This paper presents the differences and relations between background knowledge and domain theories in learning systems. The roles they play during learning procedures are discussed. It is emphasized that background k...This paper presents the differences and relations between background knowledge and domain theories in learning systems. The roles they play during learning procedures are discussed. It is emphasized that background knowledge plays an important role in enhancing the ability of a learning system. An explanation based learning system with domain theory in primary knowledge base and background knowledge in secondary knowledge base is introduced as an example. It shows how background knowledge can be used to solve some of the problems caused by incomplete domain theory in an explanation based learning system. The system can accomplish knowledge level learning through purely deductive approach. At last the acquisition of background knowledge is briefly discussed.展开更多
Ovarian cancer is one of the three most common gynecological cancers in the world,and is regarded as a priority in terms of women’s cancer.In the past few years,many researchers have attempted to develop and apply ar...Ovarian cancer is one of the three most common gynecological cancers in the world,and is regarded as a priority in terms of women’s cancer.In the past few years,many researchers have attempted to develop and apply artificial intelligence(AI)techniques to multiple clinical scenarios of ovarian cancer,especially in the field of medical imaging.AI-assisted imaging studies have involved computer tomography(CT),ultrasonography(US),and magnetic resonance imaging(MRI).In this review,we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer,and bring up the advances in terms of four clinical aspects,including medical diagnosis,pathological classification,targeted biopsy guidance,and prognosis prediction.Meanwhile,current status and existing issues of the researches on AI application in ovarian cancer are discussed.展开更多
Cardiovascular diseases(CVDs)are major disease burdens with high mortality worldwide.Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates o...Cardiovascular diseases(CVDs)are major disease burdens with high mortality worldwide.Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates of patients with CVDs.The pathological mechanisms and multiple factors involved in CVDs are complex;thus,traditional data analysis is insufficient and inefficient to manage multidimensional data for the risk prediction of CVDs and heart attacks,medical image interpretations,therapeutic decision-making,and disease prognosis prediction.Meanwhile,traditional Chinese medicine(TCM)has been widely used for treating CVDs.TCM offers unique theoretical and practical applications in the diagnosis and treatment of CVDs.Big data have been generated to investigate the scientific basis of TCM diagnostic methods.TCM formulae contain multiple herbal items.Elucidating the complicated interactions between the active compounds and network modulations requires advanced data-analysis capability.Recent progress in artificial intelligence(AI)technology has allowed these challenges to be resolved,which significantly facilitates the development of integrative diagnostic and therapeutic strategies for CVDs and the understanding of the therapeutic principles of TCM formulae.Herein,we briefly introduce the basic concept and current progress of AI and machine learning(ML)technology,and summarize the applications of advanced AI and ML for the diagnosis and treatment of CVDs.Furthermore,we review the progress of AI and ML technology for investigating the scientific basis of TCM diagnosis and treatment for CVDs.We expect the application of AI and ML technology to promote synergy between western medicine and TCM,which can then boost the development of integrative medicine for the diagnosis and treatment of CVDs.展开更多
The application of reinforcement learning is widely used by multi-agent systems in recent years. An agent uses a multi-agent system to cooperate with other agents to accomplish the given task, and one agent′s behavio...The application of reinforcement learning is widely used by multi-agent systems in recent years. An agent uses a multi-agent system to cooperate with other agents to accomplish the given task, and one agent′s behavior usually affects the others′ behaviors. In traditional reinforcement learning, one agent takes the others location, so it is difficult to consider the others′ behavior, which decreases the learning efficiency. This paper proposes multi-agent reinforcement learning with cooperation based on eligibility traces, i.e. one agent estimates the other agent′s behavior with the other agent′s eligibility traces. The results of this simulation prove the validity of the proposed learning method.展开更多
When you go to Fashion Week,the talk is all about what’s trending in colors,cuts,hemlines,and finishes.When you go to retail seminars,it＇s about data.And how artificial intelligence（AI）,machine learning,
Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage...Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design.展开更多
With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbul...With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbulence in aerodynamics,our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions.The results calculated by the proposed model are encouraging.In this work,we aim to model the turbulence of transonic wing flows with fully connected deep neural networks,where there is less research at present.The proposed model is driven by two flow cases of the ONERA(Office National d'Etudes et de Recherches Aerospatiales)wing and coupled with the Navier-Stokes equation solver.Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance.The iteration process is stable,and final convergence is achieved.The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model.Compared with the data calculated by the Spallart-Allmaras model,the results show that the proposed model can be well generalized to the test cases.The mean relative error of the drag coefficient at different sections is below 4%for each case.This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective.展开更多
文摘This paper presents the differences and relations between background knowledge and domain theories in learning systems. The roles they play during learning procedures are discussed. It is emphasized that background knowledge plays an important role in enhancing the ability of a learning system. An explanation based learning system with domain theory in primary knowledge base and background knowledge in secondary knowledge base is introduced as an example. It shows how background knowledge can be used to solve some of the problems caused by incomplete domain theory in an explanation based learning system. The system can accomplish knowledge level learning through purely deductive approach. At last the acquisition of background knowledge is briefly discussed.
