pwc-archive/papers-with-abstracts huggingface dataset:
Verifiable Source Code Documentation in Controlled Natural Language
Authors: Tobias Kuhn, Alexandre Bergel
Date: 2013-11-12 00:00:00

Writing documentation about software internals is rarely considered a
rewarding activity. It is highly time-consuming and the resulting documentation
is fragile when the software is continuously evolving in a multi-developer
setting. Unfortunately, traditional programming environments poorly support the
writing and maintenance of documentation. Consequences are severe as the lack
of documentation on software structure negatively impacts the overall quality
of the software product. We show that using a controlled natural language with
a reasoner and a query engine is a viable technique for verifying the
consistency and accuracy of documentation and source code. Using ACE, a
state-of-the-art controlled natural language, we present positive results on
the comprehensibility and the general feasibility of creating and verifying
documentation. As a case study, we used automatic documentation verification to
identify and fix severe flaws in the architecture of a non-trivial piece of
software. Moreover, a user experiment shows that our language is faster and
easier to learn and understand than other formal languages for software
documentation.
Sampling Based Approaches to Handle Imbalances in Network Traffic Dataset for Machine Learning Techniques
Authors: Raman Singh, Harish Kumar, R. K. Singla
Date: 2013-11-12 00:00:00
Tasks: BIG-bench Machine Learning

Network traffic data is huge, varying and imbalanced because various classes
are not equally distributed. Machine learning (ML) algorithms for traffic
analysis uses the samples from this data to recommend the actions to be taken
by the network administrators as well as training. Due to imbalances in
dataset, it is difficult to train machine learning algorithms for traffic
analysis and these may give biased or false results leading to serious
degradation in performance of these algorithms. Various techniques can be
applied during sampling to minimize the effect of imbalanced instances. In this
paper various sampling techniques have been analysed in order to compare the
decrease in variation in imbalances of network traffic datasets sampled for
these algorithms. Various parameters like missing classes in samples,
probability of sampling of the different instances have been considered for
comparison.
Performing edge detection by difference of Gaussians using q-Gaussian kernels
Authors: Lucas Assirati, Núbia R. da Silva, Lilian Berton, Alneu de A. Lopes, Odemir M. Bruno
Date: 2013-11-11 00:00:00
Tasks: Edge Detection

In image processing, edge detection is a valuable tool to perform the
extraction of features from an image. This detection reduces the amount of
information to be processed, since the redundant information (considered less
relevant) can be unconsidered. The technique of edge detection consists of
determining the points of a digital image whose intensity changes sharply. This
changes are due to the discontinuities of the orientation on a surface for
example. A well known method of edge detection is the Difference of Gaussians
(DoG). The method consists of subtracting two Gaussians, where a kernel has a
standard deviation smaller than the previous one. The convolution between the
subtraction of kernels and the input image results in the edge detection of
this image. This paper introduces a method of extracting edges using DoG with
kernels based on the q-Gaussian probability distribution, derived from the
q-statistic proposed by Constantino Tsallis. To demonstrate the method's
potential, we compare the introduced method with the traditional DoG using
Gaussians kernels. The results showed that the proposed method can extract
edges with more accurate details.
A comparative analysis of methods for estimating axon diameter using DWI
Authors: Hamed Yousefi Mesri
Date: 2013-08-24 00:00:00

The importance of studying the brain microstructure is described and the
existing and state of the art non-invasive methods for the investigation of the
brain microstructure using Diffusion Weighted Magnetic Resonance Imaging (DWI)
is studied. In the next step, Cramer-Rao Lower Bound (CRLB) analysis is
described and utilised for assessment of the minimum estimation error and
uncertainty level of different Diffusion Weighted Magnetic Resonance (DWMR)
signal decay models. The analyses are performed considering the best scenario
through which, we assume that the models are the appropriate representation of
the measured phenomena. This includes the study of the sensitivity of the
estimations to the measurement and model parameters. It is demonstrated that
none of the existing models can achieve a reasonable minimum uncertainty level
under typical measurement setup. At the end, the practical obstacles for
achieving higher performance in clinical and experimental environments are
studied and their effects on feasibility of the methods are discussed.
Volumetric Reconstruction Applied to Perceptual Studies of Size and Weight
Authors: J. Balzer, M. Peters, S. Soatto
Date: 2013-11-11 00:00:00

