pwc-archive/papers-with-abstracts huggingface dataset:
Scalable and Robust Construction of Topical Hierarchies
Authors: Chi Wang, Xueqing Liu, Yanglei Song, Jiawei Han
Date: 2014-03-13 00:00:00

Automated generation of high-quality topical hierarchies for a text
collection is a dream problem in knowledge engineering with many valuable
applications. In this paper a scalable and robust algorithm is proposed for
constructing a hierarchy of topics from a text collection. We divide and
conquer the problem using a top-down recursive framework, based on a tensor
orthogonal decomposition technique. We solve a critical challenge to perform
scalable inference for our newly designed hierarchical topic model. Experiments
with various real-world datasets illustrate its ability to generate robust,
high-quality hierarchies efficiently. Our method reduces the time of
construction by several orders of magnitude, and its robust feature renders it
possible for users to interactively revise the hierarchy.
Neighborhood Selection for Thresholding-based Subspace Clustering
Authors: Reinhard Heckel, Eirikur Agustsson, Helmut Bölcskei
Date: 2014-03-13 00:00:00
Tasks: Clustering

Subspace clustering refers to the problem of clustering high-dimensional data
points into a union of low-dimensional linear subspaces, where the number of
subspaces, their dimensions and orientations are all unknown. In this paper, we
propose a variation of the recently introduced thresholding-based subspace
clustering (TSC) algorithm, which applies spectral clustering to an adjacency
matrix constructed from the nearest neighbors of each data point with respect
to the spherical distance measure. The new element resides in an individual and
data-driven choice of the number of nearest neighbors. Previous performance
results for TSC, as well as for other subspace clustering algorithms based on
spectral clustering, come in terms of an intermediate performance measure,
which does not address the clustering error directly. Our main analytical
contribution is a performance analysis of the modified TSC algorithm (as well
as the original TSC algorithm) in terms of the clustering error directly.
Semantic Unification A sheaf theoretic approach to natural language
Authors: Samson Abramsky, Mehrnoosh Sadrzadeh
Date: 2014-03-13 00:00:00
Tasks: Sentence

Language is contextual and sheaf theory provides a high level mathematical
framework to model contextuality. We show how sheaf theory can model the
contextual nature of natural language and how gluing can be used to provide a
global semantics for a discourse by putting together the local logical
semantics of each sentence within the discourse. We introduce a presheaf
structure corresponding to a basic form of Discourse Representation Structures.
Within this setting, we formulate a notion of semantic unification --- gluing
meanings of parts of a discourse into a coherent whole --- as a form of
sheaf-theoretic gluing. We illustrate this idea with a number of examples where
it can used to represent resolutions of anaphoric references. We also discuss
multivalued gluing, described using a distributions functor, which can be used
to represent situations where multiple gluings are possible, and where we may
need to rank them using quantitative measures.
  Dedicated to Jim Lambek on the occasion of his 90th birthday.
The Potential Benefits of Filtering Versus Hyper-Parameter Optimization
Authors: Michael R. Smith, Tony Martinez, Christophe Giraud-Carrier
Date: 2014-03-13 00:00:00

The quality of an induced model by a learning algorithm is dependent on the
quality of the training data and the hyper-parameters supplied to the learning
algorithm. Prior work has shown that improving the quality of the training data
(i.e., by removing low quality instances) or tuning the learning algorithm
hyper-parameters can significantly improve the quality of an induced model. A
comparison of the two methods is lacking though. In this paper, we estimate and
compare the potential benefits of filtering and hyper-parameter optimization.
Estimating the potential benefit gives an overly optimistic estimate but also
empirically demonstrates an approximation of the maximum potential benefit of
each method. We find that, while both significantly improve the induced model,
improving the quality of the training set has a greater potential effect than
hyper-parameter optimization.
A Framework for the Analysis of Computational Imaging Systems with Practical Applications
Authors: Kaushik Mitra, Oliver Cossairt, Ashok Veeraraghavan
Date: 2013-08-08 00:00:00
Tasks: Deblurring

