pwc-archive/papers-with-abstracts huggingface dataset:
Information quantity in a pixel of digital image
Authors: M. Kharinov
Date: 2014-01-29 00:00:00

The paper is devoted to the problem of integer-valued estimating of
information quantity in a pixel of digital image. The definition of an integer
estimation of information quantity based on constructing of the certain binary
hierarchy of pixel clusters is proposed. The methods for constructing
hierarchies of clusters and generating of hierarchical sequences of image
approximations that minimally differ from the image by a standard deviation are
developed. Experimental results on integer-valued estimation of information
quantity are compared with the results obtained by utilizing of the classical
formulas.
Use HMM and KNN for classifying corneal data
Authors: Payam Porkar Rezaeiye, mehrnoosh bazrafkan, ali akbar movassagh, Mojtaba Sedigh Fazli, Gholam hossein bazyari
Date: 2014-01-29 00:00:00
Tasks: General Classification

These days to gain classification system with high accuracy that can classify
complicated pattern are so useful in medicine and industry. In this article a
process for getting the best classifier for Lasik data is suggested. However at
first it's been tried to find the best line and curve by this classifier in
order to gain classifier fitting, and in the end by using the Markov method a
classifier for topographies is gained.
Propagators and Violation Functions for Geometric and Workload Constraints Arising in Airspace Sectorisation
Authors: Pierre Flener, Justin Pearson
Date: 2014-01-29 00:00:00

Airspace sectorisation provides a partition of a given airspace into sectors,
subject to geometric constraints and workload constraints, so that some cost
metric is minimised. We make a study of the constraints that arise in airspace
sectorisation. For each constraint, we give an analysis of what algorithms and
properties are required under systematic search and stochastic local search.
The parametrized probabilistic finite-state transducer probe game player fingerprint model
Authors: Jeffrey Tsang
Date: 2014-01-29 00:00:00

Fingerprinting operators generate functional signatures of game players and
are useful for their automated analysis independent of representation or
encoding. The theory for a fingerprinting operator which returns the
length-weighted probability of a given move pair occurring from playing the
investigated agent against a general parametrized probabilistic finite-state
transducer (PFT) is developed, applicable to arbitrary iterated games. Results
for the distinguishing power of the 1-state opponent model, uniform
approximability of fingerprints of arbitrary players, analyticity and Lipschitz
continuity of fingerprints for logically possible players, and equicontinuity
of the fingerprints of bounded-state probabilistic transducers are derived.
Algorithms for the efficient computation of special instances are given; the
shortcomings of a previous model, strictly generalized here from a simple
projection of the new model, are explained in terms of regularity condition
violations, and the extra power and functional niceness of the new fingerprints
demonstrated. The 2-state deterministic finite-state transducers (DFTs) are
fingerprinted and pairwise distances computed; using this the structure of DFTs
in strategy space is elucidated.
Bounding Embeddings of VC Classes into Maximum Classes
Authors: J. Hyam Rubinstein, Benjamin I. P. Rubinstein, Peter L. Bartlett
Date: 2014-01-29 00:00:00
Tasks: Learning Theory

One of the earliest conjectures in computational learning theory-the Sample
Compression conjecture-asserts that concept classes (equivalently set systems)
admit compression schemes of size linear in their VC dimension. To-date this
statement is known to be true for maximum classes---those that possess maximum
cardinality for their VC dimension. The most promising approach to positively
resolving the conjecture is by embedding general VC classes into maximum
classes without super-linear increase to their VC dimensions, as such
embeddings would extend the known compression schemes to all VC classes. We
show that maximum classes can be characterised by a local-connectivity property
of the graph obtained by viewing the class as a cubical complex. This geometric
characterisation of maximum VC classes is applied to prove a negative embedding
result which demonstrates VC-d classes that cannot be embedded in any maximum
class of VC dimension lower than 2d. On the other hand, we show that every VC-d
class C embeds in a VC-(d+D) maximum class where D is the deficiency of C,
i.e., the difference between the cardinalities of a maximum VC-d class and of
C. For VC-2 classes in binary n-cubes for 4 <= n <= 6, we give best possible
results on embedding into maximum classes. For some special classes of Boolean
functions, relationships with maximum classes are investigated. Finally we give
a general recursive procedure for embedding VC-d classes into VC-(d+k) maximum
classes for smallest k.
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts
Authors: Vu Nguyen, Dinh Phung, XuanLong Nguyen, Svetha Venkatesh, Hung Hai Bui
Date: 2014-01-09 00:00:00
Tasks: Clustering

