Dimension embedding for big data in radio interferometry

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The nitrogen-vacancy (NV) center in diamond is a powerful and versatile quantum sensor for diverse quantities. In particular, relaxometry (or T1), can be used to detect magnetic noise at the nanoscale. For experiments with single NV centers the analysis of ...

One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...

Orbital angular momentum (OAM) spectrum diagnosis is a fundamental building block for diverse OAM-based systems. Among others, the simple on-axis interferometric measurement can retrieve the amplitude and phase information of complex OAM spectra in a few s ...

We study the energy-critical nonlinear Schrodinger equation with randomised initial data in dimensions d > 6. We prove that the Cauchy problem is almost surely globally well-posed with scattering for randomised supercritical initial data in H-s(Rd) wheneve ...

Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significan ...

Data and code associated with the following paper: V. Vento et al, Nano Lett. 2023 A thorough explanation of the experiment performed is available there. The name of each sub-folder and file in Maps_data_code.zip indicates the correspondin ...

This upload contains the relevant scripts, notebooks, and datasets to reproduce the numerically obtained results of the Journal article "Three-dimensional buoyant hydraulic fractures: finite volume release" by Möri and Lecampion, (2023). ...

Dynamical Systems (DS) are fundamental to the modeling and understanding time evolving phenomena, and have application in physics, biology and control. As determining an analytical description of the dynamics is often difficult, data-driven approaches are ...

Object detection plays a critical role in various computer vision applications, encompassing domains like autonomous vehicles, object tracking, and scene understanding. These applica- tions rely on detectors that generate bounding boxes around known object ...

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. Our method learns the features necessary for an effective low-dimensi ...

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...

Low-dimensional representations of underdamped systems often provide useful insights and analytical tractability. Here, we build such representations via information projections, obtaining an optimal model that captures the most information on observed spa ...

Dimension embedding for big data in radio interferometry

Chattez avec Graph Search

Optimizing Data Processing for Nanodiamond Based Relaxometry

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

Single-shot Kramers-Kronig complex orbital angular momentum spectrum retrieval

ALMOST SURE SCATTERING OF THE ENERGY-CRITICAL NLS IN d > 6

Dated: Guidelines for Creating Synthetic Datasets for Engineering Design Applications

Data and code associated with the paper 'Mode-Specific Coupling of Nanoparticle-on-Mirror Cavities with Cylindrical Vector Beams'

Data and Workflow to: Three-dimensional buoyant hydraulic fractures: finite volume release (Möri and Lecampion, (2023))

Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps

Improving Object Detection under Domain Shifts

Validating functional redundancy with mixed generative adversarial networks

A machine learning approach to portfolio pricing and risk management for high-dimensional problems

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Information-driven transitions in projections of underdamped dynamics