The cross-sectional regression models included a practice-level random-intercept to account for unmeasured practice-level heterogeneity.54 The longitudinal models included only time-varying covariates ...

Predicting gut microbiota dynamics in obese individuals from cross-sectional data. Ena Melvan 1,2* Andrew P. Allen 1 Tea Vuckovic 2 Irena Soljic 2 Antonio Starcevic 3. 1 School of Natural Sciences ...

The problem of time series segmentation (TSS) is to find a meaningful segmentation of a time series (TS) that captures a data-generating process with distinct states and transitions. We consider a ...

A machine learning framework for real-time gravitational wave (GW) signal classification using 1D-CNN and ResNet architectures. Developed and tested entirely on Apple Silicon hardware, the models are ...

Data science needs room to experiment, but enterprise environments want everything production-ready from day one. This tension creates a massive roadblock to innovation. The Time-To-Market Challenge ...

Methods This cross-sectional study linked primary care data from the National Cancer Diagnosis Audit (NCDA) to national cancer registration and Route to Diagnosis records. Patient symptoms recorded in ...

This innovative research highlights the promise of human mobility data, measured via smartphones, as a transformative tool for tracking when and where people move across wild landscapes ...

Data is pivotal to the advancement of intelligent communication and network systems. However, the availability of relevant data is hampered by different challenges, including scarcity, privacy issues, ...

Snorkel AI’s offerings, including Snorkel Evaluate and Snorkel Expert Data-as-a-Service, accelerate evaluation and tuning of specialized AI systems with expert data—helping teams move from ...

Remote sensing images often suffer from substantial data loss due to factors such as thick cloud cover and sensor limitations. Existing methods for imputing missing values in remote sensing images ...