Publications associées à Materials science experiments by robotic automation

Publications associées (26)

Robust and Personalized Federated Learning: Handling Data Corruption and Heterogeneity

Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling collaborative model training across decentralized devices while preserving data privacy. However, FL's success is highly contingent on the quality and integrity ...

EPFL2025

Learning transferable land cover semantics for open vocabulary interactions with remote sensing images

Devis Tuia, Javiera Francisca Castillo Navarro, Valérie Zermatten

Why should we confine land cover classes to rigid and arbitrary definitions? Land cover mapping is a central task in remote sensing image processing, but the rigorous class definitions can sometimes restrict the transferability of annotations between datas ...

2025

T-matrix representation of optical scattering response: Suggestion for a data format

Karim Achouri

The transition matrix, frequently abbreviated as T-matrix, contains the complete information in a linear approximation of how a spatially localized object scatters an incident field. The T-matrix is used to study the scattering response of an isolated obje ...

2025

Reconstructing illusory camouflage patterns on moth wings using computer vision

Laurent Valentin Jospin

Monocular depth cues, such as shading, are fundamental for resolving three-dimensional information, such as an object's shape. Animal colour patterns may potentially exploit this mechanism of depth perception, generating false illusions for functions such ...

2025

Non-regular Inference: Universal Inference and Discrete Profiling

Timmy Rong Tian Tse

Non-regular or irregular statistical problems are those that do not satisfy a set of standard regularity conditions that allow useful theoretical properties of inferential procedures to be proven. Non-regular problems are prevalent; a classical example is ...

EPFL2025

Deep learning approach for identification of Hii regions during reionization in 21-cm observations – III. image recovery

Michele Bianco, Tianyue Chen, Shreyam Parth Krishna

The low-frequency component of the upcoming Square Kilometre Array Observatory (SKA-Low) will be sensitive enough to construct 3D tomographic images of the 21-cm signal distribution during reionization. However, foreground contamination poses challenges fo ...

Oxford University Press (OUP)2025

Adaptive Interface-Pinns (Adai-Pinns) for Transient Diffusion: Applications to Forward and Inverse Problems in Heterogeneous Media

Brice Tanguy Alphonse Lecampion

We model transient diffusion in heterogeneous materials using a novel physics-informed neural networks framework (PINNs) termed Adaptive interface physics-informed neural networks or AdaI-PINNs (Roy et al. arXiv preprint arXiv:2406.04626, 2024). AdaI-PINNs ...

Elsevier BV2025

Intelligent and Self-Driving Microscopy of Protein Aggregation in Neurodegenerative Diseases

Khalid Ashraf Mohie Ibrahim

Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's, afflict tens of millions of patients worldwide. They are characterized by protein aggregation and progressive neuronal loss, leading to cognitive and motor impairments, and ult ...

EPFL2025

Material-like robotic collectives with spatiotemporal control of strength and shape

Sangwoo Kim

The vision of robotic materials—cohesive collectives of robotic units that can arrange into virtually any form with any physical properties—has long intrigued both science and fiction. Yet, this vision requires a fundamental physical challenge to be overco ...

American Association for the Advancement of Science (AAAS)2025

Graph Neural Networks With Adaptive Structures

Zepeng Zhang

Graph neural networks (GNNs) have made significant progress in various machine learning tasks. Despite their success, many existing GNN models are shown to be vulnerable to adversarial attacks, creating a stringent need to build robust GNN architectures. I ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

NAT: Learning to Attack Neurons for Enhanced Adversarial Transferability

Alexandre Massoud Alahi, Krishna Kanth Nakka

The generation of transferable adversarial perturbations typically involves training a generator to maximize embedding separation between clean and adversarial images at a single mid-layer of a source model. In this work, we build on this approach and intr ...

IEEE2025

Bridging Discrete Choice Modeling and Neural Networks: Unifying Statistical Rigor and Predictive Power

Brian Alan Tappy-Sifringer

This thesis explores the innovative integration of Discrete Choice Modeling (DCM) and Machine Learning (ML), specifically Neural Networks (NN), within the transportation sector. DCM, with its highly interpretable and hand-designed models grounded in robust ...

EPFL2024

RoBétArmé Project: Human-robot collaborative construction system for shotcrete digitization and automation through advanced perception, cognition, mobility and additive manufacturing skills

Aude Billard

The importance of construction automation has grown worldwide, aiming to deliver new machineries for the automation of roads, tunnels, bridges, buildings and earth-work construction. This need is mainly driven by (i) the shortage and rising costs of skille ...

F1000 Research Ltd2024

Distributed Machine Learning Targeting Embedded Systems for Epilepsy Detection

Saleh Baghersalimi

Epileptic seizure detection and monitoring is critical in healthcare, particularly for individuals requiring continuous oversight. Current methods, primarily based on electroencephalogram (EEG) technologies, face limitations due to their complexity in term ...

EPFL2024

Fiber-Optic Force Sensing of Modular Robotic Skin for Remote and Autonomous Robot Control

Sudong Lee

2024

Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling

Michele Ceriotti, Alexander Jan Goscinski, Lorenzo Gigli

The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of density functional theory (DFT). However, running large simulation cells with DFT is computationally demanding, wh ...

2024

PyTorch 2 Model for the prediction of single-artery segmentations for "AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation"

Edward Carlo Giorgio Andò, Thabodhan Mahendiran

These are pytorch model weights for the angiopy-segmentation deep learning model, descriped in a paper published in 2024 in the International Journal of Cardiology: AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery se ...

Zenodo2024

Constrained Zero-Shot Neural Architecture Search on Small Classification Dataset

Liza Carol Andrea Dunbar

The rapid evolution of Deep Learning (DL) has brought about significant transformations across scientific domains, marked by the development of increasingly intricate models demanding powerful GPU platforms. However, edge applications like wearables and mo ...

IEEE2024

Quantum Variational Algorithms: Exploring Applications and Potentials Amid Trainability and Classical Simulability Limitations

Su Yeon Chang

Since its proposal, quantum computing has made significant strides in various domains. Among the different emerging techniques, Variational Quantum Algorithms (VQAs) have become one of the most promising approaches in the current era, where quantum computa ...

EPFL2024

Annotation Cost-Efficient Active Learning for Deep Metric Learning-Driven Remote Sensing Image Retrieval

Gencer Sümbül

Deep metric learning (DML) has shown to be effective for content-based image retrieval (CBIR) in remote sensing (RS). Most of the DML methods for CBIR rely on a high number of annotated images to accurately learn model parameters of deep neural networks (D ...

2024