Action recognition using Kinematics Posture Feature on 3D skeleton joint locations

Action recognition is a very widely explored research area in computer vision and related fields. We propose Kinematics Posture Feature (KPF) extraction from 3D joint positions based on skeleton data for improving the performance of action recognition. In this approach, we consider the skeleton 3D joints as kinematics sensors. We propose Linear Joint Position Feature (LJPF) and Angular Joint Position Feature (AJPF) based on 3D linear joint positions and angles between bone segments. We then combine these two kinematics features for each video frame for each action to create the KPF feature sets. These feature sets encode the variation of motion in the temporal domain as if each body joint represents kinematics position and orientation sensors. In the next stage, we process the extracted KPF feature descriptor by using a low pass filter, and segment them by using sliding windows with optimized length. This concept resembles the approach of processing kinematics sensor data. From the segmented windows, we compute the Position-based Statistical Feature (PSF). These features consist of temporal domain statistical features (e.g., mean, standard deviation, variance, etc.). These statistical features encode the variation of postures (i.e., joint positions and angles) across the video frames. For performing classification, we explore Support Vector Machine (Linear), RNN, CNNRNN, and ConvRNN model. The proposed PSF feature sets demonstrate prominent performance in both statistical machine learning- and deep learning-based models. For evaluation, we explore five benchmark datasets namely UTKinect-Action3D, Kinect Activity Recognition Dataset (KARD), MSR 3D Action Pairs, Florence 3D, and Office Activity Dataset (OAD). To prevent overfitting, we consider the leave-one-subject-out framework as the experimental setup and perform 10-fold cross-validation. Our approach outperforms several existing methods in these benchmark datasets and achieves very promising classification performance

Annotator-dependent uncertainty-aware estimation of gait relative attributes

In this paper, we describe an uncertainty-aware estimation framework for gait relative attributes. We specifically design a two-stream network model that takes a pair of gait videos as input. It then outputs a corresponding pair of Gaussian distributions of gait absolute attribute scores and annotator-dependent gait relative attribute label distributions. Moreover, we propose a differentiable annotator-independent uncertainty layer to estimate the gait relative attribute score distribution from the absolute distributions then map it to a relative attribute label distribution using the computation of cumulative distribution functions. Furthermore, we propose another annotator-dependent uncertainty layer to estimate the uncertainty on the gait relative attribute labels in terms of a set of trainable transition matrices. Finally, we design a joint loss function on the relative attribute label distribution to learn the model parameters. Experiments on two gait relative attribute datasets demonstrated the effectiveness of the proposed method against baselines in quantitative and qualitative evaluations

A crystallographic phase transition within the magnetically ordered state of Ce_2Fe_17

X-ray diffraction experiments were performed on polycrystalline and single-crystal specimens of Ce$_{2}$Fe$_{17}$ at temperatures between 10 K and 300 K. Below $T_{\mathrm{t}}$ = 118$\pm$2 K, additional weak superstructure reflections were observed in the antiferromagnetically ordered state. The superstructure can be described by a doubling of the chemical unit cell along the $\mathbf{c}$ direction in hexagonal notation with the same space group $R \bar{3} m$ as the room-temperature structure. The additional antiferromagnetic satellite reflections observed in earlier neutron diffraction experiments can be conclusively related to the appearance of this superstructure.Comment: 8 pages, figures, submitted for publication in Phys. Rev.

Strong dispersive coupling between a mechanical resonator and a fluxonium superconducting qubit

We demonstrate strong dispersive coupling between a fluxonium superconducting qubit and a 690 megahertz mechanical oscillator, extending the reach of circuit quantum acousto-dynamics (cQAD) experiments into a new range of frequencies. We have engineered a qubit-phonon coupling rate of $g\approx2\pi\times14~\text{MHz}$, and achieved a dispersive interaction that exceeds the decoherence rates of both systems while the qubit and mechanics are highly nonresonant ($\Delta/g\gtrsim10$). Leveraging this strong coupling, we perform phonon number-resolved measurements of the mechanical resonator and investigate its dissipation and dephasing properties. Our results demonstrate the potential for fluxonium-based hybrid quantum systems, and a path for developing new quantum sensing and information processing schemes with phonons at frequencies below 700 MHz to significantly expand the toolbox of cQAD.Comment: 22 pages, 12 figure