Inspiring the Wonderment: Emotional Intelligence in Higher Education

The purpose of this research was to shed insight on the degree to which instructor Emotional Intelligence (EI) may moderate the student/teacher relationship. Interviews were conducted to gather qualitative data on the experience of several students at a private university in the Midwest. The findings suggest that there appears to be a positive relationship between instructor EI and a positive academic experience by the student. Further research on this topic may indicate that institutions may also benefit from incorporating the tracking and evaluating of EI in their faculty body to enhance academic success student

Quantum measurement and the first law of thermodynamics: the energy cost of measurement is the work value of the acquired information

The energy cost of measurement is an interesting fundamental question, and may have profound implications for quantum technologies. In the context of Maxwell's demon, it is often stated that measurement has no minimum energy cost, while information has a work value, even though these statements can appear contradictory. However, as we elucidate, these statements do no refer to the cost paid by the measuring device. Here we show that it is only when a measuring device has access to a zero temperature reservoir - that is, never - that the measurement requires no energy. All real measuring devices pay the cost that a heat engine pays to obtain the work value of the information they acquire.Comment: 4 pages, revtex4-1. v2: added a referenc

Classification of Material Mixtures in Volume Data for Visualization and Modeling

Material classification is a key stop in creating computer graphics models and images from volume data, We present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with Magnetic Resonance Imaging (NMI) or Computed Tomography (CT). The algorithm assumes that voxels can contain more than one material, e.g. both muscle and fat; we wish to compute the relative proportion of each material in the voxels. Other classification methods have utilized Gaussian probability density functions to model the distribution of values within a dataset. These Gaussian basis functions work well for voxels with unmixed materials, but do not work well where the materials are mixed together. We extend this approach by deriving non-Gaussian "mixture" basis functions. We treat a voxel as a volume, not as a single point. We use the distribution of values within each voxel-sized volume to identify materials within the voxel using a probabilistic approach. The technique reduces the classification artifacts that occur along boundaries between materials. The technique is useful for making higher quality geometric models and renderings from volume data, and has the potential to make more accurate volume measurements. It also classifies noisy, low-resolution data well

Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms

The authors present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with magnetic resonance imaging (MRI) or computed tomography (CT). Because the authors allow for mixtures of materials and treat voxels as regions, their technique reduces errors that other classification techniques can create along boundaries between materials and is particularly useful for creating accurate geometric models and renderings from volume data. It also has the potential to make volume measurements more accurately and classifies noisy, low-resolution data well. There are two unusual aspects to the authors' approach. First, they assume that, due to partial-volume effects, or blurring, voxels can contain more than one material, e.g., both muscle and fat; the authors compute the relative proportion of each material in the voxels. Second, they incorporate information from neighboring voxels into the classification process by reconstructing a continuous function, ρ(x), from the samples and then looking at the distribution of values that ρ(x) takes on within the region of a voxel. This distribution of values is represented by a histogram taken over the region of the voxel; the mixture of materials that those values measure is identified within the voxel using a probabilistic Bayesian approach that matches the histogram by finding the mixture of materials within each voxel most likely to have created the histogram. The size of regions that the authors classify is chosen to match the sparing of the samples because the spacing is intrinsically related to the minimum feature size that the reconstructed continuous function can represent