pwc-archive/papers-with-abstracts huggingface dataset:
Invertibility and Robustness of Phaseless Reconstruction
Authors: Radu Balan, Yang Wang
Date: 2013-08-21 00:00:00

This paper is concerned with the question of reconstructing a vector in a
finite-dimensional real Hilbert space when only the magnitudes of the
coefficients of the vector under a redundant linear map are known. We analyze
various Lipschitz bounds of the nonlinear analysis map and we establish
theoretical performance bounds of any reconstruction algorithm. We show that
robust and stable reconstruction requires additional redundancy than the
critical threshold.
A proposal for a Chinese keyboard for cellphones, smartphones, ipads and tablets
Authors: Maurice Margenstern, Lan Wu
Date: 2013-08-21 00:00:00

In this paper, we investigate the possibility to use two tilings of the
hyperbolic plane as basic frame for devising a way to input texts in Chinese
characters into messages of cellphones, smartphones, ipads and tablets.
Can inferred provenance and its visualisation be used to detect erroneous annotation? A case study using UniProtKB
Authors: Michael J. Bell, Matthew Collison, Phillip Lord
Date: 2013-08-21 00:00:00
Tasks: Sentence

A constant influx of new data poses a challenge in keeping the annotation in
biological databases current. Most biological databases contain significant
quantities of textual annotation, which often contains the richest source of
knowledge. Many databases reuse existing knowledge, during the curation process
annotations are often propagated between entries. However, this is often not
made explicit. Therefore, it can be hard, potentially impossible, for a reader
to identify where an annotation originated from. Within this work we attempt to
identify annotation provenance and track its subsequent propagation.
Specifically, we exploit annotation reuse within the UniProt Knowledgebase
(UniProtKB), at the level of individual sentences. We describe a visualisation
approach for the provenance and propagation of sentences in UniProtKB which
enables a large-scale statistical analysis. Initially levels of sentence reuse
within UniProtKB were analysed, showing that reuse is heavily prevalent, which
enables the tracking of provenance and propagation. By analysing sentences
throughout UniProtKB, a number of interesting propagation patterns were
identified, covering over 100, 000 sentences. Over 8000 sentences remain in the
database after they have been removed from the entries where they originally
occurred. Analysing a subset of these sentences suggest that approximately 30%
are erroneous, whilst 35% appear to be inconsistent. These results suggest that
being able to visualise sentence propagation and provenance can aid in the
determination of the accuracy and quality of textual annotation. Source code
and supplementary data are available from the authors website.
A study of retrieval algorithms of sparse messages in networks of neural cliques
Authors: Ala Aboudib, Vincent Gripon, Xiaoran Jiang
Date: 2013-08-21 00:00:00
Tasks: Retrieval

Associative memories are data structures addressed using part of the content
rather than an index. They offer good fault reliability and biological
plausibility. Among different families of associative memories, sparse ones are
known to offer the best efficiency (ratio of the amount of bits stored to that
of bits used by the network itself). Their retrieval process performance has
been shown to benefit from the use of iterations. However classical algorithms
require having prior knowledge about the data to retrieve such as the number of
nonzero symbols. We introduce several families of algorithms to enhance the
retrieval process performance in recently proposed sparse associative memories
based on binary neural networks. We show that these algorithms provide better
performance, along with better plausibility than existing techniques. We also
analyze the required number of iterations and derive corresponding curves.
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso
Authors: Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Eric P. Xing, Masashi Sugiyama
Date: 2012-02-02 00:00:00
Tasks: feature selection, Vocal Bursts Intensity Prediction

The goal of supervised feature selection is to find a subset of input
features that are responsible for predicting output values. The least absolute
shrinkage and selection operator (Lasso) allows computationally efficient
feature selection based on linear dependency between input features and output
values. In this paper, we consider a feature-wise kernelized Lasso for
capturing non-linear input-output dependency. We first show that, with
particular choices of kernel functions, non-redundant features with strong
statistical dependence on output values can be found in terms of kernel-based
independence measures. We then show that the globally optimal solution can be
efficiently computed; this makes the approach scalable to high-dimensional
problems. The effectiveness of the proposed method is demonstrated through
feature selection experiments with thousands of features.
An Investigation of the Sampling-Based Alignment Method and Its Contributions
Authors: Juan Luo, Yves Lepage
Date: 2013-08-21 00:00:00
Tasks: Machine Translation, Translation

