We presented the first photometric light curve solutions of four W Ursae Majoris-type contact binary systems.This investigation utilized photometric data from the Transiting Exoplanet Survey Satellite and Gaia Data Re...We presented the first photometric light curve solutions of four W Ursae Majoris-type contact binary systems.This investigation utilized photometric data from the Transiting Exoplanet Survey Satellite and Gaia Data Release 3(DR3).We used the PHysics Of Eclipsing BinariEs Python code and the Markov Chain Monte Carlo method for these light curve solutions.Only TIC 249064185 among the target systems needed a cold starspot to be included in the analysis.Based on the estimated mass ratios for these total eclipse systems,three of them are categorized as low mass ratio contact binary stars.The absolute parameters of the systems were estimated using the Gaia DR3 parallax method and the orbital period and semimajor axis(P-a)empirical relationship.We ascertained that the TIC 318015356 and TIC 55522736 systems are A-subtypes,while TIC 249064185 and TIC 397984843 are W-subtypes,depending on each component’s effective temperature and mass.We estimated the initial masses of the stars,the mass lost by the binary system,and the systems’ages.We displayed star positions in the mass-radius,mass-luminosity,and total mass-orbital angular momentum diagrams.In addition,our findings indicate a good agreement with the mass-temperature empirical parameter relationship for the primary stars.展开更多
Consistency identification in task-oriented dialogue(CI-ToD)can prevent inconsistent dialogue response generation,which has recently emerged as an important and growing research area.This paper takes the first step to...Consistency identification in task-oriented dialogue(CI-ToD)can prevent inconsistent dialogue response generation,which has recently emerged as an important and growing research area.This paper takes the first step to explore a pre-training paradigm for CI-ToD.Nevertheless,pre-training for CI-ToD is non-trivial because it requires a large amount of multi-turn KB-grounded dialogues,which are extremely hard to collect.To alleviate the data scarcity problem for pre-training,we introduce a modularized pre-training framework(MPFToD),which is capable of utilizing large amounts of KB-free dialogues.Specifically,such modularization allows us to decouple CI-ToD into three sub-modules and propose three pre-training tasks including(i)query response matching pre-training;(ii)dialogue history consistent identification pre-training;and(iii)KB mask language modeling to enhance different abilities of CI-ToD model.As different sub-tasks are solved separately,MPFToD can learn from large amounts of KB-free dialogues for different modules,which are much easier to obtain.Results on the CI-ToD benchmark show that MPFToD pushes the state-of-the-art performance from 56.3%to 61.0%.Furthermore,we show its transferability with promising performance on other downstream tasks(i.e.,dialog act recognition,sentiment classification and table fact checking).展开更多
目的:分析人工智能(AI)在辅助肺结节诊断方面的研究现状、热点及问题,以探究我国在该领域的优势与不足,厘清后续发展思路。方法:以Web of Science核心合集数据库作为数据来源,纳入2003年1月至2023年1月AI辅助肺结节诊断相关文献1 468篇...目的:分析人工智能(AI)在辅助肺结节诊断方面的研究现状、热点及问题,以探究我国在该领域的优势与不足,厘清后续发展思路。方法:以Web of Science核心合集数据库作为数据来源,纳入2003年1月至2023年1月AI辅助肺结节诊断相关文献1 468篇,利用CiteSpace绘制可视化知识图谱,依次分析合作网络、共被引网络和关键词共现。结果:AI辅助肺结节诊断研究存在核心国家、机构;国内已经形成稳定的研究与合作团队,但跨国家的合作明显不足;美国在研究中处于领先、核心地位,中国是后起之秀,韩国是近年的先锋;AI算法导致研究热点的大幅度转移,目前研究重点是利用CT断层扫描和深度学习算法辅助判断肺结节和低密度磨玻璃结节,进行诊断和病因分析。结论:AI辅助肺结节诊断的研究近年飞速发展,需增强各国研究团队的合作。此领域受算法性能影响较大,后继应该继续关注AI等用于计算机辅助诊断的新算法性能的提升。展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid ...Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).展开更多
Brain midline delineation can facilitate the clinical evaluation of brain midline shift,which has a pivotal role in the diagnosis and prognosis of various brain pathology.However,there are still challenges for brain m...Brain midline delineation can facilitate the clinical evaluation of brain midline shift,which has a pivotal role in the diagnosis and prognosis of various brain pathology.However,there are still challenges for brain midline delineation:1)the largely deformed midline is hard to localize if mixed with severe cerebral hemorrhage;2)the predicted midlines of recent methods are not smooth and continuous which violates the structural priority.To overcome these challenges,we propose an anisotropic three dimensional(3D)network with context-aware refinement(A3D-CAR)for brain midline modeling.The proposed network fuses 3D context from different two dimensional(2D)slices through asymmetric context fusion.To exploit the elongated structure of the midline,an anisotropic block is designed to balance the difference between the adjacent pixels in the horizontal and vertical directions.For maintaining the structural priority of a brain midline,we present a novel 3D connectivity regular loss(3D CRL)to penalize the disconnectivity between nearby coordinates.Extensive experiments on the CQ dataset and one in-house dataset show that the proposed method outperforms three state-of-the-art methods on four evaluation metrics without excessive computational burden.展开更多
