7,854 research outputs found
Texture-based crowd detection and localisation
This paper presents a crowd detection system based on texture analysis. The state-of-the-art techniques based on co-occurrence matrix have been revisited and a novel set of features proposed. These features provide a richer description of the co-occurrence matrix, and can be exploited to obtain stronger classification results, especially when smaller portions of the image are considered. This is extremely useful for crowd localisation: acquired images are divided into smaller regions in order to perform a classification on each one. A thorough evaluation of the proposed system on a real world data set is also presented: this validates the improvements in reliability of the crowd detection and localisation
Optimum size of a molecular bond cluster in adhesion
The strength of a bonded interface is considered for the case in which bonding is the result of clusters of discrete bonds distributed along the interface. Assumptions appropriate for the case of adhesion of biological cells to an extracellular matrix are introduced as a basis for the discussion. It is observed that those individual bonds nearest to the edges of a cluster are necessarily subjected to disproportionately large forces in transmitting loads across the interface, in analogy with well-known behavior in elastic crack mechanics. Adopting Bell's model for the kinetics of bond response under force, a stochastic model leading to a dependence of interface strength on cluster size is developed and analyzed. On the basis of this model, it is demonstrated that there is an optimum cluster size for maximum strength. This size arises from the competition between the nonuniform force distribution among bonds, which tends to promote smaller clusters, and stochastic response allowing bond reformation, which tends to promote larger clusters. The model results have been confirmed by means of direct Monte Carlo simulations. This analysis may be relevant to the observation that mature focal adhesion zones in cell bonding are found to have a relatively uniform size. © 2008 The American Physical Society.published_or_final_versio
Statistical Mechanics of Linear and Nonlinear Time-Domain Ensemble Learning
Conventional ensemble learning combines students in the space domain. In this
paper, however, we combine students in the time domain and call it time-domain
ensemble learning. We analyze, compare, and discuss the generalization
performances regarding time-domain ensemble learning of both a linear model and
a nonlinear model. Analyzing in the framework of online learning using a
statistical mechanical method, we show the qualitatively different behaviors
between the two models. In a linear model, the dynamical behaviors of the
generalization error are monotonic. We analytically show that time-domain
ensemble learning is twice as effective as conventional ensemble learning.
Furthermore, the generalization error of a nonlinear model features
nonmonotonic dynamical behaviors when the learning rate is small. We
numerically show that the generalization performance can be improved remarkably
by using this phenomenon and the divergence of students in the time domain.Comment: 11 pages, 7 figure
Robust topology optimization of three-dimensional photonic-crystal band-gap structures
We perform full 3D topology optimization (in which "every voxel" of the unit
cell is a degree of freedom) of photonic-crystal structures in order to find
optimal omnidirectional band gaps for various symmetry groups, including fcc
(including diamond), bcc, and simple-cubic lattices. Even without imposing the
constraints of any fabrication process, the resulting optimal gaps are only
slightly larger than previous hand designs, suggesting that current photonic
crystals are nearly optimal in this respect. However, optimization can discover
new structures, e.g. a new fcc structure with the same symmetry but slightly
larger gap than the well known inverse opal, which may offer new degrees of
freedom to future fabrication technologies. Furthermore, our band-gap
optimization is an illustration of a computational approach to 3D dispersion
engineering which is applicable to many other problems in optics, based on a
novel semidefinite-program formulation for nonconvex eigenvalue optimization
combined with other techniques such as a simple approach to impose symmetry
constraints. We also demonstrate a technique for \emph{robust} topology
optimization, in which some uncertainty is included in each voxel and we
optimize the worst-case gap, and we show that the resulting band gaps have
increased robustness to systematic fabrication errors.Comment: 17 pages, 9 figures, submitted to Optics Expres
Minimalist AdaBoost for blemish identification in potatoes
We present a multi-class solution based on minimalist Ad-
aBoost for identifying blemishes present in visual images of potatoes.
Using training examples we use Real AdaBoost to rst reduce the fea-
ture set by selecting ve features for each class, then train binary clas-
siers for each class, classifying each testing example according to the
binary classier with the highest certainty. Against hand-drawn ground
truth data we achieve a pixel match of 83% accuracy in white potatoes
and 82% in red potatoes. For the task of identifying which blemishes
are present in each potato within typical industry dened criteria (10%
coverage) we achieve accuracy rates of 93% and 94%, respectively
A Non-Sequential Representation of Sequential Data for Churn Prediction
We investigate the length of event sequence giving best predictions
when using a continuous HMM approach to churn prediction from sequential
data. Motivated by observations that predictions based on only the few most recent
events seem to be the most accurate, a non-sequential dataset is constructed
from customer event histories by averaging features of the last few events. A simple
K-nearest neighbor algorithm on this dataset is found to give significantly
improved performance. It is quite intuitive to think that most people will react
only to events in the fairly recent past. Events related to telecommunications occurring
months or years ago are unlikely to have a large impact on a customer’s
future behaviour, and these results bear this out. Methods that deal with sequential
data also tend to be much more complex than those dealing with simple nontemporal
data, giving an added benefit to expressing the recent information in a
non-sequential manner
Tests of a Novel Design of Resistive Plate Chambers
A novel design of Resistive Plate Chambers (RPCs), using only a single
resistive plate, is being proposed. Based on this design, two large size
prototype chambers were constructed and were tested with cosmic rays and in
particle beams. The tests confirmed the viability of this new approach. In
addition to showing an improved single-particle response compared to the
traditional 2-plate design, the novel chambers also prove to be suitable for
calorimetric applications
Ensemble learning of linear perceptron; Online learning theory
Within the framework of on-line learning, we study the generalization error
of an ensemble learning machine learning from a linear teacher perceptron. The
generalization error achieved by an ensemble of linear perceptrons having
homogeneous or inhomogeneous initial weight vectors is precisely calculated at
the thermodynamic limit of a large number of input elements and shows rich
behavior. Our main findings are as follows. For learning with homogeneous
initial weight vectors, the generalization error using an infinite number of
linear student perceptrons is equal to only half that of a single linear
perceptron, and converges with that of the infinite case with O(1/K) for a
finite number of K linear perceptrons. For learning with inhomogeneous initial
weight vectors, it is advantageous to use an approach of weighted averaging
over the output of the linear perceptrons, and we show the conditions under
which the optimal weights are constant during the learning process. The optimal
weights depend on only correlation of the initial weight vectors.Comment: 14 pages, 3 figures, submitted to Physical Review
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