8,235 research outputs found
Note on islands in path-length sequences of binary trees
An earlier characterization of topologically ordered (lexicographic)
path-length sequences of binary trees is reformulated in terms of an
integrality condition on a scaled Kraft sum of certain subsequences (full
segments, or islands). The scaled Kraft sum is seen to count the set of
ancestors at a certain level of a set of topologically consecutive leaves is a
binary tree.Comment: 4 page
Part Detector Discovery in Deep Convolutional Neural Networks
Current fine-grained classification approaches often rely on a robust
localization of object parts to extract localized feature representations
suitable for discrimination. However, part localization is a challenging task
due to the large variation of appearance and pose. In this paper, we show how
pre-trained convolutional neural networks can be used for robust and efficient
object part discovery and localization without the necessity to actually train
the network on the current dataset. Our approach called "part detector
discovery" (PDD) is based on analyzing the gradient maps of the network outputs
and finding activation centers spatially related to annotated semantic parts or
bounding boxes.
This allows us not just to obtain excellent performance on the CUB200-2011
dataset, but in contrast to previous approaches also to perform detection and
bird classification jointly without requiring a given bounding box annotation
during testing and ground-truth parts during training. The code is available at
http://www.inf-cv.uni-jena.de/part_discovery and
https://github.com/cvjena/PartDetectorDisovery.Comment: Accepted for publication on Asian Conference on Computer Vision
(ACCV) 201
Momentum-Resolved Ultrafast Electron Dynamics in Superconducting Bi2Sr2CaCu2O8+delta
The non-equilibrium state of the high-Tc superconductor Bi2Sr2CaCu2O8+delta
and its ultrafast dynamics have been investigated by femtosecond time- and
angle-resolved photoemission spectroscopy well below the critical temperature.
We probe optically excited quasiparticles at different electron momenta along
the Fermi surface and detect metastable quasiparticles near the antinode. Their
decay through e-e scattering is blocked by a phase space restricted to the
nodal region. The lack of momentum dependence in the decay rates is in
agreement with relaxation dominated by Cooper pair recombination in a boson
bottleneck limit
Lagged and instantaneous dynamical influences related to brain structural connectivity
Contemporary neuroimaging methods can shed light on the basis of human neural
and cognitive specializations, with important implications for neuroscience and
medicine. Different MRI acquisitions provide different brain networks at the
macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural
connectivity (SC) coincident with the bundles of parallel fibers between brain
areas, functional MRI (fMRI) accounts for the variations in the
blood-oxygenation-level-dependent T2* signal, providing functional connectivity
(FC).Understanding the precise relation between FC and SC, that is, between
brain dynamics and structure, is still a challenge for neuroscience. To
investigate this problem, we acquired data at rest and built the corresponding
SC (with matrix elements corresponding to the fiber number between brain areas)
to be compared with FC connectivity matrices obtained by 3 different methods:
directed dependencies by an exploratory version of structural equation modeling
(eSEM), linear correlations (C) and partial correlations (PC). We also
considered the possibility of using lagged correlations in time series; so, we
compared a lagged version of eSEM and Granger causality (GC). Our results were
two-fold: firstly, eSEM performance in correlating with SC was comparable to
those obtained from C and PC, but eSEM (not C nor PC) provides information
about directionality of the functional interactions. Second, interactions on a
time scale much smaller than the sampling time, captured by instantaneous
connectivity methods, are much more related to SC than slow directed influences
captured by the lagged analysis. Indeed the performance in correlating with SC
was much worse for GC and for the lagged version of eSEM. We expect these
results to supply further insights to the interplay between SC and functional
patterns, an important issue in the study of brain physiology and function.Comment: Accepted and published in Frontiers in Psychology in its current
form. 27 pages, 1 table, 5 figures, 2 suppl. figure
Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy and Mobility
Experimental data have revealed that neuronal connection efficacy exhibits
two forms of short-term plasticity, namely, short-term depression (STD) and
short-term facilitation (STF). They have time constants residing between fast
neural signaling and rapid learning, and may serve as substrates for neural
systems manipulating temporal information on relevant time scales. The present
study investigates the impact of STD and STF on the dynamics of continuous
attractor neural networks (CANNs) and their potential roles in neural
information processing. We find that STD endows the network with slow-decaying
plateau behaviors-the network that is initially being stimulated to an active
state decays to a silent state very slowly on the time scale of STD rather than
on the time scale of neural signaling. This provides a mechanism for neural
systems to hold sensory memory easily and shut off persistent activities
gracefully. With STF, we find that the network can hold a memory trace of
external inputs in the facilitated neuronal interactions, which provides a way
to stabilize the network response to noisy inputs, leading to improved accuracy
in population decoding. Furthermore, we find that STD increases the mobility of
the network states. The increased mobility enhances the tracking performance of
the network in response to time-varying stimuli, leading to anticipative neural
responses. In general, we find that STD and STP tend to have opposite effects
on network dynamics and complementary computational advantages, suggesting that
the brain may employ a strategy of weighting them differentially depending on
the computational purpose.Comment: 40 pages, 17 figure
Collaborative Layer-wise Discriminative Learning in Deep Neural Networks
Intermediate features at different layers of a deep neural network are known
to be discriminative for visual patterns of different complexities. However,
most existing works ignore such cross-layer heterogeneities when classifying
samples of different complexities. For example, if a training sample has
already been correctly classified at a specific layer with high confidence, we
argue that it is unnecessary to enforce rest layers to classify this sample
correctly and a better strategy is to encourage those layers to focus on other
samples.
