pwc-archive/papers-with-abstracts huggingface dataset:
Block matching algorithm based on Harmony Search optimization for motion estimation
Authors: Erik Cuevas
Date: 2014-06-17 00:00:00
Tasks: Motion Estimation

Motion estimation is one of the major problems in developing video coding
applications. Among all motion estimation approaches, Block-matching (BM)
algorithms are the most popular methods due to their effectiveness and
simplicity for both software and hardware implementations. A BM approach
assumes that the movement of pixels within a defined region of the current
frame can be modeled as a translation of pixels contained in the previous
frame. In this procedure, the motion vector is obtained by minimizing a certain
matching metric that is produced for the current frame over a determined search
window from the previous frame. Unfortunately, the evaluation of such matching
measurement is computationally expensive and represents the most consuming
operation in the BM process. Therefore, BM motion estimation can be viewed as
an optimization problem whose goal is to find the best-matching block within a
search space. The simplest available BM method is the Full Search Algorithm
(FSA) which finds the most accurate motion vector through an exhaustive
computation of all the elements of the search space. Recently, several fast BM
algorithms have been proposed to reduce the search positions by calculating
only a fixed subset of motion vectors despite lowering its accuracy. On the
other hand, the Harmony Search (HS) algorithm is a population-based
optimization method that is inspired by the music improvisation process in
which a musician searches for harmony and continues to polish the pitches to
obtain a better harmony. In this paper, a new BM algorithm that combines HS
with a fitness approximation model is proposed. The approach uses motion
vectors belonging to the search window as potential solutions. A fitness
function evaluates the matching quality of each motion vector candidate.
Authorship Attribution through Function Word Adjacency Networks
Authors: Santiago Segarra, Mark Eisen, Alejandro Ribeiro
Date: 2014-06-17 00:00:00
Tasks: Authorship Attribution

A method for authorship attribution based on function word adjacency networks
(WANs) is introduced. Function words are parts of speech that express
grammatical relationships between other words but do not carry lexical meaning
on their own. In the WANs in this paper, nodes are function words and directed
edges stand in for the likelihood of finding the sink word in the ordered
vicinity of the source word. WANs of different authors can be interpreted as
transition probabilities of a Markov chain and are therefore compared in terms
of their relative entropies. Optimal selection of WAN parameters is studied and
attribution accuracy is benchmarked across a diverse pool of authors and
varying text lengths. This analysis shows that, since function words are
independent of content, their use tends to be specific to an author and that
the relational data captured by function WANs is a good summary of stylometric
fingerprints. Attribution accuracy is observed to exceed the one achieved by
methods that rely on word frequencies alone. Further combining WANs with
methods that rely on word frequencies alone, results in larger attribution
accuracy, indicating that both sources of information encode different aspects
of authorial styles.
Automatic Fado Music Classification
Authors: Pedro Girão Antunes, David Martins de Matos, Ricardo Ribeiro, Isabel Trancoso
Date: 2014-06-17 00:00:00
Tasks: Classification, General Classification, Music Classification

In late 2011, Fado was elevated to the oral and intangible heritage of
humanity by UNESCO. This study aims to develop a tool for automatic detection
of Fado music based on the audio signal. To do this, frequency spectrum-related
characteristics were captured form the audio signal: in addition to the Mel
Frequency Cepstral Coefficients (MFCCs) and the energy of the signal, the
signal was further analysed in two frequency ranges, providing additional
information. Tests were run both in a 10-fold cross-validation setup (97.6%
accuracy), and in a traditional train/test setup (95.8% accuracy). The good
results reflect the fact that Fado is a very distinctive musical style.
A Kernel Independence Test for Random Processes
Authors: Kacper Chwialkowski, Arthur Gretton
Date: 2014-02-18 00:00:00

