pwc-archive/papers-with-abstracts huggingface dataset:
How good is the Shapley value-based approach to the influence maximization problem?
Authors: Kamil Adamczewski, Szymon Matejczyk, Tomasz P. Michalak
Date: 2014-09-27 00:00:00

The Shapley value has been recently advocated as a method to choose the seed
nodes for the process of information diffusion. Intuitively, since the Shapley
value evaluates the average marginal contribution of a player to the
coalitional game, it can be used in the network context to evaluate the
marginal contribution of a node in the process of information diffusion given
various groups of already 'infected' nodes. Although the above direction of
research seems promising, the current liter- ature is missing a throughout
assessment of its performance. The aim of this work is to provide such an
assessment of the existing Shapley value-based approaches to information
diffusion.
Comparing apples to apples in the evaluation of binary coding methods
Authors: Mohammad Rastegari, Shobeir Fakhraei, Jonghyun Choi, David Jacobs, Larry S. Davis
Date: 2014-05-05 00:00:00

We discuss methodological issues related to the evaluation of unsupervised
binary code construction methods for nearest neighbor search. These issues have
been widely ignored in literature. These coding methods attempt to preserve
either Euclidean distance or angular (cosine) distance in the binary embedding
space. We explain why when comparing a method whose goal is preserving cosine
similarity to one designed for preserving Euclidean distance, the original
features should be normalized by mapping them to the unit hypersphere before
learning the binary mapping functions. To compare a method whose goal is to
preserves Euclidean distance to one that preserves cosine similarity, the
original feature data must be mapped to a higher dimension by including a bias
term in binary mapping functions. These conditions ensure the fair comparison
between different binary code methods for the task of nearest neighbor search.
Our experiments show under these conditions the very simple methods (e.g. LSH
and ITQ) often outperform recent state-of-the-art methods (e.g. MDSH and
OK-means).
PAINTER: a spatio-spectral image reconstruction algorithm for optical interferometry
Authors: Antony Schutz, André Ferrari, David Mary, Férréol Soulez, Éric Thiébaut, Martin Vannier
Date: 2014-06-29 00:00:00
Tasks: Image Reconstruction

Astronomical optical interferometers sample the Fourier transform of the
intensity distribution of a source at the observation wavelength. Because of
rapid perturbations caused by atmospheric turbulence, the phases of the complex
Fourier samples (visibilities) cannot be directly exploited. Consequently,
specific image reconstruction methods have been devised in the last few
decades. Modern polychromatic optical interferometric instruments are now
paving the way to multiwavelength imaging. This paper is devoted to the
derivation of a spatio-spectral (3D) image reconstruction algorithm, coined
PAINTER (Polychromatic opticAl INTErferometric Reconstruction software). The
algorithm relies on an iterative process, which alternates estimation of
polychromatic images and of complex visibilities. The complex visibilities are
not only estimated from squared moduli and closure phases, but also
differential phases, which helps to better constrain the polychromatic
reconstruction. Simulations on synthetic data illustrate the efficiency of the
algorithm and in particular the relevance of injecting a differential phases
model in the reconstruction.
The automatic creation of concept maps from documents written using morphologically rich languages
Authors: Krunoslav Zubrinic, Damir Kalpic, Mario Milicevic
Date: 2012-10-29 00:00:00
Tasks: Management

Concept map is a graphical tool for representing knowledge. They have been
used in many different areas, including education, knowledge management,
business and intelligence. Constructing of concept maps manually can be a
complex task; an unskilled person may encounter difficulties in determining and
positioning concepts relevant to the problem area. An application that
recommends concept candidates and their position in a concept map can
significantly help the user in that situation. This paper gives an overview of
different approaches to automatic and semi-automatic creation of concept maps
from textual and non-textual sources. The concept map mining process is
defined, and one method suitable for the creation of concept maps from
unstructured textual sources in highly inflected languages such as the Croatian
language is described in detail. Proposed method uses statistical and data
mining techniques enriched with linguistic tools. With minor adjustments, that
method can also be used for concept map mining from textual sources in other
morphologically rich languages.
Audio Surveillance: a Systematic Review
Authors: Marco Crocco, Marco Cristani, Andrea Trucco, Vittorio Murino
Date: 2014-09-27 00:00:00
Tasks: Object Tracking

