Imaging X-ray spectrometer

An X-ray spectrometer for providing imaging and energy resolution of an X-ray source is described. This spectrometer is comprised of a thick silicon wafer having an embedded matrix or grid of aluminum completely through the wafer fabricated, for example, by thermal migration. The aluminum matrix defines the walls of a rectangular array of silicon X-ray detector cells or pixels. A thermally diffused aluminum electrode is also formed centrally through each of the silicon cells with biasing means being connected to the aluminum cell walls and causes lateral charge carrier depletion between the cell walls so that incident X-ray energy causes a photoelectric reaction within the silicon producing collectible charge carriers in the form of electrons which are collected and used for imaging

Update of High Resolution (e,e'K^+) Hypernuclear Spectroscopy at Jefferson Lab's Hall A

Updated results of the experiment E94-107 hypernuclear spectroscopy in Hall A of the Thomas Jefferson National Accelerator Facility (Jefferson Lab), are presented. The experiment provides high resolution spectra of excitation energy for 12B_\Lambda, 16N_\Lambda, and 9Li_\Lambda hypernuclei obtained by electroproduction of strangeness. A new theoretical calculation for 12B_\Lambda, final results for 16N_\Lambda, and discussion of the preliminary results of 9Li_\Lambda are reported.Comment: 8 pages, 5 figures, submitted to the proceedings of Hyp-X Conferenc

The Outstanding Decisions of the United States Supreme Court in 1954

We perform a kinematic and morphological analysis of 44 star-forming galaxies at z ̃ 2 in the COSMOS legacy field using near-infrared spectroscopy from Keck/MOSFIRE and F160W imaging from CANDELS/3D-HST as part of the ZFIRE survey. Our sample consists of cluster and field galaxies from 2.0 < z < 2.5 with K-band multi-object slit spectroscopic measurements of their Hα emission lines. Hα rotational velocities and gas velocity dispersions are measured using the Heidelberg Emission Line Algorithm (HELA), which compares directly to simulated 3D data cubes. Using a suite of simulated emission lines, we determine that HELA reliably recovers input S 0.5 and angular momentum at small offsets, but V 2.2/σ g values are offset and highly scattered. We examine the role of regular and irregular morphology in the stellar mass kinematic scaling relations, deriving the kinematic measurement S 0.5, and finding {log}({S}0.5)=(0.38+/- 0.07){log}(M/{M}☉ -10)+(2.04+/- 0.03) with no significant offset between morphological populations and similar levels of scatter (̃0.16 dex). Additionally, we identify a correlation between M ⋆ and V 2.2/σ g for the total sample, showing an increasing level of rotation dominance with increasing M ⋆, and a high level of scatter for both regular and irregular galaxies. We estimate the specific angular momenta (j disk) of these galaxies and find a slope of 0.36 ± 0.12, shallower than predicted without mass-dependent disk growth, but this result is possibly due to measurement uncertainty at M ⋆ < 9.5 However, through a Kolmogorov-Smirnov test we find irregular galaxies to have marginally higher j disk values than regular galaxies, and high scatter at low masses in both populations

AQuaMaM: An Autoregressive, Quaternion Manifold Model for Rapidly Estimating Complex SO(3) Distributions

Accurately modeling complex, multimodal distributions is necessary for optimal decision-making, but doing so for rotations in three-dimensions, i.e., the SO(3) group, is challenging due to the curvature of the rotation manifold. The recently described implicit-PDF (IPDF) is a simple, elegant, and effective approach for learning arbitrary distributions on SO(3) up to a given precision. However, inference with IPDF requires $N$ forward passes through the network's final multilayer perceptron (where $N$ places an upper bound on the likelihood that can be calculated by the model), which is prohibitively slow for those without the computational resources necessary to parallelize the queries. In this paper, I introduce AQuaMaM, a neural network capable of both learning complex distributions on the rotation manifold and calculating exact likelihoods for query rotations in a single forward pass. Specifically, AQuaMaM autoregressively models the projected components of unit quaternions as mixtures of uniform distributions that partition their geometrically-restricted domain of values. When trained on an "infinite" toy dataset with ambiguous viewpoints, AQuaMaM rapidly converges to a sampling distribution closely matching the true data distribution. In contrast, the sampling distribution for IPDF dramatically diverges from the true data distribution, despite IPDF approaching its theoretical minimum evaluation loss during training. When trained on a constructed dataset of 500,000 renders of a die in different rotations, AQuaMaM reaches a test log-likelihood 14% higher than IPDF. Further, compared to IPDF, AQuaMaM uses 24% fewer parameters, has a prediction throughput 52$\times$ faster on a single GPU, and converges in a similar amount of time during training

Paved2Paradise: Cost-Effective and Scalable LiDAR Simulation by Factoring the Real World

