Time dependence in perpendicular media with a soft underlayer

In this paper we describe measurements of magnetic viscosity or time dependence in magnetic thin films suitable for use as perpendicular recording media. Generally, such effects cannot be measured using conventional magnetometry techniques due to the presence of a thin (0.1 mum) soft underlayer (SUL) in the media necessary to focus the head field. To achieve our results we have developed an ultrastable MOKE magnetometer, the construction of which is described. This has enabled us to measure nominally identical films with and without the presence of the SUL. We find that the presence of the SUL narrows the energy barrier distribution in the perpendicular film increasing the nucleation field (H-n), reducing the coercivity (H-c) and results in an increase in the squareness of the loop. This in turn results in an increase in the magnitude of the viscosity in the region of the H-c but that the range of fields over which the viscosity occurs is reduced

Determination of activation volumes of reversal in perpendicular media

We discuss a method for the determination of activation volumes of reversal in perpendicular media. This method does not require correction for the self-demagnetizing field normally associated with these media. This is achieved by performing time dependence measurements at a constant level of magnetization. From the difference in time taken for the magnetization to decay to a fixed value at two fields-separated by a small increment DeltaH, the activation volume can be determined. We report data for both CoCrPt alloy films and a multilayer film, typical of those materials under consideration for use as perpendicular media. We find activation volumes that are consistent with the hysteresis curves of the materials. The activation volume scales qualitatively with the exchange coupling. The alloy films have significantly lower activation volumes, implying that they would be capable of supporting a higher data density

Functional renormalization group study of an eight-band model for the iron arsenides

We investigate the superconducting pairing instabilities of eight-band models for the iron arsenides. Using a functional renormalization group treatment, we determine how the critical energy scale for superconductivity depends on the electronic band structure. Most importantly, if we vary the parameters from values corresponding to LaFeAsO to SmFeAsO, the pairing scale is strongly enhanced, in accordance with the experimental observation. We analyze the reasons for this trend and compare the results of the eight-band approach to those found using five-band models.Comment: 11 pages, 10 figure

Bubble popper: considering body contact in games

Exertion games, digital games that involve physical effort, are becoming more popular. Although some of these games support social experiences, they rarely consider or support body contact. We believe overlooking body contact as part of social play experiences limits opportunities to design engaging exertion games. To explore this opportunity, we present Bubble Popper, an exertion game that considers and facilitates body contact. Bubble Popper, which uses very simple technology, also demonstrates that considering and facilitating body contact can be achieved without the need to sense body contact. Through reflecting on our design and analyzing observations of play we are able to articulate what impact physical space layout in relation to digital game elements, and physical disparity between input and digital display can have on body contact. Our results aid game designers in creating engaging exertion game experiences by guiding them when considering body contact, ultimately helping players benefiting from more engaging exertion games

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

On landmark selection and sampling in high-dimensional data analysis

In recent years, the spectral analysis of appropriately defined kernel matrices has emerged as a principled way to extract the low-dimensional structure often prevalent in high-dimensional data. Here we provide an introduction to spectral methods for linear and nonlinear dimension reduction, emphasizing ways to overcome the computational limitations currently faced by practitioners with massive datasets. In particular, a data subsampling or landmark selection process is often employed to construct a kernel based on partial information, followed by an approximate spectral analysis termed the Nystrom extension. We provide a quantitative framework to analyse this procedure, and use it to demonstrate algorithmic performance bounds on a range of practical approaches designed to optimize the landmark selection process. We compare the practical implications of these bounds by way of real-world examples drawn from the field of computer vision, whereby low-dimensional manifold structure is shown to emerge from high-dimensional video data streams.Comment: 18 pages, 6 figures, submitted for publicatio

A Land-Use and Water-Quality History of White Rock Lake Reservoir, Dallas, Texas, Based on Paleolimnological Analyses

White Rock Lake reservoir in Dallas, Texas contains a 150-cm sediment record of silty clay that documents land-use changes since its construction in 1912. Pollen analysis corroborates historical evidence that between 1912 and 1950 the watershed was primarily agricultural. Land disturbance by plowing coupled with strong and variable spring precipitation caused large amounts of sediment to enter the lake during this period. Diatoms were not preserved at this time probably because of low productivity compared to diatom dissolution by warm, alkaline water prior to burial in the sediments. After 1956, the watershed became progressively urbanized. Erosion decreased, land stabilized, and pollen of riparian trees increased as the lake water became somewhat less turbid. By 1986 the sediment record indicates that diatom productivity had increased beyond rates of diatom destruction. Neither increased nutrients nor reduced pesticides can account for increased diatom productivity, but grain size studies imply that before 1986 diatoms were light limited by high levels of turbidity. This study documents how reservoirs may relate to land-use practices and how watershed management could extend reservoir life and improve water quality

Conserving slow-growing, long-lived tree species: Input from the demography of a rare understory conifer, Taxus floridana

Although land preservation and promotion of successful regeneration are important conservation actions, their ability to increase population growth rates of slow-growing, long-lived trees is limited. We investigated the demography of Taxus floridana Nutt., a rare understory conifer, in three populations in different ravine forests spanning its entire geographic range along the Apalachicola River Bluffs in northern Florida (U.S.A.). We examined spatial and temporal patterns in demographic parameters and projected population growth rates by using four years of data on the recruitment and survival of seedlings and established stems, and on diameter growth from cross-sections of dead stems. All populations experienced a roughly 10-fold increase in seedling recruitment in 1996 compared with other years. The fates of seedlings and stems between 8 and 16 mm differed among populations. The fates of stems in two other size classes (the 2- to 4-mm class and the 4- to 8-mm class) differed among both populations and years. Individual stems in all populations exhibited similarly slow growth rates. Stochastic matrix models projected declines in all populations. Stochastic matrix analysis revealed the high elasticity of a measure of stochastic population growth rate to perturbations in the stasis of large reproductive stems for all populations. Additional analyses also indicated that occasional episodes of high recruitment do not greatly affect population growth rates. Conservation efforts directed at long-lived, slow-growing rare plants like Taxus floridana should both protect established reproductive individuals and further enhance survival of individuals in other life-history stages, such as juveniles, that often do not appear to contribute greatly to population growth rates

Active Sampling-based Binary Verification of Dynamical Systems

Nonlinear, adaptive, or otherwise complex control techniques are increasingly relied upon to ensure the safety of systems operating in uncertain environments. However, the nonlinearity of the resulting closed-loop system complicates verification that the system does in fact satisfy those requirements at all possible operating conditions. While analytical proof-based techniques and finite abstractions can be used to provably verify the closed-loop system's response at different operating conditions, they often produce conservative approximations due to restrictive assumptions and are difficult to construct in many applications. In contrast, popular statistical verification techniques relax the restrictions and instead rely upon simulations to construct statistical or probabilistic guarantees. This work presents a data-driven statistical verification procedure that instead constructs statistical learning models from simulated training data to separate the set of possible perturbations into "safe" and "unsafe" subsets. Binary evaluations of closed-loop system requirement satisfaction at various realizations of the uncertainties are obtained through temporal logic robustness metrics, which are then used to construct predictive models of requirement satisfaction over the full set of possible uncertainties. As the accuracy of these predictive statistical models is inherently coupled to the quality of the training data, an active learning algorithm selects additional sample points in order to maximize the expected change in the data-driven model and thus, indirectly, minimize the prediction error. Various case studies demonstrate the closed-loop verification procedure and highlight improvements in prediction error over both existing analytical and statistical verification techniques.Comment: 23 page