Causality extraction has become a crucial task in natural language processing and knowledge graph.However,most existing methods divide causality extraction into two subtasks:extraction of candidate causal pairs and cl...Causality extraction has become a crucial task in natural language processing and knowledge graph.However,most existing methods divide causality extraction into two subtasks:extraction of candidate causal pairs and classification of causality.These methods result in cascading errors and the loss of associated contextual information.Therefore,in this study,based on graph theory,an End-to-end Multi-Granulation Causality Extraction model(EMGCE)is proposed to extract explicit causality and directly mine implicit causality.First,the sentences are represented on different granulation layers,that contain character,word,and contextual string layers.The word layer is fine-grained into three layers:word-index,word-embedding and word-position-embedding layers.Then,a granular causality tree of dataset is built based on the word-index layer.Next,an improved tagREtriplet algorithm is designed to obtain the labeled causality based on the granular causality tree.It can transform the task into a sequence labeling task.Subsequently,the multi-granulation semantic representation is fed into the neural network model to extract causality.Finally,based on the extended public SemEval 2010 Task 8 dataset,the experimental results demonstrate that EMGCE is effective.展开更多
Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the los...Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the loss function.The performance of PINNs is generally affected by both training and sampling.Specifically,training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs,and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished.However,a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category,namely,time-dependent PDEs,where temporal information plays a key role in the algorithms used.There is one method,called Causal PINN,that considers temporal causality at the training level but not special temporal utilization at the sampling level.Incorporating temporal knowledge into sampling remains to be studied.To fill this gap,we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality.By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain,we provide a practical solution by incorporating temporal information into sampling.Numerical experiments of several nonlinear time-dependent PDEs,including the Cahn–Hilliard,Korteweg–de Vries,Allen–Cahn and wave equations,show that our proposed sampling method can improve the performance.We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods,especially when points are limited.展开更多
An improved safety analysis based on the causality diagram for the complex system of micro aero-engines is presented.The study is examined by using the causality diagram in analytical failure cases due to rupture or p...An improved safety analysis based on the causality diagram for the complex system of micro aero-engines is presented.The study is examined by using the causality diagram in analytical failure cases due to rupture or pentration in the receiver of micro turbojet engine casing,and the comparisons are also made with the results from the traditional fault tree analysis.Experimental results show two main advantages：（1）Quantitative analysis which is more reliable for the failure analysis in jet engines can be produced by the causality diagram analysis;（2）Graphical representation of causality diagram is easier to apply in real test cases and more effective for the safety assessment.展开更多
In multi-label learning,the label-specific features learning framework can effectively solve the dimensional catastrophe problem brought by high-dimensional data.The classification performance and robustness of the mo...In multi-label learning,the label-specific features learning framework can effectively solve the dimensional catastrophe problem brought by high-dimensional data.The classification performance and robustness of the model are effectively improved.Most existing label-specific features learning utilizes the cosine similarity method to measure label correlation.It is well known that the correlation between labels is asymmetric.However,existing label-specific features learning only considers the private features of labels in classification and does not take into account the common features of labels.Based on this,this paper proposes a Causality-driven Common and Label-specific Features Learning,named CCSF algorithm.Firstly,the causal learning algorithm GSBN is used to calculate the asymmetric correlation between labels.Then,in the optimization,both l_(2,1)-norm and l_(1)-norm are used to select the corresponding features,respectively.Finally,it is compared with six state-of-the-art algorithms on nine datasets.The experimental results prove the effectiveness of the algorithm in this paper.展开更多
As physicalisms of various kinds have faced difficulties in recent years, the time has come to explore possible alternatives, one of which is yinyang ontology. A yinyang theorist is expected to provide a plausible acc...As physicalisms of various kinds have faced difficulties in recent years, the time has come to explore possible alternatives, one of which is yinyang ontology. A yinyang theorist is expected to provide a plausible account of causation to replace the traditional notion of causation. The present paper is critical of the Humean tradition, which understands the relata of causal relations in terms of passive materiality so that humans use referential terms to describe causal relations constructively. But an alternative notion of reference is available according to which causal relata are active processors of the information with which they interact. On this latter view, humans use referential language to describe the structure in which the relata interrelate themselves so that the structure can be understood hermeneutically. Reference on this view is naturalized. In this article, I advance two arguments for this thesis, one concerning the informationality of states and the other related to the essentiality of properties.展开更多
