pwc-archive/papers-with-abstracts huggingface dataset:
Nested Regular Path Queries in Description Logics
Authors: Meghyn Bienvenu, Diego Calvanese, Magdalena Ortiz, Mantas Simkus
Date: 2014-02-28 00:00:00

Two-way regular path queries (2RPQs) have received increased attention
recently due to their ability to relate pairs of objects by flexibly navigating
graph-structured data. They are present in property paths in SPARQL 1.1, the
new standard RDF query language, and in the XML query language XPath. In line
with XPath, we consider the extension of 2RPQs with nesting, which allows one
to require that objects along a path satisfy complex conditions, in turn
expressed through (nested) 2RPQs. We study the computational complexity of
answering nested 2RPQs and conjunctions thereof (CN2RPQs) in the presence of
domain knowledge expressed in description logics (DLs). We establish tight
complexity bounds in data and combined complexity for a variety of DLs, ranging
from lightweight DLs (DL-Lite, EL) up to highly expressive ones. Interestingly,
we are able to show that adding nesting to (C)2RPQs does not affect worst-case
data complexity of query answering for any of the considered DLs. However, in
the case of lightweight DLs, adding nesting to 2RPQs leads to a surprising jump
in combined complexity, from P-complete to Exp-complete.
Matroid Regression
Authors: Franz J. Király, Louis Theran
Date: 2014-03-04 00:00:00
Tasks: regression

We propose an algebraic combinatorial method for solving large sparse linear
systems of equations locally - that is, a method which can compute single
evaluations of the signal without computing the whole signal. The method scales
only in the sparsity of the system and not in its size, and allows to provide
error estimates for any solution method. At the heart of our approach is the
so-called regression matroid, a combinatorial object associated to sparsity
patterns, which allows to replace inversion of the large matrix with the
inversion of a kernel matrix that is constant size. We show that our method
provides the best linear unbiased estimator (BLUE) for this setting and the
minimum variance unbiased estimator (MVUE) under Gaussian noise assumptions,
and furthermore we show that the size of the kernel matrix which is to be
inverted can be traded off with accuracy.
A proof challenge: multiple alignment and information compression
Authors: J. Gerard Wolff
Date: 2014-03-04 00:00:00

These notes pose a "proof challenge": a proof, or disproof, of the
proposition that "For any given body of information, I, expressed as a
one-dimensional sequence of atomic symbols, a multiple alignment concept,
described in the document, provides a means of encoding all the redundancy that
may exist in I. Aspects of the challenge are described.
Decomposition of Optical Flow on the Sphere
Authors: Clemens Kirisits, Lukas F. Lang, Otmar Scherzer
Date: 2013-12-16 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation

We propose a number of variational regularisation methods for the estimation
and decomposition of motion fields on the $2$-sphere. While motion estimation
is based on the optical flow equation, the presented decomposition models are
motivated by recent trends in image analysis. In particular we treat $u+v$
decomposition as well as hierarchical decomposition. Helmholtz decomposition of
motion fields is obtained as a natural by-product of the chosen numerical
method based on vector spherical harmonics. All models are tested on time-lapse
microscopy data depicting fluorescently labelled endodermal cells of a
zebrafish embryo.
Dynamic Move Chains -- a Forward Pruning Approach to Tree Search in Computer Chess
Authors: Kieran Greer
Date: 2014-03-04 00:00:00
Tasks: Position

This paper proposes a new mechanism for pruning a search game-tree in
computer chess. The algorithm stores and then reuses chains or sequences of
moves, built up from previous searches. These move sequences have a built-in
forward-pruning mechanism that can radically reduce the search space. A typical
search process might retrieve a move from a Transposition Table, where the
decision of what move to retrieve would be based on the position itself. This
algorithm stores move sequences based on what previous sequences were better,
or caused cutoffs. This is therefore position independent and so it could also
be useful in games with imperfect information or uncertainty, where the whole
situation is not known at any one time. Over a small set of tests, the
algorithm was shown to clearly out-perform Transposition Tables, both in terms
of search reduction and game-play results.
Clustering Concept Chains from Ordered Data without Path Descriptions
Authors: Kieran Greer
Date: 2014-03-04 00:00:00
Tasks: Clustering

