pwc-archive/papers-with-abstracts huggingface dataset:
Cognitive Learning of Statistical Primary Patterns via Bayesian Network
Authors: Weijia Han, Huiyan Sang, Min Sheng, Jiandong Li, Shuguang Cui
Date: 2014-09-28 00:00:00

In cognitive radio (CR) technology, the trend of sensing is no longer to only
detect the presence of active primary users. A large number of applications
demand for more comprehensive knowledge on primary user behaviors in spatial,
temporal, and frequency domains. To satisfy such requirements, we study the
statistical relationship among primary users by introducing a Bayesian network
(BN) based framework. How to learn such a BN structure is a long standing
issue, not fully understood even in the statistical learning community.
Besides, another key problem in this learning scenario is that the CR has to
identify how many variables are in the BN, which is usually considered as prior
knowledge in statistical learning applications. To solve such two issues
simultaneously, this paper proposes a BN structure learning scheme consisting
of an efficient structure learning algorithm and a blind variable
identification scheme. The proposed approach incurs significantly lower
computational complexity compared with previous ones, and is capable of
determining the structure without assuming much prior knowledge about
variables. With this result, cognitive users could efficiently understand the
statistical pattern of primary networks, such that more efficient cognitive
protocols could be designed across different network layers.
Structural Decompositions for Problems with Global Constraints
Authors: Evgenij Thorstensen
Date: 2015-02-09 00:00:00

A wide range of problems can be modelled as constraint satisfaction problems
(CSPs), that is, a set of constraints that must be satisfied simultaneously.
Constraints can either be represented extensionally, by explicitly listing
allowed combinations of values, or implicitly, by special-purpose algorithms
provided by a solver.
  Such implicitly represented constraints, known as global constraints, are
widely used; indeed, they are one of the key reasons for the success of
constraint programming in solving real-world problems. In recent years, a
variety of restrictions on the structure of CSP instances have been shown to
yield tractable classes of CSPs. However, most such restrictions fail to
guarantee tractability for CSPs with global constraints. We therefore study the
applicability of structural restrictions to instances with such constraints.
  We show that when the number of solutions to a CSP instance is bounded in key
parts of the problem, structural restrictions can be used to derive new
tractable classes. Furthermore, we show that this result extends to
combinations of instances drawn from known tractable classes, as well as to CSP
instances where constraints assign costs to satisfying assignments.
On the Dynamics of a Recurrent Hopfield Network
Authors: Rama Garimella, Berkay Kicanaoglu, Moncef Gabbouj
Date: 2015-02-09 00:00:00

In this research paper novel real/complex valued recurrent Hopfield Neural
Network (RHNN) is proposed. The method of synthesizing the energy landscape of
such a network and the experimental investigation of dynamics of Recurrent
Hopfield Network is discussed. Parallel modes of operation (other than fully
parallel mode) in layered RHNN is proposed. Also, certain potential
applications are proposed.
Semantics-based services for a low carbon society: An application on emissions trading system data and scenarios management
Authors: Cecilia Camporeale, Antonio De Nicola, Maria Luisa Villani
Date: 2015-02-09 00:00:00
Tasks: Management

A low carbon society aims at fighting global warming by stimulating synergic
efforts from governments, industry and scientific communities. Decision support
systems should be adopted to provide policy makers with possible scenarios,
options for prompt countermeasures in case of side effects on environment,
economy and society due to low carbon society policies, and also options for
information management. A necessary precondition to fulfill this agenda is to
face the complexity of this multi-disciplinary domain and to reach a common
understanding on it as a formal specification. Ontologies are widely accepted
means to share knowledge. Together with semantic rules, they enable advanced
semantic services to manage knowledge in a smarter way. Here we address the
European Emissions Trading System (EU-ETS) and we present a knowledge base
consisting of the EREON ontology and a catalogue of rules. Then we describe two
innovative semantic services to manage ETS data and information on ETS
scenarios.
Out-of-sample generalizations for supervised manifold learning for classification
Authors: Elif Vural, Christine Guillemot
Date: 2015-02-09 00:00:00
Tasks: Classification, General Classification

