27,229 research outputs found
Notions of optimal transport theory and how to implement them on a computer
This article gives an introduction to optimal transport, a mathematical
theory that makes it possible to measure distances between functions (or
distances between more general objects), to interpolate between objects or to
enforce mass/volume conservation in certain computational physics simulations.
Optimal transport is a rich scientific domain, with active research
communities, both on its theoretical aspects and on more applicative
considerations, such as geometry processing and machine learning. This article
aims at explaining the main principles behind the theory of optimal transport,
introduce the different involved notions, and more importantly, how they
relate, to let the reader grasp an intuition of the elegant theory that
structures them. Then we will consider a specific setting, called
semi-discrete, where a continuous function is transported to a discrete sum of
Dirac masses. Studying this specific setting naturally leads to an efficient
computational algorithm, that uses classical notions of computational geometry,
such as a generalization of Voronoi diagrams called Laguerre diagrams.Comment: 32 pages, 17 figure
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
A Review of Traffic Signal Control.
The aim of this paper is to provide a starting point for the future research within the SERC sponsored project "Gating and Traffic Control: The Application of State Space Control Theory". It will provide an introduction to State Space Control Theory, State Space applications in transportation in general, an in-depth review of congestion control (specifically traffic signal control in congested situations), a review of theoretical works, a review of existing systems and will conclude with recommendations for the research to be undertaken within this project
Stabilizing Training of Generative Adversarial Networks through Regularization
Deep generative models based on Generative Adversarial Networks (GANs) have
demonstrated impressive sample quality but in order to work they require a
careful choice of architecture, parameter initialization, and selection of
hyper-parameters. This fragility is in part due to a dimensional mismatch or
non-overlapping support between the model distribution and the data
distribution, causing their density ratio and the associated f-divergence to be
undefined. We overcome this fundamental limitation and propose a new
regularization approach with low computational cost that yields a stable GAN
training procedure. We demonstrate the effectiveness of this regularizer across
several architectures trained on common benchmark image generation tasks. Our
regularization turns GAN models into reliable building blocks for deep
learning
RFCs, MOOs, LMSs: Assorted Educational Devices\ud
This paper discusses implicit social consequences of four basic internet protocols. The results are then related to the field of computer-assisted teaching. An educational on-line community is described and compared to the emerging standard of web-based learning management.\u
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