In order to provide scientists with a computational methodology and some computational tools to program their epistemic processes in scientific discovery, we are establishing a novel programming paradigm, named ‘Epis...In order to provide scientists with a computational methodology and some computational tools to program their epistemic processes in scientific discovery, we are establishing a novel programming paradigm, named ‘Epistemic Programming’, which regards conditionals as the subject of computing, takes primary epistemic operations as basic operations of computing, and regards epistemic processes as the subject of programming. This paper presents our fundamental observations and assumptions on scientific discovery processes and their automation, research problems on modeling, automating, and programming epistemic processes, and an outline of our research project of Epistemic Programming.展开更多
Purpose:We attempt to find out whether OA or TA really affects the dissemination of scientific discoveries.Design/methodology/approach:We design the indicators,hot-degree,and R-index to indicate a topic OA or TA advan...Purpose:We attempt to find out whether OA or TA really affects the dissemination of scientific discoveries.Design/methodology/approach:We design the indicators,hot-degree,and R-index to indicate a topic OA or TA advantages.First,according to the OA classification of the Web of Science(WoS),we collect data from the WoS by downloading OA and TA articles,letters,and reviews published in Nature and Science during 2010–2019.These papers are divided into three broad disciplines,namely biomedicine,physics,and others.Then,taking a discipline in a journal and using the classical Latent Dirichlet Allocation(LDA)to cluster 100 topics of OA and TA papers respectively,we apply the Pearson correlation coefficient to match the topics of OA and TA,and calculate the hot-degree and R-index of every OA-TA topic pair.Finally,characteristics of the discipline can be presented.In qualitative comparison,we choose some high-quality papers which belong to Nature remarkable papers or Science breakthroughs,and analyze the relations between OA/TA and citation numbers.Findings:The result shows that OA hot-degree in biomedicine is significantly greater than that of TA,but significantly less than that of TA in physics.Based on the R-index,it is found that OA advantages exist in biomedicine and TA advantages do in physics.Therefore,the dissemination of average scientific discoveries in all fields is not necessarily affected by OA or TA.However,OA promotes the spread of important scientific discoveries in high-quality papers.Research limitations:We lost some citations by ignoring other open sources such as arXiv and bioArxiv.Another limitation came from that Nature employs some strong measures for access-promoting subscription-based articles,on which the boundary between OA and TA became fuzzy.Practical implications:It is useful to select hot topics in a set of publications by the hotdegree index.The finding comprehensively reflects the differences of OA and TA in different disciplines,which is a useful reference when researchers choose the publishing way as OA or TA.Originality/value:We propose a new method,including two indicators,to explore and measure OA or TA advantages.展开更多
大规模科学装置与重大科学实验使得科学发现进入了数据密集型的第四范式,借助蓬勃发展的人工智能技术促进智能科学发现势在必行.机器学习作为人工智能中的一项重要技术,已广泛应用于各个科学领域.然而,现有工作仅研究特定任务下的机器...大规模科学装置与重大科学实验使得科学发现进入了数据密集型的第四范式,借助蓬勃发展的人工智能技术促进智能科学发现势在必行.机器学习作为人工智能中的一项重要技术,已广泛应用于各个科学领域.然而,现有工作仅研究特定任务下的机器学习方法,没能抽象出一个通用的智能科学发现研究框架.本文首先总结了科学发现任务中常用的机器学习方法,并将科学任务归类为五大机器学习问题.其次,提出了基于机器学习的智能科学发现研究框架,作为“AI for Science”的典型范例,阐述了一种高效的智能科学发现模式.再次,本文以时域天文学中发现瞬变事件这一科学任务为例,通过实验证明了唯有恰当地结合领域知识后,机器学习算法才能更好地服务于智能科学发现,验证了该框架的有效性.最后进行总结与展望,以期对各领域进行智能科学发现形成参考意义.展开更多
So long as economics is regarded as a science,its mission should be to discover and explain the patterns and phenomena of the world as other scientific disciplines do.Economics is also a theoretical paradigm structure...So long as economics is regarded as a science,its mission should be to discover and explain the patterns and phenomena of the world as other scientific disciplines do.Economics is also a theoretical paradigm structure with which humankind comprehends the real world.As far as its identification and discovery functions are concerned,economics aims to identify and appraise the economic value(value of exchange)of the nonmaterial form of material existence(things)in price or various forms of price.The object of economic research can be expanded to the identification and evaluation of nonmaterial existence.Nonmaterial existence also has its material form(physical carrier)and nonmaterial form.Various applied disciplines have derived from economics to form a system of management science.Though having exceeded certain limitations,such applied disciplines are still subject to the commitments of economic paradigms.Most problems of identification and evaluation facing economics are related to“relational existence,”which takes the forms of intra-domain relationship(and realm state),inter-realm relationship(interactions between various realms),and realms within a realm(multi-tier realm).Change in the commitment of economic paradigms,i.e.the commitment to introduce the realm paradigm into the structure of macro-paradigm aims to overcome the isolation and narrowness of traditional economic paradigms,so that economics becomes tolerant of more important factors and more capable of identifying,evaluating,and explaining the real world.By broadening the horizon of economics,the realm paradigm explores a greater“blue ocean”for the academic research of economics.Only a world perceived as consisting of realm economies with different characteristics is a realistic and sustainable one.Economics must develop a logical system of identification and evaluation commensurate with the realm paradigm to discover and explain the economic patterns and complex phenomena in the real world.A historic task of China’s economics community in academic innovation is to transform paradigms,enhance the identification and discovery functions and explanatory power of economics,and restore the scientific nature of economics.展开更多
