Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet

Multi-person pose understanding from RGB videos involves three complex tasks: pose estimation, tracking and motion forecasting. Intuitively, accurate multi-person pose estimation facilitates robust tracking, and robust tracking builds crucial history for correct motion forecasting. Most existing works either focus on a single task or employ multi-stage approaches to solving multiple tasks separately, which tends to make sub-optimal decision at each stage and also fail to exploit correlations among the three tasks. In this paper, we propose Snipper, a unified framework to perform multi-person 3D pose estimation, tracking, and motion forecasting simultaneously in a single stage. We propose an efficient yet powerful deformable attention mechanism to aggregate spatiotemporal information from the video snippet. Building upon this deformable attention, a video transformer is learned to encode the spatiotemporal features from the multi-frame snippet and to decode informative pose features for multi-person pose queries. Finally, these pose queries are regressed to predict multi-person pose trajectories and future motions in a single shot. In the experiments, we show the effectiveness of Snipper on three challenging public datasets where our generic model rivals specialized state-of-art baselines for pose estimation, tracking, and forecasting

Numerical solution of fractional differential equations using the generalized block pulse operational matrix

AbstractThe Riemann–Liouville fractional integral for repeated fractional integration is expanded in block pulse functions to yield the block pulse operational matrices for the fractional order integration. Also, the generalized block pulse operational matrices of differentiation are derived. Based on the above results we propose a way to solve the fractional differential equations. The method is computationally attractive and applications are demonstrated through illustrative examples

Time-fractional diffusion equation for signal smoothing

The time-fractional diffusion equation is used for signal smoothing. Compared to the classical diffusion equation, the time-fractional diffusion equation has another adjustable time-fractional derivative order to control the diffusion process. Therefore, some simulated signals are used to compare the smoothing performance between the time-fractional diffusion equation and the classical diffusion equation as well as between classical smoothing methods (regularization method, Savitzky–Golay method and wavelet method). In the end, the time-fractional diffusion filtering is applied in an NMR spectrum smoothing. Results indicate that the time-fractional diffusion filtering is advantage over the classical diffusion filtering and their smoothing performance is better than that of classical smoothing methods

Recent Progress of Atomic Layer Technology in Spintronics: Mechanism, Materials and Prospects

The atomic layer technique is generating a lot of excitement and study due to its profound physics and enormous potential in device fabrication. This article reviews current developments in atomic layer technology for spintronics, including atomic layer deposition (ALD) and atomic layer etching (ALE). To begin, we introduce the main atomic layer deposition techniques. Then, in a brief review, we discuss ALE technology for insulators, semiconductors, metals, and newly created two-dimensional van der Waals materials. Additionally, we compare the critical factors learned from ALD to constructing ALE technology. Finally, we discuss the future prospects and challenges of atomic layer technology in the field of spinronics

Time fractional super-diffusion model and its application in peak-preserving smoothing

The super-diffusion model is suggested for peak-preserving smoothing. In this model, the time derivative on the left of the classical diffusion model is replaced with the time fractional derivative. Because of the weight property of the fractional derivative, the super-diffusion model can further improve the smooth performance of the classical nonlinear diffusion model. An explicit difference scheme and an implicit difference scheme are given. Then some comparisons between the proposed model and the classical nonlinear diffusion model are done. The results indicate the proposed model outperforms the classic nonlinear diffusion model. In the end, the proposed method is used to smooth a nuclear magnetic resonance spectroscopy and a mass spectrometry.</p

catena-Poly[[dichloridomercury(II)]-N&amp;#8242;-nicotinoylnicotinohydrazide]

The title complex, [HgCl2(C12H10N4O2)]n, is composed of one HgII ion, one nnh ligand (nnh = N&amp;#8242;-nicotinoylnicotinohydrazide) and two coordinated chloride ions. The HgII ion shows a distorted tetrahedral geometry, being surrounded by two N atoms from two nnh ligands and two chloride ions. Due to the bridging role of nnh, the HgII atoms are connected into polymeric chains along the c axis, which are further interlinked via N&amp;#8212;H...O and C&amp;#8212;H...Cl hydrogen-bonding interactions, forming a three-dimensional network