327 research outputs found

    Kernelized Back-Projection Networks for Blind Super Resolution

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    Since non-blind Super Resolution (SR) fails to super-resolve Low-Resolution (LR) images degraded by arbitrary degradations, SR with the degradation model is required. However, this paper reveals that non-blind SR that is trained simply with various blur kernels exhibits comparable performance as those with the degradation model for blind SR. This result motivates us to revisit high-performance non-blind SR and extend it to blind SR with blur kernels. This paper proposes two SR networks by integrating kernel estimation and SR branches in an iterative end-to-end manner. In the first model, which is called the Kernel Conditioned Back-Projection Network (KCBPN), the low-dimensional kernel representations are estimated for conditioning the SR branch. In our second model, the Kernelized BackProjection Network (KBPN), a raw kernel is estimated and directly employed for modeling the image degradation. The estimated kernel is employed not only for back-propagating its residual but also for forward-propagating the residual to iterative stages. This forward-propagation encourages these stages to learn a variety of different features in different stages by focusing on pixels with large residuals in each stage. Experimental results validate the effectiveness of our proposed networks for kernel estimation and SR. We will release the code for this work.Comment: The first two authors contributed equally to this wor

    Definitive Identification of the Transition between Small- to Large-Scale Clustering for Lyman Break Galaxies

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    We report angular correlation function (ACF) of Lyman Break Galaxies (LBGs) with unprecedented statistical quality on the basis of 16,920 LBGs at z=4 detected in the 1 deg^2 sky of the Subaru/XMM-Newton Deep Field. The ACF significantly departs from a power law, and shows an excess on small scale. Particularly, the ACF of LBGs with i'<27.5 have a clear break between the small and large-scale regimes at the angular separation of ~7'' whose projected length corresponds to the virial radius of dark halos with a mass of 10^11-12 Mo, indicating multiple LBGs residing in a single dark halo. Both on small (2''<theta<3'') and large (40''<theta<400'') scales, clustering amplitudes monotonically increase with luminosity for the magnitude range of i'=24.5-27.5, and the small-scale clustering shows a stronger luminosity dependence than the large-scale clustering. The small-scale bias reaches b~10-50, and the outskirts of small-scale excess extend to a larger angular separation for brighter LBGs. The ACF and number density of LBGs can be explained by the cold dark matter model.Comment: Accepted for publication in ApJL. 5 pages, 4 figures. The text and Figures 2-4 have been revised. There is no major change which affects to the main discussion shown in the original preprint. This paper with high resolution figures is available at http://www-int.stsci.edu/~ouchi/work/astroph/sxds_z4LBG/ouchi_highres.pdf (PDF

    Wheezing Infant

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    Relationship Between Personality Patterns and Harmfulness : Analysis and Prediction Based on Sentence Embedding

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    This paper hypothesizes that harmful utterances need to be judged in the context of whole sentences, and the authors extract features of harmful expressions using a general-purpose language model. Based on the extracted features, the authors propose a method to predict the presence or absence of harmful categories. In addition, the authors believe that it is possible to analyze users who incite others by combining this method with research on analyzing the personality of the speaker from statements on social networking sites. The results confirmed that the proposed method can judge the possibility of harmful comments with higher accuracy than simple dictionary-based models or models using a distributed representation of words. The relationship between personality patterns and harmful expressions was also confirmed by an analysis based on a harmful judgment model

    gwpcorMapper: an interactive mapping tool for exploring geographically weighted correlation and partial correlation in high-dimensional geospatial datasets

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    Exploratory spatial data analysis (ESDA) plays a key role in research that includes geographic data. In ESDA, analysts often want to be able to visualize observations and local relationships on a map. However, software dedicated to visualizing local spatial relations be-tween multiple variables in high dimensional datasets remains undeveloped. This paper introduces gwpcorMapper, a newly developed software application for mapping geographically weighted correlation and partial correlation in large multivariate datasets. gwpcorMap-per facilitates ESDA by giving researchers the ability to interact with map components that describe local correlative relationships. We built gwpcorMapper using the R Shiny framework. The software inherits its core algorithm from GWpcor, an R library for calculating the geographically weighted correlation and partial correlation statistics. We demonstrate the application of gwpcorMapper by using it to explore census data in order to find meaningful relationships that describe the work-life environment in the 23 special wards of Tokyo, Japan. We show that gwpcorMapper is useful in both variable selection and parameter tuning for geographically weighted statistics. gwpcorMapper highlights that there are strong statistically clear local variations in the relationship between the number of commuters and the total number of hours worked when considering the total population in each district across the 23 special wards of Tokyo. Our application demonstrates that the ESDA process with high-dimensional geospatial data using gwpcorMapper has applications across multiple fields.Comment: 18 pages, 8 figures, 2 table
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