Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association betw...Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.展开更多
With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or p...With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.展开更多
Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subju...Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subjunctive conditionals.Motivated by the so-called OK pairs,they try to provide a convincing explanation of the intuition underlying the OK pairs.As far as we know,what they have done is to provide not only an explanation of the OK pairs,but also a way of distinguishing between indicative and subjunctive conditionals.Although we agree with their success in explaining the OK pairs within a causal modelling framework,we argue that their ways of distinguishing between indicative and subjunctive conditionals fail.Instead,we argue that their approaches can be used to distinguish between two readings of conditionals,the epistemic reading and the ontic reading.which can be applied to both indicative and subjunctive conditionals.We conclude by arguing that these two readings are related to two approaches to asking and answering causal questions:the“auses-of-effects"approach and the"effects-of-causes"approach.展开更多
The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitatio...The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitations:1)unrealistic assumptions of complete evidence independence,and 2)a lack of mechanisms to differentiate causal relationships from spurious correlations.Existing similarity-based approaches often misinterpret interdependent evidence,leading to unreliable decision outcomes.To address these gaps,this study proposes a causality-enhanced ER rule(CER-e)framework with three key methodological innovations:1)a multidimensional causal representation of evidence to capture dependency structures;2)probabilistic quantification of causal strength using transfer entropy,a model-free information-theoretic measure;3)systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity.The PC algorithm is employed during causal discovery to eliminate spurious correlations,ensuring robust causal inference.Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios.Under simulated incomplete information conditions,the framework demonstrates superior algorithmic robustness compared to traditional ER.Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results,establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation.展开更多
Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(M...Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(ML)nature,causal models hope to tie the cause(s)to the effect(s)pertaining to a phenomenon(i.e.,data generating process)through causal principles.This paper presents one of the first works at creating causal models in the area of structural and construction engineering.To this end,this paper starts with a brief review of the principles of causality and then adopts four causal discovery algorithms,namely,PC(Peter-Clark),FCI(fast causal inference),GES(greedy equivalence search),and GRa SP(greedy relaxation of the sparsest permutation),have been used to examine four phenomena,including predicting the load-bearing capacity of axially loaded members,fire resistance of structural members,shear strength of beams,and resistance of walls against impulsive(blast)loading.Findings from this study reveal the possibility and merit of discovering complete and partial causal models.Finally,this study also proposes two simple metrics that can help assess the performance of causal discovery algorithms.展开更多
Objective Vitamin deficiencies,particularly in vitamins A,B12,and D,are prevalent across populations and contribute significantly to a range of health issues.While these deficiencies are well documented,the underlying...Objective Vitamin deficiencies,particularly in vitamins A,B12,and D,are prevalent across populations and contribute significantly to a range of health issues.While these deficiencies are well documented,the underlying etiology remains complex.Recent studies suggest a close link between the gut microbiota and the synthesis,absorption,and metabolism of these vitamins.However,the specific causal relationships between the gut microbiota composition and vitamin deficiencies remain poorly understood.Identifying key bacterial species and understanding their role in vitamin metabolism could provide critical insights for targeted interventions.Methods We conducted a two-sample Mendelian randomization(MR)study to assess the causal relationship between the gut microbiota and vitamin deficiencies(A,B12,D).The genome-wide association study data for vitamin deficiencies were sourced from the FinnGen biobank,and the gut microbiota data were from the MiBioGen consortium.MR analyses included inverse variance-weighted(IVW),MR‒Egger,weighted median,and weighted mode approaches.Sensitivity analyses and reverse causality assessments were performed to ensure robustness and validate the findings.Results After FDR adjustment,vitamin B12 deficiency was associated with the class Verrucomicrobiae,order Verrucomicrobiales,family Verrucomicrobiaceae,and genus Akkermansia.Vitamin A deficiency was associated with the phylum Firmicutes and the genera Fusicatenibacter and Ruminiclostridium 6.Additional associations for vitamin B12 deficiency included the Enterobacteriaceae and Rhodospirillaceae and the genera Coprococcus 2,Lactococcus,and Ruminococcaceae UCG002.Vitamin D deficiency was associated with the genera Allisonella,Eubacterium,and Tyzzerella 3.Lachnospiraceae and Lactococcus were common risk factors for both B12 and D deficiency.Sensitivity analyses confirmed the robustness of the findings against heterogeneity and horizontal pleiotropy,and reverse MR tests indicated no evidence of reverse causality.Conclusions Our findings reveal a possible causal relationship between specific gut microbiota characteristics and vitamin A,B12 and D deficiencies,providing a theoretical basis for addressing these nutritional deficiencies through the modulation of the gut microbiota in the future and laying the groundwork for related interventions.展开更多
