EFFFECTS OF SIMPLIFIED TAICHI ON THE BALANCE ABILITY OF THE ELDERLY

Purpose: This study explores whether simplified Tai Chi can improve the balance ability of the elderly and provides a theoretical basis for studying fall prevention among the elderly. Methods: A three-dimensional force plate was used to test the balance of 30 healthy elderly. Balance ability was tested by using four postures, namely, standing with both eyes open,standing with both eyes closed,standing with one eye open, and standing with one eyeclosed.Results: The DO RMS-AP, DO RMS-ML, SC COP-ML, and SOT indexes of the Tai hi group were significantly higher than those of the control group. Conclusion: Simplified Tai Chi can improve the balance ability of the elderly. If one wants to improve his/her posture control ability when standing with one eye closed, then s/he needs to practice tai chi for an extended period

Effects of sintering temperatures on the microstructure and mechanical properties of S390 powder metallurgy high-speed steel

High-performance complex gear cutters and high-temperature bearings are just some of the applications where high-speed steels (HSSs) shine as a preferred material choice owing to their high hardness and outstanding wear resistance. In this work, the effects of sintering temperature on the microstructure and mechanical properties of S390 HSS prepared via spark plasma sintering (SPS) were investigated with a range of sintering temperatures from 930°C to 1,090°C, a uniaxial pressure of 50 MPa, and a holding time of 5 min. The results demonstrated that the improvements in density, hardness, red hardness, and three-point bending strength were confirmed as the sintering temperature increased from 930°C to 1,090°C. Temperature-induced microstructure evolutions were assessed for their contribution to property enhancement, such as powders with varying dimensions and carbides with diverse morphology and diameter. The specimen with the best comprehensive mechanical properties (67.1 HRC and 1,196.67 MPa) was prepared at 1,050°C via SPS. The wear coefficients decreased as the sintering temperature increased, and the observation results of worn surfaces of test pins confirmed that abrasive wear and oxidation wear dominated the wear experiments. Furthermore, the wear mechanism of dense and porous SPS HSS was illustrated and analyzed in terms of the debris and trapped carbides

An AMR-based Link Prediction Approach for Document-level Event Argument Extraction

Recent works have introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE), since AMR provides a useful interpretation of complex semantic structures and helps to capture long-distance dependency. However, in these works AMR is used only implicitly, for instance, as additional features or training signals. Motivated by the fact that all event structures can be inferred from AMR, this work reformulates EAE as a link prediction problem on AMR graphs. Since AMR is a generic structure and does not perfectly suit EAE, we propose a novel graph structure, Tailored AMR Graph (TAG), which compresses less informative subgraphs and edge types, integrates span information, and highlights surrounding events in the same document. With TAG, we further propose a novel method using graph neural networks as a link prediction model to find event arguments. Our extensive experiments on WikiEvents and RAMS show that this simpler approach outperforms the state-of-the-art models by 3.63pt and 2.33pt F1, respectively, and do so with reduced 56% inference time. The code is availabel at https://github.com/ayyyq/TARA.Comment: Accepted to ACL 202

Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models

Cross-document event coreference resolution (CDECR) involves clustering event mentions across multiple documents that refer to the same real-world events. Existing approaches utilize fine-tuning of small language models (SLMs) like BERT to address the compatibility among the contexts of event mentions. However, due to the complexity and diversity of contexts, these models are prone to learning simple co-occurrences. Recently, large language models (LLMs) like ChatGPT have demonstrated impressive contextual understanding, yet they encounter challenges in adapting to specific information extraction (IE) tasks. In this paper, we propose a collaborative approach for CDECR, leveraging the capabilities of both a universally capable LLM and a task-specific SLM. The collaborative strategy begins with the LLM accurately and comprehensively summarizing events through prompting. Then, the SLM refines its learning of event representations based on these insights during fine-tuning. Experimental results demonstrate that our approach surpasses the performance of both the large and small language models individually, forming a complementary advantage. Across various datasets, our approach achieves state-of-the-art performance, underscoring its effectiveness in diverse scenarios.Comment: Accepted to ACL-24 Mai

Vector Quantized Diffusion Model with CodeUnet for Text-to-Sign Pose Sequences Generation

Sign Language Production (SLP) aims to translate spoken languages into sign sequences automatically. The core process of SLP is to transform sign gloss sequences into their corresponding sign pose sequences (G2P). Most existing G2P models usually perform this conditional long-range generation in an autoregressive manner, which inevitably leads to an accumulation of errors. To address this issue, we propose a vector quantized diffusion method for conditional pose sequences generation, called PoseVQ-Diffusion, which is an iterative non-autoregressive method. Specifically, we first introduce a vector quantized variational autoencoder (Pose-VQVAE) model to represent a pose sequence as a sequence of latent codes. Then we model the latent discrete space by an extension of the recently developed diffusion architecture. To better leverage the spatial-temporal information, we introduce a novel architecture, namely CodeUnet, to generate higher quality pose sequence in the discrete space. Moreover, taking advantage of the learned codes, we develop a novel sequential k-nearest-neighbours method to predict the variable lengths of pose sequences for corresponding gloss sequences. Consequently, compared with the autoregressive G2P models, our model has a faster sampling speed and produces significantly better results. Compared with previous non-autoregressive G2P methods, PoseVQ-Diffusion improves the predicted results with iterative refinements, thus achieving state-of-the-art results on the SLP evaluation benchmark

Evaluating Open-QA Evaluation

This study focuses on the evaluation of the Open Question Answering (Open-QA) task, which can directly estimate the factuality of large language models (LLMs). Current automatic evaluation methods have shown limitations, indicating that human evaluation still remains the most reliable approach. We introduce a new task, Evaluating QA Evaluation (QA-Eval) and the corresponding dataset EVOUNA, designed to assess the accuracy of AI-generated answers in relation to standard answers within Open-QA. Our evaluation of these methods utilizes human-annotated results to measure their performance. Specifically, the work investigates methods that show high correlation with human evaluations, deeming them more reliable. We also discuss the pitfalls of current methods and methods to improve LLM-based evaluators. We believe this new QA-Eval task and corresponding dataset EVOUNA will facilitate the development of more effective automatic evaluation tools and prove valuable for future research in this area. All resources are available at \url{https://github.com/wangcunxiang/QA-Eval} and it is under the Apache-2.0 License

RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Models

Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents RefChecker, a framework that introduces claim-triplets to represent claims in LLM responses, aiming to detect fine-grained hallucinations. In RefChecker, an extractor generates claim-triplets from a response, which are then evaluated by a checker against a reference. We delineate three task settings: Zero, Noisy and Accurate Context, to reflect various real-world use cases. We curated a benchmark spanning various NLP tasks and annotated 11k claim-triplets from 2.1k responses by seven LLMs. RefChecker supports both proprietary and open-source models as the extractor and checker. Experiments demonstrate that claim-triplets enable superior hallucination detection, compared to other granularities such as response, sentence and sub-sentence level claims. RefChecker outperforms prior methods by 6.8 to 26.1 points on our benchmark and the checking results of RefChecker are strongly aligned with human judgments. This work is open sourced at https://github.com/amazon-science/RefChecke