pwc-archive/papers-with-abstracts huggingface dataset:
Modified Soft Brood Crossover in Genetic Programming
Authors: Hardik M. Parekh, Vipul K. Dabhi
Date: 2013-04-12 00:00:00
Tasks: Diversity, regression, Symbolic Regression

Premature convergence is one of the important issues while using Genetic
Programming for data modeling. It can be avoided by improving population
diversity. Intelligent genetic operators can help to improve the population
diversity. Crossover is an important operator in Genetic Programming. So, we
have analyzed number of intelligent crossover operators and proposed an
algorithm with the modification of soft brood crossover operator. It will help
to improve the population diversity and reduce the premature convergence. We
have performed experiments on three different symbolic regression problems.
Then we made the performance comparison of our proposed crossover (Modified
Soft Brood Crossover) with the existing soft brood crossover and subtree
crossover operators.
Sparsity regret bounds for individual sequences in online linear regression
Authors: Sébastien Gerchinovitz
Date: 2011-01-05 00:00:00
Tasks: regression

We consider the problem of online linear regression on arbitrary
deterministic sequences when the ambient dimension d can be much larger than
the number of time rounds T. We introduce the notion of sparsity regret bound,
which is a deterministic online counterpart of recent risk bounds derived in
the stochastic setting under a sparsity scenario. We prove such regret bounds
for an online-learning algorithm called SeqSEW and based on exponential
weighting and data-driven truncation. In a second part we apply a
parameter-free version of this algorithm to the stochastic setting (regression
model with random design). This yields risk bounds of the same flavor as in
Dalalyan and Tsybakov (2011) but which solve two questions left open therein.
In particular our risk bounds are adaptive (up to a logarithmic factor) to the
unknown variance of the noise if the latter is Gaussian. We also address the
regression model with fixed design.
Astronomical Image Denoising Using Dictionary Learning
Authors: Simon Beckouche, Jean-Luc Starck, Jalal Fadili
Date: 2013-04-12 00:00:00
Tasks: Denoising, Dictionary Learning, Image Denoising

Astronomical images suffer a constant presence of multiple defects that are
consequences of the intrinsic properties of the acquisition equipments, and
atmospheric conditions. One of the most frequent defects in astronomical
imaging is the presence of additive noise which makes a denoising step
mandatory before processing data. During the last decade, a particular modeling
scheme, based on sparse representations, has drawn the attention of an ever
growing community of researchers. Sparse representations offer a promising
framework to many image and signal processing tasks, especially denoising and
restoration applications. At first, the harmonics, wavelets, and similar bases
and overcomplete representations have been considered as candidate domains to
seek the sparsest representation. A new generation of algorithms, based on
data-driven dictionaries, evolved rapidly and compete now with the
off-the-shelf fixed dictionaries. While designing a dictionary beforehand leans
on a guess of the most appropriate representative elementary forms and
functions, the dictionary learning framework offers to construct the dictionary
upon the data themselves, which provides us with a more flexible setup to
sparse modeling and allows to build more sophisticated dictionaries. In this
paper, we introduce the Centered Dictionary Learning (CDL) method and we study
its performances for astronomical image denoising. We show how CDL outperforms
wavelet or classic dictionary learning denoising techniques on astronomical
images, and we give a comparison of the effect of these different algorithms on
the photometry of the denoised images.
Distributed dictionary learning over a sensor network
Authors: Pierre Chainais, Cédric Richard
Date: 2013-04-12 00:00:00
Tasks: Dictionary Learning, regression

We consider the problem of distributed dictionary learning, where a set of
nodes is required to collectively learn a common dictionary from noisy
measurements. This approach may be useful in several contexts including sensor
networks. Diffusion cooperation schemes have been proposed to solve the
distributed linear regression problem. In this work we focus on a
diffusion-based adaptive dictionary learning strategy: each node records
observations and cooperates with its neighbors by sharing its local dictionary.
The resulting algorithm corresponds to a distributed block coordinate descent
(alternate optimization). Beyond dictionary learning, this strategy could be
adapted to many matrix factorization problems and generalized to various
settings. This article presents our approach and illustrates its efficiency on
some numerical examples.
Merging Satellite Measurements of Rainfall Using Multi-scale Imagery Technique
Authors: Seyed Hamed Alemohammad, Dara Entekhabi
Date: 2013-04-11 00:00:00

