pwc-archive/papers-with-abstracts huggingface dataset:
An improved quasar detection method in EROS-2 and MACHO LMC datasets
Authors: Karim Pichara, Pavlos Protopapas, Dae-Won Kim, Jean-Baptiste Marquette, Patrick Tisserand
Date: 2013-04-01 00:00:00
Tasks: General Classification

We present a new classification method for quasar identification in the
EROS-2 and MACHO datasets based on a boosted version of Random Forest
classifier. We use a set of variability features including parameters of a
continuous auto regressive model. We prove that continuous auto regressive
parameters are very important discriminators in the classification process. We
create two training sets (one for EROS-2 and one for MACHO datasets) using
known quasars found in the LMC. Our model's accuracy in both EROS-2 and MACHO
training sets is about 90% precision and 86% recall, improving the state of the
art models accuracy in quasar detection. We apply the model on the complete,
including 28 million objects, EROS-2 and MACHO LMC datasets, finding 1160 and
2551 candidates respectively. To further validate our list of candidates, we
crossmatched our list with a previous 663 known strong candidates, getting 74%
of matches for MACHO and 40% in EROS-2. The main difference on matching level
is because EROS-2 is a slightly shallower survey which translates to
significantly lower signal-to-noise ratio lightcurves.
Introducing Nominals to the Combined Query Answering Approaches for EL
Authors: Giorgio Stefanoni, Boris Motik, Ian Horrocks
Date: 2013-03-29 00:00:00

So-called combined approaches answer a conjunctive query over a description
logic ontology in three steps: first, they materialise certain consequences of
the ontology and the data; second, they evaluate the query over the data; and
third, they filter the result of the second phase to eliminate unsound answers.
Such approaches were developed for various members of the DL-Lite and the EL
families of languages, but none of them can handle ontologies containing
nominals. In our work, we bridge this gap and present a combined query
answering approach for ELHO---a logic that contains all features of the OWL 2
EL standard apart from transitive roles and complex role inclusions. This
extension is nontrivial because nominals require equality reasoning, which
introduces complexity into the first and the third step. Our empirical
evaluation suggests that our technique is suitable for practical application,
and so it provides a practical basis for conjunctive query answering in a large
fragment of OWL 2 EL.
On Power-law Kernels, corresponding Reproducing Kernel Hilbert Space and Applications
Authors: Debarghya Ghoshdastidar, Ambedkar Dukkipati
Date: 2012-04-09 00:00:00
Tasks: General Classification, regression

The role of kernels is central to machine learning. Motivated by the
importance of power-law distributions in statistical modeling, in this paper,
we propose the notion of power-law kernels to investigate power-laws in
learning problem. We propose two power-law kernels by generalizing Gaussian and
Laplacian kernels. This generalization is based on distributions, arising out
of maximization of a generalized information measure known as nonextensive
entropy that is very well studied in statistical mechanics. We prove that the
proposed kernels are positive definite, and provide some insights regarding the
corresponding Reproducing Kernel Hilbert Space (RKHS). We also study practical
significance of both kernels in classification and regression, and present some
simulation results.
Fast Feature Reduction in intrusion detection datasets
Authors: Shafigh Parsazad, Ehsan Saboori, Amin Allahyar
Date: 2013-04-01 00:00:00
Tasks: feature selection, Intrusion Detection

In the most intrusion detection systems (IDS), a system tries to learn
characteristics of different type of attacks by analyzing packets that sent or
received in network. These packets have a lot of features. But not all of them
is required to be analyzed to detect that specific type of attack. Detection
speed and computational cost is another vital matter here, because in these
types of problems, datasets are very huge regularly. In this paper we tried to
propose a very simple and fast feature selection method to eliminate features
with no helpful information on them. Result faster learning in process of
redundant feature omission. We compared our proposed method with three most
successful similarity based feature selection algorithm including Correlation
Coefficient, Least Square Regression Error and Maximal Information Compression
Index. After that we used recommended features by each of these algorithms in
two popular classifiers including: Bayes and KNN classifier to measure the
quality of the recommendations. Experimental result shows that although the
proposed method can't outperform evaluated algorithms with high differences in
accuracy, but in computational cost it has huge superiority over them.
A cookbook of translating English to Xapi
Authors: Ladislau Bölöni
Date: 2013-03-31 00:00:00