Elucidating the complex mechanism between urbanization, economic growth, car- bon dioxide emissions is fundamental necessary to inform effective strategies on energy saving and emission reduction in China. Based on a ...Elucidating the complex mechanism between urbanization, economic growth, car- bon dioxide emissions is fundamental necessary to inform effective strategies on energy saving and emission reduction in China. Based on a balanced panel data of 31 provinces in China over the period 1997-2010, this study empirically examines the relationships among urbanization, economic growth and carbon dioxide （CO2） emissions at the national and re- gional levels using panel cointegration and vector error correction model and Granger cau- sality tests. Results showed that urbanization, economic growth and CO2 emissions are inte- grated of order one. Urbanization contributes to economic growth, both of which increase CO2 emissions in China and its eastern, central and western regions. The impact of urbanization on CO2 emissions in the western region was larger than that in the eastern and central re- gions. But economic growth had a larger impact on CO2 emissions in the eastern region than that in the central and western regions. Panel causality analysis revealed a bidirectional long-run causal relationship among urbanization, economic growth and CO2 emissions, in- dicating that in the long run, urbanization does have a causal effect on economic growth in China, both of which have causal effect on CO2 emissions. At the regional level, we also found a bidirectional long-run causality between land urbanization and economic growth in eastern and central China. These results demonstrated that it might be difficult for China to pursue carbon emissions reduction policy and to control urban expansion without impeding economic growth in the long run. In the short-run, we observed a unidirectional causation running from land urbanization to CO2 emissions and from economic growth to CO2 emissions in the eastern and central regions. Further investigations revealed an inverted N-shaped re- lationship between CO2 emissions and economic growth in China, not supporting the envi- ronmental Kuznets curve （EKC） hypothesis. Our empirical findings have an important refer- ence value for policy-makers in formulating effective energy saving and emission reduction strategies for China.展开更多
The diagnosis of herbal hepatotoxicity or herb induced liver injury(HILI) represents a particular clinical and regulatory challenge with major pitfalls for the causality evaluation.At the day HILI is suspected in a pa...The diagnosis of herbal hepatotoxicity or herb induced liver injury(HILI) represents a particular clinical and regulatory challenge with major pitfalls for the causality evaluation.At the day HILI is suspected in a patient,physicians should start assessing the quality of the used herbal product,optimizing the clinical data for completeness,and applying the Council for International Organizations of Medical Sciences(CIOMS) scale for initial causality assessment.This scale is structured,quantitative,liver specific,and validated for hepatotoxicity cases.Its items provide individual scores,which together yield causality levels of highly probable,probable,possible,unlikely,and excluded.After completion by additional information including raw data,this scale with all items should be reported to regulatory agencies and manufacturers for further evaluation.The CIOMS scale is preferred as tool for assessing causality in hepatotoxicity cases,compared to numerous other causality assessment methods,which are inferior on various grounds.Among these disputed methods are the Maria and Victorino scale,an insufficiently qualified,shortened version of the CIOMS scale,as well as various liver unspecific methods such as thead hoc causality approach,the Naranjo scale,the World Health Organization(WHO) method,and the Karch and Lasagna method.An expert panel is required for the Drug Induced Liver Injury Network method,the WHO method,and other approaches based on expert opinion,which provide retrospective analyses with a long delay and thereby prevent a timely assessment of the illness in question by the physician.In conclusion,HILI causality assessment is challenging and is best achieved by the liver specific CIOMS scale,avoiding pitfalls commonly observed with other approaches.展开更多
In this review,we summarize the recent microbiome studies related to diabetes disease and discuss the key findings that show the early emerging potential causal roles for diabetes.On a global scale,diabetes causes a s...In this review,we summarize the recent microbiome studies related to diabetes disease and discuss the key findings that show the early emerging potential causal roles for diabetes.On a global scale,diabetes causes a significant negative impact to the health status of human populations.This review covers type 1 diabetes and type 2 diabetes.We examine promising studies which lead to a better understanding of the potential mechanism of microbiota in diabetes diseases.It appears that the human oral and gut microbiota are deeply interdigitated with diabetes.It is that simple.Recent studies of the human microbiome are capturing the attention of scientists and healthcare practitioners worldwide by focusing on the interplay of gut microbiome and diabetes.These studies focus on the role and the potential impact of intestinal microflora in diabetes.We paint a clear picture of how strongly microbes are linked and associated,both positively and negatively,with the fundamental and essential parts of diabetes in humans.The microflora seems to have an endless capacity to impact and transform diabetes.We conclude that there is clear and growing evidence of a close relationship between the microbiota and diabetes and this is worthy of future investments and research efforts.展开更多
