pwc-archive/papers-with-abstracts huggingface dataset:
Parsimonious module inference in large networks
Authors: Tiago P. Peixoto
Date: 2012-12-19 00:00:00

We investigate the detectability of modules in large networks when the number
of modules is not known in advance. We employ the minimum description length
(MDL) principle which seeks to minimize the total amount of information
required to describe the network, and avoid overfitting. According to this
criterion, we obtain general bounds on the detectability of any prescribed
block structure, given the number of nodes and edges in the sampled network. We
also obtain that the maximum number of detectable blocks scales as $\sqrt{N}$,
where $N$ is the number of nodes in the network, for a fixed average degree
$<k>$. We also show that the simplicity of the MDL approach yields an efficient
multilevel Monte Carlo inference algorithm with a complexity of $O(\tau N\log
N)$, if the number of blocks is unknown, and $O(\tau N)$ if it is known, where
$\tau$ is the mixing time of the Markov chain. We illustrate the application of
the method on a large network of actors and films with over $10^6$ edges, and a
dissortative, bipartite block structure.
Dynamic Amelioration of Resolution Mismatches for Local Feature Based Identity Inference
Authors: Yongkang Wong, Conrad Sanderson, Sandra Mau, Brian C. Lovell
Date: 2013-04-08 00:00:00
Tasks: Face Recognition

While existing face recognition systems based on local features are robust to
issues such as misalignment, they can exhibit accuracy degradation when
comparing images of differing resolutions. This is common in surveillance
environments where a gallery of high resolution mugshots is compared to low
resolution CCTV probe images, or where the size of a given image is not a
reliable indicator of the underlying resolution (eg. poor optics). To alleviate
this degradation, we propose a compensation framework which dynamically chooses
the most appropriate face recognition system for a given pair of image
resolutions. This framework applies a novel resolution detection method which
does not rely on the size of the input images, but instead exploits the
sensitivity of local features to resolution using a probabilistic multi-region
histogram approach. Experiments on a resolution-modified version of the
"Labeled Faces in the Wild" dataset show that the proposed resolution detector
frontend obtains a 99% average accuracy in selecting the most appropriate face
recognition system, resulting in higher overall face discrimination accuracy
(across several resolutions) compared to the individual baseline face
recognition systems.
Automatic Fingerprint Recognition Using Minutiae Matching Technique for the Large Fingerprint Database
Authors: S. M. Mohsen, S. M. Zamshed Farhan, M. M. A. Hashem
Date: 2013-04-08 00:00:00

Extracting minutiae from fingerprint images is one of the most important
steps in automatic fingerprint identification system. Because minutiae matching
are certainly the most well-known and widely used method for fingerprint
matching, minutiae are local discontinuities in the fingerprint pattern. In
this paper a fingerprint matching algorithm is proposed using some specific
feature of the minutiae points, also the acquired fingerprint image is
considered by minimizing its size by generating a corresponding fingerprint
template for a large fingerprint database. The results achieved are compared
with those obtained through some other methods also shows some improvement in
the minutiae detection process in terms of memory and time required.
A powerful and efficient set test for genetic markers that handles confounders
Authors: Jennifer Listgarten, Christoph Lippert, Eun Yong Kang, Jing Xiang, Carl M. Kadie, David Heckerman
Date: 2012-05-03 00:00:00
Tasks: Two-sample testing

Approaches for testing sets of variants, such as a set of rare or common
variants within a gene or pathway, for association with complex traits are
important. In particular, set tests allow for aggregation of weak signal within
a set, can capture interplay among variants, and reduce the burden of multiple
hypothesis testing. Until now, these approaches did not address confounding by
family relatedness and population structure, a problem that is becoming more
important as larger data sets are used to increase power.
  Results: We introduce a new approach for set tests that handles confounders.
Our model is based on the linear mixed model and uses two random effects-one to
capture the set association signal and one to capture confounders. We also
introduce a computational speedup for two-random-effects models that makes this
approach feasible even for extremely large cohorts. Using this model with both
the likelihood ratio test and score test, we find that the former yields more
power while controlling type I error. Application of our approach to richly
structured GAW14 data demonstrates that our method successfully corrects for
population structure and family relatedness, while application of our method to
a 15,000 individual Crohn's disease case-control cohort demonstrates that it
additionally recovers genes not recoverable by univariate analysis.
  Availability: A Python-based library implementing our approach is available
at http://mscompbio.codeplex.com
Solving Linear Equations by Classical Jacobi-SR Based Hybrid Evolutionary Algorithm with Uniform Adaptation Technique
Authors: R. M. Jalal Uddin Jamali, M. M. A. Hashem, M. Mahfuz Hasan, Md. Bazlar Rahman
Date: 2013-04-08 00:00:00

