pwc-archive/papers-with-abstracts huggingface dataset:
A new integral loss function for Bayesian optimization
Authors: Emmanuel Vazquez, Julien Bect
Date: 2014-08-20 00:00:00
Tasks: Bayesian Optimization

We consider the problem of maximizing a real-valued continuous function $f$
using a Bayesian approach. Since the early work of Jonas Mockus and Antanas
\v{Z}ilinskas in the 70's, the problem of optimization is usually formulated by
considering the loss function $\max f - M_n$ (where $M_n$ denotes the best
function value observed after $n$ evaluations of $f$). This loss function puts
emphasis on the value of the maximum, at the expense of the location of the
maximizer. In the special case of a one-step Bayes-optimal strategy, it leads
to the classical Expected Improvement (EI) sampling criterion. This is a
special case of a Stepwise Uncertainty Reduction (SUR) strategy, where the risk
associated to a certain uncertainty measure (here, the expected loss) on the
quantity of interest is minimized at each step of the algorithm. In this
article, assuming that $f$ is defined over a measure space $(\mathbb{X},
\lambda)$, we propose to consider instead the integral loss function
$\int_{\mathbb{X}} (f - M_n)_{+}\, d\lambda$, and we show that this leads, in
the case of a Gaussian process prior, to a new numerically tractable sampling
criterion that we call $\rm EI^2$ (for Expected Integrated Expected
Improvement). A numerical experiment illustrates that a SUR strategy based on
this new sampling criterion reduces the error on both the value and the
location of the maximizer faster than the EI-based strategy.
EURETILE D7.3 - Dynamic DAL benchmark coding, measurements on MPI version of DPSNN-STDP (distributed plastic spiking neural net) and improvements to other DAL codes
Authors: Pier Stanislao Paolucci, Iuliana Bacivarov, Devendra Rai, Lars Schor, Lothar Thiele, Hoeseok Yang, Elena Pastorelli, Roberto Ammendola, Andrea Biagioni, Ottorino Frezza, Francesca Lo Cicero, Alessandro Lonardo, Francesco Simula, Laura Tosoratto, Piero Vicini
Date: 2014-08-20 00:00:00

The EURETILE project required the selection and coding of a set of dedicated
benchmarks. The project is about the software and hardware architecture of
future many-tile distributed fault-tolerant systems. We focus on dynamic
workloads characterised by heavy numerical processing requirements. The
ambition is to identify common techniques that could be applied to both the
Embedded Systems and HPC domains. This document is the first public deliverable
of Work Package 7: Challenging Tiled Applications.
Optimal rates for zero-order convex optimization: the power of two function evaluations
Authors: John C. Duchi, Michael. I. Jordan, Martin J. Wainwright, Andre Wibisono
Date: 2013-12-07 00:00:00

We consider derivative-free algorithms for stochastic and non-stochastic
convex optimization problems that use only function values rather than
gradients. Focusing on non-asymptotic bounds on convergence rates, we show that
if pairs of function values are available, algorithms for $d$-dimensional
optimization that use gradient estimates based on random perturbations suffer a
factor of at most $\sqrt{d}$ in convergence rate over traditional stochastic
gradient methods. We establish such results for both smooth and non-smooth
cases, sharpening previous analyses that suggested a worse dimension
dependence, and extend our results to the case of multiple ($m \ge 2$)
evaluations. We complement our algorithmic development with
information-theoretic lower bounds on the minimax convergence rate of such
problems, establishing the sharpness of our achievable results up to constant
(sometimes logarithmic) factors.
Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network
Authors: Yu Liu, Osvaldo Simeone, Alexander M. Haimovich, Wei Su
Date: 2014-08-04 00:00:00
Tasks: Classification, General Classification

A novel Bayesian modulation classification scheme is proposed for a
single-antenna system over frequency-selective fading channels. The method is
based on Gibbs sampling as applied to a latent Dirichlet Bayesian network (BN).
The use of the proposed latent Dirichlet BN provides a systematic solution to
the convergence problem encountered by the conventional Gibbs sampling approach
for modulation classification. The method generalizes, and is shown to improve
upon, the state of the art.
Unsupervised Parallel Extraction based Texture for Efficient Image Representation
Authors: Mohammed M. Abdelsamea
Date: 2014-08-20 00:00:00