We explore the application of volumetric reconstruction from structured-light
sensors in cognitive neuroscience, specifically in the quantification of the
size-weight illusion, whereby humans tend to systematically perceive smaller
objects as heavier. We investigate the performance of two commercial
structured-light scanning systems in comparison to one we developed
specifically for this application. Our method has two main distinct features:
First, it only samples a sparse series of viewpoints, unlike other systems such
as the Kinect Fusion. Second, instead of building a distance field for the
purpose of points-to-surface conversion directly, we pursue a first-order
approach: the distance function is recovered from its gradient by a screened
Poisson reconstruction, which is very resilient to noise and yet preserves
high-frequency signal components. Our experiments show that the quality of
metric reconstruction from structured light sensors is subject to systematic
biases, and highlights the factors that influence it. Our main performance
index rates estimates of volume (a proxy of size), for which we review a
well-known formula applicable to incomplete meshes. Our code and data will be
made publicly available upon completion of the anonymous review process.
Determining Leishmania Infection Levels by Automatic Analysis of Microscopy Images
Authors: P. A Nogueira
Date: 2013-11-11 00:00:00
Tasks: General Classification

Analysis of microscopy images is one important tool in many fields of
biomedical research, as it allows the quantification of a multitude of
parameters at the cellular level. However, manual counting of these images is
both tiring and unreliable and ultimately very time-consuming for biomedical
researchers. Not only does this slow down the overall research process, it also
introduces counting errors due to a lack of objectivity and consistency
inherent to the researchers own human nature.
  This thesis addresses this issue by automatically determining infection
indexes of macrophages parasite by Leishmania in microscopy images using
computer vision and pattern recognition methodologies. Initially images are
submitted to a pre-processing stage that consists in a normalization of
illumination conditions. Three algorithms are then applied in parallel to each
image. Algorithm A intends to detect macrophage nuclei and consists of
segmentation via adaptive multi-threshold, and classification of resulting
regions using a set of collected features. Algorithm B intends to detect
parasites and is similar to Algorithm A but the adaptive multi-threshold is
parameterized with a different constraints vector. Algorithm C intends to
detect the macrophages and parasites cytoplasm and consists of a cut-off
version of the previous two algorithms, where the classification step is
skipped. Regions with multiple nuclei or parasites are processed by a voting
system that employs both a Support Vector Machine and a set of region features
for determining the number of objects present in each region. The previous vote
is then taken into account as the number of mixtures to be used in a Gaussian
Mixture Model to decluster the said region. Finally each parasite is assigned
to, at most, a single macrophage using minimum Euclidean distance to a cell
nucleus, thus quantifying Leishmania infection levels.
Stitched Panoramas from Toy Airborne Video Cameras
Authors: Camille Goudeseune
Date: 2013-11-11 00:00:00

Effective panoramic photographs are taken from vantage points that are high.
High vantage points have recently become easier to reach as the cost of
quadrotor helicopters has dropped to nearly disposable levels. Although cameras
carried by such aircraft weigh only a few grams, their low-quality video can be
converted into panoramas of high quality and high resolution. Also, the small
size of these aircraft vastly reduces the risks inherent to flight.
Motility at the origin of life: Its characterization and a model
Authors: Tom Froese, Nathaniel Virgo, Takashi Ikegami
Date: 2013-11-11 00:00:00
Tasks: Artificial Life

