pwc-archive/papers-with-abstracts huggingface dataset:
Hardware based Scale- and Rotation-Invariant Feature Extraction: A Retrospective Analysis and Future Directions
Authors: Shoaib Ehsan, Adrian F. Clark, Klaus D. McDonald-Maier
Date: 2015-04-29 00:00:00

Computer Vision techniques represent a class of algorithms that are highly
computation and data intensive in nature. Generally, performance of these
algorithms in terms of execution speed on desktop computers is far from
real-time. Since real-time performance is desirable in many applications,
special-purpose hardware is required in most cases to achieve this goal. Scale-
and rotation-invariant local feature extraction is a low level computer vision
task with very high computational complexity. The state-of-the-art algorithms
that currently exist in this domain, like SIFT and SURF, suffer from slow
execution speeds and at best can only achieve rates of 2-3 Hz on modern desktop
computers. Hardware-based scale- and rotation-invariant local feature
extraction is an emerging trend enabling real-time performance for these
computationally complex algorithms. This paper takes a retrospective look at
the advances made so far in this field, discusses the hardware design
strategies employed and results achieved, identifies current research gaps and
suggests future research directions.
Exploring Integral Image Word Length Reduction Techniques for SURF Detector
Authors: Shoaib Ehsan, Klaus D. McDonald-Maier
Date: 2015-04-29 00:00:00

Speeded Up Robust Features (SURF) is a state of the art computer vision
algorithm that relies on integral image representation for performing fast
detection and description of image features that are scale and rotation
invariant. Integral image representation, however, has major draw back of large
binary word length that leads to substantial increase in memory size. When
designing a dedicated hardware to achieve real-time performance for the SURF
algorithm, it is imperative to consider the adverse effects of integral image
on memory size, bus width and computational resources. With the objective of
minimizing hardware resources, this paper presents a novel implementation
concept of a reduced word length integral image based SURF detector. It
evaluates two existing word length reduction techniques for the particular case
of SURF detector and extends one of these to achieve more reduction in word
length. This paper also introduces a novel method to achieve integral image
word length reduction for SURF detector.
Designing Optimal Mortality Risk Prediction Scores that Preserve Clinical Knowledge
Authors: Natalia M. Arzeno, Karla A. Lawson, Sarah V. Duzinski, Haris Vikalo
Date: 2014-11-19 00:00:00
Tasks: Clinical Knowledge

Many in-hospital mortality risk prediction scores dichotomize predictive
variables to simplify the score calculation. However, hard thresholding in
these additive stepwise scores of the form "add x points if variable v is
above/below threshold t" may lead to critical failures. In this paper, we seek
to develop risk prediction scores that preserve clinical knowledge embedded in
features and structure of the existing additive stepwise scores while
addressing limitations caused by variable dichotomization. To this end, we
propose a novel score structure that relies on a transformation of predictive
variables by means of nonlinear logistic functions facilitating smooth
differentiation between critical and normal values of the variables. We develop
an optimization framework for inferring parameters of the logistic functions
for a given patient population via cyclic block coordinate descent. The
parameters may readily be updated as the patient population and standards of
care evolve. We tested the proposed methodology on two populations: (1) brain
trauma patients admitted to the intensive care unit of the Dell Children's
Medical Center of Central Texas between 2007 and 2012, and (2) adult ICU
patient data from the MIMIC II database. The results are compared with those
obtained by the widely used PRISM III and SOFA scores. The prediction power of
a score is evaluated using area under ROC curve, Youden's index, and
precision-recall balance in a cross-validation study. The results demonstrate
that the new framework enables significant performance improvements over PRISM
III and SOFA in terms of all three criteria.
Robust hyperspectral image classification with rejection fields
Authors: Filipe Condessa, Jose Bioucas-Dias, Jelena Kovacevic
Date: 2015-04-29 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification, Robust classification