Solving a set of simultaneous linear equations is probably the most important
topic in numerical methods. For solving linear equations, iterative methods are
preferred over the direct methods especially when the coefficient matrix is
sparse. The rate of convergence of iteration method is increased by using
Successive Relaxation (SR) technique. But SR technique is very much sensitive
to relaxation factor, {\omega}. Recently, hybridization of classical
Gauss-Seidel based successive relaxation technique with evolutionary
computation techniques have successfully been used to solve large set of linear
equations in which relaxation factors are self-adapted. In this paper, a new
hybrid algorithm is proposed in which uniform adaptive evolutionary computation
techniques and classical Jacobi based SR technique are used instead of
classical Gauss-Seidel based SR technique. The proposed Jacobi-SR based uniform
adaptive hybrid algorithm, inherently, can be implemented in parallel
processing environment efficiently. Whereas Gauss-Seidel-SR based hybrid
algorithms cannot be implemented in parallel computing environment efficiently.
The convergence theorem and adaptation theorem of the proposed algorithm are
proved theoretically. And the performance of the proposed Jacobi-SR based
uniform adaptive hybrid evolutionary algorithm is compared with Gauss-Seidel-SR
based uniform adaptive hybrid evolutionary algorithm as well as with both
classical Jacobi-SR method and Gauss-Seidel-SR method in the experimental
domain. The proposed Jacobi-SR based hybrid algorithm outperforms the
Gauss-Seidel-SR based hybrid algorithm as well as both classical Jacobi-SR
method and Gauss-Seidel-SR method in terms of convergence speed and
effectiveness.
Image Compression predicated on Recurrent Iterated Function Systems
Authors: Chol-Hui Yun, W. Metzler, M. Barski
Date: 2013-04-07 00:00:00
Tasks: Image Compression

Recurrent iterated function systems (RIFSs) are improvements of iterated
function systems (IFSs) using elements of the theory of Marcovian stochastic
processes which can produce more natural looking images. We construct new RIFSs
consisting substantially of a vertical contraction factor function and
nonlinear transformations. These RIFSs are applied to image compression.
Facial transformations of ancient portraits: the face of Caesar
Authors: Amelia Carolina Sparavigna
Date: 2013-04-07 00:00:00

Some software solutions used to obtain the facial transformations can help
investigating the artistic metamorphosis of the ancient portraits of the same
person. An analysis with a freely available software of portraitures of Julius
Caesar is proposed, showing his several "morphs". The software helps enhancing
the mood the artist added to a portrait.
Client-Driven Content Extraction Associated with Table
Authors: K. C. Santosh, Abdel Belaïd
Date: 2013-04-06 00:00:00

The goal of the project is to extract content within table in document images
based on learnt patterns. Real-world users i.e., clients first provide a set of
key fields within the table which they think are important. These are first
used to represent the graph where nodes are labelled with semantics including
other features and edges are attributed with relations. Attributed relational
graph (ARG) is then employed to mine similar graphs from a document image. Each
mined graph will represent an item within the table, and hence a set of such
graphs will compose a table. We have validated the concept by using a
real-world industrial problem.
RPA: Probabilistic analysis of probe performance and robust summarization
Authors: Leo Lahti, Laura L. Elo, Tero Aittokallio, Samuel Kaski
Date: 2011-09-22 00:00:00

