pwc-archive/papers-with-abstracts huggingface dataset:
Offline Handwritten MODI Character Recognition Using HU, Zernike Moments and Zoning
Authors: Sadanand A. Kulkarni, Prashant L. Borde, Ramesh R. Manza, Pravin L. Yannawar
Date: 2014-06-24 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

HOCR is abbreviated as Handwritten Optical Character Recognition. HOCR is a
process of recognition of different handwritten characters from a digital image
of documents. Handwritten automatic character recognition has attracted many
researchers all over the world to contribute handwritten character recognition
domain. Shape identification and feature extraction is very important part of
any character recognition system and success of method is highly dependent on
selection of features. However feature extraction is the most important step in
defining the shape of the character as precisely and as uniquely as possible.
This is indeed the most important step and complex task as well and achieved
success by using invariance property, irrespective of position and orientation.
Zernike moments describes shape, identify rotation invariant due to its
Orthogonality property. MODI is an ancient script of India had cursive and
complex representation of characters. The work described in this paper presents
efficiency of Zernike moments over Hu 7 moment with zoning for automatic
recognition of handwritten MODI characters. Offline approach is used in this
paper because MODI Script was very popular and widely used for writing purpose
till 19th century before Devanagari was officially adopted.
Recognition of Handwritten MODI Numerals using Hu and Zernike features
Authors: Sadanand A. Kulkarni, Prashant L. Borde, Ramesh R. Manza, Pravin L. Yannawar
Date: 2014-04-04 00:00:00
Tasks: Position

Handwritten automatic character recognition has attracted many researchers
all over the world to contribute automatic character recognition domain. Shape
identification and feature extraction is very important part of any character
recognition system and success of method is highly dependent on selection of
features. However feature extraction is the most important step in defining the
shape of the character as precisely and as uniquely as possible. This is indeed
the most important step and complex task as well and achieved success by using
invariance property, irrespective of position and orientation. Zernike moments
describes shape, identify rotation invariant due to its Orthogonality property.
MODI is an ancient script of India had cursive and complex representation of
characters. The work described in this paper presents efficiency of Zernike
moments over Hus moment for automatic recognition of handwritten MODI numerals.
The probatilistic Quantifier Fuzzification Mechanism FA: A theoretical analysis
Authors: Felix Diaz-Hermida, Alberto Bugarin, David E. Losada
Date: 2014-10-27 00:00:00

The main goal of this work is to analyze the behaviour of the FA quantifier
fuzzification mechanism. As we prove in the paper, this model has a very solid
theorethical behaviour, superior to most of the models defined in the
literature. Moreover, we show that the underlying probabilistic interpretation
has very interesting consequences.
A method for context-based adaptive QRS clustering in real-time
Authors: Daniel Castro, Paulo Félix, Jesús Presedo
Date: 2014-10-27 00:00:00
Tasks: Clustering, Dynamic Time Warping

Continuous follow-up of heart condition through long-term electrocardiogram
monitoring is an invaluable tool for diagnosing some cardiac arrhythmias. In
such context, providing tools for fast locating alterations of normal
conduction patterns is mandatory and still remains an open issue. This work
presents a real-time method for adaptive clustering QRS complexes from
multilead ECG signals that provides the set of QRS morphologies that appear
during an ECG recording. The method processes the QRS complexes sequentially,
grouping them into a dynamic set of clusters based on the information content
of the temporal context. The clusters are represented by templates which evolve
over time and adapt to the QRS morphology changes. Rules to create, merge and
remove clusters are defined along with techniques for noise detection in order
to avoid their proliferation. To cope with beat misalignment, Derivative
Dynamic Time Warping is used. The proposed method has been validated against
the MIT-BIH Arrhythmia Database and the AHA ECG Database showing a global
purity of 98.56% and 99.56%, respectively. Results show that our proposal not
only provides better results than previous offline solutions but also fulfills
real-time requirements.
Predicting Parameters in Deep Learning
Authors: Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas
Date: 2013-06-03 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Deep Learning