Blockchain is a technology that uses community validation to synchronize the content of ledgers replicated by multiple users.Although Blockchain derives its origins from technologies introduced decades ago,recently it...Blockchain is a technology that uses community validation to synchronize the content of ledgers replicated by multiple users.Although Blockchain derives its origins from technologies introduced decades ago,recently it has received an astonishing amount of attention in both academic and industry due to its charac-teristics of decentralization,point-to-point transmission,transparency,traceability,non-tampering,and data security.Both researchers and practitioners have recognized that Blockchain can be used to solve complex technical or socio-economic problems.展开更多
Because some efficient motion estimation algorithms need considering global camera motion such as zooming, panning, rotating and tilting, a frequency domain algorithm is proposed to solve this problem. Utilizing the m...Because some efficient motion estimation algorithms need considering global camera motion such as zooming, panning, rotating and tilting, a frequency domain algorithm is proposed to solve this problem. Utilizing the magnitude relationship and phase relationship between successive images’ spectra, the authors can solve the full parameter camera motion robustly with acceptable computational complexity.展开更多
In the wave of digital and intelligent applications,artificial intelligence(AI)is transforming the development trajectories of industries across the globe.Traditional Chinese medicine(TCM),as a cultural treasure of th...In the wave of digital and intelligent applications,artificial intelligence(AI)is transforming the development trajectories of industries across the globe.Traditional Chinese medicine(TCM),as a cultural treasure of the Chinese nation,carries thousands of years of wisdom and practical experience.However,in the context of the rapid advancements in modern medicine and technology,TCM faces dual challenges:preserving its heritage while innovating.DeepSeek,a major achievement in the field of AI,offers a new opportunity for the development of TCM with its powerful technological capabilities.Exploring the integration of DeepSeek with TCM not only helps modernize the practice but also promises unique contributions to global health.展开更多
Single-and dual-atom catalysts(SACs and DACs)on single-layer graphene are widely investigated for a wide range of electrochemical reactions.However,the effect of van der Waals interactions on the activity of these cat...Single-and dual-atom catalysts(SACs and DACs)on single-layer graphene are widely investigated for a wide range of electrochemical reactions.However,the effect of van der Waals interactions on the activity of these catalysts has not been investigated through systematic high-throughput screening.Here we introduce the concept of van der Waals interactions through a double-layer DAC structure which has axial d orbital modification towards enhanced CO_(2) reduction reaction(CO_(2)RR),hydrogen evolution reaction(HER),oxygen reduction reaction(ORR),and oxygen evolution reaction(OER).We applied density functional theory(DFT)to screen 3d,4d,and 5d transition metals supported by double-layer nitrogen-doped graphene,denoted as M2N8.We sought catalysts with high thermodynamic and electrochemical stabilities along with low overpotentials for CO_(2)RR,ORR,OER,or HER.We find that HER can take place inside the van der Waals gap of V2N8 and Co2N8 leading to overpotentials of 0.10 and 0.16 V.Moreover,ORR and OER can take place on the surface of Fe2N8 and Ir2N8,respectively,leading to overpotentials of 0.39 and 0.37 V.DFT predicts a CO_(2)RR overpotential of 0.85 V towards CO on the surface of Co2N8 along with the HER overpotential of 0.16 V inside the van der Waals gap of Co2N8 towards the production of syngas(CO+H_(2)).This paper provides fundamental insights into the design of advanced multi-layer catalysts by applying the concept of van der Waals interactions for electrochemistry at room temperature.展开更多