By investigating the distribution of phrase pairs in phrase translation
tables, the work in this paper describes an approach to increase the number of
n-gram alignments in phrase translation tables output by a sampling-based
alignment method. This approach consists in enforcing the alignment of n-grams
in distinct translation subtables so as to increase the number of n-grams.
Standard normal distribution is used to allot alignment time among translation
subtables, which results in adjustment of the distribution of n- grams. This
leads to better evaluation results on statistical machine translation tasks
than the original sampling-based alignment approach. Furthermore, the
translation quality obtained by merging phrase translation tables computed from
the sampling-based alignment method and from MGIZA++ is examined.
Influences Combination of Multi-Sensor Images on Classification Accuracy
Authors: AL-Wassai Firouz, N. V. Kalyankar
Date: 2013-08-20 00:00:00
Tasks: Classification, General Classification

This paper focuses on two main issues; first one is the impact of combination
of multi-sensor images on the supervised learning classification accuracy using
segment Fusion (SF). The second issue attempts to undertake the study of
supervised machine learning classification technique of remote sensing images
by using four classifiers like Parallelepiped (Pp), Mahalanobis Distance (MD),
Maximum-Likelihood (ML) and Euclidean Distance(ED) classifiers, and their
accuracies have been evaluated on their respected classification to choose the
best technique for classification of remote sensing images. QuickBird
multispectral data (MS) and panchromatic data (PAN) have been used in this
study to demonstrate the enhancement and accuracy assessment of fused image
over the original images using ALwassaiProcess software. According to
experimental result of this study, is that the test results indicate the
supervised classification results of fusion image, which generated better than
the MS did. As well as the result with Euclidean classifier is robust and
provides better results than the other classifiers do, despite of the popular
belief that the maximum-likelihood classifier is the most accurate classifier.
SAR Image Despeckling Algorithms using Stochastic Distances and Nonlocal Means
Authors: Leonardo Torres, Alejandro C. Frery
Date: 2013-08-20 00:00:00
Tasks: Sar Image Despeckling

This paper presents two approaches for filter design based on stochastic
distances for intensity speckle reduction. A window is defined around each
pixel, overlapping samples are compared and only those which pass a
goodness-of-fit test are used to compute the filtered value. The tests stem
from stochastic divergences within the Information Theory framework. The
technique is applied to intensity Synthetic Aperture Radar (SAR) data with
homogeneous regions using the Gamma model. The first approach uses a
Nagao-Matsuyama-type procedure for setting the overlapping samples, and the
second uses the nonlocal method. The proposals are compared with the Improved
Sigma filter and with anisotropic diffusion for speckled data (SRAD) using a
protocol based on Monte Carlo simulation. Among the criteria used to quantify
the quality of filters, we employ the equivalent number of looks, and line and
edge preservation. Moreover, we also assessed the filters by the Universal
Image Quality Index and by the Pearson correlation between edges. Applications
to real images are also discussed. The proposed methods show good results.
Support Recovery for the Drift Coefficient of High-Dimensional Diffusions
Authors: Jose Bento, Morteza Ibrahimi
Date: 2013-08-19 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Consider the problem of learning the drift coefficient of a $p$-dimensional
stochastic differential equation from a sample path of length $T$. We assume
that the drift is parametrized by a high-dimensional vector, and study the
support recovery problem when both $p$ and $T$ can tend to infinity. In
particular, we prove a general lower bound on the sample-complexity $T$ by
using a characterization of mutual information as a time integral of
conditional variance, due to Kadota, Zakai, and Ziv. For linear stochastic
differential equations, the drift coefficient is parametrized by a $p\times p$
matrix which describes which degrees of freedom interact under the dynamics. In
this case, we analyze a $\ell_1$-regularized least squares estimator and prove
an upper bound on $T$ that nearly matches the lower bound on specific classes
of sparse matrices.
Pylearn2: a machine learning research library
Authors: Ian J. Goodfellow, David Warde-Farley, Pascal Lamblin, Vincent Dumoulin, Mehdi Mirza, Razvan Pascanu, James Bergstra, Frédéric Bastien, Yoshua Bengio
Date: 2013-08-20 00:00:00
Tasks: BIG-bench Machine Learning, Philosophy

