33,555 research outputs found
Invariance of visual operations at the level of receptive fields
Receptive field profiles registered by cell recordings have shown that
mammalian vision has developed receptive fields tuned to different sizes and
orientations in the image domain as well as to different image velocities in
space-time. This article presents a theoretical model by which families of
idealized receptive field profiles can be derived mathematically from a small
set of basic assumptions that correspond to structural properties of the
environment. The article also presents a theory for how basic invariance
properties to variations in scale, viewing direction and relative motion can be
obtained from the output of such receptive fields, using complementary
selection mechanisms that operate over the output of families of receptive
fields tuned to different parameters. Thereby, the theory shows how basic
invariance properties of a visual system can be obtained already at the level
of receptive fields, and we can explain the different shapes of receptive field
profiles found in biological vision from a requirement that the visual system
should be invariant to the natural types of image transformations that occur in
its environment.Comment: 40 pages, 17 figure
Order-Invariant First-Order Logic over Hollow Trees
We show that the expressive power of order-invariant first-order logic collapses to first-order logic over hollow trees. A hollow tree is an unranked ordered tree where every non leaf node has at most four adjacent nodes: two siblings (left and right) and its first and last children. In particular there is no predicate for the linear order among siblings nor for the descendant relation. Moreover only the first and last nodes of a siblinghood are linked to their parent node, and the parent-child relation cannot be completely reconstructed in first-order
Idealized computational models for auditory receptive fields
This paper presents a theory by which idealized models of auditory receptive
fields can be derived in a principled axiomatic manner, from a set of
structural properties to enable invariance of receptive field responses under
natural sound transformations and ensure internal consistency between
spectro-temporal receptive fields at different temporal and spectral scales.
For defining a time-frequency transformation of a purely temporal sound
signal, it is shown that the framework allows for a new way of deriving the
Gabor and Gammatone filters as well as a novel family of generalized Gammatone
filters, with additional degrees of freedom to obtain different trade-offs
between the spectral selectivity and the temporal delay of time-causal temporal
window functions.
When applied to the definition of a second-layer of receptive fields from a
spectrogram, it is shown that the framework leads to two canonical families of
spectro-temporal receptive fields, in terms of spectro-temporal derivatives of
either spectro-temporal Gaussian kernels for non-causal time or the combination
of a time-causal generalized Gammatone filter over the temporal domain and a
Gaussian filter over the logspectral domain. For each filter family, the
spectro-temporal receptive fields can be either separable over the
time-frequency domain or be adapted to local glissando transformations that
represent variations in logarithmic frequencies over time. Within each domain
of either non-causal or time-causal time, these receptive field families are
derived by uniqueness from the assumptions.
It is demonstrated how the presented framework allows for computation of
basic auditory features for audio processing and that it leads to predictions
about auditory receptive fields with good qualitative similarity to biological
receptive fields measured in the inferior colliculus (ICC) and primary auditory
cortex (A1) of mammals.Comment: 55 pages, 22 figures, 3 table
Efficient Identification of Equivalences in Dynamic Graphs and Pedigree Structures
We propose a new framework for designing test and query functions for complex
structures that vary across a given parameter such as genetic marker position.
The operations we are interested in include equality testing, set operations,
isolating unique states, duplication counting, or finding equivalence classes
under identifiability constraints. A motivating application is locating
equivalence classes in identity-by-descent (IBD) graphs, graph structures in
pedigree analysis that change over genetic marker location. The nodes of these
graphs are unlabeled and identified only by their connecting edges, a
constraint easily handled by our approach. The general framework introduced is
powerful enough to build a range of testing functions for IBD graphs, dynamic
populations, and other structures using a minimal set of operations. The
theoretical and algorithmic properties of our approach are analyzed and proved.
Computational results on several simulations demonstrate the effectiveness of
our approach.Comment: Code for paper available at
http://www.stat.washington.edu/~hoytak/code/hashreduc
Exploring self-similarity of complex cellular networks: The edge-covering method with simulated annealing and log-periodic sampling
Song, Havlin and Makse (2005) have recently used a version of the
box-counting method, called the node-covering method, to quantify the
self-similar properties of 43 cellular networks: the minimal number of
boxes of size needed to cover all the nodes of a cellular network was
found to scale as the power law with a fractal
dimension . We propose a new box-counting method based on
edge-covering, which outperforms the node-covering approach when applied to
strictly self-similar model networks, such as the Sierpinski network. The
minimal number of boxes of size in the edge-covering method is
obtained with the simulated annealing algorithm. We take into account the
possible discrete scale symmetry of networks (artifactual and/or real), which
is visualized in terms of log-periodic oscillations in the dependence of the
logarithm of as a function of the logarithm of . In this way, we
are able to remove the bias of the estimator of the fractal dimension, existing
for finite networks. With this new methodology, we find that scales with
respect to as a power law with
for the 43 cellular networks previously analyzed by Song, Havlin and Makse
(2005). Bootstrap tests suggest that the analyzed cellular networks may have a
significant log-periodicity qualifying a discrete hierarchy with a scaling
ratio close to 2. In sum, we propose that our method of edge-covering with
simulated annealing and log-periodic sampling minimizes the significant bias in
the determination of fractal dimensions in log-log regressions.Comment: 19 elsart pages including 9 eps figure
A Deep Representation for Invariance And Music Classification
Representations in the auditory cortex might be based on mechanisms similar
to the visual ventral stream; modules for building invariance to
transformations and multiple layers for compositionality and selectivity. In
this paper we propose the use of such computational modules for extracting
invariant and discriminative audio representations. Building on a theory of
invariance in hierarchical architectures, we propose a novel, mid-level
representation for acoustical signals, using the empirical distributions of
projections on a set of templates and their transformations. Under the
assumption that, by construction, this dictionary of templates is composed from
similar classes, and samples the orbit of variance-inducing signal
transformations (such as shift and scale), the resulting signature is
theoretically guaranteed to be unique, invariant to transformations and stable
to deformations. Modules of projection and pooling can then constitute layers
of deep networks, for learning composite representations. We present the main
theoretical and computational aspects of a framework for unsupervised learning
of invariant audio representations, empirically evaluated on music genre
classification.Comment: 5 pages, CBMM Memo No. 002, (to appear) IEEE 2014 International
Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014
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