pwc-archive/papers-with-abstracts huggingface dataset:
Robust Binary Fused Compressive Sensing using Adaptive Outlier Pursuit
Authors: Xiangrong Zeng, Mário A. T. Figueiredo
Date: 2014-02-20 00:00:00
Tasks: Compressive Sensing

We propose a new method, {\it robust binary fused compressive sensing}
(RoBFCS), to recover sparse piece-wise smooth signals from 1-bit compressive
measurements. The proposed method is a modification of our previous {\it binary
fused compressive sensing} (BFCS) algorithm, which is based on the {\it binary
iterative hard thresholding} (BIHT) algorithm. As in BIHT, the data term of the
objective function is a one-sided $\ell_1$ (or $\ell_2$) norm. Experiments show
that the proposed algorithm is able to take advantage of the piece-wise
smoothness of the original signal and detect sign flips and correct them,
achieving more accurate recovery than BFCS and BIHT.
Clinical TempEval
Authors: Steven Bethard, Leon Derczynski, James Pustejovsky, Marc Verhagen
Date: 2014-03-19 00:00:00
Tasks: Relation

We describe the Clinical TempEval task which is currently in preparation for
the SemEval-2015 evaluation exercise. This task involves identifying and
describing events, times and the relations between them in clinical text. Six
discrete subtasks are included, focusing on recognising mentions of times and
events, describing those mentions for both entity types, identifying the
relation between an event and the document creation time, and identifying
narrative container relations.
Evolutionary Algorithm for Drug Discovery Interim Design Report
Authors: Mark Shackelford
Date: 2014-03-19 00:00:00
Tasks: Drug Discovery

A software program which aims to provide an exploration capability over the
Search Space of potential drug molecules. The program explores the search space
by generating random molecules, determining their fitness and then breeding a
new generation from the fittest individuals. The search space, in theory any
combination of any elements in any order, is constrained by the use of a subset
of elements and a list of fragments, molecular parts that are known to be
useful in drug development. The resultant molecules from each generation are
stored in a searchable database, so that the user can browse through previous
generations looking for interesting molecules.
Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit
Authors: Cagdas Bilen, Gilles Puy, Rémi Gribonval, Laurent Daudet
Date: 2014-03-17 00:00:00
Tasks: Compressive Sensing, Retrieval

We investigate the methods that simultaneously enforce sparsity and low-rank
structure in a matrix as often employed for sparse phase retrieval problems or
phase calibration problems in compressive sensing. We propose a new approach
for analyzing the trade off between the sparsity and low rank constraints in
these approaches which not only helps to provide guidelines to adjust the
weights between the aforementioned constraints, but also enables new simulation
strategies for evaluating performance. We then provide simulation results for
phase retrieval and phase calibration cases both to demonstrate the consistency
of the proposed method with other approaches and to evaluate the change of
performance with different weights for the sparsity and low rank structure
constraints.
A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation
Authors: Subhadip Mukherjee, Chandra Sekhar Seelamantula
Date: 2014-03-19 00:00:00
Tasks: Denoising, Dictionary Learning, Image Denoising

In big data image/video analytics, we encounter the problem of learning an
overcomplete dictionary for sparse representation from a large training
dataset, which can not be processed at once because of storage and
computational constraints. To tackle the problem of dictionary learning in such
scenarios, we propose an algorithm for parallel dictionary learning. The
fundamental idea behind the algorithm is to learn a sparse representation in
two phases. In the first phase, the whole training dataset is partitioned into
small non-overlapping subsets, and a dictionary is trained independently on
each small database. In the second phase, the dictionaries are merged to form a
global dictionary. We show that the proposed algorithm is efficient in its
usage of memory and computational complexity, and performs on par with the
standard learning strategy operating on the entire data at a time. As an
application, we consider the problem of image denoising. We present a
comparative analysis of our algorithm with the standard learning techniques,
that use the entire database at a time, in terms of training and denoising
performance. We observe that the split-and-merge algorithm results in a
remarkable reduction of training time, without significantly affecting the
denoising performance.
MFCC based Enlargement of the Training Set for Emotion Recognition in Speech
Authors: Inma Mohino-Herranz, Roberto Gil-Pita, Sagrario Alonso-Diaz, Manuel Rosa-Zurera
Date: 2014-03-19 00:00:00
Tasks: Emotion Recognition