We demonstrate that there is significant redundancy in the parameterization
of several deep learning models. Given only a few weight values for each
feature it is possible to accurately predict the remaining values. Moreover, we
show that not only can the parameter values be predicted, but many of them need
not be learned at all. We train several different architectures by learning
only a small number of weights and predicting the rest. In the best case we are
able to predict more than 95% of the weights of a network without any drop in
accuracy.
Analysis of Named Entity Recognition and Linking for Tweets
Authors: Leon Derczynski, Diana Maynard, Giuseppe Rizzo, Marieke van Erp, Genevieve Gorrell, Raphaël Troncy, Johann Petrak, Kalina Bontcheva
Date: 2014-10-27 00:00:00
Tasks: Entity Disambiguation, Language Identification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging

Applying natural language processing for mining and intelligent information
access to tweets (a form of microblog) is a challenging, emerging research
area. Unlike carefully authored news text and other longer content, tweets pose
a number of new challenges, due to their short, noisy, context-dependent, and
dynamic nature. Information extraction from tweets is typically performed in a
pipeline, comprising consecutive stages of language identification,
tokenisation, part-of-speech tagging, named entity recognition and entity
disambiguation (e.g. with respect to DBpedia). In this work, we describe a new
Twitter entity disambiguation dataset, and conduct an empirical analysis of
named entity recognition and disambiguation, investigating how robust a number
of state-of-the-art systems are on such noisy texts, what the main sources of
error are, and which problems should be further investigated to improve the
state of the art.
Directional Bilateral Filters
Authors: Manasij Venkatesh, Chandra Sekhar Seelamantula
Date: 2014-10-27 00:00:00
Tasks: Denoising

We propose a bilateral filter with a locally controlled domain kernel for
directional edge-preserving smoothing. Traditional bilateral filters use a
range kernel, which is responsible for edge preservation, and a fixed domain
kernel that performs smoothing. Our intuition is that orientation and
anisotropy of image structures should be incorporated into the domain kernel
while smoothing. For this purpose, we employ an oriented Gaussian domain kernel
locally controlled by a structure tensor. The oriented domain kernel combined
with a range kernel forms the directional bilateral filter. The two kernels
assist each other in effectively suppressing the influence of the outliers
while smoothing. To find the optimal parameters of the directional bilateral
filter, we propose the use of Stein's unbiased risk estimate (SURE). We test
the capabilities of the kernels separately as well as together, first on
synthetic images, and then on real endoscopic images. The directional bilateral
filter has better denoising performance than the Gaussian bilateral filter at
various noise levels in terms of peak signal-to-noise ratio (PSNR).
Ordered {AND, OR}-Decomposition and Binary-Decision Diagram
Authors: Yong Lai, Dayou Liu
Date: 2012-08-14 00:00:00

In the context of knowledge compilation (KC), we study the effect of
augmenting Ordered Binary Decision Diagrams (OBDD) with two kinds of
decomposition nodes, i.e., AND-vertices and OR-vertices which denote
conjunctive and disjunctive decomposition of propositional knowledge bases,
respectively. The resulting knowledge compilation language is called Ordered
{AND, OR}-decomposition and binary-Decision Diagram (OAODD). Roughly speaking,
several previous languages can be seen as special types of OAODD, including
OBDD, AND/OR Binary Decision Diagram (AOBDD), OBDD with implied Literals
(OBDD-L), Multi-Level Decomposition Diagrams (MLDD). On the one hand, we
propose some families of algorithms which can convert some fragments of OAODD
into others; on the other hand, we present a rich set of polynomial-time
algorithms that perform logical operations. According to these algorithms, as
well as theoretical analysis, we characterize the space efficiency and
tractability of OAODD and its some fragments with respect to the evaluating
criteria in the KC map. Finally, we present a compilation algorithm which can
convert formulas in negative normal form into OAODD.
Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters
Authors: Ozan Sener, Kemal Ugur, A. Aydin Alatan
Date: 2013-01-22 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation

Segmentation of an object from a video is a challenging task in multimedia
applications. Depending on the application, automatic or interactive methods
are desired; however, regardless of the application type, efficient computation
of video object segmentation is crucial for time-critical applications;
specifically, mobile and interactive applications require near real-time
efficiencies. In this paper, we address the problem of video segmentation from
the perspective of efficiency. We initially redefine the problem of video
object segmentation as the propagation of MRF energies along the temporal
domain. For this purpose, a novel and efficient method is proposed to propagate
MRF energies throughout the frames via bilateral filters without using any
global texture, color or shape model. Recently presented bi-exponential filter
is utilized for efficiency, whereas a novel technique is also developed to
dynamically solve graph-cuts for varying, non-lattice graphs in general linear
filtering scenario. These improvements are experimented for both automatic and
interactive video segmentation scenarios. Moreover, in addition to the
efficiency, segmentation quality is also tested both quantitatively and
qualitatively. Indeed, for some challenging examples, significant time
efficiency is observed without loss of segmentation quality.
Estimating the intrinsic dimension in fMRI space via dataset fractal analysis - Counting the `cpu cores' of the human brain
Authors: Harris V. Georgiou
Date: 2014-10-27 00:00:00
Tasks: blind source separation, CPU

Functional Magnetic Resonance Imaging (fMRI) is a powerful non-invasive tool
for localizing and analyzing brain activity. This study focuses on one very
important aspect of the functional properties of human brain, specifically the
estimation of the level of parallelism when performing complex cognitive tasks.
Using fMRI as the main modality, the human brain activity is investigated
through a purely data-driven signal processing and dimensionality analysis
approach. Specifically, the fMRI signal is treated as a multi-dimensional data
space and its intrinsic `complexity' is studied via dataset fractal analysis
and blind-source separation (BSS) methods. One simulated and two real fMRI
datasets are used in combination with Independent Component Analysis (ICA) and
fractal analysis for estimating the intrinsic (true) dimensionality, in order
to provide data-driven experimental evidence on the number of independent brain
processes that run in parallel when visual or visuo-motor tasks are performed.
Although this number is can not be defined as a strict threshold but rather as
a continuous range, when a specific activation level is defined, a
corresponding number of parallel processes or the casual equivalent of `cpu
cores' can be detected in normal human brain activity.
Concavity of reweighted Kikuchi approximation
Authors: Po-Ling Loh, Andre Wibisono
Date: 2014-10-26 00:00:00
Proceeding: NeurIPS 2014 12

We analyze a reweighted version of the Kikuchi approximation for estimating
the log partition function of a product distribution defined over a region
graph. We establish sufficient conditions for the concavity of our reweighted
objective function in terms of weight assignments in the Kikuchi expansion, and
show that a reweighted version of the sum product algorithm applied to the
Kikuchi region graph will produce global optima of the Kikuchi approximation
whenever the algorithm converges. When the region graph has two layers,
corresponding to a Bethe approximation, we show that our sufficient conditions
for concavity are also necessary. Finally, we provide an explicit
characterization of the polytope of concavity in terms of the cycle structure
of the region graph. We conclude with simulations that demonstrate the
advantages of the reweighted Kikuchi approach.
On Chord and Sagitta in ${\mathbb Z}^2$: An Analysis towards Fast and Robust Circular Arc Detection
Authors: Sahadev Bera, Shyamosree Pal, Partha Bhowmick, Bhargab B. Bhattacharya
Date: 2014-10-26 00:00:00
Tasks: ARC

