pwc-archive/papers-with-abstracts huggingface dataset:
Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome
Authors: Lorenzo Livi, Antonello Rizzi, Alireza Sadeghian
Date: 2014-08-17 00:00:00
Tasks: Classification, General Classification, Structured Data Classification

We evaluate a version of the recently-proposed classification system named
Optimized Dissimilarity Space Embedding (ODSE) that operates in the input space
of sequences of generic objects. The ODSE system has been originally presented
as a classification system for patterns represented as labeled graphs. However,
since ODSE is founded on the dissimilarity space representation of the input
data, the classifier can be easily adapted to any input domain where it is
possible to define a meaningful dissimilarity measure. Here we demonstrate the
effectiveness of the ODSE classifier for sequences by considering an
application dealing with the recognition of the solubility degree of the
Escherichia coli proteome. Solubility, or analogously aggregation propensity,
is an important property of protein molecules, which is intimately related to
the mechanisms underlying the chemico-physical process of folding. Each protein
of our dataset is initially associated with a solubility degree and it is
represented as a sequence of symbols, denoting the 20 amino acid residues. The
herein obtained computational results, which we stress that have been achieved
with no context-dependent tuning of the ODSE system, confirm the validity and
generality of the ODSE-based approach for structured data classification.
Dirichlet Process Parsimonious Mixtures for clustering
Authors: Faicel Chamroukhi, Marius Bartcus, Hervé Glotin
Date: 2015-01-14 00:00:00
Tasks: Clustering, Model Selection

The parsimonious Gaussian mixture models, which exploit an eigenvalue
decomposition of the group covariance matrices of the Gaussian mixture, have
shown their success in particular in cluster analysis. Their estimation is in
general performed by maximum likelihood estimation and has also been considered
from a parametric Bayesian prospective. We propose new Dirichlet Process
Parsimonious mixtures (DPPM) which represent a Bayesian nonparametric
formulation of these parsimonious Gaussian mixture models. The proposed DPPM
models are Bayesian nonparametric parsimonious mixture models that allow to
simultaneously infer the model parameters, the optimal number of mixture
components and the optimal parsimonious mixture structure from the data. We
develop a Gibbs sampling technique for maximum a posteriori (MAP) estimation of
the developed DPMM models and provide a Bayesian model selection framework by
using Bayes factors. We apply them to cluster simulated data and real data
sets, and compare them to the standard parsimonious mixture models. The
obtained results highlight the effectiveness of the proposed nonparametric
parsimonious mixture models as a good nonparametric alternative for the
parametric parsimonious models.
Image enhancement in intensity projected multichannel MRI using spatially adaptive directional anisotropic diffusion
Authors: P. K. Akshara, J. S. Paul
Date: 2015-01-14 00:00:00
Tasks: Image Enhancement

Anisotropic Diffusion is widely used for noise reduction with simultaneous
preservation of vascular structures in maximum intensity projected (MIP)
angiograms. However, extension to minimum intensity projected (mIP) venograms
in Susceptibility Weighted Imaging (SWI) poses difficulties due to spatially
varying baseline. Here, we introduce a modified version of the directional
anisotropic diffusion which allows us to simultaneously reduce the noise and
enhance vascular structures reconstructed using both M/mIP angiograms. This
method is based on spatial adaptation of the diffusion function, separately in
the directions of the gradient, and along those of the minimum and maximum
curvatures. The existing approach of directional anisotropic diffusion uses
binary switched diffusion function to ensure diffusion along the direction of
maximum curvature stopped near the vessel borders. Here, the choice of a
threshold for detecting the upper limit of diffusion becomes difficult in the
presence of spatially varying baseline. Also, the approach of using vesselness
measure to steer the diffusion process results in structural discontinuities
due to junction suppression in mIP. The merits of the proposed method include
elimination of the need for an apriori choice of a threshold to detect the
vessel, and problems due to junction suppression. The proposed method is also
extended to multi-channel phase contrast angiogram.
Classification with Low Rank and Missing Data
Authors: Elad Hazan, Roi Livni, Yishay Mansour
Date: 2015-01-14 00:00:00
Tasks: Classification, General Classification, regression