Due to recent advances in synthetic biology and artificial life, the origin
of life is currently a hot topic of research. We review the literature and
argue that the two traditionally competing "replicator-first" and
"metabolism-first" approaches are merging into one integrated theory of
individuation and evolution. We contribute to the maturation of this more
inclusive approach by highlighting some problematic assumptions that still lead
to an impoverished conception of the phenomenon of life. In particular, we
argue that the new consensus has so far failed to consider the relevance of
intermediate timescales. We propose that an adequate theory of life must
account for the fact that all living beings are situated in at least four
distinct timescales, which are typically associated with metabolism, motility,
development, and evolution. On this view, self-movement, adaptive behavior and
morphological changes could have already been present at the origin of life. In
order to illustrate this possibility we analyze a minimal model of life-like
phenomena, namely of precarious, individuated, dissipative structures that can
be found in simple reaction-diffusion systems. Based on our analysis we suggest
that processes in intermediate timescales could have already been operative in
prebiotic systems. They may have facilitated and constrained changes occurring
in the faster- and slower-paced timescales of chemical self-individuation and
evolution by natural selection, respectively.
Two-View Matching with View Synthesis Revisited
Authors: Dmytro Mishkin, Michal Perdoch, Jiri Matas
Date: 2013-06-17 00:00:00
Tasks: Vocal Bursts Valence Prediction

Wide-baseline matching focussing on problems with extreme viewpoint change is
considered. We introduce the use of view synthesis with affine-covariant
detectors to solve such problems and show that matching with the Hessian-Affine
or MSER detectors outperforms the state-of-the-art ASIFT.
  To minimise the loss of speed caused by view synthesis, we propose the
Matching On Demand with view Synthesis algorithm (MODS) that uses progressively
more synthesized images and more (time-consuming) detectors until reliable
estimation of geometry is possible. We show experimentally that the MODS
algorithm solves problems beyond the state-of-the-art and yet is comparable in
speed to standard wide-baseline matchers on simpler problems.
  Minor contributions include an improved method for tentative correspondence
selection, applicable both with and without view synthesis and a view synthesis
setup greatly improving MSER robustness to blur and scale change that increase
its running time by 10% only.
Predictable Feature Analysis
Authors: Stefan Richthofer, Laurenz Wiskott
Date: 2013-11-11 00:00:00
Tasks: Decision Making

Every organism in an environment, whether biological, robotic or virtual,
must be able to predict certain aspects of its environment in order to survive
or perform whatever task is intended. It needs a model that is capable of
estimating the consequences of possible actions, so that planning, control, and
decision-making become feasible. For scientific purposes, such models are
usually created in a problem specific manner using differential equations and
other techniques from control- and system-theory. In contrast to that, we aim
for an unsupervised approach that builds up the desired model in a
self-organized fashion. Inspired by Slow Feature Analysis (SFA), our approach
is to extract sub-signals from the input, that behave as predictable as
possible. These "predictable features" are highly relevant for modeling,
because predictability is a desired property of the needed
consequence-estimating model by definition. In our approach, we measure
predictability with respect to a certain prediction model. We focus here on the
solution of the arising optimization problem and present a tractable algorithm
based on algebraic methods which we call Predictable Feature Analysis (PFA). We
prove that the algorithm finds the globally optimal signal, if this signal can
be predicted with low error. To deal with cases where the optimal signal has a
significant prediction error, we provide a robust, heuristically motivated
variant of the algorithm and verify it empirically. Additionally, we give
formal criteria a prediction-model must meet to be suitable for measuring
predictability in the PFA setting and also provide a suitable default-model
along with a formal proof that it meets these criteria.
Notes on Elementary Spectral Graph Theory. Applications to Graph Clustering Using Normalized Cuts
Authors: Jean Gallier
Date: 2013-11-11 00:00:00
Tasks: Clustering, Graph Clustering

