33,576 research outputs found
Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates
Computational drug design based on artificial intelligence is an emerging
research area. At the time of writing this paper, the world suffers from an
outbreak of the coronavirus SARS-CoV-2. A promising way to stop the virus
replication is via protease inhibition. We propose an evolutionary
multi-objective algorithm (EMOA) to design potential protease inhibitors for
SARS-CoV-2's main protease. Based on the SELFIES representation the EMOA
maximizes the binding of candidate ligands to the protein using the docking
tool QuickVina 2, while at the same time taking into account further objectives
like drug-likeliness or the fulfillment of filter constraints. The experimental
part analyzes the evolutionary process and discusses the inhibitor candidates.Comment: 15 pages, 7 figures, submitted to PPSN 202
Building with Drones: Accurate 3D Facade Reconstruction using MAVs
Automatic reconstruction of 3D models from images using multi-view
Structure-from-Motion methods has been one of the most fruitful outcomes of
computer vision. These advances combined with the growing popularity of Micro
Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools
ubiquitous for large number of Architecture, Engineering and Construction
applications among audiences, mostly unskilled in computer vision. However, to
obtain high-resolution and accurate reconstructions from a large-scale object
using SfM, there are many critical constraints on the quality of image data,
which often become sources of inaccuracy as the current 3D reconstruction
pipelines do not facilitate the users to determine the fidelity of input data
during the image acquisition. In this paper, we present and advocate a
closed-loop interactive approach that performs incremental reconstruction in
real-time and gives users an online feedback about the quality parameters like
Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We
also propose a novel multi-scale camera network design to prevent scene drift
caused by incremental map building, and release the first multi-scale image
sequence dataset as a benchmark. Further, we evaluate our system on real
outdoor scenes, and show that our interactive pipeline combined with a
multi-scale camera network approach provides compelling accuracy in multi-view
reconstruction tasks when compared against the state-of-the-art methods.Comment: 8 Pages, 2015 IEEE International Conference on Robotics and
Automation (ICRA '15), Seattle, WA, US
Thermal Transients in District Heating Systems
Heat fluxes in a district heating pipeline systems need to be controlled on
the scale from minutes to an hour to adjust to evolving demand. There are two
principal ways to control the heat flux - keep temperature fixed but adjust
velocity of the carrier (typically water) or keep the velocity flow steady but
then adjust temperature at the heat producing source (heat plant). We study the
latter scenario, commonly used for operations in Russia and Nordic countries,
and analyze dynamics of the heat front as it propagates through the system.
Steady velocity flows in the district heating pipelines are typically turbulent
and incompressible. Changes in the heat, on either consumption or production
sides, lead to slow transients which last from tens of minutes to hours. We
classify relevant physical phenomena in a single pipe, e.g. turbulent spread of
the turbulent front. We then explain how to describe dynamics of temperature
and heat flux evolution over a network efficiently and illustrate the network
solution on a simple example involving one producer and one consumer of heat
connected by "hot" and "cold" pipes. We conclude the manuscript motivating
future research directions.Comment: 31 pages, 7 figure
ROAM: a Rich Object Appearance Model with Application to Rotoscoping
Rotoscoping, the detailed delineation of scene elements through a video shot,
is a painstaking task of tremendous importance in professional post-production
pipelines. While pixel-wise segmentation techniques can help for this task,
professional rotoscoping tools rely on parametric curves that offer the artists
a much better interactive control on the definition, editing and manipulation
of the segments of interest. Sticking to this prevalent rotoscoping paradigm,
we propose a novel framework to capture and track the visual aspect of an
arbitrary object in a scene, given a first closed outline of this object. This
model combines a collection of local foreground/background appearance models
spread along the outline, a global appearance model of the enclosed object and
a set of distinctive foreground landmarks. The structure of this rich
appearance model allows simple initialization, efficient iterative optimization
with exact minimization at each step, and on-line adaptation in videos. We
demonstrate qualitatively and quantitatively the merit of this framework
through comparisons with tools based on either dynamic segmentation with a
closed curve or pixel-wise binary labelling
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