Visualization of heterogeneous data

Abstract — Both the Resource Description Framework (RDF), used in the semantic web, and Maya Viz u-forms represent data as a graph of objects connected by labeled edges. Existing systems for flexible visualization of this kind of data require manual specification of the possible visualization roles for each data attribute. When the schema is large and unfamiliar, this requirement inhibits exploratory visualization by requiring a costly up-front data integration step. To eliminate this step, we propose an automatic technique for mapping data attributes to visualization attributes. We formulate this as a schema matching problem, finding appropriate paths in the data model for each required visualization attribute in a visualization template. Index Terms—Data integration, RDF, attribute inference.

Estimates of crop responses to climate change with quantified ranges of uncertainty

In estimating responses of crops to future climate realisations, it is necessary to understand and differentiate between the sources of uncertainty in climate models and how these lead to errors in estimating the past climate and biases in future projections, and how these affect crop model estimates. This paper investigates the complexities in using climate model projections representing different spatial scales within climate change impacts and adaptation studies. This is illustrated by simulating spring barley with three crop models run using site-specific observed, original (50•50 km) and bias corrected downscaled (site-specific) hindcast (1960-1990) weather data from the HadRM3 Regional Climate Model (RCM). Original and bias corrected downscaled weather data were evaluated against the observed data. The comparisons made between the crop models were in the light of lessons learned from this data evaluation. Though the bias correction downscaling method improved the match between observed and hindcast data, this did not always translate into better matching of crop models estimates. At four sites the original HadRM3 data produced near identical mean simulated yield values as from the observed weather data, despite differences in the weather data, giving a situation of ‘right results for the wrong reasons’. This was likely due to compensating errors in the input weather data and non-linearity in crop models processes, making interpretation of results problematic. Overall, bias correction downscaling improved the quality of simulated outputs. Understanding how biases in climate data manifest themselves in crop models gives greater confidence in the utility of the estimates produced using downscaled future climate projections. The results indicate implications on how future projections of climate change impacts are interpreted. Fundamentally, considerable care is required in determining the impact weather data sources have in climate change impact and adaptation studies, whether from individual models or ensembles

Time Dependent Photon Mapping

The photon map technique for global illumination does not specifically address animated scenes. In particular, prior work has not considered the problem of temporal sampling (motion blur) while using the photon map. In this paper we examine several approaches for simulating motion blur with the photon map. In particular we show that a distribution of photons in time combined with the standard photon map radiance estimate is incorrect, and we introduce a simple generalization that correctly handles photons distributed in both time and space. Our results demonstrate that this time dependent photon map extension allows fast and correct estimates of motion-blurred illumination including motion-blurred caustics. 1

Implications of climate model biases and downscaling on crop model simulated climate change impacts

Implications of climate model biases and downscaling on crop model simulated climate change impacts. ASA/CSSA/SSSA/ESA 2015 Joint Annual Meetin