Despite surveillance systems are becoming increasingly ubiquitous in our
living environment, automated surveillance, currently based on video sensory
modality and machine intelligence, lacks most of the time the robustness and
reliability required in several real applications. To tackle this issue, audio
sensory devices have been taken into account, both alone or in combination with
video, giving birth, in the last decade, to a considerable amount of research.
In this paper audio-based automated surveillance methods are organized into a
comprehensive survey: a general taxonomy, inspired by the more widespread video
surveillance field, is proposed in order to systematically describe the methods
covering background subtraction, event classification, object tracking and
situation analysis. For each of these tasks, all the significant works are
reviewed, detailing their pros and cons and the context for which they have
been proposed. Moreover, a specific section is devoted to audio features,
discussing their expressiveness and their employment in the above described
tasks. Differently, from other surveys on audio processing and analysis, the
present one is specifically targeted to automated surveillance, highlighting
the target applications of each described methods and providing the reader
tables and schemes useful to retrieve the most suited algorithms for a specific
requirement.
Maximum mutual information regularized classification
Authors: Jim Jing-Yan Wang, Yi Wang, Shiguang Zhao, Xin Gao
Date: 2014-09-27 00:00:00
Tasks: Classification, General Classification

In this paper, a novel pattern classification approach is proposed by
regularizing the classifier learning to maximize mutual information between the
classification response and the true class label. We argue that, with the
learned classifier, the uncertainty of the true class label of a data sample
should be reduced by knowing its classification response as much as possible.
The reduced uncertainty is measured by the mutual information between the
classification response and the true class label. To this end, when learning a
linear classifier, we propose to maximize the mutual information between
classification responses and true class labels of training samples, besides
minimizing the classification error and reduc- ing the classifier complexity.
An objective function is constructed by modeling mutual information with
entropy estimation, and it is optimized by a gradi- ent descend method in an
iterative algorithm. Experiments on two real world pattern classification
problems show the significant improvements achieved by maximum mutual
information regularization.
Using Answer Set Programming for pattern mining
Authors: Thomas Guyet, Yves Moinard, René Quiniou
Date: 2014-09-27 00:00:00
Tasks: Sequential Pattern Mining

Serial pattern mining consists in extracting the frequent sequential patterns
from a unique sequence of itemsets. This paper explores the ability of a
declarative language, such as Answer Set Programming (ASP), to solve this issue
efficiently. We propose several ASP implementations of the frequent sequential
pattern mining task: a non-incremental and an incremental resolution. The
results show that the incremental resolution is more efficient than the
non-incremental one, but both ASP programs are less efficient than dedicated
algorithms. Nonetheless, this approach can be seen as a first step toward a
generic framework for sequential pattern mining with constraints.
Combating Corrupt Messages in Sparse Clustered Associative Memories
Authors: Zhe Yao, Vincent Gripon, Michael Rabbat
Date: 2014-09-27 00:00:00
Tasks: Retrieval

In this paper we analyze and extend the neural network based associative
memory proposed by Gripon and Berrou. This associative memory resembles the
celebrated Willshaw model with an added partite cluster structure. In the
literature, two retrieving schemes have been proposed for the network dynamics,
namely sum-of-sum and sum-of-max. They both offer considerably better
performance than Willshaw and Hopfield networks, when comparable retrieval
scenarios are considered. Former discussions and experiments concentrate on the
erasure scenario, where a partial message is used as a probe to the network, in
the hope of retrieving the full message. In this regard, sum-of-max outperforms
sum-of-sum in terms of retrieval rate by a large margin. However, we observe
that when noise and errors are present and the network is queried by a corrupt
probe, sum-of-max faces a severe limitation as its stringent activation rule
prevents a neuron from reviving back into play once deactivated. In this
manuscript, we categorize and analyze different error scenarios so that both
the erasure and the corrupt scenarios can be treated consistently. We make an
amendment to the network structure to improve the retrieval rate, at the cost
of an extra scalar per neuron. Afterwards, five different approaches are
proposed to deal with corrupt probes. As a result, we extend the network
capability, and also increase the robustness of the retrieving procedure. We
then experimentally compare all these proposals and discuss pros and cons of
each approach under different types of errors. Simulation results show that if
carefully designed, the network is able to preserve both a high retrieval rate
and a low running time simultaneously, even when queried by a corrupt probe.
Active Dictionary Learning in Sparse Representation Based Classification
Authors: Jin Xu, Haibo He, Hong Man
Date: 2014-09-19 00:00:00
Tasks: Active Learning, Classification, Dictionary Learning, General Classification, Sparse Representation-based Classification

Sparse representation, which uses dictionary atoms to reconstruct input
vectors, has been studied intensively in recent years. A proper dictionary is a
key for the success of sparse representation. In this paper, an active
dictionary learning (ADL) method is introduced, in which classification error
and reconstruction error are considered as the active learning criteria in
selection of the atoms for dictionary construction. The learned dictionaries
are caculated in sparse representation based classification (SRC). The
classification accuracy and reconstruction error are used to evaluate the
proposed dictionary learning method. The performance of the proposed dictionary
learning method is compared with other methods, including unsupervised
dictionary learning and whole-training-data dictionary. The experimental
results based on the UCI data sets and face data set demonstrate the efficiency
of the proposed method.
How close are we to understanding image-based saliency?
Authors: Matthias Kümmerer, Thomas Wallis, Matthias Bethge
Date: 2014-09-26 00:00:00
Tasks: Point Processes