Over the last decade, a number of Computational Imaging (CI) systems have
been proposed for tasks such as motion deblurring, defocus deblurring and
multispectral imaging. These techniques increase the amount of light reaching
the sensor via multiplexing and then undo the deleterious effects of
multiplexing by appropriate reconstruction algorithms. Given the widespread
appeal and the considerable enthusiasm generated by these techniques, a
detailed performance analysis of the benefits conferred by this approach is
important.
  Unfortunately, a detailed analysis of CI has proven to be a challenging
problem because performance depends equally on three components: (1) the
optical multiplexing, (2) the noise characteristics of the sensor, and (3) the
reconstruction algorithm. A few recent papers have performed analysis taking
multiplexing and noise characteristics into account. However, analysis of CI
systems under state-of-the-art reconstruction algorithms, most of which exploit
signal prior models, has proven to be unwieldy. In this paper, we present a
comprehensive analysis framework incorporating all three components.
  In order to perform this analysis, we model the signal priors using a
Gaussian Mixture Model (GMM). A GMM prior confers two unique characteristics.
Firstly, GMM satisfies the universal approximation property which says that any
prior density function can be approximated to any fidelity using a GMM with
appropriate number of mixtures. Secondly, a GMM prior lends itself to
analytical tractability allowing us to derive simple expressions for the
`minimum mean square error' (MMSE), which we use as a metric to characterize
the performance of CI systems. We use our framework to analyze several
previously proposed CI techniques, giving conclusive answer to the question:
`How much performance gain is due to use of a signal prior and how much is due
to multiplexing?
Noise Facilitation in Associative Memories of Exponential Capacity
Authors: Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, Lav R. Varshney
Date: 2014-03-13 00:00:00
Tasks: Hippocampus, Retrieval

Recent advances in associative memory design through structured pattern sets
and graph-based inference algorithms have allowed reliable learning and recall
of an exponential number of patterns. Although these designs correct external
errors in recall, they assume neurons that compute noiselessly, in contrast to
the highly variable neurons in brain regions thought to operate associatively
such as hippocampus and olfactory cortex.
  Here we consider associative memories with noisy internal computations and
analytically characterize performance. As long as the internal noise level is
below a specified threshold, the error probability in the recall phase can be
made exceedingly small. More surprisingly, we show that internal noise actually
improves the performance of the recall phase while the pattern retrieval
capacity remains intact, i.e., the number of stored patterns does not reduce
with noise (up to a threshold). Computational experiments lend additional
support to our theoretical analysis. This work suggests a functional benefit to
noisy neurons in biological neuronal networks.
Ambiguity in language networks
Authors: Ricard V. Solé, Luís F. Seoane
Date: 2014-02-18 00:00:00

Human language defines the most complex outcomes of evolution. The emergence
of such an elaborated form of communication allowed humans to create extremely
structured societies and manage symbols at different levels including, among
others, semantics. All linguistic levels have to deal with an astronomic
combinatorial potential that stems from the recursive nature of languages. This
recursiveness is indeed a key defining trait. However, not all words are
equally combined nor frequent. In breaking the symmetry between less and more
often used and between less and more meaning-bearing units, universal scaling
laws arise. Such laws, common to all human languages, appear on different
stages from word inventories to networks of interacting words. Among these
seemingly universal traits exhibited by language networks, ambiguity appears to
be a specially relevant component. Ambiguity is avoided in most computational
approaches to language processing, and yet it seems to be a crucial element of
language architecture. Here we review the evidence both from language network
architecture and from theoretical reasonings based on a least effort argument.
Ambiguity is shown to play an essential role in providing a source of language
efficiency, and is likely to be an inevitable byproduct of network growth.
Sensing-Aware Kernel SVM
Authors: Weicong Ding, Prakash Ishwar, Venkatesh Saligrama, W. Clem Karl
Date: 2013-12-02 00:00:00
Tasks: General Classification, image-classification, Image Classification

