Fine structures of solar radio type III bursts and their possible relationship with coronal density turbulence

Solar radio type III bursts are believed to be the most sensitive signatures of near-relativistic electron beam propagation in the corona. A solar radio type IIIb-III pair burst with fine frequency structures, observed by the Low Frequency Array (LOFAR) with high temporal (~10 ms) and spectral (12.5 kHz) resolutions at 30–80 MHz, is presented. The observations show that the type III burst consists of many striae, which have a frequency scale of about 0.1 MHz in both the fundamental (plasma) and the harmonic (double plasma) emission. We investigate the effects of background density fluctuations based on the observation of striae structure to estimate the density perturbation in the solar corona. It is found that the spectral index of the density fluctuation spectrum is about −1.7, and the characteristic spatial scale of the density perturbation is around 700 km. This spectral index is very close to a Kolmogorov turbulence spectral index of −5/3, consistent with a turbulent cascade. This fact indicates that the coronal turbulence may play the important role of modulating the time structures of solar radio type III bursts, and the fine structure of radio type III bursts could provide a useful and unique tool to diagnose the turbulence in the solar corona

Effect of a sausage oscillation on radio zebra pattern structures in a solar flare

Sausage modes that are axisymmetric fast magnetoacoustic oscillations of solar coronal loops are characterized by variation of the plasma density and magnetic field, and hence cause time variations of the electron plasma frequency and cyclotron frequency. The latter parameters determine the condition for the double plasma resonance (DPR), which is responsible for the appearance of zebra-pattern (ZP) structures in time spectra of solar type IV radio bursts. We perform numerical simulations of standing and propagating sausage oscillations in a coronal loop modeled as a straight, field-aligned plasma slab, and determine the time variation of the DPR layer locations. Instant values of the plasma density and magnetic field at the DPR layers allowed us to construct skeletons of the time variation of ZP stripes in radio spectra. In the presence of a sausage oscillation, the ZP structures are shown to have characteristic wiggles with the time period prescribed by the sausage oscillation. Standing and propagating sausage oscillations are found to have different signatures in ZP patterns. We conclude that ZP wiggles can be used for the detection of short-period sausage oscillations and the exploitation of their seismological potential

Aerobic exercise training at maximal fat oxidation intensity improves body composition, glycemic control, and physical capacity in older people with type 2 diabetes

Background: Aerobic training has been used as one of the common treatments for type 2 diabetes; however, further research on the individualized exercise program with the optimal intensity is still necessary. The purpose of this study was to investigate the effects of supervised exercise training at the maximal fat oxidation (FATmax) intensity on body composition, glycemic control, lipid profile, and physical capacity in older people with type 2 diabetes. Methods: Twenty-four women and 25 men with type 2 diabetes, aged 60–69 years. The exercise groups trained at the individualized FATmax intensity for 1 h/day for 3 days/week over 16 weeks. No dietary intervention was introduced during the experimental period. Whole body fat, abdominal fat, oral glucose tolerance test, lipid profile, and physical capacity were measured before and after the interventions. Results: FATmax intensity was at 41.3 ± 3.2% VO2max for women and 46.1 ± 10.3% VO2max for men. Exercise groups obtained significant improvements in body composition, with a special decrease in abdominal obesity; decreased resting blood glucose concentration and HbA1c; and increased VO2max, walking ability, and lower body strength, compared to the non-exercising controls. Daily energy intake and medication remained unchanged for all participants during the experimental period. Conclusion: Beside the improvements in the laboratory variables, the individualized FATmax training can also benefit daily physical capacity of older people with type 2 diabetes

A face recognition system for assistive robots

Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience. To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance. In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots. Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet. We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS). The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform.This work is supported by the Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech, the research projects WISER ([DPI2017-84827-R]),funded by the Spanish Government, and financed by European RegionalDevelopment’s funds (FEDER), and MoveCare ([ICT-26-2016b-GA-732158]), funded by the European H2020 program, and by a postdoc contract from the I-PPIT-UMA program financed by the University of Málaga

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and difficulties of generalization. In this work, we propose a novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data. This formulation not only leads to greater expressive power but also stronger generalization capability. On two large datasets, Kinetics and NTU-RGBD, it achieves substantial improvements over mainstream methods.Comment: Accepted by AAAI 201