pwc-archive/papers-with-abstracts huggingface dataset:
Statistical models and regularization strategies in statistical image reconstruction of low-dose X-ray CT: a survey
Authors: Hao Zhang, Jing Wang, Jianhua Ma, Hongbing Lu, Zhengrong Liang
Date: 2014-12-04 00:00:00
Tasks: Computed Tomography (CT), Image Reconstruction

Statistical image reconstruction (SIR) methods have shown potential to
substantially improve the image quality of low-dose X-ray computed tomography
(CT) as compared to the conventional filtered back-projection (FBP) method for
various clinical tasks. According to the maximum a posterior (MAP) estimation,
the SIR methods can be typically formulated by an objective function consisting
of two terms: (1) data-fidelity (or equivalently, data-fitting or
data-mismatch) term modeling the statistics of projection measurements, and (2)
regularization (or equivalently, prior or penalty) term reflecting prior
knowledge or expectation on the characteristics of the image to be
reconstructed. Existing SIR methods for low-dose CT can be divided into two
groups: (1) those that use calibrated transmitted photon counts (before
log-transform) with penalized maximum likelihood (pML) criterion, and (2) those
that use calibrated line-integrals (after log-transform) with penalized
weighted least-squares (PWLS) criterion. Accurate statistical modeling of the
projection measurements is a prerequisite for SIR, while the regularization
term in the objective function also plays a critical role for successful image
reconstruction. This paper reviews several statistical models on CT projection
measurements and various regularization strategies incorporating prior
knowledge or expected properties of the image to be reconstructed, which
together formulate the objective function of the SIR methods for low-dose X-ray
CT.
Fast and numerically stable circle fit
Authors: Houssam Abdul-Rahman, Nikolai Chernov
Date: 2015-05-14 00:00:00

We develop a new algorithm for fitting circles that does not have drawbacks
commonly found in existing circle fits. Our fit achieves ultimate accuracy (to
machine precision), avoids divergence, and is numerically stable even when
fitting circles get arbitrary large. Lastly, our algorithm takes less than 10
iterations to converge, on average.
Rank diversity of languages: Generic behavior in computational linguistics
Authors: Germinal Cocho, Jorge Flores, Carlos Gershenson, Carlos Pineda, Sergio Sánchez
Date: 2015-05-14 00:00:00
Tasks: Diversity

Statistical studies of languages have focused on the rank-frequency
distribution of words. Instead, we introduce here a measure of how word ranks
change in time and call this distribution \emph{rank diversity}. We calculate
this diversity for books published in six European languages since 1800, and
find that it follows a universal lognormal distribution. Based on the mean and
standard deviation associated with the lognormal distribution, we define three
different word regimes of languages: "heads" consist of words which almost do
not change their rank in time, "bodies" are words of general use, while "tails"
are comprised by context-specific words and vary their rank considerably in
time. The heads and bodies reflect the size of language cores identified by
linguists for basic communication. We propose a Gaussian random walk model
which reproduces the rank variation of words in time and thus the diversity.
Rank diversity of words can be understood as the result of random variations in
rank, where the size of the variation depends on the rank itself. We find that
the core size is similar for all languages studied.
On the Complexity of Finding Second-Best Abductive Explanations
Authors: Paolo Liberatore, Marco Schaerf
Date: 2012-04-26 00:00:00

While looking for abductive explanations of a given set of manifestations, an
ordering between possible solutions is often assumed. The complexity of
finding/verifying optimal solutions is already known. In this paper we consider
the computational complexity of finding second-best solutions. We consider
different orderings, and consider also different possible definitions of what a
second-best solution is.
Modified Hausdorff Fractal Dimension (MHFD)
Authors: Reza Farrahi Moghaddam, Mohamed Cheriet
Date: 2015-05-13 00:00:00