Causality assessment of suspected drug induced liver injury(DILI) and herb induced liver injury(HILI) is hampered by the lack of a standardized approach to be used by attending physicians and at various subsequent eva...Causality assessment of suspected drug induced liver injury(DILI) and herb induced liver injury(HILI) is hampered by the lack of a standardized approach to be used by attending physicians and at various subsequent evaluating levels. The aim of this review was to analyze the suitability of the liver specific Council for International Organizations of Medical Sciences(CIOMS) scale as a standard tool for causality assessment in DILI and HILI cases. PubMed database was searched for the following terms: drug induced liver injury; herb induced liver injury; DILI causality assessment; and HILI causality assessment. The strength of the CIOMS lies in its potential as a standardized scale for DILI and HILI causality assessment. Other advantages include its liver specificity and its validation for hepatotoxicity with excellent sensitivity, specificity and predictive validity, based on cases with a positive reexposure test. This scale allows prospective collection of all relevant data required for a valid causality assessment. It does not require expert knowledge in hepatotoxicity and its results may subsequently be refined. Weaknesses of the CIOMS scale include the limited exclusion of alternative causes and qualitatively graded risk factors. In conclusion, CIOMS appears to be suitable as a standard scale for attending physicians, regulatory agencies, expert panels and other scientists to provide a standardized, reproducible causality assessment in suspected DILI and HILI cases, applicable primarily at all assessing levels involved. 2014 Baishideng Publishing Group Co., Limited. All展开更多
Rational use of blast furnace gas(BFG) in steel industry can raise economic profit, save fossil energy resources and alleviate the environment pollution. In this paper, a causality diagram is established to describe t...Rational use of blast furnace gas(BFG) in steel industry can raise economic profit, save fossil energy resources and alleviate the environment pollution. In this paper, a causality diagram is established to describe the causal relationships among the decision objective and the variables of the scheduling process for the industrial system, based on which the total scheduling amount of the BFG system can be computed by using a causal fuzzy C-means(CFCM) clustering algorithm. In this algorithm,not only the distances among the historical samples but also the effects of different solutions on the gas tank level are considered.The scheduling solution can be determined based on the proposed causal probability of the causality diagram calculated by the total amount and the conditions of the adjustable units. The causal probability quantifies the impact of different allocation schemes of the total scheduling amount on the BFG system. An evaluation method is then proposed to evaluate the effectiveness of the scheduling solutions. The experiments by using the practical data coming from a steel plant in China indicate that the proposed approach can effectively improve the scheduling accuracy and reduce the gas diffusion.展开更多
Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neuro...Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neurofuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph(DUCG)has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree(FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm(GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.展开更多
This paper examines the causal relationship between oil prices and the Gross Domestic Product(GDP)in the Kingdom of Saudi Arabia.The study is carried out by a data set collected quarterly,by Saudi Arabian Monetary Aut...This paper examines the causal relationship between oil prices and the Gross Domestic Product(GDP)in the Kingdom of Saudi Arabia.The study is carried out by a data set collected quarterly,by Saudi Arabian Monetary Authority,over a period from 1974 to 2016.We seek how a change in real crude oil price affects the GDP of KSA.Based on a new technique,we treat this data in its continuous path.Precisely,we analyze the causality between these two variables,i.e.,oil prices and GDP,by using their yearly curves observed in the four quarters of each year.We discuss the causality in the sense of Granger,which requires the stationarity of the data.Thus,in the first Step,we test the stationarity by using the Monte Carlo test of a functional time series stationarity.Our main goal is treated in the second step,where we use the functional causality idea to model the co-variability between these variables.We show that the two series are not integrated;there is one causality between these two variables.All the statistical analyzes were performed using R software.展开更多
A new approach for abnormal behavior detection was proposed using causality analysis and sparse reconstruction. To effectively represent multiple-object behavior, low level visual features and causality features were ...A new approach for abnormal behavior detection was proposed using causality analysis and sparse reconstruction. To effectively represent multiple-object behavior, low level visual features and causality features were adopted. The low level visual features, which included trajectory shape descriptor, speeded up robust features and histograms of optical flow, were used to describe properties of individual behavior, and causality features obtained by causality analysis were introduced to depict the interaction information among a set of objects. In order to cope with feature noisy and uncertainty, a method for multiple-object anomaly detection was presented via a sparse reconstruction. The abnormality of the testing sample was decided by the sparse reconstruction cost from an atomically learned dictionary. Experiment results show the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases for abnormal behavior detection.展开更多
Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations.However, difficulties associated with computational overload, ubiquitous uncertainties and i...Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations.However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.展开更多
Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is faidy difficult to distinguish the cause o...Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is faidy difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A ＂chaining＂ inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic rea- soning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.展开更多