Pylearn2 is a machine learning research library. This does not just mean that
it is a collection of machine learning algorithms that share a common API; it
means that it has been designed for flexibility and extensibility in order to
facilitate research projects that involve new or unusual use cases. In this
paper we give a brief history of the library, an overview of its basic
philosophy, a summary of the library's architecture, and a description of how
the Pylearn2 community functions socially.
Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations
Authors: Erik Rodner, Judy Hoffman, Jeff Donahue, Trevor Darrell, Kate Saenko
Date: 2013-08-20 00:00:00
Tasks: Domain Adaptation, Scene Understanding

Images seen during test time are often not from the same distribution as
images used for learning. This problem, known as domain shift, occurs when
training classifiers from object-centric internet image databases and trying to
apply them directly to scene understanding tasks. The consequence is often
severe performance degradation and is one of the major barriers for the
application of classifiers in real-world systems. In this paper, we show how to
learn transform-based domain adaptation classifiers in a scalable manner. The
key idea is to exploit an implicit rank constraint, originated from a
max-margin domain adaptation formulation, to make optimization tractable.
Experiments show that the transformation between domains can be very
efficiently learned from data and easily applied to new categories. This begins
to bridge the gap between large-scale internet image collections and object
images captured in everyday life environments.
Predicting protein contact map using evolutionary and physical constraints by integer programming (extended version)
Authors: Zhiyong Wang, Jinbo Xu
Date: 2013-08-08 00:00:00

Motivation. Protein contact map describes the pairwise spatial and functional
relationship of residues in a protein and contains key information for protein
3D structure prediction. Although studied extensively, it remains very
challenging to predict contact map using only sequence information. Most
existing methods predict the contact map matrix element-by-element, ignoring
correlation among contacts and physical feasibility of the whole contact map. A
couple of recent methods predict contact map based upon residue co-evolution,
taking into consideration contact correlation and enforcing a sparsity
restraint, but these methods require a very large number of sequence homologs
for the protein under consideration and the resultant contact map may be still
physically unfavorable.
  Results. This paper presents a novel method PhyCMAP for contact map
prediction, integrating both evolutionary and physical restraints by machine
learning and integer linear programming (ILP). The evolutionary restraints
include sequence profile, residue co-evolution and context-specific statistical
potential. The physical restraints specify more concrete relationship among
contacts than the sparsity restraint. As such, our method greatly reduces the
solution space of the contact map matrix and thus, significantly improves
prediction accuracy. Experimental results confirm that PhyCMAP outperforms
currently popular methods no matter how many sequence homologs are available
for the protein under consideration. PhyCMAP can predict contacts within
minutes after PSIBLAST search for sequence homologs is done, much faster than
the two recent methods PSICOV and EvFold.
  See http://raptorx.uchicago.edu for the web server.
A Literature Survey of Benchmark Functions For Global Optimization Problems
Authors: Momin Jamil, Xin-She Yang
Date: 2013-08-19 00:00:00
Tasks: global-optimization, Survey

Test functions are important to validate and compare the performance of
optimization algorithms. There have been many test or benchmark functions
reported in the literature; however, there is no standard list or set of
benchmark functions. Ideally, test functions should have diverse properties so
that can be truly useful to test new algorithms in an unbiased way. For this
purpose, we have reviewed and compiled a rich set of 175 benchmark functions
for unconstrained optimization problems with diverse properties in terms of
modality, separability, and valley landscape. This is by far the most complete
set of functions so far in the literature, and tt can be expected this complete
set of functions can be used for validation of new optimization in the future.
Clustering of Complex Networks and Community Detection Using Group Search Optimization
Authors: G. Kishore Kumar, V. K. Jayaraman
Date: 2013-07-04 00:00:00
Tasks: Clustering, Community Detection

