An Alternative Approach to Functional Linear Partial Quantile Regression

We have previously proposed the partial quantile regression (PQR) prediction procedure for functional linear model by using partial quantile covariance techniques and developed the simple partial quantile regression (SIMPQR) algorithm to efficiently extract PQR basis for estimating functional coefficients. However, although the PQR approach is considered as an attractive alternative to projections onto the principal component basis, there are certain limitations to uncovering the corresponding asymptotic properties mainly because of its iterative nature and the non-differentiability of the quantile loss function. In this article, we propose and implement an alternative formulation of partial quantile regression (APQR) for functional linear model by using block relaxation method and finite smoothing techniques. The proposed reformulation leads to insightful results and motivates new theory, demonstrating consistency and establishing convergence rates by applying advanced techniques from empirical process theory. Two simulations and two real data from ADHD-200 sample and ADNI are investigated to show the superiority of our proposed methods

Beyond Scalar Treatment: A Causal Analysis of Hippocampal Atrophy on Behavioral Deficits in Alzheimer's Studies

Alzheimer's disease is a progressive form of dementia that results in problems with memory, thinking and behavior. It often starts with abnormal aggregation and deposition of beta-amyloid and tau, followed by neuronal damage such as atrophy of the hippocampi, and finally leads to behavioral deficits. Despite significant progress in finding biomarkers associated with behavioral deficits, the underlying causal mechanism remains largely unknown. Here we investigate whether and how hippocampal atrophy contributes to behavioral deficits based on a large-scale observational study conducted by the Alzheimer's Disease Neuroimaging Initiative (ADNI). As a key novelty, we use 2D representations of the hippocampi, which allows us to better understand atrophy associated with different subregions. It, however, introduces methodological challenges as existing causal inference methods are not well suited for exploiting structural information embedded in the 2D exposures. Moreover, our data contain more than 6 million clinical and genetic covariates, necessitating appropriate confounder selection methods. We hence develop a novel two-step causal inference approach tailored for our ADNI data application. Analysis results suggest that atrophy of CA1 and subiculum subregions may cause more severe behavioral deficits compared to CA2 and CA3 subregions. We further evaluate our method using simulations and provide theoretical guarantees

Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving

With widening deployments of natural language processing (NLP) in daily life, inherited social biases from NLP models have become more severe and problematic. Previous studies have shown that word embeddings trained on human-generated corpora have strong gender biases that can produce discriminative results in downstream tasks. Previous debiasing methods focus mainly on modeling bias and only implicitly consider semantic information while completely overlooking the complex underlying causal structure among bias and semantic components. To address these issues, we propose a novel methodology that leverages a causal inference framework to effectively remove gender bias. The proposed method allows us to construct and analyze the complex causal mechanisms facilitating gender information flow while retaining oracle semantic information within word embeddings. Our comprehensive experiments show that the proposed method achieves state-of-the-art results in gender-debiasing tasks. In addition, our methods yield better performance in word similarity evaluation and various extrinsic downstream NLP tasks

Numerical Research on Migration Law of Typical Chlorinated Organic Matter in Shallow Groundwater of Yangtze Delta Region

With the reform of China’s urbanization increasing in popularity, the security issues posed by urban groundwater, especially groundwater in industrial areas, have attracted scholars’ attention. This research aimed to predict and quantify the migration process of contaminants in a microconfined aquifer by conducting a groundwater contamination investigation in an abandoned chemical plant in the Jiangsu Province of China. First, data such as regional hydrogeological parameters and types of contaminants were obtained via hydrogeological drilling, groundwater well monitoring, pumping tests, and laboratory permeability tests, which helped identify the most serious pollution factor: chloroform. Then, a groundwater flow model was built using the Groundwater Modeling System (GMS) and verified using the general-purpose parameter estimation (PEST) package. In addition, based on the three-dimensional multi-species model for transport (MT3DMS) in GMS, a transport model was established. The results illustrate that the plume range of chloroform diffuses with water flow, but, because of its slow diffusion rate and inability to degrade naturally, the concentration of the contaminant has remained several times higher than the safety standard for a long time. The contaminant spread vertically to the soil layer above the microconfined aquifer under pressure, resulting in direct pollution. In addition, the contaminant in the microconfined aquifer is anticipated to migrate down to the clay layer and become enriched. However, the first confined aquifer has not been seriously polluted in the past 20 years. Finally, a sensitivity analysis of the parameters shows that groundwater contamination in the Yangtze delta region is greatly affected by precipitation recharge and hydraulic conductivity