Emerging evidence highlights the role of thyroid hormones in cancer,although findings are controversial.Research on thyroid-related traits in lung carcinogenesis is limited.Using UK Biobank data,we performed bidirecti...Emerging evidence highlights the role of thyroid hormones in cancer,although findings are controversial.Research on thyroid-related traits in lung carcinogenesis is limited.Using UK Biobank data,we performed bidirectional Mendelian randomization(MR)to assess causal associations between lung cancer risk and thyroid dysfunction(hypothyroidism and hyperthyroidism)or functional traits(free thyroxine[FT4]and normal-range thyroid-stimulating hormone[TSH]).Furthermore,in the smoking-behavior-stratified MR analysis,we evaluated the mediating effect of thyroid-related phenotypes on the association between smoking behaviors and lung cancer.We demonstrated significant associations between lung cancer risk and hypothyroidism(hazard ratio[HR]=1.14,95%confidence interval[CI]=1.03–1.26,P=0.009)and hyperthyroidism(HR=1.55,95%CI=1.29–1.87,P=1.90×10^(-6))in the UKB.Moreover,the MR analysis indicated a causal effect of thyroid dysfunction on lung cancer risk(ORinverse variance weighted[IVW]=1.09,95%CI=1.05–1.13,P=3.12×10^(-6)for hypothyroidism;ORIVW=1.08,95%CI=1.04–1.12,P=8.14×10^(-5)for hyperthyroidism).We found that FT4 levels were protective against lung cancer risk(ORIVW=0.93,95%CI=0.87–0.99,P=0.030).Additionally,the stratified MR analysis demonstrated distinct causal effects of thyroid dysfunction on lung cancer risk among smokers.Hyperthyroidism mediated the effect of smoking behaviors,especially the age of smoking initiation(17.66%mediated),on lung cancer risk.Thus,thyroid dysfunction phenotypes play causal roles in lung cancer development exclusively among smokers and act as mediators in the causal pathway from smoking to lung cancer.展开更多
Financial time series have been analyzed with a wide variety of models and approaches,some of which can forecast with great accuracy.However,most of these models,especially the machine learning ones,cannot show additi...Financial time series have been analyzed with a wide variety of models and approaches,some of which can forecast with great accuracy.However,most of these models,especially the machine learning ones,cannot show additional information for the decision maker or the financial analyst.The notion of causality is a concept that provides a more complete understanding of a problem beyond improved forecasts.In this study,we propose integrating the treatment/control concept of causality into a forecasting framework to better predict financial time series.Our results show that the proposed methodology outperforms classic econometric approaches such as ARIMA and Random Walk,as well as machine learning approaches without the proposed methodology.This improvement is statistically significant,as indicated by the Model Confidence Set test in the complete test set and quarterly analysis.展开更多
Dear Editor,Observational studies in epidemiology have identified a correlation between hypothyroidism and cholelithiasis[1–2].However,the causal relationship between the two diseases remains unclear.To investigate t...Dear Editor,Observational studies in epidemiology have identified a correlation between hypothyroidism and cholelithiasis[1–2].However,the causal relationship between the two diseases remains unclear.To investigate the potential causal relationship,we employed a two-sample bidirectional Mendelian randomization(MR)analysis.展开更多
Liquid rocket engine(LRE)fault diagnosis is critical for successful space launch missions,enabling timely avoidance of safety hazards,while accurate post-failure analysis prevents subsequent economic losses.However,th...Liquid rocket engine(LRE)fault diagnosis is critical for successful space launch missions,enabling timely avoidance of safety hazards,while accurate post-failure analysis prevents subsequent economic losses.However,the complexity of LRE systems and the“black-box”nature of current deep learning-based diagnostic methods hinder interpretable fault diagnosis.This paper establishes Granger causality(GC)extraction-based component-wise multi-layer perceptron(GCMLP),achieving high fault diagnosis accuracy while leveraging GC to enhance diagnostic interpretability.First,component-wise MLP networks are constructed for distinct LRE variables to extract inter-variable GC relationships.Second,dedicated predictors are designed for each variable,leveraging historical data and GC relationships to forecast future states,thereby ensuring GC reliability.Finally,the extracted GC features are utilized for fault classification,guaranteeing feature discriminability and diagnosis accuracy.This study simulates six critical fault modes in LRE using Simulink.Based on the generated simulation data,GCMLP demonstrates superior fault localization accuracy compared to benchmark methods,validating its efficacy and robustness.展开更多
Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed ...Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials.In this study,we integrated large-scale plasma proteomics,genetic-driven causal inference,and experimental validation to identify prioritized targets for DKD using the UK Biobank(UKB)and FinnGen cohorts.Among 2844 diabetic patients(528 with DKD),we identified 37 targets significantly associated with incident DKD,supported by both observational and causal evidence.Of these,22%(8/37)of the potential targets are currently under investigation for DKD or other diseases.Our prospective study confirmed that higher levels of three prioritized targetsdinsulin-like growth factor binding protein 4(IGFBP4),family with sequence similarity 3 member C(FAM3C),and prostaglandin D2 synthase(PTGDS)dwere associated with a 4.35,3.51,and 3.57-fold increased likelihood of developing DKD,respectively.In addition,population-level protein-altering variants(PAVs)analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD,through the classic NLR family pyrin domain containing 3(NLRP3)-caspase-1-gasdermin D(GSDMD)apoptotic axis.Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.展开更多
