pwc-archive/papers-with-abstracts huggingface dataset:
Natural Language Processing in Biomedicine: A Unified System Architecture Overview
Authors: Son Doan, Mike Conway, Tu Minh Phuong, Lucila Ohno-Machado
Date: 2014-01-03 00:00:00

In modern electronic medical records (EMR) much of the clinically important
data - signs and symptoms, symptom severity, disease status, etc. - are not
provided in structured data fields, but rather are encoded in clinician
generated narrative text. Natural language processing (NLP) provides a means of
"unlocking" this important data source for applications in clinical decision
support, quality assurance, and public health. This chapter provides an
overview of representative NLP systems in biomedicine based on a unified
architectural view. A general architecture in an NLP system consists of two
main components: background knowledge that includes biomedical knowledge
resources and a framework that integrates NLP tools to process text. Systems
differ in both components, which we will review briefly. Additionally,
challenges facing current research efforts in biomedical NLP include the
paucity of large, publicly available annotated corpora, although initiatives
that facilitate data sharing, system evaluation, and collaborative work between
researchers in clinical NLP are starting to emerge.
Content Based Image Indexing and Retrieval
Authors: Avinash N Bhute, B. B. Meshram
Date: 2014-01-08 00:00:00
Tasks: Content-Based Image Retrieval, Edge Detection, Image Compression, Image Retrieval, Retrieval

In this paper, we present the efficient content based image retrieval systems
which employ the color, texture and shape information of images to facilitate
the retrieval process. For efficient feature extraction, we extract the color,
texture and shape feature of images automatically using edge detection which is
widely used in signal processing and image compression. For facilitated the
speedy retrieval we are implements the antipole-tree algorithm for indexing the
images.
Application of the Modified Fractal Signature Method for Terrain Classification from Synthetic Aperture Radar Images
Authors: A. Malamou, C. Pandis, P. Frangos, P. Stefaneas, A. Karakasiliotis, D. Kodokostas
Date: 2014-01-08 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

In this paper the Modified Fractal Signature method is applied to real
Synthetic Aperture Radar images provided to our research group by SET 163
Working Group on SAR radar techniques. This method uses the blanket technique
to provide useful information for SAR image classification. It is based on the
calculation of the volume of a blanket, corresponding to the image to be
classified, and then on the calculation of the corresponding Fractal Area curve
and Fractal Dimension curve of the image. The main idea concerning this
proposed technique is the fact that different terrain types encountered in SAR
images yield different values of Fractal Area curves and Fractal Dimension
curves, upon which classification of different types of terrain is possible. As
a result, a classification technique for five different terrain types, i.e.
urban, suburban, rural, mountain and sea, is presented in this paper.
Smart machines and the SP theory of intelligence
Authors: J. Gerard Wolff
Date: 2014-01-08 00:00:00
Tasks: Computational Efficiency, Management, Medical Diagnosis

These notes describe how the "SP theory of intelligence", and its embodiment
in the "SP machine", may help to realise cognitive computing, as described in
the book "Smart Machines". In the SP system, information compression and a
concept of "multiple alignment" are centre stage. The system is designed to
integrate such things as unsupervised learning, pattern recognition,
probabilistic reasoning, and more. It may help to overcome the problem of
variety in big data, it may serve in pattern recognition and in the
unsupervised learning of structure in data, and it may facilitate the
management and transmission of big data. There is potential, via information
compression, for substantial gains in computational efficiency, especially in
the use of energy. The SP system may help to realise data-centric computing,
perhaps via a development of Hebb's concept of a "cell assembly", or via the
use of light or DNA for the processing of information. It has potential in the
management of errors and uncertainty in data, in medical diagnosis, in
processing streams of data, and in promoting adaptability in robots.
Estimating the historical and future probabilities of large terrorist events
Authors: Aaron Clauset, Ryan Woodard
Date: 2012-09-01 00:00:00