This article describes a study by co-integration test and Granger causality test on the relationships between China's services trades and employment using the data of services trade from the WTO website and the em...This article describes a study by co-integration test and Granger causality test on the relationships between China's services trades and employment using the data of services trade from the WTO website and the employment data from China Statistic Yearbook for the years from 1982 to 2003. Co-integration test showed that 1% increase in export value and import value of services created respectively 0.205% and 0.068 7% more job opportunities in the service sector. Both export and import of services impacted positively on employment in service industry, and export did more than import. However, in the short run, the impacts of services export and import on employment in service industry were both very small, though positive; and the impacts of employment in service industry on both export and import of services were very big, but not stable. Granger causality test indicated that employment in service industry was a Granger cause of services export. The findings highlight the importance of facilitating services import and reducing import barriers, and suggest that the competitiveness of China's labor- intensive services trade can be exploited to boost services export and help employment in service sector, and that the structure of services trade should be optimized by shifting from labor-intensive to knowledge-and technology-intensive services thus to enhance China's competitiveness of services export.展开更多
Causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely ben...Causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely benefit from the advancement in this field. Here we introduce into this community a recent finding in physics on causality and the subsequent rigorous and quantitative causality analysis. The resulting formula is concise in form, involving only the common statistics namely sample covariance. A corollary is that causation implies correlation, but not vice versa, resolving the long-standing philosophical debate over correlation versus causation. The applicability to big data analysis is validated with time series purportedly generated with hidden processes. As a demonstration, a preliminary application to the gross domestic product (GDP) data of United States, China, and Japan reveals some subtle USA-China-Japan relations in certain periods. 展开更多
Causality Diagram (CD) is a new graphical knowledge representation based on probability theory. The application of this methodology in the safety analysis of the gas explosion in collieries was discussed in this paper...Causality Diagram (CD) is a new graphical knowledge representation based on probability theory. The application of this methodology in the safety analysis of the gas explosion in collieries was discussed in this paper, and the Minimal Cut Set, the Minimal Path Set and the Importance were introduced to develop the methodology. These concepts are employed to analyze the influence each event has on the top event ? the gas explosion, so as to find out about the defects of the system and accordingly help to work out the emphasis of the precautionary work and some preventive measures as well. The results of the safety analysis are in accordance with the practical requirements; therefore the preventive measures are certain to work effectively. In brief, according to the research CD is so effective in the safety analysis and the safety assessment that it can be a qualitative and quantitative method to predict the accident as well as offer some effective measures for the investigation, the prevention and the control of the accident.展开更多
Currently, Granger-Geweke causality models have been widely applied to investigate the dynamic direction relationships among brain regions. In a previous study, we have found that the right hand finger-tapping task ca...Currently, Granger-Geweke causality models have been widely applied to investigate the dynamic direction relationships among brain regions. In a previous study, we have found that the right hand finger-tapping task can produce relatively reliable brain response. As an extension of our previous study, we developed an algorithm based on the classical Granger- Geweke causality model to further investigate the effective connectivity of three brain regions (left primary motor cortex (M1), supplementary motor area (SMA) and right cerebellum) that showed the most robust brain activations. Our computational results not only confirm the strong linear feedback among SMA, M1 and right cerebellum, but also demonstrate that M1 is the hub of these three regions indicated by the anatomy research. Moreover, the model predicts the high intermediate node density existing in the area between SMA and M1, which will stimulate the imaging experimentalists to carry out new experiments to validate this postulation.展开更多
Market efficiency is based on efficient market hypothesis(EMH).EMH claims that market totally contains the available information.In case of EMH,valid investors who take position will not gain abnormal profits.If the e...Market efficiency is based on efficient market hypothesis(EMH).EMH claims that market totally contains the available information.In case of EMH,valid investors who take position will not gain abnormal profits.If the efficiency can not be established,that is,if markets are not efficient,investors will have the opportunity of abnormal profits.This paper investigates the causality relations to determine validity of EMH among G7(Canada,France,Germany,Italy,Japan,United Kingdom,and United States)countries'stock exchange markets for the period from July 2003 to October 2014.To find out whether the variables cause each other or not provides knowledge about the market efficiency.The implication of this analysis is twofold.One implication is that if the markets are informationally efficient,the possibility of abnormal returns through arbitrage is ruled out and investors can reduce the risk of their investment for the same expected returns,if they establish portfolios that consist of both markets rather than consisting of only one market.Based on this,Hacker-Hatemi-J.bootstrap causality test that is newer and has many advantages contrary to other tests was used.Results showed that EMH is valid among each G7 countries'stock exchange markets.Also portfolio diversification benefits exist among these markets.展开更多
