EFFECTS OF SHOE COLLAR HEIGHT ON SAGITTAL ANKLE ROM, KINETICS AND POWER OUTPUT DURING SINGLE-LEG AND DOUBLE-LEG JUMPS

The aim of this research was to examine the effects of high-top shoes and low-top shoes on sagittal ankle ROM, kinetics and power output during single-leg and double-leg jumps. Twelve male subjects were requested to wear high-top and low-top shoes to perform single-leg and double-leg jumps. Ankle joint kinematics and kinetics data were collected using Vicon system and force plates. Shoe collar heights did not influence the jump height in both single-leg and double-leg jump tasks. However, high-top shoes adopted in this study resulted in a significant smaller sagittal ankle ROM during a quasi-static movement. In addition, wearing high-top shoe could also decrease the dorsiflexion ankle joint torque and power output during the push-off phase in single-leg jump. These findings provide preliminary evidence suggesting that a changed ankle kinematic and kinetic behaviour in the sagittal plane may be induced when wearing high-top shoes

Surface terraces in pure tungsten formed by high-temperature oxidation

We observe large-scale surface terraces in tungsten oxidised at high temperature and in high vacuum. Their formation is highly dependent on crystal orientation, with only {111} grains showing prominent terraces. Terrace facets are aligned with {100} crystallographic planes, leading to an increase in total surface energy, making a diffusion-driven formation mechanism unlikely. Instead we hypothesize that preferential oxidation of {100} crystal planes controls terrace formation. Grain height profiles after oxidation and the morphology of samples heat treated with limited oxygen supply are consistent with this hypothesis. Our observations have important implications for the use of tungsten in extreme environments.Comment: 10 pages, 4 figures & supplementar

High-Fidelity Lake Extraction via Two-Stage Prompt Enhancement: Establishing a Novel Baseline and Benchmark

The extraction of lakes from remote sensing images is a complex challenge due to the varied lake shapes and data noise. Current methods rely on multispectral image datasets, making it challenging to learn lake features accurately from pixel arrangements. This, in turn, affects model learning and the creation of accurate segmentation masks. This paper introduces a unified prompt-based dataset construction approach that provides approximate lake locations using point, box, and mask prompts. We also propose a two-stage prompt enhancement framework, LEPrompter, which involves prompt-based and prompt-free stages during training. The prompt-based stage employs a prompt encoder to extract prior information, integrating prompt tokens and image embeddings through self- and cross-attention in the prompt decoder. Prompts are deactivated once the model is trained to ensure independence during inference, enabling automated lake extraction. Evaluations on Surface Water and Qinghai-Tibet Plateau Lake datasets show consistent performance improvements compared to the previous state-of-the-art method. LEPrompter achieves mIoU scores of 91.48% and 97.43% on the respective datasets without introducing additional parameters or GFLOPs. Supplementary materials provide the source code, pre-trained models, and detailed user studies.Comment: 8 pages, 7 figure

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role. Recent graph neural networks (GNNs) based SBR methods regard the item transitions as pairwise relations, which neglect the complex high-order information among items. Hypergraph provides a natural way to capture beyond-pairwise relations, while its potential for SBR has remained unexplored. In this paper, we fill this gap by modeling session-based data as a hypergraph and then propose a hypergraph convolutional network to improve SBR. Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task. Since the two types of networks both are based on hypergraph, which can be seen as two channels for hypergraph modeling, we name our model \textbf{DHCN} (Dual Channel Hypergraph Convolutional Networks). Extensive experiments on three benchmark datasets demonstrate the superiority of our model over the SOTA methods, and the results validate the effectiveness of hypergraph modeling and self-supervised task. The implementation of our model is available at https://github.com/xiaxin1998/DHCNComment: 9 pages, 4 figures, accepted by AAAI'2