pwc-archive/papers-with-abstracts huggingface dataset:
An evolutionary computational based approach towards automatic image registration
Authors: P. V. Arun, S. K. Katiyar
Date: 2014-02-05 00:00:00
Tasks: Image Registration

Image registration is a key component of various image processing operations
which involve the analysis of different image data sets. Automatic image
registration domains have witnessed the application of many intelligent
methodologies over the past decade; however inability to properly model object
shape as well as contextual information had limited the attainable accuracy. In
this paper, we propose a framework for accurate feature shape modeling and
adaptive resampling using advanced techniques such as Vector Machines, Cellular
Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be
effective in improving feature matching as well as resampling stages of
registration and complexity of the approach has been considerably reduced using
corset optimization The salient features of this work are cellular neural
network approach based SIFT feature point optimisation, adaptive resampling and
intelligent object modelling. Developed methodology has been compared with
contemporary methods using different statistical measures. Investigations over
various satellite images revealed that considerable success was achieved with
the approach. System has dynamically used spectral and spatial information for
representing contextual knowledge using CNN-prolog approach. Methodology also
illustrated to be effective in providing intelligent interpretation and
adaptive resampling.
Cellular Automata based adaptive resampling technique for the processing of remotely sensed imagery
Authors: S. K. Katiyar, P. V. Arun
Date: 2014-02-05 00:00:00

Resampling techniques are being widely used at different stages of satellite
image processing. The existing methodologies cannot perfectly recover features
from a completely under sampled image and hence an intelligent adaptive
resampling methodology is required. We address these issues and adopt an error
metric from the available literature to define interpolation quality. We also
propose a new resampling scheme that adapts itself with regard to the pixel and
texture variation in the image. The proposed CNN based hybrid method has been
found to perform better than the existing methods as it adapts itself with
reference to the image features.
A review over the applicability of image entropy in analyses of remote sensing datasets
Authors: S. K. Katiyar, P. V. Arun
Date: 2014-02-05 00:00:00

Entropy is the measure of uncertainty in any data and is adopted for
maximisation of mutual information in many remote sensing operations. The
availability of wide entropy variations motivated us for an investigation over
the suitability preference of these versions to specific operations.
An SIR Graph Growth Model for the Epidemics of Communicable Diseases
Authors: Charanpal Dhanjal, Stéphan Clémençon
Date: 2013-12-09 00:00:00

It is the main purpose of this paper to introduce a graph-valued stochastic
process in order to model the spread of a communicable infectious disease. The
major novelty of the SIR model we promote lies in the fact that the social
network on which the epidemics is taking place is not specified in advance but
evolves through time, accounting for the temporal evolution of the interactions
involving infective individuals. Without assuming the existence of a fixed
underlying network model, the stochastic process introduced describes, in a
flexible and realistic manner, epidemic spread in non-uniformly mixing and
possibly heterogeneous populations. It is shown how to fit such a
(parametrised) model by means of Approximate Bayesian Computation methods based
on graph-valued statistics. The concepts and statistical methods described in
this paper are finally applied to a real epidemic dataset, related to the
spread of HIV in Cuba in presence of a contact tracing system, which permits
one to reconstruct partly the evolution of the graph of sexual partners
diagnosed HIV positive between 1986 and 2006.
Sample Complexity of Bayesian Optimal Dictionary Learning
Authors: Ayaka Sakata, Yoshiyuki Kabashima
Date: 2013-01-26 00:00:00
Tasks: Dictionary Learning

