A Novel Technique for Online Partial Discharge Pattern Recognition in Large Electrical Motors

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Understanding the processes that transform star-forming galaxies into quiescent ones is key to unraveling the role of environment in galaxy evolution. We present measurements of the luminosity functions (LFs) and stellar mass functions (SMFs) of passive re ...

In real-world speech data processing, the scarcity of annotated data and the abundance of unlabelled speech data present a significant challenge. To address this, we propose an efficient data selection pipeline for fine-tuning ASR models by generating pseu ...

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

Urban proximity planning is foreseen as a solution to foster a “sustainable” city, including economic viability, environmental soundness and social inclusivity. This paper focuses on the inclusivity aspects by questioning the adoption of urban proximities: ...

This thesis reports high energy-density electrostatic actuators for use in soft robotics. This thesis has two main parts: a) a detailed study of electro-adhesion using microfabricated electrodes, and b) a flexi-ble fiber-shaped linear motor. Electro-adhesi ...

Multi-object spectroscopic galaxy surveys typically make use of photometric and colour criteria to select their targets. That is not the case of Euclid, which will use the NISP slitless spectrograph to record spectra for every source over its field of view ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

While classical in many theoretical settings-and in particular in statistical physics-inspired works-the assumption of Gaussian i.i.d. input data is often perceived as a strong limitation in the context of statistics and machine learning. In this study, we ...

Developing advanced control strategies is a relevant pathway to achieve energy savings in heating, ventilation, and air conditioning systems. Nevertheless, the studies presenting these controls usually quantify the potential energy savings by analysing a s ...

Terrestrial particle accelerators collide charged particles, then watch the trajectory of outgoing debris - but they cannot manipulate dark matter. Fortunately, dark matter is the main component of galaxy clusters, which are continuously pulled together by ...

We investigate the training dynamics of two-layer neural networks when learning multi-index target functions. We focus on multi-pass gradient descent (GD) that reuses the batches multiple times and show that it significantly changes the conclusion about wh ...

Context. The intracluster medium (ICM) is the low-density diffuse gas that fills the space between galaxies within galaxy clusters. It is primarily composed of magnetized plasma, which reaches virial temperatures of up to 10(8) K, probably due to mergers o ...

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...

In this thesis we present and analyze approximation algorithms for three different clustering problems. The formulations of these problems are motivated by fairness and explainability considerations, two issues that have recently received attention in the ...

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

The modern world is heavily reliant on electromagnetic devices to convert mechanical energy into electrical energy and vice versa. These devices are fundamental to powering our society, and the growing need for automated production lines and electrified tr ...

Molecular machines offer many opportunities for the development of responsive materials and introduce autono-mous motion in molecular systems. While basic molecular switches and motors carry out one type of motion upon being exposed to an external stimulus ...

There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

Chatter is a harmful self-excited vibration that commonly occurs during milling processes. Data -driven chatter detection and prediction is critical to achieve high surface quality and process efficiency. Most existing chatter detection approaches are base ...

Studies showed that motor expertise was found to induce improvement in language processing. Grounded and situated approaches attributed this effect to an underlying automatic simulation of the motor experience elicited by action words, similar to motor ima ...

A Novel Technique for Online Partial Discharge Pattern Recognition in Large Electrical Motors

Chattez avec Graph Search

Insights into environmental quenching at z ∼ 1: an enhancement of faint, low-mass passive galaxies in clusters

Speech Data Selection for Efficient ASR Fine-Tuning using Domain Classifier and Pseudo-Label Filtering

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Planning urban proximities: An empirical analysis of how residential preferences conflict with the urban morphologies and residential practices

High energy-density electrostatic actuators for soft robotics

Euclid: Testing photometric selection of emission-line galaxy targets?

Partial discharge localization in power transformer tanks using machine learning methods

Gaussian universality of perceptrons with random labels

Towards a classification of the building stock in terms of potential energy savings through advanced control

Hydrodynamical simulations of merging galaxy clusters: giant dark matter particle colliders, powered by gravity

The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents

The effect of pressure-anisotropy-driven kinetic instabilities on magnetic field amplification in galaxy clusters

Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

Fairness and Explainability in Clustering Problems

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Computational design and additive manufacturing of electromagnetic coils

Coupled Rotary and Oscillatory Motion in a Second-Generation Molecular Motor Pd Complex

Transfer learning application of self-supervised learning in ARPES

Exploring the effectiveness of using internal CNC system signals for chatter detection in milling process

Motor imagery training to improve language processing: What are the arguments?