A new non parametric approach to the problem of testing the independence of
two random process is developed. The test statistic is the Hilbert Schmidt
Independence Criterion (HSIC), which was used previously in testing
independence for i.i.d pairs of variables. The asymptotic behaviour of HSIC is
established when computed from samples drawn from random processes. It is shown
that earlier bootstrap procedures which worked in the i.i.d. case will fail for
random processes, and an alternative consistent estimate of the p-values is
proposed. Tests on artificial data and real-world Forex data indicate that the
new test procedure discovers dependence which is missed by linear approaches,
while the earlier bootstrap procedure returns an elevated number of false
positives. The code is available online:
https://github.com/kacperChwialkowski/HSIC .
Fast Computation of Wasserstein Barycenters
Authors: Marco Cuturi, Arnaud Doucet
Date: 2013-10-16 00:00:00
Tasks: Clustering, Constrained Clustering

We present new algorithms to compute the mean of a set of empirical
probability measures under the optimal transport metric. This mean, known as
the Wasserstein barycenter, is the measure that minimizes the sum of its
Wasserstein distances to each element in that set. We propose two original
algorithms to compute Wasserstein barycenters that build upon the subgradient
method. A direct implementation of these algorithms is, however, too costly
because it would require the repeated resolution of large primal and dual
optimal transport problems to compute subgradients. Extending the work of
Cuturi (2013), we propose to smooth the Wasserstein distance used in the
definition of Wasserstein barycenters with an entropic regularizer and recover
in doing so a strictly convex objective whose gradients can be computed for a
considerably cheaper computational cost using matrix scaling algorithms. We use
these algorithms to visualize a large family of images and to solve a
constrained clustering problem.
Single camera pose estimation using Bayesian filtering and Kinect motion priors
Authors: Michael Burke, Joan Lasenby
Date: 2014-05-20 00:00:00
Tasks: 2D Pose Estimation, Camera Pose Estimation, Computational Efficiency, Face Detection, Object Recognition, Pose Estimation, Pose Tracking

Traditional approaches to upper body pose estimation using monocular vision
rely on complex body models and a large variety of geometric constraints. We
argue that this is not ideal and somewhat inelegant as it results in large
processing burdens, and instead attempt to incorporate these constraints
through priors obtained directly from training data. A prior distribution
covering the probability of a human pose occurring is used to incorporate
likely human poses. This distribution is obtained offline, by fitting a
Gaussian mixture model to a large dataset of recorded human body poses, tracked
using a Kinect sensor. We combine this prior information with a random walk
transition model to obtain an upper body model, suitable for use within a
recursive Bayesian filtering framework. Our model can be viewed as a mixture of
discrete Ornstein-Uhlenbeck processes, in that states behave as random walks,
but drift towards a set of typically observed poses. This model is combined
with measurements of the human head and hand positions, using recursive
Bayesian estimation to incorporate temporal information. Measurements are
obtained using face detection and a simple skin colour hand detector, trained
using the detected face. The suggested model is designed with analytical
tractability in mind and we show that the pose tracking can be
Rao-Blackwellised using the mixture Kalman filter, allowing for computational
efficiency while still incorporating bio-mechanical properties of the upper
body. In addition, the use of the proposed upper body model allows reliable
three-dimensional pose estimates to be obtained indirectly for a number of
joints that are often difficult to detect using traditional object recognition
strategies. Comparisons with Kinect sensor results and the state of the art in
2D pose estimation highlight the efficacy of the proposed approach.
Towards a theory of granular sets
Authors: Garimella Rama Murthy
Date: 2014-06-17 00:00:00
Tasks: Classification, General Classification, Sensor Fusion

Motivated by the application problem of sensor fusion the author introduced
the concept of graded set. It is reasoned that in classification problem
arising in an information system (represented by information table), a novel
set called Granular set naturally arises. It is realized that in any
hierarchical classification problem, Granular set naturally arises. Also when
the target set of objects forms a graded set the lower and upper approximations
of target sets form a graded set. This generalizes the concept of rough set. It
is hoped that a detailed theory of granular/ graded sets finds several
applications.
Sparse Estimation with the Swept Approximated Message-Passing Algorithm
Authors: Andre Manoel, Florent Krzakala, Eric W. Tramel, Lenka Zdeborová
Date: 2014-06-17 00:00:00
Tasks: Computational Efficiency