Several passive microwave satellites orbit the Earth and measure rainfall.
These measurements have the advantage of almost full global coverage when
compared to surface rain gauges. However, these satellites have low temporal
revisit and missing data over some regions. Image fusion is a useful technique
to fill in the gaps of one image (one satellite measurement) using another one.
The proposed algorithm uses an iterative fusion scheme to integrate information
from two satellite measurements. The algorithm is implemented on two datasets
for 7 years of half-hourly data. The results show significant improvements in
rain detection and rain intensity in the merged measurements.
Generic Behaviour Similarity Measures for Evolutionary Swarm Robotics
Authors: Jorge Gomes, Anders Lyhne Christensen
Date: 2013-04-11 00:00:00

Novelty search has shown to be a promising approach for the evolution of
controllers for swarm robotics. In existing studies, however, the experimenter
had to craft a domain dependent behaviour similarity measure to use novelty
search in swarm robotics applications. The reliance on hand-crafted similarity
measures places an additional burden to the experimenter and introduces a bias
in the evolutionary process. In this paper, we propose and compare two
task-independent, generic behaviour similarity measures: combined state count
and sampled average state. The proposed measures use the values of sensors and
effectors recorded for each individual robot of the swarm. The characterisation
of the group-level behaviour is then obtained by combining the sensor-effector
values from all the robots. We evaluate the proposed measures in an aggregation
task and in a resource sharing task. We show that the generic measures match
the performance of domain dependent measures in terms of solution quality. Our
results indicate that the proposed generic measures operate as effective
behaviour similarity measures, and that it is possible to leverage the benefits
of novelty search without having to craft domain specific similarity measures.
Evolution of Swarm Robotics Systems with Novelty Search
Authors: Jorge Gomes, Paulo Urbano, Anders Lyhne Christensen
Date: 2013-04-11 00:00:00

Novelty search is a recent artificial evolution technique that challenges
traditional evolutionary approaches. In novelty search, solutions are rewarded
based on their novelty, rather than their quality with respect to a predefined
objective. The lack of a predefined objective precludes premature convergence
caused by a deceptive fitness function. In this paper, we apply novelty search
combined with NEAT to the evolution of neural controllers for homogeneous
swarms of robots. Our empirical study is conducted in simulation, and we use a
common swarm robotics task - aggregation, and a more challenging task - sharing
of an energy recharging station. Our results show that novelty search is
unaffected by deception, is notably effective in bootstrapping the evolution,
can find solutions with lower complexity than fitness-based evolution, and can
find a broad diversity of solutions for the same task. Even in non-deceptive
setups, novelty search achieves solution qualities similar to those obtained in
traditional fitness-based evolution. Our study also encompasses variants of
novelty search that work in concert with fitness-based evolution to combine the
exploratory character of novelty search with the exploitatory character of
objective-based evolution. We show that these variants can further improve the
performance of novelty search. Overall, our study shows that novelty search is
a promising alternative for the evolution of controllers for robotic swarms.
Probabilistic Classification using Fuzzy Support Vector Machines
Authors: Marzieh Parandehgheibi
Date: 2013-04-11 00:00:00
Tasks: Classification, Diagnostic, General Classification