This paper describes a process for clustering concepts into chains from data
presented randomly to an evaluating system. There are a number of rules or
guidelines that help the system to determine more accurately what concepts
belong to a particular chain and what ones do not, but it should be possible to
write these in a generic way. This mechanism also uses a flat structure without
any hierarchical path information, where the link between two concepts is made
at the level of the concept itself. It does not require related metadata, but
instead, a simple counting mechanism is used. Key to this is a count for both
the concept itself and also the group or chain that it belongs to. To test the
possible success of the mechanism, concept chain parts taken randomly from a
larger ontology were presented to the system, but only at a depth of 2 concepts
each time. That is - root concept plus a concept that it is linked to. The
results show that this can still lead to very variable structures being formed
and can also accommodate some level of randomness.
EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines
Authors: Marc Claesen, Frank De Smet, Johan Suykens, Bart De Moor
Date: 2014-03-04 00:00:00
Tasks: Ensemble Learning

EnsembleSVM is a free software package containing efficient routines to
perform ensemble learning with support vector machine (SVM) base models. It
currently offers ensemble methods based on binary SVM models. Our
implementation avoids duplicate storage and evaluation of support vectors which
are shared between constituent models. Experimental results show that using
ensemble approaches can drastically reduce training complexity while
maintaining high predictive accuracy. The EnsembleSVM software package is
freely available online at http://esat.kuleuven.be/stadius/ensemblesvm.
On the Intersection Property of Conditional Independence and its Application to Causal Discovery
Authors: Jonas Peters
Date: 2014-03-03 00:00:00
Tasks: Causal Discovery, Causal Inference

This work investigates the intersection property of conditional independence.
It states that for random variables $A,B,C$ and $X$ we have that $X$
independent of $A$ given $B,C$ and $X$ independent of $B$ given $A,C$ implies
$X$ independent of $(A,B)$ given $C$. Under the assumption that the joint
distribution has a continuous density, we provide necessary and sufficient
conditions under which the intersection property holds. The result has direct
applications to causal inference: it leads to strictly weaker conditions under
which the graphical structure becomes identifiable from the joint distribution
of an additive noise model.
A Novel Method for Vectorization
Authors: Tolga Birdal, Emrah Bala
Date: 2014-03-04 00:00:00

Vectorization of images is a key concern uniting computer graphics and
computer vision communities. In this paper we are presenting a novel idea for
efficient, customizable vectorization of raster images, based on Catmull Rom
spline fitting. The algorithm maintains a good balance between photo-realism
and photo abstraction, and hence is applicable to applications with artistic
concerns or applications where less information loss is crucial. The resulting
algorithm is fast, parallelizable and can satisfy general soft realtime
requirements. Moreover, the smoothness of the vectorized images aesthetically
outperforms outputs of many polygon-based methods
Random Projections on Manifolds of Symmetric Positive Definite Matrices for Image Classification
Authors: Azadeh Alavi, Arnold Wiliem, Kun Zhao, Brian C. Lovell, Conrad Sanderson
Date: 2014-03-04 00:00:00
Tasks: Face Recognition, General Classification, image-classification, Image Classification, Person Re-Identification, Texture Classification

Recent advances suggest that encoding images through Symmetric Positive
Definite (SPD) matrices and then interpreting such matrices as points on
Riemannian manifolds can lead to increased classification performance. Taking
into account manifold geometry is typically done via (1) embedding the
manifolds in tangent spaces, or (2) embedding into Reproducing Kernel Hilbert
Spaces (RKHS). While embedding into tangent spaces allows the use of existing
Euclidean-based learning algorithms, manifold shape is only approximated which
can cause loss of discriminatory information. The RKHS approach retains more of
the manifold structure, but may require non-trivial effort to kernelise
Euclidean-based learning algorithms. In contrast to the above approaches, in
this paper we offer a novel solution that allows SPD matrices to be used with
unmodified Euclidean-based learning algorithms, with the true manifold shape
well-preserved. Specifically, we propose to project SPD matrices using a set of
random projection hyperplanes over RKHS into a random projection space, which
leads to representing each matrix as a vector of projection coefficients.
Experiments on face recognition, person re-identification and texture
classification show that the proposed approach outperforms several recent
methods, such as Tensor Sparse Coding, Histogram Plus Epitome, Riemannian
Locality Preserving Projection and Relational Divergence Classification.
Multi-Shot Person Re-Identification via Relational Stein Divergence
Authors: Azadeh Alavi, Yan Yang, Mehrtash Harandi, Conrad Sanderson
Date: 2014-03-04 00:00:00
Tasks: General Classification, Person Re-Identification

