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Found 9 papers, 1 papers with code
How neural networks learn to classify chaotic time series
In particular, we show that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
Towards a Numerical Proof of Turbulence Closure
The development of turbulence closure models, parametrizing the influence of small non-resolved scales on the dynamics of large resolved ones, is an outstanding theoretical challenge with vast applicative relevance.
Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control
To successfully employ pedestrian tracking techniques in research and technology, it is crucial to validate and benchmark them for accuracy.
Controlling Rayleigh-Bénard convection via Reinforcement Learning
Thermal convection is ubiquitous in nature as well as in many industrial applications.
Pedestrian orientation dynamics from high-fidelity measurements
We investigate in real-life conditions and with very high accuracy the dynamics of body rotation, or yawing, of walking pedestrians - an highly complex task due to the wide variety in shapes, postures and walking gestures.
Deep learning velocity signals allows to quantify turbulence intensity
Turbulence, the ubiquitous and chaotic state of fluid motions, is characterized by strong and statistically non-trivial fluctuations of the velocity field, over a wide range of length- and time-scales, and it can be quantitatively described only in terms of statistical averages.
StampNet: unsupervised multi-class object discovery
Unsupervised object discovery in images involves uncovering recurring patterns that define objects and discriminates them against the background.
Accurate pedestrian localization in overhead depth images via Height-Augmented HOG
We tackle the challenge of reliably and automatically localizing pedestrians in real-life conditions through overhead depth imaging at unprecedented high-density conditions.
Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields
Even though hand-crafted image analysis algorithms are successful in many common cases, they fail frequently when there are complex interactions of multiple objects in the image.