In medical applications such as recognizing the type of a tumor as Malignant
or Benign, a wrong diagnosis can be devastating. Methods like Fuzzy Support
Vector Machines (FSVM) try to reduce the effect of misplaced training points by
assigning a lower weight to the outliers. However, there are still uncertain
points which are similar to both classes and assigning a class by the given
information will cause errors. In this paper, we propose a two-phase
classification method which probabilistically assigns the uncertain points to
each of the classes. The proposed method is applied to the Breast Cancer
Wisconsin (Diagnostic) Dataset which consists of 569 instances in 2 classes of
Malignant and Benign. This method assigns certain instances to their
appropriate classes with probability of one, and the uncertain instances to
each of the classes with associated probabilities. Therefore, based on the
degree of uncertainty, doctors can suggest further examinations before making
the final diagnosis.
Extension of hidden markov model for recognizing large vocabulary of sign language
Authors: Maher Jebali, Patrice Dalle, Mohamed Jemni
Date: 2013-04-11 00:00:00
Tasks: Gesture Recognition, Sign Language Recognition, speech-recognition, Speech Recognition

Computers still have a long way to go before they can interact with users in
a truly natural fashion. From a users perspective, the most natural way to
interact with a computer would be through a speech and gesture interface.
Although speech recognition has made significant advances in the past ten
years, gesture recognition has been lagging behind. Sign Languages (SL) are the
most accomplished forms of gestural communication. Therefore, their automatic
analysis is a real challenge, which is interestingly implied to their lexical
and syntactic organization levels. Statements dealing with sign language occupy
a significant interest in the Automatic Natural Language Processing (ANLP)
domain. In this work, we are dealing with sign language recognition, in
particular of French Sign Language (FSL). FSL has its own specificities, such
as the simultaneity of several parameters, the important role of the facial
expression or movement and the use of space for the proper utterance
organization. Unlike speech recognition, Frensh sign language (FSL) events
occur both sequentially and simultaneously. Thus, the computational processing
of FSL is too complex than the spoken languages. We present a novel approach
based on HMM to reduce the recognition complexity.
From Constraints to Resolution Rules, Part II: chains, braids, confluence and T&E
Authors: Denis Berthier
Date: 2013-04-11 00:00:00

In this Part II, we apply the general theory developed in Part I to a
detailed analysis of the Constraint Satisfaction Problem (CSP). We show how
specific types of resolution rules can be defined. In particular, we introduce
the general notions of a chain and a braid. As in Part I, these notions are
illustrated in detail with the Sudoku example - a problem known to be
NP-complete and which is therefore typical of a broad class of hard problems.
For Sudoku, we also show how far one can go in 'approximating' a CSP with a
resolution theory and we give an empirical statistical analysis of how the
various puzzles, corresponding to different sets of entries, can be classified
along a natural scale of complexity. For any CSP, we also prove the confluence
property of some Resolution Theories based on braids and we show how it can be
used to define different resolution strategies. Finally, we prove that, in any
CSP, braids have the same solving capacity as Trial-and-Error (T&E) with no
guessing and we comment this result in the Sudoku case.
Improvement studies on neutron-gamma separation in HPGe detectors by using neural networks
Authors: Serkan Akkoyun, Tuncay Bayram, S. Okan Kara
Date: 2013-04-11 00:00:00

The neutrons emitted in heavy-ion fusion-evaporation (HIFE) reactions
together with the gamma-rays cause unwanted backgrounds in gamma-ray spectra.
Especially in the nuclear reactions, where relativistic ion beams (RIBs) are
used, these neutrons are serious problem. They have to be rejected in order to
obtain clearer gamma-ray peaks. In this study, the radiation energy and three
criteria which were previously determined for separation between neutron and
gamma-rays in the HPGe detectors have been used in artificial neural network
(ANN) for improving of the decomposition power. According to the preliminary
results obtained from ANN method, the ratio of neutron rejection has been
improved by a factor of 1.27 and the ratio of the lost in gamma-rays has been
decreased by a factor of 0.50.
From Constraints to Resolution Rules, Part I: Conceptual Framework
Authors: Denis Berthier
Date: 2013-04-11 00:00:00

