14,861 research outputs found
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution
framework to generate continuous (grid-free) spatio-temporal solutions from the
low-resolution inputs. While being computationally efficient, MeshfreeFlowNet
accurately recovers the fine-scale quantities of interest. MeshfreeFlowNet
allows for: (i) the output to be sampled at all spatio-temporal resolutions,
(ii) a set of Partial Differential Equation (PDE) constraints to be imposed,
and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal
domains owing to its fully convolutional encoder. We empirically study the
performance of MeshfreeFlowNet on the task of super-resolution of turbulent
flows in the Rayleigh-Benard convection problem. Across a diverse set of
evaluation metrics, we show that MeshfreeFlowNet significantly outperforms
existing baselines. Furthermore, we provide a large scale implementation of
MeshfreeFlowNet and show that it efficiently scales across large clusters,
achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of
less than 4 minutes.Comment: Supplementary Video: https://youtu.be/mjqwPch9gDo. Accepted to SC2
The Visual Social Distancing Problem
One of the main and most effective measures to contain the recent viral
outbreak is the maintenance of the so-called Social Distancing (SD). To comply
with this constraint, workplaces, public institutions, transports and schools
will likely adopt restrictions over the minimum inter-personal distance between
people. Given this actual scenario, it is crucial to massively measure the
compliance to such physical constraint in our life, in order to figure out the
reasons of the possible breaks of such distance limitations, and understand if
this implies a possible threat given the scene context. All of this, complying
with privacy policies and making the measurement acceptable. To this end, we
introduce the Visual Social Distancing (VSD) problem, defined as the automatic
estimation of the inter-personal distance from an image, and the
characterization of the related people aggregations. VSD is pivotal for a
non-invasive analysis to whether people comply with the SD restriction, and to
provide statistics about the level of safety of specific areas whenever this
constraint is violated. We then discuss how VSD relates with previous
literature in Social Signal Processing and indicate which existing Computer
Vision methods can be used to manage such problem. We conclude with future
challenges related to the effectiveness of VSD systems, ethical implications
and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this
manuscript and they are listed by alphabetical order. Under submissio
Dynamical strategies for obstacle avoidance during Dictyostelium discoideum aggregation: a Multi-agent system model
Chemotaxis, the movement of an organism in response to chemical stimuli, is a
typical feature of many microbiological systems. In particular, the social
amoeba \textit{Disctyostelium discoideum} is widely used as a model organism,
but it is not still clear how it behaves in heterogeneous environments. A few
models focusing on mechanical features have already addressed the question;
however, we suggest that phenomenological models focusing on the population
dynamics may provide new meaningful data. Consequently, by means of a specific
Multi-agent system model, we study the dynamical features emerging from complex
social interactions among individuals belonging to amoeba colonies.\\ After
defining an appropriate metric to quantitatively estimate the gathering
process, we find that: a) obstacles play the role of local topological
perturbation, as they alter the flux of chemical signals; b) physical obstacles
(blocking the cellular motion and the chemical flux) and purely chemical
obstacles (only interfering with chemical flux) elicit similar dynamical
behaviors; c) a minimal program for robustly gathering simulated cells does not
involve mechanisms for obstacle sensing and avoidance; d) fluctuations of the
dynamics concur in preventing multiple stable clusters. Comparing those
findings with previous results, we speculate about the fact that chemotactic
cells can avoid obstacles by simply following the altered chemical gradient.
Social interactions are sufficient to guarantee the aggregation of the whole
colony past numerous obstacles
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