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ABSTRACT Detailed spectropolarimetric studies may hold the key to probing the explosion mechanisms and the progenitor scenarios of Type Ia supernovae (SNe Ia). We present multi-epoch spectropolarimetry and imaging polarimetry of SN 2019ein, an SN Ia showing high expansion velocities at early phases. The spectropolarimetry sequence spans from ∼−11 to +10 d relative to peak brightness in the B band. We find that the level of the continuum polarization of SN 2019ein, after subtracting estimated interstellar polarization, is in the range 0.0–0.3 per cent, typical for SNe Ia. The polarization position angle remains roughly constant before and after the SN light-curve peak, implying that the inner regions share the same axisymmetry as the outer layers. We observe high polarization (∼1 per cent) across both the Si ii λ6355 and Ca ii near-infrared triplet features. These two lines also display complex polarization modulations. The spectropolarimetric properties of SN 2019ein rule out a significant departure from spherical symmetry of the ejecta for up to a month after the explosion. These observations disfavour merger-induced and double-detonation models for SN 2019ein. The imaging polarimetry shows weak evidence for a modest increase in polarization after ∼20 d since the B-band maximum. If this rise is real and is observed in other SNe Ia at similar phases, we may have seen, for the first time, an aspherical interior similar to what has been previously observed for SNe IIP. Future polarization observations of SNe Ia extending to post-peak epochs will help to examine the inner structure of the explosion.
Vehicle trajectories contain rich information on microscopic phenomena such as car following and lane changing. Despite many efforts to retrieve reliable trajectories from video images, previous approaches do not give high enough quality of trajectories that can be used in microscopic analysis. We introduce a new vehicle tracking approach based on a model-based 3-D vehicle detection and description algorithm. The proposed algorithm uses a probabilistic line feature grouping method to detect vehicles with little computation. A dynamic programming algorithm is proposed for fast reasoning. We present the system implementation and the vehicle detection and tracking results.
Abstract ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.
Objective: Delayed onset posttraumatic stress disorder (PTSD) refers to PTSD that develops at least 6 months after the traumatic event. This study aimed to index the features of patients who develop delayed-onset PTSD. Method: This study investigated delayed onset PTSD by prospectively assessing 103 motor vehicle accident survivors within 1 month of the motor vehicle accident for acute stress disorder, and subsequently assessing them for PTSD 6 months post-accident, and 2 years post-accident. Patients were initially assessed for symptoms of traumatic stress, anxiety, depression, and resting heart rate. Results: Five patients displayed PTSD 2 years post-trauma without meeting PTSD criteria 6 months posttrauma. Delayed onset cases were characterized by elevated psychopathology scores and resting heart rate levels within the initial month and elevated psychopathology 6 months posttrauma. Conclusions: These findings suggest that cases of delayed onset PTSD suffer subsyndromal levels of posttraumatic stress prior to the diagnosis of PTSD. These findings challenge the notion of PTSD developing after a period without symptoms.
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTKinetics of the Oxidation of Carbon Monoxide and the Decomposition of Carbon Dioxide in a Radiofrequency Electric Discharge. I. Experimental ResultsLloyd C. Brown and Alexis T. BellCite this: Ind. Eng. Chem. Fundamen. 1974, 13, 3, 203–210Publication Date (Print):August 1, 1974Publication History Published online1 May 2002Published inissue 1 August 1974https://pubs.acs.org/doi/10.1021/i160051a008https://doi.org/10.1021/i160051a008research-articleACS PublicationsRequest reuse permissionsArticle Views217Altmetric-Citations38LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access options Get e-Alerts
Abstract ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.
In this paper we extend the "shape, illumination and reflectance from shading" (SIRFS) model [3, 4], which recovers intrinsic scene properties from a single image. Though SIRFS performs well on images of segmented objects, it performs poorly on images of natural scenes, which contain occlusion and spatially-varying illumination. We therefore present Scene-SIRFS, a generalization of SIRFS in which we have a mixture of shapes and a mixture of illuminations, and those mixture components are embedded in a "soft" segmentation of the input image. We additionally use the noisy depth maps provided by RGB-D sensors (in this case, the Kinect) to improve shape estimation. Our model takes as input a single RGB-D image and produces as output an improved depth map, a set of surface normals, a reflectance image, a shading image, and a spatially varying model of illumination. The output of our model can be used for graphics applications, or for any application involving RGB-D images.