The Hausdorff fractal dimension has been a fast-to-calculate method to
estimate complexity of fractal shapes. In this work, a modified version of this
fractal dimension is presented in order to make it more robust when applied in
estimating complexity of non-fractal images. The modified Hausdorff fractal
dimension stands on two features that weaken the requirement of presence of a
shape and also reduce the impact of the noise possibly presented in the input
image. The new algorithm has been evaluated on a set of images of different
character with promising performance.
A PCA-Based Convolutional Network
Authors: Yanhai Gan, Jun Liu, Junyu Dong, Guoqiang Zhong
Date: 2015-05-14 00:00:00
Tasks: Deep Learning, Face Recognition, Texture Classification

In this paper, we propose a novel unsupervised deep learning model, called
PCA-based Convolutional Network (PCN). The architecture of PCN is composed of
several feature extraction stages and a nonlinear output stage. Particularly,
each feature extraction stage includes two layers: a convolutional layer and a
feature pooling layer. In the convolutional layer, the filter banks are simply
learned by PCA. In the nonlinear output stage, binary hashing is applied. For
the higher convolutional layers, the filter banks are learned from the feature
maps that were obtained in the previous stage. To test PCN, we conducted
extensive experiments on some challenging tasks, including handwritten digits
recognition, face recognition and texture classification. The results show that
PCN performs competitive with or even better than state-of-the-art deep
learning models. More importantly, since there is no back propagation for
supervised finetuning, PCN is much more efficient than existing deep networks.
Leveraging Image based Prior for Visual Place Recognition
Authors: Tsukamoto Taisho, Tanaka Kanji
Date: 2015-05-13 00:00:00
Tasks: Visual Place Recognition

In this study, we propose a novel scene descriptor for visual place
recognition. Unlike popular bag-of-words scene descriptors which rely on a
library of vector quantized visual features, our proposed descriptor is based
on a library of raw image data, such as publicly available photo collections
from Google StreetView and Flickr. The library images need not to be associated
with spatial information regarding the viewpoint and orientation of the scene.
As a result, these images are cheaper than the database images; in addition,
they are readily available. Our proposed descriptor directly mines the image
library to discover landmarks (i.e., image patches) that suitably match an
input query/database image. The discovered landmarks are then compactly
described by their pose and shape (i.e., library image ID, bounding boxes) and
used as a compact discriminative scene descriptor for the input image. We
evaluate the effectiveness of our scene description framework by comparing its
performance to that of previous approaches.
CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research
Authors: Ali Borji, Laurent Itti
Date: 2015-05-14 00:00:00

Saliency modeling has been an active research area in computer vision for
about two decades. Existing state of the art models perform very well in
predicting where people look in natural scenes. There is, however, the risk
that these models may have been overfitting themselves to available small scale
biased datasets, thus trapping the progress in a local minimum. To gain a
deeper insight regarding current issues in saliency modeling and to better
gauge progress, we recorded eye movements of 120 observers while they freely
viewed a large number of naturalistic and artificial images. Our stimuli
includes 4000 images; 200 from each of 20 categories covering different types
of scenes such as Cartoons, Art, Objects, Low resolution images, Indoor,
Outdoor, Jumbled, Random, and Line drawings. We analyze some basic properties
of this dataset and compare some successful models. We believe that our dataset
opens new challenges for the next generation of saliency models and helps
conduct behavioral studies on bottom-up visual attention.
Vanishing Point Attracts Eye Movements in Scene Free-viewing
Authors: Ali Borji, Mengyang Feng, Huchuan Lu
Date: 2015-05-14 00:00:00

Eye movements are crucial in understanding complex scenes. By predicting
where humans look in natural scenes, we can understand how they percieve scenes
and priotriaze information for further high-level processing. Here, we study
the effect of a particular type of scene structural information known as
vanishing point and show that human gaze is attracted to vanishing point
regions. We then build a combined model of traditional saliency and vanishing
point channel that outperforms state of the art saliency models.
Graphlet-based lazy associative graph classification
Authors: Yury Kashnitsky, Sergei O. Kuznetsov
Date: 2015-04-21 00:00:00
Tasks: Classification, General Classification, Graph Classification

