Publications related to Phi coefficient

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Jean-Paul Richard Kneib, Emma Elizabeth Tolley, Tianyue Chen, Michele Bianco

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Oxford Univ Press2024

Revisiting adversarial training for the worst-performing class

Volkan Cevher, Grigorios Chrysos, Thomas Michaelsen Pethick

Despite progress in adversarial training (AT), there is a substantial gap between the topperforming and worst-performing classes in many datasets. For example, on CIFAR10, the accuracies for the best and worst classes are 74% and 23%, respectively. We argu ...

2023

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

Marilyne Andersen, Sabine Süsstrunk, Caroline Karmann, Kynthia Chamilothori, Seungryong Kim, Bahar Aydemir

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the pe ...

2023

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

Kamiar Aminian, Anisoara Ionescu, Abolfazl Soltani, Francesca Salis

Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validat ...

BioMed Central2023

SiML: Sieved Maximum Likelihood for Array Signal Processing

Paul Hurley, Matthieu Martin Jean-André Simeoni

Stochastic Maximum Likelihood (SML) is a popular direction of arrival (DOA) estimation technique in array signal processing. It is a parametric method that jointly estimates signal and instrument noise by maximum likelihood, achieving excellent statistical ...

2021

Constructing a provably adversarially-robust classifier from a high accuracy one

Rüdiger Urbanke, Grzegorz Adam Gluch

Modern machine learning models with very high accuracy have been shown to be vulnerable to small, adversarially chosen perturbations of the input. Given black-box access to a high-accuracy classifier f, we show how to construct a new classifier g that has ...

ADDISON-WESLEY PUBL CO2020

Distributed Fault Detection for Interconnected Large-Scale Systems: A Scalable Plug & Play Approach

Giancarlo Ferrari Trecate, Marcello Farina

In this paper, we propose a novel distributed fault detection method to monitor the state of a-possibly large scale-linear system, partitioned into interconnected subsystems. The approach hinges on the definition of a partition-based distributed Luenberger ...

2019

Concrete Dam Displacement Prediction Based on an ISODATA-GMM Clustering and Random Coefficient Model

Zhenzhu Meng, Yating Hu, Chenfei Shao

Displacement data modelling is of great importance for the safety control of concrete dams. The commonly used artificial intelligence method modelled the displacement data at each monitoring point individually, i.e., the data correlations between the monit ...

2019

Using Robust Principal Component Analysis to Reduce EEG Intra-Trial Variability

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Ping-Keng Jao

Practical brain-computer interfaces need to overcome several challenges, including tolerance to signal variability within- and across sessions. We introduce Robust Principal Component Analysis (RPCA) as a potential approach to tackle intra-trial variabilit ...

2018

A Non-linear Filter to Detect Atrial Activation from Intracardiac Electrograms

Jean-Marc Vesin, Sasan Yazdani, Andréa Buttu, Etienne Pruvot

Atrial activation (AA) detection during atrial fibrillation (AF) can be considered as a first step in estimating AF characteristics such as the AF cycle length. The detection of AA from intracardiac electrograms (EGM) remains challenging due to the constan ...

2017

Semi-supervised multiview embedding for hyperspectral data classification

Devis Tuia, Giona Matasci

In this paper, a method for semi-supervised multiview feature extraction based on the multiset regularized kernel canonical correlation analysis (kCCA) is proposed for the classification of hyperspectral images. The covariance matrix of this type of data i ...

Elsevier Science Bv2014

A Critical Assessment of Storytelling: Gene Ontology Categories and the Importance of Validating Genomic Scans

Jeffrey David Jensen, Alexandros Stamatakis

In the age of whole-genome population genetics, so-called genomic scan studies often conclude with a long list of putatively selected loci. These lists are then further scrutinized to annotate these regions by gene function, corresponding biological proces ...

Oxford University Press2012

Sleep and Wake Classification With ECG and Respiratory Effort Signals

Dario Floreano, Claudio Mattiussi, Walter Karlen

We describe a method for the online classification of sleep/wake states based on cardiorespiratory signals produced by wearable sensors. The method was conceived in view of its applicability to a wearable sleepiness monitoring device. The method uses a Fas ...

2009

An efficient P300-based brain-computer interface for disabled subjects

Touradj Ebrahimi, Jean-Marc Vesin, Ulrich Hoffmann

A brain-computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral n ...

2008

A new approach to accurate measurement of uniaxial joint angles based on a combination of accelerometers and gyroscopes

Kamiar Aminian, Hooman Dejnabadi

A new method of measuring joint angle using a combination of accelerometers and gyroscopes is presented. The method proposes a minimal sensor configuration with one sensor module mounted on each segment. The model is based on estimating the acceleration of ...

2005