PASTA OR PARADIGM: THE PLACE OF ITALIAN-AMERICAN WOMEN IN POPULAR FILM

The year is 1930, the film is Little Caesar, and Hollywood begins its long and often irresponsible tradition of portraying the Italian-American male as gangster, thug, sociopath. The gangster genre has traditionally focused on male activities--men in groups, their rites of passage into underworld manhood, and their perverted American dreams of success achieved through community extortion, syndicated corruption, and blood murder. But hidden in the story of Caesar Enrico Bandello, who has justifiably been called our archetypal film gangster, we also discover fragmentary, but important, early portrayals of the Italian woman in America

Mellin Bootstrap for Scalars in Generic Dimension

We use the recently developed framework of the Mellin bootstrap to study perturbatively free scalar CFTs in arbitrary dimensions. This approach uses the crossing-symmetric Mellin space formulation of correlation functions to generate algebraic bootstrap equations by demanding that only physical operators contribute to the OPE. We find that there are no perturbatively interacting CFTs with only fundamental scalars in $d>6$ dimensions (to at least second order in the perturbation). Our results can be seen as a modest step towards understanding the space of interacting CFTs in $d>6$ and are consistent with the intuition that no such CFTs exist.Comment: 11 pages + appendices. v2: references added, few minor revisions; published versio

Computationally efficient cardiac views projection using 3D Convolutional Neural Networks

4D Flow is an MRI sequence which allows acquisition of 3D images of the heart. The data is typically acquired volumetrically, so it must be reformatted to generate cardiac long axis and short axis views for diagnostic interpretation. These views may be generated by placing 6 landmarks: the left and right ventricle apex, and the aortic, mitral, pulmonary, and tricuspid valves. In this paper, we propose an automatic method to localize landmarks in order to compute the cardiac views. Our approach consists of first calculating a bounding box that tightly crops the heart, followed by a landmark localization step within this bounded region. Both steps are based on a 3D extension of the recently introduced ENet. We demonstrate that the long and short axis projections computed with our automated method are of equivalent quality to projections created with landmarks placed by an experienced cardiac radiologist, based on a blinded test administered to a different cardiac radiologist

ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans

Medical images with specific pathologies are scarce, but a large amount of data is usually required for a deep convolutional neural network (DCNN) to achieve good accuracy. We consider the problem of segmenting the left ventricular (LV) myocardium on late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) scans of which only some of the scans have scar tissue. We propose ScarGAN to simulate scar tissue on healthy myocardium using chained generative adversarial networks (GAN). Our novel approach factorizes the simulation process into 3 steps: 1) a mask generator to simulate the shape of the scar tissue; 2) a domain-specific heuristic to produce the initial simulated scar tissue from the simulated shape; 3) a refining generator to add details to the simulated scar tissue. Unlike other approaches that generate samples from scratch, we simulate scar tissue on normal scans resulting in highly realistic samples. We show that experienced radiologists are unable to distinguish between real and simulated scar tissue. Training a U-Net with additional scans with scar tissue simulated by ScarGAN increases the percentage of scar pixels correctly included in LV myocardium prediction from 75.9% to 80.5%.Comment: 12 pages, 5 figures. To appear in MICCAI DLMIA 201

Modeling of Complex Systems II: A minimalist and unified semantics for heterogeneous integrated systems

International audienceThe purpose of this paper is to contribute to a unified formal framework for complex systems modeling. To this aim, we define a unified semantics for systems including integration operators. We consider complex systems as functional blackboxes (with internal states), whose structure and behaviors can be constructed through a recursive integration of heterogeneous components. We first introduce formal definitions of time (allowing to deal uniformly with both continuous and discrete times) and data (allowing to handle heterogeneous data), and introduce a generic synchronization mechanism for dataflows. We then define a system as a mathematical object characterized by coupled functional and states behaviors. This definition is expressive enough to capture the functional behavior of any real system with sequential transitions. We finally provide formal operators for integrating systems and show that they are consistent with the classical definitions of those operators on transfer functions which model real systems

Developing Reflective Learners: Serendipity and Synergy at Wheaton College

For the past decade, Wheaton College, a small liberal arts institution located in Norton, Massachusetts, has been creating, refining, and integrating a range of initiatives designed to develop students into active and reflective learners. Some of these initiatives grew out of explicit institutional commitments to teaching and learning innovation, while others were in fact happy accidents of circumstance, or situations where an individual interesting program idea took hold, spread into other units of the College, and was itself transformed in the process