With the progression of modern information techniques,such as next generation sequencing(NGS),Internet of Everything(IoE)based smart sensors,and artificial intelligence algorithms,data-intensive research and applicati...With the progression of modern information techniques,such as next generation sequencing(NGS),Internet of Everything(IoE)based smart sensors,and artificial intelligence algorithms,data-intensive research and applications are emerging as the fourth paradigm for scientific discovery.However,we facemany challenges to practical application of this paradigm.In this article,10 challenges to data-intensive discovery and applications in precision medicine and healthcare are summarized and the future perspectives on next generation medicine are discussed.展开更多
Although much has been known about how humans psychologically perform data-driven scientific discovery,less has been known about its brain mechanism.The number series completion is a typical data-driven scientific dis...Although much has been known about how humans psychologically perform data-driven scientific discovery,less has been known about its brain mechanism.The number series completion is a typical data-driven scientific discovery task,and has been demonstrated to possess the priming effect,which is attributed to the regularity identification and its subsequent extrapolation.In order to reduce the heterogeneities and make the experimental task proper for a brain imaging study,the number magnitude and arithmetic operation involved in number series completion tasks are further restricted.Behavioral performance in Experiment 1 shows the reliable priming effect for targets as expected.Then,a factorial design (the priming effect:prime vs.target;the period length:simple vs.complex) of event-related functional magnetic resonance imaging (fMRI) is used in Experiment 2 to examine the neural basis of data-driven scientific discovery.The fMRI results reveal a double dissociation of the left DLPFC (dorsolateral prefrontal cortex) and the left APFC (anterior prefrontal cortex) between the simple (period length=1) and the complex (period length=2) number series completion task.The priming effect in the left DLPFC is more significant for the simple task than for the complex task,while the priming effect in the left APFC is more significant for the complex task than for the simple task.The reliable double dissociation may suggest the different roles of the left DLPFC and left APFC in data-driven scientific discovery.The left DLPFC (BA 46) may play a crucial role in rule identification,while the left APFC (BA 10) may be related to mental set maintenance needed during rule identification and extrapolation.展开更多
基金Supported in part by The Ministry of EducationCulture+1 种基金SportsScience and Technology of Japan under Grant-in-Aid for Explor
文摘In order to provide scientists with a computational methodology and some computational tools to program their epistemic processes in scientific discovery, we are establishing a novel programming paradigm, named ‘Epistemic Programming’, which regards conditionals as the subject of computing, takes primary epistemic operations as basic operations of computing, and regards epistemic processes as the subject of programming. This paper presents our fundamental observations and assumptions on scientific discovery processes and their automation, research problems on modeling, automating, and programming epistemic processes, and an outline of our research project of Epistemic Programming.
文摘Purpose:We attempt to find out whether OA or TA really affects the dissemination of scientific discoveries.Design/methodology/approach:We design the indicators,hot-degree,and R-index to indicate a topic OA or TA advantages.First,according to the OA classification of the Web of Science(WoS),we collect data from the WoS by downloading OA and TA articles,letters,and reviews published in Nature and Science during 2010–2019.These papers are divided into three broad disciplines,namely biomedicine,physics,and others.Then,taking a discipline in a journal and using the classical Latent Dirichlet Allocation(LDA)to cluster 100 topics of OA and TA papers respectively,we apply the Pearson correlation coefficient to match the topics of OA and TA,and calculate the hot-degree and R-index of every OA-TA topic pair.Finally,characteristics of the discipline can be presented.In qualitative comparison,we choose some high-quality papers which belong to Nature remarkable papers or Science breakthroughs,and analyze the relations between OA/TA and citation numbers.Findings:The result shows that OA hot-degree in biomedicine is significantly greater than that of TA,but significantly less than that of TA in physics.Based on the R-index,it is found that OA advantages exist in biomedicine and TA advantages do in physics.Therefore,the dissemination of average scientific discoveries in all fields is not necessarily affected by OA or TA.However,OA promotes the spread of important scientific discoveries in high-quality papers.Research limitations:We lost some citations by ignoring other open sources such as arXiv and bioArxiv.Another limitation came from that Nature employs some strong measures for access-promoting subscription-based articles,on which the boundary between OA and TA became fuzzy.Practical implications:It is useful to select hot topics in a set of publications by the hotdegree index.The finding comprehensively reflects the differences of OA and TA in different disciplines,which is a useful reference when researchers choose the publishing way as OA or TA.Originality/value:We propose a new method,including two indicators,to explore and measure OA or TA advantages.
