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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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science China(Information sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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Constructing 2D bismuth-based heterostructure for highly efficient photocatalytic CO2 reduction

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Tungsten 2025年第1期7卷 71-79页

作者： Ruo-Hang Xu Hao-Yu Jiang Dan-Dan Cui Kun-Rong Du Hong-Run Zhang Sheng-Jie Xu Ya-Qi Li Zhi-Zhen Ren Liang Wang Wei-Chang Hao Yi Du School of Physics and BUAA-UOW Joint Research centre Beihang University Shenzhen Institute of Beihang University centre for Catalysis and Clean Energy School of Environment and Science Griffith University

Reforming carbon dioxide（CO2） into fuels and valuable chemicals using solar energy holds significant potential for emission reduction and addressing energy supply *** recombination of photocarriers remains a major obstacle,however,to enhancing solar energy conversion efficiency in the photocatalytic reaction *** two-dimensional materials,constructing heterogeneous structures has emerged as an effective strategy to enhance the separation of photoexcited carriers by leveraging the internal electric field at the heterogeneous ***,we present a facile strategy for preparing Bi4NbO8Br/Bi2WO6plate-on-plate Z-scheme junctions with a strong internal electric field by in situ *** unique heterostructure facilitates efficient separation and transport of photoexcited charges by harnessing the internal electric field induced by chemical bonding at the lateral ***,the Bi4NbO8Br/Bi2WO6heterostructure exhibits enhanced CO2photoreduction properties,along with a CO yield rate of approximately 35 μmol·g-1·h-1under full light *** work offers novel insights into the synthesis of unique hetero structures intended to enhance the efficiency of photocatalytic CO2reduction.

关键词： Bi4NbO8Br/Bi2WO6 CO2 reduction Heterostructures Photocatalysts

Is dark energy necessary for the sustainability of traversable wormholes?

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Chinese Physics C 2025年第1期49卷 290-304页

作者： Ayan Banerjee Sudan Hansraj Anirudh Pradhan Abdelghani Errehymy Astrophysics Research centre School of MathematicsStatistics and Computer ScienceUniversity of KwaZulu-NatalPrivate Bag X54001Durban 4000South Africa Atrophysics Research centre School of MathematicsStatistics and Computer ScienceUniversity of KwaZulu-NatalPrivate Bag X54001Durban 4000South Africa centre for Cosmology Astrophysics and Space ScienceGLA UniversityMathura-281406Uttar PradeshIndia

Dark energy is typically the principal component needed for the traversability of wormholes(WH),as it provides the negative gravity effect required to keep the throat ***,can this be achieved without dark energy?It turns out that if we couple the trace of energy-momentum with the standard Einstein-Hilbert Lagrangian and utilize a specific equation of state(EoS),dark energy may be *** Casimir stress energy is known to result in the violation of the null energy condition(NEC)on the energy momentum *** phenomenon makes such an EoS an ideal candidate for generating traversable WH *** laboratory proven phenomenon provides a natural mechanism to sustain an open WH throat without relying on dark ***,we generate two classes of WH solutions using this in energy-momentum trace-coupling *** the specific choice of the Casimir EoS relating the energy-momentum tensor components[Kar and Sahdev:***.D 52,2030(1995)]and different choices of redshift functions,we determine the WH geometry *** obtained WH solutions violate the NECs,and all qualitative constraints demanded for physically realizable WHs are *** is demonstrated via graphical plots for a suitably chosen range of coupling parameter ***,our study investigates the repulsive effect of gravity,revealing that its presence leads to a negative deflection angle for photons traveling along null ***,we observe a consistent pattern of negative values for the deflection angle across all values of in the three scenarios considered,thus indicating the clear manifestation of the repulsive gravity *** of this is possible without invoking the existence of dark energy.

关键词： wormholes traversability casimir f(R,T)gravity deflection angle

Asymptotic false discovery control of the Benjamini-Hochberg procedure for pairwise comparisons

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science China Mathematics 2025年第1期68卷 223-252页

作者： Weidong Liu Dennis Leung Qi-Man Shao School of Mathematical sciences Shanghai Jiao Tong UniversityShanghai 200240China School of Mathematics and Statistics University of MelbourneVictoria 3010Australia Department of Statistics and data science SICMSouthern University of Science and TechnologyShenzhen 518055China

In a one-way analysis-of-variance(ANOVA) model,the number of pairwise comparisons can become large even with a moderate number of *** by this,we consider a regime with a growing number of groups and prove that,when testing pairwise comparisons,the Benjamini-Hochberg(BH) procedure can asymptotically control false discoveries,despite the fact that the involved t-statistics do not exhibit the wellknown positive dependence structure required for exact false discovery rate(FDR) *** Tukey's perspective that the difference between the means of any two groups cannot be exactly zero,our main result provides control over the directional false discovery rate and directional false discovery proportion.A key technical contribution of our work is demonstrating that the dependence among the t-statistics is sufficiently weak to establish the convergence result typically required for asymptotic FDR *** analysis does not rely on conventional assumptions such as normality,variance homogeneity,or a balanced design,thereby offering a theoretical foundation for applications in more general settings.