基金supported in part by the National Natural Science Foundation of China(No.62221005)the National Key Research and Development Program of China(No.2021YFF0704101,No.2020YFC2003502)+2 种基金the National Natural Science Foundation of China(No.61876201)the Natural Science Foundation of Chongqing(No.cstc2019jcyj-cxtt X0002,No.cstc2021ycjh-bgzxm0013)the key cooperation project of chongqing municipal education commission(HZ2021008)。
文摘Causality extraction has become a crucial task in natural language processing and knowledge graph.However,most existing methods divide causality extraction into two subtasks:extraction of candidate causal pairs and classification of causality.These methods result in cascading errors and the loss of associated contextual information.Therefore,in this study,based on graph theory,an End-to-end Multi-Granulation Causality Extraction model(EMGCE)is proposed to extract explicit causality and directly mine implicit causality.First,the sentences are represented on different granulation layers,that contain character,word,and contextual string layers.The word layer is fine-grained into three layers:word-index,word-embedding and word-position-embedding layers.Then,a granular causality tree of dataset is built based on the word-index layer.Next,an improved tagREtriplet algorithm is designed to obtain the labeled causality based on the granular causality tree.It can transform the task into a sequence labeling task.Subsequently,the multi-granulation semantic representation is fed into the neural network model to extract causality.Finally,based on the extended public SemEval 2010 Task 8 dataset,the experimental results demonstrate that EMGCE is effective.
基金Project supported by the Key National Natural Science Foundation of China(Grant No.62136005)the National Natural Science Foundation of China(Grant Nos.61922087,61906201,and 62006238)。
文摘Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the loss function.The performance of PINNs is generally affected by both training and sampling.Specifically,training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs,and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished.However,a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category,namely,time-dependent PDEs,where temporal information plays a key role in the algorithms used.There is one method,called Causal PINN,that considers temporal causality at the training level but not special temporal utilization at the sampling level.Incorporating temporal knowledge into sampling remains to be studied.To fill this gap,we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality.By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain,we provide a practical solution by incorporating temporal information into sampling.Numerical experiments of several nonlinear time-dependent PDEs,including the Cahn–Hilliard,Korteweg–de Vries,Allen–Cahn and wave equations,show that our proposed sampling method can improve the performance.We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods,especially when points are limited.
文摘An improved safety analysis based on the causality diagram for the complex system of micro aero-engines is presented.The study is examined by using the causality diagram in analytical failure cases due to rupture or pentration in the receiver of micro turbojet engine casing,and the comparisons are also made with the results from the traditional fault tree analysis.Experimental results show two main advantages：（1）Quantitative analysis which is more reliable for the failure analysis in jet engines can be produced by the causality diagram analysis;（2）Graphical representation of causality diagram is easier to apply in real test cases and more effective for the safety assessment.
基金2022 University Research Priorities,No.2022AH051989.
文摘In multi-label learning,the label-specific features learning framework can effectively solve the dimensional catastrophe problem brought by high-dimensional data.The classification performance and robustness of the model are effectively improved.Most existing label-specific features learning utilizes the cosine similarity method to measure label correlation.It is well known that the correlation between labels is asymmetric.However,existing label-specific features learning only considers the private features of labels in classification and does not take into account the common features of labels.Based on this,this paper proposes a Causality-driven Common and Label-specific Features Learning,named CCSF algorithm.Firstly,the causal learning algorithm GSBN is used to calculate the asymmetric correlation between labels.Then,in the optimization,both l_(2,1)-norm and l_(1)-norm are used to select the corresponding features,respectively.Finally,it is compared with six state-of-the-art algorithms on nine datasets.The experimental results prove the effectiveness of the algorithm in this paper.
文摘As physicalisms of various kinds have faced difficulties in recent years, the time has come to explore possible alternatives, one of which is yinyang ontology. A yinyang theorist is expected to provide a plausible account of causation to replace the traditional notion of causation. The present paper is critical of the Humean tradition, which understands the relata of causal relations in terms of passive materiality so that humans use referential terms to describe causal relations constructively. But an alternative notion of reference is available according to which causal relata are active processors of the information with which they interact. On this latter view, humans use referential language to describe the structure in which the relata interrelate themselves so that the structure can be understood hermeneutically. Reference on this view is naturalized. In this article, I advance two arguments for this thesis, one concerning the informationality of states and the other related to the essentiality of properties.