In this paper we present a novel method for robust hyperspectral image
classification using context and rejection. Hyperspectral image classification
is generally an ill-posed image problem where pixels may belong to unknown
classes, and obtaining representative and complete training sets is costly.
Furthermore, the need for high classification accuracies is frequently greater
than the need to classify the entire image.
  We approach this problem with a robust classification method that combines
classification with context with classification with rejection. A rejection
field that will guide the rejection is derived from the classification with
contextual information obtained by using the SegSALSA algorithm. We validate
our method in real hyperspectral data and show that the performance gains
obtained from the rejection fields are equivalent to an increase the dimension
of the training sets.
Comparative study of image registration techniques for bladder video-endoscopy
Authors: Achraf Ben-Hamadou, Charles Soussen, Walter Blondel, Christian Daul, Didier Wolf
Date: 2015-04-29 00:00:00
Tasks: Image Registration

Bladder cancer is widely spread in the world. Many adequate diagnosis
techniques exist. Video-endoscopy remains the standard clinical procedure for
visual exploration of the bladder internal surface. However, video-endoscopy
presents the limit that the imaged area for each image is about nearly 1cm2.
And, lesions are, typically, spread over several images. The aim of this
contribution is to assess the performance of two mosaicing algorithms leading
to the construction of panoramic maps (one unique image) of bladder walls. The
quantitative comparison study is performed on a set of real endoscopic exam
data and on simulated data relative to bladder phantom.
Visual Information Retrieval in Endoscopic Video Archives
Authors: Jennifer Roldan-Carlos, Mathias Lux, Xavier Giró-i-Nieto, Pia Muñoz, Nektarios Anagnostopoulos
Date: 2015-04-29 00:00:00
Tasks: Information Retrieval, Retrieval, Video Retrieval

In endoscopic procedures, surgeons work with live video streams from the
inside of their subjects. A main source for documentation of procedures are
still frames from the video, identified and taken during the surgery. However,
with growing demands and technical means, the streams are saved to storage
servers and the surgeons need to retrieve parts of the videos on demand. In
this submission we present a demo application allowing for video retrieval
based on visual features and late fusion, which allows surgeons to re-find
shots taken during the procedure.
ASTROMLSKIT: A New Statistical Machine Learning Toolkit: A Platform for Data Analytics in Astronomy
Authors: Snehanshu Saha, Surbhi Agrawal, Manikandan. R, Kakoli Bora, Swati Routh, Anand Narasimhamurthy
Date: 2015-04-29 00:00:00
Tasks: Astronomy, BIG-bench Machine Learning, General Classification

Astroinformatics is a new impact area in the world of astronomy, occasionally
called the final frontier, where several astrophysicists, statisticians and
computer scientists work together to tackle various data intensive astronomical
problems. Exponential growth in the data volume and increased complexity of the
data augments difficult questions to the existing challenges. Classical
problems in Astronomy are compounded by accumulation of astronomical volume of
complex data, rendering the task of classification and interpretation
incredibly laborious. The presence of noise in the data makes analysis and
interpretation even more arduous. Machine learning algorithms and data analytic
techniques provide the right platform for the challenges posed by these
problems. A diverse range of open problem like star-galaxy separation,
detection and classification of exoplanets, classification of supernovae is
discussed. The focus of the paper is the applicability and efficacy of various
machine learning algorithms like K Nearest Neighbor (KNN), random forest (RF),
decision tree (DT), Support Vector Machine (SVM), Na\"ive Bayes and Linear
Discriminant Analysis (LDA) in analysis and inference of the decision theoretic
problems in Astronomy. The machine learning algorithms, integrated into
ASTROMLSKIT, a toolkit developed in the course of the work, have been used to
analyze HabCat data and supernovae data. Accuracy has been found to be
appreciably good.
Intelligent Health Recommendation System for Computer Users
Authors: Qi Guo, Zixuan Wang, Ming Li, Hamid Aghajan
Date: 2015-04-29 00:00:00

The time people spend in front of computers has been increasing steadily due
to the role computers play in modern society. Individuals who sit in front of
computers for an extended period of time, specifically with improper postures
may incur various health issues. In this work, individuals' behaviors in front
of computers are studied using web cameras. By means of non-rigid face tracking
system, data are analyzed to determine the 3D head pose, blink rate and yawn
frequency of computer users. When combining these visual cues, a system of
intelligent personal assistants for computer users is proposed.
On the universal structure of human lexical semantics
Authors: Hyejin Youn, Logan Sutton, Eric Smith, Cristopher Moore, Jon F. Wilkins, Ian Maddieson, William Croft, Tanmoy Bhattacharya
Date: 2015-04-29 00:00:00