Supervised manifold learning methods for data classification map data samples
residing in a high-dimensional ambient space to a lower-dimensional domain in a
structure-preserving way, while enhancing the separation between different
classes in the learned embedding. Most nonlinear supervised manifold learning
methods compute the embedding of the manifolds only at the initially available
training points, while the generalization of the embedding to novel points,
known as the out-of-sample extension problem in manifold learning, becomes
especially important in classification applications. In this work, we propose a
semi-supervised method for building an interpolation function that provides an
out-of-sample extension for general supervised manifold learning algorithms
studied in the context of classification. The proposed algorithm computes a
radial basis function (RBF) interpolator that minimizes an objective function
consisting of the total embedding error of unlabeled test samples, defined as
their distance to the embeddings of the manifolds of their own class, as well
as a regularization term that controls the smoothness of the interpolation
function in a direction-dependent way. The class labels of test data and the
interpolation function parameters are estimated jointly with a progressive
procedure. Experimental results on face and object images demonstrate the
potential of the proposed out-of-sample extension algorithm for the
classification of manifold-modeled data sets.
A Social Spider Algorithm for Global Optimization
Authors: James J. Q. Yu, Victor O. K. Li
Date: 2015-02-09 00:00:00
Tasks: global-optimization

The growing complexity of real-world problems has motivated computer
scientists to search for efficient problem-solving methods. Metaheuristics
based on evolutionary computation and swarm intelligence are outstanding
examples of nature-inspired solution techniques. Inspired by the social
spiders, we propose a novel Social Spider Algorithm to solve global
optimization problems. This algorithm is mainly based on the foraging strategy
of social spiders, utilizing the vibrations on the spider web to determine the
positions of preys. Different from the previously proposed swarm intelligence
algorithms, we introduce a new social animal foraging strategy model to solve
optimization problems. In addition, we perform preliminary parameter
sensitivity analysis for our proposed algorithm, developing guidelines for
choosing the parameter values. The Social Spider Algorithm is evaluated by a
series of widely-used benchmark functions, and our proposed algorithm has
superior performance compared with other state-of-the-art metaheuristics.
Tensor Canonical Correlation Analysis for Multi-view Dimension Reduction
Authors: Yong Luo, DaCheng Tao, Yonggang Wen, Kotagiri Ramamohanarao, Chao Xu
Date: 2015-02-09 00:00:00
Tasks: Dimensionality Reduction, MULTI-VIEW LEARNING

Canonical correlation analysis (CCA) has proven an effective tool for
two-view dimension reduction due to its profound theoretical foundation and
success in practical applications. In respect of multi-view learning, however,
it is limited by its capability of only handling data represented by two-view
features, while in many real-world applications, the number of views is
frequently many more. Although the ad hoc way of simultaneously exploring all
possible pairs of features can numerically deal with multi-view data, it
ignores the high order statistics (correlation information) which can only be
discovered by simultaneously exploring all features.
  Therefore, in this work, we develop tensor CCA (TCCA) which straightforwardly
yet naturally generalizes CCA to handle the data of an arbitrary number of
views by analyzing the covariance tensor of the different views. TCCA aims to
directly maximize the canonical correlation of multiple (more than two) views.
Crucially, we prove that the multi-view canonical correlation maximization
problem is equivalent to finding the best rank-1 approximation of the data
covariance tensor, which can be solved efficiently using the well-known
alternating least squares (ALS) algorithm. As a consequence, the high order
correlation information contained in the different views is explored and thus a
more reliable common subspace shared by all features can be obtained. In
addition, a non-linear extension of TCCA is presented. Experiments on various
challenge tasks, including large scale biometric structure prediction, internet
advertisement classification and web image annotation, demonstrate the
effectiveness of the proposed method.
Sparse And Low Rank Decomposition Based Batch Image Alignment for Speckle Reduction of retinal OCT Images
Authors: Ahmadreza Baghaie, Roshan M. D'souza, Zeyun Yu
Date: 2014-11-14 00:00:00