Within the set of the many complex factors driving gaze placement, the
properities of an image that are associated with fixations under free viewing
conditions have been studied extensively. There is a general impression that
the field is close to understanding this particular association. Here we frame
saliency models probabilistically as point processes, allowing the calculation
of log-likelihoods and bringing saliency evaluation into the domain of
information. We compared the information gain of state-of-the-art models to a
gold standard and find that only one third of the explainable spatial
information is captured. We additionally provide a principled method to show
where and how models fail to capture information in the fixations. Thus,
contrary to previous assertions, purely spatial saliency remains a significant
challenge.
Order-invariant prior specification in Bayesian factor analysis
Authors: Dennis Leung, Mathias Drton
Date: 2014-09-26 00:00:00
Tasks: Bayesian Inference

In (exploratory) factor analysis, the loading matrix is identified only up to
orthogonal rotation. For identifiability, one thus often takes the loading
matrix to be lower triangular with positive diagonal entries. In Bayesian
inference, a standard practice is then to specify a prior under which the
loadings are independent, the off-diagonal loadings are normally distributed,
and the diagonal loadings follow a truncated normal distribution. This prior
specification, however, depends in an important way on how the variables and
associated rows of the loading matrix are ordered. We show how a minor
modification of the approach allows one to compute with the identifiable lower
triangular loading matrix but maintain invariance properties under reordering
of the variables.
Deconvolution of High-Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain
Authors: Charles Zheng, Franco Pestilli, Ariel Rokem
Date: 2014-09-25 00:00:00
Tasks: Spike Sorting

Diffusion-weighted magnetic resonance imaging (DWI) and fiber tractography
are the only methods to measure the structure of the white matter in the living
human brain. The diffusion signal has been modelled as the combined
contribution from many individual fascicles of nerve fibers passing through
each location in the white matter. Typically, this is done via basis pursuit,
but estimation of the exact directions is limited due to discretization. The
difficulties inherent in modeling DWI data are shared by many other problems
involving fitting non-parametric mixture models. Ekanadaham et al. proposed an
approach, continuous basis pursuit, to overcome discretization error in the
1-dimensional case (e.g., spike-sorting). Here, we propose a more general
algorithm that fits mixture models of any dimensionality without
discretization. Our algorithm uses the principles of L2-boost, together with
refitting of the weights and pruning of the parameters. The addition of these
steps to L2-boost both accelerates the algorithm and assures its accuracy. We
refer to the resulting algorithm as elastic basis pursuit, or EBP, since it
expands and contracts the active set of kernels as needed. We show that in
contrast to existing approaches to fitting mixtures, our boosting framework (1)
enables the selection of the optimal bias-variance tradeoff along the solution
path, and (2) scales with high-dimensional problems. In simulations of DWI, we
find that EBP yields better parameter estimates than a non-negative least
squares (NNLS) approach, or the standard model used in DWI, the tensor model,
which serves as the basis for diffusion tensor imaging (DTI). We demonstrate
the utility of the method in DWI data acquired in parts of the brain containing
crossings of multiple fascicles of nerve fibers.
Topic Similarity Networks: Visual Analytics for Large Document Sets
Authors: Arun S. Maiya, Robert M. Rolfe
Date: 2014-09-26 00:00:00
Tasks: Topic Models

We investigate ways in which to improve the interpretability of LDA topic
models by better analyzing and visualizing their outputs. We focus on examining
what we refer to as topic similarity networks: graphs in which nodes represent
latent topics in text collections and links represent similarity among topics.
We describe efficient and effective approaches to both building and labeling
such networks. Visualizations of topic models based on these networks are shown
to be a powerful means of exploring, characterizing, and summarizing large
collections of unstructured text documents. They help to "tease out"
non-obvious connections among different sets of documents and provide insights
into how topics form larger themes. We demonstrate the efficacy and
practicality of these approaches through two case studies: 1) NSF grants for
basic research spanning a 14 year period and 2) the entire English portion of
Wikipedia.
Gradient-based Taxis Algorithms for Network Robotics
Authors: Christian Blum, Verena V. Hafner
Date: 2014-09-26 00:00:00

