1,405 research outputs found

    Automated detection of block falls in the north polar region of Mars

    Full text link
    We developed a change detection method for the identification of ice block falls using NASA's HiRISE images of the north polar scarps on Mars. Our method is based on a Support Vector Machine (SVM), trained using Histograms of Oriented Gradients (HOG), and on blob detection. The SVM detects potential new blocks between a set of images; the blob detection, then, confirms the identification of a block inside the area indicated by the SVM and derives the shape of the block. The results from the automatic analysis were compared with block statistics from visual inspection. We tested our method in 6 areas consisting of 1000x1000 pixels, where several hundreds of blocks were identified. The results for the given test areas produced a true positive rate of ~75% for blocks with sizes larger than 0.7 m (i.e., approx. 3 times the available ground pixel size) and a false discovery rate of ~8.5%. Using blob detection we also recover the size of each block within 3 pixels of their actual size

    Is megestrol acetate safe and effective for malnourished nursing home residents?

    Get PDF
    Q: Is megestrol acetate safe and effective for malnourished nursing home residents? A: no. Megestrol acetate (MA) is neither safe nor effective for stimulating appetite in malnourished nursing home residents. It increases the risk of deep vein thrombosis (DVT) (strength of recommendation [SOR]: C, 2 retrospective chart reviews), but isn't associated with other new or worsening events or disorders (SOR: B, single randomized controlled trial [RCT]). Over a 25-week period, MA wasn't associated with increased mortality (SOR: B, single RCT). After 44 months, however, MA-treated patients showed decreased median survival (SOR: B, single case- control study). Consistent, meaningful weight gain was not observed with MA treatment (SOR: B, single case-control study, single RCT, 2 retrospective chart reviews, single prospective case-series).Authors: Frances K. Wen, PhD; James Millar, MD University of Oklahoma School of Community Medicine, Tulsa; Linda Oberst-Walsh, MD University of Colorado School of Medicine, Denver; Joan Nashelsky, MLS Family Physicians Inquiries Network, Iowa City

    Ubiquitination and proteasomal degradation of ATG12 regulates its proapoptotic activity

    Get PDF
    During macroautophagy, conjugation of ATG12 to ATG5 is essential for LC3 lipidation and autophagosome formation. Additionally, ATG12 has ATG5-independent functions in diverse processes including mitochondrial fusion and mitochondrial-dependent apoptosis. In this study, we investigated the regulation of free ATG12. In stark contrast to the stable ATG12–ATG5 conjugate, we find that free ATG12 is highly unstable and rapidly degraded in a proteasome-dependent manner. Surprisingly, ATG12, itself a ubiquitin-like protein, is directly ubiquitinated and this promotes its proteasomal degradation. As a functional consequence of its turnover, accumulation of free ATG12 contributes to proteasome inhibitor-mediated apoptosis, a finding that may be clinically important given the use of proteasome inhibitors as anticancer agents. Collectively, our results reveal a novel interconnection between autophagy, proteasome activity, and cell death mediated by the ubiquitin-like properties of ATG12

    End-user feature labeling: a locally-weighted regression approach

    Get PDF
    When intelligent interfaces, such as intelligent desktop assistants, email classifiers, and recommender systems, customize themselves to a particular end user, such customizations can decrease productivity and increase frustration due to inaccurate predictions - especially in early stages, when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amount of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose a new learning algorithm based on locally weighted regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. In our user study, the first allowing ordinary end users to freely choose features to label directly from text documents, our algorithm was both more effective than others at leveraging end users' feature labels to improve the learning algorithm, and more robust to real users' noisy feature labels. These results strongly suggest that allowing users to freely choose features to label is a promising method for allowing end users to improve learning algorithms effectively

    Phobos DTM and Coordinate Refinement for Phobos-Grunt Mission Support.

    Get PDF
    Images obtained by the High Resolution Stereo Camera (HRSC) during recent Phobos flybys were used to study the proposed new landing site area of the Russian Phobos-Grunt mission, scheduled for launch in 2011 [1]. From the stereo images (resolution of up to 4.4 m/pixel), a digital terrain model (DTM) with a lateral resolution of 100 m per pixel and a relative point accuracy of ±15 m, was determined. Images and DTM were registered to the established Phobos control point network [7]. A map of the landing site area was produced enabling mission planers and scientists to extract accurate body-fixed coordinates of features in the Phobos Grunt landing site area

    End-User Feature Labeling via Locally Weighted Logistic Regression

    Get PDF
    Applications that adapt to a particular end user often make inaccurate predictions during the early stages when training data is limited. Although an end user can improve the learning algorithm by labeling more training data, this process is time consuming and too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose a new learning algorithm based on Locally Weighted Logistic Regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. In our user study, the first allowing ordinary end users to freely choose features to label directly from text documents, our algorithm was more effective than others at leveraging end users’ feature labels to improve the learning algorithm. Our results strongly suggest that allowing users to freely choose features to label is a promising method for allowing end users to improve learning algorithms effectively
    • …
    corecore