Plasma confinement mode classification using a sequence-to-sequence neural network with attention

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Object detection, a fundamental task in computer vision, is crucial for various intelligent edge computing applications. However, object detection algorithms are usually heavy in computation, hindering their deployments on resource-constrained edge devices ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

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 ...

The optimistic gradient method is useful in addressing minimax optimization problems. Motivated by the observation that the conventional stochastic version suffers from the need for a large batch size on the order of O(ϵ-2) to achieve an ϵ-stationary solut ...

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 ...

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 ...

The digitization of 3D deformable objects remains a significant challenge in computer graphics and vision, particularly in the accurate modeling of garments. Garments exhibit complex shape variability, non-rigid deformations, and frequent self-occlusion, m ...

Phase contrast imaging (PCI) is an established and powerful technique for measuring density fluctuations in plasmas and has been successfully applied to several fusion devices. Rooted in a concept first developed for microscopy, PCI belongs to the category ...

Magnons are the quanta of spin waves and transport angular momenta through magnetically ordered materials. They can be used to distribute and control on-chip GHz signals without charge flow, thereby avoiding Joule heating. Beyond multiplexed signal process ...

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Jointly estimating hand and object shape facilitates the grasping task in human-to-robot handovers. Relying on handcrafted prior knowledge about the geometric structure of the object fails when generalising to unseen objects, and depth sensors fail to dete ...

The performance of magnetic confinement fusion devices, such as tokamaks, is strongly correlated to the phenomena that occur in the boundary region of the plasma core that faces the wall of the device. The dominant cross-field transport mechanisms from the ...

MammAlps is a multimodal and multi-view dataset of wildlife behavior monitoring. Nine camera-traps were placed at three monitoring sites in the Swiss National Park, from which we curated over 14 hours of video with audio, 2D segmentation maps and 8.5 hours ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Here we report the progress of the development and optimization of operational scenarios for ITER and beyond, focusing upon baseline, hybrid, and steady-state scenarios since 2007. This includes advancements made by the integrated operation scenarios (IOS) ...

Under the auspices of EUROfusion, the ITER baseline (IBL) scenario has been jointly investigated on AUG and TCV in the past years and this paper reports on the developments on TCV. It is found that the performance of TCV IBL is mainly limited by (neoclassi ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

Image steganalysis, detecting hidden data in digital images, is essential for enhancing digital security. Traditional steganalysis methods typically rely on large, pre-labeled image datasets, which are difficult and costly to compile. To address this, this ...

Plasma confinement mode classification using a sequence-to-sequence neural network with attention

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Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

Topics in statistical physics of high-dimensional machine learning

Diffusion Stochastic Optimization for Min-Max Problems

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Partial discharge localization in power transformer tanks using machine learning methods

Neural Garment Modeling

Tangentially viewing phase contrast imaging for turbulence measurements in toroidal fusion devices

Perspective on nonvolatile magnon-signal storage and in-memory computation for low-power consuming magnonics

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Robust machine learning for neuroscientific inference

Stereo Hand-Object Reconstruction for Human-to-Robot Handover

Divertor turbulence characterisation using Gas Puff Imaging in the TCV tokamak

MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps

Aggregating Spatial and Photometric Context for Photometric Stereo

Integrated operation scenarios: Chapter 6 of the special issue: on the path to tokamak burning plasma operation

Progress in the development of the ITER baseline scenario in TCV

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Image steganalysis using active learning and hyperparameter optimization