We consider classification and regression tasks where we have missing data
and assume that the (clean) data resides in a low rank subspace. Finding a
hidden subspace is known to be computationally hard. Nevertheless, using a
non-proper formulation we give an efficient agnostic algorithm that classifies
as good as the best linear classifier coupled with the best low-dimensional
subspace in which the data resides. A direct implication is that our algorithm
can linearly (and non-linearly through kernels) classify provably as well as
the best classifier that has access to the full data.
Higher dimensional homodyne filtering for suppression of incidental phase artifacts in multichannel MRI
Authors: Joseph Suresh Paul, Uma Krishna Swamy Pillai
Date: 2015-01-14 00:00:00

The aim of this paper is to introduce procedural steps for extension of the
1D homodyne phase correction for k-space truncation in all gradient encoding
directions. Compared to the existing method applied to 2D partial k-space,
signal losses introduced by the phase correction filter is observed to be
minimal for the extended approach. In addition, the modified form of phase
correction mitigates Incidental Phase Artifacts (IPA) due to truncation. For
parallel imaging with undersampling along phase encode direction, the extended
homodyne filtering is shown to be effective for minimizing these artifacts when
each of the channel k-spaces are truncated along both phase and frequency
encode directions. This is illustrated with 2D partial k-space for flow
compensated multichannel Susceptibility Weighted Imaging (SWI). Extension of
our method to 3D partial k-space shows improved reconstruction of flow
information in phase contrast angiography.
Quantifying Prosodic Variability in Middle English Alliterative Poetry
Authors: Roger Bilisoly
Date: 2015-01-14 00:00:00

Interest in the mathematical structure of poetry dates back to at least the
19th century: after retiring from his mathematics position, J. J. Sylvester
wrote a book on prosody called $\textit{The Laws of Verse}$. Today there is
interest in the computer analysis of poems, and this paper discusses how a
statistical approach can be applied to this task. Starting with the definition
of what Middle English alliteration is, $\textit{Sir Gawain and the Green
Knight}$ and William Langland's $\textit{Piers Plowman}$ are used to illustrate
the methodology. Theory first developed for analyzing data from a Riemannian
manifold turns out to be applicable to strings allowing one to compute a
generalized mean and variance for textual data, which is applied to the poems
above. The ratio of these two variances produces the analogue of the F test,
and resampling allows p-values to be estimated. Consequently, this methodology
provides a way to compare prosodic variability between two texts.
Towards Deep Semantic Analysis Of Hashtags
Authors: Piyush Bansal, Romil Bansal, Vasudeva Varma
Date: 2015-01-13 00:00:00
Tasks: Entity Linking, Sentiment Analysis

Hashtags are semantico-syntactic constructs used across various social
networking and microblogging platforms to enable users to start a topic
specific discussion or classify a post into a desired category. Segmenting and
linking the entities present within the hashtags could therefore help in better
understanding and extraction of information shared across the social media.
However, due to lack of space delimiters in the hashtags (e.g #nsavssnowden),
the segmentation of hashtags into constituent entities ("NSA" and "Edward
Snowden" in this case) is not a trivial task. Most of the current
state-of-the-art social media analytics systems like Sentiment Analysis and
Entity Linking tend to either ignore hashtags, or treat them as a single word.
In this paper, we present a context aware approach to segment and link entities
in the hashtags to a knowledge base (KB) entry, based on the context within the
tweet. Our approach segments and links the entities in hashtags such that the
coherence between hashtag semantics and the tweet is maximized. To the best of
our knowledge, no existing study addresses the issue of linking entities in
hashtags for extracting semantic information. We evaluate our method on two
different datasets, and demonstrate the effectiveness of our technique in
improving the overall entity linking in tweets via additional semantic
information provided by segmenting and linking entities in a hashtag.
Annotating Cognates and Etymological Origin in Turkic Languages
Authors: Benjamin S. Mericli, Michael Bloodgood
Date: 2015-01-13 00:00:00
Tasks: Diversity, Translation

