The calculation of efficient high precision orbits by optimum matching of the formulation and numerical integrator

The development of improved computer algorithms is considered for calculating earth satellite orbital trajectories by optimum selection of the analytical method that minimizes the number of perturbative acceleration computations for a given accuracy. A variation of parameter algorithm considering the equation of motion for a satellite proved superior for the geosynchronous orbit

On the use of machine learning algorithms in the measurement of stellar magnetic fields

Regression methods based in Machine Learning Algorithms (MLA) have become an important tool for data analysis in many different disciplines. In this work, we use MLA in an astrophysical context; our goal is to measure the mean longitudinal magnetic field in stars (H_ eff) from polarized spectra of high resolution, through the inversion of the so-called multi-line profiles. Using synthetic data, we tested the performance of our technique considering different noise levels: In an ideal scenario of noise-free multi-line profiles, the inversion results are excellent; however, the accuracy of the inversions diminish considerably when noise is taken into account. In consequence, we propose a data pre-process in order to reduce the noise impact, which consists in a denoising profile process combined with an iterative inversion methodology. Applying this data pre-process, we have found a considerable improvement of the inversions results, allowing to estimate the errors associated to the measurements of stellar magnetic fields at different noise levels. We have successfully applied our data analysis technique to two different stars, attaining by first time the measurement of H_eff from multi-line profiles beyond the condition of line autosimilarity assumed by other techniques.Comment: Accepted for publication in A&

Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

Neural networks are among the most accurate supervised learning methods in use today, but their opacity makes them difficult to trust in critical applications, especially when conditions in training differ from those in test. Recent work on explanations for black-box models has produced tools (e.g. LIME) to show the implicit rules behind predictions, which can help us identify when models are right for the wrong reasons. However, these methods do not scale to explaining entire datasets and cannot correct the problems they reveal. We introduce a method for efficiently explaining and regularizing differentiable models by examining and selectively penalizing their input gradients, which provide a normal to the decision boundary. We apply these penalties both based on expert annotation and in an unsupervised fashion that encourages diverse models with qualitatively different decision boundaries for the same classification problem. On multiple datasets, we show our approach generates faithful explanations and models that generalize much better when conditions differ between training and test

Mathematical theory of the Goddard trajectory determination system

Basic mathematical formulations depict coordinate and time systems, perturbation models, orbital estimation techniques, observation models, and numerical integration methods

TRPA1 CHANNELS IN COCHLEAR SUPPORTING CELLS REGULATE HEARING SENSITIVITY AFTER NOISE EXPOSURE

TRPA1 channels are sensors for noxious stimuli in a subset of nociceptive neurons. TRPA1 channels are also expressed in cells of the mammalian inner ear, but their function in this tissue remains unknown given that Trpa1–/– mice exhibit normal hearing, balance and sensory mechanotransduction. Here we show that non-sensory (supporting) cells of the hearing organ in the cochlea detect tissue damage via the activation of TRPA1 channels and subsequently modulate cochlear amplification through active cellshape changes. We found that cochlear supporting cells of wild type but not Trpa1–/– mice generate inward currents and robust long-lasting Ca2+ responses after stimulation with TRPA1 agonists. These Ca2+ responses often propagated between different types of supporting cells and were accompanied by prominent tissue displacements. The most prominent shape changes were observed in pillar cells which here we show possess Ca2+-dependent contractile machinery. Increased oxidative stress following acoustic overstimulation leads to the generation of lipid peroxidation byproducts such as 4-hydroxynonenal (4-HNE) that could directly activate TRPA1. Therefore, we exposed mice to mild noise and found a longer-lasting inhibition of cochlear amplification in wild type than in Trpa1–/– mice. Our results suggest that TRPA1-dependent changes in pillar cell shape can alter the tissue geometry and affect cochlear amplification. We believe this novel mechanism of cochlear regulation may protect or fine-tune the organ of Corti after noise exposure or other cochlear injuries

Flight Mechanics/Estimation Theory Symposium

The conference emphasizes orbital position estimation and trajectory calculation methods that consider flight mechanical parameters

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step through the process behind their predictions in little time. Specifically, we train deep time-series models so their class-probability predictions have high accuracy while being closely modeled by decision trees with few nodes. Using intuitive toy examples as well as medical tasks for treating sepsis and HIV, we demonstrate that this new tree regularization yields models that are easier for humans to simulate than simpler L1 or L2 penalties without sacrificing predictive power.Comment: To appear in AAAI 2018. Contains 9-page main paper and appendix with supplementary materia