基金founded by the Gordon and Betty Moore Foundation through grants GBMF5490 and GBMF10501 to Ohio State Universitythe Alfred P.Sloan Foundation grant G-2021-14192
文摘We presented the first photometric light curve solutions of four W Ursae Majoris-type contact binary systems.This investigation utilized photometric data from the Transiting Exoplanet Survey Satellite and Gaia Data Release 3(DR3).We used the PHysics Of Eclipsing BinariEs Python code and the Markov Chain Monte Carlo method for these light curve solutions.Only TIC 249064185 among the target systems needed a cold starspot to be included in the analysis.Based on the estimated mass ratios for these total eclipse systems,three of them are categorized as low mass ratio contact binary stars.The absolute parameters of the systems were estimated using the Gaia DR3 parallax method and the orbital period and semimajor axis(P-a)empirical relationship.We ascertained that the TIC 318015356 and TIC 55522736 systems are A-subtypes,while TIC 249064185 and TIC 397984843 are W-subtypes,depending on each component’s effective temperature and mass.We estimated the initial masses of the stars,the mass lost by the binary system,and the systems’ages.We displayed star positions in the mass-radius,mass-luminosity,and total mass-orbital angular momentum diagrams.In addition,our findings indicate a good agreement with the mass-temperature empirical parameter relationship for the primary stars.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant Nos.62306342,62176076)the Excellent Young Scientists Fund in Hunan Province(2024JJ4070)+3 种基金supported by the Natural Science Foundation of Guangdong(2023A1515012922)Shenzhen Foundational Research Funding(JCYJ20220818102415032)The Major Key Project of PCL(PCL2023A09)Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies(2022B1212010005k).
文摘Consistency identification in task-oriented dialogue(CI-ToD)can prevent inconsistent dialogue response generation,which has recently emerged as an important and growing research area.This paper takes the first step to explore a pre-training paradigm for CI-ToD.Nevertheless,pre-training for CI-ToD is non-trivial because it requires a large amount of multi-turn KB-grounded dialogues,which are extremely hard to collect.To alleviate the data scarcity problem for pre-training,we introduce a modularized pre-training framework(MPFToD),which is capable of utilizing large amounts of KB-free dialogues.Specifically,such modularization allows us to decouple CI-ToD into three sub-modules and propose three pre-training tasks including(i)query response matching pre-training;(ii)dialogue history consistent identification pre-training;and(iii)KB mask language modeling to enhance different abilities of CI-ToD model.As different sub-tasks are solved separately,MPFToD can learn from large amounts of KB-free dialogues for different modules,which are much easier to obtain.Results on the CI-ToD benchmark show that MPFToD pushes the state-of-the-art performance from 56.3%to 61.0%.Furthermore,we show its transferability with promising performance on other downstream tasks(i.e.,dialog act recognition,sentiment classification and table fact checking).
文摘目的:分析人工智能(AI)在辅助肺结节诊断方面的研究现状、热点及问题,以探究我国在该领域的优势与不足,厘清后续发展思路。方法:以Web of Science核心合集数据库作为数据来源,纳入2003年1月至2023年1月AI辅助肺结节诊断相关文献1 468篇,利用CiteSpace绘制可视化知识图谱,依次分析合作网络、共被引网络和关键词共现。结果:AI辅助肺结节诊断研究存在核心国家、机构;国内已经形成稳定的研究与合作团队,但跨国家的合作明显不足;美国在研究中处于领先、核心地位,中国是后起之秀,韩国是近年的先锋;AI算法导致研究热点的大幅度转移,目前研究重点是利用CT断层扫描和深度学习算法辅助判断肺结节和低密度磨玻璃结节,进行诊断和病因分析。结论:AI辅助肺结节诊断的研究近年飞速发展,需增强各国研究团队的合作。此领域受算法性能影响较大,后继应该继续关注AI等用于计算机辅助诊断的新算法性能的提升。
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
基金the support of the Leverhulme Centre for Wildfires,Environment and Society through the Leverhulme Trust(RC-2018-023)Sibo Cheng,César Quilodran-Casas,and Rossella Arcucci acknowledge the support of the PREMIERE project(EP/T000414/1)+5 种基金the support of EPSRC grant:PURIFY(EP/V000756/1)the Fundamental Research Funds for the Central Universitiesthe support of the SASIP project(353)funded by Schmidt Futures–a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologiesDFG for the Heisenberg Programm Award(JA 1077/4-1)the National Natural Science Foundation of China(61976120)the Natural Science Key Foundat ion of Jiangsu Education Department(21KJA510004)。
文摘Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).
