Preliminary evaluation of a thin organic film coating Final report

High temperature and humidity resistance of thin siloxane films on metal substrate

EMPATH: A Neural Network that Categorizes Facial Expressions

There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of "categorical perception." In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, "surprise" expressions lie between "happiness" and "fear" expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks' implementations in the brain

On the physical association of the peculiar emission: Line stars HD 122669 and HD 122691

Spectroscopic and photometric observations indicate a physical association between the peculiar early-type emission-line stars HD 122669 and HD 122691. The latter has undergone a drastic change in the strength of its emission lines during the past twenty years. There is some indication that both stars vary with shorter time scales

Experiment-Based Validation and Uncertainty Quantification of Partitioned Models: Improving Predictive Capability of Multi-Scale Plasticity Models

Partitioned analysis involves coupling of constituent models that resolve their own scales or physics by exchanging inputs and outputs in an iterative manner. Through partitioning, simulations of complex physical systems are becoming evermore present in scientific modeling, making Verification and Validation of partitioned models for the purpose of quantifying the predictive capability of their simulations increasingly important. Parameterization of the constituent models as well as the coupling interface requires a significant amount of information about the system, which is often imprecisely known. Consequently, uncertainties as well as biases in constituent models and their interface lead to concerns about the accumulation and compensation of these uncertainties and errors during the iterative procedures of partitioned analysis. Furthermore, partitioned analysis relies on the availability of reliable constituent models for each component of a system. When a constituent is unavailable, assumptions must be made to represent the coupling relationship, often through uncertain parameters that are then calibrated. This dissertation contributes to the field of computational modeling by presenting novel methods that take advantage of the transparency of partitioned analysis to compare constituent models with separate-effect experiments (measurements contained to the constituent domain) and coupled models with integral-effect experiments (measurements capturing behavior of the full system). The methods developed herein focus on these two types of experiments seeking to maximize the information that can be gained from each, thus progressing our capability to assess and improve the predictive capability of partitioned models through inverse analysis. The importance of this study stems from the need to make coupled models available for widespread use for predicting the behavior of complex systems with confidence to support decision-making in high-risk scenarios. Methods proposed herein address the challenges currently limiting the predictive capability of coupled models through a focused analysis with available experiments. Bias-corrected partitioned analysis takes advantage of separate-effect experiments to reduce parametric uncertainty and quantify systematic bias at the constituent level followed by an integration of bias-correction to the coupling framework, thus ‘correcting’ the constituent model during coupling iterations and preventing the accumulation of errors due to the final predictions. Model bias is the result of assumptions made in the modeling process, often due to lack of understanding of the underlying physics. Such is the case when a constituent model of a system component is entirely unavailable and cannot be developed due to lack of knowledge. However, if this constituent model were to be available and coupled to existing models of the other system components, bias in the coupled system would be reduced. This dissertation proposes a novel statistical inference method for developing empirical constituent models where integral-effect experiments are used to infer relationships missing from system models. Thus, the proposed inverse analysis may be implemented to infer underlying coupled relationships, not only improving the predictive capability of models by producing empirical constituents to allow for coupling, but also advancing our fundamental understanding of dependencies in the coupled system. Throughout this dissertation, the applicability and feasibility of the proposed methods are demonstrated with advanced multi-scale and multi-physics material models simulating complex material behaviors under extreme loading conditions, thus specifically contributing advancements to the material modeling community

An evaluation of oxygen-hydrogen propulsion systems for the Space Station

Conceptual designs for O2/H2 chemical and resistojet propulsion systems for the space station was developed and evaluated. The evolution of propulsion requirements was considered as the space station configuration and its utilization as a space transportation node change over the first decade of operation. The characteristics of candidate O2/H2 auxiliary propulsion systems are determined, and opportunities for integration with the OTV tank farm and the space station life support, power and thermal control subsystems are investigated. OTV tank farm boiloff can provide a major portion of the growth station impulse requirements and CO2 from the life support system can be a significant propellant resource, provided it is not denied by closure of that subsystem. Waste heat from the thermal control system is sufficient for many propellant conditioning requirements. It is concluded that the optimum level of subsystem integration must be based on higher level space station studies