These are notes on the method of normalized graph cuts and its applications
to graph clustering. I provide a fairly thorough treatment of this deeply
original method due to Shi and Malik, including complete proofs. I include the
necessary background on graphs and graph Laplacians. I then explain in detail
how the eigenvectors of the graph Laplacian can be used to draw a graph. This
is an attractive application of graph Laplacians. The main thrust of this paper
is the method of normalized cuts. I give a detailed account for K = 2 clusters,
and also for K > 2 clusters, based on the work of Yu and Shi. Three points that
do not appear to have been clearly articulated before are elaborated:
  1. The solutions of the main optimization problem should be viewed as tuples
in the K-fold cartesian product of projective space RP^{N-1}.
  2. When K > 2, the solutions of the relaxed problem should be viewed as
elements of the Grassmannian G(K,N).
  3. Two possible Riemannian distances are available to compare the closeness
of solutions: (a) The distance on (RP^{N-1})^K. (b) The distance on the
Grassmannian.
  I also clarify what should be the necessary and sufficient conditions for a
matrix to represent a partition of the vertices of a graph to be clustered.
Global Sensitivity Analysis with Dependence Measures
Authors: Sébastien da Veiga
Date: 2013-11-11 00:00:00
Tasks: feature selection, Sensitivity

Global sensitivity analysis with variance-based measures suffers from several
theoretical and practical limitations, since they focus only on the variance of
the output and handle multivariate variables in a limited way. In this paper,
we introduce a new class of sensitivity indices based on dependence measures
which overcomes these insufficiencies. Our approach originates from the idea to
compare the output distribution with its conditional counterpart when one of
the input variables is fixed. We establish that this comparison yields
previously proposed indices when it is performed with Csiszar f-divergences, as
well as sensitivity indices which are well-known dependence measures between
random variables. This leads us to investigate completely new sensitivity
indices based on recent state-of-the-art dependence measures, such as distance
correlation and the Hilbert-Schmidt independence criterion. We also emphasize
the potential of feature selection techniques relying on such dependence
measures as alternatives to screening in high dimension.
Exploiting correlation and budget constraints in Bayesian multi-armed bandit optimization
Authors: Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas
Date: 2013-03-27 00:00:00
Tasks: Bayesian Optimization, Thompson Sampling

We address the problem of finding the maximizer of a nonlinear smooth
function, that can only be evaluated point-wise, subject to constraints on the
number of permitted function evaluations. This problem is also known as
fixed-budget best arm identification in the multi-armed bandit literature. We
introduce a Bayesian approach for this problem and show that it empirically
outperforms both the existing frequentist counterpart and other Bayesian
optimization methods. The Bayesian approach places emphasis on detailed
modelling, including the modelling of correlations among the arms. As a result,
it can perform well in situations where the number of arms is much larger than
the number of allowed function evaluation, whereas the frequentist counterpart
is inapplicable. This feature enables us to develop and deploy practical
applications, such as automatic machine learning toolboxes. The paper presents
comprehensive comparisons of the proposed approach, Thompson sampling,
classical Bayesian optimization techniques, more recent Bayesian bandit
approaches, and state-of-the-art best arm identification methods. This is the
first comparison of many of these methods in the literature and allows us to
examine the relative merits of their different features.
An Empirical Evaluation of Sequence-Tagging Trainers
Authors: P. Balamurugan, Shirish Shevade, S. Sundararajan, S. S Keerthi
Date: 2013-11-11 00:00:00

The task of assigning label sequences to a set of observed sequences is
common in computational linguistics. Several models for sequence labeling have
been proposed over the last few years. Here, we focus on discriminative models
for sequence labeling. Many batch and online (updating model parameters after
visiting each example) learning algorithms have been proposed in the
literature. On large datasets, online algorithms are preferred as batch
learning methods are slow. These online algorithms were designed to solve
either a primal or a dual problem. However, there has been no systematic
comparison of these algorithms in terms of their speed, generalization
performance (accuracy/likelihood) and their ability to achieve steady state
generalization performance fast. With this aim, we compare different algorithms
and make recommendations, useful for a practitioner. We conclude that the
selection of an algorithm for sequence labeling depends on the evaluation
criterion used and its implementation simplicity.
Efficient Optimization for Sparse Gaussian Process Regression
Authors: Yanshuai Cao, Marcus A. Brubaker, David J. Fleet, Aaron Hertzmann
Date: 2013-10-22 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: regression