Emotional state recognition through speech is being a very interesting
research topic nowadays. Using subliminal information of speech, denominated as
prosody, it is possible to recognize the emotional state of the person. One of
the main problems in the design of automatic emotion recognition systems is the
small number of available patterns. This fact makes the learning process more
difficult, due to the generalization problems that arise under these
conditions. In this work we propose a solution to this problem consisting in
enlarging the training set through the creation the new virtual patterns. In
the case of emotional speech, most of the emotional information is included in
speed and pitch variations. So, a change in the average pitch that does not
modify neither the speed nor the pitch variations does not affect the expressed
emotion. Thus, we use this prior information in order to create new patterns
applying a gender dependent pitch shift modification in the feature extraction
process of the classification system. For this purpose, we propose a frequency
scaling modification of the Mel Frequency Cepstral Coefficients, used to
classify the emotion. For this purpose, we propose a gender dependent frequency
scaling modification. This proposed process allows us to synthetically increase
the number of available patterns in the training set, thus increasing the
generalization capability of the system and reducing the test error. Results
carried out with two different classifiers with different degree of
generalization capability demonstrate the suitability of the proposal.
Spelling Error Trends and Patterns in Sindhi
Authors: Zeeshan Bhatti, Imdad Ali Ismaili, Asad Ali Shaikh, Waseem Javaid
Date: 2014-03-19 00:00:00

Statistical error Correction technique is the most accurate and widely used
approach today, but for a language like Sindhi which is a low resourced
language the trained corpora's are not available, so the statistical techniques
are not possible at all. Instead a useful alternative would be to exploit
various spelling error trends in Sindhi by using a Rule based approach. For
designing such technique an essential prerequisite would be to study the
various error patterns in a language. This pa per presents various studies of
spelling error trends and their types in Sindhi Language. The research shows
that the error trends common to all languages are also encountered in Sindhi
but their do exist some error patters that are catered specifically to a Sindhi
language.
Study on performance improvement of oil paint image filter algorithm using parallel pattern library
Authors: Siddhartha Mukherjee
Date: 2014-03-19 00:00:00

This paper gives a detailed study on the performance of oil paint image
filter algorithm with various parameters applied on an image of RGB model. Oil
Paint image processing, being very performance hungry, current research tries
to find improvement using parallel pattern library. With increasing
kernel-size, the processing time of oil paint image filter algorithm increases
exponentially.
A Proximal Stochastic Gradient Method with Progressive Variance Reduction
Authors: Lin Xiao, Tong Zhang
Date: 2014-03-19 00:00:00

We consider the problem of minimizing the sum of two convex functions: one is
the average of a large number of smooth component functions, and the other is a
general convex function that admits a simple proximal mapping. We assume the
whole objective function is strongly convex. Such problems often arise in
machine learning, known as regularized empirical risk minimization. We propose
and analyze a new proximal stochastic gradient method, which uses a multi-stage
scheme to progressively reduce the variance of the stochastic gradient. While
each iteration of this algorithm has similar cost as the classical stochastic
gradient method (or incremental gradient method), we show that the expected
objective value converges to the optimum at a geometric rate. The overall
complexity of this method is much lower than both the proximal full gradient
method and the standard proximal stochastic gradient method.
Structured Sparse Method for Hyperspectral Unmixing
Authors: Feiyun Zhu, Ying Wang, Shiming Xiang, Bin Fan, Chunhong Pan
Date: 2014-03-19 00:00:00
Tasks: Hyperspectral Unmixing