We present a Bayesian nonparametric framework for multilevel clustering which
utilizes group-level context information to simultaneously discover
low-dimensional structures of the group contents and partitions groups into
clusters. Using the Dirichlet process as the building block, our model
constructs a product base-measure with a nested structure to accommodate
content and context observations at multiple levels. The proposed model
possesses properties that link the nested Dirichlet processes (nDP) and the
Dirichlet process mixture models (DPM) in an interesting way: integrating out
all contents results in the DPM over contexts, whereas integrating out
group-specific contexts results in the nDP mixture over content variables. We
provide a Polya-urn view of the model and an efficient collapsed Gibbs
inference procedure. Extensive experiments on real-world datasets demonstrate
the advantage of utilizing context information via our model in both text and
image domains.
Infrared face recognition: a comprehensive review of methodologies and databases
Authors: Reza Shoja Ghiass, Ognjen Arandjelovic, Hakim Bendada, Xavier Maldague
Date: 2014-01-29 00:00:00
Tasks: Face Recognition

Automatic face recognition is an area with immense practical potential which
includes a wide range of commercial and law enforcement applications. Hence it
is unsurprising that it continues to be one of the most active research areas
of computer vision. Even after over three decades of intense research, the
state-of-the-art in face recognition continues to improve, benefitting from
advances in a range of different research fields such as image processing,
pattern recognition, computer graphics, and physiology. Systems based on
visible spectrum images, the most researched face recognition modality, have
reached a significant level of maturity with some practical success. However,
they continue to face challenges in the presence of illumination, pose and
expression changes, as well as facial disguises, all of which can significantly
decrease recognition accuracy. Amongst various approaches which have been
proposed in an attempt to overcome these limitations, the use of infrared (IR)
imaging has emerged as a particularly promising research direction. This paper
presents a comprehensive and timely review of the literature on this subject.
Our key contributions are: (i) a summary of the inherent properties of infrared
imaging which makes this modality promising in the context of face recognition,
(ii) a systematic review of the most influential approaches, with a focus on
emerging common trends as well as key differences between alternative
methodologies, (iii) a description of the main databases of infrared facial
images available to the researcher, and lastly (iv) a discussion of the most
promising avenues for future research.
Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks
Authors: Andrew Davis, Itamar Arel
Date: 2013-12-16 00:00:00

Scalability properties of deep neural networks raise key research questions,
particularly as the problems considered become larger and more challenging.
This paper expands on the idea of conditional computation introduced by Bengio,
et. al., where the nodes of a deep network are augmented by a set of gating
units that determine when a node should be calculated. By factorizing the
weight matrix into a low-rank approximation, an estimation of the sign of the
pre-nonlinearity activation can be efficiently obtained. For networks using
rectified-linear hidden units, this implies that the computation of a hidden
unit with an estimated negative pre-nonlinearity can be ommitted altogether, as
its value will become zero when nonlinearity is applied. For sparse neural
networks, this can result in considerable speed gains. Experimental results
using the MNIST and SVHN data sets with a fully-connected deep neural network
demonstrate the performance robustness of the proposed scheme with respect to
the error introduced by the conditional computation process.
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units
Authors: Guido F. Montúfar
Date: 2013-03-29 00:00:00