The isotope effect on zonal flows(ZFs)and turbulence remains a key issue that is not completely solved in fusion plasmas.This paper presents the first experimental results of the ab initio prediction of causal relatio...The isotope effect on zonal flows(ZFs)and turbulence remains a key issue that is not completely solved in fusion plasmas.This paper presents the first experimental results of the ab initio prediction of causal relation between geodesic acoustic mode(GAM)and ambient turbulence at different isotope masses in the edge of HL-2A tokamak,where transfer entropy method based on information-theoretical approach is utilized as a quantified indicator of causality.Analysis shows that GAM is more pronounced in deuterium plasmas than in hydrogen,leading to a lower heat transport as well as more peaked profiles in the former situation.The causal impact of GAM on conductive heat flux component is stronger than on the convective component,which is resulted from a larger causal influence of zonal flow on temperature fluctuation.While a stronger GAM in deuterium plasmas has larger influence on all flux components,the relative change in temperature fluctuation and coefficient is more obvious when the ion mass varies.These findings not only offer an in-depth understanding of the real causality between zonal flow and turbulence in the present isotope experiments,but also provide useful ways for the physical understandings of transport and zonal flow dynamics in future deuterium-tritium fusion plasmas.展开更多
This study uses the Bayesian structural model to assess the causal effect of the futures exchange(FTX)insolvency on cryptocurrencies from October 2022 to December 14,2022.Findings show that FTX insolvency negatively i...This study uses the Bayesian structural model to assess the causal effect of the futures exchange(FTX)insolvency on cryptocurrencies from October 2022 to December 14,2022.Findings show that FTX insolvency negatively impacts cryptocurrencies.Moreover,the results indicate rapid divergence from counterfactual predictions,and the actual cryptocurrencies are consistently lower than would have been expected in the absence of the FTX collapse.Cryptocurrency is reacting strongly to the uncertainty caused by insolvency.In relative terms,the collapse of FTX has been highly detrimental to Solana and Ethereum.Furthermore,the outcomes show that cryptocurrencies would not have been negatively affected if the intervention had not occurred.FTX collapsed owing to a mismatch between the assets and liabilities.The industry is still mostly unregulated,and regulators must act quickly,highlighting the need for outstanding innovation and decentralized and trustless technology adoption.展开更多
Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult f...Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult for the current ROP models to guarantee the prediction accuracy and the robustness of the models at the same time.To address the current issues,a new ROP prediction model was developed in this study,which considers ROP as a time series signal(ROP signal).The model is based on the time convolutional network(TCN)framework and integrates ensemble empirical modal decomposition(EEMD)and Bayesian network causal inference(BN),the model is named EEMD-BN-TCN.Within the proposed model,the EEMD decomposes the original ROP signal into multiple sets of sub-signals.The BN determines the causal relationship between the sub-signals and the key physical parameters(weight on bit and revolutions per minute)and carries out preliminary reconstruction of the sub-signals based on the causal relationship.The TCN predicts signals reconstructed by BN.When applying this model to an actual production well,the average absolute percentage error of the EEMD-BN-TCN prediction decreased from 18.4%with TCN to 9.2%.In addition,compared with other models,the EEMD-BN-TCN can improve the decomposition signal of ROP by regulating weight on bit and revolutions per minute,ultimately enhancing ROP.展开更多
Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that ...Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships,particularly in the presence of non-linearities and confounding factors.This limits their value for informing policy and market design in the context of the energy transition.To address this gap,we propose a novel causal inference framework based on local partially linear double machine learning(DML).Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for non-linear,context-dependent effects.This represents a substantial methodological advancement over standard econometric techniques.Applying this framework to the UK electricity market over the period 2018-2024,we produce the first robust causal estimates of how renewables affect dayahead wholesale electricity prices.We find that wind power exerts a U-shaped causal effect:at low penetration levels,a 1 GWh increase reduces prices by up to£7/MWh,the effect weakens at mid-levels,and intensifies again at higher penetration.Solar power consistently reduces prices at low penetration levels,up to£9/MWh per additional GWh,but its marginal effect diminishes quickly.Importantly,the magnitude of these effects has increased over time,reflecting the growing influence of renewables on price formation as their share in the energy mix rises.These findings offer a sound empirical basis for improving the design of support schemes,refining capacity planning,and enhancing electricity market efficiency.By providing a robust causal understanding of renewable impacts,our study contributes both methodological innovation and actionable insights to guide future energy policy.展开更多
