Photo-production of lowest Σ1/2−∗\Sigma^*_{1/2^-} state within the Regge-effective Lagrangian approach

Since the lowest $\Sigma^{*}$ state, with quantum numbers spin-parity $J^{P} =1/2^{-}$, is far from established experimentally and theoretically, we have performed a theoretical study on the $\Sigma^*_{1/2^-}$ photo-production within the Regge-effective Lagrangian approach. Taking into account that the $\Sigma^*_{1/2^-}$ couples to the $\bar{K}N$ channel, we have considered the contributions from the $t$-channel $K$ exchange diagram. Moreover, these contributions from $t$-channel $K^*$ exchange, $s$-channel nucleon pole, $u$-channel $\Sigma$ exchange, and the contact term, are considered. The differential and total cross sections of the process $\gamma n \to K^{+}\Sigma^{*-}_{1/2^-}$ are predicted with our model parameters. The results should be helpful to search for the $\Sigma^*_{1/2^-}$ state experimentally in future.Comment: 7 pages, 4 figure

RSG: Fast Learning Adaptive Skills for Quadruped Robots by Skill Graph

Developing robotic intelligent systems that can adapt quickly to unseen wild situations is one of the critical challenges in pursuing autonomous robotics. Although some impressive progress has been made in walking stability and skill learning in the field of legged robots, their ability to fast adaptation is still inferior to that of animals in nature. Animals are born with massive skills needed to survive, and can quickly acquire new ones, by composing fundamental skills with limited experience. Inspired by this, we propose a novel framework, named Robot Skill Graph (RSG) for organizing massive fundamental skills of robots and dexterously reusing them for fast adaptation. Bearing a structure similar to the Knowledge Graph (KG), RSG is composed of massive dynamic behavioral skills instead of static knowledge in KG and enables discovering implicit relations that exist in be-tween of learning context and acquired skills of robots, serving as a starting point for understanding subtle patterns existing in robots' skill learning. Extensive experimental results demonstrate that RSG can provide rational skill inference upon new tasks and environments and enable quadruped robots to adapt to new scenarios and learn new skills rapidly

MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining

Text images contain both visual and linguistic information. However, existing pre-training techniques for text recognition mainly focus on either visual representation learning or linguistic knowledge learning. In this paper, we propose a novel approach MaskOCR to unify vision and language pre-training in the classical encoder-decoder recognition framework. We adopt the masked image modeling approach to pre-train the feature encoder using a large set of unlabeled real text images, which allows us to learn strong visual representations. In contrast to introducing linguistic knowledge with an additional language model, we directly pre-train the sequence decoder. Specifically, we transform text data into synthesized text images to unify the data modalities of vision and language, and enhance the language modeling capability of the sequence decoder using a proposed masked image-language modeling scheme. Significantly, the encoder is frozen during the pre-training phase of the sequence decoder. Experimental results demonstrate that our proposed method achieves superior performance on benchmark datasets, including Chinese and English text images

Do visual and step height factors cause imbalance during bipedal and unipedal stances? A plantar pressure perspective

Objective: The plantar pressure analysis technique was used to explore the static balance ability and stability of healthy adult males under the influence of visual and step height factors during bipedal and unipedal stances.Methods: Thirty healthy adult males volunteered for the study. Experiments used the F-scan plantar pressure analysis insoles to carry out with eyes open (EO) and eyes closed (EC) at four different step heights. The plantar pressure data were recorded for 10 s and pre-processed to derive kinematic and dynamic parameters.Results: For unipedal stance, most of kinematic parameters of the subjects’ right and left feet were significantly greater when the eyes were closed compared to the EO condition and increased with step height. The differences in toe load between right and left feet, open and closed eyes were extremely statistically significant (p < 0.001). The differences in midfoot load between the EO and EC conditions were statistically significant (p = 0.024) and extremely statistically significant between the right and left feet (p < 0.001). The difference in rearfoot load between EO and EC conditions was extremely statistically significant (p < 0.001) and statistically significant (p = 0.002) between the right and left feet. For bipedal stance, most of kinematic parameters of the subjects’ EO and EC conditions were statistically significant between the right and left feet and increased with step height. The overall load’s difference between EO and EC states was statistically significant (p = 0.003) for both feet. The overall load’s difference between the right and left feet was extremely statistically significant (p < 0.001) in the EC state. The differences between the right and left feet of the forefoot and rearfoot load with EO and EC suggested that the right foot had a smaller forefoot load, but a larger rearfoot load than the left foot (p < 0.001). The differences between the forefoot and rearfoot load of the subjects’ both feet with EO and EC were extremely statistically significant (p < 0.001).Conclusion: Both visual input and step height factors, even the dominant foot, act on kinematic and dynamic parameters that affect the maintenance of static balance ability