Probe-level models have led to improved performance in microarray studies but
the various sources of probe-level contamination are still poorly understood.
Data-driven analysis of probe performance can be used to quantify the
uncertainty in individual probes and to highlight the relative contribution of
different noise sources. Improved understanding of the probe-level effects can
lead to improved preprocessing techniques and microarray design.
  We have implemented probabilistic tools for probe performance analysis and
summarization on short oligonucleotide arrays. In contrast to standard
preprocessing approaches, the methods provide quantitative estimates of
probe-specific noise and affinity terms and tools to investigate these
parameters. Tools to incorporate prior information of the probes in the
analysis are provided as well. Comparisons to known probe-level error sources
and spike-in data sets validate the approach.
  Implementation is freely available in R/BioConductor:
http://www.bioconductor.org/packages/release/bioc/html/RPA.html
Logical Stochastic Optimization
Authors: Emad Saad
Date: 2013-04-06 00:00:00
Tasks: Stochastic Optimization

We present a logical framework to represent and reason about stochastic
optimization problems based on probability answer set programming. This is
established by allowing probability optimization aggregates, e.g., minimum and
maximum in the language of probability answer set programming to allow
minimization or maximization of some desired criteria under the probabilistic
environments. We show the application of the proposed logical stochastic
optimization framework under the probability answer set programming to two
stages stochastic optimization problems with recourse.
Nested Aggregates in Answer Sets: An Application to a Priori Optimization
Authors: Emad Saad
Date: 2013-04-05 00:00:00
Tasks: Traveling Salesman Problem

We allow representing and reasoning in the presence of nested multiple
aggregates over multiple variables and nested multiple aggregates over
functions involving multiple variables in answer sets, precisely, in answer set
optimization programming and in answer set programming. We show the
applicability of the answer set optimization programming with nested multiple
aggregates and the answer set programming with nested multiple aggregates to
the Probabilistic Traveling Salesman Problem, a fundamental a priori
optimization problem in Operation Research.
Logical Probability Preferences
Authors: Emad Saad
Date: 2013-04-05 00:00:00

We present a unified logical framework for representing and reasoning about
both probability quantitative and qualitative preferences in probability answer
set programming, called probability answer set optimization programs. The
proposed framework is vital to allow defining probability quantitative
preferences over the possible outcomes of qualitative preferences. We show the
application of probability answer set optimization programs to a variant of the
well-known nurse restoring problem, called the nurse restoring with probability
preferences problem. To the best of our knowledge, this development is the
first to consider a logical framework for reasoning about probability
quantitative preferences, in general, and reasoning about both probability
quantitative and qualitative preferences in particular.
Logical Fuzzy Preferences
Authors: Emad Saad
Date: 2013-04-05 00:00:00
Tasks: Scheduling

We present a unified logical framework for representing and reasoning about
both quantitative and qualitative preferences in fuzzy answer set programming,
called fuzzy answer set optimization programs. The proposed framework is vital
to allow defining quantitative preferences over the possible outcomes of
qualitative preferences. We show the application of fuzzy answer set
optimization programs to the course scheduling with fuzzy preferences problem.
To the best of our knowledge, this development is the first to consider a
logical framework for reasoning about quantitative preferences, in general, and
reasoning about both quantitative and qualitative preferences in particular.
Logical Fuzzy Optimization
Authors: Emad Saad
Date: 2013-04-05 00:00:00

We present a logical framework to represent and reason about fuzzy
optimization problems based on fuzzy answer set optimization programming. This
is accomplished by allowing fuzzy optimization aggregates, e.g., minimum and
maximum in the language of fuzzy answer set optimization programming to allow
minimization or maximization of some desired criteria under fuzzy environments.
We show the application of the proposed logical fuzzy optimization framework
under the fuzzy answer set optimization programming to the fuzzy water
allocation optimization problem.
Fuzzy Aggregates in Fuzzy Answer Set Programming
Authors: Emad Saad
Date: 2013-04-05 00:00:00