Group Search Optimizer(GSO) is one of the best algorithms, is very new in the
field of Evolutionary Computing. It is very robust and efficient algorithm,
which is inspired by animal searching behaviour. The paper describes an
application of GSO to clustering of networks. We have tested GSO against five
standard benchmark datasets, GSO algorithm is proved very competitive in terms
of accuracy and convergence speed.
Optimal Algorithms for Testing Closeness of Discrete Distributions
Authors: Siu-On Chan, Ilias Diakonikolas, Gregory Valiant, Paul Valiant
Date: 2013-08-19 00:00:00

We study the question of closeness testing for two discrete distributions.
More precisely, given samples from two distributions $p$ and $q$ over an
$n$-element set, we wish to distinguish whether $p=q$ versus $p$ is at least
$\eps$-far from $q$, in either $\ell_1$ or $\ell_2$ distance. Batu et al. gave
the first sub-linear time algorithms for these problems, which matched the
lower bounds of Valiant up to a logarithmic factor in $n$, and a polynomial
factor of $\eps.$
  In this work, we present simple (and new) testers for both the $\ell_1$ and
$\ell_2$ settings, with sample complexity that is information-theoretically
optimal, to constant factors, both in the dependence on $n$, and the dependence
on $\eps$; for the $\ell_1$ testing problem we establish that the sample
complexity is $\Theta(\max\{n^{2/3}/\eps^{4/3}, n^{1/2}/\eps^2 \}).$
A Likelihood Ratio Approach for Probabilistic Inequalities
Authors: Xinjia Chen
Date: 2013-08-18 00:00:00
Tasks: parameter estimation

We propose a new approach for deriving probabilistic inequalities based on
bounding likelihood ratios. We demonstrate that this approach is more general
and powerful than the classical method frequently used for deriving
concentration inequalities such as Chernoff bounds. We discover that the
proposed approach is inherently related to statistical concepts such as
monotone likelihood ratio, maximum likelihood, and the method of moments for
parameter estimation. A connection between the proposed approach and the large
deviation theory is also established. We show that, without using moment
generating functions, tightest possible concentration inequalities may be
readily derived by the proposed approach. We have derived new concentration
inequalities using the proposed approach, which cannot be obtained by the
classical approach based on moment generating functions.
Bat Algorithm: Literature Review and Applications
Authors: Xin-She Yang
Date: 2013-08-18 00:00:00

Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and
BA has been found to be very efficient. As a result, the literature has
expanded significantly in the last 3 years. This paper provides a timely review
of the bat algorithm and its new variants. A wide range of diverse applications
and case studies are also reviewed and summarized briefly here. Further
research topics are also discussed.
Firefly Algorithm: Recent Advances and Applications
Authors: Xin-She Yang, Xing-Shi He
Date: 2013-08-18 00:00:00

Nature-inspired metaheuristic algorithms, especially those based on swarm
intelligence, have attracted much attention in the last ten years. Firefly
algorithm appeared in about five years ago, its literature has expanded
dramatically with diverse applications. In this paper, we will briefly review
the fundamentals of firefly algorithm together with a selection of recent
publications. Then, we discuss the optimality associated with balancing
exploration and exploitation, which is essential for all metaheuristic
algorithms. By comparing with intermittent search strategy, we conclude that
metaheuristics such as firefly algorithm are better than the optimal
intermittent search strategy. We also analyse algorithms and their implications
for higher-dimensional optimization problems.
Understanding Dropout: Training Multi-Layer Perceptrons with Auxiliary Independent Stochastic Neurons
Authors: Kyunghyun Cho
Date: 2013-06-12 00:00:00

In this paper, a simple, general method of adding auxiliary stochastic
neurons to a multi-layer perceptron is proposed. It is shown that the proposed
method is a generalization of recently successful methods of dropout (Hinton et
al., 2012), explicit noise injection (Vincent et al., 2010; Bishop, 1995) and
semantic hashing (Salakhutdinov & Hinton, 2009). Under the proposed framework,
an extension of dropout which allows using separate dropping probabilities for
different hidden neurons, or layers, is found to be available. The use of
different dropping probabilities for hidden layers separately is empirically
investigated.
KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization
Authors: Ilya Loshchilov, Marc Schoenauer, Michèle Sebag
Date: 2013-08-12 00:00:00