SOM is a type of unsupervised learning where the goal is to discover some
underlying structure of the data. In this paper, a new extraction method based
on the main idea of Concurrent Self-Organizing Maps (CSOM), representing a
winner-takes-all collection of small SOM networks is proposed. Each SOM of the
system is trained individually to provide best results for one class only. The
experiments confirm that the proposed features based CSOM is capable to
represent image content better than extracted features based on a single big
SOM and these proposed features improve the final decision of the CAD.
Experiments held on Mammographic Image Analysis Society (MIAS) dataset.
On Optimal Decision-Making in Ant Colonies
Authors: Mahnush Movahedi, Mahdi Zamani
Date: 2014-08-19 00:00:00
Tasks: Decision Making

Colonies of ants can collectively choose the best of several nests, even when
many of the active ants who organize the move visit only one site.
Understanding such a behavior can help us design efficient distributed decision
making algorithms. Marshall et al. propose a model for house-hunting in
colonies of ant Temnothorax albipennis. Unfortunately, their model does not
achieve optimal decision-making while laboratory experiments show that, in
fact, colonies usually achieve optimality during the house-hunting process. In
this paper, we argue that the model of Marshall et al. can achieve optimality
by including nest size information in their mathematical model. We use lab
results of Pratt et al. to re-define the differential equations of Marshall et
al. Finally, we sketch our strategy for testing the optimality of the new
model.
PGMHD: A Scalable Probabilistic Graphical Model for Massive Hierarchical Data Problems
Authors: Khalifeh AlJadda, Mohammed Korayem, Camilo Ortiz, Trey Grainger, John A. Miller, William S. York
Date: 2014-07-21 00:00:00

In the big data era, scalability has become a crucial requirement for any
useful computational model. Probabilistic graphical models are very useful for
mining and discovering data insights, but they are not scalable enough to be
suitable for big data problems. Bayesian Networks particularly demonstrate this
limitation when their data is represented using few random variables while each
random variable has a massive set of values. With hierarchical data - data that
is arranged in a treelike structure with several levels - one would expect to
see hundreds of thousands or millions of values distributed over even just a
small number of levels. When modeling this kind of hierarchical data across
large data sets, Bayesian networks become infeasible for representing the
probability distributions for the following reasons: i) Each level represents a
single random variable with hundreds of thousands of values, ii) The number of
levels is usually small, so there are also few random variables, and iii) The
structure of the network is predefined since the dependency is modeled top-down
from each parent to each of its child nodes, so the network would contain a
single linear path for the random variables from each parent to each child
node. In this paper we present a scalable probabilistic graphical model to
overcome these limitations for massive hierarchical data. We believe the
proposed model will lead to an easily-scalable, more readable, and expressive
implementation for problems that require probabilistic-based solutions for
massive amounts of hierarchical data. We successfully applied this model to
solve two different challenging probabilistic-based problems on massive
hierarchical data sets for different domains, namely, bioinformatics and latent
semantic discovery over search logs.
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
Authors: Nenad Markuš, Miroslav Frljak, Igor S. Pandžić, Jörgen Ahlberg, Robert Forchheimer
Date: 2013-05-20 00:00:00
Tasks: Face Detection, Object, object-detection, Object Detection, PICO, Sensitivity

We describe a method for visual object detection based on an ensemble of
optimized decision trees organized in a cascade of rejectors. The trees use
pixel intensity comparisons in their internal nodes and this makes them able to
process image regions very fast. Experimental analysis is provided through a
face detection problem. The obtained results are encouraging and demonstrate
that the method has practical value. Additionally, we analyse its sensitivity
to noise and show how to perform fast rotation invariant object detection.
Complete source code is provided at https://github.com/nenadmarkus/pico.
Object Segmentation in Images using EEG Signals
Authors: Eva Mohedano, Graham Healy, Kevin McGuinness, Xavier Giro-i-Nieto, Noel E. O'Connor, Alan F. Smeaton
Date: 2014-08-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Object, Semantic Segmentation

