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Deep learning classification of inverted papilloma malignant transformation using 3D convolutional neural networks and m...

Published in International forum of allergy & rhinology

Distinguishing benign inverted papilloma (IP) tumors from those that have undergone malignant transformation to squamous cell carcinoma (IP-SCC) is important but challenging to do preoperatively. Magnetic resonance imaging (MRI) can help differentiate these 2 entities, but no established method exists that can automatically synthesize all potential...

Data-driven predictive modeling of Hubble parameter

Published in Physica Scripta

We redesign the generalized pressure dark energy (GPDE) model, which is covering three common types of pressure parameterizations, with the help of a caloric framework to construct a theoretical ground for the machine learning (ML) analysis of cosmic Hubble parameter. The theoretical setup was optimized to find out appropriate values of its arbitra...

Nanoscale mapping to assess the asymmetry of local C–V curves obtained from ferroelectric materials

Published in Japanese Journal of Applied Physics

The asymmetry in the capacitance–voltage (C–V) curves obtained from a ferroelectric material can provide information concerning the internal microstructure of a specimen. The present study visualized nanoscale switching of a HfO2-based ferroelectric thin film in real space based on assessing asymmetry using a local C–V mapping method. Several param...

Mitigating the uncertainty in small field dosimetry by leveraging machine learning strategies

Published in Physics in Medicine & Biology

Small field dosimetry is significantly different from the dosimetry of broad beams due to loss of electron side scatter equilibrium, source occlusion, and effects related to the choice of detector. However, use of small fields is increasing with the increase in indications for intensity-modulated radiation therapy and stereotactic body radiation th...

High-speed liquid crystal display simulation using parallel reservoir computing approach

Published in Japanese Journal of Applied Physics

The performance of liquid crystal displays (LCDs) is expected to be improved further with the emergence of their new applications. Numerical simulations such as the finite differential method (FDM) and the finite element method are useful in optimum design. However, they take a long time because dynamical systems in LCDs are nonlinear multiphysics ...

Magnetic phase transition of monolayer chromium trihalides investigated with machine learning: toward a universal magnet...

Published in Journal of Physics: Condensed Matter

The prediction of magnetic phase transitions often requires model Hamiltonians to describe the necessary magnetic interactions. The advance of machine learning provides an opportunity to build a unified approach that can treat various magnetic systems without proposing new model Hamiltonians. Here, we develop such an approach by proposing a novel s...

Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Il...

Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but ...

Benchmarking structural evolution methods for training of machine learned interatomic potentials

Published in Journal of Physics: Condensed Matter

When creating training data for machine-learned interatomic potentials (MLIPs), it is common to create initial structures and evolve them using molecular dynamics (MD) to sample a larger configuration space. We benchmark two other modalities of evolving structures, contour exploration (CE) and dimer-method (DM) searches against MD for their ability...

Recognising Gestures Using Ambient Light and Convolutional Neural Networks: Adapting Convolutional Neural Networks for G...

This paper presents how a convolutional neural network can be constructed in order to recognise gestures using photodiodes and ambient light. A number of candidates are presented and evaluated, with the most performant being adopted for in-depth analysis. This network is then compressed in order to be ran on an Arduino Nano 33 BLE microcontroller t...

From text to data inside bibliographic records. Entity recognition and entity linking of contributors and their roles fr...

Sudoc is the french higher education union catalogue. It is run by Abes. As any large database (15 million records), Sudoc has some quality issues that can negatively impact the user experience or the database maintenance efforts, e.g. the process towards a LRM compliant catalogue. Quality issues are diverse: data can be inaccurate, ambiguous, misc...