We generalize recent theoretical work on the minimal number of layers of
narrow deep belief networks that can approximate any probability distribution
on the states of their visible units arbitrarily well. We relax the setting of
binary units (Sutskever and Hinton, 2008; Le Roux and Bengio, 2008, 2010;
Mont\'ufar and Ay, 2011) to units with arbitrary finite state spaces, and the
vanishing approximation error to an arbitrary approximation error tolerance.
For example, we show that a $q$-ary deep belief network with $L\geq
2+\frac{q^{\lceil m-\delta \rceil}-1}{q-1}$ layers of width $n \leq m +
\log_q(m) + 1$ for some $m\in \mathbb{N}$ can approximate any probability
distribution on $\{0,1,\ldots,q-1\}^n$ without exceeding a Kullback-Leibler
divergence of $\delta$. Our analysis covers discrete restricted Boltzmann
machines and na\"ive Bayes models as special cases.
Tempering by Subsampling
Authors: Jan-Willem van de Meent, Brooks Paige, Frank Wood
Date: 2014-01-28 00:00:00

In this paper we demonstrate that tempering Markov chain Monte Carlo samplers
for Bayesian models by recursively subsampling observations without replacement
can improve the performance of baseline samplers in terms of effective sample
size per computation. We present two tempering by subsampling algorithms,
subsampled parallel tempering and subsampled tempered transitions. We provide
an asymptotic analysis of the computational cost of tempering by subsampling,
verify that tempering by subsampling costs less than traditional tempering, and
demonstrate both algorithms on Bayesian approaches to learning the mean of a
high dimensional multivariate Normal and estimating Gaussian process
hyperparameters.
Bayesian Properties of Normalized Maximum Likelihood and its Fast Computation
Authors: Andrew Barron, Teemu Roos, Kazuho Watanabe
Date: 2014-01-28 00:00:00
Tasks: Data Compression, Prediction

The normalized maximized likelihood (NML) provides the minimax regret
solution in universal data compression, gambling, and prediction, and it plays
an essential role in the minimum description length (MDL) method of statistical
modeling and estimation. Here we show that the normalized maximum likelihood
has a Bayes-like representation as a mixture of the component models, even in
finite samples, though the weights of linear combination may be both positive
and negative. This representation addresses in part the relationship between
MDL and Bayes modeling. This representation has the advantage of speeding the
calculation of marginals and conditionals required for coding and prediction
applications.
Kaldi+PDNN: Building DNN-based ASR Systems with Kaldi and PDNN
Authors: Yajie Miao
Date: 2014-01-27 00:00:00
Tasks: speech-recognition, Speech Recognition

The Kaldi toolkit is becoming popular for constructing automated speech
recognition (ASR) systems. Meanwhile, in recent years, deep neural networks
(DNNs) have shown state-of-the-art performance on various ASR tasks. This
document describes our open-source recipes to implement fully-fledged DNN
acoustic modeling using Kaldi and PDNN. PDNN is a lightweight deep learning
toolkit developed under the Theano environment. Using these recipes, we can
build up multiple systems including DNN hybrid systems, convolutional neural
network (CNN) systems and bottleneck feature systems. These recipes are
directly based on the Kaldi Switchboard 110-hour setup. However, adapting them
to new datasets is easy to achieve.
Efficient Eigen-updating for Spectral Graph Clustering
Authors: Charanpal Dhanjal, Romaric Gaudel, Stéphan Clémençon
Date: 2013-01-07 00:00:00
Tasks: Clustering, Graph Clustering, Spectral Graph Clustering

Partitioning a graph into groups of vertices such that those within each
group are more densely connected than vertices assigned to different groups,
known as graph clustering, is often used to gain insight into the organisation
of large scale networks and for visualisation purposes. Whereas a large number
of dedicated techniques have been recently proposed for static graphs, the
design of on-line graph clustering methods tailored for evolving networks is a
challenging problem, and much less documented in the literature. Motivated by
the broad variety of applications concerned, ranging from the study of
biological networks to the analysis of networks of scientific references
through the exploration of communications networks such as the World Wide Web,
it is the main purpose of this paper to introduce a novel, computationally
efficient, approach to graph clustering in the evolutionary context. Namely,
the method promoted in this article can be viewed as an incremental eigenvalue
solution for the spectral clustering method described by Ng. et al. (2001). The
incremental eigenvalue solution is a general technique for finding the
approximate eigenvectors of a symmetric matrix given a change. As well as
outlining the approach in detail, we present a theoretical bound on the quality
of the approximate eigenvectors using perturbation theory. We then derive a
novel spectral clustering algorithm called Incremental Approximate Spectral
Clustering (IASC). The IASC algorithm is simple to implement and its efficacy
is demonstrated on both synthetic and real datasets modelling the evolution of
a HIV epidemic, a citation network and the purchase history graph of an
e-commerce website.
SKYNET: an efficient and robust neural network training tool for machine learning in astronomy
Authors: Philip Graff, Farhan Feroz, Michael P. Hobson, Anthony N. Lasenby
Date: 2013-09-03 00:00:00
Tasks: Astronomy, BIG-bench Machine Learning, Clustering, Denoising, Density Estimation, Dimensionality Reduction