Finding the physical location of a specific network node is a prototypical
task for navigation inside a wireless network. In this paper, we consider in
depth the implications of wireless communication as a measurement input of
gradient-based taxis algorithms. We discuss how gradients can be measured and
determine the errors of this estimation. We then introduce a gradient-based
taxis algorithm as an example of a family of gradient-based, convergent
algorithms and discuss its convergence in the context of network robotics. We
also conduct an exemplary experiment to show how to overcome some of the
specific problems related to network robotics. Finally, we show how to adapt
this framework to more complex objectives.
An Analysis on Selection for High-Resolution Approximations in Many-Objective Optimization
Authors: Hernan Aguirre, Arnaud Liefooghe, Sébastien Verel, Kiyoshi Tanaka
Date: 2014-09-26 00:00:00
Tasks: Evolutionary Algorithms

This work studies the behavior of three elitist multi- and many-objective
evolutionary algorithms generating a high-resolution approximation of the
Pareto optimal set. Several search-assessment indicators are defined to trace
the dynamics of survival selection and measure the ability to simultaneously
keep optimal solutions and discover new ones under different population sizes,
set as a fraction of the size of the Pareto optimal set.
Short-term solar irradiance and irradiation forecasts via different time series techniques: A preliminary study
Authors: Cédric Join, Cyril Voyant, Michel Fliess, Marc Muselli, Marie Laure Nivet, Christophe Paoli, Frédéric Chaxel
Date: 2014-09-26 00:00:00
Tasks: Time Series, Time Series Analysis

This communication is devoted to solar irradiance and irradiation short-term
forecasts, which are useful for electricity production. Several different time
series approaches are employed. Our results and the corresponding numerical
simulations show that techniques which do not need a large amount of historical
data behave better than those which need them, especially when those data are
quite noisy.
Extracting man-made objects from remote sensing images via fast level set evolutions
Authors: Zhongbin Li, Wenzhong Shi, Qunming Wang, Zelang Miao
Date: 2014-09-26 00:00:00
Tasks: Computational Efficiency, Object

Object extraction from remote sensing images has long been an intensive
research topic in the field of surveying and mapping. Most existing methods are
devoted to handling just one type of object and little attention has been paid
to improving the computational efficiency. In recent years, level set evolution
(LSE) has been shown to be very promising for object extraction in the
community of image processing and computer vision because it can handle
topological changes automatically while achieving high accuracy. However, the
application of state-of-the-art LSEs is compromised by laborious parameter
tuning and expensive computation. In this paper, we proposed two fast LSEs for
man-made object extraction from high spatial resolution remote sensing images.
The traditional mean curvature-based regularization term is replaced by a
Gaussian kernel and it is mathematically sound to do that. Thus a larger time
step can be used in the numerical scheme to expedite the proposed LSEs. In
contrast to existing methods, the proposed LSEs are significantly faster. Most
importantly, they involve much fewer parameters while achieving better
performance. The advantages of the proposed LSEs over other state-of-the-art
approaches have been verified by a range of experiments.
Using graph transformation algorithms to generate natural language equivalents of icons expressing medical concepts
Authors: Pascal Vaillant, Jean-Baptiste Lamy
Date: 2014-09-26 00:00:00
Tasks: Text Generation

A graphical language addresses the need to communicate medical information in
a synthetic way. Medical concepts are expressed by icons conveying fast visual
information about patients' current state or about the known effects of drugs.
In order to increase the visual language's acceptance and usability, a natural
language generation interface is currently developed. In this context, this
paper describes the use of an informatics method ---graph transformation--- to
prepare data consisting of concepts in an OWL-DL ontology for use in a natural
language generation component. The OWL concept may be considered as a
star-shaped graph with a central node. The method transforms it into a graph
representing the deep semantic structure of a natural language phrase. This
work may be of future use in other contexts where ontology concepts have to be
mapped to half-formalized natural language expressions.
Semi-supervised Spectral Clustering for Classification
Authors: Arif Mahmood, Ajmal S. Mian
Date: 2014-05-22 00:00:00
Tasks: Classification, Clustering, General Classification

We propose a Classification Via Clustering (CVC) algorithm which enables
existing clustering methods to be efficiently employed in classification
problems. In CVC, training and test data are co-clustered and class-cluster
distributions are used to find the label of the test data. To determine an
efficient number of clusters, a Semi-supervised Hierarchical Clustering (SHC)
algorithm is proposed. Clusters are obtained by hierarchically applying two-way
NCut by using signs of the Fiedler vector of the normalized graph Laplacian. To
this end, a Direct Fiedler Vector Computation algorithm is proposed. The graph
cut is based on the data structure and does not consider labels. Labels are
used only to define the stopping criterion for graph cut. We propose clustering
to be performed on the Grassmannian manifolds facilitating the formation of
spectral ensembles. The proposed algorithm outperformed state-of-the-art
image-set classification algorithms on five standard datasets.