Understanding the characteristics and driving factors behind changes in vegetation ecosystem resilience is crucial for mitigating both current and future impacts of climate change. Despite recent advances in resilienc...Understanding the characteristics and driving factors behind changes in vegetation ecosystem resilience is crucial for mitigating both current and future impacts of climate change. Despite recent advances in resilience research, significant knowledge gaps remain regarding the drivers of resilience changes. In this study, we investigated the dynamics of ecosystem resilience across China and identified potential driving factors using the kernel normalized difference vegetation index(kNDVI) from 2000 to 2020. Our results indicate that vegetation resilience in China has exhibited an increasing trend over the past two decades, with a notable breakpoint occurring around 2012. We found that precipitation was the dominant driver of changes in ecosystem resilience, accounting for 35.82% of the variation across China, followed by monthly average maximum temperature(Tmax) and vapor pressure deficit(VPD), which explained 28.95% and 28.31% of the variation, respectively. Furthermore, we revealed that daytime and nighttime warming has asymmetric impacts on vegetation resilience, with temperature factors such as Tmin and Tmax becoming more influential, while the importance of precipitation slightly decreases after the resilience change point. Overall, our study highlights the key roles of water availability and temperature in shaping vegetation resilience and underscores the asymmetric effects of daytime and nighttime warming on ecosystem resilience.展开更多
Hemorrhagic stroke,the second leading cause of stroke,is a severe medical emergency that often leads to severe disability or death;however,the causal relationship between antibody-mediated immune responses and hemorrh...Hemorrhagic stroke,the second leading cause of stroke,is a severe medical emergency that often leads to severe disability or death;however,the causal relationship between antibody-mediated immune responses and hemorrhagic stroke remains unknown.This study aimed to investigate the potential causal relationship between antibody-mediated immune responses to infectious agents and hemorrhagic stroke using the two-sample Mendelian randomization(MR)method.Comprehensive analyses were conducted using publicly available data from genome-wide association study(GWAS),which involved the whole genomes of 9724 European participants and 46 antibody measurement phenotypes,and summary statistics from the FinnGen dataset R12(including intracerebral hemorrhage and subarachnoid hemorrhage)were used.The causal relationship between the aforementioned immune responses and hemorrhagic stroke was analyzed using inverse-variance weighting,MR-Egger regression,weighted median,weighted mode,simple mode,and MR-pleiotropy residual sum and outlier(MR-PRESSO),while various sensitivity analyses were performed to assess heterogeneity and pleiotropy in the study findings.Results showed that human herpes virus 7(HHV-7)U14 antibody levels(OR:0.877,95%CI:0.797-0.964,P=0.007)exerted a protective effect against hemorrhagic stroke,and Chlamydia trachomatis(CT)tarp-D F2 antibody levels(OR:0.937,95%CI:0.885-0.992,P=0.025)had a potential protective effect;additionally,Epstein-Barr virus(EBV)ZEBRA antibody levels(OR:1.062,95%CI:1.012-1.114,P=0.014),human herpesvirus 6(HHV-6)p101k antibody levels(OR:1.054,95%CI:1.002-1.108,P=0.042),and cytomegalovirus(CMV)pp150 antibody levels(OR:1.086,95%CI:1.002-1.176,P=0.045)were potential risk factors for the disease.No significant pleiotropy or heterogeneity was observed in any of the MR analyses.Collectively,these findings confirmed a significant causal relationship between antibody-mediated immune responses and hemorrhagic stroke,and this study contributed to a deeper understanding of the potential mechanisms underlying hemorrhagic stroke onset.展开更多
The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,direc...The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,directly predicting missing components via global characteristics extracted from incomplete inputs.However,this makes detail re-covery challenging,as global characteristics fail to provide complete missing component specifics.A new point cloud completion method named Point-PC is proposed.A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion.Concretely,a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format.The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain,enabling retrieval of analogous shapes from incomplete inputs.In addition,the authors employ backdoor adjustment to eliminate confounders,which are shape prior components sharing identical semantic structures with incomplete inputs.Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches.The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.展开更多
The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to...The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to falling into local optima.To address these issues,an improved GA with domain knowledge(IGADK)is proposed.Firstly,domain knowledge is incorporated into the learning process of causality to construct a new fitness function.Secondly,a dynamical mutation operator is introduced in the algorithm to accelerate the convergence rate.Finally,an experiment is conducted on simulation data,which compares the classical GA with IGADK with domain knowledge of varying accuracy.The IGADK can greatly reduce the number of iterations,populations,and samples required for learning,which illustrates the efficiency and effectiveness of the proposed algorithm.展开更多
Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.Howev...Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.展开更多
基金supported by the Yanzhao Gold Talent Project of Hebei Province(NO.HJZD202506)。
文摘Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.
基金funded by the Hunan Provincial Natural Science Foundation of China(Grant No.2025JJ70105)the Hunan Provincial College Students’Innovation and Entrepreneurship Training Program(Project No.S202411342056)The article processing charge(APC)was funded by the Project No.2025JJ70105.
文摘With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.
基金supported by China’s MOE project of Key Research Institute of Humanities and Social Sciences at Universities(22JJD720021)the project of Shandong University(11090087395308).
