pwc-archive/papers-with-abstracts huggingface dataset:
Robust Subspace System Identification via Weighted Nuclear Norm Optimization
Authors: Dorsa Sadigh, Henrik Ohlsson, S. Shankar Sastry, Sanjit A. Seshia
Date: 2013-12-07 00:00:00

Subspace identification is a classical and very well studied problem in
system identification. The problem was recently posed as a convex optimization
problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this
framework to handle outliers. The proposed framework takes the form of a convex
optimization problem with an objective that trades off fit, rank and sparsity.
As in robust PCA, it can be problematic to find a suitable regularization
parameter. We show how the space in which a suitable parameter should be sought
can be limited to a bounded open set of the two dimensional parameter space. In
practice, this is very useful since it restricts the parameter space that is
needed to be surveyed.
Towards Structural Natural Language Formalization: Mapping Discourse to Controlled Natural Language
Authors: Nicholas H. Kirk
Date: 2013-12-07 00:00:00

The author describes a conceptual study towards mapping grounded natural
language discourse representation structures to instances of controlled
language statements. This can be achieved via a pipeline of preexisting state
of the art technologies, namely natural language syntax to semantic discourse
mapping, and a reduction of the latter to controlled language discourse, given
a set of previously learnt reduction rules. Concludingly a description on
evaluation, potential and limitations for ontology-based reasoning is
presented.
A Novel Hierarchical Ant based QoS aware Intelligent Routing Scheme for MANETS
Authors: Debajit Sensarma, Koushik Majumder
Date: 2013-12-07 00:00:00
Tasks: Clustering, Management

MANET is a collection of mobile devices with no centralized control and no
pre-existing infrastructures. Due to the nodal mobility, supporting QoS during
routing in this type of networks is a very challenging task. To tackle this
type of overhead many routing algorithms with clustering approach have been
proposed. Clustering is an effective method for resource management regarding
network performance, routing protocol design, QoS etc. Most of the flat network
architecture contains homogeneous capacity of nodes but in real time nodes are
with heterogeneous capacity and transmission power. Hierarchical routing
provides routing through this kind of heterogeneous nodes. Here, routes can be
recorded hierarchically, across clusters to increase routing flexibility.
Besides this, it increases scalability and robustness of routes. In this paper,
a novel ant based QoS aware routing is proposed on a three level hierarchical
cluster based topology in MANET which will be more scalable and efficient
compared to flat architecture and will give better throughput.
Region and Location Based Indexing and Retrieval of MR-T2 Brain Tumor Images
Authors: Krishna A N, B. G. Prasad
Date: 2013-12-07 00:00:00
Tasks: Retrieval

In this paper, region based and location based retrieval systems have been
implemented for retrieval of MR-T2 axial 2-D brain images. This is done by
extracting and characterizing the tumor portion of 2-D brain slices by use of a
suitable threshold computed over the entire image. Indexing and retrieval is
then performed by computing texture features based on gray-tone
spatial-dependence matrix of segmented regions. A Hash structure is used to
index all images. A combined index is adopted to point to all similar images in
terms of the texture features. At query time, only those images that are in the
same hash bucket as those of the queried image are compared for similarity,
thus reducing the search space and time.
Semistability-Based Convergence Analysis for Paracontracting Multiagent Coordination Optimization
Authors: Qing Hui, Haopeng Zhang
Date: 2013-08-13 00:00:00

This sequential technical report extends some of the previous results we
posted at arXiv:1306.0225.
A Component Lasso
Authors: Nadine Hussami, Robert Tibshirani
Date: 2013-11-18 00:00:00
Tasks: regression