Quantities with right-skewed distributions are ubiquitous in complex social
systems, including political conflict, economics and social networks, and these
systems sometimes produce extremely large events. For instance, the 9/11
terrorist events produced nearly 3000 fatalities, nearly six times more than
the next largest event. But, was this enormous loss of life statistically
unlikely given modern terrorism's historical record? Accurately estimating the
probability of such an event is complicated by the large fluctuations in the
empirical distribution's upper tail. We present a generic statistical algorithm
for making such estimates, which combines semi-parametric models of tail
behavior and a nonparametric bootstrap. Applied to a global database of
terrorist events, we estimate the worldwide historical probability of observing
at least one 9/11-sized or larger event since 1968 to be 11-35%. These results
are robust to conditioning on global variations in economic development,
domestic versus international events, the type of weapon used and a truncated
history that stops at 1998. We then use this procedure to make a data-driven
statistical forecast of at least one similar event over the next decade.
A New Continuous-Time Equality-Constrained Optimization Method to Avoid Singularity
Authors: Quan Quan, Kai-Yuan Cai
Date: 2012-09-24 00:00:00

In equality-constrained optimization, a standard regularity assumption is
often associated with feasible point methods, namely the gradients of
constraints are linearly independent. In practice, the regularity assumption
may be violated. To avoid such a singularity, we propose a new projection
matrix, based on which a feasible point method for the continuous-time,
equality-constrained optimization problem is developed. First, the equality
constraint is transformed into a continuous-time dynamical system with
solutions that always satisfy the equality constraint. Then, the singularity is
explained in detail and a new projection matrix is proposed to avoid
singularity. An update (or say a controller) is subsequently designed to
decrease the objective function along the solutions of the transformed system.
The invariance principle is applied to analyze the behavior of the solution. We
also propose a modified approach for addressing cases in which solutions do not
satisfy the equality constraint. Finally, the proposed optimization approaches
are applied to two examples to demonstrate its effectiveness.
Beyond One-Step-Ahead Forecasting: Evaluation of Alternative Multi-Step-Ahead Forecasting Models for Crude Oil Prices
Authors: Tao Xiong, Yukun Bao, Zhongyi Hu
Date: 2014-01-08 00:00:00
Tasks: Prediction

An accurate prediction of crude oil prices over long future horizons is
challenging and of great interest to governments, enterprises, and investors.
This paper proposes a revised hybrid model built upon empirical mode
decomposition (EMD) based on the feed-forward neural network (FNN) modeling
framework incorporating the slope-based method (SBM), which is capable of
capturing the complex dynamic of crude oil prices. Three commonly used
multi-step-ahead prediction strategies proposed in the literature, including
iterated strategy, direct strategy, and MIMO (multiple-input multiple-output)
strategy, are examined and compared, and practical considerations for the
selection of a prediction strategy for multi-step-ahead forecasting relating to
crude oil prices are identified. The weekly data from the WTI (West Texas
Intermediate) crude oil spot price are used to compare the performance of the
alternative models under the EMD-SBM-FNN modeling framework with selected
counterparts. The quantitative and comprehensive assessments are performed on
the basis of prediction accuracy and computational cost. The results obtained
in this study indicate that the proposed EMD-SBM-FNN model using the MIMO
strategy is the best in terms of prediction accuracy with accredited
computational load.
The Continuity of Images by Transmission Imaging Revisited
Authors: Zhitao Fan, Feng Guan, Chunlin Wu, Ming Yan
Date: 2014-01-08 00:00:00
Tasks: Astronomy, Medical Diagnosis, Object Reconstruction

