Reducing Sensitivity on Speaker Names for Text Generation from Dialogues

Changing speaker names consistently throughout a dialogue should not affect its meaning and corresponding outputs for text generation from dialogues. However, pre-trained language models, serving as the backbone for dialogue-processing tasks, have shown to be sensitive to nuances. This may result in unfairness in real-world applications. No comprehensive analysis of this problem has been done in the past. In this work, we propose to quantitatively measure a model's sensitivity on speaker names, and comprehensively evaluate a number of known methods for reducing speaker name sensitivity, including a novel approach of our own. Extensive experiments on multiple datasets provide a benchmark for this problem and show the favorable performance of our approach in sensitivity reduction and quality of generation.Comment: findings of ACL'2

In-sample Curriculum Learning by Sequence Completion for Natural Language Generation

Curriculum learning has shown promising improvements in multiple domains by training machine learning models from easy samples to hard ones. Previous works which either design rules or train models for scoring the difficulty highly rely on task-specific expertise, and cannot generalize. Inspired by the ``easy-to-hard'' intuition, we propose to do in-sample curriculum learning for natural language generation tasks. Our learning strategy starts training the model to generate the last few words, i.e., do sequence completion, and gradually extends to generate the whole output sequence. Comprehensive experiments show that it generalizes well to different tasks and achieves significant improvements over strong baselines

Role of Cytokines and Chemokines in Alcohol-Induced Tumor Promotion

Excessive chronic alcohol consumption has become a worldwide health problem. The oncogenic effect of chronic alcohol consumption is one of the leading concerns. The mechanisms of alcohol-induced tumorigenesis and tumor progression are largely unknown, although many factors have been implicated in the process. This review discusses the recent progress in this research area with concentration on alcohol-induced dysregulation of cytokines and chemokines. Based on the available evidence, we propose that alcohol promotes tumor progression by the dysregulation of the cytokine/chemokine system. In addition, we discuss specific transcription factors and signaling pathways that are involved in the action of these cytokines/chemokines and the oncogenic effect of alcohol. This review provides novel insight into the mechanisms of alcohol-induced tumor promotion

Direct imaging of single UvrD helicase dynamics on long single-stranded DNA

Fluorescence imaging of single-protein dynamics on DNA has been largely limited to double-stranded DNA or short single-stranded DNA. We have developed a hybrid approach for observing single proteins moving on laterally stretched kilobase-sized ssDNA. Here we probed the single-stranded DNA translocase activity of Escherichia coli UvrD by single fluorophore tracking, while monitoring DNA unwinding activity with optical tweezers to capture the entire sequence of protein binding, single-stranded DNA translocation and multiple pathways of unwinding initiation. The results directly demonstrate that the UvrD monomer is a highly processive single-stranded DNA translocase that is stopped by a double-stranded DNA, whereas two monomers are required to unwind DNA to a detectable degree. The single-stranded DNA translocation rate does not depend on the force applied and displays a remarkable homogeneity, whereas the unwinding rate shows significant heterogeneity. These findings demonstrate that UvrD assembly state regulates its DNA helicase activity with functional implications for its stepping mechanism, and also reveal a previously unappreciated complexity in the active species during unwinding

Association of obstructive sleep apnea with hypertension: A systematic review and meta-analysis

Results: Twenty-six studies with 51 623 participants (28 314 men, 23 309 women; mean age 51.8 years) met inclusion criteria and were included in this study. Among them, six studies showed a significant association between OSA and resistant hypertension (pooled OR = 2.842, 95% CI = 1.703-3.980, P \u3c 0.05). Meanwhile, the combination of 20 original studies on the association of OSA with essential hypertension also presented significant results with the pooled ORs of 1.184 (95% CI = 1.093-1.274, P \u3c 0.05) for mild OSA, 1.316 (95% CI = 1.197-1.433, P \u3c 0.05) for moderate OSA and 1.561 (95% CI = 1.287-1.835, P \u3c 0.05) for severe OSA. Conclusions: Our findings indicated that OSA is related to an increased risk of resistant hypertension. Mild, moderate and severe OSA are associated essential hypertension, as well a dose-response manner relationship is manifested. The associations are relatively stronger among Caucasians and male OSA patients. Background: Obstructive sleep apnea (OSA) is a sleep disorder characterized as complete or partial upper airflow cessation during sleep. Although it has been widely accepted that OSA is a risk factor for the development of hypertension, the studies focusing on this topic revealed inconsistent results. We aimed to clarify the association between OSA and hypertension, including essential and medication-resistant hypertension. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was followed. PubMed and Embase databases were used for searching the relevant studies published up to December 31, 2016. A quantitative approach of meta-analysis was performed to estimate the pooled odds ratio (OR) and 95% confidence interval (CI)

Unveiling spatial inequalities: Exploring county-level disaster damages and social vulnerability on public disaster assistance in contiguous US

Understanding the dynamics between public disaster assistance, disaster damages, and social vulnerability at county-level is crucial for designing effective disaster mitigation strategies. This study utilized the Local Bivariate Moran Index (LBMI) and geographically weighted regression (GWR) models to examine spatial patterns and relationships between disaster damages, social vulnerability, and public disaster assistance in contiguous US counties from 2001 to 2021. LBMI results reveal that public disaster assistance has predominantly been directed towards post-disaster recovery efforts, with a particular focus on coastal communities affected by major declared disasters. However, the distributions of public assistance and individual housing assistance, which are the two primary sources of public disaster assistance, do not adequately cover physically and socially vulnerable communities. The distribution of pre-disaster risk mitigation also falls short of sufficiently covering vulnerable communities. Results further indicate the complex interactions between different categories of natural disasters and public assistances. The GWR model results demonstrate spatial variations in predicting each category of public disaster assistance. These findings indicate the need to address disparities in accessing public disaster assistance in the US, and advocate for more equitable disaster mitigation strategies