Optical Coherence Tomography (OCT) is an emerging technique in the field of
biomedical imaging, with applications in ophthalmology, dermatology, coronary
imaging etc. Due to the underlying physics, OCT images usually suffer from a
granular pattern, called speckle noise, which restricts the process of
interpretation. Here, a sparse and low rank decomposition based method is used
for speckle reduction in retinal OCT images. This technique works on input data
that consists of several B-scans of the same location. The next step is the
batch alignment of the images using a sparse and low-rank decomposition based
technique. Finally the denoised image is created by median filtering of the
low-rank component of the processed data. Simultaneous decomposition and
alignment of the images result in better performance in comparison to simple
registration-based methods that are used in the literature for noise reduction
of OCT images.
Supervised Hashing Using Graph Cuts and Boosted Decision Trees
Authors: Guosheng Lin, Chunhua Shen, Anton Van Den Hengel
Date: 2014-08-24 00:00:00
Tasks: Descriptive, Image Retrieval, Retrieval

Embedding image features into a binary Hamming space can improve both the
speed and accuracy of large-scale query-by-example image retrieval systems.
Supervised hashing aims to map the original features to compact binary codes in
a manner which preserves the label-based similarities of the original data.
Most existing approaches apply a single form of hash function, and an
optimization process which is typically deeply coupled to this specific form.
This tight coupling restricts the flexibility of those methods, and can result
in complex optimization problems that are difficult to solve. In this work we
proffer a flexible yet simple framework that is able to accommodate different
types of loss functions and hash functions. The proposed framework allows a
number of existing approaches to hashing to be placed in context, and
simplifies the development of new problem-specific hashing methods. Our
framework decomposes the into two steps: binary code (hash bits) learning, and
hash function learning. The first step can typically be formulated as a binary
quadratic problem, and the second step can be accomplished by training standard
binary classifiers. For solving large-scale binary code inference, we show how
to ensure that the binary quadratic problems are submodular such that an
efficient graph cut approach can be used. To achieve efficiency as well as
efficacy on large-scale high-dimensional data, we propose to use boosted
decision trees as the hash functions, which are nonlinear, highly descriptive,
and very fast to train and evaluate. Experiments demonstrate that our proposed
method significantly outperforms most state-of-the-art methods, especially on
high-dimensional data.
Measuring the functional connectome "on-the-fly": towards a new control signal for fMRI-based brain-computer interfaces
Authors: Ricardo Pio Monti, Romy Lorenz, Christoforos Anagnostopoulos, Robert Leech, Giovanni Montana
Date: 2015-02-08 00:00:00
Tasks: Brain Computer Interface, Functional Connectivity

There has been an explosion of interest in functional Magnetic Resonance
Imaging (MRI) during the past two decades. Naturally, this has been accompanied
by many major advances in the understanding of the human connectome. These
advances have served to pose novel challenges as well as open new avenues for
research. One of the most promising and exciting of such avenues is the study
of functional MRI in real-time. Such studies have recently gained momentum and
have been applied in a wide variety of settings; ranging from training of
healthy subjects to self-regulate neuronal activity to being suggested as
potential treatments for clinical populations. To date, the vast majority of
these studies have focused on a single region at a time. This is due in part to
the many challenges faced when estimating dynamic functional connectivity
networks in real-time. In this work we propose a novel methodology with which
to accurately track changes in functional connectivity networks in real-time.
We adapt the recently proposed SINGLE algorithm for estimating sparse and
temporally homo- geneous dynamic networks to be applicable in real-time. The
proposed method is applied to motor task data from the Human Connectome Project
as well as to real-time data ob- tained while exploring a virtual environment.
We show that the algorithm is able to estimate significant task-related changes
in network structure quickly enough to be useful in future brain-computer
interface applications.
Improving Term Frequency Normalization for Multi-topical Documents, and Application to Language Modeling Approaches
Authors: Seung-Hoon Na, In-Su Kang, Jong-Hyeok Lee
Date: 2015-02-08 00:00:00
Tasks: Language Modeling, Language Modelling, Retrieval

