Boosting Speech-to-Text software potential

The article focuses on finding ways of boosting efficiency and accuracy of Speech-to-Text (STT)-powered input. The effort is triggered by the growing popularity of the software among professional translators, which is in line with the general trend of abandoning typing in favor of speech-to-text application

Leveraging VBA in translation

The article is focused on finding ways for the VBA application in translation. Most of the macro code solutions available online date back to the early 2000s. Given the fact, the researchers decided to look into the usefulness of the instrument in today's environment, which has changed significantly over the two decade

EPR detection of presumable quantum behavior of iron oxide nanoparticles in dendrimeric nanocomposite

© 2017 Elsevier B.V.The superparamagnetic γ-Fe2O3 nanoparticles (average diameter of 2.5 nm) encapsulated in poly(propylene imine) dendrimer have been investigated by Electron Magnetic Resonance (EMR). EMR measurements have been recorded in perpendicular and parallel configurations in the wide temperature range (4.2–300 K). It has been shown that the model based on the spin value S = 30, corresponding to the total magnetic moment of the nanoparticle, can be used to interpret the experimental results and the proof of the quantum behavior of γ-Fe2O3 nanoparticles

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Plasma Electron Source with a Beam of Large Cross Section

Plasma Electron Source with a Beam of Large Cross Section / V. A. Gruzdev, V. G. Zalesskii, D. A. Antonovich, Yu. P. Golubev (Polotsk State University)We present the design and characteristics of a plasma electron source based on a discharge in crossed E × H fields, which provides the formation of technological high‐energy beams with a large cross section in steady‐state and pulsed regimes, and consider conditions for excitation of a high‐current anomalous glow discharge forming an emitting plasma in a pulsed regime

EPR detection of presumable quantum behavior of iron oxide nanoparticles in dendrimeric nanocomposite

© 2017 Elsevier B.V.The superparamagnetic γ-Fe2O3 nanoparticles (average diameter of 2.5 nm) encapsulated in poly(propylene imine) dendrimer have been investigated by Electron Magnetic Resonance (EMR). EMR measurements have been recorded in perpendicular and parallel configurations in the wide temperature range (4.2–300 K). It has been shown that the model based on the spin value S = 30, corresponding to the total magnetic moment of the nanoparticle, can be used to interpret the experimental results and the proof of the quantum behavior of γ-Fe2O3 nanoparticles