periodic GBs. © 2024, CC BY-NC-SA.

关键词： Grain boundaries

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