基金National Natural Science Foundation of ChinaNo.41130748+2 种基金No.41471143Major Program of National Social Science Foundation of ChinaNo.15ZDA021
文摘Elucidating the complex mechanism between urbanization, economic growth, car- bon dioxide emissions is fundamental necessary to inform effective strategies on energy saving and emission reduction in China. Based on a balanced panel data of 31 provinces in China over the period 1997-2010, this study empirically examines the relationships among urbanization, economic growth and carbon dioxide （CO2） emissions at the national and re- gional levels using panel cointegration and vector error correction model and Granger cau- sality tests. Results showed that urbanization, economic growth and CO2 emissions are inte- grated of order one. Urbanization contributes to economic growth, both of which increase CO2 emissions in China and its eastern, central and western regions. The impact of urbanization on CO2 emissions in the western region was larger than that in the eastern and central re- gions. But economic growth had a larger impact on CO2 emissions in the eastern region than that in the central and western regions. Panel causality analysis revealed a bidirectional long-run causal relationship among urbanization, economic growth and CO2 emissions, in- dicating that in the long run, urbanization does have a causal effect on economic growth in China, both of which have causal effect on CO2 emissions. At the regional level, we also found a bidirectional long-run causality between land urbanization and economic growth in eastern and central China. These results demonstrated that it might be difficult for China to pursue carbon emissions reduction policy and to control urban expansion without impeding economic growth in the long run. In the short-run, we observed a unidirectional causation running from land urbanization to CO2 emissions and from economic growth to CO2 emissions in the eastern and central regions. Further investigations revealed an inverted N-shaped re- lationship between CO2 emissions and economic growth in China, not supporting the envi- ronmental Kuznets curve （EKC） hypothesis. Our empirical findings have an important refer- ence value for policy-makers in formulating effective energy saving and emission reduction strategies for China.
文摘The diagnosis of herbal hepatotoxicity or herb induced liver injury(HILI) represents a particular clinical and regulatory challenge with major pitfalls for the causality evaluation.At the day HILI is suspected in a patient,physicians should start assessing the quality of the used herbal product,optimizing the clinical data for completeness,and applying the Council for International Organizations of Medical Sciences(CIOMS) scale for initial causality assessment.This scale is structured,quantitative,liver specific,and validated for hepatotoxicity cases.Its items provide individual scores,which together yield causality levels of highly probable,probable,possible,unlikely,and excluded.After completion by additional information including raw data,this scale with all items should be reported to regulatory agencies and manufacturers for further evaluation.The CIOMS scale is preferred as tool for assessing causality in hepatotoxicity cases,compared to numerous other causality assessment methods,which are inferior on various grounds.Among these disputed methods are the Maria and Victorino scale,an insufficiently qualified,shortened version of the CIOMS scale,as well as various liver unspecific methods such as thead hoc causality approach,the Naranjo scale,the World Health Organization(WHO) method,and the Karch and Lasagna method.An expert panel is required for the Drug Induced Liver Injury Network method,the WHO method,and other approaches based on expert opinion,which provide retrospective analyses with a long delay and thereby prevent a timely assessment of the illness in question by the physician.In conclusion,HILI causality assessment is challenging and is best achieved by the liver specific CIOMS scale,avoiding pitfalls commonly observed with other approaches.
基金Supported by Shandong Provincial Key Research and Development Program,No.2018CXGC1219City of Weihai Technique Extension Project,No.2016GNS023+1 种基金TaiShan Scholars Program of Shandong Province,No.tshw20120206TaiShan Industrial Experts Program,No.tscy20190612.
文摘In this review,we summarize the recent microbiome studies related to diabetes disease and discuss the key findings that show the early emerging potential causal roles for diabetes.On a global scale,diabetes causes a significant negative impact to the health status of human populations.This review covers type 1 diabetes and type 2 diabetes.We examine promising studies which lead to a better understanding of the potential mechanism of microbiota in diabetes diseases.It appears that the human oral and gut microbiota are deeply interdigitated with diabetes.It is that simple.Recent studies of the human microbiome are capturing the attention of scientists and healthcare practitioners worldwide by focusing on the interplay of gut microbiome and diabetes.These studies focus on the role and the potential impact of intestinal microflora in diabetes.We paint a clear picture of how strongly microbes are linked and associated,both positively and negatively,with the fundamental and essential parts of diabetes in humans.The microflora seems to have an endless capacity to impact and transform diabetes.We conclude that there is clear and growing evidence of a close relationship between the microbiota and diabetes and this is worthy of future investments and research efforts.
