pwc-archive/papers-with-abstracts huggingface dataset:
Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series
Authors: Zitao Liu, Milos Hauskrecht
Date: 2013-11-27 00:00:00
Tasks: Time Series, Time Series Analysis

Linear Dynamical System (LDS) is an elegant mathematical framework for
modeling and learning multivariate time series. However, in general, it is
difficult to set the dimension of its hidden state space. A small number of
hidden states may not be able to model the complexities of a time series, while
a large number of hidden states can lead to overfitting. In this paper, we
study methods that impose an $\ell_1$ regularization on the transition matrix
of an LDS model to alleviate the problem of choosing the optimal number of
hidden states. We incorporate a generalized gradient descent method into the
Maximum a Posteriori (MAP) framework and use Expectation Maximization (EM) to
iteratively achieve sparsity on the transition matrix of an LDS model. We show
that our Sparse Linear Dynamical System (SLDS) improves the predictive
performance when compared to ordinary LDS on a multivariate clinical time
series dataset.
Feature Extraction of Human Lip Prints
Authors: Samir Kumar Bandyopadhyay, S Arunkumar, Saptarshi Bhattacharjee
Date: 2013-12-03 00:00:00

Methods have been used for identification of human by recognizing lip prints.
Human lips have a number of elevation and depressions features called lip
prints and examination of lip prints is referred to as cheiloscopy. Lip prints
of each human being are unique in nature like many others features of human. In
this paper lip print is first smoothened using a Gaussian Filter. Next Sobel
Edge Detector and Canny Edge Detector are used to detect the vertical and
horizontal groove pattern in the lip. This method of identification will be
useful both in criminal forensics and personal identification. It is our
assumption that study of lip prints and their types are well connected to play
a song in a better way that are well accepted to people who loves to hear
songs.
Combining Simulated Annealing and Monte Carlo Tree Search for Expression Simplification
Authors: Ben Ruijl, Jos Vermaseren, Aske Plaat, Jaap van den Herik
Date: 2013-12-03 00:00:00

In many applications of computer algebra large expressions must be simplified
to make repeated numerical evaluations tractable. Previous works presented
heuristically guided improvements, e.g., for Horner schemes. The remaining
expression is then further reduced by common subexpression elimination. A
recent approach successfully applied a relatively new algorithm, Monte Carlo
Tree Search (MCTS) with UCT as the selection criterion, to find better variable
orderings. Yet, this approach is fit for further improvements since it is
sensitive to the so-called exploration-exploitation constant $C_p$ and the
number of tree updates $N$. In this paper we propose a new selection criterion
called Simulated Annealing UCT (SA-UCT) that has a dynamic
exploration-exploitation parameter, which decreases with the iteration number
$i$ and thus reduces the importance of exploration over time. First, we provide
an intuitive explanation in terms of the exploration-exploitation behavior of
the algorithm. Then, we test our algorithm on three large expressions of
different origins. We observe that SA-UCT widens the interval of good initial
values $C_p$ where best results are achieved. The improvement is large (more
than a tenfold) and facilitates the selection of an appropriate $C_p$.
Medical Aid for Automatic Detection of Malaria
Authors: Pramit Ghosh, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu
Date: 2013-12-03 00:00:00
Tasks: Morphological Analysis

The analysis and counting of blood cells in a microscope image can provide
useful information concerning to the health of a person. In particular,
morphological analysis of red blood cells deformations can effectively detect
important disease like malaria. Blood images, obtained by the microscope, which
is coupled with a digital camera, are analyzed by the computer for diagnosis or
can be transmitted easily to clinical centers than liquid blood samples.
Automatic analysis system for the presence of Plasmodium in microscopic image
of blood can greatly help pathologists and doctors that typically inspect blood
films manually. Unfortunately, the analysis made by human experts is not rapid
and not yet standardized due to the operators capabilities and tiredness. The
paper shows how effectively and accurately it is possible to identify the
Plasmodium in the blood film. In particular, the paper presents how to enhance
the microscopic image and filter out the unnecessary segments followed by the
threshold based segmentation and recognize the presence of Plasmodium. The
proposed system can be deployed in the remote area as a supporting aid for
telemedicine technology and only basic training is sufficient to operate it.
This system achieved more than 98 percentage accuracy for the samples collected
to test this system.
Automatic White Blood Cell Measuring Aid for Medical Diagnosis
Authors: Pramit Ghosh, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu
Date: 2013-12-03 00:00:00
Tasks: Medical Diagnosis