Although chord and sagitta, when considered in tandem, may reflect many
underlying geometric properties of circles on the Euclidean plane, their
implications on the digital plane are not yet well-understood. In this paper,
we explore some of their fundamental properties on the digital plane that have
a strong bearing on the unsupervised detection of circles and circular arcs in
a digital image. We show that although the chord-and-sagitta properties of a
real circle do not readily migrate to the digital plane, they can indeed be
used for the analysis in the discrete domain based on certain bounds on their
deviations, which are derived from the real domain. In particular, we derive an
upper bound on the circumferential angular deviation of a point in the context
of chord property, and an upper bound on the relative error in radius
estimation with regard to the sagitta property. Using these two bounds, we
design a novel algorithm for the detection and parameterization of circles and
circular arcs, which does not require any heuristic initialization or manual
tuning. The chord property is deployed for the detection of circular arcs,
whereas the sagitta property is used to estimate their centers and radii.
Finally, to improve the accuracy of estimation, the notion of restricted Hough
transform is used. Experimental results demonstrate superior efficiency and
robustness of the proposed methodology compared to existing techniques.
Sparse Distributed Learning via Heterogeneous Diffusion Adaptive Networks
Authors: Bijit Kumar Das, Mrityunjoy Chakraborty, Jerónimo Arenas-García
Date: 2014-10-26 00:00:00

In-network distributed estimation of sparse parameter vectors via diffusion
LMS strategies has been studied and investigated in recent years. In all the
existing works, some convex regularization approach has been used at each node
of the network in order to achieve an overall network performance superior to
that of the simple diffusion LMS, albeit at the cost of increased computational
overhead. In this paper, we provide analytical as well as experimental results
which show that the convex regularization can be selectively applied only to
some chosen nodes keeping rest of the nodes sparsity agnostic, while still
enjoying the same optimum behavior as can be realized by deploying the convex
regularization at all the nodes. Due to the incorporation of unregularized
learning at a subset of nodes, less computational cost is needed in the
proposed approach. We also provide a guideline for selection of the sparsity
aware nodes and a closed form expression for the optimum regularization
parameter.
Performance Guarantees for Schatten-$p$ Quasi-Norm Minimization in Recovery of Low-Rank Matrices
Authors: Mohammadreza Malek-Mohammadi, Massoud Babaie-Zadeh, Mikael Skoglund
Date: 2014-07-14 00:00:00
Tasks: Automated Theorem Proving

We address some theoretical guarantees for Schatten-$p$ quasi-norm
minimization ($p \in (0,1]$) in recovering low-rank matrices from compressed
linear measurements. Firstly, using null space properties of the measurement
operator, we provide a sufficient condition for exact recovery of low-rank
matrices. This condition guarantees unique recovery of matrices of ranks equal
or larger than what is guaranteed by nuclear norm minimization. Secondly, this
sufficient condition leads to a theorem proving that all restricted isometry
property (RIP) based sufficient conditions for $\ell_p$ quasi-norm minimization
generalize to Schatten-$p$ quasi-norm minimization. Based on this theorem, we
provide a few RIP-based recovery conditions.
Learning-Assisted Automated Reasoning with Flyspeck
Authors: Cezary Kaliszyk, Josef Urban
Date: 2012-11-29 00:00:00
Tasks: CPU

The considerable mathematical knowledge encoded by the Flyspeck project is
combined with external automated theorem provers (ATPs) and machine-learning
premise selection methods trained on the proofs, producing an AI system capable
of answering a wide range of mathematical queries automatically. The
performance of this architecture is evaluated in a bootstrapping scenario
emulating the development of Flyspeck from axioms to the last theorem, each
time using only the previous theorems and proofs. It is shown that 39% of the
14185 theorems could be proved in a push-button mode (without any high-level
advice and user interaction) in 30 seconds of real time on a fourteen-CPU
workstation. The necessary work involves: (i) an implementation of sound
translations of the HOL Light logic to ATP formalisms: untyped first-order,
polymorphic typed first-order, and typed higher-order, (ii) export of the
dependency information from HOL Light and ATP proofs for the machine learners,
and (iii) choice of suitable representations and methods for learning from
previous proofs, and their integration as advisors with HOL Light. This work is
described and discussed here, and an initial analysis of the body of proofs
that were found fully automatically is provided.
A Novel Statistical Method Based on Dynamic Models for Classification
Authors: Lerong Li, Momiao Xiong
Date: 2014-10-26 00:00:00
Tasks: Classification, General Classification