文摘Causality assessment of suspected drug induced liver injury(DILI) and herb induced liver injury(HILI) is hampered by the lack of a standardized approach to be used by attending physicians and at various subsequent evaluating levels. The aim of this review was to analyze the suitability of the liver specific Council for International Organizations of Medical Sciences(CIOMS) scale as a standard tool for causality assessment in DILI and HILI cases. PubMed database was searched for the following terms: drug induced liver injury; herb induced liver injury; DILI causality assessment; and HILI causality assessment. The strength of the CIOMS lies in its potential as a standardized scale for DILI and HILI causality assessment. Other advantages include its liver specificity and its validation for hepatotoxicity with excellent sensitivity, specificity and predictive validity, based on cases with a positive reexposure test. This scale allows prospective collection of all relevant data required for a valid causality assessment. It does not require expert knowledge in hepatotoxicity and its results may subsequently be refined. Weaknesses of the CIOMS scale include the limited exclusion of alternative causes and qualitatively graded risk factors. In conclusion, CIOMS appears to be suitable as a standard scale for attending physicians, regulatory agencies, expert panels and other scientists to provide a standardized, reproducible causality assessment in suspected DILI and HILI cases, applicable primarily at all assessing levels involved. 2014 Baishideng Publishing Group Co., Limited. All
基金supported by the National Natural Sciences Foundation of China(61473056,61533005,61522304,61603068,U1560102)
文摘Rational use of blast furnace gas(BFG) in steel industry can raise economic profit, save fossil energy resources and alleviate the environment pollution. In this paper, a causality diagram is established to describe the causal relationships among the decision objective and the variables of the scheduling process for the industrial system, based on which the total scheduling amount of the BFG system can be computed by using a causal fuzzy C-means(CFCM) clustering algorithm. In this algorithm,not only the distances among the historical samples but also the effects of different solutions on the gas tank level are considered.The scheduling solution can be determined based on the proposed causal probability of the causality diagram calculated by the total amount and the conditions of the adjustable units. The causal probability quantifies the impact of different allocation schemes of the total scheduling amount on the BFG system. An evaluation method is then proposed to evaluate the effectiveness of the scheduling solutions. The experiments by using the practical data coming from a steel plant in China indicate that the proposed approach can effectively improve the scheduling accuracy and reduce the gas diffusion.
文摘Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neurofuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph(DUCG)has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree(FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm(GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.
基金the financial support through the General Research Program under project number GRP-73-41.
文摘This paper examines the causal relationship between oil prices and the Gross Domestic Product(GDP)in the Kingdom of Saudi Arabia.The study is carried out by a data set collected quarterly,by Saudi Arabian Monetary Authority,over a period from 1974 to 2016.We seek how a change in real crude oil price affects the GDP of KSA.Based on a new technique,we treat this data in its continuous path.Precisely,we analyze the causality between these two variables,i.e.,oil prices and GDP,by using their yearly curves observed in the four quarters of each year.We discuss the causality in the sense of Granger,which requires the stationarity of the data.Thus,in the first Step,we test the stationarity by using the Monte Carlo test of a functional time series stationarity.Our main goal is treated in the second step,where we use the functional causality idea to model the co-variability between these variables.We show that the two series are not integrated;there is one causality between these two variables.All the statistical analyzes were performed using R software.
基金Project(50808025) supported by the National Natural Science Foundation of ChinaProject(20090162110057) supported by the Doctoral Fund of Ministry of Education,China
文摘A new approach for abnormal behavior detection was proposed using causality analysis and sparse reconstruction. To effectively represent multiple-object behavior, low level visual features and causality features were adopted. The low level visual features, which included trajectory shape descriptor, speeded up robust features and histograms of optical flow, were used to describe properties of individual behavior, and causality features obtained by causality analysis were introduced to depict the interaction information among a set of objects. In order to cope with feature noisy and uncertainty, a method for multiple-object anomaly detection was presented via a sparse reconstruction. The abnormality of the testing sample was decided by the sparse reconstruction cost from an atomically learned dictionary. Experiment results show the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases for abnormal behavior detection.
基金supported by the National Natural Science Foundation of China(Nos.61050005 and 61273330)Research Foundation for the Doctoral Program of China Ministry of Education(No.20120002110037)+1 种基金the 2014 Teaching Reform Project of Shandong Normal UniversityDevelopment Project of China Guangdong Nuclear Power Group(No.CNPRI-ST10P005)
文摘Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations.However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.