Many real world problems naturally appear as constraints satisfaction
problems (CSP), for which very efficient algorithms are known. Most of these
involve the combination of two techniques: some direct propagation of
constraints between variables (with the goal of reducing their sets of possible
values) and some kind of structured search (depth-first, breadth-first,...).
But when such blind search is not possible or not allowed or when one wants a
'constructive' or a 'pattern-based' solution, one must devise more complex
propagation rules instead. In this case, one can introduce the notion of a
candidate (a 'still possible' value for a variable). Here, we give this
intuitive notion a well defined logical status, from which we can define the
concepts of a resolution rule and a resolution theory. In order to keep our
analysis as concrete as possible, we illustrate each definition with the well
known Sudoku example. Part I proposes a general conceptual framework based on
first order logic; with the introduction of chains and braids, Part II will
give much deeper results.
The Pascal Triangle of a Discrete Image: Definition, Properties and Application to Shape Analysis
Authors: Mireille Boutin, Shanshan Huang
Date: 2012-09-21 00:00:00

We define the Pascal triangle of a discrete (gray scale) image as a pyramidal
arrangement of complex-valued moments and we explore its geometric
significance. In particular, we show that the entries of row k of this triangle
correspond to the Fourier series coefficients of the moment of order k of the
Radon transform of the image. Group actions on the plane can be naturally
prolonged onto the entries of the Pascal triangle. We study the prolongation of
some common group actions, such as rotations and reflections, and we propose
simple tests for detecting equivalences and self-equivalences under these group
actions. The motivating application of this work is the problem of
characterizing the geometry of objects on images, for example by detecting
approximate symmetries.
An Approach to Solve Linear Equations Using a Time-Variant Adaptation Based Hybrid Evolutionary Algorithm
Authors: A. R. M. Jalal Uddin Jamali, M. M. A. Hashem, Md. Bazlar Rahman
Date: 2013-04-11 00:00:00
Tasks: Evolutionary Algorithms

For small number of equations, systems of linear (and sometimes nonlinear)
equations can be solved by simple classical techniques. However, for large
number of systems of linear (or nonlinear) equations, solutions using classical
method become arduous. On the other hand evolutionary algorithms have mostly
been used to solve various optimization and learning problems. Recently,
hybridization of evolutionary algorithm with classical Gauss-Seidel based
Successive Over Relaxation (SOR) method has successfully been used to solve
large number of linear equations; where a uniform adaptation (UA) technique of
relaxation factor is used. In this paper, a new hybrid algorithm is proposed in
which a time-variant adaptation (TVA) technique of relaxation factor is used
instead of uniform adaptation technique to solve large number of linear
equations. The convergence theorems of the proposed algorithms are proved
theoretically. And the performance of the proposed TVA-based algorithm is
compared with the UA-based hybrid algorithm in the experimental domain. The
proposed algorithm outperforms the hybrid one in terms of efficiency.
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition
Authors: Yulan Guo, Ferdous Sohel, Mohammed Bennamoun, Min Lu, Jianwei Wan
Date: 2013-04-11 00:00:00
Tasks: 3D Object Recognition, Object, Object Recognition

Recognizing 3D objects in the presence of noise, varying mesh resolution,
occlusion and clutter is a very challenging task. This paper presents a novel
method named Rotational Projection Statistics (RoPS). It has three major
modules: Local Reference Frame (LRF) definition, RoPS feature description and
3D object recognition. We propose a novel technique to define the LRF by
calculating the scatter matrix of all points lying on the local surface. RoPS
feature descriptors are obtained by rotationally projecting the neighboring
points of a feature point onto 2D planes and calculating a set of statistics
(including low-order central moments and entropy) of the distribution of these
projected points. Using the proposed LRF and RoPS descriptor, we present a
hierarchical 3D object recognition algorithm. The performance of the proposed
LRF, RoPS descriptor and object recognition algorithm was rigorously tested on
a number of popular and publicly available datasets. Our proposed techniques
exhibited superior performance compared to existing techniques. We also showed
that our method is robust with respect to noise and varying mesh resolution.
Our RoPS based algorithm achieved recognition rates of 100%, 98.9%, 95.4% and
96.0% respectively when tested on the Bologna, UWA, Queen's and Ca' Foscari
Venezia Datasets.
Sustainable Cooperative Coevolution with a Multi-Armed Bandit
Authors: François-Michel De Rainville, Michèle Sebag, Christian Gagné, Marc Schoenauer, Denis Laurendeau
Date: 2013-04-10 00:00:00

