The importance of priors for l2 regularization and total variation methods in quantitative susceptibility mapping

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The detailed anisotropic dispersion of the low-temperature, low-energy magnetic excitations of the candidate spin-triplet superconductor UTe2 is revealed using inelastic neutron scattering. The magnetic excitations emerge from the Brillouin zone boundary a ...

Catastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to 0%). For models trained with multi-step AT, it has been observed that the loss function behaves locally linearly w ...

Magnetic resonance imaging (MRI) is a valuable tool for studying brain structure and function in animal and clinical studies. With the growth of public MRI repositories, access to data has finally become easier. However, filtering large datasets for potent ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of high dimensional kernels under covariate shifts. By a bias-variance decomposi ...

Here, we introduce a design, fabrication, and control methodology for large amplitude torsional microactuators powered by ultrasound. The microactuators are 3D printed from two polymers with drastically different elastic moduli as a monolithic compliant me ...

Context. Gaia DR3 has offered the scientific community a remarkable dataset of approximately one million spectra acquired with the radial velocity spectrometer (RVS) in the calcium II triplet region, which is well suited to identify very metal-poor (VMP) s ...

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 ...

The shapes of galaxies, their outer regions in particular, are important guideposts to their formation and evolution. In this work, we report on the discovery of strongly box-shaped morphologies of the otherwise well-studied elliptical and lenticular galax ...

The space industry has experienced substantial growth in recent years, leading to rapid advancements in space exploration and space-based technologies. Consequently, the study of electronics and sensor performance in extreme environments has become crucial ...

At the Large Hadron Collider (LHC), absolute luminosity calibrations obtained by the van der Meer (vdM) method are affected by the mutual electromagnetic interaction of the two beams. The colliding bunches experience relative orbit shifts, as well as optic ...

An efficient characterization of scientifically significant locations is essential prior to the return of humans to the Moon. The highest resolution imagery acquired from orbit of south-polar shadowed regions and other relevant locations remains, at best, ...

This article summarizes the recent advancements in the design, fabrication, and control of microrobotic devices for the diagnosis and treatment of brain disorders. With a focus on diverse actuation methods, we discuss how advancements in materials science ...

Recently there has been a surge of interest in understanding implicit regularization properties of iterative gradient-based optimization algorithms. In this paper, we study the statistical guarantees on the excess risk achieved by early-stopped unconstrain ...

Carbon dioxide generated by the human body and exhaled (VCO2) is commonly used for ventilation controls, while the metabolic rate (i.e., energy expenditure EE) is an important parameter defining human thermal comfort in buildings. These two parameters are ...

Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Polynomial Networks (PNs) have emerged as an alternative method with a promising ...

The first generation of S-band injectors for free-electron lasers has illustrated high reliability and performance by driving the latest generation of high brightness machines. However, the ultimate electron beam brightness of these devices is limited by t ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

The material composition of asteroids is an essential piece of knowledge in the quest to understand the formation and evolution of the Solar System. Visual to near-infrared spectra or multiband photometry is required to constrain the material composition o ...

The importance of priors for l2 regularization and total variation methods in quantitative susceptibility mapping

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Connection between f-electron correlations and magnetic excitations in UTe2

Efficient local linearity regularization to overcome catastrophic overfitting

Automated quality control of small animal MR neuroimaging data

Towards Trustworthy Deep Learning for Image Reconstruction

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

3D printed large amplitude torsional microactuators powered by ultrasound

Gaia's brightest very metal-poor (VMP) stars Metallicity catalogue of a thousand VMP stars from Gaia's radial velocity spectrometer spectra

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

The Halos and Environments of Nearby Galaxies (HERON) survey IV. Complexity in the boxy galaxies NGC720 and NGC2768

Single-Photon Avalanche Diode Image Sensors for Harsh Radiation Environments

Impact of beam-beam effects on absolute luminosity calibrations at the CERN Large Hadron Collider

A lunar reconnaissance drone for cooperative exploration and high-resolution mapping of extreme locations

Small-scale robotic devices for medical interventions in the brain

The statistical complexity of early-stopped mirror descent

Inter- and intra-individual variability in CO2 production and metabolic rate

Regularization of polynomial networks for image recognition

Regularization of polynomial networks for image recognition

Toward a brightness upgrade to the SwissFEL: A high gradient traveling-wave rf photogun

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Euclid: Identification of asteroid streaks in simulated images using deep learning