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A Comprehensive Overview of CFN From a Commonsense Perspective

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Machine Intelligence Research 2024年第2期21卷 239-256页

作者： Ru Li Yunxiao Zhao Zhiqiang Wang Xuefeng Su Shaoru Guo Yong Guan Xiaoqi Han Hongyan Zhao Key Laboratory of computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information TechnologyShanxi UniversityTaiyuan 030006China

Chinese FrameNet(CFN)is a scenario commonsense knowledge base(CKB)that plays an important role in research on Chinese language understanding.It is based on the theory of frame semantics and English FrameNet(FN).The CFN knowledge base contains a wealth of scenario commonsense knowledge,including frames,frame elements,and frame relations,as well as annotated instances with rich scenario-related labels on Chinese sentences and discourses.In this paper,we conduct a comprehensive overview of CFN from a commonsense perspective,covering topics such as scenario commonsense representation,CFN resources,and its applications.We also summarize recent breakthroughs and identify future research directions.First,we introduce the concept of scenario commonsense,including its definitions,examples,and representation methods,with a focus on the relationship between scenario commonsense and the frame concept in CFN.In addition,we provide a comprehensive overview of CFN resources and their applications,highlighting the newly proposed frame-based discourse representation and a human-machine collaboration framework for expanding the CFN corpus.Furthermore,we explore emerging topics such as expanding the CFN resource,improving the interpretability of machine reading comprehension,and using scenario CKBs for text generation.

关键词： Chinese FrameNet(CFN) commonsense scenario commonsense frame knowledge

Nadel-type multiplier ideal sheaves on complex spaces with singularities

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Science China Mathematics 2024年第5期67卷 951-974页

作者： Zhenqian Li School of Mathematics and computational Science Xiangtan UniversityXiangtan 411105China Siwuge Technology Co.Ltd. Chengdu 610093China

In this article,we introduce multiplier ideal sheaves on complex spaces with singularities(not necessarily normal)via Ohsawa’s extension measure,as a special case of which,it turns out to be the socalled Mather-Jacobian multiplier ideals in the algebro-geometric setting.Inspired by Nadel’s coherence and Guan-Zhou’s strong openness properties of the multiplier ideal sheaves,we discuss similar properties for the generalized multiplier ideal sheaves.As applications,we obtain a reasonable generalization of(algebraic)adjoint ideal sheaves to the analytic setting and establish some extension theorems on K?hler manifolds from singular hypersurfaces.Relying on our multiplier and adjoint ideals,we also give characterizations for several important classes of singularities of pairs associated with plurisubharmonic functions.

关键词： multiplier ideal sheaves plurisubharmonic functions Ohsawa-Takegoshi Luextension theorem rational singularities

New product formulas for classical Gauss sums

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Acta Mathematica Sinica,English Series 2024年

作者： Wenpeng ZHANG Li WANG Research Center for Number Theory and Its applications Northwest University Xi’an

The main purpose of this article is using the elementary techniques and the properties of the character sums to study the computational problem of one kind products of Gauss sums, and give an interesting triplication ...

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On weighted compactness of commutators of bilinear vector-valued singular integral operators and applications

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Acta Mathematica Sinica,English Series 2024年

作者： Zheng Yang LI Liu LU Fang Hui LIAO Qing Ying XUE School of Mathematics & computational Science Hunan University of Science and Technology School of Mathematics & computational Science Xiangtan University School of Mathematical Sciences Laboratory of Mathematics and Complex Systems Ministry of Education Beijing Normal University

Let T be a bilinear vector-valued singular integral operator satisfies some mild regularity conditions,which may not fall under the scope of the theory of standard Calderón-Zygmund classes.For any b=(b1,b2) ∈(CMO(Rn))2,let [T,bj]ej(j=1,2),[T,■]_α be the commutators in the j-th entry and the iterated commutators of T,respectively.In this paper,for all p0> 1,p0/2 applications,we obtained the weighted compactness of commutators in the j-th entry and the iterated commutators of several kinds of bilinear Littlewood-Paley square operators with some mild kernel regularity,including bilinear g function,bilinear g_λ~* function and bilinear Lusin’s area integral.In addition,we also get the weighted compactness of commutators in the j-th entry and the iterated commutators of bilinear Fourier multiplier operators,and bilinear square Fourier multiplier operators associated with bilinear g function,bilinear g_λ~* function and bilinear Lusin’s area integral,respectively.