Too Big to Fail in the Local Group

We compare the dynamical masses of dwarf galaxies in the Local Group (LG) to the predicted masses of halos in the ELVIS suite of $\Lambda$CDM simulations, a sample of 48 Galaxy-size hosts, 24 of which are in paired configuration similar to the LG. We enumerate unaccounted-for dense halos ($V_\mathrm{max} \gtrsim 25$ km s$^{-1}$) in these volumes that at some point in their histories were massive enough to have formed stars in the presence of an ionizing background ($V_\mathrm{peak} > 30$ km s$^{-1}$). Within 300 kpc of the Milky Way, the number of unaccounted-for massive halos ranges from 2 - 25 over our full sample. Moreover, this "too big to fail" count grows as we extend our comparison to the outer regions of the Local Group: within 1.2 Mpc of either giant we find that there are 12-40 unaccounted-for massive halos. This count excludes volumes within 300 kpc of both the MW and M31, and thus should be largely unaffected by any baryonically-induced environmental processes. According to abundance matching -- specifically abundance matching that reproduces the Local Group stellar mass function -- all of these missing massive systems should have been quite bright, with $M_\star > 10^6M_\odot$. Finally, we use the predicted density structure of outer LG dark matter halos together with observed dwarf galaxy masses to derive an $M_\star-V_\mathrm{max}$ relation for LG galaxies that are outside the virial regions of either giant. We find that there is no obvious trend in the relation over three orders of magnitude in stellar mass (a "common mass" relation), from $M_\star \sim 10^8 - 10^5 M_\odot$, in drastic conflict with the tight relation expected for halos that are unaffected by reionization. Solutions to the too big to fail problem that rely on ram pressure stripping, tidal effects, or statistical flukes appear less likely in the face of these results.Comment: 16 pages, 14 figures, 2 tables, submitted to MNRA

Running with BICEP2: Implications for Small-Scale Problems in CDM

The BICEP2 results, when interpreted as a gravitational wave signal and combined with other CMB data, suggest a roll-off in power towards small scales in the primordial matter power spectrum. Among the simplest possibilities is a running of the spectral index. Here we show that the preferred level of running alleviates small-scale issues within the $\Lambda$CDM model, more so even than viable WDM models. We use cosmological zoom-in simulations of a Milky Way-size halo along with full-box simulations to compare predictions among four separate cosmologies: a BICEP2-inspired running index model ($\alpha_s$ = -0.024), two fixed-tilt $\Lambda$CDM models motivated by Planck, and a 2.6 keV thermal WDM model. We find that the running BICEP2 model reduces the central densities of large dwarf-size halos ($V_\mathrm{max}$ ~ 30 - 80 km s$^{-1}$) and alleviates the too-big-to-fail problem significantly compared to our adopted Planck and WDM cases. Further, the BICEP2 model suppresses the count of small subhalos by ~50% relative to Planck models, and yields a significantly lower "boost" factor for dark matter annihilation signals. Our findings highlight the need to understand the shape of the primordial power spectrum in order to correctly interpret small-scale data.Comment: 10 pages, 8 figures, 2 tables, published in MNRA

Sterile neutrino dark matter bounds from galaxies of the Local Group

We show that the canonical oscillation-based (non-resonant) production of sterile neutrino dark matter is inconsistent at $>99$% confidence with observations of galaxies in the Local Group. We set lower limits on the non-resonant sterile neutrino mass of $2.5$ keV (equivalent to $0.7$ keV thermal mass) using phase-space densities derived for dwarf satellite galaxies of the Milky Way, as well as limits of $8.8$ keV (equivalent to $1.8$ keV thermal mass) based on subhalo counts of $N$-body simulations of M 31 analogues. Combined with improved upper mass limits derived from significantly deeper X-ray data of M 31 with full consideration for background variations, we show that there remains little room for non-resonant production if sterile neutrinos are to explain $100$% of the dark matter abundance. Resonant and non-oscillation sterile neutrino production remain viable mechanisms for generating sufficient dark matter sterile neutrinos.Comment: 10 pages, 4 figures, 2 tables. Submitted to PR