Preliminary control variates to improve empirical regression methods

International audienceWe design a variance reduction method to reduce the estimation error in regression problems. It is based on an appropriate use of other known regression functions. Theoretical estimates are supporting this improvement and numerical experiments are illustrating the efficiency of the method

On the Sum of Order Statistics and Applications to Wireless Communication Systems Performances

We consider the problem of evaluating the cumulative distribution function (CDF) of the sum of order statistics, which serves to compute outage probability (OP) values at the output of generalized selection combining receivers. Generally, closed-form expressions of the CDF of the sum of order statistics are unavailable for many practical distributions. Moreover, the naive Monte Carlo (MC) method requires a substantial computational effort when the probability of interest is sufficiently small. In the region of small OP values, we propose instead two effective variance reduction techniques that yield a reliable estimate of the CDF with small computing cost. The first estimator, which can be viewed as an importance sampling estimator, has bounded relative error under a certain assumption that is shown to hold for most of the challenging distributions. An improvement of this estimator is then proposed for the Pareto and the Weibull cases. The second is a conditional MC estimator that achieves the bounded relative error property for the Generalized Gamma case and the logarithmic efficiency in the Log-normal case. Finally, the efficiency of these estimators is compared via various numerical experiments

Evaluating droplet distribution of spray-nozzles for dust reduction in livestock buildings using machine vision

Previous studies have demonstrated the negative effects of sub-optimal air quality on profitability, production efficiency, environmental sustainability and animal welfare. Experiments were conducted to assess potential environmental improvement techniques such as installing oil-spraying systems in piggery buildings. The developed spray system worked very well and it was easy to assemble and operate. However, before selecting the most suitable spray heads, their capacity to uniformly distribute the oily mixture and the area covered by the spray heads had to be assessed. Machine vision techniques were used to evaluate the ability of different spray heads to evenly distribute the oil/water mixture. The results indicated that the best coverage was achieved by spray head No.4 and spray head No.1 which covered 79% and 67% of the target area, respectively. Spray distribution uniformity (variance) value was the lowest for spray head No.4 (0.015). Spray head No.3 had the highest variance value (0.064). As the lowest variance means higher uniformity, nozzle No.4 was identified as the most suitable spray head for dust reduction in livestock buildings

State-Level Electricity Generation Efficiency: Do Restructuring and Regulatory Institutions Matter in the US?

This paper examines the impact of deregulation and the political support for it on the electric power industry using a consistent state-level electricity generation dataset for the US contiguous states from 1997-2014. Recent analyses of productivity growth suggests that institutional factors are important and we wish to study the role of deregulation as a statelevel institutional change through two measures: (a) restructuring and (b) the political support for it, measured by the majority political affiliation of public utility commissions. We find evidence of positive impacts of deregulation (both restructuring and the political support for it) on technical efficiency across the models estimated. Our preferred model which allows for the control for deregulation variables on the mean and variance of the inefficiency shows an average technical efficiency of 73.1 percent. The results of the marginal effects reveal that the impact of deregulation including its political support on inefficiency is negative and monotonic, with the potential reduction of 8.4 percent in the mean of technical inefficiency, thereby suggesting a compelling evidence for generation efficiency improvement via deregulation

TEEM: Online Thermal- and Energy-Efficiency Management on CPU-GPU MPSoCs

Heterogeneous Multiprocessor System-on-Chip (MPSoC) are progressively becoming predominant in most modern mobile devices. These devices are required to perform processing of applications within thermal, energy and performance constraints. However, most stock power and thermal management mechanisms either neglect some of these constraints or rely on frequency scaling to achieve energy-efficiency and temperature reduction on the device. Although this inefficient technique can reduce temporal thermal gradient, but at the same time hurts the performance of the executing task. In this paper, we propose a thermal and energy management mechanism which achieves reduction in thermal gradient as well as energy-efficiency through resource mapping and thread-partitioning of applications with online optimization in heterogeneous MPSoCs. The efficacy of the proposed approach is experimentally appraised using different applications from Polybench benchmark suite on Odroid-XU4 developmental platform. Results show 28% performance improvement, 28.32% energy saving and reduced thermal variance of over 76% when compared to the existing approaches. Additionally, the method is able to free more than 90% in memory storage on the MPSoC, which would have been previously utilized to store several task-to-thread mapping configurations

Adaptive Quantum Homodyne Tomography

An adaptive optimization technique to improve precision of quantum homodyne tomography is presented. The method is based on the existence of so-called null functions, which have zero average for arbitrary state of radiation. Addition of null functions to the tomographic kernels does not affect their mean values, but changes statistical errors, which can then be reduced by an optimization method that "adapts" kernels to homodyne data. Applications to tomography of the density matrix and other relevant field-observables are studied in detail.Comment: Latex (RevTex class + psfig), 9 Figs, Submitted to PR