208 research outputs found
Learning Behavioural Context
The original publication is available at www.springerlink.co
Data driven estimation of building interior plans
This work investigates constructing plans of building interiors using learned building measurements. In particular, we address the problem of accurately estimating dimensions of rooms when measurements of the interior space have not been captured. Our approach focuses on learning the geometry, orientation and occurrence of rooms from a corpus of real-world building plan data to form a predictive model. The trained predictive model may then be queried to generate estimates of room dimensions and orientations. These estimates are then integrated with the overall building footprint and iteratively improved using a two-stage optimisation process to form complete interior plans.
The approach is presented as a semi-automatic method for constructing plans which can cope with a limited set of known information and constructs likely representations of building plans through modelling of soft and hard constraints. We evaluate the method in the context of estimating residential house plans and demonstrate that predictions can effectively be used for constructing plans given limited prior knowledge about the types of rooms and their topology
Labour Relations and Modes of Employment
Seventh Framework Programme (FP7)info:eu-repo/grantAgreement/EC/FP7/266833Hervorming Sociale Regelgevin
Deep Learning of Representations: Looking Forward
Deep learning research aims at discovering learning algorithms that discover
multiple levels of distributed representations, with higher levels representing
more abstract concepts. Although the study of deep learning has already led to
impressive theoretical results, learning algorithms and breakthrough
experiments, several challenges lie ahead. This paper proposes to examine some
of these challenges, centering on the questions of scaling deep learning
algorithms to much larger models and datasets, reducing optimization
difficulties due to ill-conditioning or local minima, designing more efficient
and powerful inference and sampling procedures, and learning to disentangle the
factors of variation underlying the observed data. It also proposes a few
forward-looking research directions aimed at overcoming these challenges
Collisional and Radiative Processes in Optically Thin Plasmas
Most of our knowledge of the physical processes in distant plasmas is obtained
through measurement of the radiation they produce. Here we provide an overview of the
main collisional and radiative processes and examples of diagnostics relevant to the microphysical
processes in the plasma. Many analyses assume a time-steady plasma with ion
populations in equilibrium with the local temperature and Maxwellian distributions of particle
velocities, but these assumptions are easily violated in many cases. We consider these
departures from equilibrium and possible diagnostics in detail
Are biological systems poised at criticality?
Many of life's most fascinating phenomena emerge from interactions among many
elements--many amino acids determine the structure of a single protein, many
genes determine the fate of a cell, many neurons are involved in shaping our
thoughts and memories. Physicists have long hoped that these collective
behaviors could be described using the ideas and methods of statistical
mechanics. In the past few years, new, larger scale experiments have made it
possible to construct statistical mechanics models of biological systems
directly from real data. We review the surprising successes of this "inverse"
approach, using examples form families of proteins, networks of neurons, and
flocks of birds. Remarkably, in all these cases the models that emerge from the
data are poised at a very special point in their parameter space--a critical
point. This suggests there may be some deeper theoretical principle behind the
behavior of these diverse systems.Comment: 21 page
The Essential Elements of a Risk Governance Framework for Current and Future Nanotechnologies
Societies worldwide are investing considerable resources into the safe development and use of nanomaterials. Although each of these protective efforts is crucial for governing the risks of nanomaterials, they are insufficient in isolation. What is missing is a more integrative governance approach that goes beyond legislation. Development of this approach must b
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