Transmission imaging, as an important imaging technique widely used in
astronomy, medical diagnosis, and biology science, has been shown in [49] quite
different from reflection imaging used in our everyday life. Understanding the
structures of images (the prior information) is important for designing,
testing, and choosing image processing methods, and good image processing
methods are helpful for further uses of the image data, e.g., increasing the
accuracy of the object reconstruction methods in transmission imaging
applications. In reflection imaging, the images are usually modeled as
discontinuous functions and even piecewise constant functions. In transmission
imaging, it was shown very recently in [49] that almost all images are
continuous functions. However, the author in [49] considered only the case of
parallel beam geometry and used some too strong assumptions in the proof, which
exclude some common cases such as cylindrical objects. In this paper, we
consider more general beam geometries and simplify the assumptions by using
totally different techniques. In particular, we will prove that almost all
images in transmission imaging with both parallel and divergent beam geometries
(two most typical beam geometries) are continuous functions, under much weaker
assumptions than those in [49], which admit almost all practical cases.
Besides, taking into accounts our analysis, we compare two image processing
methods for Poisson noise (which is the most significant noise in transmission
imaging) removal. Numerical experiments will be provided to demonstrate our
analysis.
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball
Authors: Andrew Miller, Luke Bornn, Ryan Adams, Kirk Goldsberry
Date: 2014-01-05 00:00:00
Tasks: Dimensionality Reduction

We develop a machine learning approach to represent and analyze the
underlying spatial structure that governs shot selection among professional
basketball players in the NBA. Typically, NBA players are discussed and
compared in an heuristic, imprecise manner that relies on unmeasured intuitions
about player behavior. This makes it difficult to draw comparisons between
players and make accurate player specific predictions. Modeling shot attempt
data as a point process, we create a low dimensional representation of
offensive player types in the NBA. Using non-negative matrix factorization
(NMF), an unsupervised dimensionality reduction technique, we show that a
low-rank spatial decomposition summarizes the shooting habits of NBA players.
The spatial representations discovered by the algorithm correspond to intuitive
descriptions of NBA player types, and can be used to model other spatial
effects, such as shooting accuracy.
Key point selection and clustering of swimmer coordination through Sparse Fisher-EM
Authors: John Komar, Romain Hérault, Ludovic Seifert
Date: 2014-01-07 00:00:00
Tasks: Clustering

To answer the existence of optimal swimmer learning/teaching strategies, this
work introduces a two-level clustering in order to analyze temporal dynamics of
motor learning in breaststroke swimming. Each level have been performed through
Sparse Fisher-EM, a unsupervised framework which can be applied efficiently on
large and correlated datasets. The induced sparsity selects key points of the
coordination phase without any prior knowledge.
Design & Development of the Graphical User Interface for Sindhi Language
Authors: Imdad Ali Ismaili, Zeeshan Bhatti, Azhar Ali Shah
Date: 2014-01-07 00:00:00

This paper describes the design and implementation of a Unicode-based GUISL
(Graphical User Interface for Sindhi Language). The idea is to provide a
software platform to the people of Sindh as well as Sindhi diasporas living
across the globe to make use of computing for basic tasks such as editing,
composition, formatting, and printing of documents in Sindhi by using GUISL.
The implementation of the GUISL has been done in the Java technology to make
the system platform independent. The paper describes several design issues of
Sindhi GUI in the context of existing software tools and technologies and
explains how mapping and concatenation techniques have been employed to achieve
the cursive shape of Sindhi script.
Belief Revision in Structured Probabilistic Argumentation
Authors: Paulo Shakarian, Gerardo I. Simari, Marcelo A. Falappa
Date: 2014-01-07 00:00:00

In real-world applications, knowledge bases consisting of all the information
at hand for a specific domain, along with the current state of affairs, are
bound to contain contradictory data coming from different sources, as well as
data with varying degrees of uncertainty attached. Likewise, an important
aspect of the effort associated with maintaining knowledge bases is deciding
what information is no longer useful; pieces of information (such as
intelligence reports) may be outdated, may come from sources that have recently
been discovered to be of low quality, or abundant evidence may be available
that contradicts them. In this paper, we propose a probabilistic structured
argumentation framework that arises from the extension of Presumptive
Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue
that this formalism is capable of addressing the basic issues of handling
contradictory and uncertain data. Then, to address the last issue, we focus on
the study of non-prioritized belief revision operations over probabilistic
PreDeLP programs. We propose a set of rationality postulates -- based on
well-known ones developed for classical knowledge bases -- that characterize
how such operations should behave, and study a class of operators along with
theoretical relationships with the proposed postulates, including a
representation theorem stating the equivalence between this class and the class
of operators characterized by the postulates.
Cortical prediction markets
Authors: David Balduzzi
Date: 2014-01-07 00:00:00
Tasks: Prediction