The Xapagy cognitive architecture had been designed to perform narrative
reasoning: to model and mimic the activities performed by humans when
witnessing, reading, recalling, narrating and talking about stories. Xapagy
communicates with the outside world using Xapi, a simplified, "pidgin" language
which is strongly tied to the internal representation model (instances, scenes
and verb instances) and reasoning techniques (shadows and headless shadows).
While not fully a semantic equivalent of natural language, Xapi can represent a
wide range of complex stories. We illustrate the representation technique used
in Xapi through examples taken from folk physics, folk psychology as well as
some more unusual literary examples. We argue that while the Xapi model
represents a conceptual shift from the English representation, the mapping is
logical and consistent, and a trained knowledge engineer can translate between
English and Xapi at near-native speed.
Compressive adaptive computational ghost imaging
Authors: Marc Aßmann, Manfred Bayer
Date: 2013-03-31 00:00:00
Tasks: Compressive Sensing

Compressive sensing is considered a huge breakthrough in signal acquisition.
It allows recording an image consisting of $N^2$ pixels using much fewer than
$N^2$ measurements if it can be transformed to a basis where most pixels take
on negligibly small values. Standard compressive sensing techniques suffer from
the computational overhead needed to reconstruct an image with typical
computation times between hours and days and are thus not optimal for
applications in physics and spectroscopy. We demonstrate an adaptive
compressive sampling technique that performs measurements directly in a sparse
basis. It needs much fewer than $N^2$ measurements without any computational
overhead, so the result is available instantly.
Modèle flou d'expression des préférences basé sur les CP-Nets
Authors: Hanene Rezgui, Minyar Sassi-Hidri
Date: 2013-03-31 00:00:00

This article addresses the problem of expressing preferences in flexible
queries while basing on a combination of the fuzzy logic theory and Conditional
Preference Networks or CP-Nets.
Phase Transition and Network Structure in Realistic SAT Problems
Authors: Soumya C. Kambhampati, Thomas Liu
Date: 2013-03-30 00:00:00
Tasks: Clustering

A fundamental question in Computer Science is understanding when a specific
class of problems go from being computationally easy to hard. Because of its
generality and applications, the problem of Boolean Satisfiability (aka SAT) is
often used as a vehicle for investigating this question. A signal result from
these studies is that the hardness of SAT problems exhibits a dramatic
easy-to-hard phase transition with respect to the problem constrainedness. Past
studies have however focused mostly on SAT instances generated using uniform
random distributions, where all constraints are independently generated, and
the problem variables are all considered of equal importance. These assumptions
are unfortunately not satisfied by most real problems. Our project aims for a
deeper understanding of hardness of SAT problems that arise in practice. We
study two key questions: (i) How does easy-to-hard transition change with more
realistic distributions that capture neighborhood sensitivity and
rich-get-richer aspects of real problems and (ii) Can these changes be
explained in terms of the network properties (such as node centrality and
small-worldness) of the clausal networks of the SAT problems. Our results,
based on extensive empirical studies and network analyses, provide important
structural and computational insights into realistic SAT problems. Our
extensive empirical studies show that SAT instances from realistic
distributions do exhibit phase transition, but the transition occurs sooner (at
lower values of constrainedness) than the instances from uniform random
distribution. We show that this behavior can be explained in terms of their
clausal network properties such as eigenvector centrality and small-worldness
(measured indirectly in terms of the clustering coefficients and average node
distance).
Is the Multiverse Hypothesis capable of explaining the Fine Tuning of Nature Laws and Constants? The Case of Cellular Automata
Authors: Francisco José Soler Gil, Manuel Alfonseca
Date: 2011-05-21 00:00:00
Tasks: Philosophy

