A probabilistic interpretation of a sequence related to Narayana polynomials

A sequence of coefficients appearing in a recurrence for the Narayana polynomials is generalized. The coefficients are given a probabilistic interpretation in terms of beta distributed random variables. The recurrence established by M. Lasalle is then obtained from a classical convolution identity. Some arithmetical properties of the generalized coefficients are also established

Learning to Reconstruct People in Clothing from a Single RGB Camera

We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our model learns to predict the parameters of a statistical body model and instance displacements that add clothing and hair to the shape. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused. Second, based on the observation that feed-forward predictions are fast but do not always align with the input images, we predict using both, bottom-up and top-down streams (one per view) allowing information to flow in both directions. Learning relies only on synthetic 3D data. Once learned, the model can take a variable number of frames as input, and is able to reconstruct shapes even from a single image with an accuracy of 6mm. Results on 3 different datasets demonstrate the efficacy and accuracy of our approach

Interface dipoles of organic molecules on Ag(111) in hybrid density-functional theory

We investigate the molecular acceptors 3,4,9,10-perylene-tetracarboxylic acid dianhydride (PTCDA), 2,3,5,6-tetra uoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ), and 4,5,9,10-pyrenetetraone (PYTON) on Ag(111) using densityfunctional theory. For two groups of the HSE(\alpha, \omega) family of exchange-correlation functionals (\omega = 0 and \omega = 0.2\AA) we study the isolated components as well as the combined systems as a function of the amount of exact-exchange (\alpha). We find that hybrid functionals favour electron transfer to the adsorbate. Comparing to experimental work-function data, we report for (\alpha) ca. 0.25 a notable but small improvement over (semi)local functionals for the interface dipole. Although Kohn-Sham eigenvalues are only approximate representations of ionization energies, incidentally, at this value also the density of states agrees well with the photoelectron spectra. However, increasing (\alpha) to values for which the energy of the lowest unoccupied molecular orbital matches the experimental electron affinity in the gas phase worsens both the interface dipole and the density of states. Our results imply that semi-local DFT calculations may often be adequate for conjugated organic molecules on metal surfaces and that the much more computationally demanding hybrid functionals yield only small improvements.Comment: submitted to New Journal of Physics (2013). More information can be found at http://th.fhi-berlin.mpg.de/site/index.php?n=Publications.Publication

The Moments of the Hydrogen Atom by the Method of Brackets

Expectation values of powers of the radial coordinate in arbitrary hydrogen states are given, in the quantum case, by an integral involving the associated Laguerre function. The method of brackets is used to evaluate the integral in closed-form and to produce an expression for this average value as a finite sum

Derivation of an integral of Boros and Moll via convolution of Student t-densities

We show that the evaluation of an integral considered by Boros and Moll is a special case of a convolution result about Student t-densities obtained by the authors in 2008

Enacted support and golf-putting performance: The role of support type and support visibility

Objectives This study examined whether the impact of enacted support on performance differed across type (esteem and informational) and visibility (visible and invisible) of support. It further tested whether self-efficacy mediated the enacted support-performance relationship. Design A one-factor (support manipulation) between subjects experiment. Method A fellow novice golfer — in reality a confederate — was scripted to randomly provide one of five support manipulations (visible informational support, invisible informational support, visible esteem support, invisible esteem support, and no support) to participants (n = 105). Immediately after, participants completed a self-efficacy measure and then performed a golf-putting task. Results The results demonstrated that participants given visible esteem support significantly outperformed those given no support and those given invisible esteem support. Participants given invisible informational support significantly outperformed those given no support. Although non-significant, the observed mean difference and moderate effect size provided weak evidence that those in the invisible informational support condition may have performed at a higher level than those in the visible informational support condition. There was no evidence that self-efficacy could explain any of these effects. Conclusion The results suggest that enacted support can benefit novices’ performance and that it is crucial to consider both the type and the visibility of the support. Esteem support is particularly effective when communicated in an explicit and direct manner but informational support appears more effective when communicated in a more subtle, indirect manner

Visually Plausible Human-Object Interaction Capture from Wearable Sensors

In everyday lives, humans naturally modify the surrounding environmentthrough interactions, e.g., moving a chair to sit on it. To reproduce suchinteractions in virtual spaces (e.g., metaverse), we need to be able to captureand model them, including changes in the scene geometry, ideally fromego-centric input alone (head camera and body-worn inertial sensors). This isan extremely hard problem, especially since the object/scene might not bevisible from the head camera (e.g., a human not looking at a chair whilesitting down, or not looking at the door handle while opening a door). In thispaper, we present HOPS, the first method to capture interactions such asdragging objects and opening doors from ego-centric data alone. Central to ourmethod is reasoning about human-object interactions, allowing to track objectseven when they are not visible from the head camera. HOPS localizes andregisters both the human and the dynamic object in a pre-scanned static scene.HOPS is an important first step towards advanced AR/VR applications based onimmersive virtual universes, and can provide human-centric training data toteach machines to interact with their surroundings. The supplementary video,data, and code will be available on our project page athttp://virtualhumans.mpi-inf.mpg.de/hops/<br

One session of fMRI-Neurofeedback training on motor imagery modulates whole-brain effective connectivity and dynamical complexity

In the past decade, several studies have shown that Neurofeedback (NFB) by functional magnetic resonance imaging can alter the functional coupling of targeted and non-targeted areas. However, the causal mechanisms underlying these changes remain uncertain. Here, we applied a whole-brain dynamical model to estimate Effective Connectivity (EC) profiles of resting-state data acquired before and immediately after a single-session NFB training for 17 participants who underwent motor imagery NFB training and 16 healthy controls who received sham feedback. Within-group and between-group classification analyses revealed that only for the NFB group it was possible to accurately discriminate between the 2 resting-state sessions. NFB training-related signatures were reflected in a support network of direct connections between areas involved in reward processing and implicit learning, together with regions belonging to the somatomotor, control, attention, and default mode networks, identified through a recursive-feature elimination procedure. By applying a data-driven approach to explore NFB-induced changes in spatiotemporal dynamics, we demonstrated that these regions also showed decreased switching between different brain states (i.e. metastability) only following real NFB training. Overall, our findings contribute to the understanding of NFB impact on the whole brain's structure and function by shedding light on the direct connections between brain areas affected by NFB training