Human-AI Collaboration in Content Moderation: The Effects of Information Cues and Time Constraints

An extremely large amount of user-generated content is produced by users worldwide every day with the rapid development of online social media. Content moderation has emerged to ensure the quality of posts on various social media platforms. This process typically demands collaboration between humans and AI because of the complementarity of the two agents in different facets. Wondering how AI can better assist humans to make final judgment in the “machine-in-the-loop” paradigm, we propose a lab experiment to explore the influence of different types of cues provided by AI through a nudging approach as well as time constraints on human moderators’ performance. The proposed study contributes to the literature on the AI-assisted decision-making pattern, and helps social media platforms in creating an effective human-AI collaboration framework for content moderation

The CHD1-KDM Axis in Prostate Cancer

https://openworks.mdanderson.org/sumexp21/1189/thumbnail.jp

PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter

The Retrieval Question Answering (ReQA) task employs the retrieval-augmented framework, composed of a retriever and generator. The generator formulates the answer based on the documents retrieved by the retriever. Incorporating Large Language Models (LLMs) as generators is beneficial due to their advanced QA capabilities, but they are typically too large to be fine-tuned with budget constraints while some of them are only accessible via APIs. To tackle this issue and further improve ReQA performance, we propose a trainable Pluggable Reward-Driven Contextual Adapter (PRCA), keeping the generator as a black box. Positioned between the retriever and generator in a Pluggable manner, PRCA refines the retrieved information by operating in a token-autoregressive strategy via maximizing rewards of the reinforcement learning phase. Our experiments validate PRCA's effectiveness in enhancing ReQA performance on three datasets by up to 20% improvement to fit black-box LLMs into existing frameworks, demonstrating its considerable potential in the LLMs era.Comment: Accepted by the Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. (EMNLP2023

Study on the relationship between tsunami waves in dam break state and initial water levels

Tsunami wave characteristics are greatly influenced by the initial water level when they attack structures. In this study, experimental and numerical investigations were conducted to investigated the relationship between tsunami wave characteristics and initial water levels. Results showed that, the wave height, wave velocity, and Froude number increase with the increase of tsunami wave intensity; the time history of water levels were influenced by the different initial water level conditions; the analytical solution proposed by Chanson (2005) may be extended to wet-bed conditions (for initial water level < 0.36 tsunami bore height in our experimental set-up). Due to the limitations of experimental ranges in the laboratory, the validated numerical model can provide more results for wider experimental ranges for tsunami bore investigations. It was observed from numerical results that, tsunami bore height increases with the increase of reservoir water level; tsunami bore velocity decreases with the increased initial water level on the bed; as the initial water level on the bed gradually increases, the mean tsunami bore Froude number shows a downward trend

RS4651 suppresses lung fibroblast activation via the TGF-β1/SMAD signalling pathway.

ABSTRACT Background Idiopathic pulmonary fibrosis (IPF) is a progressive disease resulting in respiratory failure with no efficient treatment options. We investigated the protective effect of RS4651 on pulmonary fibrosis in mice and the mechanism. Methods Intratracheal injection of bleomycin (BLM) was used to induce pulmonary fibrosis in mice. RS4561 was administered intraperitoneally at different doses. Histopathological changes were observed. The level of alpha-smooth muscle actin (α-SMA) were also tested. In vitro, the proliferation and migratory effects of RS4651 treatment on MRC-5 cells pre-treated with transforming growth factor (TGF-β1) were examined. RNA-sequencing was used to detect differentially expressed target genes. Then, the expression of α-SMA, pSMAD2 and SMAD7 were analysed during RS4651 treatment of MRC-5 cells with or without silencing by SMAD7 siRNA. Results Histopathological staining results showed decreased collagen deposition in RS4651 administered mice. Additionally, a lower level of α-SMA was also observed compared to the BLM group. The results of in vitro studies confirmed that RS4651 can inhibit the proliferation and migration, as well as α-SMA and pSMAD2 expression in MRC-5 cells treated with TGF-β1. RNA-sequencing data identified the target gene SMAD7. We found that RS4651 could upregulate SMAD7 expression and inhibit the proliferation and migration of MRC-5 cells via SMAD7, and RS4651 inhibition of α-SMA and pSMAD2 expression was blocked in SMAD7-siRNA MRC-5 cells. In vivo studies further confirmed that RS4651 could upregulate SMAD7 expression in BLM-induced lung fibrosis in mice. Conclusions Our data suggest that RS4651 alleviates BLM-induced pulmonary fibrosis in mice by inhibiting the TGF-β1/SMAD signalling pathway

OpenFE: Automated Feature Generation beyond Expert-level Performance

The goal of automated feature generation is to liberate machine learning experts from the laborious task of manual feature generation, which is crucial for improving the learning performance of tabular data. The major challenge in automated feature generation is to efficiently and accurately identify useful features from a vast pool of candidate features. In this paper, we present OpenFE, an automated feature generation tool that provides competitive results against machine learning experts. OpenFE achieves efficiency and accuracy with two components: 1) a novel feature boosting method for accurately estimating the incremental performance of candidate features. 2) a feature-scoring framework for retrieving effective features from a large number of candidates through successive featurewise halving and feature importance attribution. Extensive experiments on seven benchmark datasets show that OpenFE outperforms existing baseline methods. We further evaluate OpenFE in two famous Kaggle competitions with thousands of data science teams participating. In one of the competitions, features generated by OpenFE with a simple baseline model can beat 99.3\% data science teams. In addition to the empirical results, we provide a theoretical perspective to show that feature generation is beneficial in a simple yet representative setting. The code is available at https://github.com/ZhangTP1996/OpenFE.Comment: 23 pages, 3 figure

A Common Variant in CLDN14 is Associated with Primary Biliary Cirrhosis and Bone Mineral Density.

Primary biliary cirrhosis (PBC), a chronic autoimmune liver disease, has been associated with increased incidence of osteoporosis. Intriguingly, two PBC susceptibility loci identified through genome-wide association studies are also involved in bone mineral density (BMD). These observations led us to investigate the genetic variants shared between PBC and BMD. We evaluated 72 genome-wide significant BMD SNPs for association with PBC using two European GWAS data sets (n = 8392), with replication of significant findings in a Chinese cohort (685 cases, 1152 controls). Our analysis identified a novel variant in the intron of the CLDN14 gene (rs170183, Pfdr = 0.015) after multiple testing correction. The three associated variants were followed-up in the Chinese cohort; one SNP rs170183 demonstrated consistent evidence of association in diverse ethnic populations (Pcombined = 2.43 × 10(-5)). Notably, expression quantitative trait loci (eQTL) data revealed that rs170183 was correlated with a decline in CLDN14 expression in both lymphoblastoid cell lines and T cells (Padj = 0.003 and 0.016, respectively). In conclusion, our study identified a novel PBC susceptibility variant that has been shown to be strongly associated with BMD, highlighting the potential of pleiotropy to improve gene discovery