pwc-archive/papers-with-abstracts huggingface dataset:
A New Path to Construct Parametric Orientation Field: Sparse FOMFE Model and Compressed Sparse FOMFE Model
Authors: Jinwei Xu, Jiankun Hu, Xiuping Jia
Date: 2014-07-01 00:00:00
Tasks: compressed sensing, model

Orientation field, representing the fingerprint ridge structure direction,
plays a crucial role in fingerprint-related image processing tasks. Orientation
field is able to be constructed by either non-parametric or parametric methods.
In this paper, the advantages and disadvantages regarding to the existing
non-parametric and parametric approaches are briefly summarized. With the
further investigation for constructing the orientation field by parametric
technique, two new models - sparse FOMFE model and compressed sparse FOMFE
model are introduced, based on the rapidly developing signal sparse
representation and compressed sensing theories. The experiments on high-quality
fingerprint image dataset (plain and rolled print) and poor-quality fingerprint
image dataset (latent print) demonstrate their feasibilities to construct the
orientation field in a sparse or even compressed sparse mode. The comparisons
among the state-of-art orientation field modeling approaches show that the
proposed two models have the potential availability in big data-oriented
fingerprint indexing tasks.
Supervised learning in Spiking Neural Networks with Limited Precision: SNN/LP
Authors: Evangelos Stromatias, John Marsland
Date: 2014-07-01 00:00:00

A new supervised learning algorithm, SNN/LP, is proposed for Spiking Neural
Networks. This novel algorithm uses limited precision for both synaptic weights
and synaptic delays; 3 bits in each case. Also a genetic algorithm is used for
the supervised training. The results are comparable or better than previously
published work. The results are applicable to the realization of large scale
hardware neural networks. One of the trained networks is implemented in
programmable hardware.
Imaging with Kantorovich-Rubinstein discrepancy
Authors: Jan Lellmann, Dirk A. Lorenz, Carola Schönlieb, Tuomo Valkonen
Date: 2014-07-01 00:00:00
Tasks: Denoising, Image Denoising

We propose the use of the Kantorovich-Rubinstein norm from optimal transport
in imaging problems. In particular, we discuss a variational regularisation
model endowed with a Kantorovich-Rubinstein discrepancy term and total
variation regularization in the context of image denoising and cartoon-texture
decomposition. We point out connections of this approach to several other
recently proposed methods such as total generalized variation and norms
capturing oscillating patterns. We also show that the respective optimization
problem can be turned into a convex-concave saddle point problem with simple
constraints and hence, can be solved by standard tools. Numerical examples
exhibit interesting features and favourable performance for denoising and
cartoon-texture decomposition.
Mind the Nuisance: Gaussian Process Classification using Privileged Noise
Authors: Daniel Hernández-Lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto
Date: 2014-07-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Classification, General Classification

The learning with privileged information setting has recently attracted a lot
of attention within the machine learning community, as it allows the
integration of additional knowledge into the training process of a classifier,
even when this comes in the form of a data modality that is not available at
test time. Here, we show that privileged information can naturally be treated
as noise in the latent function of a Gaussian Process classifier (GPC). That
is, in contrast to the standard GPC setting, the latent function is not just a
nuisance but a feature: it becomes a natural measure of confidence about the
training data by modulating the slope of the GPC sigmoid likelihood function.
Extensive experiments on public datasets show that the proposed GPC method
using privileged noise, called GPC+, improves over a standard GPC without
privileged knowledge, and also over the current state-of-the-art SVM-based
method, SVM+. Moreover, we show that advanced neural networks and deep learning
methods can be compressed as privileged information.
Mathematical Language Processing Project
Authors: Robert Pagael, Moritz Schubotz
Date: 2014-07-01 00:00:00
Tasks: Sentence, TAG