This paper explores the potential of brain-computer interfaces in segmenting
objects from images. Our approach is centered around designing an effective
method for displaying the image parts to the users such that they generate
measurable brain reactions. When an image region, specifically a block of
pixels, is displayed we estimate the probability of the block containing the
object of interest using a score based on EEG activity. After several such
blocks are displayed, the resulting probability map is binarized and combined
with the GrabCut algorithm to segment the image into object and background
regions. This study shows that BCI and simple EEG analysis are useful in
locating object boundaries in images.
What Regularized Auto-Encoders Learn from the Data Generating Distribution
Authors: Guillaume Alain, Yoshua Bengio
Date: 2012-11-18 00:00:00
Tasks: Denoising

What do auto-encoders learn about the underlying data generating
distribution? Recent work suggests that some auto-encoder variants do a good
job of capturing the local manifold structure of data. This paper clarifies
some of these previous observations by showing that minimizing a particular
form of regularized reconstruction error yields a reconstruction function that
locally characterizes the shape of the data generating density. We show that
the auto-encoder captures the score (derivative of the log-density with respect
to the input). It contradicts previous interpretations of reconstruction error
as an energy function. Unlike previous results, the theorems provided here are
completely generic and do not depend on the parametrization of the
auto-encoder: they show what the auto-encoder would tend to if given enough
capacity and examples. These results are for a contractive training criterion
we show to be similar to the denoising auto-encoder training criterion with
small corruption noise, but with contraction applied on the whole
reconstruction function rather than just encoder. Similarly to score matching,
one can consider the proposed training criterion as a convenient alternative to
maximum likelihood because it does not involve a partition function. Finally,
we show how an approximate Metropolis-Hastings MCMC can be setup to recover
samples from the estimated distribution, and this is confirmed in sampling
experiments.
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion
Authors: Franz J. Király, Louis Theran, Ryota Tomioka
Date: 2012-11-17 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

We present a novel algebraic combinatorial view on low-rank matrix completion
based on studying relations between a few entries with tools from algebraic
geometry and matroid theory. The intrinsic locality of the approach allows for
the treatment of single entries in a closed theoretical and practical
framework. More specifically, apart from introducing an algebraic combinatorial
theory of low-rank matrix completion, we present probability-one algorithms to
decide whether a particular entry of the matrix can be completed. We also
describe methods to complete that entry from a few others, and to estimate the
error which is incurred by any method completing that entry. Furthermore, we
show how known results on matrix completion and their sampling assumptions can
be related to our new perspective and interpreted in terms of a completability
phase transition.
What makes an Image Iconic? A Fine-Grained Case Study
Authors: Yangmuzi Zhang, Diane Larlus, Florent Perronnin
Date: 2014-08-19 00:00:00

A natural approach to teaching a visual concept, e.g. a bird species, is to
show relevant images. However, not all relevant images represent a concept
equally well. In other words, they are not necessarily iconic. This observation
raises three questions. Is iconicity a subjective property? If not, can we
predict iconicity? And what exactly makes an image iconic? We provide answers
to these questions through an extensive experimental study on a challenging
fine-grained dataset of birds. We first show that iconicity ratings are
consistent across individuals, even when they are not domain experts, thus
demonstrating that iconicity is not purely subjective. We then consider an
exhaustive list of properties that are intuitively related to iconicity and
measure their correlation with these iconicity ratings. We combine them to
predict iconicity of new unseen images. We also propose a direct iconicity
predictor that is discriminatively trained with iconicity ratings. By combining
both systems, we get an iconicity prediction that approaches human performance.
Hierarchical Clustering of Hyperspectral Images using Rank-Two Nonnegative Matrix Factorization
Authors: Nicolas Gillis, Da Kuang, Haesun Park
Date: 2013-09-14 00:00:00
Tasks: Clustering, Vocal Bursts Valence Prediction

