Saad Sadiq

TL;DR: A comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing is presented, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications.

TL;DR: In this paper, a useful inferential framework to estimate individual treatment effect in observational data is proposed, which is the counter-factual counterfactual framework. But it is not suitable for observational data, due to the challenges of confounding and selection bias.

TL;DR: The single-cell RNA sequencing (scRNA-seq) of ALK3bright+-sorted ductal cells, a fraction that harbors BMP-responsive progenitor-like cells are reported, indicating that progenitors might be activated in situ for therapeutic purposes and suggesting the existence of a third ducto-endocrine axis.

TL;DR: It is found that accurate estimation of individual treatment effects is possible even in complex heterogenous settings but that the type of RF approach plays an important role in accuracy.

TL;DR: This paper will use the Patient Rule Induction Method (PRIM) based bump hunting method to identify the spaces of higher modes and masses to indicate the peak anomalies in the CMS 2014 dataset to characterize the attribute space and explain the events incurring losses to the medicare/medicaid program.