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Journal of Systems Science & Complexity 2024年

作者： ZHOU Pei ZHU Chungang School of Mathematical Sciences Dalian University of Technology Key lab.ratory for computational Mathematics and Data Intelligence of Liaoning Province

Isogeometric collocation method(IGC) shows high computational efficiency compared with isogeometric Galerkin method(IGG) when solving partial differential equations(PDEs). However, few studies about IGC have focused on multi-sided physical domains. In this paper, the authors propose a new IGC method based on toric parameterization(IGCT) for the multi-sided planar physical *** to the high order continuity of toric basis functions, the IGCT method shows more accurate numerical approximation. Moreover, the authors generalize the adaptive w-refinement method into IGCT(IGCT-w), in which the weights of basis functions in physical domains are optimized independently for geometry representation. The numerical accuracy of IGCT-w is significantly improved by an order of magnitude in comparison with IGCT method. To save the computational cost of IGCT-w, the authors devise a selection of weights scheme according to relative residuals. Finally, several numerical examples demonstrate the effectiveness and robustness of the proposed method.

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基于能量稳定的不可压单相流和两相流的光滑粒子流体动力学

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中国科学:数学 2024年

作者：王修平章涛孙树瑜 computational Transport Phenomena lab King Abdullahh University of Science and Technology 中国石油大学(华东)新能源学院

本文综述光滑粒子流体动力学的在能量稳定格式上的一些最新进展,分析部分光滑粒子流体动力学离散格式的性质,并给出求解不可压缩单相流以及两相流的全离散能量稳定格式.光滑粒子流体动力学作为一种无网格粒子方法,目前已广泛应用于工程... 详细信息

本文综述光滑粒子流体动力学的在能量稳定格式上的一些最新进展,分析部分光滑粒子流体动力学离散格式的性质,并给出求解不可压缩单相流以及两相流的全离散能量稳定格式.光滑粒子流体动力学作为一种无网格粒子方法,目前已广泛应用于工程工业界,但绝大部分均采用显式时间格式,对时间步长有严格要求.近年来,离散能量稳定性在设计数值格式时越来越受到重视.满足离散能量稳定性给予我们使用大时间步长的可能性并同时保证准确模拟.本文以不可压缩单相流为例分析光滑粒子流体动力学对应的半离散及全离散系统的能量稳定性,并展示部分扩散界面不可压缩两相流数值算例.

关键词：能量稳定性光滑粒子流体动力学不可压缩流两相流

Low-Complexity Integrated Super-Resolution Sensing and Communication with Signal Decimation and Ambiguity Removal

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ZTE Communications 2024年

作者： DAI Qianglong ZHOU Zhiwen XIAO Zhiqiang ZENG Yong YANG Fei CHEN Yan National Mobile Communications Research lab.ratory， Southeast University Purple Mountain lab.ratories Wireless Technology lab.， 2012 lab. Shanghai Huawei Technologies Co.， Ltd

Integrated sensing and communication (ISAC) is one of the main usage scenarios for the sixth-generation (6G) wireless networks. To most efficiently utilize the limited wireless resources， integrated super-resolution sensing and communication (ISSAC) has been recently proposed to significantly improve sensing performance with super-resolution algorithms for ISAC systems， such as the Multiple Signal Classification (MUSIC) algorithm. However， traditional super-resolution sensing algorithms suffer from prohibitive computational complexity for orthogonal-frequency division multiplexing (OFDM) systems due to the large dimensions of the signals in the subcarrier and symbol domains. To address such issues， we propose a novel two-stage approach to significantly reduce the computational complexity for super-resolution range estimation. The key idea of the proposed scheme is to first uniformly decimate signals in the subcarrier domain， so that the computational complexity is significantly reduced without missing any target in the range domain. However， the decimation operation may result in range ambiguity due to pseudo peaks， which is addressed by the second stage where the total collocated subcarrier data are used for verification of the detected peaks. Compared with traditional MUSIC algorithms， the proposed scheme reduces computational complexity by two orders of magnitude， while maintaining the range resolution and unambiguity. Simulation results verify the effectiveness of the proposed scheme.

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Radiative heat transfer analysis of a concave porous fin under the local thermal non-equilibrium condition:application of the clique polynomial method and physics-informed neural networks

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Applied Mathematics and Mechanics(English Edition) 2024年第9期45卷 1613-1632页

作者： K.CHANDAN K.KARTHIK K.V.NAGARAJA B.C.PRASANNAKUMARA R.S.VARUN KUMAR T.MUHAMMAD Amrita School of Artificial Intelligence Amrita Vishwa VidyapeethamBengaluru 560035KarnatakaIndia Department of Studies in Mathematics Davangere UniversityDavangere 577002KarnatakaIndia computational Science lab Amrita School of EngineeringAmrita Vishwa VidyapeethamBengaluru 560035KarnatakaIndia Department of Pure and Applied Mathematics School of Mathematical SciencesSunway UniversityPetaling Jaya 47500Malaysia Department of Mathematics College of ScienceKing Khalid UniversityAbha 61413Saudi Arabia

The heat transfer through a concave permeable fin is analyzed by the local thermal non-equilibrium(LTNE)*** governing dimensional temperature equations for the solid and fluid phases of the porous extended surface are modeled,and then are nondimensionalized by suitable dimensionless ***,the obtained nondimensional equations are solved by the clique polynomial method(CPM).The effects of several dimensionless parameters on the fin's thermal profiles are shown by graphical ***,the current study implements deep neural structures to solve physics-governed coupled equations,and the best-suited hyperparameters are attained by comparison with various network *** results of the CPM and physicsinformed neural network(PINN)exhibit good agreement,signifying that both methods effectively solve the thermal modeling problem.