Fuzzy answer set programming is a declarative framework for representing and
reasoning about knowledge in fuzzy environments. However, the unavailability of
fuzzy aggregates in disjunctive fuzzy logic programs, DFLP, with fuzzy answer
set semantics prohibits the natural and concise representation of many
interesting problems. In this paper, we extend DFLP to allow arbitrary fuzzy
aggregates. We define fuzzy answer set semantics for DFLP with arbitrary fuzzy
aggregates including monotone, antimonotone, and nonmonotone fuzzy aggregates.
We show that the proposed fuzzy answer set semantics subsumes both the original
fuzzy answer set semantics of DFLP and the classical answer set semantics of
classical disjunctive logic programs with classical aggregates, and
consequently subsumes the classical answer set semantics of classical
disjunctive logic programs. We show that the proposed fuzzy answer sets of DFLP
with fuzzy aggregates are minimal fuzzy models and hence incomparable, which is
an important property for nonmonotonic fuzzy reasoning.
Model-based Bayesian Reinforcement Learning for Dialogue Management
Authors: Pierre Lison
Date: 2013-04-05 00:00:00
Tasks: Bayesian Inference, Dialogue Management, Management, model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning methods are increasingly used to optimise dialogue
policies from experience. Most current techniques are model-free: they directly
estimate the utility of various actions, without explicit model of the
interaction dynamics. In this paper, we investigate an alternative strategy
grounded in model-based Bayesian reinforcement learning. Bayesian inference is
used to maintain a posterior distribution over the model parameters, reflecting
the model uncertainty. This parameter distribution is gradually refined as more
data is collected and simultaneously used to plan the agent's actions. Within
this learning framework, we carried out experiments with two alternative
formalisations of the transition model, one encoded with standard multinomial
distributions, and one structured with probabilistic rules. We demonstrate the
potential of our approach with empirical results on a user simulator
constructed from Wizard-of-Oz data in a human-robot interaction scenario. The
results illustrate in particular the benefits of capturing prior domain
knowledge with high-level rules.
Probability Aggregates in Probability Answer Set Programming
Authors: Emad Saad
Date: 2013-04-05 00:00:00

Probability answer set programming is a declarative programming that has been
shown effective for representing and reasoning about a variety of probability
reasoning tasks. However, the lack of probability aggregates, e.g. {\em
expected values}, in the language of disjunctive hybrid probability logic
programs (DHPP) disallows the natural and concise representation of many
interesting problems. In this paper, we extend DHPP to allow arbitrary
probability aggregates. We introduce two types of probability aggregates; a
type that computes the expected value of a classical aggregate, e.g., the
expected value of the minimum, and a type that computes the probability of a
classical aggregate, e.g, the probability of sum of values. In addition, we
define a probability answer set semantics for DHPP with arbitrary probability
aggregates including monotone, antimonotone, and nonmonotone probability
aggregates. We show that the proposed probability answer set semantics of DHPP
subsumes both the original probability answer set semantics of DHPP and the
classical answer set semantics of classical disjunctive logic programs with
classical aggregates, and consequently subsumes the classical answer set
semantics of the original disjunctive logic programs. We show that the proposed
probability answer sets of DHPP with probability aggregates are minimal
probability models and hence incomparable, which is an important property for
nonmonotonic probability reasoning.
Bug Classification: Feature Extraction and Comparison of Event Model using Naïve Bayes Approach
Authors: Sunil Joy Dommati, Ruchi Agrawal, Ram Mohana Reddy G., S. Sowmya Kamath
Date: 2013-04-05 00:00:00
Tasks: General Classification

In software industries, individuals at different levels from customer to an
engineer apply diverse mechanisms to detect to which class a particular bug
should be allocated. Sometimes while a simple search in Internet might help, in
many other cases a lot of effort is spent in analyzing the bug report to
classify the bug. So there is a great need of a structured mining algorithm -
where given a crash log, the existing bug database could be mined to find out
the class to which the bug should be allocated. This would involve Mining
patterns and applying different classification algorithms. This paper focuses
on the feature extraction, noise reduction in data and classification of
network bugs using probabilistic Na\"ive Bayes approach. Different event models
like Bernoulli and Multinomial are applied on the extracted features. When new,
unseen bugs are given as input to the algorithms, the performance comparison of
different algorithms is done on the basis of accuracy and recall parameters.
Pattern-Based Constraint Satisfaction and Logic Puzzles
Authors: Denis Berthier
Date: 2013-04-05 00:00:00

