A Unified Approach to Robust Inference for Genetic Covariance

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits. Many complex traits are found to have shared genetic etiology. Genetic covariance is defined as the underlying covariance of genetic values and can be used to measure the shared genetic architecture. The data of two outcomes may be collected from the same group or different groups of individuals and the outcomes can be of different types or collected based on different study designs. This paper proposes a unified approach to robust estimation and inference for genetic covariance of general outcomes that may be associated with genetic variants nonlinearly. We provide the asymptotic properties of the proposed estimator and show that our proposal is robust under certain model mis-specification. Our method under linear working models provides robust inference for the narrow-sense genetic covariance, even when both linear models are mis-specified. Various numerical experiments are performed to support the theoretical results. Our method is applied to an outbred mice GWAS data set to study the overlapping genetic effects between the behavioral and physiological phenotypes. The real data results demonstrate the robustness of the proposed method and reveal interesting genetic covariance among different mice developmental traits

Inference Of Shared Genetic Architecture With Genome-Wide Association Data

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits. Many complex traits and diseases share common genetic architecture. Studying the shared genetic architecture provides valuable insights into the underlying disease mechanisms. In this dissertation, we develop several statistical methods for investigating the shared genetic architecture based on GWAS data. We first discuss the quantification and estimation of the shared genetic architecture based on genetic covariance, which is defined as the underlying covariance of the genetic effects. We develop a unified approach to robust estimation and inference for genetic covariance of general outcomes that can be associated with genetic variants nonlinearly. The theoretical analysis shows that the proposed estimator is robust under certain model mis-specification. Various numerical experiments are performed to support the theoretical results. Application of this method to an outbred mice GWAS data set reveals interesting genetic covariance among different mice developmental traits. We then consider a practical challenge when the raw genotype data are unavailable, but only the GWAS summary association statistics are available. We develop a method of moments estimator of genetic correlation between two traits in the framework of high dimensional linear models. Theoretical properties of the estimator in terms of consistency and asymptotic normality are provided. Simulations and real data analysis results show that the proposed estimator is more robust and has better interpretability than the LD score regression method under different genetic architectures. Finally, in chapter 4 we discuss the problem of genome-wide detection and identification of shared genetic association, which is a global assessment of the existence of shared genetic architecture. The challenge is that the linkage disequilibrium (LD) between the SNPs makes test statistics highly dependent, which complicates the detection and identification. To account for such a dependency, an eigenvector-projected score statistic is proposed and a max-type test statistic (max-block) is developed for the genome-wide detection of shared associations. The max-block method is easy to calculate and is shown to control the genome-wide error rate. The method is applied to study shared cross-trait associations in 10 pediatric autoimmune diseases, leading to several regions that explain the genetic sharing between diseases

Numerical Simulation on the Influence of Bridge Construction on River Flood Control in a Bottleneck Reach

Bottleneck reach regions with narrow and deep cross sections prevent sediment transport and weaken flood control capacity. In addition, bridge constructions can exacerbate the risk of flooding in these areas. In this study, the Longhai Railway Extension Project at the Xianyang reach of the Weihe River in China was selected as a typical object. A horizontal 2-D numerical model was used to assess the effects of three engineering plans on flood discharge capacity under three flood frequencies. Plan 1 was designed to include building a new bridge, demolishing the three original bridges and dredging a single section of the channel. Plan 2 was the same as Plan 1, except for the compound sections. Plan 3 was designed with the four bridges coexisting and no dredging projects carried out. The results indicated that Plan 3 will increase the water level by 0.2-0.3 m in the upstream reach. The cross-sectional area was approximately 370 m2 larger under Plan 1 than under Plan 2. Water levels of 300-, 100-, and 5-year flooding around the bridge were reduced by 0.9, 0.9, and 0.6 m, respectively. To improve flood control capacity, an effective dredging project must be executed to widen the river and reduce the water stage in the bottleneck reach where the bridge is constructed

NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation

In this paper, we propose a Chinese multi-turn topic-driven conversation dataset, NaturalConv, which allows the participants to chat anything they want as long as any element from the topic is mentioned and the topic shift is smooth. Our corpus contains 19.9K conversations from six domains, and 400K utterances with an average turn number of 20.1. These conversations contain in-depth discussions on related topics or widely natural transition between multiple topics. We believe either way is normal for human conversation. To facilitate the research on this corpus, we provide results of several benchmark models. Comparative results show that for this dataset, our current models are not able to provide significant improvement by introducing background knowledge/topic. Therefore, the proposed dataset should be a good benchmark for further research to evaluate the validity and naturalness of multi-turn conversation systems. Our dataset is available at https://ai.tencent.com/ailab/nlp/dialogue/#datasets.Comment: Accepted as a main track paper at AAAI 202

Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation

Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text. It is thus natural to ask whether it is possible to leverage these large models as knowledge bases for downstream tasks. In this work, we answer the aforementioned question in unsupervised knowledge-grounded conversation. We explore various methods that best elicit knowledge from large models. Our human study indicates that, though hallucinations exist, large models post the unique advantage of being able to output common sense and summarize facts that cannot be directly retrieved from the search engine. To better exploit such generated knowledge in dialogue generation, we treat the generated knowledge as a noisy knowledge source and propose the posterior-based reweighing as well as the noisy training strategy. Empirical results on two benchmarks show advantages over the state-of-the-art methods.Comment: Accepted to EMNLP 2022 Main Conference. The code is publicly available at https://github.com/lyy1994/PLM_as_KB/tree/main/projects/plm_as_k

Leaching resistance of hazardous waste cement solidification after accelerated carbonation

When cement-based materials are carbonated, some of their physicochemical properties are changed, which includes reductions of porosity by 20% and pH from 12-13 to 8–9. These changes can enhance the retention ability of cementitious solids containing hazard waste. This research studied the effect of carbonation on the leaching resistance of hazardous waste cement solidification. The finite element software COMSOL Multiphysics was used to simulate the process of accelerated carbonation and the effect of carbonation on leaching. Laboratory tests were conducted to validate the numerical models. Parametric studies from the numerical simulations revealed that carbonation could significantly improve leaching retention capabilities of cementitious solids containing hazardous wastes

Abnormal traffic detection system in SDN based on deep learning hybrid models

Software defined network (SDN) provides technical support for network construction in smart cities, However, the openness of SDN is also prone to more network attacks. Traditional abnormal traffic detection methods have complex algorithms and find it difficult to detect abnormalities in the network promptly, which cannot meet the demand for abnormal detection in the SDN environment. Therefore, we propose an abnormal traffic detection system based on deep learning hybrid model. The system adopts a hierarchical detection technique, which first achieves rough detection of abnormal traffic based on port information. Then it uses wavelet transform and deep learning techniques for fine detection of all traffic data flowing through suspicious switches. The experimental results show that the proposed detection method based on port information can quickly complete the approximate localization of the source of abnormal traffic. the accuracy, precision, and recall of the fine detection are significantly improved compared with the traditional method of abnormal traffic detection in SDN