关键词： heat transfer fin porous fin local thermal non-equilibrium(LTNE)model physics-informed neural network(PINN)

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Predicting Chinese Abbreviations from Definitions:An Empirical Learning Approach Using Support Vector Regression

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Journal of Computer Science & Technology 2008年第4期23卷 602-611页

作者：孙栩王厚峰王波 Institute of computational linguistics School of Electronics Engineering and Computer Science

In Chinese, phrases and named entities play a central role in information retrieval. Abbreviations, however make keyword-based approaches less effective. This paper presents an empirical learning approach to Chinese abbreviation prediction. In this study, each abbreviation is taken as a reduced form of the corresponding definition （expanded form）, and the abbreviation prediction is formalized as a scoring and ranking problem among abbreviation candidates, which are automatically generated from the corresponding definition. By employing Support Vector Regression （SVR） for scoring, we can obtain multiple abbreviation candidates together with their SVR values, which are used for candidate ranking. Experimental results show that the SVR method performs better than the popular heuristic rule of abbreviation prediction. In addition, in abbreviation prediction, the SVR method outperforms the hidden Markov model （HMM）.

关键词： statistical natural language processing abbreviation prediction support vector regression word clustering

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A prompt-based approach to adversarial example generation and robustness enhancement

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Frontiers of Computer Science 2024年第4期18卷 85-96页

作者： Yuting YANG Pei HUANG Juan CAO Jintao LI Yun LIN Feifei MA Key lab.of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Computer Science Stanford UniversityCA 94305USA School of Computing National University of SingaporeSingapore 119077Singapore lab.ratory of Parallel Software and computational Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Recent years have seen the wide application of natural language processing(NLP)models in crucial areas such as finance,medical treatment,and news media,raising concerns about the model robustness and *** find that prompt paradigm can probe special robust defects of pre-trained language *** prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via *** results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym ***,we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization *** on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.

关键词： robustness adversarial example prompt learning pre-trained language model

Learning layout generation for virtual worlds

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computational Visual Media 2024年第3期10卷 577-592页

作者： Weihao Cheng Ying Shan ARC lab TencentShenzhen 518057China

The emergence of the metaverse has ledto the rapidly increasing demand for the generation ofextensive 3D worlds. We consider that an engagingworld is built upon a rational layout of multiple landuse areas (e.g., forest, meadow, and farmland). Tothis end, we propose a generative model of landuse distribution that learns from geographic *** model is based on a transformer architecturethat generates a 2D map of the land-use layout,which can be conditioned on spatial and semanticcontrols, depending on whether either one or bothare provided. This model enables diverse layoutgeneration with user control and layout expansion byextending borders with partial inputs. To generatehigh-quality and satisfactory layouts, we devise ageometric objective function that supervises the modelto perceive layout shapes and regularize generationsusing geometric priors. Additionally, we devise aplanning objective function that supervises the modelto perceive progressive composition demands andsuppress generations deviating from controls. Toevaluate the spatial distribution of the generations, wetrain an autoencoder to embed land-use layouts intovectors to enable comparison between the real andgenerated data using the Wasserstein metric, which isinspired by the Fr´echet inception distance.

关键词： layout generation virtual worlds Transformer land-use

A Tensor Neural Network with Layerwise Pretraining： Towards Effective Answer Retrieval

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Journal of Computer Science & Technology 2016年第6期31卷 1151-1160页

作者： Xin-Qi Bao Yun-Fang Wu Key lab.ratory of computational linguistics Peking University Beijing 100871 China

In this paper we address the answer retrieval problem in community-based question answering. To fully capture the interactions between question-answer pairs, we propose an original tensor neural network to model the relevance between them. The question and candidate answers are separately embedded into different latent semantic spaces, and a 3-way tensor is then utilized to model the interactions between latent semantics. To initialize the network layers properly, we propose a novel algorithm called denoising tensor autoencoder （DTAE）, and then implement a layerwise pretraining strategy using denoising autoencoders （DAE） on word embedding layers and DTAE on the tensor layer. The experimental results show that our tensor neural network outperforms various baselines with other competitive neural network methods, and our pretraining DTAE strategy improves the system＇s performance and robustness.

关键词： artificial intelligence language parsing and understanding machine learning

Interlayer Magnetic Interaction in the CrI_(3)/CrSe_(2) Heterostructure

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Chinese Physics Letters 2024年第5期41卷 85-89页

作者：王秋皓倪美燕李淑静郑法伟路洪艳张平 School of Physics and Physical Engineering Qufu Normal UniversityQufu 273165China Center for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and MeasurementSchool of PhysicsBeijing Institute of TechnologyBeijing 100081China Beijing Key lab.of Nanophotonics and Ultrafine Optoelectronic Systems School of PhysicsBeijing Institute of TechnologyBeijing 100081China College of Mathematics and Physics Beijing University of Chemical TechnologyBeijing 100029China Institute of Applied Physics and computational Mathematics Beijing 100088China

Based on first-principles calculations, we systematically study the stacking energy and interlayer magnetic interaction of the heterobilayer composed of CrI_(3) and CrSe_(2) monolayers. It is found that the stacking order plays a crucial role in the interlayer magnetic coupling. Among all possible stacking structures, the AA-stacking is the most stable heterostructure, exhibiting interlayer antiferromagnetic interactions. Interestingly, the interlayer magnetic interaction can be effectively tuned by biaxial strain. A 4.3% compressive strain would result in a ferromagnetic interlayer interaction in all stacking orders. These results reveal the magnetic properties of CrI_(3)/CrSe_(2) heterostructure, which is expected to be applied to spintronic devices.

关键词： strain stacking interlayer

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