Pattern-Based Constraint Satisfaction and Logic Puzzles develops a pure
logic, pattern-based perspective of solving the finite Constraint Satisfaction
Problem (CSP), with emphasis on finding the "simplest" solution. Different ways
of reasoning with the constraints are formalised by various families of
"resolution rules", each of them carrying its own notion of simplicity. A large
part of the book illustrates the power of the approach by applying it to
various popular logic puzzles. It provides a unified view of how to model and
solve them, even though they involve very different types of constraints:
obvious symmetric ones in Sudoku, non-symmetric but transitive ones
(inequalities) in Futoshiki, topological and geometric ones in Map colouring,
Numbrix and Hidato, and even much more complex non-binary arithmetic ones in
Kakuro (or Cross Sums). It also shows that the most familiar techniques for
these puzzles can indeed be understood as mere application-specific
presentations of the general rules. Sudoku is used as the main example
throughout the book, making it also an advanced level sequel to "The Hidden
Logic of Sudoku" (another book by the same author), with: many examples of
relationships among different rules and of exceptional situations; comparisons
of the resolution potential of various families of rules; detailed statistics
of puzzles hardness; analysis of extreme instances.
Joint-ViVo: Selecting and Weighting Visual Words Jointly for Bag-of-Features based Tissue Classification in Medical Images
Authors: Jing-Yan Wang
Date: 2012-08-19 00:00:00
Tasks: General Classification

Automatically classifying the tissues types of Region of Interest (ROI) in
medical imaging has been an important application in Computer-Aided Diagnosis
(CAD), such as classification of breast parenchymal tissue in the mammogram,
classify lung disease patterns in High-Resolution Computed Tomography (HRCT)
etc. Recently, bag-of-features method has shown its power in this field,
treating each ROI as a set of local features. In this paper, we investigate
using the bag-of-features strategy to classify the tissue types in medical
imaging applications. Two important issues are considered here: the visual
vocabulary learning and weighting. Although there are already plenty of
algorithms to deal with them, all of them treat them independently, namely, the
vocabulary learned first and then the histogram weighted. Inspired by
Auto-Context who learns the features and classifier jointly, we try to develop
a novel algorithm that learns the vocabulary and weights jointly. The new
algorithm, called Joint-ViVo, works in an iterative way. In each iteration, we
first learn the weights for each visual word by maximizing the margin of ROI
triplets, and then select the most discriminate visual words based on the
learned weights for the next iteration. We test our algorithm on three tissue
classification tasks: identifying brain tissue type in magnetic resonance
imaging (MRI), classifying lung tissue in HRCT images, and classifying breast
tissue density in mammograms. The results show that Joint-ViVo can perform
effectively for classifying tissues.
Generalization Bounds for Domain Adaptation
Authors: Chao Zhang, Lei Zhang, Jieping Ye
Date: 2013-04-04 00:00:00
Proceeding: NeurIPS 2012 12
Tasks: Domain Adaptation, Generalization Bounds

In this paper, we provide a new framework to obtain the generalization bounds
of the learning process for domain adaptation, and then apply the derived
bounds to analyze the asymptotical convergence of the learning process. Without
loss of generality, we consider two kinds of representative domain adaptation:
one is with multiple sources and the other is combining source and target data.
  In particular, we use the integral probability metric to measure the
difference between two domains. For either kind of domain adaptation, we
develop a related Hoeffding-type deviation inequality and a symmetrization
inequality to achieve the corresponding generalization bound based on the
uniform entropy number. We also generalized the classical McDiarmid's
inequality to a more general setting where independent random variables can
take values from different domains. By using this inequality, we then obtain
generalization bounds based on the Rademacher complexity. Afterwards, we
analyze the asymptotic convergence and the rate of convergence of the learning
process for such kind of domain adaptation. Meanwhile, we discuss the factors
that affect the asymptotic behavior of the learning process and the numerical
experiments support our theoretical findings as well.