Blood related invasive pathological investigations play a major role in
diagnosis of diseases. But in India and other third world countries there are
no enough pathological infrastructures for medical diagnosis. Moreover, most of
the remote places of those countries have neither pathologists nor physicians.
Telemedicine partially solves the lack of physicians. But the pathological
investigation infrastructure can not be integrated with the telemedicine
technology. The objective of this work is to automate the blood related
pathological investigation process. Detection of different white blood cells
has been automated in this work. This system can be deployed in the remote area
as a supporting aid for telemedicine technology and only high school education
is sufficient to operate it. The proposed system achieved 97.33 percent
accuracy for the samples collected to test this system.
Template-Based Active Contours
Authors: Jayanth Krishna Mogali, Adithya Kumar Pediredla, Chandra Sekhar Seelamantula
Date: 2013-12-03 00:00:00
Tasks: Image Segmentation, Semantic Segmentation, Translation

We develop a generalized active contour formalism for image segmentation
based on shape templates. The shape template is subjected to a restricted
affine transformation (RAT) in order to segment the object of interest. RAT
allows for translation, rotation, and scaling, which give a total of five
degrees of freedom. The proposed active contour comprises an inner and outer
contour pair, which are closed and concentric. The active contour energy is a
contrast function defined based on the intensities of pixels that lie inside
the inner contour and those that lie in the annulus between the inner and outer
contours. We show that the contrast energy functional is optimal under certain
conditions. The optimal RAT parameters are computed by maximizing the contrast
function using a gradient descent optimizer. We show that the calculations are
made efficient through use of Green's theorem. The proposed formalism is
capable of handling a variety of shapes because for a chosen template,
optimization is carried with respect to the RAT parameters only. The proposed
formalism is validated on multiple images to show robustness to Gaussian and
Poisson noise, to initialization, and to partial loss of structure in the
object to be segmented.
A semi-automatic semantic method for mapping SNOMED CT concepts to VCM Icons
Authors: Jean-Baptiste Lamy, Rosy Tsopra, Alain Venot, Catherine Duclos
Date: 2013-12-03 00:00:00

VCM (Visualization of Concept in Medicine) is an iconic language for
representing key medical concepts by icons. However, the use of this language
with reference terminologies, such as SNOMED CT, will require the mapping of
its icons to the terms of these terminologies. Here, we present and evaluate a
semi-automatic semantic method for the mapping of SNOMED CT concepts to VCM
icons. Both SNOMED CT and VCM are compositional in nature; SNOMED CT is
expressed in description logic and VCM semantics are formalized in an OWL
ontology. The proposed method involves the manual mapping of a limited number
of underlying concepts from the VCM ontology, followed by automatic generation
of the rest of the mapping. We applied this method to the clinical findings of
the SNOMED CT CORE subset, and 100 randomly-selected mappings were evaluated by
three experts. The results obtained were promising, with 82 of the SNOMED CT
concepts correctly linked to VCM icons according to the experts. Most of the
errors were easy to fix.
A generic system for critiquing physicians' prescriptions: usability, satisfaction and lessons learnt
Authors: Jean-Baptiste Lamy, Vahid Ebrahiminia, Brigitte Seroussi, Jacques Bouaud, Christian Simon, Madeleine Favre, Hector Falcoff, Alain Venot
Date: 2013-12-03 00:00:00