Person re-identification is particularly challenging due to significant
appearance changes across separate camera views. In order to re-identify
people, a representative human signature should effectively handle differences
in illumination, pose and camera parameters. While general appearance-based
methods are modelled in Euclidean spaces, it has been argued that some
applications in image and video analysis are better modelled via non-Euclidean
manifold geometry. To this end, recent approaches represent images as
covariance matrices, and interpret such matrices as points on Riemannian
manifolds. As direct classification on such manifolds can be difficult, in this
paper we propose to represent each manifold point as a vector of similarities
to class representers, via a recently introduced form of Bregman matrix
divergence known as the Stein divergence. This is followed by using a
discriminative mapping of similarity vectors for final classification. The use
of similarity vectors is in contrast to the traditional approach of embedding
manifolds into tangent spaces, which can suffer from representing the manifold
structure inaccurately. Comparative evaluations on benchmark ETHZ and iLIDS
datasets for the person re-identification task show that the proposed approach
obtains better performance than recent techniques such as Histogram Plus
Epitome, Partial Least Squares, and Symmetry-Driven Accumulation of Local
Features.
Network In Network
Authors: Min Lin, Qiang Chen, Shuicheng Yan
Date: 2013-12-16 00:00:00
Tasks: Face Identification, General Classification, Image Classification

We propose a novel deep network structure called "Network In Network" (NIN)
to enhance model discriminability for local patches within the receptive field.
The conventional convolutional layer uses linear filters followed by a
nonlinear activation function to scan the input. Instead, we build micro neural
networks with more complex structures to abstract the data within the receptive
field. We instantiate the micro neural network with a multilayer perceptron,
which is a potent function approximator. The feature maps are obtained by
sliding the micro networks over the input in a similar manner as CNN; they are
then fed into the next layer. Deep NIN can be implemented by stacking mutiple
of the above described structure. With enhanced local modeling via the micro
network, we are able to utilize global average pooling over feature maps in the
classification layer, which is easier to interpret and less prone to
overfitting than traditional fully connected layers. We demonstrated the
state-of-the-art classification performances with NIN on CIFAR-10 and
CIFAR-100, and reasonable performances on SVHN and MNIST datasets.
An Efficient Method for Face Recognition System In Various Assorted Conditions
Authors: V. Karthikeyan, K. Vijayalakshmi, P. Jeyakumar
Date: 2014-03-04 00:00:00
Tasks: Face Recognition, Face Verification

In the beginning stage, face verification is done using easy method of geometric algorithm models, but the verification route has now developed into a scientific progress of complicated geometric representation and identical procedure. In recent years the technologies have boosted face recognition system into the healthy focus. Researchers currently undergoing strong research on finding face recognition system for wider area information taken under hysterical elucidation dissimilarity. The proposed face recognition system consists of a narrative expositionindiscreet preprocessing method, a hybrid Fourier-based facial feature extraction and a score fusion scheme. We have verified the face recognition in different lightening conditions (day or night) and at different locations (indoor or outdoor). Preprocessing, Image detection, Feature- extraction and Face recognition are the methods used for face verification system. This paper focuses mainly on the issue of toughness to lighting variations. The proposed system has obtained an average of 88.1% verification rate on Two-Dimensional images under different lightening conditions.
Multi-period Trading Prediction Markets with Connections to Machine Learning
Authors: Jinli Hu, Amos Storkey
Date: 2014-03-04 00:00:00
Tasks: BIG-bench Machine Learning