We investigate cortical learning from the perspective of mechanism design.
First, we show that discretizing standard models of neurons and synaptic
plasticity leads to rational agents maximizing simple scoring rules. Second,
our main result is that the scoring rules are proper, implying that neurons
faithfully encode expected utilities in their synaptic weights and encode
high-scoring outcomes in their spikes. Third, with this foundation in hand, we
propose a biologically plausible mechanism whereby neurons backpropagate
incentives which allows them to optimize their usefulness to the rest of
cortex. Finally, experiments show that networks that backpropagate incentives
can learn simple tasks.
Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint
Authors: Ji Liu, Ryohei Fujimaki, Jieping Ye
Date: 2013-12-31 00:00:00
Tasks: Activity Recognition, feature selection

We consider forward-backward greedy algorithms for solving sparse feature
selection problems with general convex smooth functions. A state-of-the-art
greedy method, the Forward-Backward greedy algorithm (FoBa-obj) requires to
solve a large number of optimization problems, thus it is not scalable for
large-size problems. The FoBa-gdt algorithm, which uses the gradient
information for feature selection at each forward iteration, significantly
improves the efficiency of FoBa-obj. In this paper, we systematically analyze
the theoretical properties of both forward-backward greedy algorithms. Our main
contributions are: 1) We derive better theoretical bounds than existing
analyses regarding FoBa-obj for general smooth convex functions; 2) We show
that FoBa-gdt achieves the same theoretical performance as FoBa-obj under the
same condition: restricted strong convexity condition. Our new bounds are
consistent with the bounds of a special case (least squares) and fills a
previously existing theoretical gap for general convex smooth functions; 3) We
show that the restricted strong convexity condition is satisfied if the number
of independent samples is more than $\bar{k}\log d$ where $\bar{k}$ is the
sparsity number and $d$ is the dimension of the variable; 4) We apply FoBa-gdt
(with the conditional random field objective) to the sensor selection problem
for human indoor activity recognition and our results show that FoBa-gdt
outperforms other methods (including the ones based on forward greedy selection
and L1-regularization).
Insights from the Wikipedia Contest (IEEE Contest for Data Mining 2011)
Authors: Kalpit V Desai, Roopesh Ranjan
Date: 2014-01-07 00:00:00

The Wikimedia Foundation has recently observed that newly joining editors on
Wikipedia are increasingly failing to integrate into the Wikipedia editors'
community, i.e. the community is becoming increasingly harder to penetrate. To
sustain healthy growth of the community, the Wikimedia Foundation aims to
quantitatively understand the factors that determine the editing behavior, and
explain why most new editors become inactive soon after joining. As a step
towards this broader goal, the Wikimedia foundation sponsored the ICDM (IEEE
International Conference for Data Mining) contest for the year 2011.
  The objective for the participants was to develop models to predict the
number of edits that an editor will make in future five months based on the
editing history of the editor. Here we describe the approach we followed for
developing predictive models towards this goal, the results that we obtained
and the modeling insights that we gained from this exercise. In addition,
towards the broader goal of Wikimedia Foundation, we also summarize the factors
that emerged during our model building exercise as powerful predictors of
future editing activity.
Time series forecasting using neural networks
Authors: Bogdan Oancea, ŞTefan Cristian Ciucu
Date: 2014-01-07 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis, Time Series Forecasting, Time Series Prediction

