Stereo Camera Calibrations with Optical Flow

Remotely Piloted Aircraft (RPA) are currently unable to refuel mid-air due to the large communication delays between their operators and the aircraft. AAR seeks to address this problem by reducing the communication delay to a fast line-of-sight signal between the tanker and the RPA. Current proposals for AAR utilize stereo cameras to estimate where the receiving aircraft is relative to the tanker, but require accurate calibrations for accurate location estimates of the receiver. This paper improves the accuracy of this calibration by improving three components of it: increasing the quantity of intrinsic calibration data with CNN preprocessing, improving the quality of the intrinsic calibration data through a novel linear regression filter, and reducing the epipolar error of the stereo calibration with optical flow for feature matching and alignment. A combination of all three approaches resulted in significant epipolar error improvements over OpenCV\u27s stereo calibration while also providing significant precision improvements

Analysis of Factors Affecting Farmers’ Willingness to Adopt Switchgrass Production

In the United States, biomass is the largest source of renewable energy accounting for over 3 percent of the energy consumed domestically and is currently the only source for liquid, renewable, transportation fuels. Continued development of biomass as a renewable energy source is being driven in large part by the Energy Independence and Security Act of 2007, which mandates that by 2022 at least 36 billion gallons of fuel ethanol be produced, with at least 16 billion gallons being derived from cellulose, hemi-cellulose, or lignin. However, the market for cellulosic biofuels is still under development. As such, little is known about producer response to feedstock prices paid for dedicated energy crops. While there have been some studies done on factors that determine farmers’ willingness to produce switchgrass, these have been very regional in nature. This study will provide information regarding potential switchgrass adoption by agricultural producers in twelve southeastern states. The objectives of this research are 1) to determine the likelihood of farmers growing switchgrass as a biomass feedstock and the acres they would be willing to devote to switchgrass production and 2) to evaluate some of the factors that are likely to influence these decisions, including the price of switchgrass.Switchgrass, Farmer Adoption, Crop Production/Industries, Research and Development/Tech Change/Emerging Technologies, Resource /Energy Economics and Policy, Q12, Q16,

Complete atrial-specific knockout of sodium-calcium exchange eliminates sinoatrial node pacemaker activity.

The origin of sinoatrial node (SAN) pacemaker activity in the heart is controversial. The leading candidates are diastolic depolarization by "funny" current (If) through HCN4 channels (the "Membrane Clock" hypothesis), depolarization by cardiac Na-Ca exchange (NCX1) in response to intracellular Ca cycling (the "Calcium Clock" hypothesis), and a combination of the two ("Coupled Clock"). To address this controversy, we used Cre/loxP technology to generate atrial-specific NCX1 KO mice. NCX1 protein was undetectable in KO atrial tissue, including the SAN. Surface ECG and intracardiac electrograms showed no atrial depolarization and a slow junctional escape rhythm in KO that responded appropriately to β-adrenergic and muscarinic stimulation. Although KO atria were quiescent they could be stimulated by external pacing suggesting that electrical coupling between cells remained intact. Despite normal electrophysiological properties of If in isolated patch clamped KO SAN cells, pacemaker activity was absent. Recurring Ca sparks were present in all KO SAN cells, suggesting that Ca cycling persists but is uncoupled from the sarcolemma. We conclude that NCX1 is required for normal pacemaker activity in murine SAN

Concert recording 2021-11-15

[Track 1] Growth / Cole Powledge -- [Track 2] Day tripper ; Lady Madonna / Lennon/McCartney ; arranged by Tommy Emmanuel -- [Track 3] Disappear / Joshua Larson -- [Track 4] Sirabhorn / Pat Metheny -- [Track 5] Parisienne walkways / Gary Moore -- [Track 6] Songbird / Kenny G -- [Track 7] Drifting / Cody Lucas -- [Track 8] Slow dancing in a burning room / John Mayer -- [Track 9] Suites of morning

Design, assessment, and in vivo evaluation of a computational model illustrating the role of CAV1 in CD4+ T-lymphocytes