Approximate Message Passing (AMP) has been shown to be a superior method for
inference problems, such as the recovery of signals from sets of noisy,
lower-dimensionality measurements, both in terms of reconstruction accuracy and
in computational efficiency. However, AMP suffers from serious convergence
issues in contexts that do not exactly match its assumptions. We propose a new
approach to stabilizing AMP in these contexts by applying AMP updates to
individual coefficients rather than in parallel. Our results show that this
change to the AMP iteration can provide theoretically expected, but hitherto
unobtainable, performance for problems on which the standard AMP iteration
diverges. Additionally, we find that the computational costs of this swept
coefficient update scheme is not unduly burdensome, allowing it to be applied
efficiently to signals of large dimensionality.
Learning States Representations in POMDP
Authors: Gabriella Contardo, Ludovic Denoyer, Thierry Artieres, Patrick Gallinari
Date: 2013-12-20 00:00:00

We propose to deal with sequential processes where only partial observations
are available by learning a latent representation space on which policies may
be accurately learned.
Identifying roles of clinical pharmacy with survey evaluation
Authors: Andreja Čufar, Aleš Mrhar, Marko Robnik-Šikonja
Date: 2014-06-17 00:00:00
Tasks: Attribute, Decision Making, Marketing, Survey

The survey data sets are important sources of data and their successful
exploitation is of key importance for informed policy-decision making. We
present how a survey analysis approach initially developed for customer
satisfaction research in marketing can be adapted for the introduction of
clinical pharmacy services into hospital. We use two analytical approaches to
extract relevant managerial consequences. With OrdEval algorithm we first
evaluate the importance of competences for the users of clinical pharmacy and
extract their nature according to the users expectations. Next, we build a
model for predicting a successful introduction of clinical pharmacy to the
clinical departments. We the wards with the highest probability of successful
cooperation with a clinical pharmacist. We obtain useful managerially relevant
information from a relatively small sample of highly relevant respondents. We
show how the OrdEval algorithm exploits the information hidden in the ordering
of class and attribute values and their inherent correlation. Its output can be
effectively visualized and complemented with confidence intervals.
An Evolutionary Approach for Optimal Citing and Sizing of Micro-Grid in Radial Distribution Systems
Authors: Eswari. J, Dr. S. Jeyadevi
Date: 2014-06-17 00:00:00

This Paper presents the methodology of penetration of Micro-Grids (MG) in the
radial distribution system (RDS). The aim of this paper is to minimize a total
real power loss that descends the performance of the radial distribution system
by integrating various renewable resources as Distributed Generation (DG). The
combination of different types of renewable energy resources contributes a
sustainable MG. These resources are optimally sized and located using
evolutionary approach in various penetration levels. The optimal solutions are
experimented with IEEE 33 radial distribution system using Particle Swarm
Optimization (PSO) technique. The results are quite promising and authenticate
its potential to solve problem in radial distribution system effectively.
CCCP Algorithms to Minimize the Bethe free energy of 3-SAT Problem
Authors: Yusupjan Habibulla
Date: 2014-06-16 00:00:00

The k-sat problem is a prototypical constraint satisfaction problem. There
are many algorithms to study k-sat problem, BP algorithm is famous one of them.
But BP algorithm does not converge when $\alpha$(constraint density)is bigger
than some threshold value. In this paper we use CCCP (Concave Convex Procedure)
algorithm to study 3-sat problem and we get better results than BP algorithm
that CCCP algorithm still converges when BP algorithm does not converge. Our
work almost builds on recent results by Yuille \cite{Yuille2002} who apply the
CCCP algorithm to Bethe and Kikuchi free energies and obtained two algorithms
on 2D and 3D spin glasses. Our implementation of CCCP algorithm on 3-sat
problem is some different from his implementation and we have some different
views about CCCP algorithm's some properties. Some difference of these maybe
because of CCCP algorithm have different properties and implementation process
on different problem and some others of these are related to the CCCP algorithm
itself. Our work indicates that CCCP algorithm has more learning and inference
applications.
Mapping the Economic Crisis: Some Preliminary Investigations
Authors: Pierre Bourreau, Thierry Poibeau
Date: 2014-06-17 00:00:00