We present a new model for prediction markets, in which we use risk measures
to model agents and introduce a market maker to describe the trading process.
This specific choice on modelling tools brings us mathematical convenience. The
analysis shows that the whole market effectively approaches a global objective,
despite that the market is designed such that each agent only cares about its
own goal. Additionally, the market dynamics provides a sensible algorithm for
optimising the global objective. An intimate connection between machine
learning and our markets is thus established, such that we could 1) analyse a
market by applying machine learning methods to the global objective, and 2)
solve machine learning problems by setting up and running certain markets.
Global solar irradiation prediction using a multi-gene genetic programming approach
Authors: Indranil Pan, Daya Shankar Pandey, Saptarshi Das
Date: 2014-03-03 00:00:00
Tasks: regression, Symbolic Regression

In this paper, a nonlinear symbolic regression technique using an
evolutionary algorithm known as multi-gene genetic programming (MGGP) is
applied for a data-driven modelling between the dependent and the independent
variables. The technique is applied for modelling the measured global solar
irradiation and validated through numerical simulations. The proposed modelling
technique shows improved results over the fuzzy logic and artificial neural
network (ANN) based approaches as attempted by contemporary researchers. The
method proposed here results in nonlinear analytical expressions, unlike those
with neural networks which is essentially a black box modelling approach. This
additional flexibility is an advantage from the modelling perspective and helps
to discern the important variables which affect the prediction. Due to the
evolutionary nature of the algorithm, it is able to get out of local minima and
converge to a global optimum unlike the back-propagation (BP) algorithm used
for training neural networks. This results in a better percentage fit than the
ones obtained using neural networks by contemporary researchers. Also a
hold-out cross validation is done on the obtained genetic programming (GP)
results which show that the results generalize well to new data and do not
over-fit the training samples. The multi-gene GP results are compared with
those, obtained using its single-gene version and also the same with four
classical regression models in order to show the effectiveness of the adopted
approach.
The Structurally Smoothed Graphlet Kernel
Authors: Pinar Yanardag, S. V. N. Vishwanathan
Date: 2014-03-03 00:00:00

A commonly used paradigm for representing graphs is to use a vector that
contains normalized frequencies of occurrence of certain motifs or sub-graphs.
This vector representation can be used in a variety of applications, such as,
for computing similarity between graphs. The graphlet kernel of Shervashidze et
al. [32] uses induced sub-graphs of k nodes (christened as graphlets by Przulj
[28]) as motifs in the vector representation, and computes the kernel via a dot
product between these vectors. One can easily show that this is a valid kernel
between graphs. However, such a vector representation suffers from a few
drawbacks. As k becomes larger we encounter the sparsity problem; most higher
order graphlets will not occur in a given graph. This leads to diagonal
dominance, that is, a given graph is similar to itself but not to any other
graph in the dataset. On the other hand, since lower order graphlets tend to be
more numerous, using lower values of k does not provide enough discrimination
ability. We propose a smoothing technique to tackle the above problems. Our
method is based on a novel extension of Kneser-Ney and Pitman-Yor smoothing
techniques from natural language processing to graphs. We use the relationships
between lower order and higher order graphlets in order to derive our method.
Consequently, our smoothing algorithm not only respects the dependency between
sub-graphs but also tackles the diagonal dominance problem by distributing the
probability mass across graphlets. In our experiments, the smoothed graphlet
kernel outperforms graph kernels based on raw frequency counts.
Representing, reasoning and answering questions about biological pathways - various applications
Authors: Saadat Anwar
Date: 2014-03-03 00:00:00

