Publications related to Optical fibre based quench detection in HTS applications using machine learning classifiers

Related publications (30)

Large impact of phonon lineshapes on the superconductivity of solid hydrogen

Phonon anharmonicity plays a crucial role in determining the stability and vibrational properties of high-pressure hydrides. Furthermore, strong anharmonicity can render phonon quasiparticle picture obsolete questioning standard approaches for modeling sup ...

Nature Portfolio2024

Validation and Application of Hysteresis Loss Model for HTS Stacks and Conductors for Fusion Applications

Nikolay Bykovskiy

Numerous conductor designs for pulsed magnets based on High Temperature Superconductors (HTS), featuring stacks of tapes are currently being proposed. A major contribution to the AC losses is expected to be given by hysteresis losses. Several numerical mod ...

IEEE Institute of Electrical and Electronics Engineers2024

Thermoelectric power of overdoped Tl2201 crystals: charge density waves and T1 and T2 resistivities

Matthias Carsten Putzke

We report measurements of the in-plane thermoelectric power (TEP) for an overdoped (OD) crystal of the single layer cuprate superconductor Tl2Ba2CuO6+x (Tl2201) at several hole concentrations (p), from 300 or 400 K to below the superconducting transition t ...

Iop Publishing Ltd2024

Technology-Aware Logic Synthesis for Superconducting Electronics

Giovanni De Micheli, Alessandro Tempia Calvino, Siang-Yun Lee, Dewmini Sudara Marakkalage, Mingfei Yu, Rassul Bairamkulov

Superconducting electronics provide us with cryogenic digital circuits that can rival established technologies in performance and energy consumption. Today, the lack of tools for the design of large-scale integrated superconducting circuits is a major obst ...

2024

Boosting likelihood learning with event reweighting

Andrea Wulzer, Alfredo Glioti, Siyu Chen

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Springer Nature2024

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

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Arnout Jan J Devos

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

EPFL2024

The magnet feeders for the European DEMO fusion reactor: Conceptual design and recent advances

Rainer Wesche, Roberto Guarino

The magnet feeders are part of the critical systems of a fusion reactor since they represent the interface between the in-cryostat components at 4.5 K and the room-temperature cryogenic plant and power supply. The European DEMO tokamak foresees 16 toroidal ...

2024

Incorporating Projective Geometry into Deep Learning

Michal Jan Tyszkiewicz

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

EPFL2024

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Romain Christophe Rémy Fleury, Janez Rus

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

2024

Studies of crystal collimation for heavy ion operation at the LHC

Rongrong Cai

At CERN's Large Hadron Collider (LHC), proton and heavy-ion beams are accelerated to multi-TeV energies to be collided for the needs of the scientific community around the world. The total stored beam energy of tens to hundreds ofMJ creates potential threa ...

EPFL2024

Parametric Wavelength Conversion in Waveguiding Structures: Efficiency, Bandwidth and Polarization

Arman Ayan

Photonics integrated circuits are a promising solution for the growing demands of data transmission and future system-on-chip technologies. Within this context, nonlinear optical interactions offer unique opportunities for all-optical processing, sampling, ...

EPFL2024

Hydrogen-induced tunable remanent polarization in a perovskite nickelate

Michele Kotiuga

Materials with field-tunable polarization are of broad interest to condensed matter sciences and solid-state device technologies. Here, using hydrogen (H) donor doping, we modify the room temperature metallic phase of a perovskite nickelate NdNiO3 into an ...

Nature Portfolio2024

Towards Scalable Electronics: Synthetic 2D Materials for Large-Area 2D Circuit Integration

Zhenyu Wang

In the past decades, a significant increase of the transistor density on a chip has led to exponential growth in computational power driven by Moore's law. To overcome the bottleneck of traditional von-Neumann architecture in computational efficiency, effo ...

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

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Sabine Süsstrunk, Mathieu Salzmann, Tong Zhang

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

2024

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Seyedbehzad Bozorgtabar

Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the pres ...

Elsevier2024

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

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

Alexandra Cameron Karpilow

Situational awareness strategies are essential for the reliable and secure operation of the electric power grid which represents critical infrastructure in modern society. With the rise of converter-interfaced renewable generation and the consequent shift ...

EPFL2024

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mattia Atzeni

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024