A Practitioner Assists In High School Career Days

During each school year, a number of staff members from the college of Veterinary Medicine at Iowa State University are asked to participate in High School Career Days. The staff members are assisted by junior and senior veterinary students. On some occasions the local veterinarians have also assisted in answering some of the questions

Adaptive cancelation of self-generated sensory signals in a whisking robot

Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme

Principal Component Analysis with Noisy and/or Missing Data

We present a method for performing Principal Component Analysis (PCA) on noisy datasets with missing values. Estimates of the measurement error are used to weight the input data such that compared to classic PCA, the resulting eigenvectors are more sensitive to the true underlying signal variations rather than being pulled by heteroskedastic measurement noise. Missing data is simply the limiting case of weight=0. The underlying algorithm is a noise weighted Expectation Maximization (EM) PCA, which has additional benefits of implementation speed and flexibility for smoothing eigenvectors to reduce the noise contribution. We present applications of this method on simulated data and QSO spectra from the Sloan Digital Sky Survey.Comment: Accepted for publication in PASP; v2 with minor updates, mostly to bibliograph

The Superluminal Character of the Compact Steep Spectrum Quasar 3C 216

We report the results of fourth epoch VLBI observations at 4990.99 MHz, with a resolution of ~1 mas, of the compact steep-spectrum quasar 3C216. Superluminal motion in this object is confirmed. Although a constant superluminal expansion at v_(app) = 3.9c ± 0.6 is not ruled out, our four epoch data are suggestive of component deceleration. In this paper we discuss the possibility of deceleration taking into account the compact steep spectrum nature of this quasar. We conclude that (a) compact steep spectrum sources may show the same beaming and orientation phenomena as extended sources and (b) the compact steep spectrum nature of the source could offer an explanation for the possible deceleration

Extracting quantum dynamics from genetic learning algorithms through principal control analysis

Genetic learning algorithms are widely used to control ultrafast optical pulse shapes for photo-induced quantum control of atoms and molecules. An unresolved issue is how to use the solutions found by these algorithms to learn about the system's quantum dynamics. We propose a simple method based on covariance analysis of the control space, which can reveal the degrees of freedom in the effective control Hamiltonian. We have applied this technique to stimulated Raman scattering in liquid methanol. A simple model of two-mode stimulated Raman scattering is consistent with the results.Comment: 4 pages, 5 figures. Presented at coherent control Ringberg conference 200