We consider a learning problem of identifying a dictionary matrix D (M times
N dimension) from a sample set of M dimensional vectors Y = N^{-1/2} DX, where
X is a sparse matrix (N times P dimension) in which the density of non-zero
entries is 0<rho< 1. In particular, we focus on the minimum sample size P_c
(sample complexity) necessary for perfectly identifying D of the optimal
learning scheme when D and X are independently generated from certain
distributions. By using the replica method of statistical mechanics, we show
that P_c=O(N) holds as long as alpha = M/N >rho is satisfied in the limit of N
to infinity. Our analysis also implies that the posterior distribution given Y
is condensed only at the correct dictionary D when the compression rate alpha
is greater than a certain critical value alpha_M(rho). This suggests that
belief propagation may allow us to learn D with a low computational complexity
using O(N) samples.
Patchwise Joint Sparse Tracking with Occlusion Detection
Authors: Ali Zarezade, Hamid R. Rabiee, Ali Soltani-Farani, Ahmad Khajenezhad
Date: 2014-02-05 00:00:00

This paper presents a robust tracking approach to handle challenges such as
occlusion and appearance change. Here, the target is partitioned into a number
of patches. Then, the appearance of each patch is modeled using a dictionary
composed of corresponding target patches in previous frames. In each frame, the
target is found among a set of candidates generated by a particle filter, via a
likelihood measure that is shown to be proportional to the sum of
patch-reconstruction errors of each candidate. Since the target's appearance
often changes slowly in a video sequence, it is assumed that the target in the
current frame and the best candidates of a small number of previous frames,
belong to a common subspace. This is imposed using joint sparse representation
to enforce the target and previous best candidates to have a common sparsity
pattern. Moreover, an occlusion detection scheme is proposed that uses
patch-reconstruction errors and a prior probability of occlusion, extracted
from an adaptive Markov chain, to calculate the probability of occlusion per
patch. In each frame, occluded patches are excluded when updating the
dictionary. Extensive experimental results on several challenging sequences
shows that the proposed method outperforms state-of-the-art trackers.
Consistency of Causal Inference under the Additive Noise Model
Authors: Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf
Date: 2013-12-19 00:00:00
Tasks: Causal Inference

We analyze a family of methods for statistical causal inference from sample
under the so-called Additive Noise Model. While most work on the subject has
concentrated on establishing the soundness of the Additive Noise Model, the
statistical consistency of the resulting inference methods has received little
attention. We derive general conditions under which the given family of
inference methods consistently infers the causal direction in a nonparametric
setting.
Comparative analysis of common edge detection techniques in context of object extraction
Authors: S. K. Katiyar, P. V. Arun
Date: 2014-02-05 00:00:00
Tasks: Edge Detection

Edges characterize boundaries and are therefore a problem of practical
importance in remote sensing.In this paper a comparative study of various edge
detection techniques and band wise analysis of these algorithms in the context
of object extraction with regard to remote sensing satellite images from the
Indian Remote Sensing Satellite (IRS) sensors LISS 3, LISS 4 and Cartosat1 as
well as Google Earth is presented.
Learning Ordered Representations with Nested Dropout
Authors: Oren Rippel, Michael A. Gelbart, Ryan P. Adams
Date: 2014-02-05 00:00:00
Tasks: Retrieval

In this paper, we study ordered representations of data in which different
dimensions have different degrees of importance. To learn these representations
we introduce nested dropout, a procedure for stochastically removing coherent
nested sets of hidden units in a neural network. We first present a sequence of
theoretical results in the simple case of a semi-linear autoencoder. We
rigorously show that the application of nested dropout enforces identifiability
of the units, which leads to an exact equivalence with PCA. We then extend the
algorithm to deep models and demonstrate the relevance of ordered
representations to a number of applications. Specifically, we use the ordered
property of the learned codes to construct hash-based data structures that
permit very fast retrieval, achieving retrieval in time logarithmic in the
database size and independent of the dimensionality of the representation. This
allows codes that are hundreds of times longer than currently feasible for
retrieval. We therefore avoid the diminished quality associated with short
codes, while still performing retrieval that is competitive in speed with
existing methods. We also show that ordered representations are a promising way
to learn adaptive compression for efficient online data reconstruction.
Discovering Latent Network Structure in Point Process Data
Authors: Scott W. Linderman, Ryan P. Adams
Date: 2014-02-04 00:00:00
Tasks: Point Processes