Term frequency normalization is a serious issue since lengths of documents
are various. Generally, documents become long due to two different reasons -
verbosity and multi-topicality. First, verbosity means that the same topic is
repeatedly mentioned by terms related to the topic, so that term frequency is
more increased than the well-summarized one. Second, multi-topicality indicates
that a document has a broad discussion of multi-topics, rather than single
topic. Although these document characteristics should be differently handled,
all previous methods of term frequency normalization have ignored these
differences and have used a simplified length-driven approach which decreases
the term frequency by only the length of a document, causing an unreasonable
penalization. To attack this problem, we propose a novel TF normalization
method which is a type of partially-axiomatic approach. We first formulate two
formal constraints that the retrieval model should satisfy for documents having
verbose and multi-topicality characteristic, respectively. Then, we modify
language modeling approaches to better satisfy these two constraints, and
derive novel smoothing methods. Experimental results show that the proposed
method increases significantly the precision for keyword queries, and
substantially improves MAP (Mean Average Precision) for verbose queries.
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
Authors: Zheng Qu, Peter Richtárik, Martin Takáč, Olivier Fercoq
Date: 2015-02-08 00:00:00

We propose a new algorithm for minimizing regularized empirical loss:
Stochastic Dual Newton Ascent (SDNA). Our method is dual in nature: in each
iteration we update a random subset of the dual variables. However, unlike
existing methods such as stochastic dual coordinate ascent, SDNA is capable of
utilizing all curvature information contained in the examples, which leads to
striking improvements in both theory and practice - sometimes by orders of
magnitude. In the special case when an L2-regularizer is used in the primal,
the dual problem is a concave quadratic maximization problem plus a separable
term. In this regime, SDNA in each step solves a proximal subproblem involving
a random principal submatrix of the Hessian of the quadratic function; whence
the name of the method. If, in addition, the loss functions are quadratic, our
method can be interpreted as a novel variant of the recently introduced
Iterative Hessian Sketch.
Contextual Markov Decision Processes
Authors: Assaf Hallak, Dotan Di Castro, Shie Mannor
Date: 2015-02-08 00:00:00

We consider a planning problem where the dynamics and rewards of the
environment depend on a hidden static parameter referred to as the context. The
objective is to learn a strategy that maximizes the accumulated reward across
all contexts. The new model, called Contextual Markov Decision Process (CMDP),
can model a customer's behavior when interacting with a website (the learner).
The customer's behavior depends on gender, age, location, device, etc. Based on
that behavior, the website objective is to determine customer characteristics,
and to optimize the interaction between them. Our work focuses on one basic
scenario--finite horizon with a small known number of possible contexts. We
suggest a family of algorithms with provable guarantees that learn the
underlying models and the latent contexts, and optimize the CMDPs. Bounds are
obtained for specific naive implementations, and extensions of the framework
are discussed, laying the ground for future research.
Hierarchical Dirichlet process for tracking complex topical structure evolution and its application to autism research literature
Authors: Adham Beykikhoshk, Ognjen Arandjelovic, Dinh Phung, Svetha Venkatesh
Date: 2015-02-08 00:00:00