Realizations of stochastic process are often observed temporal data or
functional data. There are growing interests in classification of dynamic or
functional data. The basic feature of functional data is that the functional
data have infinite dimensions and are highly correlated. An essential issue for
classifying dynamic and functional data is how to effectively reduce their
dimension and explore dynamic feature. However, few statistical methods for
dynamic data classification have directly used rich dynamic features of the
data. We propose to use second order ordinary differential equation (ODE) to
model dynamic process and principal differential analysis to estimate constant
or time-varying parameters in the ODE. We examine differential dynamic
properties of the dynamic system across different conditions including
stability and transient-response, which determine how the dynamic systems
maintain their functions and performance under a broad range of random internal
and external perturbations. We use the parameters in the ODE as features for
classifiers. As a proof of principle, the proposed methods are applied to
classifying normal and abnormal QRS complexes in the electrocardiogram (ECG)
data analysis, which is of great clinical values in diagnosis of cardiovascular
diseases. We show that the ODE-based classification methods in QRS complex
classification outperform the currently widely used neural networks with
Fourier expansion coefficients of the functional data as their features. We
expect that the dynamic model-based classification methods may open a new
avenue for functional data classification.
Semantic clustering of Russian web search results: possibilities and problems
Authors: Andrey Kutuzov
Date: 2014-09-04 00:00:00
Tasks: Clustering, Word Sense Induction

The paper deals with word sense induction from lexical co-occurrence graphs.
We construct such graphs on large Russian corpora and then apply this data to
cluster Mail.ru Search results according to meanings of the query. We compare
different methods of performing such clustering and different source corpora.
Models of applying distributional semantics to big linguistic data are
described.
Sparse Estimation using Bayesian Hierarchical Prior Modeling for Real and Complex Linear Models
Authors: Niels Lovmand Pedersen, Carles Navarro Manchón, Mihai-Alin Badiu, Dmitriy Shutin, Bernard Henri Fleury
Date: 2011-08-22 00:00:00
Tasks: Vocal Bursts Type Prediction

In sparse Bayesian learning (SBL), Gaussian scale mixtures (GSMs) have been
used to model sparsity-inducing priors that realize a class of concave penalty
functions for the regression task in real-valued signal models. Motivated by
the relative scarcity of formal tools for SBL in complex-valued models, this
paper proposes a GSM model - the Bessel K model - that induces concave penalty
functions for the estimation of complex sparse signals. The properties of the
Bessel K model are analyzed when it is applied to Type I and Type II
estimation. This analysis reveals that, by tuning the parameters of the mixing
pdf different penalty functions are invoked depending on the estimation type
used, the value of the noise variance, and whether real or complex signals are
estimated. Using the Bessel K model, we derive a sparse estimator based on a
modification of the expectation-maximization algorithm formulated for Type II
estimation. The estimator includes as a special instance the algorithms
proposed by Tipping and Faul [1] and by Babacan et al. [2]. Numerical results
show the superiority of the proposed estimator over these state-of-the-art
estimators in terms of convergence speed, sparseness, reconstruction error, and
robustness in low and medium signal-to-noise ratio regimes.
Improved depth imaging by constrained full-waveform inversion
Authors: Musa Maharramov, Biondo Biondi
Date: 2014-10-26 00:00:00

We propose a formulation of full-wavefield inversion (FWI) as a constrained
optimization problem, and describe a computationally efficient technique for
solving constrained full-wavefield inversion (CFWI). The technique is based on
using a total-variation regularization method, with the regularization weighted
in favor of constraining deeper subsurface model sections. The method helps to
promote "edge-preserving" blocky model inversion where fitting the seismic data
alone fails to adequately constrain the model. The method is demonstrated on
synthetic datasets with added noise, and is shown to enhance the sharpness of
the inverted model and correctly reposition mispositioned reflectors by better
constraining the velocity model at depth.