An Assessment of Scientific Claim Verification Frameworks: Final Presentation

https://digitalcommons.odu.edu/reu2022_computerscience/1005/thumbnail.jp

Anion receptor chemistry

This review covers advances in anion complexation in the year 2013, 2014 and 2015. The review focuses on the applications of anion receptor chemistry including sensing, self-assembly, extraction, transport, catalysis, as well as fundamental advances in the area. <br/

Synchronous Image-Label Diffusion Probability Model with Application to Stroke Lesion Segmentation on Non-contrast CT

Stroke lesion volume is a key radiologic measurement for assessing the prognosis of Acute Ischemic Stroke (AIS) patients, which is challenging to be automatically measured on Non-Contrast CT (NCCT) scans. Recent diffusion probabilistic models have shown potentials of being used for image segmentation. In this paper, a novel Synchronous image-label Diffusion Probability Model (SDPM) is proposed for stroke lesion segmentation on NCCT using Markov diffusion process. The proposed SDPM is fully based on a Latent Variable Model (LVM), offering a complete probabilistic elaboration. An additional net-stream, parallel with a noise prediction stream, is introduced to obtain initial noisy label estimates for efficiently inferring the final labels. By optimizing the specified variational boundaries, the trained model can infer multiple label estimates for reference given the input images with noises. The proposed model was assessed on three stroke lesion datasets including one public and two private datasets. Compared to several U-net and transformer-based segmentation methods, our proposed SDPM model is able to achieve state-of-the-art performance. The code is publicly available

Block-Recurrent Transformers

We introduce the Block-Recurrent Transformer, which applies a transformer layer in a recurrent fashion along a sequence, and has linear complexity with respect to sequence length. Our recurrent cell operates on blocks of tokens rather than single tokens during training, and leverages parallel computation within a block in order to make efficient use of accelerator hardware. The cell itself is strikingly simple. It is merely a transformer layer: it uses self-attention and cross-attention to efficiently compute a recurrent function over a large set of state vectors and tokens. Our design was inspired in part by LSTM cells, and it uses LSTM-style gates, but it scales the typical LSTM cell up by several orders of magnitude. Our implementation of recurrence has the same cost in both computation time and parameter count as a conventional transformer layer, but offers dramatically improved perplexity in language modeling tasks over very long sequences. Our model out-performs a long-range Transformer XL baseline by a wide margin, while running twice as fast. We demonstrate its effectiveness on PG19 (books), arXiv papers, and GitHub source code. Our code has been released as open source.Comment: Update to NeurIPS camera-ready versio

On the Effectiveness of Offline RL for Dialogue Response Generation

A common training technique for language models is teacher forcing (TF). TF attempts to match human language exactly, even though identical meanings can be expressed in different ways. This motivates use of sequence-level objectives for dialogue response generation. In this paper, we study the efficacy of various offline reinforcement learning (RL) methods to maximize such objectives. We present a comprehensive evaluation across multiple datasets, models, and metrics. Offline RL shows a clear performance improvement over teacher forcing while not inducing training instability or sacrificing practical training budgets.Comment: Accepted at ICML 2023. 18 pages, 12 figures. Code available at https://github.com/asappresearch/dialogue-offline-r

All together now: findings from a PCORI workshop to align patient-reported outcomes in the electronic health record

In recent years, patient-reported outcomes have become increasingly collected and integrated into electronic health records. However, there are few cross-cutting recommendations and limited guidance available in this rapidly developing research area. Our goal is to report key findings from a 2013 Patient-Centered Outcomes Research Institute workshop on this topic and a summary of actions that followed from the workshop, and present resulting recommendations that address patient, clinical and research/quality improvement barriers to regular use. These findings provide actionable guidance across research and practice settings to promote and sustain widespread adoption of patient-reported outcomes across patient populations, healthcare settings and electronic health record systems

Accessibility Rating Form for Websites and Other Online Platforms

Background. This file provides a coding form developed to judge how accessible websites and other online platforms are to users. Accessibility may be defined as the ease to which a person can perceive content and navigate material (Ross & Ross, 2021). Users are encouraged to adapt this form for their use. Purpose. The rating form can be used to judge the pages of online media, using 14 criteria under two areas: Accessible Media and Accessible Design. One of three grades could be assigned to each criterion: Not Accessible (0 point), Somewhat Accessible (1 point), Accessible (2 points), adapted from published research by Wallace et al. (2010). Initially, this form was developed to rate the website created using the Learning Management System platform, Canvas (Instructure, n.d.), which was adapted as a research survey website. Form validity and reliability. This form was based on guidelines for accessible websites, provided from the World Wide Web Consortium (Zahra, 2019). This form was found to have excellent rater agreement within a preliminary study, which was presented at the 2022 Southwest Chapter Conference Meeting of the American College of Sports Medicine (October 28-29, Costa Mesa, California). The intraclass coefficient statistic was used (four raters, M = .91, LL = .82, UL = .94; Landers, 2015). Results were interpreted using Cicchetti’s (1994) interpretive cut-points. Further detail is reported in the published abstract to the study’s presentation (Wu et al., in press)