How universal is human conceptual structure? The way concepts are organized
in the human brain may reflect distinct features of cultural, historical, and
environmental background in addition to properties universal to human
cognition. Semantics, or meaning expressed through language, provides direct
access to the underlying conceptual structure, but meaning is notoriously
difficult to measure, let alone parameterize. Here we provide an empirical
measure of semantic proximity between concepts using cross-linguistic
dictionaries. Across languages carefully selected from a phylogenetically and
geographically stratified sample of genera, translations of words reveal cases
where a particular language uses a single polysemous word to express concepts
represented by distinct words in another. We use the frequency of polysemies
linking two concepts as a measure of their semantic proximity, and represent
the pattern of such linkages by a weighted network. This network is highly
uneven and fragmented: certain concepts are far more prone to polysemy than
others, and there emerge naturally interpretable clusters loosely connected to
each other. Statistical analysis shows such structural properties are
consistent across different language groups, largely independent of geography,
environment, and literacy. It is therefore possible to conclude the conceptual
structure connecting basic vocabulary studied is primarily due to universal
features of human cognition and language use.
Learning to Recognize Pedestrian Attribute
Authors: Yubin Deng, Ping Luo, Chen Change Loy, Xiaoou Tang
Date: 2015-01-05 00:00:00
Tasks: Attribute, Informativeness

Learning to recognize pedestrian attributes at far distance is a challenging
problem in visual surveillance since face and body close-shots are hardly
available; instead, only far-view image frames of pedestrian are given. In this
study, we present an alternative approach that exploits the context of
neighboring pedestrian images for improved attribute inference compared to the
conventional SVM-based method. In addition, we conduct extensive experiments to
evaluate the informativeness of background and foreground features for
attribute recognition. Experiments are based on our newly released pedestrian
attribute dataset, which is by far the largest and most diverse of its kind.
Dual Averaging on Compactly-Supported Distributions And Application to No-Regret Learning on a Continuum
Authors: Walid Krichene
Date: 2015-04-29 00:00:00

We consider an online learning problem on a continuum. A decision maker is
given a compact feasible set $S$, and is faced with the following sequential
problem: at iteration~$t$, the decision maker chooses a distribution $x^{(t)}
\in \Delta(S)$, then a loss function $\ell^{(t)} : S \to \mathbb{R}_+$ is
revealed, and the decision maker incurs expected loss $\langle \ell^{(t)},
x^{(t)} \rangle = \mathbb{E}_{s \sim x^{(t)}} \ell^{(t)}(s)$. We view the
problem as an online convex optimization problem on the space $\Delta(S)$ of
Lebesgue-continnuous distributions on $S$. We prove a general regret bound for
the Dual Averaging method on $L^2(S)$, then prove that dual averaging with
$\omega$-potentials (a class of strongly convex regularizers) achieves
sublinear regret when $S$ is uniformly fat (a condition weaker than convexity).
Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization
Authors: Yanfeng Sun, Junbin Gao, Xia Hong, Bamdev Mishra, Bao-Cai Yin
Date: 2015-04-07 00:00:00
Tasks: Clustering, Tensor Decomposition

Tensors or multiarray data are generalizations of matrices. Tensor clustering
has become a very important research topic due to the intrinsically rich
structures in real-world multiarray datasets. Subspace clustering based on
vectorizing multiarray data has been extensively researched. However,
vectorization of tensorial data does not exploit complete structure
information. In this paper, we propose a subspace clustering algorithm without
adopting any vectorization process. Our approach is based on a novel
heterogeneous Tucker decomposition model. In contrast to existing techniques,
we propose a new clustering algorithm that alternates between different modes
of the proposed heterogeneous tensor model. All but the last mode have
closed-form updates. Updating the last mode reduces to optimizing over the
so-called multinomial manifold, for which we investigate second order
Riemannian geometry and propose a trust-region algorithm. Numerical experiments
show that our proposed algorithm compete effectively with state-of-the-art
clustering algorithms that are based on tensor factorization.
Joint cross-domain classification and subspace learning for unsupervised adaptation
Authors: Basura Fernando, Tatiana Tommasi, Tinne Tuytelaars
Date: 2014-11-17 00:00:00
Tasks: Domain Adaptation, domain classification, General Classification

