The conditional correlation attack: a practical attack on Bluetooth encryption

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Discomfort glare from daylight is common in workspaces, where daylight and luminance contrast between light source and background can be excessive. Fabric shadings can mitigate glare, but their light transmission properties remain difficult to characterize ...

Current fatigue load models (FLMs) for road bridges are typically calibrated based on steel and reinforced-concrete bridges. These models are not likely applicable to fiber–polymer composite bridges, primarily because the damage rate—defined as the slope o ...

Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease1,2. However, existing data-sharing regulations limit the scope of such collaborations3. Although crypt ...

Our understanding of the patterns of internal migration that underlie the fast urbanization in developing countries is mainly based on theory and country case studies. We test the hypothesis of a mobility transition from a comparative perspective by analyz ...

AIM: To characterise the corticoreticular pathway (CRP) in a case -control cohort of adolescent idiopathic scoliosis (AIS) patients using high -resolution slice -accelerated readoutsegmented echo -planar diffusion tensor imaging (DTI) to enhance the discri ...

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 ...

Central aortic diastolic pressure decay time constant ( ) is according to the two-element Windkessel model equal to the product of total peripheral resistance (R) times total arterial compliance (C ). As such, it is related to arterial stiffness, which has ...

Non-contact, fluid-mediated manipulation of objects using underactuated soft robots or structures presents unique control challenges, demanding a nuanced understanding of fluid dynamics and innovative control methods. In this study, we investigate methods ...

Context. The Euclid mission of the European Space Agency will perform a survey of weak lensing cosmic shear and galaxy clustering in order to constrain cosmological models and fundamental physics. Aims. We expand and adjust the mock Euclid likelihoods of t ...

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

Primordial features, in particular oscillatory signals, imprinted in the primordial power spectrum of density perturbations represent a clear window of opportunity for detecting new physics at high-energy scales. Future spectroscopic and photometric measur ...

Breast cancer is a significant global health burden, causing a substantial number of deaths. Systemic metastatic tumour cell dissemination is a major cause of poor outcomes. Understanding the mechanisms underlying metastasis is crucial for effective interv ...

In tokamak experiments, sufficiently strong E x B flow shear reduces turbulent transport, thereby improving the prospects for fusion power plants. It is therefore of great importance to efficiently explore parameter space to find where strong plasma flow c ...

Point clouds are effective data structures for the rep- resentation of three-dimensional media and hence adopted in a wide range of practical applications. In many cases, the portrayed data is expected to be visualized by humans. After acquisition, point c ...

Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that this may result in sub-optimal convergence due to the challenges associated ...

Background: Compared with conventional unimodal analysis, understanding how brain function and structure relate to one another opens a new biologically relevant assessment of neural mechanisms. However, how function-structure dependencies (FSDs) evolve thr ...

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the pe ...

Current cryptographic solutions will become obsolete with the arrival of large-scale universal quantum computers. As a result, the National Institute of Standards and Technology supervises a post-quantum standardization process which involves evaluating ca ...

Fluid antenna systems (FAS) are an emerging technology that promises a significant diversity gain even in the smallest spaces. It consists of a freely moving antenna in a small linear space to pick up the strongest received signal. Previous works in the li ...

The conditional correlation attack: a practical attack on Bluetooth encryption

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User evaluations of fabric shades with sun in the field of view: Glare model performance and implications for EN14501 Classification

Methodology for Calibrating Fatigue Load Models for Composite Road Bridges

Secure and federated genome-wide association studies for biobank-scale datasets

Internal Migration Over the Course of Urbanization in Developing Countries

The hindbrain and cortico-reticular pathway in adolescent idiopathic scoliosis

Partial discharge localization in power transformer tanks using machine learning methods

Novel theory and potential applications of central diastolic pressure decay time constant

Non-Contact Manipulation of Objects in Fluid with Soft Robots Through Behavioral Primitives and Finite State Machine

Euclid: Validation of the MontePython forecasting tools

Efficient and insightful descriptors for representing molecular and material space

Spectral Estimators for High-Dimensional Matrix Inference

Euclid: The search for primordial features

Unveiling breast cancer metastasis through an advanced X-ray imaging approach

A new quasilinear model for turbulent momentum transport in tokamaks with flow shear and plasma shaping

Towards a multiscale point cloud structural similarity metric

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Longitudinal Analysis of Brain Function-Structure Dependencies in 22q11.2 Deletion Syndrome and Psychotic Symptoms

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

Side-channel analysis of isogeny-based key encapsulation mechanisms and hash-based digital signatures

A New Analytical Approximation of the Fluid Antenna System Channel