In natural language, words and phrases themselves imply the semantics. In
contrast, the meaning of identifiers in mathematical formulae is undefined.
Thus scientists must study the context to decode the meaning. The Mathematical
Language Processing (MLP) project aims to support that process. In this paper,
we compare two approaches to discover identifier-definition tuples. At first we
use a simple pattern matching approach. Second, we present the MLP approach
that uses part-of-speech tag based distances as well as sentence positions to
calculate identifier-definition probabilities. The evaluation of our
prototypical system, applied on the Wikipedia text corpus, shows that our
approach augments the user experience substantially. While hovering the
identifiers in the formula, tool-tips with the most probable definitions occur.
Tests with random samples show that the displayed definitions provide a good
match with the actual meaning of the identifiers.
Multilinear Wavelets: A Statistical Shape Space for Human Faces
Authors: Alan Brunton, Timo Bolkart, Stefanie Wuhrer
Date: 2014-01-13 00:00:00
Tasks: 3D Face Modelling, 3D Face Reconstruction, Face Model, Face Reconstruction

We present a statistical model for $3$D human faces in varying expression,
which decomposes the surface of the face using a wavelet transform, and learns
many localized, decorrelated multilinear models on the resulting coefficients.
Using this model we are able to reconstruct faces from noisy and occluded $3$D
face scans, and facial motion sequences. Accurate reconstruction of face shape
is important for applications such as tele-presence and gaming. The localized
and multi-scale nature of our model allows for recovery of fine-scale detail
while retaining robustness to severe noise and occlusion, and is
computationally efficient and scalable. We validate these properties
experimentally on challenging data in the form of static scans and motion
sequences. We show that in comparison to a global multilinear model, our model
better preserves fine detail and is computationally faster, while in comparison
to a localized PCA model, our model better handles variation in expression, is
faster, and allows us to fix identity parameters for a given subject.
A Unified Framework of Elementary Geometric Transformation Representation
Authors: F. Lu, Z. Chen
Date: 2013-07-03 00:00:00
Tasks: All, Translation

As an extension of projective homology, stereohomology is proposed via an
extension of Desargues theorem and the extended Desargues configuration.
Geometric transformations such as reflection, translation, central symmetry,
central projection, parallel projection, shearing, central dilation, scaling,
and so on are all included in stereohomology and represented as
Householder-Chen elementary matrices. Hence all these geometric transformations
are called elementary. This makes it possible to represent these elementary
geometric transformations in homogeneous square matrices independent of a
particular choice of coordinate system.
Newton-Type Iterative Solver for Multiple View $L2$ Triangulation
Authors: F. Lu, Z. Chen
Date: 2014-05-14 00:00:00
Tasks: CPU, Vocal Bursts Type Prediction

In this note, we show that the L2 optimal solutions to most real multiple
view L2 triangulation problems can be efficiently obtained by two-stage
Newton-like iterative methods, while the difficulty of such problems mainly
lies in how to verify the L2 optimality. Such a working two-stage bundle
adjustment approach features: first, the algorithm is initialized by symmedian
point triangulation, a multiple-view generalization of the mid-point method;
second, a symbolic-numeric method is employed to compute derivatives
accurately; third, globalizing strategy such as line search or trust region is
smoothly applied to the underlying iteration which assures algorithm robustness
in general cases.
  Numerical comparison with tfml method shows that the local minimizers
obtained by the two-stage iterative bundle adjustment approach proposed here
are also the L2 optimal solutions to all the calibrated data sets available
online by the Oxford visual geometry group. Extensive numerical experiments
indicate the bundle adjustment approach solves more than 99% the real
triangulation problems optimally. An IEEE 754 double precision C++
implementation shows that it takes only about 0.205 second tocompute allthe
4983 points in the Oxford dinosaur data setvia Gauss-Newton iteration hybrid
with a line search strategy on a computer with a 3.4GHz Intel i7 CPU.
Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)
Authors: Erik Cuevas, Daniel Zaldivar, Marco Perez, Humberto Sossa, Valentin Osuna
Date: 2014-06-30 00:00:00
Tasks: Motion Estimation