Networks play a central role in modern data analysis, enabling us to reason
about systems by studying the relationships between their parts. Most often in
network analysis, the edges are given. However, in many systems it is difficult
or impossible to measure the network directly. Examples of latent networks
include economic interactions linking financial instruments and patterns of
reciprocity in gang violence. In these cases, we are limited to noisy
observations of events associated with each node. To enable analysis of these
implicit networks, we develop a probabilistic model that combines
mutually-exciting point processes with random graph models. We show how the
Poisson superposition principle enables an elegant auxiliary variable
formulation and a fully-Bayesian, parallel inference algorithm. We evaluate
this new model empirically on several datasets.
Jointly Clustering Rows and Columns of Binary Matrices: Algorithms and Trade-offs
Authors: Jiaming Xu, Rui Wu, Kai Zhu, Bruce Hajek, R. Srikant, Lei Ying
Date: 2013-10-01 00:00:00
Tasks: Clustering

In standard clustering problems, data points are represented by vectors, and
by stacking them together, one forms a data matrix with row or column cluster
structure. In this paper, we consider a class of binary matrices, arising in
many applications, which exhibit both row and column cluster structure, and our
goal is to exactly recover the underlying row and column clusters by observing
only a small fraction of noisy entries. We first derive a lower bound on the
minimum number of observations needed for exact cluster recovery. Then, we
propose three algorithms with different running time and compare the number of
observations needed by them for successful cluster recovery. Our analytical
results show smooth time-data trade-offs: one can gradually reduce the
computational complexity when increasingly more observations are available.
Learning Loosely Connected Markov Random Fields
Authors: Rui Wu, R. Srikant, Jian Ni
Date: 2012-04-25 00:00:00

We consider the structure learning problem for graphical models that we call
loosely connected Markov random fields, in which the number of short paths
between any pair of nodes is small, and present a new conditional independence
test based algorithm for learning the underlying graph structure. The novel
maximization step in our algorithm ensures that the true edges are detected
correctly even when there are short cycles in the graph. The number of samples
required by our algorithm is C*log p, where p is the size of the graph and the
constant C depends on the parameters of the model. We show that several
previously studied models are examples of loosely connected Markov random
fields, and our algorithm achieves the same or lower computational complexity
than the previously designed algorithms for individual cases. We also get new
results for more general graphical models, in particular, our algorithm learns
general Ising models on the Erdos-Renyi random graph G(p, c/p) correctly with
running time O(np^5).
Cognitive Aging as Interplay between Hebbian Learning and Criticality
Authors: Sakyasingha Dasgupta
Date: 2014-02-04 00:00:00

Cognitive ageing seems to be a story of global degradation. As one ages there
are a number of physical, chemical and biological changes that take place.
Therefore it is logical to assume that the brain is no exception to this
phenomenon. The principle purpose of this project is to use models of neural
dynamics and learning based on the underlying principle of self-organised
criticality, to account for the age related cognitive effects. In this regard
learning in neural networks can serve as a model for the acquisition of skills
and knowledge in early development stages i.e. the ageing process and
criticality in the network serves as the optimum state of cognitive abilities.
Possible candidate mechanisms for ageing in a neural network are loss of
connectivity and neurons, increase in the level of noise, reduction in white
matter or more interestingly longer learning history and the competition among
several optimization objectives. In this paper we are primarily interested in
the affect of the longer learning history on memory and thus the optimality in
the brain. Hence it is hypothesized that prolonged learning in the form of
associative memory patterns can destroy the state of criticality in the
network. We base our model on Tsodyks and Markrams [49] model of dynamic
synapses, in the process to explore the effect of combining standard Hebbian
learning with the phenomenon of Self-organised criticality. The project mainly
consists of evaluations and simulations of networks of integrate and
fire-neurons that have been subjected to various combinations of neural-level
ageing effects, with the aim of establishing the primary hypothesis and
understanding the decline of cognitive abilities due to ageing, using one of
its important characteristics, a longer learning history.
Corrupted Sensing: Novel Guarantees for Separating Structured Signals
Authors: Rina Foygel, Lester Mackey
Date: 2013-05-11 00:00:00
Tasks: compressed sensing