In this paper we describe our contribution to the PoliInformatics 2014
Challenge on the 2007-2008 financial crisis. We propose a state of the art
technique to extract information from texts and provide different
representations, giving first a static overview of the domain and then a
dynamic representation of its main evolutions. We show that this strategy
provides a practical solution to some recent theories in social sciences that
are facing a lack of methods and tools to automatically extract information
from natural language texts.
Replicating Kernels with a Short Stride Allows Sparse Reconstructions with Fewer Independent Kernels
Authors: Peter F. Schultz, Dylan M. Paiton, Wei Lu, Garrett T. Kenyon
Date: 2014-06-17 00:00:00

In sparse coding it is common to tile an image into nonoverlapping patches,
and then use a dictionary to create a sparse representation of each tile
independently. In this situation, the overcompleteness of the dictionary is the
number of dictionary elements divided by the patch size. In deconvolutional
neural networks (DCNs), dictionaries learned on nonoverlapping tiles are
replaced by a family of convolution kernels. Hence adjacent points in the
feature maps (V1 layers) have receptive fields in the image that are
translations of each other. The translational distance is determined by the
dimensions of V1 in comparison to the dimensions of the image space. We refer
to this translational distance as the stride.
  We implement a type of DCN using a modified Locally Competitive Algorithm
(LCA) to investigate the relationship between the number of kernels, the
stride, the receptive field size, and the quality of reconstruction. We find,
for example, that for 16x16-pixel receptive fields, using eight kernels and a
stride of 2 leads to sparse reconstructions of comparable quality as using 512
kernels and a stride of 16 (the nonoverlapping case). We also find that for a
given stride and number of kernels, the patch size does not significantly
affect reconstruction quality. Instead, the learned convolution kernels have a
natural support radius independent of the patch size.
Construction of non-convex polynomial loss functions for training a binary classifier with quantum annealing
Authors: Ryan Babbush, Vasil Denchev, Nan Ding, Sergei Isakov, Hartmut Neven
Date: 2014-06-17 00:00:00

Quantum annealing is a heuristic quantum algorithm which exploits quantum
resources to minimize an objective function embedded as the energy levels of a
programmable physical system. To take advantage of a potential quantum
advantage, one needs to be able to map the problem of interest to the native
hardware with reasonably low overhead. Because experimental considerations
constrain our objective function to take the form of a low degree PUBO
(polynomial unconstrained binary optimization), we employ non-convex loss
functions which are polynomial functions of the margin. We show that these loss
functions are robust to label noise and provide a clear advantage over convex
methods. These loss functions may also be useful for classical approaches as
they compile to regularized risk expressions which can be evaluated in constant
time with respect to the number of training examples.
Matroid Bandits: Fast Combinatorial Optimization with Learning
Authors: Branislav Kveton, Zheng Wen, Azin Ashkan, Hoda Eydgahi, Brian Eriksson
Date: 2014-03-20 00:00:00
Tasks: Combinatorial Optimization, Computational Efficiency

A matroid is a notion of independence in combinatorial optimization which is
closely related to computational efficiency. In particular, it is well known
that the maximum of a constrained modular function can be found greedily if and
only if the constraints are associated with a matroid. In this paper, we bring
together the ideas of bandits and matroids, and propose a new class of
combinatorial bandits, matroid bandits. The objective in these problems is to
learn how to maximize a modular function on a matroid. This function is
stochastic and initially unknown. We propose a practical algorithm for solving
our problem, Optimistic Matroid Maximization (OMM); and prove two upper bounds,
gap-dependent and gap-free, on its regret. Both bounds are sublinear in time
and at most linear in all other quantities of interest. The gap-dependent upper
bound is tight and we prove a matching lower bound on a partition matroid
bandit. Finally, we evaluate our method on three real-world problems and show
that it is practical.
Automatic Channel Fault Detection and Diagnosis System for a Small Animal APD-Based Digital PET Scanner
Authors: Jonathan Charest, Jean-François Beaudoin, Jules Cadorette, Roger Lecomte, Charles-Antoine Brunet, Réjean Fontaine
Date: 2014-06-16 00:00:00
Tasks: Fault Detection, Fault Diagnosis

