COMPARATIVE KINEMATIC ANALYSIS OF ENQVIST AND MOYA’S TENNIS SERVE TECHNOLOGY

Serving occupies a more important role in the modern tennis. The tennis serve of two players, Thomas Enqvist and Carles Moya, were filmed in the semifinals of Chengdu Open-ATP Champions Tour and analysed with three-dimensional video analysis. The serve was divided into three stages as follows: throwing ball rising racket stage, backward swing stage, forward swing hitting stage. It is found that: in the first stage, the maximum value of shoulder-hip level projection angle of Enqvist and Moya are 18.5° and 28.7° respectively. In the second stage, Enqvist and Moya’s extension range of left knee joint were 55.1° and 34.6°.Their e angular velocity were 182.6°/s and 170.4°/s. In the third stage, Enqvist and Moya’s hitting height were 2.23m and 2.15m, Hitting height and body height ratio were 1.18 and 1.13, there are significant differences

Control and mitigation of microwave crosstalk effect with superconducting qubits

Improving gate performance is vital for scalable quantum computing. The universal quantum computing also requires the gate fidelity to reach a high level. For superconducting quantum processor, which operates in the microwave band, the single-qubit gates are usually realized with microwave driving. The crosstalk between microwave pulses is a non-negligible error source. In this article, we propose an error mitigation scheme to address this crosstalk issue for single-qubit gates. There are three steps in our method. First, by controlling the detuning between qubits, the microwave induced classical crosstalk error can be constrained within the computational subspace. Second, by applying the general decomposition procedure, arbitrary single-qubit gate can be decomposed as a sequence of $\sqrt{X}$ and virtual Z gates. Finally, by optimizing the parameters in virtual Z gates, the error constrained in the computational space can be corrected. Using our method, no additional compensation signals are needed, arbitrary single-qubit gate time will not be prolonged, and the circuit depth containing simultaneous single-qubit gates will also not increase. The simulation results show that, in specific regime of qubit-qubit detuning, the infidelities of simultaneous single-qubit gates can be as low as which without microwave crosstalk

Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene Classification

Deep neural networks have achieved promising progress in remote sensing (RS) image classification, for which the training process requires abundant samples for each class. However, it is time-consuming and unrealistic to annotate labels for each RS category, given the fact that the RS target database is increasing dynamically. Zero-shot learning (ZSL) allows for identifying novel classes that are not seen during training, which provides a promising solution for the aforementioned problem. However, previous ZSL models mainly depend on manually-labeled attributes or word embeddings extracted from language models to transfer knowledge from seen classes to novel classes. Besides, pioneer ZSL models use convolutional neural networks pre-trained on ImageNet, which focus on the main objects appearing in each image, neglecting the background context that also matters in RS scene classification. To address the above problems, we propose to collect visually detectable attributes automatically. We predict attributes for each class by depicting the semantic-visual similarity between attributes and images. In this way, the attribute annotation process is accomplished by machine instead of human as in other methods. Moreover, we propose a Deep Semantic-Visual Alignment (DSVA) that take advantage of the self-attention mechanism in the transformer to associate local image regions together, integrating the background context information for prediction. The DSVA model further utilizes the attribute attention maps to focus on the informative image regions that are essential for knowledge transfer in ZSL, and maps the visual images into attribute space to perform ZSL classification. With extensive experiments, we show that our model outperforms other state-of-the-art models by a large margin on a challenging large-scale RS scene classification benchmark.Comment: Published in ISPRS P&RS. The code is available at https://github.com/wenjiaXu/RS_Scene_ZS

A study of 10 Rotating Radio Transients using Parkes radio telescope

Rotating Radio Transients (RRATs) are a relatively new subclass of pulsars that emit detectable radio bursts sporadically. We conducted an analysis of 10 RRATs observed using the Parkes telescope, with 8 of these observed via the Ultra-Wideband Receiver. We measured the burst rate and produced integrated profiles spanning multiple frequency bands for 3 RRATs. We also conducted a spectral analysis on both integrated pulses and individual pulses of 3 RRATs. All of their integrated pulses follow a simple power law, consistent with the known range of pulsar spectral indices. Their average spectral indices of single pulses are -0.9, -1.2, and -1.0 respectively, which are within the known range of pulsar spectral indices. Additionally, we find that the spreads of single-pulse spectral indices for these RRATs (ranging from -3.5 to +0.5) are narrower compared to what has been observed in other RRATs (Shapiro-Albert et al. 2018; Xie et al. 2022). It is notable that the average spectral index and scatter of single pulses are both relatively small. For the remaining 5 RRATs observed at the UWL receiver, we also provided the upper limits on fluence and flux density. In addition, we obtained the timing solution of PSR J1709-43. Our analysis shows that PSRs J1919+1745, J1709-43 and J1649-4653 are potentially nulling pulsars or weak pulsars with sparse strong pulses.Comment: 16 pages, 8 figures, RAA accepte