We study the problem of corrupted sensing, a generalization of compressed
sensing in which one aims to recover a signal from a collection of corrupted or
unreliable measurements. While an arbitrary signal cannot be recovered in the
face of arbitrary corruption, tractable recovery is possible when both signal
and corruption are suitably structured. We quantify the relationship between
signal recovery and two geometric measures of structure, the Gaussian
complexity of a tangent cone and the Gaussian distance to a subdifferential. We
take a convex programming approach to disentangling signal and corruption,
analyzing both penalized programs that trade off between signal and corruption
complexity, and constrained programs that bound the complexity of signal or
corruption when prior information is available. In each case, we provide
conditions for exact signal recovery from structured corruption and stable
signal recovery from structured corruption with added unstructured noise. Our
simulations demonstrate close agreement between our theoretical recovery bounds
and the sharp phase transitions observed in practice. In addition, we provide
new interpretable bounds for the Gaussian complexity of sparse vectors,
block-sparse vectors, and low-rank matrices, which lead to sharper guarantees
of recovery when combined with our results and those in the literature.
Sequential Model-Based Ensemble Optimization
Authors: Alexandre Lacoste, Hugo Larochelle, François Laviolette, Mario Marchand
Date: 2014-02-04 00:00:00
Tasks: model, Model Selection

One of the most tedious tasks in the application of machine learning is model
selection, i.e. hyperparameter selection. Fortunately, recent progress has been
made in the automation of this process, through the use of sequential
model-based optimization (SMBO) methods. This can be used to optimize a
cross-validation performance of a learning algorithm over the value of its
hyperparameters. However, it is well known that ensembles of learned models
almost consistently outperform a single model, even if properly selected. In
this paper, we thus propose an extension of SMBO methods that automatically
constructs such ensembles. This method builds on a recently proposed ensemble
construction paradigm known as agnostic Bayesian learning. In experiments on 22
regression and 39 classification data sets, we confirm the success of this
proposed approach, which is able to outperform model selection with SMBO.
Signal to Noise Ratio in Lensless Compressive Imaging
Authors: Hong Jiang, Gang Huang, Paul Wilford
Date: 2014-02-04 00:00:00
Tasks: Compressive Sensing

We analyze the signal to noise ratio (SNR) in a lensless compressive imaging
(LCI) architecture. The architecture consists of a sensor of a single detecting
element and an aperture assembly of an array of programmable elements. LCI can
be used in conjunction with compressive sensing to capture images in a
compressed form of compressive measurements. In this paper, we perform SNR
analysis of the LCI and compare it with imaging with a pinhole or a lens. We
will show that the SNR in the LCI is independent of the image resolution, while
the SNR in either pinhole aperture imaging or lens aperture imaging decreases
as the image resolution increases. Consequently, the SNR in the LCI is much
higher if the image resolution is large enough.
Microstrip Coupler Design Using Bat Algorithm
Authors: Ezgi Deniz Ulker, Sadik Ulker
Date: 2014-02-04 00:00:00

Evolutionary and swarm algorithms have found many applications in design
problems since todays computing power enables these algorithms to find
solutions to complicated design problems very fast. Newly proposed hybrid
algorithm, bat algorithm, has been applied for the design of microwave
microstrip couplers for the first time. Simulation results indicate that the
bat algorithm is a very fast algorithm and it produces very reliable results.
A Study of Local Binary Pattern Method for Facial Expression Detection
Authors: Ms. Drashti H. Bhatt, Mr. Kirit R. Rathod, Mr. Shardul J. Agravat
Date: 2014-02-04 00:00:00
Tasks: Face Detection, Face Recognition, Facial Expression Recognition, Facial Expression Recognition (FER)