The paper addresses the graph classification problem and introduces a
modification of the lazy associative classification method to efficiently
handle intersections of graphs. Graph intersections are approximated with all
common subgraphs up to a fixed size similarly to what is done with graphlet
kernels. We explain the idea of the algorithm with a toy example and describe
our experiments with a predictive toxicology dataset.
Bootstrapped Adaptive Threshold Selection for Statistical Model Selection and Estimation
Authors: Kristofer E. Bouchard
Date: 2015-05-13 00:00:00
Tasks: Model Selection

A central goal of neuroscience is to understand how activity in the nervous
system is related to features of the external world, or to features of the
nervous system itself. A common approach is to model neural responses as a
weighted combination of external features, or vice versa. The structure of the
model weights can provide insight into neural representations. Often, neural
input-output relationships are sparse, with only a few inputs contributing to
the output. In part to account for such sparsity, structured regularizers are
incorporated into model fitting optimization. However, by imposing priors,
structured regularizers can make it difficult to interpret learned model
parameters. Here, we investigate a simple, minimally structured model
estimation method for accurate, unbiased estimation of sparse models based on
Bootstrapped Adaptive Threshold Selection followed by ordinary least-squares
refitting (BoATS). Through extensive numerical investigations, we show that
this method often performs favorably compared to L1 and L2 regularizers. In
particular, for a variety of model distributions and noise levels, BoATS more
accurately recovers the parameters of sparse models, leading to more
parsimonious explanations of outputs. Finally, we apply this method to the task
of decoding human speech production from ECoG recordings.
Prediction and Quantification of Individual Athletic Performance
Authors: Duncan A. J. Blythe, Franz J. Király
Date: 2015-05-05 00:00:00
Tasks: Form, Matrix Completion, Prediction

We provide scientific foundations for athletic performance prediction on an
individual level, exposing the phenomenology of individual athletic running
performance in the form of a low-rank model dominated by an individual power
law. We present, evaluate, and compare a selection of methods for prediction of
individual running performance, including our own, \emph{local matrix
completion} (LMC), which we show to perform best. We also show that many
documented phenomena in quantitative sports science, such as the form of
scoring tables, the success of existing prediction methods including Riegel's
formula, the Purdy points scheme, the power law for world records performances
and the broken power law for world record speeds may be explained on the basis
of our findings in a unified way.
A Review Paper: Noise Models in Digital Image Processing
Authors: Ajay Kumar Boyat, Brijendra Kumar Joshi
Date: 2015-05-13 00:00:00
Tasks: Denoising, Image Denoising

Noise is always presents in digital images during image acquisition, coding,
transmission, and processing steps. Noise is very difficult to remove it from
the digital images without the prior knowledge of noise model. That is why,
review of noise models are essential in the study of image denoising
techniques. In this paper, we express a brief overview of various noise models.
These noise models can be selected by analysis of their origin. In this way, we
present a complete and quantitative analysis of noise models available in
digital images.
Exploring Strategy-Proofness, Uniqueness, and Pareto Optimality for the Stable Matching Problem with Couples
Authors: Andrew Perrault, Joanna Drummond, Fahiem Bacchus
Date: 2015-05-13 00:00:00

The Stable Matching Problem with Couples (SMP-C) is a ubiquitous real-world
extension of the stable matching problem (SMP) involving complementarities.
Although SMP can be solved in polynomial time, SMP-C is NP-Complete. Hence, it
is not clear which, if any, of the theoretical results surrounding the
canonical SMP problem apply in this setting. In this paper, we use a
recently-developed SAT encoding to solve SMP-C exactly. This allows us to
enumerate all stable matchings for any given instance of SMP-C. With this tool,
we empirically evaluate some of the properties that have been hypothesized to
hold for SMP-C.
  We take particular interest in investigating if, as the size of the market
grows, the percentage of instances with unique stable matchings also grows.
While we did not find this trend among the random problem instances we sampled,
we did find that the percentage of instances with an resident optimal matching
seems to more closely follow the trends predicted by previous conjectures. We
also define and investigate resident Pareto optimal stable matchings, finding
that, even though this is important desideratum for the deferred acceptance
style algorithms previously designed to solve SMP-C, they do not always find
one.
  We also investigate strategy-proofness for SMP-C, showing that even if only
one stable matching exists, residents still have incentive to misreport their
preferences. However, if a problem has a resident optimal stable matching, we
show that residents cannot manipulate via truncation.
A Survey of Predictive Modelling under Imbalanced Distributions
Authors: Paula Branco, Luis Torgo, Rita Ribeiro
Date: 2015-05-07 00:00:00
Tasks: Fraud Detection, Survey

