The Terroir of Swiss Cheese: A Temporal and Geomorphological Investigation of the Martian CO2 Sublimation Pits

Observations by NASA Mars Global Surveyor showed evidence of rough topography on the South Pole of Mars. The topography is the result of CO2 sublimation processes that occur through the changing seasons on the red planet. These sublimation areas are known to scientists as Swiss Cheese Features (SCF). SCF are erosional degradation pits that have been studied for over two decades. Studies show that these SCF increase in area over time, but these values are collected by hand on a per feature basis. Models for the pit evolution have also played a part in understanding these SCF. This work is time-intensive and can only produce results from relatively small selections of data. Current research lacks the ability to complete image pit object-based and multi-image measurements as well as simultaneously estimate areal statistics. This research investigated the use of object-based image analysis (OBIA) techniques on sublimation pits at high spatial resolution over large temporal domains. This was done using images acquired through the High-Resolution Imager and Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter (MRO). The approach was tested on selected pits and compared to previous work for stand-alone growth rates. The results of this investigation increase both our capacity to effectively study the CO2 pits as well as our knowledge of pit evolution as a factor of time with implications to understand the CO2 cycle on the Martian South Pole

The Terroir of Swiss Cheese: A Temporal and Geomorphological Investigation of the Martian CO2 Sublimation Pits

Observations by NASA Mars Global Surveyor showed evidence of rough topography on the South Pole of Mars. The topography is the result of CO2 sublimation processes that occur through the changing seasons on the red planet. These sublimation areas are known to scientists as Swiss Cheese Features (SCF). SCF are erosional degradation pits that have been studied for over two decades. Studies show that these SCF increase in area over time, but these values are collected by hand on a per feature basis. Models for the pit evolution have also played a part in understanding these SCF. This work is time-intensive and can only produce results from relatively small selections of data. Current research lacks the ability to complete image pit object-based and multi-image measurements as well as simultaneously estimate areal statistics. This research investigated the use of object-based image analysis (OBIA) techniques on sublimation pits at high spatial resolution over large temporal domains. This was done using images acquired through the High-Resolution Imager and Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter (MRO). The approach was tested on selected pits and compared to previous work for stand-alone growth rates. The results of this investigation increase both our capacity to effectively study the CO2 pits as well as our knowledge of pit evolution as a factor of time with implications to understand the CO2 cycle on the Martian South Pole

Local Linear GMM Estimation of Functional Coefficient IV Models with Application to the Estimation of Rate of Return to Schooling

We consider the local linear GMM estimation of functional coefficient models with a mix of discrete and continuous data and in the presence of endogenous regressors. We establish the asymptotic normality of the estimator and derive the optimal instrumental variable that minimizes the asymptotic variance-covariance matrix among the class of all local linear GMM estimators. Data-dependent bandwidth sequences are also allowed for. We propose a nonparametric test for the constancy of the functional coefficients, study its asymptotic properties under the null hypothesis as well as a sequence of local alternatives and global alternatives, and propose a bootstrap version for it. Simulations are conducted to evaluate both the estimator and test. Applications to the 1985 Australian Longitudina

Reproducible Research in Computational Economics: Guidelines, Integrated Approaches and Open Source Software

Traditionally, computer and software applications have been used by economists to off-load otherwise complex or tedious tasks onto technology, freeing up time and intellect to address other, intellectually more rewarding, aspects of research. On the negative side, this increasing dependence on computers has resulted in research that has become increasingly difficult to replicate. In this paper, we propose some basic standards to improve the production and reporting of computational results in economics for the purpose of accuracy and reproducibility. In particular, we make recommendations on four aspects of the process: computational practice, published reporting, supporting documentation, and visualization. Also, we reflect on current developments in the practice of computing and visualization, such as integrated dynamic electronic documents, distributed computing systems, open source software, and their potential usefulness in making computational and empirical research in economics more easily reproducible

Locally weighted learning

This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control