Publications related to Z-test

Quantitative T2 Mapping of Acute Pancreatitis

Background: Quantification of the T2 signal by means of T2 mapping in acute pancreatitis (AP) has the potential to quantify the parenchymal edema. Quantitative T2 mapping may overcome the limitations of previously reported scoring systems for reliable asse ...

Wiley2024

Parallel flows as a key component to interpret Super-X divertor experiments

Basil Duval, Holger Reimerdes, Christian Gabriel Theiler, Joaquim Loizu Cisquella, Artur Perek, Guang-Yu Sun, Luke Simons, Olivier Claude Martin Février, Garance Hélène Salomé Durr-Legoupil-Nicoud, Davide Galassi, Lorenzo Martinelli, Sophie Danielle Angelica Gorno, Claudia Colandrea, Curdin Tobias Wüthrich

The Super-X Divertor (SXD) is an alternative divertor configuration leveraging total flux expansion at the Outer Strike Point (OSP). While the extended 2-Point Model (2PM) predicts facilitated detachment access and control in the SXD configuration, these a ...

IOP Publishing Ltd2024

Establishment of Reference Intervals of Blood Parameters Among the Healthy Afghan Population

Najia Sherzay

Purpose: This study was designed and conducted to validate the reference values of hematological parameters for healthy adult male and female residents of Kabul city, Afghanistan. Methodology: In this cross-sectional study, the samples were collected accor ...

Dove Medical Press Ltd2024

Near Collision Attack Against Grain V1

Subhadeep Banik, Willi Meier, Daniel Patrick Collins

A near collision attack against the Grain v1 stream cipher was proposed by Zhang et al. in Eurocrypt 18. The attack uses the fact that two internal states of the stream cipher with very low hamming distance between them, produce similar keystream sequences ...

2023

Surrogate modelling for seismic collapse risk assessment of steel moment resisting frames

Dimitrios Lignos, Andronikos Skiadopoulos, Nenad Bijelic

With the overall goal of reducing the numerical burden of uncertainty quantification involved in seismic risk assessments, this paper examines opportunities for assessment of seismic collapse capacity and risk using data-driven surrogates as a complement t ...

2023

Time vs. Truth: Age-Distortion Tradeoffs and Strategies for Distributed Inference

Yunus Inan

In 1948, Claude Shannon laid the foundations of information theory, which grew out of a study to find the ultimate limits of source compression, and of reliable communication. Since then, information theory has proved itself not only as a quest to find the ...

EPFL2023

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Soumaya Elkantassi

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

EPFL2023

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems"

Michaël Unser, Pakshal Narendra Bohra, Alexis Marie Frederic Goujon, Sebastian Jonas Neumayer, Stanislas Ducotterd

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems". The corresponding scripts can be accessed on GitHub (https://github.com/axgoujon/convex_ridge_regularizers). The data is organized as follows: - ct_data_sets.tar.gz: contains prepr ...

Zenodo2023

SmartSwim, a Novel IMU-Based Coaching Assistance

Kamiar Aminian, Farzin Dadashi, Fabien Massé, Mahdi Hamidi Rad, Vincent Gremeaux

Swimming coaches provide regular timed and technical feedback to swimmers and guide them efficiently in training sessions. Due to the complexity of swimmers' performance, which is not visible in qualitative observation, quantitative and objective performan ...

MDPI2022

A note on universal inference

Anthony Christopher Davison, Timmy Rong Tian Tse

Universal inference enables the construction of confidence intervals and tests without regularity conditions by splitting the data into two parts and appealing to Markov's inequality. Previous investigations have shown that the cost of this generality is a ...

WILEY2022

Learning new physics from an imperfect machine

Raffaele Tito D'Agnolo, Andrea Wulzer

We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties a ...

SPRINGER2022

Data-Driven Predictive Models: Calculational Bias in Characterization of Spent Nuclear Fuel

Characteristics of the spent nuclear fuel (SNF) are typically calculated, requiring validation a priori. The validation process relies on the difference between calculations and measurements, namely the bias. Usually, predicting the bias based on benchmark ...

EPFL2022

Improved Inference On Risk Measures For Univariate Extremes

Anthony Christopher Davison, Léo Raymond-Belzile

We discuss the use of likelihood asymptotics for inference on risk measures in univariate extreme value problems, focusing on estimation of high quantiles and similar summaries of risk for uncertainty quantification. We study whether higher-order approxima ...

INST MATHEMATICAL STATISTICS-IMS2022

Experimental characterisation of textile compaction response: A benchmark exercise

Véronique Michaud, Baris Çaglar, Christophe Lopez

This paper reports the results of an international benchmark exercise on the measurement of fibre bed compaction behaviour. The aim was to identify aspects of the test method critical to obtain reliable results and to arrive at a recommended test procedure ...

2021

Determining 1D fast-ion velocity distribution functions from ion cyclotron emission data using deep neural networks

Ambrogio Fasoli, Marcelo Baquero Ruiz

The relationship between simulated ion cyclotron emission (ICE) signals s and the corresponding 1D velocity distribution function f(upsilon(perpendicular to)) of the fast ions triggering the ICE is modeled using a two-layer deep neural network. The network ...

2021

Improving filling level classification with adversarial training

Pascal Frossard, Andrea Cavallaro, Apostolos Modas

We investigate the problem of classifying - from a single image the level of content in a cup or a drinking glass. This problem is made challenging by several ambiguities caused by transparencies, shape variations and partial occlusions, and by the availab ...

IEEE2021

Multiple testing with test statistics following heavy-tailed distributions

Zhiwen Jiang

In multiple testing problems where the components come from a mixture model of noise and true effect, we seek to first test for the existence of the non-zero components, and then identify the true alternatives under a fixed significance level

\alpha

. Two ...

EPFL2021

The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey quasar sample: testing observational systematics on the Baryon Acoustic Oscillation measurement

Cheng Zhao

Baryon Acoustic Oscillations (BAO) are considered to be a very robust standard ruler against various systematics. This premise has been tested against observational systematics, but not to the level required for the next generation of galaxy surveys such a ...

OXFORD UNIV PRESS2021

Lattice-Free Mmi Adaptation Of Self-Supervised Pretrained Acoustic Models

Hervé Bourlard, Apoorv Vyas

In this work, we propose lattice-free MMI (LFMMI) for supervised adaptation of self-supervised pretrained acoustic model. We pretrain a Transformer model on thousand hours of untranscribed Librispeech data followed by supervised adaptation with LFMMI on th ...

IEEE2021

Built-in Self-Evaluation of First-Order Power Side-Channel Leakage for FPGAs

Francesco Regazzoni, Mirjana Stojilovic, Ognjen Glamocanin, Louis Coulon

Embedded and cyber-physical systems are pervading all aspects of our lives, including sensitive and critical ones. As a result, they are an alluring target for cyber attacks. These systems, whose implementation is often based on reconfigurable hardware, ar ...

2020