Clinical decision support systems have been developed to help physicians to
take clinical guidelines into account during consultations. The ASTI critiquing
module is one such systems; it provides the physician with automatic criticisms
when a drug prescription does not follow the guidelines. It was initially
developed for hypertension and type 2 diabetes, but is designed to be generic
enough for application to all chronic diseases. We present here the results of
usability and satisfaction evaluations for the ASTI critiquing module, obtained
with GPs for a newly implemented guideline concerning dyslipaemia, and we
discuss the lessons learnt and the difficulties encountered when building a
generic DSS for critiquing physicians' prescriptions.
Use of the C4.5 machine learning algorithm to test a clinical guideline-based decision support system
Authors: Jean-Baptiste Lamy, Anis Ellini, Vahid Ebrahiminia, Jean-Daniel Zucker, Hector Falcoff, Alain Venot
Date: 2013-12-03 00:00:00
Tasks: BIG-bench Machine Learning

Well-designed medical decision support system (DSS) have been shown to
improve health care quality. However, before they can be used in real clinical
situations, these systems must be extensively tested, to ensure that they
conform to the clinical guidelines (CG) on which they are based. Existing
methods cannot be used for the systematic testing of all possible test cases.
We describe here a new exhaustive dynamic verification method. In this method,
the DSS is considered to be a black box, and the Quinlan C4.5 algorithm is used
to build a decision tree from an exhaustive set of DSS input vectors and
outputs. This method was successfully used for the testing of a medical DSS
relating to chronic diseases: the ASTI critiquing module for type 2 diabetes.
Phase Transitions in Community Detection: A Solvable Toy Model
Authors: Greg Ver Steeg, Cristopher Moore, Aram Galstyan, Armen E. Allahverdyan
Date: 2013-12-02 00:00:00
Tasks: Community Detection

Recently, it was shown that there is a phase transition in the community
detection problem. This transition was first computed using the cavity method,
and has been proved rigorously in the case of $q=2$ groups. However, analytic
calculations using the cavity method are challenging since they require us to
understand probability distributions of messages. We study analogous
transitions in so-called "zero-temperature inference" model, where this
distribution is supported only on the most-likely messages. Furthermore,
whenever several messages are equally likely, we break the tie by choosing
among them with equal probability. While the resulting analysis does not give
the correct values of the thresholds, it does reproduce some of the qualitative
features of the system. It predicts a first-order detectability transition
whenever $q > 2$, while the finite-temperature cavity method shows that this is
the case only when $q > 4$. It also has a regime analogous to the "hard but
detectable" phase, where the community structure can be partially recovered,
but only when the initial messages are sufficiently accurate. Finally, we study
a semisupervised setting where we are given the correct labels for a fraction
$\rho$ of the nodes. For $q > 2$, we find a regime where the accuracy jumps
discontinuously at a critical value of $\rho$.
SpeedMachines: Anytime Structured Prediction
Authors: Alexander Grubb, Daniel Munoz, J. Andrew Bagnell, Martial Hebert
Date: 2013-12-02 00:00:00
Tasks: General Classification, Prediction, Scene Understanding, Structured Prediction

Structured prediction plays a central role in machine learning applications
from computational biology to computer vision. These models require
significantly more computation than unstructured models, and, in many
applications, algorithms may need to make predictions within a computational
budget or in an anytime fashion. In this work we propose an anytime technique
for learning structured prediction that, at training time, incorporates both
structural elements and feature computation trade-offs that affect test-time
inference. We apply our technique to the challenging problem of scene
understanding in computer vision and demonstrate efficient and anytime
predictions that gradually improve towards state-of-the-art classification
performance as the allotted time increases.
A Junction Tree Framework for Undirected Graphical Model Selection
Authors: Divyanshu Vats, Robert Nowak
Date: 2013-04-17 00:00:00
Tasks: Model Selection