Many real world data mining applications involve obtaining predictive models
using data sets with strongly imbalanced distributions of the target variable.
Frequently, the least common values of this target variable are associated with
events that are highly relevant for end users (e.g. fraud detection, unusual
returns on stock markets, anticipation of catastrophes, etc.). Moreover, the
events may have different costs and benefits, which when associated with the
rarity of some of them on the available training data creates serious problems
to predictive modelling techniques. This paper presents a survey of existing
techniques for handling these important applications of predictive analytics.
Although most of the existing work addresses classification tasks (nominal
target variables), we also describe methods designed to handle similar problems
within regression tasks (numeric target variables). In this survey we discuss
the main challenges raised by imbalanced distributions, describe the main
approaches to these problems, propose a taxonomy of these methods and refer to
some related problems within predictive modelling.
Answering Conjunctive Queries over $\mathcal{EL}$ Knowledge Bases with Transitive and Reflexive Roles
Authors: Giorgio Stefanoni, Boris Motik
Date: 2014-11-10 00:00:00

Answering conjunctive queries (CQs) over $\mathcal{EL}$ knowledge bases (KBs)
with complex role inclusions is PSPACE-hard and in PSPACE in certain cases;
however, if complex role inclusions are restricted to role transitivity, the
tight upper complexity bound has so far been unknown. Furthermore, the existing
algorithms cannot handle reflexive roles, and they are not practicable.
Finally, the problem is tractable for acyclic CQs and $\mathcal{ELH}$, and
NP-complete for unrestricted CQs and $\mathcal{ELHO}$ KBs. In this paper we
complete the complexity landscape of CQ answering for several important cases.
In particular, we present a practicable NP algorithm for answering CQs over
$\mathcal{ELHO}^s$ KBs---a logic containing all of OWL 2 EL, but with complex
role inclusions restricted to role transitivity. Our preliminary evaluation
suggests that the algorithm can be suitable for practical use. Moreover, we
show that, even for a restricted class of so-called arborescent acyclic
queries, CQ answering over $\mathcal{EL}$ KBs becomes NP-hard in the presence
of either transitive or reflexive roles. Finally, we show that answering
arborescent CQs over $\mathcal{ELHO}$ KBs is tractable, whereas answering
acyclic CQs is NP-hard.
MRF Optimization by Graph Approximation
Authors: Wonsik Kim, Kyoung Mu Lee
Date: 2015-05-13 00:00:00
Proceeding: CVPR 2015 6

Graph cuts-based algorithms have achieved great success in energy
minimization for many computer vision applications. These algorithms provide
approximated solutions for multi-label energy functions via move-making
approach. This approach fuses the current solution with a proposal to generate
a lower-energy solution. Thus, generating the appropriate proposals is
necessary for the success of the move-making approach. However, not much
research efforts has been done on the generation of "good" proposals,
especially for non-metric energy functions. In this paper, we propose an
application-independent and energy-based approach to generate "good" proposals.
With these proposals, we present a graph cuts-based move-making algorithm
called GA-fusion (fusion with graph approximation-based proposals). Extensive
experiments support that our proposal generation is effective across different
classes of energy functions. The proposed algorithm outperforms others both on
real and synthetic problems.
A Vision Based System for Monitoring the Loss of Attention in Automotive Drivers
Authors: Anirban Dasgupta, Anjith George, S. L. Happy, Aurobinda Routray
Date: 2015-05-13 00:00:00
Tasks: Management