文摘Ovarian cancer is one of the three most common gynecological cancers in the world,and is regarded as a priority in terms of women’s cancer.In the past few years,many researchers have attempted to develop and apply artificial intelligence(AI)techniques to multiple clinical scenarios of ovarian cancer,especially in the field of medical imaging.AI-assisted imaging studies have involved computer tomography(CT),ultrasonography(US),and magnetic resonance imaging(MRI).In this review,we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer,and bring up the advances in terms of four clinical aspects,including medical diagnosis,pathological classification,targeted biopsy guidance,and prognosis prediction.Meanwhile,current status and existing issues of the researches on AI application in ovarian cancer are discussed.
基金The Health and Medical Research Fund,Hong Kong(17181811)。
文摘Cardiovascular diseases(CVDs)are major disease burdens with high mortality worldwide.Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates of patients with CVDs.The pathological mechanisms and multiple factors involved in CVDs are complex;thus,traditional data analysis is insufficient and inefficient to manage multidimensional data for the risk prediction of CVDs and heart attacks,medical image interpretations,therapeutic decision-making,and disease prognosis prediction.Meanwhile,traditional Chinese medicine(TCM)has been widely used for treating CVDs.TCM offers unique theoretical and practical applications in the diagnosis and treatment of CVDs.Big data have been generated to investigate the scientific basis of TCM diagnostic methods.TCM formulae contain multiple herbal items.Elucidating the complicated interactions between the active compounds and network modulations requires advanced data-analysis capability.Recent progress in artificial intelligence(AI)technology has allowed these challenges to be resolved,which significantly facilitates the development of integrative diagnostic and therapeutic strategies for CVDs and the understanding of the therapeutic principles of TCM formulae.Herein,we briefly introduce the basic concept and current progress of AI and machine learning(ML)technology,and summarize the applications of advanced AI and ML for the diagnosis and treatment of CVDs.Furthermore,we review the progress of AI and ML technology for investigating the scientific basis of TCM diagnosis and treatment for CVDs.We expect the application of AI and ML technology to promote synergy between western medicine and TCM,which can then boost the development of integrative medicine for the diagnosis and treatment of CVDs.
文摘The application of reinforcement learning is widely used by multi-agent systems in recent years. An agent uses a multi-agent system to cooperate with other agents to accomplish the given task, and one agent′s behavior usually affects the others′ behaviors. In traditional reinforcement learning, one agent takes the others location, so it is difficult to consider the others′ behavior, which decreases the learning efficiency. This paper proposes multi-agent reinforcement learning with cooperation based on eligibility traces, i.e. one agent estimates the other agent′s behavior with the other agent′s eligibility traces. The results of this simulation prove the validity of the proposed learning method.
文摘When you go to Fashion Week,the talk is all about what’s trending in colors,cuts,hemlines,and finishes.When you go to retail seminars,it＇s about data.And how artificial intelligence（AI）,machine learning,
基金supported by the National Natural Science Foundation of China (21210003 and 81230076 to H.J., 81773634 to M.Z. and 81430084 to K.C.)the “Personalized Medicines-Molecular Signature-based Drug Discovery and Development”, Strategic Priority Research Program of the Chinese Academy of Sciences (XDA12050201 to M.Z.)+1 种基金National Key Research & Development Plan (2016YFC1201003 to M.Z.)the National Basic Research Program (2015CB910304 to X.L.)
文摘Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design.
基金supported by the National Natural Science Foundation of China(Grant Nos.92152301,and 91852115)the National Numerical Wind tunnel Project(Grand No.NNW2018-ZT1B01).
文摘With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbulence in aerodynamics,our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions.The results calculated by the proposed model are encouraging.In this work,we aim to model the turbulence of transonic wing flows with fully connected deep neural networks,where there is less research at present.The proposed model is driven by two flow cases of the ONERA(Office National d'Etudes et de Recherches Aerospatiales)wing and coupled with the Navier-Stokes equation solver.Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance.The iteration process is stable,and final convergence is achieved.The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model.Compared with the data calculated by the Spallart-Allmaras model,the results show that the proposed model can be well generalized to the test cases.The mean relative error of the drag coefficient at different sections is below 4%for each case.This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective.