Hyperspectral Unmixing (HU) has received increasing attention in the past
decades due to its ability of unveiling information latent in hyperspectral
data. Unfortunately, most existing methods fail to take advantage of the
spatial information in data. To overcome this limitation, we propose a
Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method
from the following two aspects. First, we incorporate a graph Laplacian to
encode the manifold structures embedded in the hyperspectral data space. In
this way, the highly similar neighboring pixels can be grouped together.
Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the
same manifold structure are sparsely mixed by a common set of relevant bases.
These two factors act as a new structured sparse constraint. With this
constraint, our method can learn a compact space, where highly similar pixels
are grouped to share correlated sparse representations. Experiments on real
hyperspectral data sets with different noise levels demonstrate that our method
outperforms the state-of-the-art methods significantly.
Bayesian Source Separation Applied to Identifying Complex Organic Molecules in Space
Authors: Kevin H. Knuth, Man Kit Tse, Joshua Choinsky, Haley A. Maunu, Duane F. Carbon
Date: 2014-03-18 00:00:00

Emission from a class of benzene-based molecules known as Polycyclic Aromatic
Hydrocarbons (PAHs) dominates the infrared spectrum of star-forming regions.
The observed emission appears to arise from the combined emission of numerous
PAH species, each with its unique spectrum. Linear superposition of the PAH
spectra identifies this problem as a source separation problem. It is, however,
of a formidable class of source separation problems given that different PAH
sources potentially number in the hundreds, even thousands, and there is only
one measured spectral signal for a given astrophysical site. Fortunately, the
source spectra of the PAHs are known, but the signal is also contaminated by
other spectral sources. We describe our ongoing work in developing Bayesian
source separation techniques relying on nested sampling in conjunction with an
ON/OFF mechanism enabling simultaneous estimation of the probability that a
particular PAH species is present and its contribution to the spectrum.
Can Cascades be Predicted?
Authors: Justin Cheng, Lada A. Adamic, P. Alex Dow, Jon Kleinberg, Jure Leskovec
Date: 2014-03-18 00:00:00

On many social networking web sites such as Facebook and Twitter, resharing
or reposting functionality allows users to share others' content with their own
friends or followers. As content is reshared from user to user, large cascades
of reshares can form. While a growing body of research has focused on analyzing
and characterizing such cascades, a recent, parallel line of work has argued
that the future trajectory of a cascade may be inherently unpredictable. In
this work, we develop a framework for addressing cascade prediction problems.
On a large sample of photo reshare cascades on Facebook, we find strong
performance in predicting whether a cascade will continue to grow in the
future. We find that the relative growth of a cascade becomes more predictable
as we observe more of its reshares, that temporal and structural features are
key predictors of cascade size, and that initially, breadth, rather than depth
in a cascade is a better indicator of larger cascades. This prediction
performance is robust in the sense that multiple distinct classes of features
all achieve similar performance. We also discover that temporal features are
predictive of a cascade's eventual shape. Observing independent cascades of the
same content, we find that while these cascades differ greatly in size, we are
still able to predict which ends up the largest.
Language Heedless of Logic - Philosophy Mindful of What? Failures of Distributive and Absorption Laws
Authors: Arthur Merin
Date: 2014-03-14 00:00:00
Tasks: Descriptive, Philosophy

Much of philosophical logic and all of philosophy of language make empirical
claims about the vernacular natural language. They presume semantics under
which `and' and `or' are related by the dually paired distributive and
absorption laws. However, at least one of each pair of laws fails in the
vernacular. `Implicature'-based auxiliary theories associated with the
programme of H.P. Grice do not prove remedial. Conceivable alternatives that
might replace the familiar logics as descriptive instruments are briefly noted:
(i) substructural logics and (ii) meaning composition in linear algebras over
the reals, occasionally constrained by norms of classical logic. Alternative
(ii) locates the problem in violations of one of the idempotent laws. Reasons
for a lack of curiosity about elementary and easily testable implications of
the received theory are considered. The concept of `reflective equilibrium' is
critically examined for its role in reconciling normative desiderata and
descriptive commitments.
Complex-Valued Autoencoders
Authors: Pierre Baldi, Zhiqin Lu
Date: 2011-08-20 00:00:00
Tasks: Clustering