Turkic languages exhibit extensive and diverse etymological relationships
among lexical items. These relationships make the Turkic languages promising
for exploring automated translation lexicon induction by leveraging cognate and
other etymological information. However, due to the extent and diversity of the
types of relationships between words, it is not clear how to annotate such
information. In this paper, we present a methodology for annotating cognates
and etymological origin in Turkic languages. Our method strives to balance the
amount of research effort the annotator expends with the utility of the
annotations for supporting research on improving automated translation lexicon
induction.
Robust and Real Time Detection of Curvy Lanes (Curves) with Desired Slopes for Driving Assistance and Autonomous Vehicles
Authors: Amartansh Dubey, K. M. Bhurchandi
Date: 2015-01-13 00:00:00
Tasks: Autonomous Vehicles

One of the biggest reasons for road accidents is curvy lanes and blind turns.
Even one of the biggest hurdles for new autonomous vehicles is to detect curvy
lanes, multiple lanes and lanes with a lot of discontinuity and noise. This
paper presents very efficient and advanced algorithm for detecting curves
having desired slopes (especially for detecting curvy lanes in real time) and
detection of curves (lanes) with a lot of noise, discontinuity and
disturbances. Overall aim is to develop robust method for this task which is
applicable even in adverse conditions. Even in some of most famous and useful
libraries like OpenCV and Matlab, there is no function available for detecting
curves having desired slopes , shapes, discontinuities. Only few predefined
shapes like circle, ellipse, etc, can be detected using presently available
functions. Proposed algorithm can not only detect curves with discontinuity,
noise, desired slope but also it can perform shadow and illumination correction
and detect/ differentiate between different curves.
MultiGain: A controller synthesis tool for MDPs with multiple mean-payoff objectives
Authors: Tomáš Brázdil, Krishnendu Chatterjee, Vojtěch Forejt, Antonín Kučera
Date: 2015-01-13 00:00:00

We present MultiGain, a tool to synthesize strategies for Markov decision
processes (MDPs) with multiple mean-payoff objectives. Our models are described
in PRISM, and our tool uses the existing interface and simulator of PRISM. Our
tool extends PRISM by adding novel algorithms for multiple mean-payoff
objectives, and also provides features such as (i)~generating strategies and
exploring them for simulation, and checking them with respect to other
properties; and (ii)~generating an approximate Pareto curve for two mean-payoff
objectives. In addition, we present a new practical algorithm for the analysis
of MDPs with multiple mean-payoff objectives under memoryless strategies.
Deep Learning with Nonparametric Clustering
Authors: Gang Chen
Date: 2015-01-13 00:00:00
Tasks: Clustering, Deep Learning, Dimensionality Reduction, Nonparametric Clustering, Object Recognition

Clustering is an essential problem in machine learning and data mining. One
vital factor that impacts clustering performance is how to learn or design the
data representation (or features). Fortunately, recent advances in deep
learning can learn unsupervised features effectively, and have yielded state of
the art performance in many classification problems, such as character
recognition, object recognition and document categorization. However, little
attention has been paid to the potential of deep learning for unsupervised
clustering problems. In this paper, we propose a deep belief network with
nonparametric clustering. As an unsupervised method, our model first leverages
the advantages of deep learning for feature representation and dimension
reduction. Then, it performs nonparametric clustering under a maximum margin
framework -- a discriminative clustering model and can be trained online
efficiently in the code space. Lastly model parameters are refined in the deep
belief network. Thus, this model can learn features for clustering and infer
model complexity in an unified framework. The experimental results show the
advantage of our approach over competitive baselines.
Feature Selection Based on Confidence Machine
Authors: Chang Liu, Yi Xu
Date: 2014-10-20 00:00:00
Tasks: feature selection, Math