We present the first public release of our generic neural network training
algorithm, called SkyNet. This efficient and robust machine learning tool is
able to train large and deep feed-forward neural networks, including
autoencoders, for use in a wide range of supervised and unsupervised learning
applications, such as regression, classification, density estimation,
clustering and dimensionality reduction. SkyNet uses a `pre-training' method to
obtain a set of network parameters that has empirically been shown to be close
to a good solution, followed by further optimisation using a regularised
variant of Newton's method, where the level of regularisation is determined and
adjusted automatically; the latter uses second-order derivative information to
improve convergence, but without the need to evaluate or store the full Hessian
matrix, by using a fast approximate method to calculate Hessian-vector
products. This combination of methods allows for the training of complicated
networks that are difficult to optimise using standard backpropagation
techniques. SkyNet employs convergence criteria that naturally prevent
overfitting, and also includes a fast algorithm for estimating the accuracy of
network outputs. The utility and flexibility of SkyNet are demonstrated by
application to a number of toy problems, and to astronomical problems focusing
on the recovery of structure from blurred and noisy images, the identification
of gamma-ray bursters, and the compression and denoising of galaxy images. The
SkyNet software, which is implemented in standard ANSI C and fully parallelised
using MPI, is available at http://www.mrao.cam.ac.uk/software/skynet/.
Computing support for advanced medical data analysis and imaging
Authors: W. Wiślicki, T. Bednarski, P. Białas, E. Czerwiński, Ł. Kapłon, A. Kochanowski, G. Korcyl, J. Kowal, P. Kowalski, T. Kozik, W. Krzemień, M. Molenda, P. Moskal, S. Niedźwiecki, M. Pałka, M. Pawlik, L. Raczyński, Z. Rudy, P. Salabura, N. G. Sharma, M. Silarski, A. Słomski, J. Smyrski, A. Strzelecki, A. Wieczorek, M. Zieliński, N. Zoń
Date: 2014-01-27 00:00:00
Tasks: Image Reconstruction

We discuss computing issues for data analysis and image reconstruction of
PET-TOF medical scanner or other medical scanning devices producing large
volumes of data. Service architecture based on the grid and cloud concepts for
distributed processing is proposed and critically discussed.
Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
Authors: Yangbo He, Jinzhu Jia, Bin Yu
Date: 2012-09-26 00:00:00

Graphical models are popular statistical tools which are used to represent
dependent or causal complex systems. Statistically equivalent causal or
directed graphical models are said to belong to a Markov equivalent class. It
is of great interest to describe and understand the space of such classes.
However, with currently known algorithms, sampling over such classes is only
feasible for graphs with fewer than approximately 20 vertices. In this paper,
we design reversible irreducible Markov chains on the space of Markov
equivalent classes by proposing a perfect set of operators that determine the
transitions of the Markov chain. The stationary distribution of a proposed
Markov chain has a closed form and can be computed easily. Specifically, we
construct a concrete perfect set of operators on sparse Markov equivalence
classes by introducing appropriate conditions on each possible operator.
Algorithms and their accelerated versions are provided to efficiently generate
Markov chains and to explore properties of Markov equivalence classes of sparse
directed acyclic graphs (DAGs) with thousands of vertices. We find
experimentally that in most Markov equivalence classes of sparse DAGs, (1) most
edges are directed, (2) most undirected subgraphs are small and (3) the number
of these undirected subgraphs grows approximately linearly with the number of
vertices. The article contains supplement arXiv:1303.0632,
http://dx.doi.org/10.1214/13-AOS1125SUPP
Safe Sample Screening for Support Vector Machines
Authors: Kohei Ogawa, Yoshiki Suzuki, Shinya Suzumura, Ichiro Takeuchi
Date: 2014-01-27 00:00:00