文摘大规模科学装置与重大科学实验使得科学发现进入了数据密集型的第四范式,借助蓬勃发展的人工智能技术促进智能科学发现势在必行.机器学习作为人工智能中的一项重要技术,已广泛应用于各个科学领域.然而,现有工作仅研究特定任务下的机器学习方法,没能抽象出一个通用的智能科学发现研究框架.本文首先总结了科学发现任务中常用的机器学习方法,并将科学任务归类为五大机器学习问题.其次,提出了基于机器学习的智能科学发现研究框架,作为“AI for Science”的典型范例,阐述了一种高效的智能科学发现模式.再次,本文以时域天文学中发现瞬变事件这一科学任务为例,通过实验证明了唯有恰当地结合领域知识后,机器学习算法才能更好地服务于智能科学发现,验证了该框架的有效性.最后进行总结与展望,以期对各领域进行智能科学发现形成参考意义.
基金supported by the key research projects of the National Social Sciences Foundation “Accelerating the Creation of the Academic,Discipline,and Discourse Systems in Philosophical and Social Sciences with Chinese Characteristics”(Grant No.18VXK002)CASS Academician Innovation Project “Theoretical Innovations in Economics and Practical Explorations”(Grant No.SKGJCX2019-2020)
文摘So long as economics is regarded as a science,its mission should be to discover and explain the patterns and phenomena of the world as other scientific disciplines do.Economics is also a theoretical paradigm structure with which humankind comprehends the real world.As far as its identification and discovery functions are concerned,economics aims to identify and appraise the economic value(value of exchange)of the nonmaterial form of material existence(things)in price or various forms of price.The object of economic research can be expanded to the identification and evaluation of nonmaterial existence.Nonmaterial existence also has its material form(physical carrier)and nonmaterial form.Various applied disciplines have derived from economics to form a system of management science.Though having exceeded certain limitations,such applied disciplines are still subject to the commitments of economic paradigms.Most problems of identification and evaluation facing economics are related to“relational existence,”which takes the forms of intra-domain relationship(and realm state),inter-realm relationship(interactions between various realms),and realms within a realm(multi-tier realm).Change in the commitment of economic paradigms,i.e.the commitment to introduce the realm paradigm into the structure of macro-paradigm aims to overcome the isolation and narrowness of traditional economic paradigms,so that economics becomes tolerant of more important factors and more capable of identifying,evaluating,and explaining the real world.By broadening the horizon of economics,the realm paradigm explores a greater“blue ocean”for the academic research of economics.Only a world perceived as consisting of realm economies with different characteristics is a realistic and sustainable one.Economics must develop a logical system of identification and evaluation commensurate with the realm paradigm to discover and explain the economic patterns and complex phenomena in the real world.A historic task of China’s economics community in academic innovation is to transform paradigms,enhance the identification and discovery functions and explanatory power of economics,and restore the scientific nature of economics.
基金This work was supported by the regional innovation cooperation between Sichuan and Guangxi Provinces(Grant No.2020YFQ0019)the National Natural Science Foundation of China(Grant No.32070671).
文摘With the progression of modern information techniques,such as next generation sequencing(NGS),Internet of Everything(IoE)based smart sensors,and artificial intelligence algorithms,data-intensive research and applications are emerging as the fourth paradigm for scientific discovery.However,we facemany challenges to practical application of this paradigm.In this article,10 challenges to data-intensive discovery and applications in precision medicine and healthcare are summarized and the future perspectives on next generation medicine are discussed.
基金supported by the National Natural Science Foundation of China (Grant Nos.60775039 and 60875075)supported by the Grant-in-aid for Scientific Research (Grant No.18300053) from the Japanese Society for the Promotion of Science+2 种基金Support Center for Advanced Telecommunications Technology Research,Foundationthe Open Foundation of Key Laboratory of Multimedia and Intelligent Software Technology (Beijing University of Technology) Beijingthe Doctoral Research Fund of Beijing University of Technology (Grant No.00243)
文摘Although much has been known about how humans psychologically perform data-driven scientific discovery,less has been known about its brain mechanism.The number series completion is a typical data-driven scientific discovery task,and has been demonstrated to possess the priming effect,which is attributed to the regularity identification and its subsequent extrapolation.In order to reduce the heterogeneities and make the experimental task proper for a brain imaging study,the number magnitude and arithmetic operation involved in number series completion tasks are further restricted.Behavioral performance in Experiment 1 shows the reliable priming effect for targets as expected.Then,a factorial design (the priming effect:prime vs.target;the period length:simple vs.complex) of event-related functional magnetic resonance imaging (fMRI) is used in Experiment 2 to examine the neural basis of data-driven scientific discovery.The fMRI results reveal a double dissociation of the left DLPFC (dorsolateral prefrontal cortex) and the left APFC (anterior prefrontal cortex) between the simple (period length=1) and the complex (period length=2) number series completion task.The priming effect in the left DLPFC is more significant for the simple task than for the complex task,while the priming effect in the left APFC is more significant for the complex task than for the simple task.The reliable double dissociation may suggest the different roles of the left DLPFC and left APFC in data-driven scientific discovery.The left DLPFC (BA 46) may play a crucial role in rule identification,while the left APFC (BA 10) may be related to mental set maintenance needed during rule identification and extrapolation.
