University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Theory and applications of guided-mode resonance filters

Abstract: The guided-mode resonance properties of planar dielectric waveguide gratings are presented and explained. It is shown that these structures function as filters that produce complete exchange of energy between forward- and backward-propagating diffracted waves with smooth line shapes and arbitrarily narrow filter linewidths. Simple expressions based on rigorous coupled-wave theory and on classical slab waveguide theory give a clear view and quantification of the inherent TE/TM polarization separation and the free spectral ranges of the filters. Furthermore, the resonance regimes, defining the parametric regions of the guided-mode resonances, can be directly visualized. It is shown that the linewidths of the resonances can be controlled by the grating modulation amplitude and by the degree of mode confinement (refractive-index difference at the boundaries). Examples presented of potential uses for these elements include a narrow-line polarized laser, a tunable polarized laser, a photorefractive tunable filter, and an electro-optic switch. The guided-mode resonance filter represents a basic new optical element with significant potential for practical applications.

Reinforcement learning and adaptive dynamic programming for feedback control

Abstract: Living organisms learn by acting on their environment, observing the resulting reward stimulus, and adjusting their actions accordingly to improve the reward. This action-based or reinforcement learning can capture notions of optimal behavior occurring in natural systems. We describe mathematical formulations for reinforcement learning and a practical implementation method known as adaptive dynamic programming. These give us insight into the design of controllers for man-made engineered systems that both learn and exhibit optimal behavior.

Transposable elements and the evolution of regulatory networks

Abstract: The control and coordination of eukaryotic gene expression rely on transcriptional and post-transcriptional regulatory networks. Although progress has been made in mapping the components and deciphering the function of these networks, the mechanisms by which such intricate circuits originate and evolve remain poorly understood. Here I revisit and expand earlier models and propose that genomic repeats, and in particular transposable elements, have been a rich source of material for the assembly and tinkering of eukaryotic gene regulatory systems.

Repeated Measures Correlation

Abstract: Repeated measures correlation (rmcorr) is a statistical technique for determining the common within-individual association for paired measures assessed on two or more occasions for multiple individuals. Simple regression/correlation is often applied to non-independent observations or aggregated data; this may produce biased, specious results due to violation of independence and/or differing patterns between-participants versus within-participants. Unlike simple regression/correlation, rmcorr does not violate the assumption of independence of observations. Also, rmcorr tends to have much greater statistical power because neither averaging nor aggregation is necessary for an intra-individual research question. Rmcorr estimates the common regression slope, the association shared among individuals. To make rmcorr accessible, we provide background information for its assumptions and equations, visualization, power, and tradeoffs with rmcorr compared to multilevel modeling. We introduce the R package (rmcorr) and demonstrate its use for inferential statistics and visualization with two example datasets. The examples are used to illustrate research questions at different levels of analysis, intra-individual, and inter-individual. Rmcorr is well-suited for research questions regarding the common linear association in paired repeated measures data. All results are fully reproducible.