Face detection is a basic task for expression recognition. The reliability of
face detection & face recognition approach has a major role on the performance
and usability of the entire system. There are several ways to undergo face
detection & recognition. We can use Image Processing Operations, various
classifiers, filters or virtual machines for the former. Various strategies are
being available for Facial Expression Detection. The field of facial expression
detection can have various applications along with its importance & can be
interacted between human being & computer. Many few options are available to
identify a face in an image in accurate & efficient manner. Local Binary
Pattern (LBP) based texture algorithms have gained popularity in these years.
LBP is an effective approach to have facial expression recognition & is a
feature-based approach.
User Friendly Line CAPTCHAs
Authors: A. K. B. Karunathilake, B. M. D. Balasuriya, R. G. Ragel
Date: 2014-02-04 00:00:00

CAPTCHAs or reverse Turing tests are real-time assessments used by programs
(or computers) to tell humans and machines apart. This is achieved by assigning
and assessing hard AI problems that could only be solved easily by human but
not by machines. Applications of such assessments range from stopping spammers
from automatically filling online forms to preventing hackers from performing
dictionary attack. Today, the race between makers and breakers of CAPTCHAs is
at a juncture, where the CAPTCHAs proposed are not even answerable by humans.
We consider such CAPTCHAs as non user friendly. In this paper, we propose a
novel technique for reverse Turing test - we call it the Line CAPTCHAs - that
mainly focuses on user friendliness while not compromising the security aspect
that is expected to be provided by such a system.
Local Gaussian Regression
Authors: Franziska Meier, Philipp Hennig, Stefan Schaal
Date: 2014-02-04 00:00:00
Tasks: regression

Locally weighted regression was created as a nonparametric learning method
that is computationally efficient, can learn from very large amounts of data
and add data incrementally. An interesting feature of locally weighted
regression is that it can work with spatially varying length scales, a
beneficial property, for instance, in control problems. However, it does not
provide a generative model for function values and requires training and test
data to be generated identically, independently. Gaussian (process) regression,
on the other hand, provides a fully generative model without significant formal
requirements on the distribution of training data, but has much higher
computational cost and usually works with one global scale per input dimension.
Using a localising function basis and approximate inference techniques, we take
Gaussian (process) regression to increasingly localised properties and toward
the same computational complexity class as locally weighted regression.
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory
Authors: Adel Javanmard, Andrea Montanari
Date: 2013-01-17 00:00:00
Tasks: Model Selection, regression, Two-sample testing

We consider linear regression in the high-dimensional regime where the number
of observations $n$ is smaller than the number of parameters $p$. A very
successful approach in this setting uses $\ell_1$-penalized least squares
(a.k.a. the Lasso) to search for a subset of $s_0< n$ parameters that best
explain the data, while setting the other parameters to zero. Considerable
amount of work has been devoted to characterizing the estimation and model
selection problems within this approach.
  In this paper we consider instead the fundamental, but far less understood,
question of \emph{statistical significance}. More precisely, we address the
problem of computing p-values for single regression coefficients.
  On one hand, we develop a general upper bound on the minimax power of tests
with a given significance level. On the other, we prove that this upper bound
is (nearly) achievable through a practical procedure in the case of random
design matrices with independent entries. Our approach is based on a debiasing
of the Lasso estimator. The analysis builds on a rigorous characterization of
the asymptotic distribution of the Lasso estimator and its debiased version.
Our result holds for optimal sample size, i.e., when $n$ is at least on the
order of $s_0 \log(p/s_0)$.
  We generalize our approach to random design matrices with i.i.d. Gaussian
rows $x_i\sim N(0,\Sigma)$. In this case we prove that a similar distributional
characterization (termed `standard distributional limit') holds for $n$ much
larger than $s_0(\log p)^2$.
  Finally, we show that for optimal sample size, $n$ being at least of order
$s_0 \log(p/s_0)$, the standard distributional limit for general Gaussian
designs can be derived from the replica heuristics in statistical physics.