Mendelian randomization(MR)is widely used in causal mediation analysis to control unmeasured confounding effects,which is valid under some strong assumptions.It is thus of great interest to assess the impact of violat...Mendelian randomization(MR)is widely used in causal mediation analysis to control unmeasured confounding effects,which is valid under some strong assumptions.It is thus of great interest to assess the impact of violations of these MR assumptions through sensitivity analysis.Sensitivity analyses have been conducted for simple MR-based causal average effect analyses,but they are not available for MR-based mediation analysis studies,and we aim to fill this gap in this paper.We propose to use two sensitivity parameters to quantify the effect due to the deviation of the IV assumptions.With these two sensitivity parameters,we derive consistent indirect causal effect estimators and establish their asymptotic propersties.Our theoretical results can be used in MR-based mediation analysis to study the impact of violations of MR as-sumptions.The finite sample performance of the proposed method is illustrated through simulation studies,sensitivity ana-lysis,and application to a real genome-wide association study.展开更多
Unsupervised extractive summarization aims to pinpoint representative sentences from raw text without relying on labeled summary data,capturing the overall content.Numerous prevalent research methods predominantly pri...Unsupervised extractive summarization aims to pinpoint representative sentences from raw text without relying on labeled summary data,capturing the overall content.Numerous prevalent research methods predominantly prioritize the significance of sentences within a document,potentially overlooking the importance of varying keywords within a sentence.Moreover,many methods confine the summarization to information present only in the current document,potentially omitting crucial details essential for comprehensive document understanding.To tackle these challenges,this paper introduces WSDSum,an algorithm rooted in word weight fusion and dynamic document comparison.This algorithm employs two distinctword weight assessmentmethods to gauge the significance of words in a sentence and subsequently combines their assessment outcomes to more effectively evaluate word importance within a sentence.Furthermore,this paper suggests a dynamic document comparison approach to enhance the diversity of the generated summaries by creating positive examples from intra-document sentences and contrasting them with inter-document sentence counterexamples.This is achieved by leveraging a cosine annealing strategy to facilitate dynamic temperature comparisons with other documents.Experimental evaluations on three public datasets indicate that WSDSum outperforms traditional methods.展开更多
基金This work was supported by the National Natural Science Foundation of China(12171451,72091212).
文摘Mendelian randomization(MR)is widely used in causal mediation analysis to control unmeasured confounding effects,which is valid under some strong assumptions.It is thus of great interest to assess the impact of violations of these MR assumptions through sensitivity analysis.Sensitivity analyses have been conducted for simple MR-based causal average effect analyses,but they are not available for MR-based mediation analysis studies,and we aim to fill this gap in this paper.We propose to use two sensitivity parameters to quantify the effect due to the deviation of the IV assumptions.With these two sensitivity parameters,we derive consistent indirect causal effect estimators and establish their asymptotic propersties.Our theoretical results can be used in MR-based mediation analysis to study the impact of violations of MR as-sumptions.The finite sample performance of the proposed method is illustrated through simulation studies,sensitivity ana-lysis,and application to a real genome-wide association study.
文摘Unsupervised extractive summarization aims to pinpoint representative sentences from raw text without relying on labeled summary data,capturing the overall content.Numerous prevalent research methods predominantly prioritize the significance of sentences within a document,potentially overlooking the importance of varying keywords within a sentence.Moreover,many methods confine the summarization to information present only in the current document,potentially omitting crucial details essential for comprehensive document understanding.To tackle these challenges,this paper introduces WSDSum,an algorithm rooted in word weight fusion and dynamic document comparison.This algorithm employs two distinctword weight assessmentmethods to gauge the significance of words in a sentence and subsequently combines their assessment outcomes to more effectively evaluate word importance within a sentence.Furthermore,this paper suggests a dynamic document comparison approach to enhance the diversity of the generated summaries by creating positive examples from intra-document sentences and contrasting them with inter-document sentence counterexamples.This is achieved by leveraging a cosine annealing strategy to facilitate dynamic temperature comparisons with other documents.Experimental evaluations on three public datasets indicate that WSDSum outperforms traditional methods.
