Large deformation diffeomorphic metric mapping

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Fast model-based T-2 mapping using SAR-reduced simultaneous multislice excitation

Jean-Philippe Thiran, Tobias Kober, José Pedro Rebelo Ferreira Marques, Gunnar Krüger, Tom Hilbert

Purpose To obtain whole-brain high-resolution T-2 maps in 2 minutes by combining simultaneous multislice excitation and low-power PINS (power independent of number of slices) refocusing pulses with undersampling and a model-based reconstruction. Methods A ...

WILEY2019

Reconciling Matching Networks of Conceptual Models

Karl Aberer, Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Zoltán Miklós

Conceptual models such as database schemas, ontologies or process models have been established as a means for effective engineering of information systems. Yet, for complex systems, conceptual models are created by a variety of stakeholders, which calls fo ...

2019

Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks

Aurelio Bay, Ekaterina Kuznetsova, Guido Haefeli, Lesya Shchutska, Sun Hee Kim, Elena Graverini, Federico Leo Redi, Maria Elena Stramaglia, Ana Bárbara Rodrigues Cavalcante, Mikhail Chapochnikov, Alexey Boyarsky, Oleg Ruchayskiy, Nikolaos Charitonidis, Inar Timiryasov, Sebastian Gerlach, Alexandre Emmanuel Grandchamp, Jean-Loup Tastet

This paper presents a fast approach to simulating muons produced in interactions of the SPS proton beams with the target of the SHiP experiment. The SHIP experiment will be able to search for new long-lived particles produced in a 400 GeV/c SPS proton beam ...

2019

Every Smile is Unique: Landmark-Guided Diverse Smile Generation

Pascal Fua, Wei Wang

Each smile is unique: one person surely smiles in different ways (e.g. closing/opening the eyes or mouth). Given one input image of a neutral face, can we generate multiple smile videos with distinctive characteristics? To tackle this one-to-many video gen ...

IEEE2018

A Compressed Sensing Approach for Distribution Matching

Vahid Aref, Mohamad Baker Dia

In this work, we formulate the fixed-length distribution matching as a Bayesian inference problem. Our proposed solution is inspired from the compressed sensing paradigm and the sparse superposition (SS) codes. First, we introduce sparsity in the binary so ...

IEEE2018

Knowledge Transfer with Jacobian Matching

François Fleuret, Suraj Srinivas

Classical distillation methods transfer representations from a “teacher” neural network to a “student” network by matching their output activations. Recent methods also match the Jacobians, or the gradient of output activations with the input. However, thi ...

2018