Block matching (BM) motion estimation plays a very important role in video
coding. In a BM approach, image frames in a video sequence are divided into
blocks. For each block in the current frame, the best matching block is
identified inside a region of the previous frame, aiming to minimize the sum of
absolute differences (SAD). Unfortunately, the SAD evaluation is
computationally expensive and represents the most consuming operation in the BM
process. Therefore, BM motion estimation can be approached as an optimization
problem, where the goal is to find the best matching block within a search
space. The simplest available BM method is the full search algorithm (FSA)
which finds the most accurate motion vector through an exhaustive computation
of SAD values for all elements of the search window. Recently, several fast BM
algorithms have been proposed to reduce the number of SAD operations by
calculating only a fixed subset of search locations at the price of poor
accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC)
optimization is proposed to reduce the number of search locations in the BM
process. In our algorithm, the computation of search locations is drastically
reduced by considering a fitness calculation strategy which indicates when it
is feasible to calculate or only estimate new search locations. Since the
proposed algorithm does not consider any fixed search pattern or any other
movement assumption as most of other BM approaches do, a high probability for
finding the true minimum (accurate motion vector) is expected. Conducted
simulations show that the proposed method achieves the best balance over other
fast BM algorithms, in terms of both estimation accuracy and computational
cost.
Infinite Structured Hidden Semi-Markov Models
Authors: Jonathan H. Huggins, Frank Wood
Date: 2014-06-30 00:00:00

This paper reviews recent advances in Bayesian nonparametric techniques for
constructing and performing inference in infinite hidden Markov models. We
focus on variants of Bayesian nonparametric hidden Markov models that enhance a
posteriori state-persistence in particular. This paper also introduces a new
Bayesian nonparametric framework for generating left-to-right and other
structured, explicit-duration infinite hidden Markov models that we call the
infinite structured hidden semi-Markov model.
Set Constraint Model and Automated Encoding into SAT: Application to the Social Golfer Problem
Authors: Frédéric Lardeux, Eric Monfroy, Broderick Crawford, Ricardo Soto
Date: 2014-06-27 00:00:00

On the one hand, Constraint Satisfaction Problems allow one to declaratively
model problems. On the other hand, propositional satisfiability problem (SAT)
solvers can handle huge SAT instances. We thus present a technique to
declaratively model set constraint problems and to encode them automatically
into SAT instances. We apply our technique to the Social Golfer Problem and we
also use it to break symmetries of the problem. Our technique is simpler, more
declarative, and less error-prone than direct and improved hand modeling. The
SAT instances that we automatically generate contain less clauses than improved
hand-written instances such as in [20], and with unit propagation they also
contain less variables. Moreover, they are well-suited for SAT solvers and they
are solved faster as shown when solving difficult instances of the Social
Golfer Problem.
An optimization algorithm for multimodal functions inspired by collective animal behavior
Authors: Erik Cuevas, Mauricio Gonzalez
Date: 2014-06-30 00:00:00

Interest in multimodal function optimization is expanding rapidly since real
world optimization problems often demand locating multiple optima within a
search space. This article presents a new multimodal optimization algorithm
named as the Collective Animal Behavior (CAB). Animal groups, such as schools
of fish, flocks of birds, swarms of locusts and herds of wildebeest, exhibit a
variety of behaviors including swarming about a food source, milling around a
central location or migrating over large distances in aligned groups. These
collective behaviors are often advantageous to groups, allowing them to
increase their harvesting efficiency to follow better migration routes, to
improve their aerodynamic and to avoid predation. In the proposed algorithm,
searcher agents are a group of animals which interact to each other based on
the biological laws of collective motion. Experimental results demonstrate that
the proposed algorithm is capable of finding global and local optima of
benchmark multimodal optimization problems with a higher efficiency in
comparison to other methods reported in the literature.
Subjective and Objective Quality Assessment of Image: A Survey
Authors: Pedram Mohammadi, Abbas Ebrahimi-Moghadam, Shahram Shirani
Date: 2014-06-30 00:00:00
Tasks: Full reference image quality assessment, Full-Reference Image Quality Assessment, Image Quality Assessment, MS-SSIM, SSIM, Survey