文摘Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subjunctive conditionals.Motivated by the so-called OK pairs,they try to provide a convincing explanation of the intuition underlying the OK pairs.As far as we know,what they have done is to provide not only an explanation of the OK pairs,but also a way of distinguishing between indicative and subjunctive conditionals.Although we agree with their success in explaining the OK pairs within a causal modelling framework,we argue that their ways of distinguishing between indicative and subjunctive conditionals fail.Instead,we argue that their approaches can be used to distinguish between two readings of conditionals,the epistemic reading and the ontic reading.which can be applied to both indicative and subjunctive conditionals.We conclude by arguing that these two readings are related to two approaches to asking and answering causal questions:the“auses-of-effects"approach and the"effects-of-causes"approach.
基金supported by the Natural Science Foundation of China(Nos.U22A2099,62273113,62203461,62203365)the Innovation Project of Guangxi Graduate Education under Grant YCBZ2023130by the Guangxi Higher Education Undergraduate Teaching Reform Project Key Project,grant number 2022JGZ130.
文摘The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitations:1)unrealistic assumptions of complete evidence independence,and 2)a lack of mechanisms to differentiate causal relationships from spurious correlations.Existing similarity-based approaches often misinterpret interdependent evidence,leading to unreliable decision outcomes.To address these gaps,this study proposes a causality-enhanced ER rule(CER-e)framework with three key methodological innovations:1)a multidimensional causal representation of evidence to capture dependency structures;2)probabilistic quantification of causal strength using transfer entropy,a model-free information-theoretic measure;3)systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity.The PC algorithm is employed during causal discovery to eliminate spurious correlations,ensuring robust causal inference.Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios.Under simulated incomplete information conditions,the framework demonstrates superior algorithmic robustness compared to traditional ER.Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results,establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation.
文摘Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(ML)nature,causal models hope to tie the cause(s)to the effect(s)pertaining to a phenomenon(i.e.,data generating process)through causal principles.This paper presents one of the first works at creating causal models in the area of structural and construction engineering.To this end,this paper starts with a brief review of the principles of causality and then adopts four causal discovery algorithms,namely,PC(Peter-Clark),FCI(fast causal inference),GES(greedy equivalence search),and GRa SP(greedy relaxation of the sparsest permutation),have been used to examine four phenomena,including predicting the load-bearing capacity of axially loaded members,fire resistance of structural members,shear strength of beams,and resistance of walls against impulsive(blast)loading.Findings from this study reveal the possibility and merit of discovering complete and partial causal models.Finally,this study also proposes two simple metrics that can help assess the performance of causal discovery algorithms.
文摘Objective Vitamin deficiencies,particularly in vitamins A,B12,and D,are prevalent across populations and contribute significantly to a range of health issues.While these deficiencies are well documented,the underlying etiology remains complex.Recent studies suggest a close link between the gut microbiota and the synthesis,absorption,and metabolism of these vitamins.However,the specific causal relationships between the gut microbiota composition and vitamin deficiencies remain poorly understood.Identifying key bacterial species and understanding their role in vitamin metabolism could provide critical insights for targeted interventions.Methods We conducted a two-sample Mendelian randomization(MR)study to assess the causal relationship between the gut microbiota and vitamin deficiencies(A,B12,D).The genome-wide association study data for vitamin deficiencies were sourced from the FinnGen biobank,and the gut microbiota data were from the MiBioGen consortium.MR analyses included inverse variance-weighted(IVW),MR‒Egger,weighted median,and weighted mode approaches.Sensitivity analyses and reverse causality assessments were performed to ensure robustness and validate the findings.Results After FDR adjustment,vitamin B12 deficiency was associated with the class Verrucomicrobiae,order Verrucomicrobiales,family Verrucomicrobiaceae,and genus Akkermansia.Vitamin A deficiency was associated with the phylum Firmicutes and the genera Fusicatenibacter and Ruminiclostridium 6.Additional associations for vitamin B12 deficiency included the Enterobacteriaceae and Rhodospirillaceae and the genera Coprococcus 2,Lactococcus,and Ruminococcaceae UCG002.Vitamin D deficiency was associated with the genera Allisonella,Eubacterium,and Tyzzerella 3.Lachnospiraceae and Lactococcus were common risk factors for both B12 and D deficiency.Sensitivity analyses confirmed the robustness of the findings against heterogeneity and horizontal pleiotropy,and reverse MR tests indicated no evidence of reverse causality.Conclusions Our findings reveal a possible causal relationship between specific gut microbiota characteristics and vitamin A,B12 and D deficiencies,providing a theoretical basis for addressing these nutritional deficiencies through the modulation of the gut microbiota in the future and laying the groundwork for related interventions.
基金funded by the National Natural Science Foundation of China(Grant Nos.82220108002 to F.C.,82273737 to R.Z.,82473728 to Y.W.)the US National Institutes of Health(Grant Nos.CA209414,HL060710,ES000002 to D.C.C.,CA209414,CA249096 to Y.L.)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).R.Z.was partially supported by the Qing Lan Project of the Higher Education Institutions of Jiangsu Province and the Outstanding Young-Level Academic Leadership Training Program of Nanjing Medical University.
