Neural View-Interpolation for Sparse Light Field Video

We suggest representing light field (LF) videos as "one-off" neural networks (NN), i.e., a learned mapping from view-plus-time coordinates to high-resolution color values, trained on sparse views. Initially, this sounds like a bad idea for three main reasons: First, a NN LF will likely have less quality than a same-sized pixel basis representation. Second, only few training data, e.g., 9 exemplars per frame are available for sparse LF videos. Third, there is no generalization across LFs, but across view and time instead. Consequently, a network needs to be trained for each LF video. Surprisingly, these problems can turn into substantial advantages: Other than the linear pixel basis, a NN has to come up with a compact, non-linear i.e., more intelligent, explanation of color, conditioned on the sparse view and time coordinates. As observed for many NN however, this representation now is interpolatable: if the image output for sparse view coordinates is plausible, it is for all intermediate, continuous coordinates as well. Our specific network architecture involves a differentiable occlusion-aware warping step, which leads to a compact set of trainable parameters and consequently fast learning and fast execution

Atom interferometers and a small-scale test of general relativity

Since the first appearance of general relativity in 1916, various experiments have been conducted to test the theory. Due to the weakness of the interactions involved, all of the documented tests were carried out in a gravitational field generated by objects of an astronomical scale. We propose an idea for an experiment that could detect purely general-relativistic effects in a lab-generated gravitational field. It is shown that a set of dense rapidly-revolving cylinders produce a frame-dragging effect substantial enough to be two orders of magnitude away from the observable range of the next generation of atomic interferometers.. The metric tensor due to a uniform rotating axisymmetric body in the weak-field limit is calculated and the phaseshift formula for the interferometer is derived. This article is meant to demonstrate feasibility of the concept and stimulate further research into the field of low-scale experiments in general relativity. It is by no means a fully developed experiment proposal.Comment: 24 pages, 4 figure

Costs and Physical inputs in Producing Red Tart Cherries, 58 Western New York Farms, 196

A.E. Res. 10

Coping with Conflict: A Study of Interpersonal Conflict Resolution Styles of Adult Children of Alcoholics and Nonalcoholics

Research on adult children of alcoholics has indicated that such children have difficulty with behavioral and communicative characteristics. Specifically they have difficulty with such behaviors as lying, intimacy, responsibility, and trust. Research also has indicated that adult children of alcoholics rely on coping mechanisms to escape from their chaotic environments and such mechanisms are manifested in behaviors of co-dependency and family roles. Although the literature on adult children of alcoholics suggests that these individuals may have trouble with problem solving in conflict, no apparent literature discusses the strategies of conflict resolution for such individuals. This study predicted that adult children of alcoholics would choose conflict resolution styles of avoidance and/or accommodation more often than would adult children of nonalcoholics, The Thomas Kilmann MODE Instrument was given to a sample of Spring 1990 Fundamentals of Public Speaking students at the University of North Dakota. Results indicated that differences in responses to conflict resolution styles between adult children of alcoholics and adult children of nonalcoholics were not significant at the .05 level. Implications of this study of conflict resolution suggest a need to incorporate a new methodology or improve the existing instrument for a higher level of reliability. Recommendations for further research include relying on a formalized adult children of alcoholics group for testing. Also incorporating rhetorical critical analyses of metaphorical analysis, content analysis, or fantasy theme analysis to better assess conflict resolution styles may be useful

An intuitive control space for material appearance

Many different techniques for measuring material appearance have been proposed in the last few years. These have produced large public datasets, which have been used for accurate, data-driven appearance modeling. However, although these datasets have allowed us to reach an unprecedented level of realism in visual appearance, editing the captured data remains a challenge. In this paper, we present an intuitive control space for predictable editing of captured BRDF data, which allows for artistic creation of plausible novel material appearances, bypassing the difficulty of acquiring novel samples. We first synthesize novel materials, extending the existing MERL dataset up to 400 mathematically valid BRDFs. We then design a large-scale experiment, gathering 56,000 subjective ratings on the high-level perceptual attributes that best describe our extended dataset of materials. Using these ratings, we build and train networks of radial basis functions to act as functionals mapping the perceptual attributes to an underlying PCA-based representation of BRDFs. We show that our functionals are excellent predictors of the perceived attributes of appearance. Our control space enables many applications, including intuitive material editing of a wide range of visual properties, guidance for gamut mapping, analysis of the correlation between perceptual attributes, or novel appearance similarity metrics. Moreover, our methodology can be used to derive functionals applicable to classic analytic BRDF representations. We release our code and dataset publicly, in order to support and encourage further research in this direction

Evolutionary algorithms for timetable problems

The university course timetabling problem is hard and time-consuming to solve. Profits from full automatisation of this process can be invaluable. This paper describes architecture and operation of two automatic timetabling systems. Both are based on evolutionary algorithms, with specialised genetic operators and penalty-based evaluation function. The paper covers two problem variations (theorethical and real-world), with different sets of constraints and different representations. Moreover, specification of both solutions and a proposal of hybrid system architecture is included

Learning to Predict Image-based Rendering Artifacts with Respect to a Hidden Reference Image

Image metrics predict the perceived per-pixel difference between a reference image and its degraded (e. g., re-rendered) version. In several important applications, the reference image is not available and image metrics cannot be applied. We devise a neural network architecture and training procedure that allows predicting the MSE, SSIM or VGG16 image difference from the distorted image alone while the reference is not observed. This is enabled by two insights: The first is to inject sufficiently many un-distorted natural image patches, which can be found in arbitrary amounts and are known to have no perceivable difference to themselves. This avoids false positives. The second is to balance the learning, where it is carefully made sure that all image errors are equally likely, avoiding false negatives. Surprisingly, we observe, that the resulting no-reference metric, subjectively, can even perform better than the reference-based one, as it had to become robust against mis-alignments. We evaluate the effectiveness of our approach in an image-based rendering context, both quantitatively and qualitatively. Finally, we demonstrate two applications which reduce light field capture time and provide guidance for interactive depth adjustment.Comment: 13 pages, 11 figure

A Model of Local Adaptation

The visual system constantly adapts to different luminance levels when viewing natural scenes. The state of visual adaptation is the key parameter in many visual models. While the time-course of such adaptation is well understood, there is little known about the spatial pooling that drives the adaptation signal. In this work we propose a new empirical model of local adaptation, that predicts how the adaptation signal is integrated in the retina. The model is based on psychophysical measurements on a high dynamic range (HDR) display. We employ a novel approach to model discovery, in which the experimental stimuli are optimized to find the most predictive model. The model can be used to predict the steady state of adaptation, but also conservative estimates of the visibility(detection) thresholds in complex images.We demonstrate the utility of the model in several applications, such as perceptual error bounds for physically based rendering, determining the backlight resolution for HDR displays, measuring the maximum visible dynamic range in natural scenes, simulation of afterimages, and gaze-dependent tone mapping