With the increasing demand for image-based applications, the efficient and
reliable evaluation of image quality has increased in importance. Measuring the
image quality is of fundamental importance for numerous image processing
applications, where the goal of image quality assessment (IQA) methods is to
automatically evaluate the quality of images in agreement with human quality
judgments. Numerous IQA methods have been proposed over the past years to
fulfill this goal. In this paper, a survey of the quality assessment methods
for conventional image signals, as well as the newly emerged ones, which
includes the high dynamic range (HDR) and 3-D images, is presented. A
comprehensive explanation of the subjective and objective IQA and their
classification is provided. Six widely used subjective quality datasets, and
performance measures are reviewed. Emphasis is given to the full-reference
image quality assessment (FR-IQA) methods, and 9 often-used quality measures
(including mean squared error (MSE), structural similarity index (SSIM),
multi-scale structural similarity index (MS-SSIM), visual information fidelity
(VIF), most apparent distortion (MAD), feature similarity measure (FSIM),
feature similarity measure for color images (FSIMC), dynamic range independent
measure (DRIM), and tone-mapped images quality index (TMQI)) are carefully
described, and their performance and computation time on four subjective
quality datasets are evaluated. Furthermore, a brief introduction to 3-D IQA is
provided and the issues related to this area of research are reviewed.
Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters
Authors: Ziyu Wang, Nando de Freitas
Date: 2014-06-30 00:00:00
Tasks: Bayesian Optimisation, Gaussian Processes, parameter estimation

Bayesian optimisation has gained great popularity as a tool for optimising
the parameters of machine learning algorithms and models. Somewhat ironically,
setting up the hyper-parameters of Bayesian optimisation methods is notoriously
hard. While reasonable practical solutions have been advanced, they can often
fail to find the best optima. Surprisingly, there is little theoretical
analysis of this crucial problem in the literature. To address this, we derive
a cumulative regret bound for Bayesian optimisation with Gaussian processes and
unknown kernel hyper-parameters in the stochastic setting. The bound, which
applies to the expected improvement acquisition function and sub-Gaussian
observation noise, provides us with guidelines on how to design hyper-parameter
estimation methods. A simple simulation demonstrates the importance of
following these guidelines.
Dispersion and Line Formation in Artificial Swarm Intelligence
Authors: Donghwa Jeong, Kiju Lee
Date: 2014-06-30 00:00:00

One of the major motifs in collective or swarm intelligence is that, even
though individuals follow simple rules, the resulting global behavior can be
complex and intelligent. In artificial swarm systems, such as swarm robots, the
goal is to use systems that are as simple and cheap as possible, deploy many of
them, and coordinate them to conduct complex tasks that each individual cannot
accomplish. Shape formation in artificial intelligence systems is usually
required for specific task-oriented performance, including 1) forming sensing
grids, 2) exploring and mapping in space, underwater, or hazardous
environments, and 3) forming a barricade for surveillance or protecting an area
or a person. This paper presents a dynamic model of an artificial swarm system
based on a virtual spring damper model and algorithms for dispersion without a
leader and line formation with an interim leader using only the distance
estimation among the neighbors.
Relevance Singular Vector Machine for low-rank matrix sensing
Authors: Martin Sundin, Saikat Chatterjee, Magnus Jansson, Cristian R. Rojas
Date: 2014-06-30 00:00:00
Tasks: Bayesian Inference, Matrix Completion

In this paper we develop a new Bayesian inference method for low rank matrix
reconstruction. We call the new method the Relevance Singular Vector Machine
(RSVM) where appropriate priors are defined on the singular vectors of the
underlying matrix to promote low rank. To accelerate computations, a
numerically efficient approximation is developed. The proposed algorithms are
applied to matrix completion and matrix reconstruction problems and their
performance is studied numerically.
Cross-calibration of Time-of-flight and Colour Cameras
Authors: Miles Hansard, Georgios Evangelidis, Quentin Pelorson, Radu Horaud
Date: 2014-01-31 00:00:00

