Adaptive statistical pattern classifiers for remotely sensed data

A technique for the adaptive estimation of nonstationary statistics necessary for Bayesian classification is developed. The basic approach to the adaptive estimation procedure consists of two steps: (1) an optimal stochastic approximation of the parameters of interest and (2) a projection of the parameters in time or position. A divergence criterion is developed to monitor algorithm performance. Comparative results of adaptive and nonadaptive classifier tests are presented for simulated four dimensional spectral scan data

Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease

We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort. In contrast, we develop a segmentation model that recursively evolves a segmentation in several steps, and implement it as a recurrent neural network. We learn model parameters by optimizing the interme- diate steps of the evolution in addition to the final segmentation. To this end, we train our segmentation propagation model by presenting incom- plete and/or inaccurate input segmentations paired with a recommended next step. Our work aims to alleviate challenges in segmenting heart structures from cardiac MRI for patients with congenital heart disease (CHD), which encompasses a range of morphological deformations and topological changes. We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels. Com- pared to direct segmentation, the iterative method yields more accurate segmentation for patients with the most severe CHD malformations.Comment: Presented at the Deep Learning in Medical Image Analysis Workshop, MICCAI 201

S12RS SGB No. 8 (New Committees)

A BILL To amend the Senate Rules of Orde

Neutrino-nucleus cross section within the extended factorization scheme

The factorization scheme, based on the impulse approximation and the spectral function formalism, has been recently generalized to allow the description of electromagnetic nuclear interactions driven by two-nucleon currents. We have extended this framework to the case of weakly charged and neutral currents, and carried out calculations of the double-differential neutrino-carbon and neutrino-oxygen cross sections using two different models of the target spectral functions. The results, showing a moderate dependence on the input spectral function, confirm that our approach provides a consistent treatment of all reaction mechanisms contributing to the signals detected by accelerator-based neutrino experiments

Meson-exchange currents and quasielastic predictions for charged-current neutrino-12C scattering in the superscaling approach

We evaluate and discuss the impact of meson-exchange currents (MECs) on charged-current quasielastic neutrino cross sections. We consider the nuclear transverse response arising from two-particle two-hole states excited by the action of electromagnetic, purely isovector meson-exchange currents in a fully relativistic framework based on the work by the Torino Collaboration [A. D. Pace, M. Nardi, W. M. Alberico, T. W. Donnelly, and A. Molinari, Nucl. Phys. A726, 303 (2003)]. An accurate parametrization of this MEC response as a function of the momentum and energy transfers involved is presented. Results of neutrino-nucleus cross sections using this MEC parametrization together with a recent scaling approach for the one-particle one-hole contributions (named SuSAv2) are compared with experimental data (MiniBooNE, MINERvA, NOMAD and T2K Collaborations).Comment: 16 pages, 19 figure