Biological organisms are composed of numerous interconnected biochemical
processes. Diseases occur when normal functionality of these processes is
disrupted. Thus, understanding these biochemical processes and their
interrelationships is a primary task in biomedical research and a prerequisite
for diagnosing diseases, and drug development. Scientists studying these
processes have identified various pathways responsible for drug metabolism, and
signal transduction, etc.
  Newer techniques and speed improvements have resulted in deeper knowledge
about these pathways, resulting in refined models that tend to be large and
complex, making it difficult for a person to remember all aspects of it. Thus,
computer models are needed to analyze them. We want to build such a system that
allows modeling of biological systems and pathways in such a way that we can
answer questions about them.
  Many existing models focus on structural and/or factoid questions, using
surface-level knowledge that does not require understanding the underlying
model. We believe these are not the kind of questions that a biologist may ask
someone to test their understanding of the biological processes. We want our
system to answer the kind of questions a biologist may ask. Such questions
appear in early college level text books.
  Thus the main goal of our thesis is to develop a system that allows us to
encode knowledge about biological pathways and answer such questions about them
demonstrating understanding of the pathway. To that end, we develop a language
that will allow posing such questions and illustrate the utility of our
framework with various applications in the biological domain. We use some
existing tools with modifications to accomplish our goal.
  Finally, we apply our system to real world applications by extracting pathway
knowledge from text and answering questions related to drug development.
We Tweet Like We Talk and Other Interesting Observations: An Analysis of English Communication Modalities
Authors: Josiah P. Zayner
Date: 2014-03-03 00:00:00

Modalities of communication for human beings are gradually increasing in
number with the advent of new forms of technology. Many human beings can
readily transition between these different forms of communication with little
or no effort, which brings about the question: How similar are these different
communication modalities? To understand technology$\text{'}$s influence on
English communication, four different corpora were analyzed and compared:
Writing from Books using the 1-grams database from the Google Books project,
Twitter, IRC Chat, and transcribed Talking. Multi-word confusion matrices
revealed that Talking has the most similarity when compared to the other modes
of communication, while 1-grams were the least similar form of communication
analyzed. Based on the analysis of word usage, word usage frequency
distributions, and word class usage, among other things, Talking is also the
most similar to Twitter and IRC Chat. This suggests that communicating using
Twitter and IRC Chat evolved from Talking rather than Writing. When we
communicate online, even though we are writing, we do not Tweet or Chat how we
write books; we Tweet and Chat how we Speak. Nonfiction and Fiction writing
were clearly differentiable from our analysis with Twitter and Chat being much
more similar to Fiction than Nonfiction writing. These hypotheses were then
tested using author and journalists Cory Doctorow. Mr. Doctorow$\text{'}$s
Writing, Twitter usage, and Talking were all found to have very similar
vocabulary usage patterns as the amalgamized populations, as long as the
writing was Fiction. However, Mr. Doctorow$\text{'}$s Nonfiction writing is
different from 1-grams and other collected Nonfiction writings. This data could
perhaps be used to create more entertaining works of Nonfiction.
Expert System Based On Neural-Fuzzy Rules for Thyroid Diseases Diagnosis
Authors: Ahmad Taher Azar, Aboul Ella Hassanien
Date: 2014-03-03 00:00:00
Tasks: Classification, feature selection, General Classification

The thyroid, an endocrine gland that secretes hormones in the blood,
circulates its products to all tissues of the body, where they control vital
functions in every cell. Normal levels of thyroid hormone help the brain,
heart, intestines, muscles and reproductive system function normally. Thyroid
hormones control the metabolism of the body. Abnormalities of thyroid function
are usually related to production of too little thyroid hormone
(hypothyroidism) or production of too much thyroid hormone (hyperthyroidism).
Therefore, the correct diagnosis of these diseases is very important topic. In
this study, Linguistic Hedges Neural-Fuzzy Classifier with Selected Features
(LHNFCSF) is presented for diagnosis of thyroid diseases. The performance
evaluation of this system is estimated by using classification accuracy and
k-fold cross-validation. The results indicated that the classification accuracy
without feature selection was 98.6047% and 97.6744% during training and testing
phases, respectively with RMSE of 0.02335. After applying feature selection
algorithm, LHNFCSF achieved 100% for all cluster sizes during training phase.
However, in the testing phase LHNFCSF achieved 88.3721% using one cluster for
each class, 90.6977% using two clusters, 91.8605% using three clusters and
97.6744% using four clusters for each class and 12 fuzzy rules. The obtained
classification accuracy was very promising with regard to the other
classification applications in literature for this problem.