In machine learning and pattern recognition, feature selection has been a hot
topic in the literature. Unsupervised feature selection is challenging due to
the loss of labels which would supply the related information.How to define an
appropriate metric is the key for feature selection. We propose a filter method
for unsupervised feature selection which is based on the Confidence Machine.
Confidence Machine offers an estimation of confidence on a feature'reliability.
In this paper, we provide the math model of Confidence Machine in the context
of feature selection, which maximizes the relevance and minimizes the
redundancy of the selected feature. We compare our method against classic
feature selection methods Laplacian Score, Pearson Correlation and Principal
Component Analysis on benchmark data sets. The experimental results demonstrate
the efficiency and effectiveness of our method.
An Adaptive Neuro-Fuzzy Inference System Modeling for Grid-Adaptive Interpolation over Depth Images
Authors: Arbaaz Singh Sidhu
Date: 2015-01-13 00:00:00

A suitable interpolation method is essential to keep the noise level minimum
along with the time-delay. In recent years, many different interpolation
filters have been developed for instance H.264-6 tap filter, and AVS- 4 tap
filter. The present work uses Adaptive Neuro-Fuzzy Inference System (ANFIS)
technique to model and investigate the effects of a four-tap low-pass tap
filter (Grid-adaptive filter) on a hole-filled depth image. The work
demonstrates the general form of uniform interpolations for both integer and
sub-pixel locations in terms of the sampling interval and filter length of
depth-images via diverse finite impulse response filtering schemes. The
demonstrated model combined modelling function of fuzzy inference with the
learning ability of artificial neural network.
Multi-modal Image Registration for Correlative Microscopy
Authors: Tian Cao, Christopher Zach, Shannon Modla, Debbie Powell, Kirk Czymmek, Marc Niethammer
Date: 2014-11-12 00:00:00
Tasks: Image Registration

Correlative microscopy is a methodology combining the functionality of light
microscopy with the high resolution of electron microscopy and other microscopy
technologies. Image registration for correlative microscopy is quite
challenging because it is a multi-modal, multi-scale and multi-dimensional
registration problem. In this report, I introduce two methods of image
registration for correlative microscopy. The first method is based on fiducials
(beads). I generate landmarks from the fiducials and compute the similarity
transformation matrix based on three pairs of nearest corresponding landmarks.
A least-squares matching process is applied afterwards to further refine the
registration. The second method is inspired by the image analogies approach. I
introduce the sparse representation model into image analogies. I first train
representative image patches (dictionaries) for pre-registered datasets from
two different modalities, and then I use the sparse coding technique to
transfer a given image to a predicted image from one modality to another based
on the learned dictionaries. The final image registration is between the
predicted image and the original image corresponding to the given image in the
different modality. The method transforms a multi-modal registration problem to
a mono-modal one. I test my approaches on Transmission Electron Microscopy
(TEM) and confocal microscopy images. Experimental results of the methods are
also shown in this report.
Exploring the efficacy of molecular fragments of different complexity in computational SAR modeling
Authors: Albrecht Zimmermann, Björn Bringmann, Luc De Raedt
Date: 2015-01-13 00:00:00

An important first step in computational SAR modeling is to transform the
compounds into a representation that can be processed by predictive modeling
techniques. This is typically a feature vector where each feature indicates the
presence or absence of a molecular fragment. While the traditional approach to
SAR modeling employed size restricted fingerprints derived from path fragments,
much research in recent years focussed on mining more complex graph based
fragments. Today, there seems to be a growing consensus in the data mining
community that these more expressive fragments should be more useful. We
question this consensus and show experimentally that fragments of low
complexity, i.e. sequences, perform better than equally large sets of more
complex ones, an effect we explain by pairwise correlation among fragments and
the ability of a fragment set to encode compounds from different classes
distinctly. The size restriction on these sets is based on ordering the
fragments by class-correlation scores. In addition, we also evaluate the
effects of using a significance value instead of a length restriction for path
fragments and find a significant reduction in the number of features with
little loss in performance.
An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context
Authors: Pascal Germain, Amaury Habrard, Francois Laviolette, Emilie Morvant
Date: 2015-01-13 00:00:00
Tasks: Domain Adaptation

This paper provides a theoretical analysis of domain adaptation based on the
PAC-Bayesian theory. We propose an improvement of the previous domain
adaptation bound obtained by Germain et al. in two ways. We first give another
generalization bound tighter and easier to interpret. Moreover, we provide a
new analysis of the constant term appearing in the bound that can be of high
interest for developing new algorithmic solutions.
On Generalizing the C-Bound to the Multiclass and Multi-label Settings
Authors: Francois Laviolette, Emilie Morvant, Liva Ralaivola, Jean-Francis Roy
Date: 2015-01-13 00:00:00