文摘Emerging evidence highlights the role of thyroid hormones in cancer,although findings are controversial.Research on thyroid-related traits in lung carcinogenesis is limited.Using UK Biobank data,we performed bidirectional Mendelian randomization(MR)to assess causal associations between lung cancer risk and thyroid dysfunction(hypothyroidism and hyperthyroidism)or functional traits(free thyroxine[FT4]and normal-range thyroid-stimulating hormone[TSH]).Furthermore,in the smoking-behavior-stratified MR analysis,we evaluated the mediating effect of thyroid-related phenotypes on the association between smoking behaviors and lung cancer.We demonstrated significant associations between lung cancer risk and hypothyroidism(hazard ratio[HR]=1.14,95%confidence interval[CI]=1.03–1.26,P=0.009)and hyperthyroidism(HR=1.55,95%CI=1.29–1.87,P=1.90×10^(-6))in the UKB.Moreover,the MR analysis indicated a causal effect of thyroid dysfunction on lung cancer risk(ORinverse variance weighted[IVW]=1.09,95%CI=1.05–1.13,P=3.12×10^(-6)for hypothyroidism;ORIVW=1.08,95%CI=1.04–1.12,P=8.14×10^(-5)for hyperthyroidism).We found that FT4 levels were protective against lung cancer risk(ORIVW=0.93,95%CI=0.87–0.99,P=0.030).Additionally,the stratified MR analysis demonstrated distinct causal effects of thyroid dysfunction on lung cancer risk among smokers.Hyperthyroidism mediated the effect of smoking behaviors,especially the age of smoking initiation(17.66%mediated),on lung cancer risk.Thus,thyroid dysfunction phenotypes play causal roles in lung cancer development exclusively among smokers and act as mediators in the causal pathway from smoking to lung cancer.
基金ANID for supporting the Fondecyt project 1200555.
文摘Financial time series have been analyzed with a wide variety of models and approaches,some of which can forecast with great accuracy.However,most of these models,especially the machine learning ones,cannot show additional information for the decision maker or the financial analyst.The notion of causality is a concept that provides a more complete understanding of a problem beyond improved forecasts.In this study,we propose integrating the treatment/control concept of causality into a forecasting framework to better predict financial time series.Our results show that the proposed methodology outperforms classic econometric approaches such as ARIMA and Random Walk,as well as machine learning approaches without the proposed methodology.This improvement is statistically significant,as indicated by the Model Confidence Set test in the complete test set and quarterly analysis.
基金by grants from the Jiangsu Province 333 High-level Talent Training Project(Grant No.LGY2016010)the Nanjing Science and Technology Development Plan(Grant No.201715003)the Jiangsu Province Six Talent Peaks(Grant No.WSN-030).
文摘Dear Editor,Observational studies in epidemiology have identified a correlation between hypothyroidism and cholelithiasis[1–2].However,the causal relationship between the two diseases remains unclear.To investigate the potential causal relationship,we employed a two-sample bidirectional Mendelian randomization(MR)analysis.
文摘Liquid rocket engine(LRE)fault diagnosis is critical for successful space launch missions,enabling timely avoidance of safety hazards,while accurate post-failure analysis prevents subsequent economic losses.However,the complexity of LRE systems and the“black-box”nature of current deep learning-based diagnostic methods hinder interpretable fault diagnosis.This paper establishes Granger causality(GC)extraction-based component-wise multi-layer perceptron(GCMLP),achieving high fault diagnosis accuracy while leveraging GC to enhance diagnostic interpretability.First,component-wise MLP networks are constructed for distinct LRE variables to extract inter-variable GC relationships.Second,dedicated predictors are designed for each variable,leveraging historical data and GC relationships to forecast future states,thereby ensuring GC reliability.Finally,the extracted GC features are utilized for fault classification,guaranteeing feature discriminability and diagnosis accuracy.This study simulates six critical fault modes in LRE using Simulink.Based on the generated simulation data,GCMLP demonstrates superior fault localization accuracy compared to benchmark methods,validating its efficacy and robustness.
基金supported by the National Natural Science Foundation of China(Grant Nos.:82204396,82304491,and 82400511).
文摘Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials.In this study,we integrated large-scale plasma proteomics,genetic-driven causal inference,and experimental validation to identify prioritized targets for DKD using the UK Biobank(UKB)and FinnGen cohorts.Among 2844 diabetic patients(528 with DKD),we identified 37 targets significantly associated with incident DKD,supported by both observational and causal evidence.Of these,22%(8/37)of the potential targets are currently under investigation for DKD or other diseases.Our prospective study confirmed that higher levels of three prioritized targetsdinsulin-like growth factor binding protein 4(IGFBP4),family with sequence similarity 3 member C(FAM3C),and prostaglandin D2 synthase(PTGDS)dwere associated with a 4.35,3.51,and 3.57-fold increased likelihood of developing DKD,respectively.In addition,population-level protein-altering variants(PAVs)analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD,through the classic NLR family pyrin domain containing 3(NLRP3)-caspase-1-gasdermin D(GSDMD)apoptotic axis.Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.
