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&

The underpotential deposition that should not be : Cu(1x1) on Au(111)

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Reinforcement learning with limited reinforcement: Using Bayes risk for active learning in POMDPs

Acting in domains where an agent must plan several steps ahead to achieve a goal can be a challenging task, especially if the agentʼs sensors provide only noisy or partial information. In this setting, Partially Observable Markov Decision Processes (POMDPs) provide a planning framework that optimally trades between actions that contribute to the agentʼs knowledge and actions that increase the agentʼs immediate reward. However, the task of specifying the POMDPʼs parameters is often onerous. In particular, setting the immediate rewards to achieve a desired balance between information-gathering and acting is often not intuitive. In this work, we propose an approximation based on minimizing the immediate Bayes risk for choosing actions when transition, observation, and reward models are uncertain. The Bayes-risk criterion avoids the computational intractability of solving a POMDP with a multi-dimensional continuous state space; we show it performs well in a variety of problems. We use policy queries—in which we ask an expert for the correct action—to infer the consequences of a potential pitfall without experiencing its effects. More important for human–robot interaction settings, policy queries allow the agent to learn the reward model without the reward values ever being specified

Broadband Microwave Filters Based on Open Split Ring Resonators (OSRRs) and Open Complementary Split Ring Resonators (OCSRRs): Improved Models and Design Optimization

The paper is focused on the design of broadband bandpass filters at microwave frequencies. The proposed filters are based on a combination of open split ring resonators (OSRRs) and open complementary split ring resonators (OCSRRs) loaded in a host transmission line. Since these resonators (OSRRs and OCSRRs) are electrically small, the resulting filters are compact. As compared to previous papers by the authors on this topic, the main aim and originality of the present paper is to demonstrate that by including a new series inductance in the circuit model of the OCSRR, it is possible to improve the predictions of these filter models and better fit the measured filter responses. Moreover, the parameter extraction method of the new circuit model and an automated filter design technique is introduced and demonstrated. The paper is complemented with the design and comparison of several prototypes

A Bayesian nonparametric approach to modeling battery health

The batteries of many consumer products are both a substantial portion of the product's cost and commonly a first point of failure. Accurately predicting remaining battery life can lower costs by reducing unnecessary battery replacements. Unfortunately, battery dynamics are extremely complex, and we often lack the domain knowledge required to construct a model by hand. In this work, we take a data-driven approach and aim to learn a model of battery time-to-death from training data. Using a Dirichlet process prior over mixture weights, we learn an infinite mixture model for battery health. The Bayesian aspect of our model helps to avoid over-fitting while the nonparametric nature of the model allows the data to control the size of the model, preventing under-fitting. We demonstrate our model's effectiveness by making time-to-death predictions using real data from nickel-metal hydride battery packs.United States. Army Research Office (Nostra Project STTR W911NF-08-C-0066)iRobo

Wealthy and healthy in the South Pacific

Objectives: The main aim of this paper is to analyse the relationship between socio-economic status and health status at the household level in Fiji, a developing country in the South Pacific, based on original household survey data compiled by the authors. Method: We exploit the geographic conditions of Viti Levu, the relatively small main island of Fiji, to isolate the effects of household wealth on health. For households on this island physical distance is not a significant impediment for access to health care and other publicly-provided services. We use a constructed index of household wealth in place of the more commonly used income measure of socio-economic status. To control for reverse causality and other possible sources of endogeneity we use an Instrumental Variable strategy in the regression analysis. Findings: We find that a household’s socio-economic status, as measured by a constructed wealth index, has a substantial impact on the household’s health status. We estimate that if a household's wealth increased from the minimum to the maximum level, this would decrease its probability of being afflicted by an incapacitating illness by almost 50 per cent. Conclusions: Health outcomes from existing health services can therefore be improved by raising the economic well-being of poor households. Conversely, the provision of additional health services alone may not necessarily improve health outcomes for the poorest

Super-Nernstian Shifts of Interfacial Proton-Coupled Electron Transfers : Origin and Effect of Noncovalent Interactions

The support of the University of Aberdeen is gratefully acknowledged. C.W. acknowledges a summer studentship from the Carnegie Trust for the Universities of Scotland. E.P.M.L. acknowledges SeCYT (Universidad Nacional de Cordoba), ́ CONICET- PIP 11220110100992, Program BID (PICT 2012-2324), and PME 2006-01581 for financial support.Peer reviewedPostprin