We propose a novel approach for designing kernels for support vector machines
(SVMs) when the class label is linked to the observation through a latent state
and the likelihood function of the observation given the state (the sensing
model) is available. We show that the Bayes-optimum decision boundary is a
hyperplane under a mapping defined by the likelihood function. Combining this
with the maximum margin principle yields kernels for SVMs that leverage
knowledge of the sensing model in an optimal way. We derive the optimum kernel
for the bag-of-words (BoWs) sensing model and demonstrate its superior
performance over other kernels in document and image classification tasks.
These results indicate that such optimum sensing-aware kernel SVMs can match
the performance of rather sophisticated state-of-the-art approaches.
Sentiment Analysis by Using Fuzzy Logic
Authors: Md. Ansarul Haque
Date: 2014-03-13 00:00:00
Tasks: Sentiment Analysis

How could a product or service is reasonably evaluated by anyone in the
shortest time? A million dollar question but it is having a simple answer:
Sentiment analysis. Sentiment analysis is consumers review on products and
services which helps both the producers and consumers (stakeholders) to take
effective and efficient decision within a shortest period of time. Producers
can have better knowledge of their products and services through the sentiment
analysis (ex. positive and negative comments or consumers likes and dislikes)
which will help them to know their products status (ex. product limitations or
market status). Consumers can have better knowledge of their interested
products and services through the sentiment analysis (ex. positive and negative
comments or consumers likes and dislikes) which will help them to know their
deserving products status (ex. product limitations or market status). For more
specification of the sentiment values, fuzzy logic could be introduced.
Therefore, sentiment analysis with the help of fuzzy logic (deals with
reasoning and gives closer views to the exact sentiment values) will help the
producers or consumers or any interested person for taking the effective
decision according to their product or service interest.
Self-similar prior and wavelet bases for hidden incompressible turbulent motion
Authors: Patrick Héas, Frédéric Lavancier, Souleymane Kadri-Harouna
Date: 2013-02-22 00:00:00

This work is concerned with the ill-posed inverse problem of estimating
turbulent flows from the observation of an image sequence. From a Bayesian
perspective, a divergence-free isotropic fractional Brownian motion (fBm) is
chosen as a prior model for instantaneous turbulent velocity fields. This
self-similar prior characterizes accurately second-order statistics of velocity
fields in incompressible isotropic turbulence. Nevertheless, the associated
maximum a posteriori involves a fractional Laplacian operator which is delicate
to implement in practice. To deal with this issue, we propose to decompose the
divergent-free fBm on well-chosen wavelet bases. As a first alternative, we
propose to design wavelets as whitening filters. We show that these filters are
fractional Laplacian wavelets composed with the Leray projector. As a second
alternative, we use a divergence-free wavelet basis, which takes implicitly
into account the incompressibility constraint arising from physics. Although
the latter decomposition involves correlated wavelet coefficients, we are able
to handle this dependence in practice. Based on these two wavelet
decompositions, we finally provide effective and efficient algorithms to
approach the maximum a posteriori. An intensive numerical evaluation proves the
relevance of the proposed wavelet-based self-similar priors.
The Gaussian Radon Transform and Machine Learning
Authors: Irina Holmes, Ambar Sengupta
Date: 2013-10-17 00:00:00
Tasks: BIG-bench Machine Learning, regression

There has been growing recent interest in probabilistic interpretations of
kernel-based methods as well as learning in Banach spaces. The absence of a
useful Lebesgue measure on an infinite-dimensional reproducing kernel Hilbert
space is a serious obstacle for such stochastic models. We propose an
estimation model for the ridge regression problem within the framework of
abstract Wiener spaces and show how the support vector machine solution to such
problems can be interpreted in terms of the Gaussian Radon transform.
On Combining Machine Learning with Decision Making
Authors: Theja Tulabandhula, Cynthia Rudin
Date: 2011-04-27 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Generalization Bounds

