Discovery of the 1.80 hr Spin Period of the White Dwarf of the Symbiotic System BF Cyg

We report on the discovery of a coherent periodicity in the B light curve of the symbiotic star BF Cyg. The signal was detected in some sections of the light curve of the star recorded in the year 2003 as double hump periodic variations with an amplitude of ~7 mmag. In the year 2004 the signal was also present in only a subsection of the light curve. In that year, the system was about twice as bright and the amplitude of the oscillations was about half of what it was in 2003. In 2004 the cycle structure was of a single hump, the phase of which coincided with the phase of one of the humps in the 2003 cycle. No periodic signal was detected in a third, short series of observations performed in the year 2007, when the star was three magnitudes brighter than in 2003. We interpret the periodicity as the spin period of the white dwarf component of this interacting binary system. We suggest that the signal in 2003 originated in two hot spots on or near the surface of the white dwarf, most likely around the two antipodes of an oblique dipole magnetic field of this star. Magnetic field lines funneled accreted matter from the wind of the cool component to the pole areas, where the falling material created the hot spots. This process is apparently intermittent in its nature. In 2004, the activity near only one pole was enhanced enough to raise the signal above the threshold of our detection ability.Comment: 7 pages, 5 figures, accepted for publication in MNRA

The NASA high temperature superconductivity program

It has been recognized from the onset that high temperature superconductivity held great promise for major advances across a broad range of NASA interests. The current effort is organized around four key areas: communications and data, sensors and cryogenics, propulsion and power, and space materials technology. Recently, laser ablated YBa2Cu3O(7-x) films on LaAIO produced far superior RF characteristics when compared to metallic films on the same substrate. This achievement has enabled a number of unique microwave device applications, such as low insertion loss phase shifters and high Q filters. Melt texturing and melt quenched techniques are being used to produce bulk materials with optimized magnetic properties. These yttrium enriched materials possess enhanced flux pinning characteristics and will lead to prototype cryocooler bearings. Significant progress has also occurred in bolometer and current lead technology. Studies are being conducted to evaluate the effect of high temperature superconducting materials on the performance and life of high power magneto-plasma-dynamic thrusters. Extended studies were also performed to evaluate the benefit of superconducting magnetic energy storage for LEO space station, lunar and Mars mission applications. The project direction and level of effort of the program are also described

Implementing Bayesian Inference with Neural Networks

Embodied agents, be they animals or robots, acquire information about the world through their senses. Embodied agents, however, do not simply lose this information once it passes by, but rather process and store it for future use. The most general theory of how an agent can combine stored knowledge with new observations is Bayesian inference. In this dissertation I present a theory of how embodied agents can learn to implement Bayesian inference with neural networks. By neural network I mean both artificial and biological neural networks, and in my dissertation I address both kinds. On one hand, I develop theory for implementing Bayesian inference in deep generative models, and I show how to train multilayer perceptrons to compute approximate predictions for Bayesian filtering. On the other hand, I show that several models in computational neuroscience are special cases of the general theory that I develop in this dissertation, and I use this theory to model and explain several phenomena in neuroscience. The key contributions of this dissertation can be summarized as follows: - I develop a class of graphical model called nth-order harmoniums. An nth-order harmonium is an n-tuple of random variables, where the conditional distribution of each variable given all the others is always an element of the same exponential family. I show that harmoniums have a recursive structure which allows them to be analyzed at coarser and finer levels of detail. - I define a class of harmoniums called rectified harmoniums, which are constrained to have priors which are conjugate to their posteriors. As a consequence of this, rectified harmoniums afford efficient sampling and learning. - I develop deep harmoniums, which are harmoniums which can be represented by hierarchical, undirected graphs. I develop the theory of rectification for deep harmoniums, and develop a novel algorithm for training deep generative models. - I show how to implement a variety of optimal and near-optimal Bayes filters by combining the solution to Bayes' rule provided by rectified harmoniums, with predictions computed by a recurrent neural network. I then show how to train a neural network to implement Bayesian filtering when the transition and emission distributions are unknown. - I show how some well-established models of neural activity are special cases of the theory I present in this dissertation, and how these models can be generalized with the theory of rectification. - I show how the theory that I present can model several neural phenomena including proprioception and gain-field modulation of tuning curves. - I introduce a library for the programming language Haskell, within which I have implemented all the simulations presented in this dissertation. This library uses concepts from Riemannian geometry to provide a rigorous and efficient environment for implementing complex numerical simulations. I also use the results presented in this dissertation to argue for the fundamental role of neural computation in embodied cognition. I argue, in other words, that before we will be able to build truly intelligent robots, we will need to truly understand biological brains

Co-Teaching: Is the Co-Teaching Model the Most Effective Strategy for Students?

