A tremendous amount of data has been generated by global financial markets everyday,and such time-series data needs to be analyzed in real time to explore its potential value.In recent years,we have witnessed the succ...A tremendous amount of data has been generated by global financial markets everyday,and such time-series data needs to be analyzed in real time to explore its potential value.In recent years,we have witnessed the successful adoption of machine learning models on financial data,where the importance of accuracy and timeliness demands highly effective computing frameworks.However,traditional financial time-series data processing frameworks have shown performance degradation and adaptation issues,such as the outlier handling with stock suspension in Pandas and TA-Lib.In this paper,we propose HXPY,a high-performance data processing package with a C++/Python interface for financial time-series data.HXPY supports miscellaneous acceleration techniques such as the streaming algorithm,the vectorization instruction set,and memory optimization,together with various functions such as time window functions,group operations,down-sampling operations,cross-section operations,row-wise or column-wise operations,shape transformations,and alignment functions.The results of benchmark and incremental analysis demonstrate the superior performance of HXPY compared with its counterparts.From MiBs to GiBs data,HXPY significantly outperforms other in-memory dataframe computing rivals even up to hundreds of times.展开更多
Ten years into the revival of deep networks and artificial intelligence,we propose a theoretical framework that sheds light on understanding deep networks within a bigger picture of intelligence in general.We introduc...Ten years into the revival of deep networks and artificial intelligence,we propose a theoretical framework that sheds light on understanding deep networks within a bigger picture of intelligence in general.We introduce two fundamental principles,Parsimony and Self-consistency,which address two fundamental questions regarding intelligence:what to learn and how to learn,respectively.We believe the two principles serve as the cornerstone for the emergence of intelligence,artificial or natural.While they have rich classical roots,we argue that they can be stated anew in entirely measurable and computable ways.More specifically,the two principles lead to an effective and efficient computational framework,compressive closed-loop transcription,which unifies and explains the evolution of modern deep networks and most practices of artificial intelligence.While we use mainly visual data modeling as an example,we believe the two principles will unify understanding of broad families of autonomous intelligent systems and provide a framework for understanding the brain.展开更多
Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches.Accurate and efficient interatomic potent...Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches.Accurate and efficient interatomic potentials are the key enabler,but their development remains a challenge for complex materials and/or complex phenomena.Machine learning potentials,such as the Deep Potential(DP)approach,provide robust means to produce general purpose interatomic potentials.Here,we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena,where general potentials do not suffice.As an example,we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium(in addition to other defect,thermodynamic and structural properties).The resulting DP correctly captures the structures,energies,elastic constants andγ-lines of Ti in both the HCP and BCC structures,as well as properties such as dislocation core structures,vacancy formation energies,phase transition temperatures,and thermal expansion.The DP thus enables direct atomistic modelling of plastic and fracture behaviour of Ti.The approach to specialising DP interatomic potential,DPspecX,for accurate reproduction of properties of interest“X”,is general and extensible to other systems and properties.展开更多
To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and be...To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;i.e.machine learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)method.In this review,we provide an introduction to DP methods in computational materials science.The theory underlying the DP method is presented along with a step-by-step introduction to their development and use.We also review materials applications of DPs in a wide range of materials systems.The DP Library provides a platform for the development of DPs and a database of extant DPs.We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.展开更多
文摘A tremendous amount of data has been generated by global financial markets everyday,and such time-series data needs to be analyzed in real time to explore its potential value.In recent years,we have witnessed the successful adoption of machine learning models on financial data,where the importance of accuracy and timeliness demands highly effective computing frameworks.However,traditional financial time-series data processing frameworks have shown performance degradation and adaptation issues,such as the outlier handling with stock suspension in Pandas and TA-Lib.In this paper,we propose HXPY,a high-performance data processing package with a C++/Python interface for financial time-series data.HXPY supports miscellaneous acceleration techniques such as the streaming algorithm,the vectorization instruction set,and memory optimization,together with various functions such as time window functions,group operations,down-sampling operations,cross-section operations,row-wise or column-wise operations,shape transformations,and alignment functions.The results of benchmark and incremental analysis demonstrate the superior performance of HXPY compared with its counterparts.From MiBs to GiBs data,HXPY significantly outperforms other in-memory dataframe computing rivals even up to hundreds of times.
文摘Ten years into the revival of deep networks and artificial intelligence,we propose a theoretical framework that sheds light on understanding deep networks within a bigger picture of intelligence in general.We introduce two fundamental principles,Parsimony and Self-consistency,which address two fundamental questions regarding intelligence:what to learn and how to learn,respectively.We believe the two principles serve as the cornerstone for the emergence of intelligence,artificial or natural.While they have rich classical roots,we argue that they can be stated anew in entirely measurable and computable ways.More specifically,the two principles lead to an effective and efficient computational framework,compressive closed-loop transcription,which unifies and explains the evolution of modern deep networks and most practices of artificial intelligence.While we use mainly visual data modeling as an example,we believe the two principles will unify understanding of broad families of autonomous intelligent systems and provide a framework for understanding the brain.
基金This work is supported by the Research Grants Council,Hong Kong SAR through the Collaborative Research Fund under project number 8730054Early Career Scheme Fund under project number 21205019+1 种基金T.Q.W.acknowledges the support of the Hong Kong institute for Advanced Study,City University of Hong Kong through a postdoctoral fellowship.The work of H.W.is supported by the National Science Foundation of China under Grant No.11871110the Beijing Academy of Artificial Intelligence(BAAI).L.F.Z.acknowledges the support of the BAAI.We are also grateful for Dr.Wanrun Jiang,Fengbo Yuan,and Denghui Lu for helpful discussions on the training,free energy calculations,and model compression.
文摘Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches.Accurate and efficient interatomic potentials are the key enabler,but their development remains a challenge for complex materials and/or complex phenomena.Machine learning potentials,such as the Deep Potential(DP)approach,provide robust means to produce general purpose interatomic potentials.Here,we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena,where general potentials do not suffice.As an example,we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium(in addition to other defect,thermodynamic and structural properties).The resulting DP correctly captures the structures,energies,elastic constants andγ-lines of Ti in both the HCP and BCC structures,as well as properties such as dislocation core structures,vacancy formation energies,phase transition temperatures,and thermal expansion.The DP thus enables direct atomistic modelling of plastic and fracture behaviour of Ti.The approach to specialising DP interatomic potential,DPspecX,for accurate reproduction of properties of interest“X”,is general and extensible to other systems and properties.
基金T W and D J S gratefully acknowledge the support of the Research Grants Council,Hong Kong SAR,through the Collaborative Research Fund Project No.8730054The work of H W is supported by the National Science Foundation of China under Grant Nos.11871110 and 12122103The work of W E is supported in part by a gift from iFlytek to Princeton University。
文摘To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;i.e.machine learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)method.In this review,we provide an introduction to DP methods in computational materials science.The theory underlying the DP method is presented along with a step-by-step introduction to their development and use.We also review materials applications of DPs in a wide range of materials systems.The DP Library provides a platform for the development of DPs and a database of extant DPs.We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.