基金supported by the National MCF Energy Research and Development Program(Grant Nos.2024YFE03190001,2024YFE03190004,2022YFE03030001,and 2019YFE03030002)the National Natural Science Foundation of China(Grant Nos.12405257,12475215,and 12475219)+2 种基金the Natural Science Foundation of Sichuan Province,China(Grant Nos.2023NSFSC1289 and 2025ZNSFSC0066)the Nuclear Technology Research and Development Program(Grant No.HJSYF2024(02))the Innovation Program of Southwestern Institute of Physics(Grant No.202301XWCX001)。
文摘The isotope effect on zonal flows(ZFs)and turbulence remains a key issue that is not completely solved in fusion plasmas.This paper presents the first experimental results of the ab initio prediction of causal relation between geodesic acoustic mode(GAM)and ambient turbulence at different isotope masses in the edge of HL-2A tokamak,where transfer entropy method based on information-theoretical approach is utilized as a quantified indicator of causality.Analysis shows that GAM is more pronounced in deuterium plasmas than in hydrogen,leading to a lower heat transport as well as more peaked profiles in the former situation.The causal impact of GAM on conductive heat flux component is stronger than on the convective component,which is resulted from a larger causal influence of zonal flow on temperature fluctuation.While a stronger GAM in deuterium plasmas has larger influence on all flux components,the relative change in temperature fluctuation and coefficient is more obvious when the ion mass varies.These findings not only offer an in-depth understanding of the real causality between zonal flow and turbulence in the present isotope experiments,but also provide useful ways for the physical understandings of transport and zonal flow dynamics in future deuterium-tritium fusion plasmas.
文摘This study uses the Bayesian structural model to assess the causal effect of the futures exchange(FTX)insolvency on cryptocurrencies from October 2022 to December 14,2022.Findings show that FTX insolvency negatively impacts cryptocurrencies.Moreover,the results indicate rapid divergence from counterfactual predictions,and the actual cryptocurrencies are consistently lower than would have been expected in the absence of the FTX collapse.Cryptocurrency is reacting strongly to the uncertainty caused by insolvency.In relative terms,the collapse of FTX has been highly detrimental to Solana and Ethereum.Furthermore,the outcomes show that cryptocurrencies would not have been negatively affected if the intervention had not occurred.FTX collapsed owing to a mismatch between the assets and liabilities.The industry is still mostly unregulated,and regulators must act quickly,highlighting the need for outstanding innovation and decentralized and trustless technology adoption.
基金the financial support by the National Natural Science Foundation of China(Grant No.U24B2029)the Key Projects of the National Natural Science Foundation of China(Grant No.52334001)+1 种基金the Strategic Cooperation Technology Projects of CNPC and CUPB(Grand No.ZLZX2020-02)the China University of Petroleum,Beijing(Grand No.ZX20230042)。
文摘Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult for the current ROP models to guarantee the prediction accuracy and the robustness of the models at the same time.To address the current issues,a new ROP prediction model was developed in this study,which considers ROP as a time series signal(ROP signal).The model is based on the time convolutional network(TCN)framework and integrates ensemble empirical modal decomposition(EEMD)and Bayesian network causal inference(BN),the model is named EEMD-BN-TCN.Within the proposed model,the EEMD decomposes the original ROP signal into multiple sets of sub-signals.The BN determines the causal relationship between the sub-signals and the key physical parameters(weight on bit and revolutions per minute)and carries out preliminary reconstruction of the sub-signals based on the causal relationship.The TCN predicts signals reconstructed by BN.When applying this model to an actual production well,the average absolute percentage error of the EEMD-BN-TCN prediction decreased from 18.4%with TCN to 9.2%.In addition,compared with other models,the EEMD-BN-TCN can improve the decomposition signal of ROP by regulating weight on bit and revolutions per minute,ultimately enhancing ROP.
文摘Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships,particularly in the presence of non-linearities and confounding factors.This limits their value for informing policy and market design in the context of the energy transition.To address this gap,we propose a novel causal inference framework based on local partially linear double machine learning(DML).Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for non-linear,context-dependent effects.This represents a substantial methodological advancement over standard econometric techniques.Applying this framework to the UK electricity market over the period 2018-2024,we produce the first robust causal estimates of how renewables affect dayahead wholesale electricity prices.We find that wind power exerts a U-shaped causal effect:at low penetration levels,a 1 GWh increase reduces prices by up to£7/MWh,the effect weakens at mid-levels,and intensifies again at higher penetration.Solar power consistently reduces prices at low penetration levels,up to£9/MWh per additional GWh,but its marginal effect diminishes quickly.Importantly,the magnitude of these effects has increased over time,reflecting the growing influence of renewables on price formation as their share in the energy mix rises.These findings offer a sound empirical basis for improving the design of support schemes,refining capacity planning,and enhancing electricity market efficiency.By providing a robust causal understanding of renewable impacts,our study contributes both methodological innovation and actionable insights to guide future energy policy.