Domain adaptation aims at adapting the knowledge acquired on a source domain
to a new different but related target domain. Several approaches have
beenproposed for classification tasks in the unsupervised scenario, where no
labeled target data are available. Most of the attention has been dedicated to
searching a new domain-invariant representation, leaving the definition of the
prediction function to a second stage. Here we propose to learn both jointly.
Specifically we learn the source subspace that best matches the target subspace
while at the same time minimizing a regularized misclassification loss. We
provide an alternating optimization technique based on stochastic sub-gradient
descent to solve the learning problem and we demonstrate its performance on
several domain adaptation tasks.
Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation
Authors: Hongzhao Huang, Larry Heck, Heng Ji
Date: 2015-04-28 00:00:00
Tasks: Entity Disambiguation, Knowledge Graphs

Entity Disambiguation aims to link mentions of ambiguous entities to a
knowledge base (e.g., Wikipedia). Modeling topical coherence is crucial for
this task based on the assumption that information from the same semantic
context tends to belong to the same topic. This paper presents a novel deep
semantic relatedness model (DSRM) based on deep neural networks (DNN) and
semantic knowledge graphs (KGs) to measure entity semantic relatedness for
topical coherence modeling. The DSRM is directly trained on large-scale KGs and
it maps heterogeneous types of knowledge of an entity from KGs to numerical
feature vectors in a latent space such that the distance between two
semantically-related entities is minimized. Compared with the state-of-the-art
relatedness approach proposed by (Milne and Witten, 2008a), the DSRM obtains
19.4% and 24.5% reductions in entity disambiguation errors on two publicly
available datasets respectively.
Evaluation of Explore-Exploit Policies in Multi-result Ranking Systems
Authors: Dragomir Yankov, Pavel Berkhin, Lihong Li
Date: 2015-04-28 00:00:00
Tasks: News Recommendation, Thompson Sampling

We analyze the problem of using Explore-Exploit techniques to improve
precision in multi-result ranking systems such as web search, query
autocompletion and news recommendation. Adopting an exploration policy directly
online, without understanding its impact on the production system, may have
unwanted consequences - the system may sustain large losses, create user
dissatisfaction, or collect exploration data which does not help improve
ranking quality. An offline framework is thus necessary to let us decide what
policy and how we should apply in a production environment to ensure positive
outcome. Here, we describe such an offline framework.
  Using the framework, we study a popular exploration policy - Thompson
sampling. We show that there are different ways of implementing it in
multi-result ranking systems, each having different semantic interpretation and
leading to different results in terms of sustained click-through-rate (CTR)
loss and expected model improvement. In particular, we demonstrate that
Thompson sampling can act as an online learner optimizing CTR, which in some
cases can lead to an interesting outcome: lift in CTR during exploration. The
observation is important for production systems as it suggests that one can get
both valuable exploration data to improve ranking performance on the long run,
and at the same time increase CTR while exploration lasts.
Nearly Optimal Deterministic Algorithm for Sparse Walsh-Hadamard Transform
Authors: Mahdi Cheraghchi, Piotr Indyk
Date: 2015-04-28 00:00:00
Tasks: compressed sensing

