32,090 research outputs found
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Multivariate Approaches to Classification in Extragalactic Astronomy
Clustering objects into synthetic groups is a natural activity of any
science. Astrophysics is not an exception and is now facing a deluge of data.
For galaxies, the one-century old Hubble classification and the Hubble tuning
fork are still largely in use, together with numerous mono-or bivariate
classifications most often made by eye. However, a classification must be
driven by the data, and sophisticated multivariate statistical tools are used
more and more often. In this paper we review these different approaches in
order to situate them in the general context of unsupervised and supervised
learning. We insist on the astrophysical outcomes of these studies to show that
multivariate analyses provide an obvious path toward a renewal of our
classification of galaxies and are invaluable tools to investigate the physics
and evolution of galaxies.Comment: Open Access paper.
http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>.
\<10.3389/fspas.2015.00003 \&g
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
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Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
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