The C-bound, introduced in Lacasse et al., gives a tight upper bound on the
risk of a binary majority vote classifier. In this work, we present a first
step towards extending this work to more complex outputs, by providing
generalizations of the C-bound to the multiclass and multi-label settings.
Improved 8-point Approximate DCT for Image and Video Compression Requiring Only 14 Additions
Authors: U. S. Potluri, A. Madanayake, R. J. Cintra, F. M. Bayer, S. Kulasekera, A. Edirisuriya
Date: 2015-01-13 00:00:00
Tasks: Video Compression

Video processing systems such as HEVC requiring low energy consumption needed
for the multimedia market has lead to extensive development in fast algorithms
for the efficient approximation of 2-D DCT transforms. The DCT is employed in a
multitude of compression standards due to its remarkable energy compaction
properties. Multiplier-free approximate DCT transforms have been proposed that
offer superior compression performance at very low circuit complexity. Such
approximations can be realized in digital VLSI hardware using additions and
subtractions only, leading to significant reductions in chip area and power
consumption compared to conventional DCTs and integer transforms. In this
paper, we introduce a novel 8-point DCT approximation that requires only 14
addition operations and no multiplications. The proposed transform possesses
low computational complexity and is compared to state-of-the-art DCT
approximations in terms of both algorithm complexity and peak signal-to-noise
ratio. The proposed DCT approximation is a candidate for reconfigurable video
standards such as HEVC. The proposed transform and several other DCT
approximations are mapped to systolic-array digital architectures and
physically realized as digital prototype circuits using FPGA technology and
mapped to 45 nm CMOS technology.
Random Bits Regression: a Strong General Predictor for Big Data
Authors: Yi Wang, Yi Li, Momiao Xiong, Li Jin
Date: 2015-01-13 00:00:00
Tasks: BIG-bench Machine Learning, regression

To improve accuracy and speed of regressions and classifications, we present
a data-based prediction method, Random Bits Regression (RBR). This method first
generates a large number of random binary intermediate/derived features based
on the original input matrix, and then performs regularized linear/logistic
regression on those intermediate/derived features to predict the outcome.
Benchmark analyses on a simulated dataset, UCI machine learning repository
datasets and a GWAS dataset showed that RBR outperforms other popular methods
in accuracy and robustness. RBR (available on
https://sourceforge.net/projects/rbr/) is very fast and requires reasonable
memories, therefore, provides a strong, robust and fast predictor in the big
data era.
Geometric Tight Frame based Stylometry for Art Authentication of van Gogh Paintings
Authors: Haixia Liu, Raymond H. Chan, Yuan YAO
Date: 2014-07-02 00:00:00
Tasks: Classification, General Classification, Outlier Detection

This paper is about authenticating genuine van Gogh paintings from forgeries.
The authentication process depends on two key steps: feature extraction and
outlier detection. In this paper, a geometric tight frame and some simple
statistics of the tight frame coefficients are used to extract features from
the paintings. Then a forward stage-wise rank boosting is used to select a
small set of features for more accurate classification so that van Gogh
paintings are highly concentrated towards some center point while forgeries are
spread out as outliers. Numerical results show that our method can achieve
86.08% classification accuracy under the leave-one-out cross-validation
procedure. Our method also identifies five features that are much more
predominant than other features. Using just these five features for
classification, our method can give 88.61% classification accuracy which is the
highest so far reported in literature. Evaluation of the five features is also
performed on two hundred datasets generated by bootstrap sampling with
replacement. The median and the mean are 88.61% and 87.77% respectively. Our
results show that a small set of statistics of the tight frame coefficients
along certain orientations can serve as discriminative features for van Gogh
paintings. It is more important to look at the tail distributions of such
directional coefficients than mean values and standard deviations. It reflects
a highly consistent style in van Gogh's brushstroke movements, where many
forgeries demonstrate a more diverse spread in these features.