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NSNO:Neumann Series Neural Operator for Solving Helmholtz Equations in Inhomogeneous Medium

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Journal of Systems Science & Complexity 2024年第2期37卷 413-440页

作者： CHEN Fukai LIU Ziyang LIN Guochang CHEN Junqing SHI Zuoqiang Department of Mathematical Sciences Tsinghua UniversityBeijing 100084China Yau Mathematical Sciences Center Tsinghua UniversityBeijing 100084China Yanqi Lake Beijing Institute of Mathematical Sciences and applications Beijing 101408China

In this paper,the authors propose Neumann series neural operator(NSNO)to learn the solution operator of Helmholtz equation from inhomogeneity coefficients and source terms to solutions.Helmholtz equation is a crucial partial differential equation(PDE)with applications in various scientific and engineering fields.However,efficient solver of Helmholtz equation is still a big challenge especially in the case of high wavenumber.Recently,deep learning has shown great potential in solving PDEs especially in learning solution operators.Inspired by Neumann series in Helmholtz equation,the authors design a novel network architecture in which U-Net is embedded inside to capture the multiscale feature.Extensive experiments show that the proposed NSNO significantly outperforms the state-of-the-art FNO with at least 60%lower relative L^(2)-error,especially in the large wavenumber case,and has 50%lower computational cost and less data requirement.Moreover,NSNO can be used as the surrogate model in inverse scattering problems.Numerical tests show that NSNO is able to give comparable results with traditional finite difference forward solver while the computational cost is reduced tremendously.

关键词： Helmholtz equation inverse problem neumann series neural network solution operator

Advancing oxygen separation:insights from experimental and computational analysis of La_(0.7)Ca_(0.3)Co_(0.3)Fe_(0.6)M_(0.1)O_(3-δ)(M=Cu,Zn)oxygen transport membranes

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Frontiers of Chemical Science and Engineering 2024年第6期18卷 17-29页

作者： Guoxing Chen Wenmei Liu Marc Widenmeyer Xiao Yu Zhijun Zhao Songhak Yoon Ruijuan Yan Wenjie Xie Armin Feldhoff Gert Homm Emanuel Ionescu Maria Fyta Anke Weidenkaff Fraunhofer Research Institution for Materials Recycling and Resource Strategies IWKS Alzenau 63755Germany Electrochemistry Laboratory Paul Scherrer InstituteVilligen PSI 5232Switzerland Department of Materials and Earth Sciences Materials and ResourcesTechnical University of DarmstadtDarmstadt 64287Germany Institute of Physical Chemistry and Electrochemistry Leibniz University HannoverHannover 30167Germany Institute for computational Physics University of StuttgartStuttgart 70569Germany computational Biotechnology RWTH AachenAachen 52074Germany

In this study,perovskite-type La_(0.7)Ca_(0.3)Co_(0.3)Fe_(0.6)M_(0.1)O_(3-δ)(M=Cu,Zn)powders were synthesized using a scalable reverse co-precipitation method,presenting them as novel materials for oxygen transport membranes.The comprehensive study covered various aspects including oxygen permeability,crystal structure,conductivity,morphology,CO_(2) tolerance,and long-term regenerative durability with a focus on phase structure and composition.The membrane La_(0.7)Ca_(0.3)Co_(0.3)Fe_(0.6)M_(0.1)O_(3-δ)exhibited high oxygen permeation fluxes,reaching up to 0.88 and 0.64 mL·min^(−1)·cm^(−2) under air/He and air/CO_(2) gradients at 1173 K,respectively.After 1600 h of CO_(2) exposure,the perovskite structure remained intact,showcasing superior CO_(2) resistance.A combination of first principles simulations and experimental measurements was employed to deepen the understanding of Cu/Zn substitution effects on the structure,oxygen vacancy formation,and transport behavior of the membranes.These findings underscore the potential of this highly CO_(2)-tolerant membrane for applications in high-temperature oxygen separation.The enhanced insights into the oxygen transport mechanism contribute to the advancement of next-generation membrane materials.

关键词： perovskite oxygen permeation membrane oxygennionsdiffusiono xygen vacancy formation energy energy barrier

Fuzzy Quasi-Pseudo-b-Metrics on Algebraic Structures

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Journal of Mathematical Research with applications 2024年第3期44卷 397-407页

作者： Jiancai WEI Yeqing SHENG Lihong XIE Piyu LI School of Mathematics and computational Science Wuyi University School of Mathematics and Statistics Xuzhou Institute of Technology

In this paper, we mainly study fuzzy (quasi-)pseudo-b-metrics on algebraic structures.We mainly establish the following results:(1) If a group G with a topologyτ, which is induced by a left invariant fuzzy quasi-pseudo-b-metric (resp., pseudo-b-metric) on the G, is a right topological group, then G with the topologyτis a paratopological group (resp., a topological group) and (2) If a semigroup S with a topologyτis induced by an invariant fuzzy quasi-pseudob-metric, then S with the topologyτis a topological semigroup.

关键词： left topological group right topological group semitopological group paratopological group topological group topological semigroup fuzzy quasi-pseudo-b-metric invariant fuzzy (quasi-)pseudo-b-metric

A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： Data privacy distributed machine learning federated learning software frameworks

A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil Engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming loop.The surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement devices.The proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential scenarios.To achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training process.The approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical examples.The study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured data.Overall,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally intensive.This approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data