基金supported by National Natural Science Foundation of China(NSFC)(Nos.62106022,62225601,and U19B2036)Key Program of Beijing Municipal Natural Science Foundation(No.7191003)Beijing Natural Science Foundation Project(No.Z200002).
文摘Brain midline delineation can facilitate the clinical evaluation of brain midline shift,which has a pivotal role in the diagnosis and prognosis of various brain pathology.However,there are still challenges for brain midline delineation:1)the largely deformed midline is hard to localize if mixed with severe cerebral hemorrhage;2)the predicted midlines of recent methods are not smooth and continuous which violates the structural priority.To overcome these challenges,we propose an anisotropic three dimensional(3D)network with context-aware refinement(A3D-CAR)for brain midline modeling.The proposed network fuses 3D context from different two dimensional(2D)slices through asymmetric context fusion.To exploit the elongated structure of the midline,an anisotropic block is designed to balance the difference between the adjacent pixels in the horizontal and vertical directions.For maintaining the structural priority of a brain midline,we present a novel 3D connectivity regular loss(3D CRL)to penalize the disconnectivity between nearby coordinates.Extensive experiments on the CQ dataset and one in-house dataset show that the proposed method outperforms three state-of-the-art methods on four evaluation metrics without excessive computational burden.
文摘Blockchain is a technology that uses community validation to synchronize the content of ledgers replicated by multiple users.Although Blockchain derives its origins from technologies introduced decades ago,recently it has received an astonishing amount of attention in both academic and industry due to its charac-teristics of decentralization,point-to-point transmission,transparency,traceability,non-tampering,and data security.Both researchers and practitioners have recognized that Blockchain can be used to solve complex technical or socio-economic problems.
文摘Because some efficient motion estimation algorithms need considering global camera motion such as zooming, panning, rotating and tilting, a frequency domain algorithm is proposed to solve this problem. Utilizing the magnitude relationship and phase relationship between successive images’ spectra, the authors can solve the full parameter camera motion robustly with acceptable computational complexity.
文摘In the wave of digital and intelligent applications,artificial intelligence(AI)is transforming the development trajectories of industries across the globe.Traditional Chinese medicine(TCM),as a cultural treasure of the Chinese nation,carries thousands of years of wisdom and practical experience.However,in the context of the rapid advancements in modern medicine and technology,TCM faces dual challenges:preserving its heritage while innovating.DeepSeek,a major achievement in the field of AI,offers a new opportunity for the development of TCM with its powerful technological capabilities.Exploring the integration of DeepSeek with TCM not only helps modernize the practice but also promises unique contributions to global health.
基金William A.Goddard III thanks the US National Science Foundation(No.CBET-2311117)for supportGuanHua Chen acknowledges financial support from the General Research Fund(Grant No.17309620)Research Grants Council(RGC:T23-713/22-R).
文摘Single-and dual-atom catalysts(SACs and DACs)on single-layer graphene are widely investigated for a wide range of electrochemical reactions.However,the effect of van der Waals interactions on the activity of these catalysts has not been investigated through systematic high-throughput screening.Here we introduce the concept of van der Waals interactions through a double-layer DAC structure which has axial d orbital modification towards enhanced CO_(2) reduction reaction(CO_(2)RR),hydrogen evolution reaction(HER),oxygen reduction reaction(ORR),and oxygen evolution reaction(OER).We applied density functional theory(DFT)to screen 3d,4d,and 5d transition metals supported by double-layer nitrogen-doped graphene,denoted as M2N8.We sought catalysts with high thermodynamic and electrochemical stabilities along with low overpotentials for CO_(2)RR,ORR,OER,or HER.We find that HER can take place inside the van der Waals gap of V2N8 and Co2N8 leading to overpotentials of 0.10 and 0.16 V.Moreover,ORR and OER can take place on the surface of Fe2N8 and Ir2N8,respectively,leading to overpotentials of 0.39 and 0.37 V.DFT predicts a CO_(2)RR overpotential of 0.85 V towards CO on the surface of Co2N8 along with the HER overpotential of 0.16 V inside the van der Waals gap of Co2N8 towards the production of syngas(CO+H_(2)).This paper provides fundamental insights into the design of advanced multi-layer catalysts by applying the concept of van der Waals interactions for electrochemistry at room temperature.