This paper investigates the control of an ML component within the Covariance
Matrix Adaptation Evolution Strategy (CMA-ES) devoted to black-box
optimization. The known CMA-ES weakness is its sample complexity, the number of
evaluations of the objective function needed to approximate the global optimum.
This weakness is commonly addressed through surrogate optimization, learning an
estimate of the objective function a.k.a. surrogate model, and replacing most
evaluations of the true objective function with the (inexpensive) evaluation of
the surrogate model. This paper presents a principled control of the learning
schedule (when to relearn the surrogate model), based on the Kullback-Leibler
divergence of the current search distribution and the training distribution of
the former surrogate model. The experimental validation of the proposed
approach shows significant performance gains on a comprehensive set of
ill-conditioned benchmark problems, compared to the best state of the art
including the quasi-Newton high-precision BFGS method.
Natural Language Web Interface for Database (NLWIDB)
Authors: Rukshan Alexander, Prashanthi Rukshan, Sinnathamby Mahesan
Date: 2013-08-18 00:00:00

It is a long term desire of the computer users to minimize the communication
gap between the computer and a human. On the other hand, almost all ICT
applications store information in to databases and retrieve from them.
Retrieving information from the database requires knowledge of technical
languages such as Structured Query Language. However majority of the computer
users who interact with the databases do not have a technical background and
are intimidated by the idea of using languages such as SQL. For above reasons,
a Natural Language Web Interface for Database (NLWIDB) has been developed. The
NLWIDB allows the user to query the database in a language more like English,
through a convenient interface over the Internet.
Reference Distance Estimator
Authors: Yanpeng Li
Date: 2013-08-18 00:00:00
Tasks: General Classification, text-classification, Text Classification

A theoretical study is presented for a simple linear classifier called
reference distance estimator (RDE), which assigns the weight of each feature j
as P(r|j)-P(r), where r is a reference feature relevant to the target class y.
The analysis shows that if r performs better than random guess in predicting y
and is conditionally independent with each feature j, the RDE will have the
same classification performance as that from P(y|j)-P(y), a classifier trained
with the gold standard y. Since the estimation of P(r|j)-P(r) does not require
labeled data, under the assumption above, RDE trained with a large number of
unlabeled examples would be close to that trained with infinite labeled
examples. For the case the assumption does not hold, we theoretically analyze
the factors that influence the closeness of the RDE to the perfect one under
the assumption, and present an algorithm to select reference features and
combine multiple RDEs from different reference features using both labeled and
unlabeled data. The experimental results on 10 text classification tasks show
that the semi-supervised learning method improves supervised methods using
5,000 labeled examples and 13 million unlabeled ones, and in many tasks, its
performance is even close to a classifier trained with 13 million labeled
examples. In addition, the bounds in the theorems provide good estimation of
the classification performance and can be useful for new algorithm design.
Implementation Of Back-Propagation Neural Network For Isolated Bangla Speech Recognition
Authors: Md. Ali Hossain, Md. Mijanur Rahman, Uzzal Kumar Prodhan, Md. Farukuzzaman Khan
Date: 2013-08-17 00:00:00
Tasks: speech-recognition, Speech Recognition

This paper is concerned with the development of Back-propagation Neural
Network for Bangla Speech Recognition. In this paper, ten bangla digits were
recorded from ten speakers and have been recognized. The features of these
speech digits were extracted by the method of Mel Frequency Cepstral
Coefficient (MFCC) analysis. The mfcc features of five speakers were used to
train the network with Back propagation algorithm. The mfcc features of ten
bangla digit speeches, from 0 to 9, of another five speakers were used to test
the system. All the methods and algorithms used in this research were
implemented using the features of Turbo C and C++ languages. From our
investigation it is seen that the developed system can successfully encode and
analyze the mfcc features of the speech signal to recognition. The developed
system achieved recognition rate about 96.332% for known speakers (i.e.,
speaker dependent) and 92% for unknown speakers (i.e., speaker independent).