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Publications associées (32)

A Comparative Analysis of Tools & Task Types for Measuring Computational Problem-Solving

Richard Lee Davis, Engin Walter Bumbacher, Jérôme Guillaume Brender

How to measure students' Computational Problem-Solving (CPS) competencies is an ongoing research topic. Prevalent approaches vary by measurement tools (e.g., interactive programming, multiple-choice tests, or programming-independent tests) and task types ( ...

Association for Computing Machinery2024

Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming

Xue Zhang, Tianshu Yang

In this paper, we propose an analytical stochastic dynamic programming (SDP) algorithm to address the optimal management problem of price-maker community energy storage. As a price-maker, energy storage smooths price differences, thus decreasing energy arb ...

China Electric Power Research Inst2024

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Evann Pierre Guy Courdier

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

EPFL2024

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Martin Weigert

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and s ...

Nature Portfolio2024

The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry

Daniel Probst

While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the sci ...

2023

Attenuated Self-Interference Synchrophasor Estimation: the SOGI Interpolated DFT

Mario Paolone, Cesar Garcia Veloso

In this paper, a synchrophasor estimation (SE) algorithm is presented to simultaneously comply with the requirements for the P and M phasor measurement unit (PMU) performance classes. The method employs a second-order generalized integrator (SOGI) quadratu ...

IEEE2023

Geometric Algebra for Optimal Control With Applications in Manipulation Tasks

Sylvain Calinon, Tobias Löw

Many problems in robotics are fundamentally problems of geometry, which have led to an increased research effort in geometric methods for robotics in recent years. The results were algorithms using the various frameworks of screw theory, Lie algebra, and d ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Learning With Your PETS

Drazen Dujic, Andrea Cervone, Renan Pillon Barcelos, Jules Christian Georges Macé, Max Dupont

Recently, we published an article [1] presenting how the Power Electronics course at the bachelor level is organized by the Power Electronics Laboratory and taught at Ecole Polytechnique Fédérale de Lausanne, EPFL, Switzerland. For those avid readers who m ...

2023

Expectation consistency for calibration of neural networks

Lenka Zdeborová, Florent Gérard Krzakala, Bruno Loureiro, Lucas Andry Clarte

Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence. Finding effective and efficient calibration methods for neural networks is therefore an important endeavour tow ...

2023

Confidence Matters: Applications to Semantic Segmentation

Prabhu Teja Sivaprasad

The successes of deep learning for semantic segmentation can in be, in part, attributed to its scale: a notion that encapsulates the largeness of these computational architectures and the labeled datasets they are trained on. These resource requirements hi ...

EPFL2023

Evaluating the effect of sparse convolutions on point cloud compression

Touradj Ebrahimi, Davi Nachtigall Lazzarotto

The use of point clouds as an imaging modality has been rapidly growing, motivating research on compression methods to enable efficient transmission and storage for many applications. While compression standards relying on conven- tional techniques such as ...

2023

Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation

Alfio Quarteroni, Andrea Manzoni

Reducing the computational time required by high-fidelity, full-order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient-specific simulations into clinical practice. Indeed, while FOMs, such as th ...

Wiley2023

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Baiwei Guo

Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...

EPFL2023

Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea

Thanh Trung Huynh

The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and timeconsuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for foreca ...

ELSEVIER2023

Democratizing Machine Learning

Nirupam Gupta, Rafaël Benjamin Pinot, Alexandre David Olivier Maurer

The increasing prevalence of personal devices motivates the design of algorithms that can leverage their computing power, together with the data they generate, in order to build privacy-preserving and effective machine learning models. However, traditional ...

IEEE2022

Towards coupling conventional with high-fidelity fuel behavior analysis tools

Andreas Pautz, Carlo Fiorina, Alessandro Scolaro, Edoardo Luciano Brunetto

Multi-dimensional fuel performance codes have the potential to enhance the understanding of complex phenomena affecting fuel integrity, but their application is often limited by the high computational cost. Recognizing that many multi-dimensional aspects s ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Mediating Computational Thinking Through Educational Robotics In Primary School

Morgane Solène Denise Chevalier

Nowadays, computational thinking (CT) is considered a skill to be taught. While educational robotics (ER) appears promising to foster students' CT, operational CT frameworks and explicit guidance in ER are lacking. This thesis thus proposes to understand h ...

EPFL2022

Random Surface Covariance Estimation by Shifted Partial Tracing

Victor Panaretos, Tomas Masák

The problem of covariance estimation for replicated surface-valued processes is examined from the functional data analysis perspective. Considerations of statistical and computational efficiency often compel the use of separability of the covariance, even ...

2023

Computation Complexity Reduction Technique for Accurate Seizure Detection Implants

Alexandre Schmid, Keyvan Farhang Razi

An automatic seizure detection method from high-resolution intracranial-EEG (iEEG) signals is presented to minimize the computational complexity and realize real-time accurate seizure detection for biomedical implants. Complex signal processing on a large ...

IEEE2022

Analytic modelling of passive microfluidic mixers

Jean-Michel Sallese

This paper deals with a new analytical model for microfluidic passive mixers. Two common approaches already exist for such a purpose. On the one hand, the resolution of the advection-diffusion-reaction equation (ADRE) is the first one and the closest to ph ...

AMER INST MATHEMATICAL SCIENCES-AIMS2022