We present a new application and covering number bound for the framework of
"Machine Learning with Operational Costs (MLOC)," which is an exploratory form
of decision theory. The MLOC framework incorporates knowledge about how a
predictive model will be used for a subsequent task, thus combining machine
learning with the decision that is made afterwards. In this work, we use the
MLOC framework to study a problem that has implications for power grid
reliability and maintenance, called the Machine Learning and Traveling
Repairman Problem ML&TRP. The goal of the ML&TRP is to determine a route for a
"repair crew," which repairs nodes on a graph. The repair crew aims to minimize
the cost of failures at the nodes, but as in many real situations, the failure
probabilities are not known and must be estimated. The MLOC framework allows us
to understand how this uncertainty influences the repair route. We also present
new covering number generalization bounds for the MLOC framework.
A predictive analytics approach to reducing avoidable hospital readmission
Authors: Issac Shams, Saeede Ajorlou, Kai Yang
Date: 2014-02-24 00:00:00
Tasks: Attribute, Management

Hospital readmission has become a critical metric of quality and cost of
healthcare. Medicare anticipates that nearly $17 billion is paid out on the 20%
of patients who are readmitted within 30 days of discharge. Although several
interventions such as transition care management and discharge reengineering
have been practiced in recent years, the effectiveness and sustainability
depends on how well they can identify and target patients at high risk of
rehospitalization. Based on the literature, most current risk prediction models
fail to reach an acceptable accuracy level; none of them considers patient's
history of readmission and impacts of patient attribute changes over time; and
they often do not discriminate between planned and unnecessary readmissions.
Tackling such drawbacks, we develop a new readmission metric based on
administrative data that can identify potentially avoidable readmissions from
all other types of readmission. We further propose a tree based classification
method to estimate the predicted probability of readmission that can directly
incorporate patient's history of readmission and risk factors changes over
time. The proposed methods are validated with 2011-12 Veterans Health
Administration data from inpatients hospitalized for heart failure, acute
myocardial infarction, pneumonia, or chronic obstructive pulmonary disease in
the State of Michigan. Results shows improved discrimination power compared to
the literature (c-statistics>80%) and good calibration.
Parallel WiSARD object tracker: a ram-based tracking system
Authors: Rodrigo da Silva Moreira, Nelson Francisco Favilla Ebecken
Date: 2014-03-12 00:00:00
Tasks: Edge Detection, Image Segmentation, Object, Object Tracking, Quantization, Semantic Segmentation

This paper proposes the Parallel WiSARD Object Tracker (PWOT), a new object
tracker based on the WiSARD weightless neural network that is robust against
quantization errors. Object tracking in video is an important and challenging
task in many applications. Difficulties can arise due to weather conditions,
target trajectory and appearance, occlusions, lighting conditions and noise.
Tracking is a high-level application and requires the object location frame by
frame in real time. This paper proposes a fast hybrid image segmentation
(threshold and edge detection) in YcbCr color model and a parallel RAM based
discriminator that improves efficiency when quantization errors occur. The
original WiSARD training algorithm was changed to allow the tracking.
Memory Capacity of Neural Networks using a Circulant Weight Matrix
Authors: Vamsi Sashank Kotagiri
Date: 2014-03-12 00:00:00

This paper presents results on the memory capacity of a generalized feedback
neural network using a circulant matrix. Children are capable of learning soon
after birth which indicates that the neural networks of the brain have prior
learnt capacity that is a consequence of the regular structures in the brain's
organization. Motivated by this idea, we consider the capacity of circulant
matrices as weight matrices in a feedback network.
Sparse Recovery with Linear and Nonlinear Observations: Dependent and Noisy Data
Authors: Cem Aksoylar, Venkatesh Saligrama
Date: 2014-03-12 00:00:00
Tasks: regression

We formulate sparse support recovery as a salient set identification problem
and use information-theoretic analyses to characterize the recovery performance
and sample complexity. We consider a very general model where we are not
restricted to linear models or specific distributions. We state non-asymptotic
bounds on recovery probability and a tight mutual information formula for
sample complexity. We evaluate our bounds for applications such as sparse
linear regression and explicitly characterize effects of correlation or noisy
features on recovery performance. We show improvements upon previous work and
identify gaps between the performance of recovery algorithms and fundamental
information.
Emerging archetypes in massive artificial societies for literary purposes using genetic algorithms
Authors: R. H. García-Ortega, P. García-Sánchez, J. J. Merelo
Date: 2014-03-12 00:00:00

