Publications associées à Estimating The Degree of Sleepiness by Integrating Articulatory Feature Knowledge In Raw Waveform Based CNNs

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

EPFL2024

Verification of the Fourier-enhanced 3D finite element Poisson solver of the gyrokinetic full-f code PICLS

Laurent Villard, Stephan Brunner, Moahan Murugappan, Alberto Bottino

We introduce and derive the Fourier -enhanced 3D electrostatic field solver of the gyrokinetic full -f PIC code PICLS. The solver makes use of a Fourier representation in one periodic direction of the domain to make the solving of the system easily paralle ...

Elsevier2024

Osmosis-driven stiffening of structured hydrogels

Esther Amstad, Ran Zhao, Alexandra Thoma

Plant cells harness osmotic pressures to stiffen their leaves through strong turgor pressures. Key to this osmosisdriven stiffening is the confinement of liquids within semipermeable membranes that can regulate the transport of water molecules and ions. In ...

Elsevier2024

Near-Room-Temperature Detection of Aromatic Compounds with Inkjet-Printed Plasticized Polymer Composites

Jürgen Brugger, Thomas Maeder, Mohammadmahdi Kiaee

Chemiresistive gas sensors composed of a thermoplastic polymer matrix and conductive fillers offer various advantages for detecting volatile organic compounds (VOCs), including low power consumption due to near-room-temperature operation, high sensitivity, ...

Amer Chemical Soc2024

Safe Deep Neural Networks

Kyle Michael Matoba

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

EPFL2024

Topics in statistical physics of high-dimensional machine learning

Hugo Chao Cui

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

EPFL2024

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Volkan Cevher, Grigorios Chrysos, Fanghui Liu

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

2024

Monitoring an Emergent Pathogen at Low Incidence in Wastewater Using qPCR: Mpox in Switzerland

Tamar Kohn, Timothy R. Julian

Wastewater-based epidemiology offers a complementary approach to clinical case-based surveillance of emergent diseases and can help identify regions with infected people to prioritize clinical surveillance strategies. However, tracking emergent diseases in ...

2024

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

Random matrix methods for high-dimensional machine learning models

Antoine Philippe Michel Bodin

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

EPFL2024