Publications related to Raman Biosensing of Sweat Metabolites: Univariate vs. Multivariate Algorithms

Related publications (16)

Vehicle Trajectory Dataset from Drone-Collected Data at Three Swiss Roundabouts

Nikolaos Geroliminis, Emmanouil Barmpounakis, Robert Fonod, Jasso Espadaler Clapés

Overview This dataset provides high-resolution, georeferenced vehicle trajectories collected via drone footage at three roundabouts located in the municipalities of Frick and Laufenburg, Canton of Aargau, Switzerland. The data were collected as part of a ...

Zenodo2025

Probabilistic Load Forecasting of distribution power systems based on empirical copulas

Mario Paolone, Fabio Nobile, Pål Forr Austnes, Celia Garcia Pareja

Accurate and reliable electricity load forecasts are becoming increasingly important as the share of intermittent resources in the system increases. Distribution System Operators (DSOs) are called to accurately forecast their production and consumption to ...

Elsevier BV2025

Remote Dual-Cavity Enhanced Second Harmonic Generation in a Hybrid Plasmonic Waveguide

Wen Chen

On-chip nanoscale optical platforms capable of efficient second harmonic generation (SHG) are highly desired for optical sensing, subwavelength coherent sources, and quantum photonic devices. Here, we develop a remotely excited dual cavity resonance scheme ...

AMER CHEMICAL SOC2022

Metric learning for kernel ridge regression: assessment of molecular similarity

Anne-Clémence Corminboeuf, Friedrich Eisenbrand, Raimon Fabregat I De Aguilar-Amat, Puck Elisabeth van Gerwen, Matthieu Nicolas Haeberle

Supervised and unsupervised kernel-based algorithms widely used in the physical sciences depend upon the notion of similarity. Their reliance on pre-defined distance metrics-e.g. the Euclidean or Manhattan distance-are problematic especially when used in c ...

IOP Publishing Ltd2022

Advances and Open Problems in Federated Learning

Martin Jaggi, Ayfer Özgür Aydin, Sebastian Urban Stich, Florian Tramèr, Lie He

Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decen ...

NOW PUBLISHERS INC2021

An End-to-End Data Pipeline for Managing Learning Analytics

Denis Gillet, Juan Carlos Farah, Sandy Ingram, Joana Catarina Soares Machado

Despite the importance of learning analytics in digital education, there is limited support for researchers in education to generate, access, and share experimental data while complying with ethical and privacy legislation. We propose a set of related tool ...

2021

Optimal radial basis for density-based atomic representations

Michele Ceriotti, Félix Benedito Clément Musil, Jigyasa Nigam, Sergey Pozdnyakov, Alexander Jan Goscinski

The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation. Many of the most popular representations ...

2021

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Jean-Philippe Thiran, Tobias Kober, Alessandro Daducci, Tom Hilbert, Erick Jorge Canales Rodriguez, Nicolas Kunz, Marco Pizzolato, Gian Franco Piredda, Thomas Yu

Multi-component T2 relaxometry allows probing tissue microstructure by assessing compartment-specific T2 relaxation times and water fractions, including the myelin water fraction. Non-negative least squares (NNLS) with zero-order Tikhonov regularization is ...

2021

Wasserstein Distributionally Robust Learning

Soroosh Shafieezadeh Abadeh

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

EPFL2020

Application of machine learning techniques at the CERN Large Hadron Collider

Frédéric Blanc, Tatiana Pieloni, Michael Schenk, Leonid Rivkin, Loïc Thomas Davies Coyle

Machine learning techniques have been used extensively in several domains of Science and Engineering for decades. These powerful tools have been applied also to the domain of high-energy physics, in the analysis of the data from particle collisions, for ye ...

Trieste2020

A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

Jan Sickmann Hesthaven, Mengwu Guo, Mariella Kast

We propose a non-intrusive reduced basis (RB) method for parametrized nonlinear partial differential equations (PDEs) that leverages models of different accuracy. From a collection of low-fidelity (LF) snapshots, parameter locations are extracted for the e ...

ELSEVIER SCIENCE SA2020

Euclid preparation: X. The Euclid photometric-redshift challenge

Georges Meylan, Gianluca Castignani

Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-zs at the required acc ...

EDP SCIENCES S A2020

Two-Tier Mapper, an unbiased topology-based clustering method for enhanced global gene expression analysis

Kathryn Hess Bellwald, Cathrin Brisken, Jacques Rougemont, Rachel Marcone

Motivation: Unbiased clustering methods are needed to analyze growing numbers of complex data sets. Currently available clustering methods often depend on parameters that are set by the user, they lack stability, and are not applicable to small data sets. ...

Oxford University Press2019

Data-driven Measurement Designs for Magnetic Resonance Imaging

Baran Gözcü

Despite being a powerful medical imaging technique which does not emit any ionizing radiation, magnetic resonance imaging (MRI) always had the major problem of long scanning times that can take up to an hour depending on the application. It also requires u ...

EPFL2019

Timely and cost-efficient data exploration through adaptive tuning

Matthaios Alexandros Olma

Modern applications accumulate data at an exponentially increasing rate and traditional database systems struggle to keep up. Decision support systems used in industry, rely heavily on data analysis, and require real-time responses irrespective of data siz ...

EPFL2019

Gap-Plasmon-Enhanced High-Spatial-Resolution Imaging by Photothermal-Induced Resonance in the Visible Range

Giovanni Dietler, Serguei Sekatski, Jiangtao Zhou, Anton Smirnov

Chemical characterization at the nanoscale is of significant importance for many applications in physics, analytical chemistry, material science, and biology. Despite the intensive studies in the infrared range, high-spatial resolution and high-sensitivity ...

AMER CHEMICAL SOC2019