Fault detection and diagnosis is critical to many applications in order to
ensure proper operation and performance over time. Positron emission tomography
(PET) systems that require regular calibrations by qualified scanner operators
are good candidates for such continuous improvements. Furthermore, for scanners
employing one-to-one coupling of crystals to photodetectors to achieve enhanced
spatial resolution and contrast, the calibration task is even more daunting
because of the large number of independent channels involved. To cope with the
additional complexity of the calibration and quality control procedures of
these scanners, an intelligent system (IS) was designed to perform fault
detection and diagnosis (FDD) of malfunctioning channels. The IS can be broken
down into four hierarchical modules: parameter extraction, channel fault
detection, fault prioritization and diagnosis. Of these modules, the first two
have previously been reported and this paper focuses on fault prioritization
and diagnosis. The purpose of the fault prioritization module is to help the
operator to zero in on the faults that need immediate attention. The fault
diagnosis module will then identify the causes of the malfunction and propose
an explanation of the reasons that lead to the diagnosis. The FDD system was
implemented on a LabPET avalanche photodiode (APD)-based digital PET scanner.
Experiments demonstrated a FDD Sensitivity of 99.3 % (with a 95% confidence
interval (CI) of: [98.7, 99.9]) for major faults. Globally, the Balanced
Accuracy of the diagnosis for varying fault severities is 92 %. This suggests
the IS can greatly benefit the operators in their maintenance task.
Embedded Controlled Languages
Authors: Aarne Ranta
Date: 2014-06-16 00:00:00
Tasks: Machine Translation, Translation

Inspired by embedded programming languages, an embedded CNL (controlled
natural language) is a proper fragment of an entire natural language (its host
language), but it has a parser that recognizes the entire host language. This
makes it possible to process out-of-CNL input and give useful feedback to
users, instead of just reporting syntax errors. This extended abstract explains
the main concepts of embedded CNL implementation in GF (Grammatical Framework),
with examples from machine translation and some other ongoing work.
Sparse coding for multitask and transfer learning
Authors: Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes
Date: 2012-09-04 00:00:00
Tasks: Dictionary Learning, Transfer Learning

We investigate the use of sparse coding and dictionary learning in the
context of multitask and transfer learning. The central assumption of our
learning method is that the tasks parameters are well approximated by sparse
linear combinations of the atoms of a dictionary on a high or infinite
dimensional space. This assumption, together with the large quantity of
available data in the multitask and transfer learning settings, allows a
principled choice of the dictionary. We provide bounds on the generalization
error of this approach, for both settings. Numerical experiments on one
synthetic and two real datasets show the advantage of our method over single
task learning, a previous method based on orthogonal and dense representation
of the tasks and a related method learning task grouping.
Impact of Exponent Parameter Value for the Partition Matrix on the Performance of Fuzzy C Means Algorithm
Authors: Dibya Jyoti Bora, Anil Kumar Gupta
Date: 2014-06-16 00:00:00
Tasks: Clustering

Soft Clustering plays a very important rule on clustering real world data
where a data item contributes to more than one cluster. Fuzzy logic based
algorithms are always suitable for performing soft clustering tasks. Fuzzy C
Means (FCM) algorithm is a very popular fuzzy logic based algorithm. In case of
fuzzy logic based algorithm, the parameter like exponent for the partition
matrix that we have to fix for the clustering task plays a very important rule
on the performance of the algorithm. In this paper, an experimental analysis is
done on FCM algorithm to observe the impact of this parameter on the
performance of the algorithm.
Blind Deconvolution with Non-local Sparsity Reweighting
Authors: Dilip Krishnan, Joan Bruna, Rob Fergus
Date: 2013-11-16 00:00:00

Blind deconvolution has made significant progress in the past decade. Most
successful algorithms are classified either as Variational or Maximum
a-Posteriori ($MAP$). In spite of the superior theoretical justification of
variational techniques, carefully constructed $MAP$ algorithms have proven
equally effective in practice. In this paper, we show that all successful $MAP$
and variational algorithms share a common framework, relying on the following
key principles: sparsity promotion in the gradient domain, $l_2$ regularization
for kernel estimation, and the use of convex (often quadratic) cost functions.
Our observations lead to a unified understanding of the principles required for
successful blind deconvolution. We incorporate these principles into a novel
algorithm that improves significantly upon the state of the art.