In this paper, we propose a layer-wise discriminative learning method to
enhance the discriminative capability of a deep network by allowing its layers
to work collaboratively for classification. Towards this target, we introduce
multiple classifiers on top of multiple layers. Each classifier not only tries
to correctly classify the features from its input layer, but also coordinates
with other classifiers to jointly maximize the final classification
performance. Guided by the other companion classifiers, each classifier learns
to concentrate on certain training examples and boosts the overall performance.
Allowing for end-to-end training, our method can be conveniently embedded into
state-of-the-art deep networks. Experiments with multiple popular deep
networks, including Network in Network, GoogLeNet and VGGNet, on scale-various
object classification benchmarks, including CIFAR100, MNIST and ImageNet, and
scene classification benchmarks, including MIT67, SUN397 and Places205,
demonstrate the effectiveness of our method. In addition, we also analyze the
relationship between the proposed method and classical conditional random
fields models.Comment: To appear in ECCV 2016. Maybe subject to minor changes before
camera-ready versio
On fiber dispersion models: exclusion of compressed fibers and spurious model comparisons
Fiber dispersion in collagenous soft tissues has an important influence on the mechanical response, and the modeling of the collagen fiber architecture and its mechanics has developed significantly over the last few years. The purpose of this paper is twofold, first to develop a method for excluding compressed fibers within a dispersion for the generalized structure tensor (GST) model, which several times in the literature has been claimed not to be possible, and second to draw attention to several erroneous and misleading statements in the literature concerning the relative values of the GST and the angular integration (AI) models. For the GST model we develop a rather simple method involving a deformation dependent dispersion parameter that allows the mechanical influence of compressed fibers within a dispersion to be excluded. The theory is illustrated by application to simple extension and simple shear in order to highlight the effect of exclusion. By means of two examples we also show that the GST and the AI models have equivalent predictive power, contrary to some claims in the literature. We conclude that from the theoretical point of view neither of these two models is superior to the other. However, as is well known and as we now emphasize, the GST model has proved to be very successful in modeling the data from experiments on a wide range of tissues, and it is easier to analyze and simpler to implement than the AI approach, and the related computational effort is much lower
A_{LT} in the Nucleon-Nucleon Polarized Drell-Yan Process
We present a leading order (LO) estimate for the longitidinal-transverse spin
asymmetry (A_{LT}) in the nucleon-nucleon polarized Drell-Yan process at RHIC
and HERA- energies in comparison with A_{LL} and A_{TT}. A_{LT}
receives contribution from g_1, the transversity ditribution h_1, and the
twist-3 distributions g_T and h_L. For the twist-3 contribution we use the bag
model prediction evolved to a high energy scale by the large-N_c evolution
equation. We found that A_{LT} (normalized by the asymmetry in the parton
level) is much smaller than the corresponding A_{TT}. Twist-3 contribution
given by the bag model also turned out to be negligible.Comment: Talk presented at the KEK-Tanashi International Conference "Physics
of Hadrons and Nuclei", Dec. 14-17, 1998, Tokyo. 4 pages in Latex. 1 figure
included as eps files using epsbox.st
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