The objective of this paper is analyzing to which extent the multiverse
hypothesis provides a real explanation of the peculiarities of the laws and
constants in our universe. First we argue in favor of the thesis that all
multiverses except Tegmark's <<mathematical multiverse>> are too small to
explain the fine tuning, so that they merely shift the problem up one level.
But the <<mathematical multiverse>> is surely too large. To prove this
assessment, we have performed a number of experiments with cellular automata of
complex behavior, which can be considered as universes in the mathematical
multiverse. The analogy between what happens in some automata (in particular
Conway's <<Game of Life>>) and the real world is very strong. But if the
results of our experiments can be extrapolated to our universe, we should
expect to inhabit -- in the context of the multiverse -- a world in which at
least some of the laws and constants of nature should show a certain time
dependence. Actually, the probability of our existence in a world such as ours
would be mathematically equal to zero. In consequence, the results presented in
this paper can be considered as an inkling that the hypothesis of the
multiverse, whatever its type, does not offer an adequate explanation for the
peculiarities of the physical laws in our world. A slightly reduced version of
this paper has been published in the Journal for General Philosophy of Science,
Springer, March 2013, DOI: 10.1007/s10838-013-9215-7.
Meaning-focused and Quantum-inspired Information Retrieval
Authors: Diederik Aerts, Jan Broekaert, Sandro Sozzo, Tomas Veloz
Date: 2013-03-30 00:00:00
Tasks: Information Retrieval, Retrieval

In recent years, quantum-based methods have promisingly integrated the
traditional procedures in information retrieval (IR) and natural language
processing (NLP). Inspired by our research on the identification and
application of quantum structures in cognition, more specifically our work on
the representation of concepts and their combinations, we put forward a
'quantum meaning based' framework for structured query retrieval in text
corpora and standardized testing corpora. This scheme for IR rests on
considering as basic notions, (i) 'entities of meaning', e.g., concepts and
their combinations and (ii) traces of such entities of meaning, which is how
documents are considered in this approach. The meaning content of these
'entities of meaning' is reconstructed by solving an 'inverse problem' in the
quantum formalism, consisting of reconstructing the full states of the entities
of meaning from their collapsed states identified as traces in relevant
documents. The advantages with respect to traditional approaches, such as
Latent Semantic Analysis (LSA), are discussed by means of concrete examples.
A Neuromorphic VLSI Design for Spike Timing and Rate Based Synaptic Plasticity
Authors: Mostafa Rahimi Azghadi, Said Al-Sarawi, Derek Abbott, Nicolangelo Iannella
Date: 2013-03-30 00:00:00
Tasks: Triplet

Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful
synaptic plasticity rule that acts beyond conventional pair-based STDP (PSTDP).
Here, the TSTDP is capable of reproducing the outcomes from a variety of
biological experiments, while the PSTDP rule fails to reproduce them.
Additionally, it has been shown that the behaviour inherent to the spike
rate-based Bienenstock-Cooper-Munro (BCM) synaptic plasticity rule can also
emerge from the TSTDP rule. This paper proposes an analog implementation of the
TSTDP rule. The proposed VLSI circuit has been designed using the AMS 0.35 um
CMOS process and has been simulated using design kits for Synopsys and Cadence
tools. Simulation results demonstrate how well the proposed circuit can alter
synaptic weights according to the timing difference amongst a set of different
patterns of spikes. Furthermore, the circuit is shown to give rise to a
BCM-like learning rule, which is a rate-based rule. To mimic implementation
environment, a 1000 run Monte Carlo (MC) analysis was conducted on the proposed
circuit. The presented MC simulation analysis and the simulation result from
fine-tuned circuits show that, it is possible to mitigate the effect of process
variations in the proof of concept circuit, however, a practical variation
aware design technique is required to promise a high circuit performance in a
large scale neural network. We believe that the proposed design can play a
significant role in future VLSI implementations of both spike timing and rate
based neuromorphic learning systems.
Learning Locality-Constrained Collaborative Representation for Face Recognition
Authors: Xi Peng, Lei Zhang, Zhang Yi, Kok Kiong Tan
Date: 2012-10-04 00:00:00
Tasks: Dimensionality Reduction, Face Recognition