基金supported by the Medical and Health Research Program of Zhejiang Province(No.2015KYB128)the Zhejiang Provincial Natural Science Foundation(No.LQ15H030004),China
文摘Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is faidy difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A ＂chaining＂ inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic rea- soning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.
文摘This article describes a study by co-integration test and Granger causality test on the relationships between China's services trades and employment using the data of services trade from the WTO website and the employment data from China Statistic Yearbook for the years from 1982 to 2003. Co-integration test showed that 1% increase in export value and import value of services created respectively 0.205% and 0.068 7% more job opportunities in the service sector. Both export and import of services impacted positively on employment in service industry, and export did more than import. However, in the short run, the impacts of services export and import on employment in service industry were both very small, though positive; and the impacts of employment in service industry on both export and import of services were very big, but not stable. Granger causality test indicated that employment in service industry was a Granger cause of services export. The findings highlight the importance of facilitating services import and reducing import barriers, and suggest that the competitiveness of China's labor- intensive services trade can be exploited to boost services export and help employment in service sector, and that the structure of services trade should be optimized by shifting from labor-intensive to knowledge-and technology-intensive services thus to enhance China's competitiveness of services export.
文摘Causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely benefit from the advancement in this field. Here we introduce into this community a recent finding in physics on causality and the subsequent rigorous and quantitative causality analysis. The resulting formula is concise in form, involving only the common statistics namely sample covariance. A corollary is that causation implies correlation, but not vice versa, resolving the long-standing philosophical debate over correlation versus causation. The applicability to big data analysis is validated with time series purportedly generated with hidden processes. As a demonstration, a preliminary application to the gross domestic product (GDP) data of United States, China, and Japan reveals some subtle USA-China-Japan relations in certain periods.
基金Supported by the Natural Science Foundation of China (No. 59677009) the National Research Foundation for the Doctoral Program of Higher Education of China (No.99061116)
文摘Causality Diagram (CD) is a new graphical knowledge representation based on probability theory. The application of this methodology in the safety analysis of the gas explosion in collieries was discussed in this paper, and the Minimal Cut Set, the Minimal Path Set and the Importance were introduced to develop the methodology. These concepts are employed to analyze the influence each event has on the top event ? the gas explosion, so as to find out about the defects of the system and accordingly help to work out the emphasis of the precautionary work and some preventive measures as well. The results of the safety analysis are in accordance with the practical requirements; therefore the preventive measures are certain to work effectively. In brief, according to the research CD is so effective in the safety analysis and the safety assessment that it can be a qualitative and quantitative method to predict the accident as well as offer some effective measures for the investigation, the prevention and the control of the accident.
文摘Currently, Granger-Geweke causality models have been widely applied to investigate the dynamic direction relationships among brain regions. In a previous study, we have found that the right hand finger-tapping task can produce relatively reliable brain response. As an extension of our previous study, we developed an algorithm based on the classical Granger- Geweke causality model to further investigate the effective connectivity of three brain regions (left primary motor cortex (M1), supplementary motor area (SMA) and right cerebellum) that showed the most robust brain activations. Our computational results not only confirm the strong linear feedback among SMA, M1 and right cerebellum, but also demonstrate that M1 is the hub of these three regions indicated by the anatomy research. Moreover, the model predicts the high intermediate node density existing in the area between SMA and M1, which will stimulate the imaging experimentalists to carry out new experiments to validate this postulation.
文摘Market efficiency is based on efficient market hypothesis(EMH).EMH claims that market totally contains the available information.In case of EMH,valid investors who take position will not gain abnormal profits.If the efficiency can not be established,that is,if markets are not efficient,investors will have the opportunity of abnormal profits.This paper investigates the causality relations to determine validity of EMH among G7(Canada,France,Germany,Italy,Japan,United Kingdom,and United States)countries'stock exchange markets for the period from July 2003 to October 2014.To find out whether the variables cause each other or not provides knowledge about the market efficiency.The implication of this analysis is twofold.One implication is that if the markets are informationally efficient,the possibility of abnormal returns through arbitrage is ruled out and investors can reduce the risk of their investment for the same expected returns,if they establish portfolios that consist of both markets rather than consisting of only one market.Based on this,Hacker-Hatemi-J.bootstrap causality test that is newer and has many advantages contrary to other tests was used.Results showed that EMH is valid among each G7 countries'stock exchange markets.Also portfolio diversification benefits exist among these markets.
