Natural Visualizations

This paper demonstrates the prevalence of a shared characteristic between visualizations and images of nature. We have analyzed visualization competitions and user studies of visualizations and found that the more preferred, better performing visualizations exhibit more natural characteristics. Due to our brain being wired to perceive natural images [SO01], testing a visualization for properties similar to those of natural images can help show how well our brain is capable of absorbing the data. In turn, a metric that finds a visualization’s similarity to a natural image may help determine the effectiveness of that visualization. We have found that the results of comparing the sizes and distribution of the objects in a visualization with those of natural standards strongly correlate to one’s preference of that visualization

Subsonic investigations of vortex interaction control for enhanced high-alpha aerodynamics of a chine forebody/Delta wing configuration

A proposed concept to alleviate high alpha asymmetry and lateral/directional instability by decoupling of forebody and wing vortices was studied on a generic chine forebody/ 60 deg. delta configuration in the NASA Langley 7 by 10 foot High Speed Tunnel. The decoupling technique involved inboard leading edge flaps of varying span and deflection angle. Six component force/moment characteristics, surface pressure distributions and vapor-screen flow visualizations were acquired, on the basic wing-body configuration and with both single and twin vertical tails at M sub infinity = 0.1 and 0.4, and in the range alpha = 0 to 50 deg and beta = -10 to +10 degs. Results are presented which highlight the potential of vortex decoupling via leading edge flaps for enhanced high alpha lateral/directional characteristics

Visualizing and Understanding Sum-Product Networks

Sum-Product Networks (SPNs) are recently introduced deep tractable probabilistic models by which several kinds of inference queries can be answered exactly and in a tractable time. Up to now, they have been largely used as black box density estimators, assessed only by comparing their likelihood scores only. In this paper we explore and exploit the inner representations learned by SPNs. We do this with a threefold aim: first we want to get a better understanding of the inner workings of SPNs; secondly, we seek additional ways to evaluate one SPN model and compare it against other probabilistic models, providing diagnostic tools to practitioners; lastly, we want to empirically evaluate how good and meaningful the extracted representations are, as in a classic Representation Learning framework. In order to do so we revise their interpretation as deep neural networks and we propose to exploit several visualization techniques on their node activations and network outputs under different types of inference queries. To investigate these models as feature extractors, we plug some SPNs, learned in a greedy unsupervised fashion on image datasets, in supervised classification learning tasks. We extract several embedding types from node activations by filtering nodes by their type, by their associated feature abstraction level and by their scope. In a thorough empirical comparison we prove them to be competitive against those generated from popular feature extractors as Restricted Boltzmann Machines. Finally, we investigate embeddings generated from random probabilistic marginal queries as means to compare other tractable probabilistic models on a common ground, extending our experiments to Mixtures of Trees.Comment: Machine Learning Journal paper (First Online), 24 page