On board monitoring of the alertness level of an automotive driver has been a
challenging research in transportation safety and management. In this paper, we
propose a robust real time embedded platform to monitor the loss of attention
of the driver during day as well as night driving conditions. The PERcentage of
eye CLOSure (PERCLOS) has been used as the indicator of the alertness level. In
this approach, the face is detected using Haar like features and tracked using
a Kalman Filter. The Eyes are detected using Principal Component Analysis (PCA)
during day time and the block Local Binary Pattern (LBP) features during night.
Finally the eye state is classified as open or closed using Support Vector
Machines(SVM). In plane and off plane rotations of the drivers face have been
compensated using Affine and Perspective Transformation respectively.
Compensation in illumination variation is carried out using Bi Histogram
Equalization (BHE). The algorithm has been cross validated using brain signals
and finally been implemented on a Single Board Computer (SBC) having Intel Atom
processor, 1 GB RAM, 1.66 GHz clock, x86 architecture, Windows Embedded XP
operating system. The system is found to be robust under actual driving
conditions.
Analysis of PCA Algorithms in Distributed Environments
Authors: Tarek Elgamal, Mohamed Hefeeda
Date: 2015-03-17 00:00:00
Tasks: Small Data Image Classification

Classical machine learning algorithms often face scalability bottlenecks when
they are applied to large-scale data. Such algorithms were designed to work
with small data that is assumed to fit in the memory of one machine. In this
report, we analyze different methods for computing an important machine learing
algorithm, namely Principal Component Analysis (PCA), and we comment on its
limitations in supporting large datasets. The methods are analyzed and compared
across two important metrics: time complexity and communication complexity. We
consider the worst-case scenarios for both metrics, and we identify the
software libraries that implement each method. The analysis in this report
helps researchers and engineers in (i) understanding the main bottlenecks for
scalability in different PCA algorithms, (ii) choosing the most appropriate
method and software library for a given application and data set
characteristics, and (iii) designing new scalable PCA algorithms.
Stability and Performance Limits of Adaptive Primal-Dual Networks
Authors: Zaid J. Towfic, Ali H. Sayed
Date: 2014-08-16 00:00:00
Tasks: Stochastic Optimization

This work studies distributed primal-dual strategies for adaptation and
learning over networks from streaming data. Two first-order methods are
considered based on the Arrow-Hurwicz (AH) and augmented Lagrangian (AL)
techniques. Several revealing results are discovered in relation to the
performance and stability of these strategies when employed over adaptive
networks. The conclusions establish that the advantages that these methods have
for deterministic optimization problems do not necessarily carry over to
stochastic optimization problems. It is found that they have narrower stability
ranges and worse steady-state mean-square-error performance than primal methods
of the consensus and diffusion type. It is also found that the AH technique can
become unstable under a partial observation model, while the other techniques
are able to recover the unknown under this scenario. A method to enhance the
performance of AL strategies is proposed by tying the selection of the
step-size to their regularization parameter. It is shown that this method
allows the AL algorithm to approach the performance of consensus and diffusion
strategies but that it remains less stable than these other strategies.
Tree-like Queries in OWL 2 QL: Succinctness and Complexity Results
Authors: Meghyn Bienvenu, Stanislav Kikot, Vladimir Podolskii
Date: 2014-06-11 00:00:00

This paper investigates the impact of query topology on the difficulty of
answering conjunctive queries in the presence of OWL 2 QL ontologies. Our first
contribution is to clarify the worst-case size of positive existential (PE),
non-recursive Datalog (NDL), and first-order (FO) rewritings for various
classes of tree-like conjunctive queries, ranging from linear queries to
bounded treewidth queries. Perhaps our most surprising result is a
superpolynomial lower bound on the size of PE-rewritings that holds already for
linear queries and ontologies of depth 2. More positively, we show that
polynomial-size NDL-rewritings always exist for tree-shaped queries with a
bounded number of leaves (and arbitrary ontologies), and for bounded treewidth
queries paired with bounded depth ontologies. For FO-rewritings, we equate the
existence of polysize rewritings with well-known problems in Boolean circuit
complexity. As our second contribution, we analyze the computational complexity
of query answering and establish tractability results (either NL- or
LOGCFL-completeness) for a range of query-ontology pairs. Combining our new
results with those from the literature yields a complete picture of the
succinctness and complexity landscapes for the considered classes of queries
and ontologies.