Search Results for author:
Found 11 papers, 2 papers with code
Oceanic frontal divergence alters phytoplankton competition and distribution
Ecological interactions among phytoplankton occur in a moving fluid environment.
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.
Spatial population genetics with fluid flow
The growth and evolution of microbial populations is often subjected to advection by fluid flows in spatially extended environments, with immediate consequences for questions of spatial population genetics in marine ecology, planktonic diversity and origin of life scenarios.
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.
The collective effect of finite-sized inhomogeneities on the spatial spread of populations in two dimensions
In a regime where front dynamics is determined by a local front speed only, a principle of least time can be employed to predict front speed and shape.
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.
