Photometric and spectroscopic variability of the FUor star V582 Aurigae

We carried out BVRI CCD photometric observations in the field of V582 Aur from 2009 August to 2013 February. We acquired high-, medium-, and low-resolution spectroscopy of V582 Aur during this period. To study the pre-outburst variability of the target and construct its historical light curve, we searched for archival observations in photographic plate collections. Both CCD and photographic observations were analyzed using a sequence of 14 stars in the field of V582 Aur calibrated in BVRI. The pre-outburst photographic observations of V582 Aur show low-amplitude light variations typical of T Tauri stars. Archival photographic observations indicate that the increase in brightness began in late 1984 or early 1985 and the star reached the maximum level of brightness at 1986 January. The spectral type of V582 Aur can be defined as G0I with strong P Cyg profiles of H alpha and Na I D lines, which are typical of FU Orionis objects. Our BVRI photometric observations show large amplitude variations V~2.8 mag. during the 3.5 year period of observations. Most of the time, however, the star remains in a state close to the maximum brightness. The deepest drop in brightness was observed in the spring of 2012, when the brightness of the star fell to a level close to the pre-outburst. The multicolor photometric data show a color reversal during the minimum in brightness, which is typical of UX Ori variables. The corresponding spectral observations show strong variability in the profiles and intensities of the spectral lines (especially H alpha), which indicate significant changes in the accretion rate. On the basis of photometric monitoring performed over the past three years, the spectral properties of the maximal light, and the shape of the long-term light curve, we confirm the affiliation of V582 Aur to the group of FU Orionis objects.Comment: 9 pages, 8 figures, accepted for publication in A&

Capillary Rise in Nanopores: Molecular Dynamics Evidence for the Lucas-Washburn Equation

When a capillary is inserted into a liquid, the liquid will rapidly flow into it. This phenomenon, well studied and understood on the macroscale, is investigated by Molecular Dynamics simulations for coarse-grained models of nanotubes. Both a simple Lennard-Jones fluid and a model for a polymer melt are considered. In both cases after a transient period (of a few nanoseconds) the meniscus rises according to a $\sqrt{\textrm{time}}$-law. For the polymer melt, however, we find that the capillary flow exhibits a slip length $\delta$, comparable in size with the nanotube radius $R$. We show that a consistent description of the imbibition process in nanotubes is only possible upon modification of the Lucas-Washburn law which takes explicitly into account the slip length $\delta$.Comment: 4 pages 4 figure

Advances in surface EMG signal simulation with analytical and numerical descriptions of the volume conductor

Surface electromyographic (EMG) signal modeling is important for signal interpretation, testing of processing algorithms, detection system design, and didactic purposes. Various surface EMG signal models have been proposed in the literature. In this study we focus on 1) the proposal of a method for modeling surface EMG signals by either analytical or numerical descriptions of the volume conductor for space-invariant systems, and 2) the development of advanced models of the volume conductor by numerical approaches, accurately describing not only the volume conductor geometry, as mainly done in the past, but also the conductivity tensor of the muscle tissue. For volume conductors that are space-invariant in the direction of source propagation, the surface potentials generated by any source can be computed by one-dimensional convolutions, once the volume conductor transfer function is derived (analytically or numerically). Conversely, more complex volume conductors require a complete numerical approach. In a numerical approach, the conductivity tensor of the muscle tissue should be matched with the fiber orientation. In some cases (e.g., multi-pinnate muscles) accurate description of the conductivity tensor may be very complex. A method for relating the conductivity tensor of the muscle tissue, to be used in a numerical approach, to the curve describing the muscle fibers is presented and applied to representatively investigate a bi-pinnate muscle with rectilinear and curvilinear fibers. The study thus propose an approach for surface EMG signal simulation in space invariant systems as well as new models of the volume conductor using numerical methods

Pyomo - Optimization Modeling in Python

INFORMS Journal of Computing, November 2012The article of record as published may be located at http://dx.doi.org/10.1287/ijoc.2012.4.brIf a simple, intuitive tool for a task exists, the task is done more often, by more people. This basic principle is as true for gardening and gadgets, as it is for computation in operations research. The book, Pyomo { Optimization Modeling in Python, documents a simple, yet versatile tool for modeling and solving optimization problems

Mapping differential responses to cognitive training using machine learning.

We used two simple unsupervised machine learning techniques to identify differential trajectories of change in children who undergo intensive working memory (WM) training. We used self-organizing maps (SOMs)-a type of simple artificial neural network-to represent multivariate cognitive training data, and then tested whether the way tasks are represented changed as a result of training. The patterns of change we observed in the SOM weight matrices implied that the processes drawn upon to perform WM tasks changed following training. This was then combined with K-means clustering to identify distinct groups of children who respond to the training in different ways. Firstly, the K-means clustering was applied to an independent large sample (N = 616, Mage  = 9.16 years, range = 5.16-17.91 years) to identify subgroups. We then allocated children who had been through cognitive training (N = 179, Mage  = 9.00 years, range = 7.08-11.50 years) to these same four subgroups, both before and after their training. In doing so, we were able to map their improvement trajectories. Scores on a separate measure of fluid intelligence were predictive of a child's improvement trajectory. This paper provides an alternative approach to analysing cognitive training data that go beyond considering changes in individual tasks. This proof-of-principle demonstrates a potentially powerful way of distinguishing task-specific from domain-general changes following training and of establishing different profiles of response to training