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Retroactive Packet Sampling for Traffic Receipts

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Rethinking Sampling in Parallel MRI: A Data-Driven Approach

Volkan Cevher, Baran Gözcü, Thomas Sanchez

In the last decade, Compressive Sensing (CS) has emerged as the most promising, model-driven approach to accelerate MRI scans. CS relies on the key sparsity assumption and proposes random sampling for data acquisition. The practical CS approaches in MRI em ...

IEEE2019

Topology classification with deep learning to improve real-time event selection at the LHC

Roberto Castello

We show how an event topology classification based on deep learning could be used to improve the purity of data samples selected in real-time at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class ...

2019

A systematic review and meta-analysis of the efficacy of aortic anastomotic devices

Nikolaos Stergiopoulos, Dimitrios Schizas

Objective: One of the factors contributing to complications related to open repair of the aorta is the construction of a hand-sewn anastomosis. Aortic anastomotic devices (AADs), such as the intraluminal ringed graft (IRG), and the anastomotic stenting tec ...

MOSBY-ELSEVIER2019

Safe Adaptive Importance Sampling

Martin Jaggi, Sebastian Urban Stich, Anant Raj

Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - ...

2017

Sampling at unknown locations, with an application in surface retrieval

Martin Vetterli, Benjamin Bejar Haro, Adam James Scholefield, Michalina Wanda Pacholska

We consider the problem of sampling at unknown locations. We prove that, in this setting, if we take arbitrarily many samples of a polynomial or real bandlimited signal, it is possible to find another function in the same class, arbitrarily far away from t ...

2017

On the performance of random reshuffling in stochastic learning

Ali H. Sayed, Stefan Vlaski, Bicheng Ying, Kun Yuan

In empirical risk optimization, it has been observed that gradient descent implementations that rely on random reshuffling of the data achieve better performance than implementations that rely on sampling the data randomly and independently of each other. ...

2017

Gauging the highs: design and realization of a water sample campaign during high and low streamflow

How does the streamwater quality change during storm events, and how to characterize it? The purpose of this study is to investigate the evolution of major ions in streamwater from base ow to storm ux. Regular manual sampling and automated sampling campaig ...

2017

Shapes from Pixels

Mitra Fatemi

In today's digital world, sampling is at the heart of any signal acquisition device. Imaging devices are ubiquitous examples that capture two-dimensional visual signals and store them as the pixels of discrete images. The main concern is whether and how th ...

EPFL2016

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Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a fea ...

2016

Gender differences in visual perception

Michael Herzog, Christine Mohr, Maya Roinishvili

Gender differences are well established in cognition and somato-sensation, but there are almost no studies on gender differences in visual perception. One reason is that sample size is often small because effect sizes are large. Small samples are not well ...

2016