Prostate Biopsy Assistance System with Gland Deformation Estimation for Enhanced Precision

Computer-assisted prostate biopsies became a very active research area during the last years. Prostate tracking makes it possi- ble to overcome several drawbacks of the current standard transrectal ultrasound (TRUS) biopsy procedure, namely the insufficient targeting accuracy which may lead to a biopsy distribution of poor quality, the very approximate knowledge about the actual location of the sampled tissues which makes it difficult to implement focal therapy strategies based on biopsy results, and finally the difficulty to precisely reach non-ultrasound (US) targets stemming from different modalities, statistical atlases or previous biopsy series. The prostate tracking systems presented so far are limited to rigid transformation tracking. However, the gland can get considerably deformed during the intervention because of US probe pres- sure and patient movements. We propose to use 3D US combined with image-based elastic registration to estimate these deformations. A fast elastic registration algorithm that copes with the frequently occurring US shadows is presented. A patient cohort study was performed, which yielded a statistically significant in-vivo accuracy of 0.83+-0.54mm.Comment: This version of the paper integrates a correction concerning the local similarity measure w.r.t. the proceedings (this typing error could not be corrected before editing the proceedings

Coral symbiodinium community composition across the Belize Mesoamerican barrier reef system is influenced by host species and thermal variability

Accepted manuscrip

Detecting Distracted Driving with Deep Learning

© Springer International Publishing AG 2017Driver distraction is the leading factor in most car crashes and near-crashes. This paper discusses the types, causes and impacts of distracted driving. A deep learning approach is then presented for the detection of such driving behaviors using images of the driver, where an enhancement has been made to a standard convolutional neural network (CNN). Experimental results on Kaggle challenge dataset have confirmed the capability of a convolutional neural network (CNN) in this complicated computer vision task and illustrated the contribution of the CNN enhancement to a better pattern recognition accuracy.Peer reviewe

Universality in D-brane Inflation

We study the six-field dynamics of D3-brane inflation for a general scalar potential on the conifold, finding simple, universal behavior. We numerically evolve the equations of motion for an ensemble of more than 7 \times 10^7 realizations, drawing the coefficients in the scalar potential from statistical distributions whose detailed properties have demonstrably small effects on our results. When prolonged inflation occurs, it has a characteristic form: the D3-brane initially moves rapidly in the angular directions, spirals down to an inflection point in the potential, and settles into single-field inflation. The probability of N_{e} e-folds of inflation is a power law, P(N_{e}) \propto N_{e}^{-3}, and we derive the same exponent from a simple analytical model. The success of inflation is relatively insensitive to the initial conditions: we find attractor behavior in the angular directions, and the D3-brane can begin far above the inflection point without overshooting. In favorable regions of the parameter space, models yielding 60 e-folds of expansion arise approximately once in 10^3 trials. Realizations that are effectively single-field and give rise to a primordial spectrum of fluctuations consistent with WMAP, for which at least 120 e-folds are required, arise approximately once in 10^5 trials. The emergence of robust predictions from a six-field potential with hundreds of terms invites an analytic approach to multifield inflation.Comment: 28 pages, 9 figure

Factors associated with the decision to investigate child protective services referrals: a systematic review

Background: Limited resources for child protection create challenging decision situations for child protective services (CPS) workers at the point of intake. A body of research has examined the factors associated with worker decisions and processes using a variety of methodological approaches to gain knowledge on decision-making. However, few attempts have been made to systematically review this literature. Objective: As part of a larger project on decision-making at intake, this systematic review addressed the question of the factors associated with worker decisions to investigate alleged maltreatment referrals. Methods: Quantitative studies that examined factors associated with screening decisions in CPS practice settings were included in the review. Database and other search methods were used to identify research published in English over a 35-year period (1980-2015). Findings: Of 1,147 identified sources, 18 studies were selected for full data extraction. The studies were conducted in the U.S., Canada, and Sweden and varied in methodological quality. Most studies examined case factors with few studies examining other domains. Conclusions: To inform CPS policy and practice, additional research is needed to examine the relationships between decision-making factors and case outcomes. Greater attention needs to be given to the organizational and external factors that influence decision-making