An undirected graphical model is a joint probability distribution defined on
an undirected graph G*, where the vertices in the graph index a collection of
random variables and the edges encode conditional independence relationships
among random variables. The undirected graphical model selection (UGMS) problem
is to estimate the graph G* given observations drawn from the undirected
graphical model. This paper proposes a framework for decomposing the UGMS
problem into multiple subproblems over clusters and subsets of the separators
in a junction tree. The junction tree is constructed using a graph that
contains a superset of the edges in G*. We highlight three main properties of
using junction trees for UGMS. First, different regularization parameters or
different UGMS algorithms can be used to learn different parts of the graph.
This is possible since the subproblems we identify can be solved independently
of each other. Second, under certain conditions, a junction tree based UGMS
algorithm can produce consistent results with fewer observations than the usual
requirements of existing algorithms. Third, both our theoretical and
experimental results show that the junction tree framework does a significantly
better job at finding the weakest edges in a graph than existing methods. This
property is a consequence of both the first and second properties. Finally, we
note that our framework is independent of the choice of the UGMS algorithm and
can be used as a wrapper around standard UGMS algorithms for more accurate
graph estimation.
Families of Parsimonious Finite Mixtures of Regression Models
Authors: Utkarsh J. Dang, Paul D. McNicholas
Date: 2013-12-02 00:00:00
Tasks: parameter estimation, regression

Finite mixtures of regression models offer a flexible framework for
investigating heterogeneity in data with functional dependencies. These models
can be conveniently used for unsupervised learning on data with clear
regression relationships. We extend such models by imposing an
eigen-decomposition on the multivariate error covariance matrix. By
constraining parts of this decomposition, we obtain families of parsimonious
mixtures of regressions and mixtures of regressions with concomitant variables.
These families of models account for correlations between multiple responses.
An expectation-maximization algorithm is presented for parameter estimation and
performance is illustrated on simulated and real data.
High-Dimensional Screening Using Multiple Grouping of Variables
Authors: Divyanshu Vats
Date: 2012-08-09 00:00:00
Tasks: Variable Selection, Vocal Bursts Intensity Prediction

Screening is the problem of finding a superset of the set of non-zero entries
in an unknown p-dimensional vector \beta* given n noisy observations.
Naturally, we want this superset to be as small as possible. We propose a novel
framework for screening, which we refer to as Multiple Grouping (MuG), that
groups variables, performs variable selection over the groups, and repeats this
process multiple number of times to estimate a sequence of sets that contains
the non-zero entries in \beta*. Screening is done by taking an intersection of
all these estimated sets. The MuG framework can be used in conjunction with any
group based variable selection algorithm. In the high-dimensional setting,
where p >> n, we show that when MuG is used with the group Lasso estimator,
screening can be consistently performed without using any tuning parameter. Our
numerical simulations clearly show the merits of using the MuG framework in
practice.
Bidirectional Recursive Neural Networks for Token-Level Labeling with Structure
Authors: Ozan İrsoy, Claire Cardie
Date: 2013-12-02 00:00:00
Tasks: Sentence

Recently, deep architectures, such as recurrent and recursive neural networks
have been successfully applied to various natural language processing tasks.
Inspired by bidirectional recurrent neural networks which use representations
that summarize the past and future around an instance, we propose a novel
architecture that aims to capture the structural information around an input,
and use it to label instances. We apply our method to the task of opinion
expression extraction, where we employ the binary parse tree of a sentence as
the structure, and word vector representations as the initial representation of
a single token. We conduct preliminary experiments to investigate its
performance and compare it to the sequential approach.
Precise Semidefinite Programming Formulation of Atomic Norm Minimization for Recovering d-Dimensional ($d\geq 2$) Off-the-Grid Frequencies
Authors: Weiyu Xu, Jian-Feng Cai, Kumar Vijay Mishra, Myung Cho, Anton Kruger
Date: 2013-12-02 00:00:00
Tasks: compressed sensing

Recent research in off-the-grid compressed sensing (CS) has demonstrated
that, under certain conditions, one can successfully recover a spectrally
sparse signal from a few time-domain samples even though the dictionary is
continuous. In particular, atomic norm minimization was proposed in
\cite{tang2012csotg} to recover $1$-dimensional spectrally sparse signal.
However, in spite of existing research efforts \cite{chi2013compressive}, it
was still an open problem how to formulate an equivalent positive semidefinite
program for atomic norm minimization in recovering signals with $d$-dimensional
($d\geq 2$) off-the-grid frequencies. In this paper, we settle this problem by
proposing equivalent semidefinite programming formulations of atomic norm
minimization to recover signals with $d$-dimensional ($d\geq 2$) off-the-grid
frequencies.
Practical Collapsed Stochastic Variational Inference for the HDP
Authors: Arnim Bleier
Date: 2013-12-02 00:00:00
Tasks: Variational Inference