Autoencoders are unsupervised machine learning circuits whose learning goal
is to minimize a distortion measure between inputs and outputs. Linear
autoencoders can be defined over any field and only real-valued linear
autoencoder have been studied so far. Here we study complex-valued linear
autoencoders where the components of the training vectors and adjustable
matrices are defined over the complex field with the $L_2$ norm. We provide
simpler and more general proofs that unify the real-valued and complex-valued
cases, showing that in both cases the landscape of the error function is
invariant under certain groups of transformations. The landscape has no local
minima, a family of global minima associated with Principal Component Analysis,
and many families of saddle points associated with orthogonal projections onto
sub-space spanned by sub-optimal subsets of eigenvectors of the covariance
matrix. The theory yields several iterative, convergent, learning algorithms, a
clear understanding of the generalization properties of the trained
autoencoders, and can equally be applied to the hetero-associative case when
external targets are provided. Partial results on deep architecture as well as
the differential geometry of autoencoders are also presented. The general
framework described here is useful to classify autoencoders and identify
general common properties that ought to be investigated for each class,
illuminating some of the connections between information theory, unsupervised
learning, clustering, Hebbian learning, and autoencoders.
Similarity networks for classification: a case study in the Horse Colic problem
Authors: Lluís Belanche, Jerónimo Hernández
Date: 2014-03-18 00:00:00
Tasks: General Classification, Missing Values

This paper develops a two-layer neural network in which the neuron model
computes a user-defined similarity function between inputs and weights. The
neuron transfer function is formed by composition of an adapted logistic
function with the mean of the partial input-weight similarities. The resulting
neuron model is capable of dealing directly with variables of potentially
different nature (continuous, fuzzy, ordinal, categorical). There is also
provision for missing values. The network is trained using a two-stage
procedure very similar to that used to train a radial basis function (RBF)
neural network. The network is compared to two types of RBF networks in a
non-trivial dataset: the Horse Colic problem, taken as a case study and
analyzed in detail.
Splitting Methods for Convex Clustering
Authors: Eric C. Chi, Kenneth Lange
Date: 2013-04-01 00:00:00
Tasks: Clustering

Clustering is a fundamental problem in many scientific applications. Standard
methods such as $k$-means, Gaussian mixture models, and hierarchical
clustering, however, are beset by local minima, which are sometimes drastically
suboptimal. Recently introduced convex relaxations of $k$-means and
hierarchical clustering shrink cluster centroids toward one another and ensure
a unique global minimizer. In this work we present two splitting methods for
solving the convex clustering problem. The first is an instance of the
alternating direction method of multipliers (ADMM); the second is an instance
of the alternating minimization algorithm (AMA). In contrast to previously
considered algorithms, our ADMM and AMA formulations provide simple and unified
frameworks for solving the convex clustering problem under the previously
studied norms and open the door to potentially novel norms. We demonstrate the
performance of our algorithm on both simulated and real data examples. While
the differences between the two algorithms appear to be minor on the surface,
complexity analysis and numerical experiments show AMA to be significantly more
efficient.
Sign Language Gibberish for syntactic parsing evaluation
Authors: Rémi Dubot, Christophe Collet
Date: 2014-03-18 00:00:00

Sign Language (SL) automatic processing slowly progresses bottom-up. The
field has seen proposition to handle the video signal, to recognize and
synthesize sublexical and lexical units. It starts to see the development of
supra-lexical processing. But the recognition, at this level, lacks data. The
syntax of SL appears very specific as it uses massively the multiplicity of
articulators and its access to the spatial dimensions. Therefore new parsing
techniques are developed. However these need to be evaluated. The shortage on
real data restrains the corpus-based models to small sizes. We propose here a
solution to produce data-sets for the evaluation of parsers on the specific
properties of SL. The article first describes the general model used to
generates dependency grammars and the phrase generation from these lasts. It
then discusses the limits of approach. The solution shows to be of particular
interest to evaluate the scalability of the techniques on big models.
Spectral Clustering with Jensen-type kernels and their multi-point extensions
Authors: Debarghya Ghoshdastidar, Ambedkar Dukkipati, Ajay P. Adsul, Aparna S. Vijayan
Date: 2014-03-18 00:00:00
Proceeding: CVPR 2014 6
Tasks: Clustering, Image Segmentation, Semantic Segmentation, Vocal Bursts Type Prediction

