Premotor cortex encoding of dynamic hand force and motor output observation underlying hand-object interaction

We studied encoding of hand force and its relations with observation-related activity in macaques trained in an isometric hand-force application and recalibration task, that required to move a visual cursor on a screen toward eight peripheral targets by exerting a force on an isometric joystick, in absence or presence of an opposing force field. Monkeys also observed the result of their action in play-back, as motion of a visual cursor on the screen. This approach combined in a single experiment isometric action performance and force adjustment with observation of its consequences in the external world, also allowing to determine whether PMd neuronal populations reflected an inverse model that specified the force necessary to move in different directions a visual object, or a forward computation encoding its desired trajectory in visual space. We found that a population of PMd cells encoded the direction of dynamic force and its recalibration when the force condition changed but did not retain memory of such change, probably reflecting an adaptation rather than a learning process. Cells with observation-related activity also modulated by change in hand force were not modulated when the force conditions changed, suggesting that their activity reflected the motion of the visual cursor on the screen, therefore the consequences of force application in the visual space. These results also allow a direct comparison of the relative contribute of different populations of PMd cells with that of cells with similar activity profile in the encoding of hand force and its consequences in the parieto-frontal system

Geometric study of Lagrangian and Eulerian structures in turbulent channel flow

We report the detailed multi-scale and multi-directional geometric study of both evolving Lagrangian and instantaneous Eulerian structures in turbulent channel flow at low and moderate Reynolds numbers. The Lagrangian structures (material surfaces) are obtained by tracking the Lagrangian scalar field, and Eulerian structures are extracted from the swirling strength field at a time instant. The multi-scale and multi-directional geometric analysis, based on the mirror-extended curvelet transform, is developed to quantify the geometry, including the averaged inclination and sweep angles, of both structures at up to eight scales ranging from the half-height δ of the channel to several viscous length scales δ_ν. Here, the inclination angle is on the plane of the streamwise and wall-normal directions, and the sweep angle is on the plane of streamwise and spanwise directions. The results show that coherent quasi-streamwise structures in the near-wall region are composed of inclined objects with averaged inclination angle 35°–45°, averaged sweep angle 30°–40° and characteristic scale 20δ_ν, and 'curved legs' with averaged inclination angle 20°–30°, averaged sweep angle 15°–30° and length scale 5δ_ν–10δ_ν. The temporal evolution of Lagrangian structures shows increasing inclination and sweep angles with time, which may correspond to the lifting process of near-wall quasi-streamwise vortices. The large-scale structures that appear to be composed of a number of individual small-scale objects are detected using cross-correlations between Eulerian structures with large and small scales. These packets are located at the near-wall region with the typical height 0.25δ and may extend over 10δ in the streamwise direction in moderate-Reynolds-number, long channel flows. In addition, the effects of the Reynolds number and comparisons between Lagrangian and Eulerian structures are discussed

Coupling BM3D with directional wavelet packets for image denoising

The paper presents an image denoising algorithm by combining a method that is based on directional quasi-analytic wavelet packets (qWPs) with the popular BM3D algorithm. The qWPs and its corresponding transforms are designed in [1]. The denoising algorithm qWP (qWPdn) applies an adaptive localized soft thresholding to the transform coefficients using the Bivariate Shrinkage methodology. The combined method consists of several iterations of qWPdn and BM3D algorithms, where the output from one algorithm updates the input to the other (cross-boosting).The qWPdn and BM3D methods complement each other. The qWPdn capabilities to capture edges and fine texture patterns are coupled with utilizing the sparsity in real images and self-similarity of patches in the image that is inherent in the BM3D. The obtained results are quite competitive with the best state-of-the-art algorithms. We compare the performance of the combined methodology with the performances of cptTP-CTF6, DAS-2 algorithms, which use directional frames, and the BM3D algorithm. In the overwhelming majority of the experiments, the combined algorithm outperformed the above methods.Comment: 26 pages. arXiv admin note: substantial text overlap with arXiv:2001.04899, arXiv:1907.01479; text overlap with arXiv:2008.0536