PLYOMETRIC WEIGHT TRAINING CAN INCREASE HIP AND ANKLE JOINT STRENTH SIGNIFICANTLY

The purposes of this study were to (1) investigate the changes in muscle strength and power at each joint of lower extremity, kinetics and stiffness of hip, knee, and ankle joints during counter-movement jump with different weights before and after plyometric weight training (PWT); (2) compare each of the joint contributions during plyometric exercises with different weights. Sixteen basketball players were asked to perform the PWT, i.e. 3 groups continued CMJ with the weight of 30% 1RM for 8-weeks with incremental-loads. Before and after the 8-week training program, kinematics and kinetics of the lower-limb were collected during CMJ performance. Joint moment, joint power, joint stiffness, and joint contirbution were then determined. The results indicated that an 8-week plyometric weight training program could significantly increase jump height, peak GRFv, and power output. The results also revealed that muscle strength and power of hip were dominantly developed during PWT and the enhanced kinetics (moment and stiffness) of hip turned out to be a major factor responsible for the improved jump performance

Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question Prompts

Zero-shot Visual Question Answering (VQA) is a prominent vision-language task that examines both the visual and textual understanding capability of systems in the absence of training data. Recently, by converting the images into captions, information across multi-modalities is bridged and Large Language Models (LLMs) can apply their strong zero-shot generalization capability to unseen questions. To design ideal prompts for solving VQA via LLMs, several studies have explored different strategies to select or generate question-answer pairs as the exemplar prompts, which guide LLMs to answer the current questions effectively. However, they totally ignore the role of question prompts. The original questions in VQA tasks usually encounter ellipses and ambiguity which require intermediate reasoning. To this end, we present Reasoning Question Prompts for VQA tasks, which can further activate the potential of LLMs in zero-shot scenarios. Specifically, for each question, we first generate self-contained questions as reasoning question prompts via an unsupervised question edition module considering sentence fluency, semantic integrity and syntactic invariance. Each reasoning question prompt clearly indicates the intent of the original question. This results in a set of candidate answers. Then, the candidate answers associated with their confidence scores acting as answer heuristics are fed into LLMs and produce the final answer. We evaluate reasoning question prompts on three VQA challenges, experimental results demonstrate that they can significantly improve the results of LLMs on zero-shot setting and outperform existing state-of-the-art zero-shot methods on three out of four data sets. Our source code is publicly released at \url{https://github.com/ECNU-DASE-NLP/RQP}