57,064 research outputs found
Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis
The widespread use of multi-sensor technology and the emergence of big
datasets has highlighted the limitations of standard flat-view matrix models
and the necessity to move towards more versatile data analysis tools. We show
that higher-order tensors (i.e., multiway arrays) enable such a fundamental
paradigm shift towards models that are essentially polynomial and whose
uniqueness, unlike the matrix methods, is guaranteed under verymild and natural
conditions. Benefiting fromthe power ofmultilinear algebra as theirmathematical
backbone, data analysis techniques using tensor decompositions are shown to
have great flexibility in the choice of constraints that match data properties,
and to find more general latent components in the data than matrix-based
methods. A comprehensive introduction to tensor decompositions is provided from
a signal processing perspective, starting from the algebraic foundations, via
basic Canonical Polyadic and Tucker models, through to advanced cause-effect
and multi-view data analysis schemes. We show that tensor decompositions enable
natural generalizations of some commonly used signal processing paradigms, such
as canonical correlation and subspace techniques, signal separation, linear
regression, feature extraction and classification. We also cover computational
aspects, and point out how ideas from compressed sensing and scientific
computing may be used for addressing the otherwise unmanageable storage and
manipulation problems associated with big datasets. The concepts are supported
by illustrative real world case studies illuminating the benefits of the tensor
framework, as efficient and promising tools for modern signal processing, data
analysis and machine learning applications; these benefits also extend to
vector/matrix data through tensorization. Keywords: ICA, NMF, CPD, Tucker
decomposition, HOSVD, tensor networks, Tensor Train
Children, Humanoid Robots and Caregivers
This paper presents developmental learning on a humanoid robot from human-robot interactions. We consider in particular teaching humanoids as children during the child's Separation and Individuation developmental phase (Mahler, 1979). Cognitive development during this phase is characterized both by the child's dependence on her mother for learning while becoming awareness of her own individuality, and by self-exploration of her physical surroundings. We propose a learning framework for a humanoid robot inspired on such cognitive development
Spectral analysis for long-term robotic mapping
This paper presents a new approach to mobile robot mapping in long-term scenarios. So far, the environment models used in mobile robotics have been tailored to capture static scenes and dealt with the environment changes by means of âmemory decayâ. While these models keep up with slowly changing environments, their utilization in dynamic, real world
environments is difficult.
The representation proposed in this paper models the environmentâs spatio-temporal dynamics by its frequency spectrum. The spectral representation of the time domain allows to identify, analyse and remember regularly occurring environment processes in a computationally efficient way. Knowledge of the periodicity of the different environment processes constitutes the model predictive capabilities, which are especially useful for long-term mobile robotics scenarios.
In the experiments presented, the proposed approach is applied to data collected by a mobile robot patrolling an indoor
environment over a period of one week. Three scenarios are investigated, including intruder detection and 4D mapping. The results indicate that the proposed method allows to represent arbitrary timescales with constant (and low) memory requirements, achieving compression rates up to 106 . Moreover, the representation allows for prediction of future environmentâs state with ⌠90% precision
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
The Pierre Auger Observatory: Contributions to the 34th International Cosmic Ray Conference (ICRC 2015)
Contributions of the Pierre Auger Collaboration to the 34th International
Cosmic Ray Conference, 30 July - 6 August 2015, The Hague, The NetherlandsComment: 24 proceedings, the 34th International Cosmic Ray Conference, 30 July
- 6 August 2015, The Hague, The Netherlands; will appear in PoS(ICRC2015
Atmospheric neutrino oscillation analysis with sub-leading effects in Super-Kamiokande I, II, and III
We present a search for non-zero theta_{13} and deviations of sin^2
theta_{23} from 0.5 in the oscillations of atmospheric neutrino data from
Super-Kamiokande -I, -II, and -III. No distortions of the neutrino flux
consistent with non-zero theta_{13} are found and both neutrino mass hierarchy
hypotheses are in agreement with the data. The data are best fit at Delta m^2 =
2.1 x 10^-3 eV^2, sin^2 theta_{13} = 0.0, and sin^2 theta_{23} =0.5. In the
normal (inverted) hierarchy theta_{13} and Delta m^2 are constrained at the
one-dimensional 90% C.L. to sin^2 theta_{13} < 0.04 (0.09) and 1.9 (1.7) x
10^-3 < Delta m^2 < 2.6 (2.7) x 10^-3 eV^2. The atmospheric mixing angle is
within 0.407 <= sin^2 theta_{23} <= 0.583 at 90% C.L.Comment: 17 Pages, 14 figures. To be submitted to Phys. Rev. D Minor update to
text after referee comments. Figures modified for better grayscale printing
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