This paper proposes a self-adaptation mechanism to manage the resources
allocated to the different species comprising a cooperative coevolutionary
algorithm. The proposed approach relies on a dynamic extension to the
well-known multi-armed bandit framework. At each iteration, the dynamic
multi-armed bandit makes a decision on which species to evolve for a
generation, using the history of progress made by the different species to
guide the decisions. We show experimentally, on a benchmark and a real-world
problem, that evolving the different populations at different paces allows not
only to identify solutions more rapidly, but also improves the capacity of
cooperative coevolution to solve more complex problems.
A Geometric Descriptor for Cell-Division Detection
Authors: Marcelo Cicconet, Italo Lima, Davi Geiger, Kris Gunsalus
Date: 2013-01-15 00:00:00

We describe a method for cell-division detection based on a geometric-driven
descriptor that can be represented as a 5-layers processing network, based
mainly on wavelet filtering and a test for mirror symmetry between pairs of
pixels. After the centroids of the descriptors are computed for a sequence of
frames, the two-steps piecewise constant function that best fits the sequence
of centroids determines the frame where the division occurs.
Orientation Determination from Cryo-EM images Using Least Unsquared Deviation
Authors: Lanhui Wang, Amit Singer, Zaiwen Wen
Date: 2012-11-29 00:00:00
Tasks: Clustering

A major challenge in single particle reconstruction from cryo-electron
microscopy is to establish a reliable ab-initio three-dimensional model using
two-dimensional projection images with unknown orientations. Common-lines based
methods estimate the orientations without additional geometric information.
However, such methods fail when the detection rate of common-lines is too low
due to the high level of noise in the images. An approximation to the least
squares global self consistency error was obtained using convex relaxation by
semidefinite programming. In this paper we introduce a more robust global self
consistency error and show that the corresponding optimization problem can be
solved via semidefinite relaxation. In order to prevent artificial clustering
of the estimated viewing directions, we further introduce a spectral norm term
that is added as a constraint or as a regularization term to the relaxed
minimization problem. The resulted problems are solved by using either the
alternating direction method of multipliers or an iteratively reweighted least
squares procedure. Numerical experiments with both simulated and real images
demonstrate that the proposed methods significantly reduce the orientation
estimation error when the detection rate of common-lines is low.
Roborobo! a Fast Robot Simulator for Swarm and Collective Robotics
Authors: Nicolas Bredeche, Jean-Marc Montanier, Berend Weel, Evert Haasdijk
Date: 2013-04-10 00:00:00

Roborobo! is a multi-platform, highly portable, robot simulator for
large-scale collective robotics experiments. Roborobo! is coded in C++, and
follows the KISS guideline ("Keep it simple"). Therefore, its external
dependency is solely limited to the widely available SDL library for fast 2D
Graphics. Roborobo! is based on a Khepera/ePuck model. It is targeted for fast
single and multi-robots simulation, and has already been used in more than a
dozen published research mainly concerned with evolutionary swarm robotics,
including environment-driven self-adaptation and distributed evolutionary
optimization, as well as online onboard embodied evolution and embodied
morphogenesis.
Sparse projections onto the simplex
Authors: Anastasios Kyrillidis, Stephen Becker, Volkan Cevher and, Christoph Koch
Date: 2012-06-07 00:00:00
Tasks: Density Estimation

Most learning methods with rank or sparsity constraints use convex
relaxations, which lead to optimization with the nuclear norm or the
$\ell_1$-norm. However, several important learning applications cannot benefit
from this approach as they feature these convex norms as constraints in
addition to the non-convex rank and sparsity constraints. In this setting, we
derive efficient sparse projections onto the simplex and its extension, and
illustrate how to use them to solve high-dimensional learning problems in
quantum tomography, sparse density estimation and portfolio selection with
non-convex constraints.