Publications associées à 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, Bahar Aydemir, Kynthia Chamilothori, Seungryong Kim

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 ...

BMC2023

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

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

Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction

Zhenzhu Meng, Yating Hu, Chenfei Shao

Both numerical simulations and data-driven methods have been applied in dam's displacement modeling. For monitored displacement data-driven methods, the physical mechanism and structural correlations were rarely discussed. In order to take the spatial and ...

2020

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

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

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Phi coefficient

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

Revisiting adversarial training for the worst-performing class

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

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

Beyond worst-case analysis, with or without predictions

SiML: Sieved Maximum Likelihood for Array Signal Processing

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

Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction

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

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

Graph Chatbot

Chattez avec Graph Search

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

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

Revisiting adversarial training for the worst-performing class

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

Beyond worst-case analysis, with or without predictions

Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction

SiML: Sieved Maximum Likelihood for Array Signal Processing

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

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

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