Sparse classifiers such as the support vector machines (SVM) are efficient in
test-phases because the classifier is characterized only by a subset of the
samples called support vectors (SVs), and the rest of the samples (non SVs)
have no influence on the classification result. However, the advantage of the
sparsity has not been fully exploited in training phases because it is
generally difficult to know which sample turns out to be SV beforehand. In this
paper, we introduce a new approach called safe sample screening that enables us
to identify a subset of the non-SVs and screen them out prior to the training
phase. Our approach is different from existing heuristic approaches in the
sense that the screened samples are guaranteed to be non-SVs at the optimal
solution. We investigate the advantage of the safe sample screening approach
through intensive numerical experiments, and demonstrate that it can
substantially decrease the computational cost of the state-of-the-art SVM
solvers such as LIBSVM. In the current big data era, we believe that safe
sample screening would be of great practical importance since the data size can
be reduced without sacrificing the optimality of the final solution.
Quality of Geographic Information: Ontological approach and Artificial Intelligence Tools
Authors: Robert Jeansoulin, Nic Wilson
Date: 2014-01-26 00:00:00
Tasks: Translation

The objective is to present one important aspect of the European IST-FET
project "REV!GIS"1: the methodology which has been developed for the
translation (interpretation) of the quality of the data into a "fitness for
use" information, that we can confront to the user needs in its application.
This methodology is based upon the notion of "ontologies" as a conceptual
framework able to capture the explicit and implicit knowledge involved in the
application. We do not address the general problem of formalizing such
ontologies, instead, we rather try to illustrate this with three applications
which are particular cases of the more general "data fusion" problem. In each
application, we show how to deploy our methodology, by comparing several
possible solutions, and we try to enlighten where are the quality issues, and
what kind of solution to privilege, even at the expense of a highly complex
computational approach. The expectation of the REV!GIS project is that
computationally tractable solutions will be available among the next generation
AI tools.
Rank-frequency relation for Chinese characters
Authors: W. B. Deng, A. E. Allahverdyan, B. Li, Q. A. Wang
Date: 2013-09-06 00:00:00
Tasks: Relation

We show that the Zipf's law for Chinese characters perfectly holds for
sufficiently short texts (few thousand different characters). The scenario of
its validity is similar to the Zipf's law for words in short English texts. For
long Chinese texts (or for mixtures of short Chinese texts), rank-frequency
relations for Chinese characters display a two-layer, hierarchic structure that
combines a Zipfian power-law regime for frequent characters (first layer) with
an exponential-like regime for less frequent characters (second layer). For
these two layers we provide different (though related) theoretical descriptions
that include the range of low-frequency characters (hapax legomena). The
comparative analysis of rank-frequency relations for Chinese characters versus
English words illustrates the extent to which the characters play for Chinese
writers the same role as the words for those writing within alphabetical
systems.
Painting Analysis Using Wavelets and Probabilistic Topic Models
Authors: Tong Wu, Gungor Polatkan, David Steel, William Brown, Ingrid Daubechies, Robert Calderbank
Date: 2014-01-26 00:00:00
Tasks: Clustering, Topic Models

In this paper, computer-based techniques for stylistic analysis of paintings
are applied to the five panels of the 14th century Peruzzi Altarpiece by Giotto
di Bondone. Features are extracted by combining a dual-tree complex wavelet
transform with a hidden Markov tree (HMT) model. Hierarchical clustering is
used to identify stylistic keywords in image patches, and keyword frequencies
are calculated for sub-images that each contains many patches. A generative
hierarchical Bayesian model learns stylistic patterns of keywords; these
patterns are then used to characterize the styles of the sub-images; this in
turn, permits to discriminate between paintings. Results suggest that such
unsupervised probabilistic topic models can be useful to distill characteristic
elements of style.