The creation of fictional stories is a very complex task that usually implies
a creative process where the author has to combine characters, conflicts and
plots to create an engaging narrative. This work presents a simulated
environment with hundreds of characters that allows the study of coherent and
interesting literary archetypes (or behaviours), plots and sub-plots. We will
use this environment to perform a study about the number of profiles
(parameters that define the personality of a character) needed to create two
emergent scenes of archetypes: "natality control" and "revenge". A Genetic
Algorithm (GA) will be used to find the fittest number of profiles and
parameter configuration that enables the existence of the desired archetypes
(played by the characters without their explicit knowledge). The results show
that parametrizing this complex system is possible and that these kind of
archetypes can emerge in the given environment.
Evaluation of Image Segmentation and Filtering With ANN in the Papaya Leaf
Authors: Maicon A. Sartin, Alexandre C. R. da Silva
Date: 2014-03-12 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Precision agriculture is area with lack of cheap technology. The refinement
of the production system brings large advantages to the producer and the use of
images makes the monitoring a more cheap methodology. Macronutrients monitoring
can to determine the health and vulnerability of the plant in specific stages.
In this paper is analyzed the method based on computational intelligence to
work with image segmentation in the identification of symptoms of plant
nutrient deficiency. Artificial neural networks are evaluated for image
segmentation and filtering, several variations of parameters and insertion
impulsive noise were evaluated too. Satisfactory results are achieved with
artificial neural for segmentation same with high noise levels.
Efficient Legendre moment computation for grey level images
Authors: Guanyu Yang, Huazhong Shu, Christine Toumoulin, Guo-Niu Han, Limin M. Luo
Date: 2014-03-12 00:00:00

Legendre orthogonal moments have been widely used in the field of image
analysis. Because their computation by a direct method is very time expensive,
recent efforts have been devoted to the reduction of computational complexity.
Nevertheless, the existing algorithms are mainly focused on binary images. We
propose here a new fast method for computing the Legendre moments, which is not
only suitable for binary images but also for grey levels. We first set up the
recurrence formula of one-dimensional (1D) Legendre moments by using the
recursive property of Legendre polynomials. As a result, the 1D Legendre
moments of order p, Lp = Lp(0), can be expressed as a linear combination of
Lp-1(1) and Lp-2(0). Based on this relationship, the 1D Legendre moments Lp(0)
is thus obtained from the array of L1(a) and L0(a) where a is an integer number
less than p. To further decrease the computation complexity, an algorithm, in
which no multiplication is required, is used to compute these quantities. The
method is then extended to the calculation of the two-dimensional Legendre
moments Lpq. We show that the proposed method is more efficient than the direct
method.
Image reconstruction from limited range projections using orthogonal moments
Authors: Huazhong Shu, Jian Zhou, Guo-Niu Han, Limin M. Luo, Jean-Louis Coatrieux
Date: 2014-03-12 00:00:00
Tasks: Image Reconstruction

A set of orthonormal polynomials is proposed for image reconstruction from
projection data. The relationship between the projection moments and image
moments is discussed in detail, and some interesting properties are
demonstrated. Simulation results are provided to validate the method and to
compare its performance with previous works.
3D Well-composed Polyhedral Complexes
Authors: Rocio Gonzalez-Diaz, Maria-Jose Jimenez, Belen Medrano
Date: 2014-03-12 00:00:00

A binary three-dimensional (3D) image $I$ is well-composed if the boundary
surface of its continuous analog is a 2D manifold. Since 3D images are not
often well-composed, there are several voxel-based methods ("repairing"
algorithms) for turning them into well-composed ones but these methods either
do not guarantee the topological equivalence between the original image and its
corresponding well-composed one or involve sub-sampling the whole image.
  In this paper, we present a method to locally "repair" the cubical complex
$Q(I)$ (embedded in $\mathbb{R}^3$) associated to $I$ to obtain a polyhedral
complex $P(I)$ homotopy equivalent to $Q(I)$ such that the boundary of every
connected component of $P(I)$ is a 2D manifold. The reparation is performed via
a new codification system for $P(I)$ under the form of a 3D grayscale image
that allows an efficient access to cells and their faces.