In this paper we describe a novel framework for the discovery of the topical
content of a data corpus, and the tracking of its complex structural changes
across the temporal dimension. In contrast to previous work our model does not
impose a prior on the rate at which documents are added to the corpus nor does
it adopt the Markovian assumption which overly restricts the type of changes
that the model can capture. Our key technical contribution is a framework based
on (i) discretization of time into epochs, (ii) epoch-wise topic discovery
using a hierarchical Dirichlet process-based model, and (iii) a temporal
similarity graph which allows for the modelling of complex topic changes:
emergence and disappearance, evolution, and splitting and merging. The power of
the proposed framework is demonstrated on the medical literature corpus
concerned with the autism spectrum disorder (ASD) - an increasingly important
research subject of significant social and healthcare importance. In addition
to the collected ASD literature corpus which we will make freely available, our
contributions also include two free online tools we built as aids to ASD
researchers. These can be used for semantically meaningful navigation and
searching, as well as knowledge discovery from this large and rapidly growing
corpus of literature.
A new variational principle for the Euclidean distance function: Linear approach to the non-linear eikonal problem
Authors: Karthik S. Gurumoorthy, Anand Rangarajan
Date: 2011-12-13 00:00:00

We present a fast convolution-based technique for computing an approximate,
signed Euclidean distance function $S$ on a set of 2D and 3D grid locations.
Instead of solving the non-linear, static Hamilton-Jacobi equation ($\|\nabla
S\|=1$), our solution stems from first solving for a scalar field $\phi$ in a
linear differential equation and then deriving the solution for $S$ by taking
the negative logarithm. In other words, when $S$ and $\phi$ are related by
$\phi = \exp \left(-\frac{S}{\tau} \right)$ and $\phi$ satisfies a specific
linear differential equation corresponding to the extremum of a variational
problem, we obtain the approximate Euclidean distance function $S = -\tau
\log(\phi)$ which converges to the true solution in the limit as $\tau
\rightarrow 0$. This is in sharp contrast to techniques like the fast marching
and fast sweeping methods which directly solve the Hamilton-Jacobi equation by
the Godunov upwind discretization scheme. Our linear formulation results in a
closed-form solution to the approximate Euclidean distance function expressible
as a discrete convolution, and hence efficiently computable using the fast
Fourier transform (FFT). Our solution also circumvents the need for spatial
discretization of the derivative operator. As $\tau\rightarrow0$ we show the
convergence of our results to the true solution and also bound the error for a
given value of $\tau$. The differentiability of our solution allows us to
compute---using a set of convolutions---the first and second derivatives of the
approximate distance function. In order to determine the sign of the distance
function (defined to be positive inside a closed region and negative outside),
we compute the winding number in 2D and the topological degree in 3D, whose
computations can also be performed via fast convolutions. We demonstrate the
efficacy of our method through a set of experimental results.
A fast eikonal equation solver using the Schrodinger wave equation
Authors: Karthik S. Gurumoorthy, Adrian M. Peter, Birmingham Hang Guan, Anand Rangarajan
Date: 2014-03-08 00:00:00

We use a Schr\"odinger wave equation formalism to solve the eikonal equation.
In our framework, a solution to the eikonal equation is obtained in the limit
as Planck's constant $\hbar$ (treated as a free parameter) tends to zero of the
solution to the corresponding linear Schr\"odinger equation. The Schr\"odinger
equation corresponding to the eikonal turns out to be a \emph{generalized,
screened Poisson equation}. Despite being linear, it does not have a
closed-form solution for arbitrary forcing functions. We present two different
techniques to solve the screened Poisson equation. In the first approach we use
a standard perturbation analysis approach to derive a new algorithm which is
guaranteed to converge provided the forcing function is bounded and positive.
The perturbation technique requires a sequence of discrete convolutions which
can be performed in $O(N\log N)$ using the Fast Fourier Transform (FFT) where
$N$ is the number of grid points. In the second method we discretize the linear
Laplacian operator by the finite difference method leading to a sparse linear
system of equations which can be solved using the plethora of sparse solvers.
The eikonal solution is recovered from the exponent of the resultant scalar
field. Our approach eliminates the need to explicitly construct viscosity
solutions as customary with direct solutions to the eikonal. Since the linear
equation is computed for a small but non-zero $\hbar$, the obtained solution is
an approximation. Though our solution framework is applicable to the general
class of eikonal problems, we detail specifics for the popular vision
applications of shape-from-shading, vessel segmentation, and path planning.