37 research outputs found
Validity of the Fitbit Ace and Moki devices for assessing steps during different walking conditions in young adolescents
Morphological and Physiological Changes in Sedum spectabile during Flower Formation Induced by Photoperiod
Sedum spectabile is an ornamental herbaceous perennial considered as a long-day plant. Varying levels of hormones and sugars possibly affect flower bud formation. This study aimed to determine the changes in endogenous hormones, sugars, and respiration levels in leaves and in apical buds. In addition, the current research was also conducted to observe the morphological changes during the induction, initiation and development of flower buds. Results showed that the periods of floral induction, initiation and development of S. spectabile were the period from 0 d to 1 d, 2 d to 10 d and after 11 d respectively under long day of 20 hours. High zeatin level in apical buds was conducive to floral induction; the increasing levels of gibberrelin and indole acetic acid favor floral initiation; floral development was regulated by mutually synergistic and antagonistic relationships of hormones. The total starch content in leaves remarkably decreased during floral induction. Moreover, soluble sugar content increased and reached the maximum level at 20 d of the treatment period. Afterward, soluble sugar content declined rapidly and was probably transported to the apical buds for rapid floral development. Furthermore, the total respiration of leaves maintained an upward trend; the cytochrome pathway also maintained an increasing trend after the plants were treated for 20 d. Such changes may favour the morphological differentiation of apical buds in floral development
3D bi-directional transformer U-Net for medical image segmentation
As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
Despite recent progress in reinforcement learning (RL) from raw pixel data,
sample inefficiency continues to present a substantial obstacle. Prior works
have attempted to address this challenge by creating self-supervised auxiliary
tasks, aiming to enrich the agent's learned representations with
control-relevant information for future state prediction. However, these
objectives are often insufficient to learn representations that can represent
the optimal policy or value function, and they often consider tasks with small,
abstract discrete action spaces and thus overlook the importance of action
representation learning in continuous control. In this paper, we introduce
TACO: Temporal Action-driven Contrastive Learning, a simple yet powerful
temporal contrastive learning approach that facilitates the concurrent
acquisition of latent state and action representations for agents. TACO
simultaneously learns a state and an action representation by optimizing the
mutual information between representations of current states paired with action
sequences and representations of the corresponding future states.
Theoretically, TACO can be shown to learn state and action representations that
encompass sufficient information for control, thereby improving sample
efficiency. For online RL, TACO achieves 40% performance boost after one
million environment interaction steps on average across nine challenging visual
continuous control tasks from Deepmind Control Suite. In addition, we show that
TACO can also serve as a plug-and-play module adding to existing offline visual
RL methods to establish the new state-of-the-art performance for offline visual
RL across offline datasets with varying quality
Effect of temperature on high strain-rate damage evolving in CFRP studied by synchrotron-based MHz X-ray phase contrast imaging
The present study demonstrates experimental evidence of subsurface mesoscale damage initiation and evolution in angle-ply CFRP laminates under high strain-rate loading at low temperatures using synchrotron-based X-ray MHz radiography. A bespoke set of loading, temperature control and in-situ X-ray imaging systems were applied to simultaneously correlate high strain-rate mechanical response with observed subsurface damage in a time-resolved manner. The results demonstrate that independent of temperature, damage evolved following a specific sequence; firstly intra-ply shear cracking along the fibre direction, developing into multi-layer cracking with continued deformation, and finally culminating in inter-ply delamination and complete failure of the specimen. The timescale for this sequence, however, was observed to strongly depend upon temperature, with low temperatures resulting in more rapid damage evolution and loss of mechanical strength
3D bi-directional transformer U-Net for medical image segmentation
As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics