Layered classification techniques for remote sensing applications

The layered classifier method is outlined and several applications to pattern classification for which the approach is suited are discussed

ECHO user's guide

There are no author-identified significant results in this report

Final Report: National Science Foundation Remote Sensing Workshop. Held at Purdue University February 28, 29, March 1, 1984

It was the purpose of this workshop to examine the potential for further advancement of the science of remote sensing, and to define directions which the Engineering aspects of remote sensing research could not take in order to maximize the scientific and technological return

The Cerebellum: A Neural System for the Study of Reinforcement Learning

In its strictest application, the term “reinforcement learning” refers to a computational approach to learning in which an agent (often a machine) interacts with a mutable environment to maximize reward through trial and error. The approach borrows essentials from several fields, most notably Computer Science, Behavioral Neuroscience, and Psychology. At the most basic level, a neural system capable of mediating reinforcement learning must be able to acquire sensory information about the external environment and internal milieu (either directly or through connectivities with other brain regions), must be able to select a behavior to be executed, and must be capable of providing evaluative feedback about the success of that behavior. Given that Psychology informs us that reinforcers, both positive and negative, are stimuli or consequences that increase the probability that the immediately antecedent behavior will be repeated and that reinforcer strength or viability is modulated by the organism's past experience with the reinforcer, its affect, and even the state of its muscles (e.g., eyes open or closed); it is the case that any neural system that supports reinforcement learning must also be sensitive to these same considerations. Once learning is established, such a neural system must finally be able to maintain continued response expression and prevent response drift. In this report, we examine both historical and recent evidence that the cerebellum satisfies all of these requirements. While we report evidence from a variety of learning paradigms, the majority of our discussion will focus on classical conditioning of the rabbit eye blink response as an ideal model system for the study of reinforcement and reinforcement learning

A Ground-Based Albedo Upper Limit for HD 189733b from Polarimetry

We present 50 nights of polarimetric observations of HD 189733 in $B$ band using the POLISH2 aperture-integrated polarimeter at the Lick Observatory Shane 3-m telescope. This instrument, commissioned in 2011, is designed to search for Rayleigh scattering from short-period exoplanets due to the polarized nature of scattered light. Since these planets are spatially unresolvable from their host stars, the relative contribution of the planet-to-total system polarization is expected to vary with an amplitude of order 10 parts per million (ppm) over the course of the orbit. Non-zero and also variable at the 10 ppm level, the inherent polarization of the Lick 3-m telescope limits the accuracy of our measurements and currently inhibits conclusive detection of scattered light from this exoplanet. However, the amplitude of observed variability conservatively sets a $3 \sigma$ upper limit to the planet-induced polarization of the system of 58 ppm in $B$ band, which is consistent with a previous upper limit from the POLISH instrument at the Palomar Observatory 5-m telescope (Wiktorowicz 2009). A physically-motivated Rayleigh scattering model, which includes the depolarizing effects of multiple scattering, is used to conservatively set a $3 \sigma$ upper limit to the geometric albedo of HD 189733b of $A_g < 0.37$. This value is consistent with the value $A_g = 0.226 \pm 0.091$ derived from occultation observations with HST STIS (Evans et al. 2013), but it is inconsistent with the large $A_g = 0.61 \pm 0.12$ albedo reported by (Berdyugina et al. 2011).Comment: 10 pages, 9 figures, submitted to Ap

SPIFI: a Direct-Detection Imaging Spectrometer for Submillimeter Wavelengths

The South Pole Imaging Fabry-Perot Interferometer (SPIFI) is the first instrument of its kind -a direct-detection imaging spectrometer for astronomy in the submillimeter band. SPIFI ’s focal plane is a square array of 25 silicon bolometers cooled to 60 mK; the spectrometer consists of two cryogenic scanning Fabry-Perot interferometers in series with a 60-mK bandpass filter. The instrument operates in the short submillimeter windows (350 and 450 μm) available from the ground, with spectral resolving power selectable between 500 and 10,000. At present, SPIFI’s sensitivity is within a factor of 1.5-3 of the photon background limit, comparable with the best heterodyne spectrometers. The instrument ’s large bandwidth and mapping capability provide substantial advantages for specific astrophysical projects, including deep extragalactic observations. We present the motivation for and design of SPIFI and its operational characteristics on the telescope

Swain Committee Report

Letter addressed to the Secretary of the Navy, the Honorable Josephus H. Daniels, from the Committee appointed by the President of the Society for the Promotion of Engineering Education, to visit the U.S. Naval Academy in Annapolis, MD. The purpose was to evaluate the work of the Post Graduate School. The committee recommended enlarging the enrollment of the Post Graduate School and providing for appropriate funding for buildings, equipment and curricula

Mobile Augmented Reality and Language-Related Episodes

Applications of locative media (e.g., place‐based mobile augmented reality [AR]) are used in various educational content areas and have been shown to provide learners with valuable opportunities for investigation‐based learning, location‐situated social and collaborative interaction, and embodied experience of place (Squire, 2009; Thorne & Hellermann, 2017; Zheng et al., 2018). Mobile locative media applications’ value for language learning, however, remains underinvestigated. To address this lacuna, this study employed the widely used construct of language‐related episodes (LREs; Swain & Lapkin, 1998) as a unit of analysis to investigate language learning through participation in a mobile AR game. Analysis of videorecorded interactions of four mixed‐proficiency groups of game players (two English language learners [ELLs] and one expert speaker of English [ESE] per group) indicates that LREs in this environment were focused on lexical items relevant to the AR tasks and physical locations. Informed by sociocultural theory and conversation analysis, the microgenesis of learners’ understanding and subsequent use of certain lexical items are indicated in the findings. This understanding of new lexical items was frequently facilitated by ESEs’ assistance and the surrounding physical environment. A strong goal orientation by both ESEs and ELLs was visible, providing implications for task‐based language teaching approaches

Asymptotic Infrared Fractal Structure of the Propagator for a Charged Fermion

It is well known that the long-range nature of the Coulomb interaction makes the definition of asymptotic ``in'' and ``out'' states of charged particles problematic in quantum field theory. In particular, the notion of a simple particle pole in the vacuum charged particle propagator is untenable and should be replaced by a more complicated branch cut structure describing an electron interacting with a possibly infinite number of soft photons. Previous work suggests a Dirac propagator raised to a fractional power dependent upon the fine structure constant, however the exponent has not been calculated in a unique gauge invariant manner. It has even been suggested that the fractal ``anomalous dimension'' can be removed by a gauge transformation. Here, a gauge invariant non-perturbative calculation will be discussed yielding an unambiguous fractional exponent. The closely analogous case of soft graviton exponents is also briefly explored.Comment: Updated with a corrected sign error, longer discussion of fractal dimension, and more reference