Time-of-flight cameras provide depth information, which is complementary to
the photometric appearance of the scene in ordinary images. It is desirable to
merge the depth and colour information, in order to obtain a coherent scene
representation. However, the individual cameras will have different viewpoints,
resolutions and fields of view, which means that they must be mutually
calibrated. This paper presents a geometric framework for this multi-view and
multi-modal calibration problem. It is shown that three-dimensional projective
transformations can be used to align depth and parallax-based representations
of the scene, with or without Euclidean reconstruction. A new evaluation
procedure is also developed; this allows the reprojection error to be
decomposed into calibration and sensor-dependent components. The complete
approach is demonstrated on a network of three time-of-flight and six colour
cameras. The applications of such a system, to a range of automatic
scene-interpretation problems, are discussed.
Bayesian Analysis for miRNA and mRNA Interactions Using Expression Data
Authors: Mingjun Zhong, Rong Liu, Bo Liu
Date: 2012-10-12 00:00:00
Tasks: regression, Specificity

MicroRNAs (miRNAs) are small RNA molecules composed of 19-22 nt, which play
important regulatory roles in post-transcriptional gene regulation by
inhibiting the translation of the mRNA into proteins or otherwise cleaving the
target mRNA. Inferring miRNA targets provides useful information for
understanding the roles of miRNA in biological processes that are potentially
involved in complex diseases. Statistical methodologies for point estimation,
such as the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm,
have been proposed to identify the interactions of miRNA and mRNA based on
sequence and expression data. In this paper, we propose using the Bayesian
LASSO (BLASSO) and the non-negative Bayesian LASSO (nBLASSO) to analyse the
interactions between miRNA and mRNA using expression data. The proposed
Bayesian methods explore the posterior distributions for those parameters
required to model the miRNA-mRNA interactions. These approaches can be used to
observe the inferred effects of the miRNAs on the targets by plotting the
posterior distributions of those parameters. For comparison purposes, the Least
Squares Regression (LSR), Ridge Regression (RR), LASSO, non-negative LASSO
(nLASSO), and the proposed Bayesian approaches were applied to four public
datasets. We concluded that nLASSO and nBLASSO perform best in terms of
sensitivity and specificity. Compared to the point estimate algorithms, which
only provide single estimates for those parameters, the Bayesian methods are
more meaningful and provide credible intervals, which take into account the
uncertainty of the inferred interactions of the miRNA and mRNA. Furthermore,
Bayesian methods naturally provide statistical significance to select
convincing inferred interactions, while point estimate algorithms require a
manually chosen threshold, which is less meaningful, to choose the possible
interactions.
Data Requirement for Phylogenetic Inference from Multiple Loci: A New Distance Method
Authors: Gautam Dasarathy, Robert Nowak, Sebastien Roch
Date: 2014-04-28 00:00:00

We consider the problem of estimating the evolutionary history of a set of
species (phylogeny or species tree) from several genes. It is known that the
evolutionary history of individual genes (gene trees) might be topologically
distinct from each other and from the underlying species tree, possibly
confounding phylogenetic analysis. A further complication in practice is that
one has to estimate gene trees from molecular sequences of finite length. We
provide the first full data-requirement analysis of a species tree
reconstruction method that takes into account estimation errors at the gene
level. Under that criterion, we also devise a novel reconstruction algorithm
that provably improves over all previous methods in a regime of interest.
Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation
Authors: Hiroaki Sasaki, Yung-Kyun Noh, Masashi Sugiyama
Date: 2014-06-30 00:00:00
Tasks: Change Detection, feature selection, Metric Learning

Estimation of density derivatives is a versatile tool in statistical data
analysis. A naive approach is to first estimate the density and then compute
its derivative. However, such a two-step approach does not work well because a
good density estimator does not necessarily mean a good density-derivative
estimator. In this paper, we give a direct method to approximate the density
derivative without estimating the density itself. Our proposed estimator allows
analytic and computationally efficient approximation of multi-dimensional
high-order density derivatives, with the ability that all hyper-parameters can
be chosen objectively by cross-validation. We further show that the proposed
density-derivative estimator is useful in improving the accuracy of
non-parametric KL-divergence estimation via metric learning. The practical
superiority of the proposed method is experimentally demonstrated in change
detection and feature selection.