The relationship between attractiveness and femininity in female gait

The evaluation of physical attractiveness has been reported to be related to the psychological process for detecting associated physiological health and fertility features. The femininity of the female gait is also associated with its attractiveness. However, it is unclear whether femininity is always attractive in female gait and what physical characteristics are perceived as being attractive and/or feminine. In this study, we aimed to understand the root of the attractiveness of human movement by examining the relationship between perceived attractiveness and femininity in female gait. First, we created 30 s gait animations by using 3D motion capture data of 10 female nonmodels and seven female runway models, where they walked either barefoot or in high heels. Then, 60 observers evaluated the attractiveness and femininity of each animation. We compared the scores of attractiveness (A-scores) and femininity (F-scores) of the models and nonmodels, and we examined the factors related to the evaluation (A-scores and F-scores), namely, the walkers’ height, weight, BMI, and the characteristics of movements. Consequently, both the A-score and the F-score were high for the models’ gait in high heels. Conversely, in the other conditions, there were two types of attractiveness−femininity relationships—a linear relationship (high A-score and F-score, or low A-score and F-score) and an unequal relationship (high F-score but low A-score). Most physical and motion factors correlated with both the A-score and the F-score; however, BMI, flexibility at the thoracolumbar joint, stride time CV, and toe-off angle were related to either the A-score or the F-score

Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks

Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks (WLANs) are discussed in EHT Study Group. The present study proposes a deep reinforcement learning-based channel allocation scheme using graph convolutional networks (GCNs). As a deep reinforcement learning method, we use a well-known method double deep Q-network. In densely deployed WLANs, the number of the available topologies of APs is extremely high, and thus we extract the features of the topological structures based on GCNs. We apply GCNs to a contention graph where APs within their carrier sensing ranges are connected to extract the features of carrier sensing relationships. Additionally, to improve the learning speed especially in an early stage of learning, we employ a game theory-based method to collect the training data independently of the neural network model. The simulation results indicate that the proposed method can appropriately control the channels when compared to extant methods

Local SiC photoluminescence evidence of non-mutualistic hot spot formation and sub-THz coherent emission from a rectangular Bi2_2Sr2_2CaCu2_2O8+δ_{8+\delta} mesa

From the photoluminescence of SiC microcrystals uniformly covering a rectangular mesa of the high transition temperature $T_c$ superconductor Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$, the local surface temperature $T({\bm r})$ was directly measured during simultaneous sub-THz emission from the $N\sim10^3$ intrinsic Josephson junctions (IJJs) in the mesa. At high bias currents $I$ and low bath temperatures $T_{\rm bath}\lesssim~35$ K, the center of a large elliptical hot spot with $T({\bm r})> T_c$ jumps dramatically with little current-voltage characteristic changes. The hot spot doesn't alter the ubiquitous primary and secondary emission conditions: the ac Josephson relation and the electromagnetic cavity resonance excitation, respectively. Since the intense sub-THz emission was observed for high $T_{\rm bath}\gtrsim~50$ K in the low $I$ bias regime where hot spots are absent, hot spots can not provide the primary mechanisms for increasing the output power, the tunability, or for promoting the synchronization of the $N$ IJJs for the sub-THz emission, but can at best coexist non-mutualistically with the emission. No $T({\bm r})$ standing waves were observed

Extracting proficiency differences and individual characteristics in golfers' swing using single-video markerless motion analysis

In this study, we analyzed golfers' swing movement to extract differences in proficiency and individual characteristics using two-dimensional video data from a single camera. We conducted an experiment with 27 golfers who had a wide range of skill levels, using a 7-iron; we acquired video data with a camera on the sagittal plane. For data extraction, we used pose estimation (using HRNet) and object detection (using DeepLabCut) methods to extract human-joint and club-head data. We examined the relationship between proficiency and individual characteristics vis-à-vis forward tilt angle and club trajectory. The results showed that the stability and reproducibility of the forward tilt angle are characteristics of proficiency. Highly skilled golfers showed low variability and high reproducibility between trials in forward tilt angle. However, we found that club trajectory may not be a characteristic of proficiency but rather an individual characteristic. Club trajectory was divided roughly into clockwise rotation and counterclockwise rotation. Thus, the analysis based on video data from a single markerless camera enabled the extraction of the differences in proficiency and individual characteristics of golf swing. This suggests the usefulness of our system for simply evaluating golf swings and applying it to motor learning and coaching situations