Portfolio Choice and Health Status

This paper analyzes the role that health status plays in household portfolio decisions using data from the Health and Retirement Study. The results indicate that health is a significant predictor of both the probability of owning different types of financial assets and the share of financial wealth held in each asset category. Households in poor health are less likely to hold risky financial assets, other things (including the level of total wealth) being the same. Poor health is associated with a smaller share of financial wealth held in risky assets and a larger share in safe assets. We find no evidence that the relationship between health status and portfolio allocation is driven by third variables' that simultaneously affect health and financial decisions. Further, the relationship between health status and portfolio choice does not appear to operate through the effect of poor health on individuals' attitudes toward risk, their planning horizons, or their health insurance status.

Investment outlook in the American petroleum industry

Thesis (M.B.A)--Boston Universit

Looking into DNA breathing dynamics via quantum physics

We study generic aspects of bubble dynamics in DNA under time dependent perturbations, for example temperature change, by mapping the associated Fokker-Planck equation to a quantum time-dependent Schroedinger equation with imaginary time. In the static case we show that the eigenequation is exactly the same as that of the $\beta$-deformed nuclear liquid drop model, without the issue of non-integer angular momentum. A universal breathing dynamics is demonstrated by using an approximate method in quantum mechanics. The calculated bubble autocorrelation function qualitatively agrees with experimental data. Under time dependent modulations, utilizing the adiabatic approximation, bubble properties reveal memory effects.Comment: 5 pages, 1 figur

QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments

Over the past decade, machine learning techniques have revolutionized how research is done, from designing new materials and predicting their properties to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor QDs are a candidate system for building quantum computers. The present-day tuning techniques for bringing the QD devices into a desirable configuration suitable for quantum computing that rely on heuristics do not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance. To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. We show that the learner's accuracy in recognizing the state of a device is ~96.5 % in both current- and charge-sensor-based training. We also introduce a tool that enables other researchers to use this approach for further research: QFlow lite - a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts.Comment: 18 pages, 6 figures, 3 table

Health Status and Portfolio Choice

This paper analyzes the role that health status plays in household portfolio decisions using data from the first wave of the Health and Retirement Study. The results indicate that health is a significant predictor of both the probability of owning different types of financial assets and the share of financial wealth held in each asset category. Households in poor health are less likely to hold both safe and risky financial assets, other things (including the level of total wealth) being the same. Poor health is associated with a smaller share of financial wealth held in risky assets and a larger share in safe assets. We find no evidence that the cross sectional relationship between health status and portfolio allocation is driven by “third variables” that simultaneously affect health and financial decisions. Further, the relationship between health status and portfolio choice does not appear to operate through the effect of poor health on individuals’ attitudes toward risk or their planning horizons.

Benthic microbial communities of coastal terrestrial and ice shelf Antarctic meltwater ponds.

The numerous perennial meltwater ponds distributed throughout Antarctica represent diverse and productive ecosystems central to the ecological functioning of the surrounding ultra oligotrophic environment. The dominant taxa in the pond benthic communities have been well described however, little is known regarding their regional dispersal and local drivers to community structure. The benthic microbial communities of 12 meltwater ponds in the McMurdo Sound of Antarctica were investigated to examine variation between pond microbial communities and their biogeography. Geochemically comparable but geomorphologically distinct ponds were selected from Bratina Island (ice shelf) and Miers Valley (terrestrial) (<40 km between study sites), and community structure within ponds was compared using DNA fingerprinting and pyrosequencing of 16S rRNA gene amplicons. More than 85% of total sequence reads were shared between pooled benthic communities at different locations (OTU0.05), which in combination with favorable prevailing winds suggests aeolian regional distribution. Consistent with previous findings Proteobacteria and Bacteroidetes were the dominant phyla representing over 50% of total sequences; however, a large number of other phyla (21) were also detected in this ecosystem. Although dominant Bacteria were ubiquitous between ponds, site and local selection resulted in heterogeneous community structures and with more than 45% of diversity being pond specific. Potassium was identified as the most significant contributing factor to the cosmopolitan community structure and aluminum to the location unique community based on a BEST analysis (Spearman's correlation coefficient of 0.632 and 0.806, respectively). These results indicate that the microbial communities in meltwater ponds are easily dispersed regionally and that the local geochemical environment drives the ponds community structure