Co-Teaching: Is the co-teaching model the most effective strategy for students? Michelle Sokoloski The purpose of this study is to identify whether co-teaching is the most effective strategy for teaching students. Participants of this study will consist of male and female, 5th grade math students of varying races and socio economic status’ in rural Minnesota. The participants consist of English Language Learners, those that are part of the free and reduced meal program, and other students in 5thgrade. This study will follow approximately 20% of the grade and track their progress over the course of four months using the AIMSWeb Computation test. The study aims to determine whether it is better to have the students in the classroom with another educator or whether is it better for students to be taken out for more individualized instruction with another educator. This study will look at these two intervention strategies more closely

Wave fronts via Fourier series coefficients

Motivated by the product of periodic distributions, we give a new description of the wave front and the Sobolev-type wave front of a distribution $f\in\mathscr{D}'(\mathbb{R}^d)$ in terms of Fourier series coefficients.Comment: 10 page

Prospects and progress of high Tc superconductivity for space applications

Current research in the area of high temperature superconductivity is organized around four key areas: communications and data, sensors and cryogenics, propulsion and power, and space materials technology. Recently, laser ablated YBa2Cu3O(7-x) films on LaAlO3 produced far superior RF characteristics when compared to metallic films on the same substrate. The achievement has enabled a number of unique microwave device applications, such as low insertion loss phase shifters and high-Q filters. Melt texturing and melt-quenched techniques are being used to produce bulk material with optimized magnetic properties. These yttrium-enriched materials possess enhanced flux pinning characteristics and could lead to prototype cryocooler bearings. Significant progress has also occurred in bolometer and current lead technology. Studies were conducted to evaluate the effect of high temperature superconducting materials on the performance and life of high power magnetoplasma-dynamic thrusters. Extended studies were also performed to evaluate the benefit of superconducting magnetic energy storage for LEO space station, lunar, and Mars mission applications

A study of the mass loss rates of symbiotic star systems

The amount of mass loss in symbiotic systems is investigated, specifically mass loss via the formation of jets in R Aquarii (R Aqr). The jets in R Aqr have been observed in the X-ray by Chandra over a four year time period. The jet changes on times scales of a year and new outflows have been observed. Understanding the amount of mass and the frequency of ejection further constrain the ability of the white dwarf in the system to accrete enough mass to become a Type 1a supernova progenitor. The details of multi-wavelength studies, such as speed, density and spatial extent of the jets will be discussed in order to understand the mass balance in the binary system. We examine other symbiotic systems to determine trends in mass loss in this class of objects.Comment: To be published in the proceedings of "The Multicoloured Landscape of Compact Objects and their Explosive Origins

Changes in Health and Physical Fitness Parameters After 6 Months of High-intensity Group Exercise in Firefighters

Cities annually budget thousands of dollars for rehabilitation services due to work-related injuries that affect firefighters. Proper training methods may be used as an effective preventative measure for many of the musculoskeletal injuries sustained as a first responder that are inherent with the profession. The physical demands of firefighting require that the individuals employed in this profession be, at minimum, in good physical condition. The traditionally low fitness levels and poor exercise habits of firefighters may predispose this population to an increased risk of chronic conditions, such as cardiovascular and metabolic disease. Purpose: The purpose of this study was to analyze changes in health and fitness parameters of professional firefighters across North Texas during a 6-month training program. Methods: Twenty-five professional firefighters (23 males and 2 females; age 37.5 ± 10.0 years; height 70.9 ± 2.7 in; weight 96.4 ± 15.7 kg; BMI 29.8 ± 7.6 kg/m2) completed 6 months of high-intensity group training, consisting of 2 training sessions per week. Exercises were chosen to mimic common activities of firefighting. These included: farmer’s walks, squats, pushups, planks, sled pushes, sled drags, rows, tire flips, sledge hammer strikes, and medicine ball slams. These individuals underwent a pre- and post-fitness testing protocol that consisted of body composition, range-of-motion, anaerobic power, muscular endurance, and cardiorespiratory fitness. A repeated-measures MANOVA was used to determine any differences between testing periods. A significance level of 0.05 was used. Results: Comparing post-testing to pre-testing values, improvements in sit-and-reach (31.8 ± 7.5 vs. 25.3 ± 6.5 cm; p \u3c 0.001), mean power (706.9 ± 125.2 vs. 660.2 ± 125.5 W; p \u3c 0.001), fatigue index (54.0 ± 8.0 vs. 60.8 ± 7.4 %; p \u3c 0.001), curl ups (48 ± 26 vs. 21 ± 15; p \u3c 0.001), pushups (35 ± 16 vs. 30 ± 15 p = 0.005), VO2max (38.2 ± 5.8 vs. 35.6 ± 4.9 ml/kg/min; p = 0.005) & energy expenditure (11.0 ± 1.5 vs. 10.1 ± 1.3 METS; p = 0.003) were found following the 6-month training program. No differences in body composition or peak power were observed (p \u3e 0.05). Conclusion: Six months of high-intensity group exercise may improve measures of physical fitness in firefighters