A general treatment of snow microstructure exemplified by an improved relation for thermal conductivity

Finding relevant microstructural parameters beyond density is a longstanding problem which hinders the formulation of accurate parameterizations of physical properties of snow. Towards a remedy, we address the effective thermal conductivity tensor of snow via anisotropic, second-order bounds. The bound provides an explicit expression for the thermal conductivity and predicts the relevance of a microstructural anisotropy parameter <i>Q</i>, which is given by an integral over the two-point correlation function and unambiguously defined for arbitrary snow structures. For validation we compiled a comprehensive data set of 167 snow samples. The set comprises individual samples of various snow types and entire time series of metamorphism experiments under isothermal and temperature gradient conditions. All samples were digitally reconstructed by micro-computed tomography to perform microstructure-based simulations of heat transport. The incorporation of anisotropy via <i>Q</i> considerably reduces the root mean square error over the usual density-based parameterization. The systematic quantification of anisotropy via the two-point correlation function suggests a generalizable route to incorporate microstructure into snowpack models. We indicate the inter-relation of the conductivity to other properties and outline a potential impact of <i>Q</i> on dielectric constant, permeability and adsorption rate of diffusing species in the pore space

Impacts of G x E x M on Nitrogen Use Efficiency in Wheat and Future Prospects

Globally it has been estimated that only one third of applied N is recovered in the harvested component of grain crops (Raun and Johnson 1999). This represents an incredible waste of resource and the overuse has detrimental environmental and economic consequences. There is substantial variation in nutrient use efficiency (NUE) from region to region, between crops and in different cropping systems. As a consequence, both local and crop specific solutions will be required for NUE improvement at local as well as at national and international levels. Strategies to improve NUE will involve improvements to germplasm and optimized agronomy adapted to climate and location. Essential to effective solutions will be an understanding of genetics (G), environment (E) and management (M) and their interactions (G x E x M). To implement appropriate solutions will require agronomic management, attention to environmental factors and improved varieties, optimized for current and future climate scenarios. As NUE is a complex trait with many contributing processes, identifying the correct trait for improvement is not trivial. Key processes include nitrogen capture (uptake efficiency), utilization efficiency (closely related to yield), partitioning (harvest index: biochemical and organ-specific) and trade-offs between yield and quality aspects (grain nitrogen content), as well as interactions with capture and utilization of other nutrients. A long-term experiment, the Broadbalk experiment at Rothamsted, highlights many factors influencing yield and nitrogen utilization in wheat over the last 175 years, particularly management and yearly variation. A more recent series of trials conducted over the past 16 years has focused on separating the key physiological sub-traits of NUE, highlighting both genetic and seasonal variation. This perspective describes these two contrasting studies which indicate G x E x M interactions involved in nitrogen utilization and summarizes prospects for the future including the utilization of high throughput phenotyping technology

Persuading consumers to reduce their consumption of electricity in the home

Previous work has identified that providing real time feedback or interventions to consumers can persuade consumers to change behaviour and reduce domestic electricity consumption. However, little work has investigated what exactly those feedback mechanisms should be. Most past work is based on an in-home display unit, possibly complemented by lower tariffs and delayed use of non-essential home appliances such as washing machines. In this paper we focus on four methods for real time feedback on domestic energy use, developed to gauge the impact on energy consumption in homes. Their feasibility had been tested using an experimental setup of 24 households collecting minute-by-minute electricity consumption data readings over a period of 18 months. Initial results are mixed, and point to the difficulties of sustaining a reduction in energy consumption, i.e. persuading consumers to change their behaviour. Some of the methods we used exploit small group social dynamics whereby people want to conform to social norms within groups they identify with. It may be that a variety of feedback mechanisms and interventions are needed in order to sustain user interest

Explanatory debugging: Supporting end-user debugging of machine-learned programs

Many machine-learning algorithms learn rules of behavior from individual end users, such as task-oriented desktop organizers and handwriting recognizers. These rules form a “program” that tells the computer what to do when future inputs arrive. Little research has explored how an end user can debug these programs when they make mistakes. We present our progress toward enabling end users to debug these learned programs via a Natural Programming methodology. We began with a formative study exploring how users reason about and correct a text-classification program. From the results, we derived and prototyped a concept based on “explanatory debugging”, then empirically evaluated it. Our results contribute methods for exposing a learned program's logic to end users and for eliciting user corrections to improve the program's predictions

Simulation of the CTF drive beam line and comparison with the experiment

The tracking of particles in accelerating structures is presented for cases where the effects of the wake-fields are high. This is particularly the case when the structures are used with high current and relatively low energy as in the drive beam of the Compact Linear Collider Test Facility (CTF 2) with its 3 GHz accelerator and its 30 GHz decelerator. High initial energy spread and transverse wake-fields may impair the beam stability and generate particle loss. The CTF modelling is made with the code PARMELA for the 3 GHz part of the beam line, which includes 3 GHz accelerating sections and a magnetic bunch compressor. For the part containing the 30 GHz power-extracting structures, simulations are done with WAKE, a new algorithm dealing with the effects of the wake-field modes 0 and 1, as well as of the group velocity. Beam transmission through the overall beam line is studied, and results are compared with measurements made on the CTF beam

Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics

Dozens of new models on fixation prediction are published every year and compared on open benchmarks such as MIT300 and LSUN. However, progress in the field can be difficult to judge because models are compared using a variety of inconsistent metrics. Here we show that no single saliency map can perform well under all metrics. Instead, we propose a principled approach to solve the benchmarking problem by separating the notions of saliency models, maps and metrics. Inspired by Bayesian decision theory, we define a saliency model to be a probabilistic model of fixation density prediction and a saliency map to be a metric-specific prediction derived from the model density which maximizes the expected performance on that metric given the model density. We derive these optimal saliency maps for the most commonly used saliency metrics (AUC, sAUC, NSS, CC, SIM, KL-Div) and show that they can be computed analytically or approximated with high precision. We show that this leads to consistent rankings in all metrics and avoids the penalties of using one saliency map for all metrics. Our method allows researchers to have their model compete on many different metrics with state-of-the-art in those metrics: "good" models will perform well in all metrics.Comment: published at ECCV 201

Genetic diversity in nitrogen fertilizer responses and N gas emission in modern wheat

Crops assimilate nitrogen (N) as ammonium via the glutamine synthetase/glutamate synthase (GS/GOGAT) pathway which is of central importance for N uptake and potentially represents a bottle neck for N fertilizer-use efficiency. The aim of this study was to assess whether genetic diversity for N-assimilation capacity exists in wheat and could be exploited for breeding. Wheat plants rapidly, within 6h, responded to N application with an increase in GS activity. This was not accompanied by an increase in GS gene transcript abundance and a comparison of GS1 and GS2 protein models revealed a high degree of sequence conservation. N responsiveness amongst ten wheat varieties was assessed by measuring GS enzyme activity, leaf tissue ammonium, and by a leaf-disc assay as a proxy for apoplastic ammonia. Based on these data, a high-GS group showing an overall positive response to N could be distinguished from an inefficient, low-GS group. Subsequent gas emission measurements confirmed plant ammonia emission in response to N application and also revealed emission of N2O when N was provided as nitrate, which is in agreement with our current understanding that N2O is a by-product of nitrate reduction. Taken together, the data suggest that there is scope for improving N assimilation capacity in wheat and that further investigations into the regulation and role of GS-GOGAT in NH3 emission is justified. Likewise, emission of the climate gas N2O needs to be reduced, and future research should focus on assessing the nitrate reductase pathway in wheat and explore fertilizer management options