Motivated by multi-distribution divergences, which originate in information
theory, we propose a notion of `multi-point' kernels, and study their
applications. We study a class of kernels based on Jensen type divergences and
show that these can be extended to measure similarity among multiple points. We
study tensor flattening methods and develop a multi-point (kernel) spectral
clustering (MSC) method. We further emphasize on a special case of the proposed
kernels, which is a multi-point extension of the linear (dot-product) kernel
and show the existence of cubic time tensor flattening algorithm in this case.
Finally, we illustrate the usefulness of our contributions using standard data
sets and image segmentation tasks.
Concept Based vs. Pseudo Relevance Feedback Performance Evaluation for Information Retrieval System
Authors: Mohammed El Amine Abderrahim
Date: 2014-03-18 00:00:00
Tasks: Information Retrieval, Retrieval

This article evaluates the performance of two techniques for query
reformulation in a system for information retrieval, namely, the concept based
and the pseudo relevance feedback reformulation. The experiments performed on a
corpus of Arabic text have allowed us to compare the contribution of these two
reformulation techniques in improving the performance of an information
retrieval system for Arabic texts.
Transduction on Directed Graphs via Absorbing Random Walks
Authors: Jaydeep De, Xiaowei Zhang, Li Cheng
Date: 2014-02-19 00:00:00

In this paper we consider the problem of graph-based transductive
classification, and we are particularly interested in the directed graph
scenario which is a natural form for many real world applications. Different
from existing research efforts that either only deal with undirected graphs or
circumvent directionality by means of symmetrization, we propose a novel random
walk approach on directed graphs using absorbing Markov chains, which can be
regarded as maximizing the accumulated expected number of visits from the
unlabeled transient states. Our algorithm is simple, easy to implement, and
works with large-scale graphs. In particular, it is capable of preserving the
graph structure even when the input graph is sparse and changes over time, as
well as retaining weak signals presented in the directed edges. We present its
intimate connections to a number of existing methods, including graph kernels,
graph Laplacian based methods, and interestingly, spanning forest of graphs.
Its computational complexity and the generalization error are also studied.
Empirically our algorithm is systematically evaluated on a wide range of
applications, where it has shown to perform competitively comparing to a suite
of state-of-the-art methods.
Active Discovery of Network Roles for Predicting the Classes of Network Nodes
Authors: Leto Peel
Date: 2013-12-27 00:00:00
Tasks: Active Learning, General Classification, Node Classification

Nodes in real world networks often have class labels, or underlying
attributes, that are related to the way in which they connect to other nodes.
Sometimes this relationship is simple, for instance nodes of the same class are
may be more likely to be connected. In other cases, however, this is not true,
and the way that nodes link in a network exhibits a different, more complex
relationship to their attributes. Here, we consider networks in which we know
how the nodes are connected, but we do not know the class labels of the nodes
or how class labels relate to the network links. We wish to identify the best
subset of nodes to label in order to learn this relationship between node
attributes and network links. We can then use this discovered relationship to
accurately predict the class labels of the rest of the network nodes.
  We present a model that identifies groups of nodes with similar link
patterns, which we call network roles, using a generative blockmodel. The model
then predicts labels by learning the mapping from network roles to class labels
using a maximum margin classifier. We choose a subset of nodes to label
according to an iterative margin-based active learning strategy. By integrating
the discovery of network roles with the classifier optimisation, the active
learning process can adapt the network roles to better represent the network
for node classification. We demonstrate the model by exploring a selection of
real world networks, including a marine food web and a network of English
words. We show that, in contrast to other network classifiers, this model
achieves good classification accuracy for a range of networks with different
relationships between class labels and network links.