Caveolin-1 (CAV1) is a vital scaffold protein heterogeneously expressed in both healthy and malignant tissue. We focus on the role of CAV1 when overexpressed in T-cell leukemia. Previously, we have shown that CAV1 is involved in cell-to-cell communication, cellular proliferation, and immune synapse formation; however, the molecular mechanisms have not been elucidated. We hypothesize that the role of CAV1 in immune synapse formation contributes to immune regulation during leukemic progression, thereby warranting studies of the role of CAV1 in CD4+ T-cells in relation to antigen-presenting cells. To address this need, we developed a computational model of a CD4+ immune effector T-cell to mimic cellular dynamics and molecular signaling under healthy and immunocompromised conditions (i.e., leukemic conditions). Using the Cell Collective computational modeling software, the CD4+ T-cell model was constructed and simulated under CAV1+/+, CAV1+/−, and CAV1−/− conditions to produce a hypothetical immune response. This model allowed us to predict and examine the heterogeneous effects and mechanisms of CAV1 in silico. Experimental results indicate a signature of molecules involved in cellular proliferation, cell survival, and cytoskeletal rearrangement that were highly affected by CAV1 knock out. With this comprehensive model of a CD4+ T-cell, we then validated in vivo protein expression levels. Based on this study, we modeled a CD4+ T-cell, manipulated gene expression in immunocompromised versus competent settings, validated these manipulations in an in vivo murine model, and corroborated acute T-cell leukemia gene expression profiles in human beings. Moreover, we can model an immunocompetent versus an immunocompromised microenvironment to better understand how signaling is regulated in patients with leukemia

Multi-Element Abundance Measurements from Medium-Resolution Spectra. III. Metallicity Distributions of Milky Way Dwarf Satellite Galaxies

We present metallicity distribution functions (MDFs) for the central regions of eight dwarf satellite galaxies of the Milky Way: Fornax, Leo I and II, Sculptor, Sextans, Draco, Canes Venatici I, and Ursa Minor. We use the published catalog of abundance measurements from the previous paper in this series. The measurements are based on spectral synthesis of iron absorption lines. For each MDF, we determine maximum likelihood fits for Leaky Box, Pre-Enriched, and Extra Gas (wherein the gas supply available for star formation increases before it decreases to zero) analytic models of chemical evolution. Although the models are too simplistic to describe any MDF in detail, a Leaky Box starting from zero metallicity gas fits none of the galaxies except Canes Venatici I well. The MDFs of some galaxies, particularly the more luminous ones, strongly prefer the Extra Gas Model to the other models. Only for Canes Venatici I does the Pre-Enriched Model fit significantly better than the Extra Gas Model. The best-fit effective yields of the less luminous half of our galaxy sample do not exceed 0.02 Z_sun, indicating that gas outflow is important in the chemical evolution of the less luminous galaxies. We surmise that the ratio of the importance of gas infall to gas outflow increases with galaxy luminosity. Strong correlations of average [Fe/H] and metallicity spread with luminosity support this hypothesis.Comment: 17 pages, 5 figures; accepted for publication in ApJ; minor corrections in v3; corrected typographical errors in Tables 1 and 3 in v

The Energy-Water Nexus

Speakers for the 2013 Symposium included Professor Joshua P. Fershee of West Virginia University; Professor Gabriel E. Eckstein of Texas A&M University School of Law; Professor Keith B. Hall, Louisiana State University; Professor Donald T. Hornstein from the University of North Carolina; Professor Shi-Ling Hsu, Florida State University; Professor Rhett Larson, of the University of Oklahoma; Professor Amanda Leiter, American University; Professor Uma Outka, University of Kansas; Professor Justin Pidot, of the University of Denver; Professor Melissa Powers from Lewis & Clark College; Mr. Jefferson D. Reynolds, Virginia Department of Environmental Quality; Dr. Benjamin K. Sovacool & Mr. Alex Gilbert from Vermont Law School; and Ms. Andrea Wortzel, of Troutman Sanders LLP

Omnidirectional Transfer for Quasilinear Lifelong Learning

In biological learning, data are used to improve performance not only on the current task, but also on previously encountered and as yet unencountered tasks. In contrast, classical machine learning starts from a blank slate, or tabula rasa, using data only for the single task at hand. While typical transfer learning algorithms can improve performance on future tasks, their performance on prior tasks degrades upon learning new tasks (called catastrophic forgetting). Many recent approaches for continual or lifelong learning have attempted to maintain performance given new tasks. But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data. We propose omnidirectional transfer learning algorithms, which includes two special cases of interest: decision forests and deep networks. Our key insight is the development of the omni-voter layer, which ensembles representations learned independently on all tasks to jointly decide how to proceed on any given new data point, thereby improving performance on both past and future tasks. Our algorithms demonstrate omnidirectional transfer in a variety of simulated and real data scenarios, including tabular data, image data, spoken data, and adversarial tasks. Moreover, they do so with quasilinear space and time complexity