In this paper, we design a hierarchical clustering algorithm for
high-resolution hyperspectral images. At the core of the algorithm, a new
rank-two nonnegative matrix factorizations (NMF) algorithm is used to split the
clusters, which is motivated by convex geometry concepts. The method starts
with a single cluster containing all pixels, and, at each step, (i) selects a
cluster in such a way that the error at the next step is minimized, and (ii)
splits the selected cluster into two disjoint clusters using rank-two NMF in
such a way that the clusters are well balanced and stable. The proposed method
can also be used as an endmember extraction algorithm in the presence of pure
pixels. The effectiveness of this approach is illustrated on several synthetic
and real-world hyperspectral images, and shown to outperform standard
clustering techniques such as k-means, spherical k-means and standard NMF.
Can Artificial Neural Networks be Applied in Seismic Predicition? Preliminary Analysis Applying Radial Topology. Case: Mexico
Authors: Cinthya Mota-Hernandez, Luis Esquivel-Rodriguez, Rafael Alvarado-Corona
Date: 2014-08-19 00:00:00

Tectonic earthquakes of high magnitude can cause considerable losses in terms
of human lives, economic and infrastructure, among others. According to an
evaluation published by the U.S. Geological Survey, 30 is the number of
earthquakes which have greatly impacted Mexico from the end of the XIX century
to this one. Based upon data from the National Seismological Service, on the
period between January 1, 2006 and May 1, 2013 there have occurred 5,826
earthquakes which magnitude has been greater than 4.0 degrees on the Richter
magnitude scale (25.54% of the total of earthquakes registered on the national
territory), being the Pacific Plate and the Cocos Plate the most important
ones. This document describes the development of an Artificial Neural Network
(ANN) based on the radial topology which seeks to generate a prediction with an
error margin lower than 20% which can inform about the probability of a future
earthquake one of the main questions is: can artificial neural networks be
applied in seismic forecasting? It can be argued that research has the
potential to bring in the forecast seismic, more research is needed to
consolidate data and help mitigate the impact caused by such events linked with
society. Keywords--- Analysis, Mexico, Neural Artificial Networks, Seismicity.
BET: Bayesian Ensemble Trees for Clustering and Prediction in Heterogeneous Data
Authors: Leo L. Duan, John P. Clancy, Rhonda D. Szczesniak
Date: 2014-08-18 00:00:00
Tasks: Classification, Clustering, General Classification, regression

We propose a novel "tree-averaging" model that utilizes the ensemble of
classification and regression trees (CART). Each constituent tree is estimated
with a subset of similar data. We treat this grouping of subsets as Bayesian
ensemble trees (BET) and model them as an infinite mixture Dirichlet process.
We show that BET adapts to data heterogeneity and accurately estimates each
component. Compared with the bootstrap-aggregating approach, BET shows improved
prediction performance with fewer trees. We develop an efficient estimating
procedure with improved sampling strategies in both CART and mixture models. We
demonstrate these advantages of BET with simulations, classification of breast
cancer and regression of lung function measurement of cystic fibrosis patients.
  Keywords: Bayesian CART; Dirichlet Process; Ensemble Approach; Heterogeneity;
Mixture of Trees.
Brain: Biological noise-based logic
Authors: Laszlo B. Kish, Claes-Goran Granqvist, Sergey M. Bezrukov, Tamas Horvath
Date: 2014-08-18 00:00:00

Neural spikes in the brain form stochastic sequences, i.e., belong to the
class of pulse noises. This stochasticity is a counterintuitive feature because
extracting information - such as the commonly supposed neural information of
mean spike frequency - requires long times for reasonably low error
probability. The mystery could be solved by noise-based logic, wherein
randomness has an important function and allows large speed enhancements for
special-purpose tasks, and the same mechanism is at work for the brain logic
version of this concept.
Offline Signature-Based Fuzzy Vault (OSFV: Review and New Results
Authors: George S. Eskander, Robert Sabourin, Eric Granger
Date: 2014-08-18 00:00:00

An offline signature-based fuzzy vault (OSFV) is a bio-cryptographic
implementation that uses handwritten signature images as biometrics instead of
traditional passwords to secure private cryptographic keys. Having a reliable
OSFV implementation is the first step towards automating financial and legal
authentication processes, as it provides greater security of confidential
documents by means of the embedded handwritten signatures. The authors have
recently proposed the first OSFV implementation which is reviewed in this
paper. In this system, a machine learning approach based on the dissimilarity
representation concept is employed to select a reliable feature representation
adapted for the fuzzy vault scheme. Some variants of this system are proposed
for enhanced accuracy and security. In particular, a new method that adapts
user key size is presented. Performance of proposed methods are compared using
the Brazilian PUCPR and GPDS signature databases and results indicate that the
key-size adaptation method achieves a good compromise between security and
accuracy. While average system entropy is increased from 45-bits to about
51-bits, the AER (average error rate) is decreased by about 21%.
On Detecting Messaging Abuse in Short Text Messages using Linguistic and Behavioral patterns
Authors: Alejandro Mosquera, Lamine Aouad, Slawomir Grzonkowski, Dylan Morss
Date: 2014-08-18 00:00:00
Tasks: Clustering, Text Normalization

The use of short text messages in social media and instant messaging has
become a popular communication channel during the last years. This rising
popularity has caused an increment in messaging threats such as spam, phishing
or malware as well as other threats. The processing of these short text message
threats could pose additional challenges such as the presence of lexical
variants, SMS-like contractions or advanced obfuscations which can degrade the
performance of traditional filtering solutions. By using a real-world SMS data
set from a large telecommunications operator from the US and a social media
corpus, in this paper we analyze the effectiveness of machine learning filters
based on linguistic and behavioral patterns in order to detect short text spam
and abusive users in the network. We have also explored different ways to deal
with short text message challenges such as tokenization and entity detection by
using text normalization and substring clustering techniques. The obtained
results show the validity of the proposed solution by enhancing baseline
approaches.
Bayesian image segmentations by Potts prior and loopy belief propagation
Authors: Kazuyuki Tanaka, Shun Kataoka, Muneki Yasuda, Yuji Waizumi, Chiou-Ting Hsu
Date: 2014-04-11 00:00:00
Tasks: Image Segmentation, Learning Theory, Semantic Segmentation

This paper presents a Bayesian image segmentation model based on Potts prior
and loopy belief propagation. The proposed Bayesian model involves several
terms, including the pairwise interactions of Potts models, and the average
vectors and covariant matrices of Gauss distributions in color image modeling.
These terms are often referred to as hyperparameters in statistical machine
learning theory. In order to determine these hyperparameters, we propose a new
scheme for hyperparameter estimation based on conditional maximization of
entropy in the Potts prior. The algorithm is given based on loopy belief
propagation. In addition, we compare our conditional maximum entropy framework
with the conventional maximum likelihood framework, and also clarify how the
first order phase transitions in LBP's for Potts models influence our
hyperparameter estimation procedures.
Opinion mining of movie reviews at document level
Authors: Richa Sharma, Shweta Nigam, Rekha Jain
Date: 2014-08-17 00:00:00
Tasks: Decision Making, Negation, Opinion Mining

The whole world is changed rapidly and using the current technologies
Internet becomes an essential need for everyone. Web is used in every field.
Most of the people use web for a common purpose like online shopping, chatting
etc. During an online shopping large number of reviews/opinions are given by
the users that reflect whether the product is good or bad. These reviews need
to be explored, analyse and organized for better decision making. Opinion
Mining is a natural language processing task that deals with finding
orientation of opinion in a piece of text with respect to a topic. In this
paper a document based opinion mining system is proposed that classify the
documents as positive, negative and neutral. Negation is also handled in the
proposed system. Experimental results using reviews of movies show the
effectiveness of the system.
Unsupervised learning segmentation for dynamic speckle activity images
Authors: Lucia I. Passoni, Ana I. Dai Pra, Gustavo J. Meschino, MArcelo Guzman, Chistian Weber, Héctor Rabal, Marcelo Trivi
Date: 2014-08-17 00:00:00

This paper proposes the design of decision models based on Computational
Intelligence techniques applied to image sequences of dynamic laser speckle.
These models aim to identify image regions of biological specimens illuminated
by a coherent beam coming from a laser. The field image is pseudo colored using
a Self Organizing Map projection. This process is carried out using a set of
descriptors applied to the intensity variations along time in every pixel of an
image sequence. The models use descriptors selected to improve effectiveness,
depending on the specific application. We present two examples of the
application of the proposed techniques to assess biological tissues. The
results obtained are encouraging and significantly improve those obtained using
a single descriptor.