The model of low-dimensional manifold and sparse representation are two
well-known concise models that suggest each data can be described by a few
characteristics. Manifold learning is usually investigated for dimension
reduction by preserving some expected local geometric structures from the
original space to a low-dimensional one. The structures are generally
determined by using pairwise distance, e.g., Euclidean distance. Alternatively,
sparse representation denotes a data point as a linear combination of the
points from the same subspace. In practical applications, however, the nearby
points in terms of pairwise distance may not belong to the same subspace, and
vice versa. Consequently, it is interesting and important to explore how to get
a better representation by integrating these two models together. To this end,
this paper proposes a novel coding algorithm, called Locality-Constrained
Collaborative Representation (LCCR), which improves the robustness and
discrimination of data representation by introducing a kind of local
consistency. The locality term derives from a biologic observation that the
similar inputs have similar code. The objective function of LCCR has an
analytical solution, and it does not involve local minima. The empirical
studies based on four public facial databases, ORL, AR, Extended Yale B, and
Multiple PIE, show that LCCR is promising in recognizing human faces from
frontal views with varying expression and illumination, as well as various
corruptions and occlusions.
Anatomical Structure Segmentation in Liver MRI Images
Authors: G. Geethu Lakshmi
Date: 2012-07-03 00:00:00
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation

Segmentation of medical images is a challenging task owing to their
complexity. A standard segmentation problem within Magnetic Resonance Imaging
(MRI) is the task of labeling voxels according to their tissue type. Image
segmentation provides volumetric quantification of liver area and thus helps in
the diagnosis of disorders, such as Hepatitis, Cirrhosis, Jaundice,
Hemochromatosis etc.This work deals with comparison of segmentation by applying
Level Set Method,Fuzzy Level Information C-Means Clustering Algorithm and
Gradient Vector Flow Snake Algorithm.The results are compared using the
parameters such as Number of pixels correctly classified, and percentage of
area segmented.
Registration of Images with Outliers Using Joint Saliency Map
Authors: Binjie Qin, Zhijun Gu, Xianjun Sun, Yisong Lv
Date: 2013-03-29 00:00:00
Tasks: Image Registration

Mutual information (MI) is a popular similarity measure for image
registration, whereby good registration can be achieved by maximizing the
compactness of the clusters in the joint histogram. However, MI is sensitive to
the "outlier" objects that appear in one image but not the other, and also
suffers from local and biased maxima. We propose a novel joint saliency map
(JSM) to highlight the corresponding salient structures in the two images, and
emphatically group those salient structures into the smoothed compact clusters
in the weighted joint histogram. This strategy could solve both the outlier and
the local maxima problems. Experimental results show that the JSM-MI based
algorithm is not only accurate but also robust for registration of challenging
image pairs with outliers.
Regret in Online Combinatorial Optimization
Authors: Jean-Yves Audibert, Sébastien Bubeck, Gábor Lugosi
Date: 2012-04-20 00:00:00
Tasks: Combinatorial Optimization

We address online linear optimization problems when the possible actions of
the decision maker are represented by binary vectors. The regret of the
decision maker is the difference between her realized loss and the best loss
she would have achieved by picking, in hindsight, the best possible action. Our
goal is to understand the magnitude of the best possible (minimax) regret. We
study the problem under three different assumptions for the feedback the
decision maker receives: full information, and the partial information models
of the so-called "semi-bandit" and "bandit" problems. Combining the Mirror
Descent algorithm and the INF (Implicitely Normalized Forecaster) strategy, we
are able to prove optimal bounds for the semi-bandit case. We also recover the
optimal bounds for the full information setting. In the bandit case we discuss
existing results in light of a new lower bound, and suggest a conjecture on the
optimal regret in that case. Finally we also prove that the standard
exponentially weighted average forecaster is provably suboptimal in the setting
of online combinatorial optimization.
Translation-Invariant Shrinkage/Thresholding of Group Sparse Signals
Authors: Po-Yu Chen, Ivan W. Selesnick
Date: 2013-03-29 00:00:00
Tasks: Blocking, Denoising, Speech Enhancement, Translation

