Machine learning and statistical physics: preface

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Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in public rep ...

Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challengi ...

The first measurement of ϕ (1020) meson production in fixed-target p Ne collisions at

\sqrt{s_{NN}}

= 68 . 5 GeV is presented. The ϕ (1020) mesons are reconstructed in their K + K − decay in a data sample consisting of proton collisions on neon nuclei ...

Accurate human trajectory prediction is one of the most crucial tasks for autonomous driving, ensuring its safety. Yet, existing models often fail to fully leverage the visual cues that humans subconsciously communicate when navigating the space. In this w ...

While writing peer reviews resembles an important task in science, education, and large organizations, providing fruitful suggestions to peers is not a straightforward task, as different user interaction designs of text suggestion interfaces can have diver ...

Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the poten ...

Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘pre ...

Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes ...

Many technologically important material properties are underpinned by disorder and short-range structural correlations; therefore, elucidating structure-property relationships in functional materials requires understanding both the average and the local st ...

At the intersection of Topological Data Analysis (TDA) and machine learning, the field of cellular signal processing has advanced rapidly in recent years. In this context, each signal on the cells of a complex is processed using the combinatorial Laplacian ...

© The Author(s) 2024.Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, h ...

We analyze and implement the kernel ridge regression (KR) method developed in Filipovic et al. (Stripping the discount curve-a robust machine learning approach. Swiss Finance Institute Research Paper No. 22-24. SSRN. https://ssrn.com/abstract=4058150, 2022 ...

Proteins are macromolecules considered as the building blocks of cells because they are at the heart of almost every cellular task, ranging from chemical to mechanical processes. For instance, proteins play a role as enzymes for digestion, transporters of ...

Reflectivity modulation is a critical feature for applicationsin telecommunications, 3D imaging and printing, advanced laser machining,or portable displays. Tunable metasurfaces have recently emerged asa promising implementation for miniaturized and high-p ...

Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system ...

Irregular sampling of time series in electronic health records (EHRs) presents a challenge for the development of machine learning models. Additionally, the pattern of missing data in certain clinical variables is not random, but depends on the decisions o ...

High-frequency (HF) signals are ubiquitous in the industrial world and are of great use for monitoring of industrial assets. Most deep-learning tools are designed for inputs of fixed and/or very limited size and many successful applications of deep learnin ...

Inorganic metal halide perovskites such as CsPbI3 are promising for high-performance, reproducible, and robust solar cells. However, inorganic perovskites are sensitive to humidity, which causes the transformation from the black phase to the yellow & delta ...

Screening Papanicolaou test samples has proven to be highly effective in reducing cervical cancer-related mortality. However, the lack of trained cytopathologists hinders its widespread implementation in low-resource settings. Deep learning-assisted telecy ...

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a communitywide challenge using the largest availab ...

Machine learning and statistical physics: preface

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Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem

Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches

Measurement of ϕ(1020) meson production in fixed-target pNe collisions at $ \sqrt{s_{NN}} $ = 68.5 GeV

Social-Pose: Enhancing Trajectory Prediction with Human Body Pose

Enhancing Peer Review with AI-Powered Suggestion Generation Assistance: Investigating the Design Dynamics

Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity

A prediction rigidity formalism for low-cost uncertainties in trained neural networks

Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process

Unravelling the components of diffuse scattering using deep learning

Morse theoretic signal compression and reconstruction on chain complexes

A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting

Stripping the Swiss discount curve using kernel ridge regression

Impact of phylogeny on inference from protein sequences: from models to natural data

Broadband Mechanically Tunable Metasurface Reflectivity Modulator in the Visible Spectrum

Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data

Point-process-based Representation Learning for Electronic Health Records

Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series

Stabilization of Inorganic Perovskite Solar Cells with a 2D Dion-Jacobson Passivating Layer

Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting