6,057 research outputs found
Study geomorphology, past and present, linear trench, tectonics relationship between Pyrenees and Alps
The author has identified the following significant results. ERTS-1 images obviously show up some large linear features trending N 80 E or N 30 E common to both Alps and Pyrenees. One of them, the Ligurian Fault, had been previously forecast by Laubscher in an interpretation of the Alps by the plate tectonic theory, but it extends westward farthest from the Alps, cutting the Pyrenees axis. These lineaments have been interpreted as reflections of deep seated wrench faults in the surficial part of the sedimentary series. A large set of such lineaments is perceptible in western Europe, such as the Guadalquivir Fault in southern Spain, Ligurian Fault, Insubrian Fault, Northern-Jura Fault, Metz Fault. Perhaps these may be interpreted as transform faults of the mid-Atlantic ridge or of a paleo-rift seated in the Rhine-Rhone graben
Out-of-sample generalizations for supervised manifold learning for classification
Supervised manifold learning methods for data classification map data samples
residing in a high-dimensional ambient space to a lower-dimensional domain in a
structure-preserving way, while enhancing the separation between different
classes in the learned embedding. Most nonlinear supervised manifold learning
methods compute the embedding of the manifolds only at the initially available
training points, while the generalization of the embedding to novel points,
known as the out-of-sample extension problem in manifold learning, becomes
especially important in classification applications. In this work, we propose a
semi-supervised method for building an interpolation function that provides an
out-of-sample extension for general supervised manifold learning algorithms
studied in the context of classification. The proposed algorithm computes a
radial basis function (RBF) interpolator that minimizes an objective function
consisting of the total embedding error of unlabeled test samples, defined as
their distance to the embeddings of the manifolds of their own class, as well
as a regularization term that controls the smoothness of the interpolation
function in a direction-dependent way. The class labels of test data and the
interpolation function parameters are estimated jointly with a progressive
procedure. Experimental results on face and object images demonstrate the
potential of the proposed out-of-sample extension algorithm for the
classification of manifold-modeled data sets
A study of the classification of low-dimensional data with supervised manifold learning
Supervised manifold learning methods learn data representations by preserving
the geometric structure of data while enhancing the separation between data
samples from different classes. In this work, we propose a theoretical study of
supervised manifold learning for classification. We consider nonlinear
dimensionality reduction algorithms that yield linearly separable embeddings of
training data and present generalization bounds for this type of algorithms. A
necessary condition for satisfactory generalization performance is that the
embedding allow the construction of a sufficiently regular interpolation
function in relation with the separation margin of the embedding. We show that
for supervised embeddings satisfying this condition, the classification error
decays at an exponential rate with the number of training samples. Finally, we
examine the separability of supervised nonlinear embeddings that aim to
preserve the low-dimensional geometric structure of data based on graph
representations. The proposed analysis is supported by experiments on several
real data sets
Synchronization recovery and state model reduction for soft decoding of variable length codes
Variable length codes exhibit de-synchronization problems when transmitted
over noisy channels. Trellis decoding techniques based on Maximum A Posteriori
(MAP) estimators are often used to minimize the error rate on the estimated
sequence. If the number of symbols and/or bits transmitted are known by the
decoder, termination constraints can be incorporated in the decoding process.
All the paths in the trellis which do not lead to a valid sequence length are
suppressed. This paper presents an analytic method to assess the expected error
resilience of a VLC when trellis decoding with a sequence length constraint is
used. The approach is based on the computation, for a given code, of the amount
of information brought by the constraint. It is then shown that this quantity
as well as the probability that the VLC decoder does not re-synchronize in a
strict sense, are not significantly altered by appropriate trellis states
aggregation. This proves that the performance obtained by running a
length-constrained Viterbi decoder on aggregated state models approaches the
one obtained with the bit/symbol trellis, with a significantly reduced
complexity. It is then shown that the complexity can be further decreased by
projecting the state model on two state models of reduced size
Probing millisecond pulsar emission geometry using light curves from the Fermi Large Area Telescope
An interesting new high-energy pulsar sub-population is emerging following
early discoveries of gamma-ray millisecond pulsars (MSPs) by the Fermi Large
Area Telescope (LAT). We present results from 3D emission modeling, including
the Special Relativistic effects of aberration and time-of-flight delays and
also rotational sweepback of B-field lines, in the geometric context of polar
cap (PC), outer gap (OG), and two-pole caustic (TPC) pulsar models. In contrast
to the general belief that these very old, rapidly-rotating neutron stars (NSs)
should have largely pair-starved magnetospheres due to the absence of
significant pair production, we find that most of the light curves are best fit
by TPC and OG models, which indicates the presence of narrow accelerating gaps
limited by robust pair production -- even in these pulsars with very low
spin-down luminosities. The gamma-ray pulse shapes and relative phase lags with
respect to the radio pulses point to high-altitude emission being dominant for
all geometries. We also find exclusive differentiation of the current gamma-ray
MSP population into two MSP sub-classes: light curve shapes and lags across
wavebands impose either pair-starved PC (PSPC) or TPC / OG-type geometries. In
the first case, the radio pulse has a small lag with respect to the single
gamma-ray pulse, while the (first) gamma-ray peak usually trails the radio by a
large phase offset in the latter case. Finally, we find that the flux
correction factor as a function of magnetic inclination and observer angles is
typically of order unity for all models. Our calculation of light curves and
flux correction factor for the case of MSPs is therefore complementary to the
"ATLAS paper" of Watters et al. for younger pulsars.Comment: 51 pages, 23 figures, 3 tables; low-resolution figures; accepted for
publication by Ap
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