This paper addresses signal denoising when large-amplitude coefficients form
clusters (groups). The L1-norm and other separable sparsity models do not
capture the tendency of coefficients to cluster (group sparsity). This work
develops an algorithm, called 'overlapping group shrinkage' (OGS), based on the
minimization of a convex cost function involving a group-sparsity promoting
penalty function. The groups are fully overlapping so the denoising method is
translation-invariant and blocking artifacts are avoided. Based on the
principle of majorization-minimization (MM), we derive a simple iterative
minimization algorithm that reduces the cost function monotonically. A
procedure for setting the regularization parameter, based on attenuating the
noise to a specified level, is also described. The proposed approach is
illustrated on speech enhancement, wherein the OGS approach is applied in the
short-time Fourier transform (STFT) domain. The denoised speech produced by OGS
does not suffer from musical noise.
Optimal discovery with probabilistic expert advice: finite time analysis and macroscopic optimality
Authors: Sebastien Bubeck, Damien Ernst, Aurelien Garivier
Date: 2012-07-22 00:00:00

We consider an original problem that arises from the issue of security
analysis of a power system and that we name optimal discovery with
probabilistic expert advice. We address it with an algorithm based on the
optimistic paradigm and on the Good-Turing missing mass estimator. We prove two
different regret bounds on the performance of this algorithm under weak
assumptions on the probabilistic experts. Under more restrictive hypotheses, we
also prove a macroscopic optimality result, comparing the algorithm both with
an oracle strategy and with uniform sampling. Finally, we provide numerical
experiments illustrating these theoretical findings.
Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion
Authors: Franz J. Király, Louis Theran
Date: 2013-02-21 00:00:00
Tasks: Denoising, Low-Rank Matrix Completion, Matrix Completion

We propose a general framework for reconstructing and denoising single
entries of incomplete and noisy entries. We describe: effective algorithms for
deciding if and entry can be reconstructed and, if so, for reconstructing and
denoising it; and a priori bounds on the error of each entry, individually. In
the noiseless case our algorithm is exact. For rank-one matrices, the new
algorithm is fast, admits a highly-parallel implementation, and produces an
error minimizing estimate that is qualitatively close to our theoretical and
the state-of-the-are Nuclear Norm and OptSpace methods.
Note on Combinatorial Engineering Frameworks for Hierarchical Modular Systems
Authors: Mark Sh. Levin
Date: 2013-03-29 00:00:00
Tasks: Combinatorial Optimization, Multiple-choice

The paper briefly describes a basic set of special combinatorial engineering
frameworks for solving complex problems in the field of hierarchical modular
systems. The frameworks consist of combinatorial problems (and corresponding
models), which are interconnected/linked (e.g., by preference relation).
Mainly, hierarchical morphological system model is used. The list of basic
standard combinatorial engineering (technological) frameworks is the following:
(1) design of system hierarchical model, (2) combinatorial synthesis
('bottom-up' process for system design), (3) system evaluation, (4) detection
of system bottlenecks, (5) system improvement (re-design, upgrade), (6)
multi-stage design (design of system trajectory), (7) combinatorial modeling of
system evolution/development and system forecasting. The combinatorial
engineering frameworks are targeted to maintenance of some system life cycle
stages. The list of main underlaying combinatorial optimization problems
involves the following: knapsack problem, multiple-choice problem, assignment
problem, spanning trees, morphological clique problem.
Age group and gender recognition from human facial images
Authors: Tizita Nesibu Shewaye
Date: 2013-03-29 00:00:00
Tasks: General Classification

This work presents an automatic human gender and age group recognition system
based on human facial images. It makes an extensive experiment with row pixel
intensity valued features and Discrete Cosine Transform (DCT) coefficient
features with Principal Component Analysis and k-Nearest Neighbor
classification to identify the best recognition approach. The final results
show approaches using DCT coefficient outperform their counter parts resulting
in a 99% correct gender recognition rate and 68% correct age group recognition
rate (considering four distinct age groups) in unseen test images. Detailed
experimental settings and obtained results are clearly presented and explained
in this report.