We propose an efficient optimization algorithm for selecting a subset of
training data to induce sparsity for Gaussian process regression. The algorithm
estimates an inducing set and the hyperparameters using a single objective,
either the marginal likelihood or a variational free energy. The space and time
complexity are linear in training set size, and the algorithm can be applied to
large regression problems on discrete or continuous domains. Empirical
evaluation shows state-of-art performance in discrete cases and competitive
results in the continuous case.
Active Contour Models for Manifold Valued Image Segmentation
Authors: Sumukh Bansal, Aditya Tatu
Date: 2013-06-26 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation is the process of partitioning a image into different
regions or groups based on some characteristics like color, texture, motion or
shape etc. Active contours is a popular variational method for object
segmentation in images, in which the user initializes a contour which evolves
in order to optimize an objective function designed such that the desired
object boundary is the optimal solution. Recently, imaging modalities that
produce Manifold valued images have come up, for example, DT-MRI images, vector
fields. The traditional active contour model does not work on such images. In
this paper, we generalize the active contour model to work on Manifold valued
images. As expected, our algorithm detects regions with similar Manifold values
in the image. Our algorithm also produces expected results on usual gray-scale
images, since these are nothing but trivial examples of Manifold valued images.
As another application of our general active contour model, we perform texture
segmentation on gray-scale images by first creating an appropriate Manifold
valued image. We demonstrate segmentation results for manifold valued images
and texture images.
Tight Lower Bound on the Probability of a Binomial Exceeding its Expectation
Authors: Spencer Greenberg, Mehryar Mohri
Date: 2013-06-06 00:00:00
Tasks: Generalization Bounds, Learning Theory

We give the proof of a tight lower bound on the probability that a binomial
random variable exceeds its expected value. The inequality plays an important
role in a variety of contexts, including the analysis of relative deviation
bounds in learning theory and generalization bounds for unbounded loss
functions.
Generalized Denoising Auto-Encoders as Generative Models
Authors: Yoshua Bengio, Li Yao, Guillaume Alain, Pascal Vincent
Date: 2013-05-29 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Denoising, valid

Recent work has shown how denoising and contractive autoencoders implicitly
capture the structure of the data-generating density, in the case where the
corruption noise is Gaussian, the reconstruction error is the squared error,
and the data is continuous-valued. This has led to various proposals for
sampling from this implicitly learned density function, using Langevin and
Metropolis-Hastings MCMC. However, it remained unclear how to connect the
training procedure of regularized auto-encoders to the implicit estimation of
the underlying data-generating distribution when the data are discrete, or
using other forms of corruption process and reconstruction errors. Another
issue is the mathematical justification which is only valid in the limit of
small corruption noise. We propose here a different attack on the problem,
which deals with all these issues: arbitrary (but noisy enough) corruption,
arbitrary reconstruction loss (seen as a log-likelihood), handling both
discrete and continuous-valued variables, and removing the bias due to
non-infinitesimal corruption noise (or non-infinitesimal contractive penalty).
A Quantitative Evaluation Framework for Missing Value Imputation Algorithms
Authors: Vinod Nair, Rahul Kidambi, Sundararajan Sellamanickam, S. Sathiya Keerthi, Johannes Gehrke, Vijay Narayanan
Date: 2013-11-10 00:00:00
Tasks: Imputation, Missing Values

We consider the problem of quantitatively evaluating missing value imputation
algorithms. Given a dataset with missing values and a choice of several
imputation algorithms to fill them in, there is currently no principled way to
rank the algorithms using a quantitative metric. We develop a framework based
on treating imputation evaluation as a problem of comparing two distributions
and show how it can be used to compute quantitative metrics. We present an
efficient procedure for applying this framework to practical datasets,
demonstrate several metrics derived from the existing literature on comparing
distributions, and propose a new metric called Neighborhood-based Dissimilarity
Score which is fast to compute and provides similar results. Results are shown
on several datasets, metrics, and imputations algorithms.