For every fixed constant $\alpha > 0$, we design an algorithm for computing
the $k$-sparse Walsh-Hadamard transform of an $N$-dimensional vector $x \in
\mathbb{R}^N$ in time $k^{1+\alpha} (\log N)^{O(1)}$. Specifically, the
algorithm is given query access to $x$ and computes a $k$-sparse $\tilde{x} \in
\mathbb{R}^N$ satisfying $\|\tilde{x} - \hat{x}\|_1 \leq c \|\hat{x} -
H_k(\hat{x})\|_1$, for an absolute constant $c > 0$, where $\hat{x}$ is the
transform of $x$ and $H_k(\hat{x})$ is its best $k$-sparse approximation. Our
algorithm is fully deterministic and only uses non-adaptive queries to $x$
(i.e., all queries are determined and performed in parallel when the algorithm
starts).
  An important technical tool that we use is a construction of nearly optimal
and linear lossless condensers which is a careful instantiation of the GUV
condenser (Guruswami, Umans, Vadhan, JACM 2009). Moreover, we design a
deterministic and non-adaptive $\ell_1/\ell_1$ compressed sensing scheme based
on general lossless condensers that is equipped with a fast reconstruction
algorithm running in time $k^{1+\alpha} (\log N)^{O(1)}$ (for the GUV-based
condenser) and is of independent interest. Our scheme significantly simplifies
and improves an earlier expander-based construction due to Berinde, Gilbert,
Indyk, Karloff, Strauss (Allerton 2008).
  Our methods use linear lossless condensers in a black box fashion; therefore,
any future improvement on explicit constructions of such condensers would
immediately translate to improved parameters in our framework (potentially
leading to $k (\log N)^{O(1)}$ reconstruction time with a reduced exponent in
the poly-logarithmic factor, and eliminating the extra parameter $\alpha$).
  Finally, by allowing the algorithm to use randomness, while still using
non-adaptive queries, the running time of the algorithm can be improved to
$\tilde{O}(k \log^3 N)$.
A novel variational model for image registration using Gaussian curvature
Authors: Mazlinda Ibrahim, Ke Chen, Carlos Brito-Loeza
Date: 2015-04-28 00:00:00
Tasks: Image Registration, Math

Image registration is one important task in many image processing
applications. It aims to align two or more images so that useful information
can be extracted through comparison, combination or superposition. This is
achieved by constructing an optimal trans- formation which ensures that the
template image becomes similar to a given reference image. Although many models
exist, designing a model capable of modelling large and smooth deformation
field continues to pose a challenge. This paper proposes a novel variational
model for image registration using the Gaussian curvature as a regulariser. The
model is motivated by the surface restoration work in geometric processing
[Elsey and Esedoglu, Multiscale Model. Simul., (2009), pp. 1549-1573]. An
effective numerical solver is provided for the model using an augmented
Lagrangian method. Numerical experiments can show that the new model
outperforms three competing models based on, respectively, a linear curvature
[Fischer and Modersitzki, J. Math. Imaging Vis., (2003), pp. 81- 85], the mean
curvature [Chumchob, Chen and Brito, Multiscale Model. Simul., (2011), pp.
89-128] and the diffeomorphic demon model [Vercauteren at al., NeuroImage,
(2009), pp. 61-72] in terms of robustness and accuracy.
Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems
Authors: Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille
Date: 2015-04-28 00:00:00
Tasks: Attribute, General Classification, Recommendation Systems

We present a machine learning algorithm for building classifiers that are
comprised of a small number of disjunctions of conjunctions (or's of and's). An
example of a classifier of this form is as follows: If X satisfies (x1 = 'blue'
AND x3 = 'middle') OR (x1 = 'blue' AND x2 = '<15') OR (x1 = 'yellow'), then we
predict that Y=1, ELSE predict Y=0. An attribute-value pair is called a literal
and a conjunction of literals is called a pattern. Models of this form have the
advantage of being interpretable to human experts, since they produce a set of
conditions that concisely describe a specific class. We present two
probabilistic models for forming a pattern set, one with a Beta-Binomial prior,
and the other with Poisson priors. In both cases, there are prior parameters
that the user can set to encourage the model to have a desired size and shape,
to conform with a domain-specific definition of interpretability. We provide
two scalable MAP inference approaches: a pattern level search, which involves
association rule mining, and a literal level search. We show stronger priors
reduce computation. We apply the Bayesian Or's of And's (BOA) model to predict
user behavior with respect to in-vehicle context-aware personalized recommender
systems.