基金National Key Research and Development Program,No.2021xjkk0303。
文摘Understanding the characteristics and driving factors behind changes in vegetation ecosystem resilience is crucial for mitigating both current and future impacts of climate change. Despite recent advances in resilience research, significant knowledge gaps remain regarding the drivers of resilience changes. In this study, we investigated the dynamics of ecosystem resilience across China and identified potential driving factors using the kernel normalized difference vegetation index(kNDVI) from 2000 to 2020. Our results indicate that vegetation resilience in China has exhibited an increasing trend over the past two decades, with a notable breakpoint occurring around 2012. We found that precipitation was the dominant driver of changes in ecosystem resilience, accounting for 35.82% of the variation across China, followed by monthly average maximum temperature(Tmax) and vapor pressure deficit(VPD), which explained 28.95% and 28.31% of the variation, respectively. Furthermore, we revealed that daytime and nighttime warming has asymmetric impacts on vegetation resilience, with temperature factors such as Tmin and Tmax becoming more influential, while the importance of precipitation slightly decreases after the resilience change point. Overall, our study highlights the key roles of water availability and temperature in shaping vegetation resilience and underscores the asymmetric effects of daytime and nighttime warming on ecosystem resilience.
基金Supported by the National Natural Science Foundations of China(82271340,82071368)。
文摘Hemorrhagic stroke,the second leading cause of stroke,is a severe medical emergency that often leads to severe disability or death;however,the causal relationship between antibody-mediated immune responses and hemorrhagic stroke remains unknown.This study aimed to investigate the potential causal relationship between antibody-mediated immune responses to infectious agents and hemorrhagic stroke using the two-sample Mendelian randomization(MR)method.Comprehensive analyses were conducted using publicly available data from genome-wide association study(GWAS),which involved the whole genomes of 9724 European participants and 46 antibody measurement phenotypes,and summary statistics from the FinnGen dataset R12(including intracerebral hemorrhage and subarachnoid hemorrhage)were used.The causal relationship between the aforementioned immune responses and hemorrhagic stroke was analyzed using inverse-variance weighting,MR-Egger regression,weighted median,weighted mode,simple mode,and MR-pleiotropy residual sum and outlier(MR-PRESSO),while various sensitivity analyses were performed to assess heterogeneity and pleiotropy in the study findings.Results showed that human herpes virus 7(HHV-7)U14 antibody levels(OR:0.877,95%CI:0.797-0.964,P=0.007)exerted a protective effect against hemorrhagic stroke,and Chlamydia trachomatis(CT)tarp-D F2 antibody levels(OR:0.937,95%CI:0.885-0.992,P=0.025)had a potential protective effect;additionally,Epstein-Barr virus(EBV)ZEBRA antibody levels(OR:1.062,95%CI:1.012-1.114,P=0.014),human herpesvirus 6(HHV-6)p101k antibody levels(OR:1.054,95%CI:1.002-1.108,P=0.042),and cytomegalovirus(CMV)pp150 antibody levels(OR:1.086,95%CI:1.002-1.176,P=0.045)were potential risk factors for the disease.No significant pleiotropy or heterogeneity was observed in any of the MR analyses.Collectively,these findings confirmed a significant causal relationship between antibody-mediated immune responses and hemorrhagic stroke,and this study contributed to a deeper understanding of the potential mechanisms underlying hemorrhagic stroke onset.
基金National Key Research and Development Program of China,Grant/Award Number:2020YFB1711704。
文摘The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,directly predicting missing components via global characteristics extracted from incomplete inputs.However,this makes detail re-covery challenging,as global characteristics fail to provide complete missing component specifics.A new point cloud completion method named Point-PC is proposed.A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion.Concretely,a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format.The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain,enabling retrieval of analogous shapes from incomplete inputs.In addition,the authors employ backdoor adjustment to eliminate confounders,which are shape prior components sharing identical semantic structures with incomplete inputs.Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches.The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.
基金supported by the National Social Science Fund of China(2022-SKJJ-B-084).
文摘The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to falling into local optima.To address these issues,an improved GA with domain knowledge(IGADK)is proposed.Firstly,domain knowledge is incorporated into the learning process of causality to construct a new fitness function.Secondly,a dynamical mutation operator is introduced in the algorithm to accelerate the convergence rate.Finally,an experiment is conducted on simulation data,which compares the classical GA with IGADK with domain knowledge of varying accuracy.The IGADK can greatly reduce the number of iterations,populations,and samples required for learning,which illustrates the efficiency and effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(Grant Nos.1217211 and 12372244).
文摘Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.
