Deep Learning

Rendering 3D virtual scenarios has become a popular alternative for generating per-pixel-labeled image datasets, especially in fields like autonomous driving. The approach is valuable for training neural perception models, such as semantic segmentation models, particularly when data might be scarce, expensive, or difficult to collect. However, fundamental questions persist within the research community regarding the generation and processing of these synthetic images, particularly a better understanding of the key factors influencing the performance of deep learning models trained with such synthetic images.

Aero-structural shape design and optimization of bridge decks rely on accurately estimating their self-excited aeroelastic forces within the design domain. The inherent nonlinear features of bluff body aerodynamics and the high cost of wind tunnel tests and computational fluid dynamics (CFD) simulations make their emulation as a function of deck shape and reduced velocity challenging. We propose a time domain emulation strategy harnessing temporal fusion transformers (TFTs) to predict the self-excited forces time series before their integration into FDs.

This Python library specializes in the generation of synthetic tabular data. It has a diverse range of statistical, Machine Learning (ML) and Deep Learning (DL) methods to accurately capture patterns in real datasets and synthetically replicate them.

Real-time play recognition and classification algorithms are crucial for automating video production and live broadcasts of sporting events. However, current methods relying on human pose estimation and deep neural networks introduce high latency on commodity hardware, limiting their usability in low-cost real-time applications. We present PlayNet, a novel approach to real-time handball play classification.