To achieve strong real world performance, neural networks must be trained on large, diverse datasets; however, obtaining and annotating such datasets is costly and time-consuming, particularly for 3D point clouds. In this paper, we describe Paved2Paradise, a simple, cost-effective approach for generating fully labeled, diverse, and realistic lidar datasets from scratch, all while requiring minimal human annotation. Our key insight is that, by deliberately collecting separate "background" and "object" datasets (i.e., "factoring the real world"), we can intelligently combine them to produce a combinatorially large and diverse training set. The Paved2Paradise pipeline thus consists of four steps: (1) collecting copious background data, (2) recording individuals from the desired object class(es) performing different behaviors in an isolated environment (like a parking lot), (3) bootstrapping labels for the object dataset, and (4) generating samples by placing objects at arbitrary locations in backgrounds. To demonstrate the utility of Paved2Paradise, we generated synthetic datasets for two tasks: (1) human detection in orchards (a task for which no public data exists) and (2) pedestrian detection in urban environments. Qualitatively, we find that a model trained exclusively on Paved2Paradise synthetic data is highly effective at detecting humans in orchards, including when individuals are heavily occluded by tree branches. Quantitatively, a model trained on Paved2Paradise data that sources backgrounds from KITTI performs comparably to a model trained on the actual dataset. These results suggest the Paved2Paradise synthetic data pipeline can help accelerate point cloud model development in sectors where acquiring lidar datasets has previously been cost-prohibitive.Comment: Accepted to the Synthetic Data for Computer Vision workshop at CVPR 202

The Endogenous Th17 Response in NO<inf>2</inf>-Promoted Allergic Airway Disease Is Dispensable for Airway Hyperresponsiveness and Distinct from Th17 Adoptive Transfer

Severe, glucocorticoid-resistant asthma comprises 5-7% of patients with asthma. IL-17 is a biomarker of severe asthma, and the adoptive transfer of Th17 cells in mice is sufficient to induce glucocorticoid-resistant allergic airway disease. Nitrogen dioxide (NO2) is an environmental toxin that correlates with asthma severity, exacerbation, and risk of adverse outcomes. Mice that are allergically sensitized to the antigen ovalbumin by exposure to NO2 exhibit a mixed Th2/Th17 adaptive immune response and eosinophil and neutrophil recruitment to the airway following antigen challenge, a phenotype reminiscent of severe clinical asthma. Because IL-1 receptor (IL-1R) signaling is critical in the generation of the Th17 response in vivo, we hypothesized that the IL-1R/Th17 axis contributes to pulmonary inflammation and airway hyperresponsiveness (AHR) in NO2-promoted allergic airway disease and manifests in glucocorticoid-resistant cytokine production. IL-17A neutralization at the time of antigen challenge or genetic deficiency in IL-1R resulted in decreased neutrophil recruitment to the airway following antigen challenge but did not protect against the development of AHR. Instead, IL-1R-/- mice developed exacerbated AHR compared to WT mice. Lung cells from NO2-allergically inflamed mice that were treated in vitro with dexamethasone (Dex) during antigen restimulation exhibited reduced Th17 cytokine production, whereas Th17 cytokine production by lung cells from recipient mice of in vitro Th17-polarized OTII T-cells was resistant to Dex. These results demonstrate that the IL-1R/Th17 axis does not contribute to AHR development in NO2-promoted allergic airway disease, that Th17 adoptive transfer does not necessarily reflect an endogenously-generated Th17 response, and that functions of Th17 responses are contingent on the experimental conditions in which they are generated. © 2013 Martin et al

Radiographers supporting radiologists in the interpretation of screening mammography: a viable strategy to meet the shortage in the number of radiologists.

BackgroundAn alternative approach to the traditional model of radiologists interpreting screening mammography is necessary due to the shortage of radiologists to interpret screening mammograms in many countries.MethodsWe evaluated the performance of 15 Mexican radiographers, also known as radiologic technologists, in the interpretation of screening mammography after a 6 months training period in a screening setting. Fifteen radiographers received 6 months standardized training with radiologists in the interpretation of screening mammography using the Breast Imaging Reporting and Data System (BI-RADS) system. A challenging test set of 110 cases developed by the Breast Cancer Surveillance Consortium was used to evaluate their performance. We estimated sensitivity, specificity, false positive rates, likelihood ratio of a positive test (LR+) and the area under the subject-specific Receiver Operating Characteristic (ROC) curve (AUC) for diagnostic accuracy. A mathematical model simulating the consequences in costs and performance of two hypothetical scenarios compared to the status quo in which a radiologist reads all screening mammograms was also performed.ResultsRadiographer's sensitivity was comparable to the sensitivity scores achieved by U.S. radiologists who took the test but their false-positive rate was higher. Median sensitivity was 73.3 % (Interquartile range, IQR: 46.7-86.7 %) and the median false positive rate was 49.5 % (IQR: 34.7-57.9 %). The median LR+ was 1.4 (IQR: 1.3-1.7 %) and the median AUC was 0.6 (IQR: 0.6-0.7). A scenario in which a radiographer reads all mammograms first, and a radiologist reads only those that were difficult for the radiographer, was more cost-effective than a scenario in which either the radiographer or radiologist reads all mammograms.ConclusionsGiven the comparable sensitivity achieved by Mexican radiographers and U.S. radiologists on a test set, screening mammography interpretation by radiographers appears to be a possible adjunct to radiologists in countries with shortages of radiologists. Further studies are required to assess the effectiveness of different training programs in order to obtain acceptable screening accuracy, as well as the best approaches for the use of non-physician readers to interpret screening mammography