关键词： BH procedure directional error FDR one-way ANOVA pairwise comparison Studentized twosample t-statistic Cramer-type moderate deviation uniform law of large numbers

A New Adaptive Prediction Algorithm for Judicial Sentencing with Empirical Studies

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Journal of Systems science & Complexity 2025年第01期 3-20页

作者： DAI Ruifen WANG Fang GUO Lei data science Institute Shandong University Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences

With the development and applications of the Smart Court System(SCS) in China, the reliability and accuracy of legal artificial intelligence have become focal points in recent years. Notably,criminal sentencing prediction, a significant component of the SCS, has also garnered widespread attention. According to the Chinese criminal law, actual sentencing data exhibits a saturated property due to statutory penalty ranges, but this mechanism has been ignored by most existing studies. Given this, the authors propose a sentencing prediction model that combines judicial sentencing mechanisms including saturated outputs and floating boundaries with neural networks. Building on the saturated structure of our model, a more effective adaptive prediction algorithm will be constructed based on the fusion of several key ideas and techniques that include the utilization of the L1 loss together with the corresponding gradient update strategy, a data pre-processing method based on large language model to extract semantically complex sentencing elements using prior legal knowledge, the choice of appropriate initial conditions for the learning algorithm and the construction of a double-hidden-layer network structure. An empirical study on the crime of disguising or concealing proceeds of crime demonstrates that our method can achieve superior sentencing prediction accuracy and significantly outperform common baseline methods.

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Least Square Estimation for Multiple Functional Linear Model with Autoregressive Errors

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Acta Mathematicae Applicatae Sinica 2025年第1期41卷 84-98页

作者： Meng WANG Ming-liang SHU Jian-jun ZHOU Si-xin WU Min CHEN School of Management University of Science and Technology of ChinaHefei 230026China Academy of Mathematics and System science Chinese Academy of SciencesBeijing 100190China Yunnan Key Laboratory of Statistical Modeling and data Analysis Yunnan UniversityKunming 650091China

As an extension of linear regression in functional data analysis, functional linear regression has been studied by many researchers and applied in various fields. However, in many cases, data is collected sequentially over time, for example the financial series, so it is necessary to consider the autocorrelated structure of errors in functional regression background. To this end, this paper considers a multiple functional linear model with autoregressive errors. Based on the functional principal component analysis, we apply the least square procedure to estimate the functional coefficients and autoregression coefficients. Under some regular conditions, we establish the asymptotic properties of the proposed estimators. A simulation study is conducted to investigate the finite sample performance of our estimators. A real example on China's weather data is applied to illustrate the validity of our model.

关键词： multiple functional linear model autoregressive errors principal component analysis consistency

Structural and microwave absorption properties of CoFe_(2)O_(4)/residual carbon composites

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International Journal of Minerals,Metallurgy and Materials 2025年第1期32卷 221-232页

作者： Yuanchun Zhang Shengtao Gao Xingzhao Zhang Dacheng Ma Chuanlei Zhu Jun He Joint National-Local Engineering Research centre for Safe and Precise Coal Mining Anhui University of Science and TechnologyHuainan 232001China School of Chemical and Blasting Engineering Anhui University of Science and TechnologyHuainan 232001China

Electromagnetic interference,which necessitates the rapid advancement of substances with exceptional capabilities for bsorbing electromagnetic waves,is of urgent concern in contemporary *** this work,CoFe_(2)O_(4)/residual carbon from coal gasification fine slag(CFO/RC)composites were created using a novel hydrothermal *** mechanisms for microwave absorption,including conductive loss,natural resonance,interfacial dipole polarization,and magnetic flux loss,are involved in these ***,compared with pure residual carbon materials,this composite offers superior capabilities in microwave *** 7.76GHz,the CFO/RC-2 composite achieves an impressive minimum reflection loss(RL_(min))of-43.99 dB with a thickness of 2.44 ***,CFO/RC-3 demonstrates an effective absorption bandwidth(EAB)of up to 4.16 GHz,accompanied by a thickness of *** study revealed the remarkable capability of the composite to diminish electromagnetic waves,providing a new generation method for microwave absorbing materials of superior quality.

关键词： coal gasification slag residual carbon hydrothermal method microwave absorption CoFe_(2)O_(4)

Transportation Cost-information Inequalities for Stochastic Heat Equations Driven by Fractional Noise

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数学进展 2025年第1期54卷 212-224页

作者： ZHANG Bin YAO Zhigang LIU Junfeng School of Statistics and data science Nanjing Audit UniversityNanjingJiangsu211815P.R.China

In this paper,we prove the transportation cost-information inequalities on the space of continuous paths with respect to the L~2-metric and the uniform metric for the law of the mild solution to the stochastic heat eq... 详细信息

关键词： transportation cost-information inequality stochastic heat equation fractional noise

Steel Surface Defect Detection Using Learnable Memory Vision Transformer

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Computers, Materials & Continua 2025年第1期82卷 499-520页

作者： Syed Tasnimul Karim Ayon Farhan Md.Siraj Jia Uddin Department of Computer science and Engineering BRAC UniversityDhaka1212Bangladesh Department of AI and Big data Endicott CollegeWoosong UniversityDaejeon34606Republic of Korea

This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel *** awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and *** techniques mitigated overfitting,stabilized training,and improved generalization *** LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,*** findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature *** additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial *** instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often *** study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are *** research m

关键词： Learnable Memory Vision Transformer(LMViT) Convolutional Neural Networks(CNN) metal surface defect detection deep learning,computer vision image classification learnable memory gradient clipping label smoothing t-SNE visualization