Recent studies have shown the classification and prediction power of the
Neural Networks. It has been demonstrated that a NN can approximate any
continuous function. Neural networks have been successfully used for
forecasting of financial data series. The classical methods used for time
series prediction like Box-Jenkins or ARIMA assumes that there is a linear
relationship between inputs and outputs. Neural Networks have the advantage
that can approximate nonlinear functions. In this paper we compared the
performances of different feed forward and recurrent neural networks and
training algorithms for predicting the exchange rate EUR/RON and USD/RON. We
used data series with daily exchange rates starting from 2005 until 2013.
A New Approach To Two-View Motion Segmentation Using Global Dimension Minimization
Authors: Bryan Poling, Gilad Lerman
Date: 2013-04-10 00:00:00
Tasks: Clustering, Motion Segmentation, Outlier Detection

We present a new approach to rigid-body motion segmentation from two views.
We use a previously developed nonlinear embedding of two-view point
correspondences into a 9-dimensional space and identify the different motions
by segmenting lower-dimensional subspaces. In order to overcome nonuniform
distributions along the subspaces, whose dimensions are unknown, we suggest the
novel concept of global dimension and its minimization for clustering subspaces
with some theoretical motivation. We propose a fast projected gradient
algorithm for minimizing global dimension and thus segmenting motions from
2-views. We develop an outlier detection framework around the proposed method,
and we present state-of-the-art results on outlier-free and outlier-corrupted
two-view data for segmenting motion.
Quantile Regression for Large-scale Applications
Authors: Jiyan Yang, Xiangrui Meng, Michael W. Mahoney
Date: 2013-05-01 00:00:00
Tasks: quantile regression, regression

Quantile regression is a method to estimate the quantiles of the conditional
distribution of a response variable, and as such it permits a much more
accurate portrayal of the relationship between the response variable and
observed covariates than methods such as Least-squares or Least Absolute
Deviations regression. It can be expressed as a linear program, and, with
appropriate preprocessing, interior-point methods can be used to find a
solution for moderately large problems. Dealing with very large problems,
\emph(e.g.), involving data up to and beyond the terabyte regime, remains a
challenge. Here, we present a randomized algorithm that runs in nearly linear
time in the size of the input and that, with constant probability, computes a
$(1+\epsilon)$ approximate solution to an arbitrary quantile regression
problem. As a key step, our algorithm computes a low-distortion
subspace-preserving embedding with respect to the loss function of quantile
regression. Our empirical evaluation illustrates that our algorithm is
competitive with the best previous work on small to medium-sized problems, and
that in addition it can be implemented in MapReduce-like environments and
applied to terabyte-sized problems.
Bangla Text Recognition from Video Sequence: A New Focus
Authors: Souvik Bhowmick, Purnendu Banerjee
Date: 2014-01-06 00:00:00
Tasks: Binarization, Optical Character Recognition (OCR)

Extraction and recognition of Bangla text from video frame images is
challenging due to complex color background, low-resolution etc. In this paper,
we propose an algorithm for extraction and recognition of Bangla text form such
video frames with complex background. Here, a two-step approach has been
proposed. First, the text line is segmented into words using information based
on line contours. First order gradient value of the text blocks are used to
find the word gap. Next, a local binarization technique is applied on each word
and text line is reconstructed using those words. Secondly, this binarized text
block is sent to OCR for recognition purpose.
Approximated Infomax Early Stopping: Revisiting Gaussian RBMs on Natural Images
Authors: Taichi Kiwaki, Takaki Makino, Kazuyuki Aihara
Date: 2013-12-19 00:00:00
Tasks: Attribute

We pursue an early stopping technique that helps Gaussian Restricted
Boltzmann Machines (GRBMs) to gain good natural image representations in terms
of overcompleteness and data fitting. GRBMs are widely considered as an
unsuitable model for natural images because they gain non-overcomplete
representations which include uniform filters that do not represent useful
image features. We have recently found that GRBMs once gain and subsequently
lose useful filters during their training, contrary to this common perspective.
We attribute this phenomenon to a tradeoff between overcompleteness of GRBM
representations and data fitting. To gain GRBM representations that are
overcomplete and fit data well, we propose a measure for GRBM representation
quality, approximated mutual information, and an early stopping technique based
on this measure. The proposed method boosts performance of classifiers trained
on GRBM representations.