Recent advances have made it feasible to apply the stochastic variational
paradigm to a collapsed representation of latent Dirichlet allocation (LDA).
While the stochastic variational paradigm has successfully been applied to an
uncollapsed representation of the hierarchical Dirichlet process (HDP), no
attempts to apply this type of inference in a collapsed setting of
non-parametric topic modeling have been put forward so far. In this paper we
explore such a collapsed stochastic variational Bayes inference for the HDP.
The proposed online algorithm is easy to implement and accounts for the
inference of hyper-parameters. First experiments show a promising improvement
in predictive performance.
Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles
Authors: Ali Shojaie, Alexandra Jauhiainen, Michael Kallitsis, George Michailidis
Date: 2013-12-02 00:00:00

Reconstructing transcriptional regulatory networks is an important task in
functional genomics. Data obtained from experiments that perturb genes by
knockouts or RNA interference contain useful information for addressing this
reconstruction problem. However, such data can be limited in size and/or are
expensive to acquire. On the other hand, observational data of the organism in
steady state (e.g. wild-type) are more readily available, but their
informational content is inadequate for the task at hand. We develop a
computational approach to appropriately utilize both data sources for
estimating a regulatory network. The proposed approach is based on a three-step
algorithm to estimate the underlying directed but cyclic network, that uses as
input both perturbation screens and steady state gene expression data. In the
first step, the algorithm determines causal orderings of the genes that are
consistent with the perturbation data, by combining an exhaustive search method
with a fast heuristic that in turn couples a Monte Carlo technique with a fast
search algorithm. In the second step, for each obtained causal ordering, a
regulatory network is estimated using a penalized likelihood based method,
while in the third step a consensus network is constructed from the highest
scored ones. Extensive computational experiments show that the algorithm
performs well in reconstructing the underlying network and clearly outperforms
competing approaches that rely only on a single data source. Further, it is
established that the algorithm produces a consistent estimate of the regulatory
network.
Reduced egomotion estimation drift using omnidirectional views
Authors: Yalin Bastanlar
Date: 2013-07-26 00:00:00

Estimation of camera motion from a given image sequence becomes degraded as
the length of the sequence increases. In this letter, this phenomenon is
demonstrated and an approach to increase the estimation accuracy is proposed.
The proposed method uses an omnidirectional camera in addition to the
perspective one and takes advantage of its enlarged view by exploiting the
correspondences between the omnidirectional and perspective images. Simulated
and real image experiments show that the proposed approach improves the
estimation accuracy.
Sparse arrays of signatures for online character recognition
Authors: Benjamin Graham
Date: 2013-08-01 00:00:00

In mathematics the signature of a path is a collection of iterated integrals,
commonly used for solving differential equations. We show that the path
signature, used as a set of features for consumption by a convolutional neural
network (CNN), improves the accuracy of online character recognition---that is
the task of reading characters represented as a collection of paths. Using
datasets of letters, numbers, Assamese and Chinese characters, we show that the
first, second, and even the third iterated integrals contain useful information
for consumption by a CNN.
  On the CASIA-OLHWDB1.1 3755 Chinese character dataset, our approach gave a
test error of 3.58%, compared with 5.61% for a traditional CNN [Ciresan et
al.]. A CNN trained on the CASIA-OLHWDB1.0-1.2 datasets won the ICDAR2013
Online Isolated Chinese Character recognition competition.
  Computationally, we have developed a sparse CNN implementation that make it
practical to train CNNs with many layers of max-pooling. Extending the MNIST
dataset by translations, our sparse CNN gets a test error of 0.31%.