The CTA Sensitivity to Lorentz-Violating Effects on the Gamma-Ray Horizon

The arrival of TeV-energy photons from distant galaxies is expected to be affected by their QED interaction with intergalactic radiation fields through electron-positron pair production. In theories where high-energy photons violate Lorentz symmetry, the kinematics of the process $\gamma + \gamma\rightarrow e^+ + e^-$ is altered and the cross-section suppressed. Consequently, one would expect more of the highest-energy photons to arrive if QED is modified by Lorentz violation than if it is not. We estimate the sensitivity of Cherenkov Telescope Array (CTA) to changes in the $\gamma$-ray horizon of the Universe due to Lorentz violation, and find that it should be competitive with other leading constraints.Comment: 13 pages, 4 figures, typos corrected + references added, results unchanged. Matches version accepted by JCA

Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection

Foundations of egalitarian justice

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, 1996.Includes bibliographical references (leaves 215-219).by Timothy John Hinton.Ph.D

HoloDetect: Few-Shot Learning for Error Detection

We introduce a few-shot learning framework for error detection. We show that data augmentation (a form of weak supervision) is key to training high-quality, ML-based error detection models that require minimal human involvement. Our framework consists of two parts: (1) an expressive model to learn rich representations that capture the inherent syntactic and semantic heterogeneity of errors; and (2) a data augmentation model that, given a small seed of clean records, uses dataset-specific transformations to automatically generate additional training data. Our key insight is to learn data augmentation policies from the noisy input dataset in a weakly supervised manner. We show that our framework detects errors with an average precision of ~94% and an average recall of ~93% across a diverse array of datasets that exhibit different types and amounts of errors. We compare our approach to a comprehensive collection of error detection methods, ranging from traditional rule-based methods to ensemble-based and active learning approaches. We show that data augmentation yields an average improvement of 20 F1 points while it requires access to 3x fewer labeled examples compared to other ML approaches.Comment: 18 pages

Parents' perceived obstacles to pediatric clinical trial participation: Findings from the clinical trials transformation initiative.

Enrollment of children into pediatric clinical trials remains challenging. More effective strategies to improve recruitment of children into trials are needed. This study used in-depth qualitative interviews with parents who were approached to enroll their children in a clinical trial in order to gain an understanding of the barriers to pediatric clinical trial participation. Twenty-four parents whose children had been offered the opportunity to participate in a clinical trial were interviewed: 19 whose children had participated in at least 1 clinical trial and 5 who had declined participation in any trial. Each study aspect, from the initial explanation of the study to the end of the study, can affect the willingness of parents to consent to the proposed study and future studies. Establishing trust, appropriate timing, a transparent discussion of risks and benefits oriented to the layperson, and providing motivation for children to participate were key factors that impacted parents' decisions. In order for clinical trial accrual to be successful, parents' priorities and considerations must be a central focus, beginning with initial trial design. The recommendations from the parents who participated in this study can be used to support budget allocations that ensure adequate training of study staff and improved staffing on nights and weekends. Studies of parent responses in outpatient settings and additional inpatient settings will provide valuable information on the consent process from the child's and parent's perspectives. Further studies are needed to explore whether implementation of such strategies will result in improved recruitment for pediatric clinical trials