We propose a new sparse regression method called the component lasso, based
on a simple idea. The method uses the connected-components structure of the
sample covariance matrix to split the problem into smaller ones. It then solves
the subproblems separately, obtaining a coefficient vector for each one. Then,
it uses non-negative least squares to recombine the different vectors into a
single solution. This step is useful in selecting and reweighting components
that are correlated with the response. Simulated and real data examples show
that the component lasso can outperform standard regression methods such as the
lasso and elastic net, achieving a lower mean squared error as well as better
support recovery.
Modeling Suspicious Email Detection using Enhanced Feature Selection
Authors: Sarwat Nizamani, Nasrullah Memon, Uffe Kock Wiil, Panagiotis Karampelas
Date: 2013-12-06 00:00:00
Tasks: feature selection, General Classification, regression

The paper presents a suspicious email detection model which incorporates
enhanced feature selection. In the paper we proposed the use of feature
selection strategies along with classification technique for terrorists email
detection. The presented model focuses on the evaluation of machine learning
algorithms such as decision tree (ID3), logistic regression, Na\"ive Bayes
(NB), and Support Vector Machine (SVM) for detecting emails containing
suspicious content. In the literature, various algorithms achieved good
accuracy for the desired task. However, the results achieved by those
algorithms can be further improved by using appropriate feature selection
mechanisms. We have identified the use of a specific feature selection scheme
that improves the performance of the existing algorithms.
An Algorithmic Theory of Dependent Regularizers, Part 1: Submodular Structure
Authors: Hoyt Koepke, Marina Meila
Date: 2013-12-06 00:00:00
Tasks: BIG-bench Machine Learning

We present an exploration of the rich theoretical connections between several
classes of regularized models, network flows, and recent results in submodular
function theory. This work unifies key aspects of these problems under a common
theory, leading to novel methods for working with several important models of
interest in statistics, machine learning and computer vision.
  In Part 1, we review the concepts of network flows and submodular function
optimization theory foundational to our results. We then examine the
connections between network flows and the minimum-norm algorithm from
submodular optimization, extending and improving several current results. This
leads to a concise representation of the structure of a large class of pairwise
regularized models important in machine learning, statistics and computer
vision.
  In Part 2, we describe the full regularization path of a class of penalized
regression problems with dependent variables that includes the graph-guided
LASSO and total variation constrained models. This description also motivates a
practical algorithm. This allows us to efficiently find the regularization path
of the discretized version of TV penalized models. Ultimately, our new
algorithms scale up to high-dimensional problems with millions of variables.
Partitioning into Expanders
Authors: Shayan Oveis Gharan, Luca Trevisan
Date: 2013-09-12 00:00:00
Tasks: Clustering

Let G=(V,E) be an undirected graph, lambda_k be the k-th smallest eigenvalue
of the normalized laplacian matrix of G. There is a basic fact in algebraic
graph theory that lambda_k > 0 if and only if G has at most k-1 connected
components. We prove a robust version of this fact. If lambda_k>0, then for
some 1\leq \ell\leq k-1, V can be {\em partitioned} into l sets P_1,\ldots,P_l
such that each P_i is a low-conductance set in G and induces a high conductance
induced subgraph. In particular, \phi(P_i)=O(l^3\sqrt{\lambda_l}) and
\phi(G[P_i]) >= \lambda_k/k^2).
  We make our results algorithmic by designing a simple polynomial time
spectral algorithm to find such partitioning of G with a quadratic loss in the
inside conductance of P_i's. Unlike the recent results on higher order
Cheeger's inequality [LOT12,LRTV12], our algorithmic results do not use higher
order eigenfunctions of G. If there is a sufficiently large gap between
lambda_k and lambda_{k+1}, more precisely, if \lambda_{k+1} >= \poly(k)
lambda_{k}^{1/4} then our algorithm finds a k partitioning of V into sets
P_1,...,P_k such that the induced subgraph G[P_i] has a significantly larger
conductance than the conductance of P_i in G. Such a partitioning may represent
the best k clustering of G. Our algorithm is a simple local search that only
uses the Spectral Partitioning algorithm as a subroutine. We expect to see
further applications of this simple algorithm in clustering applications.
CEAI: CCM based Email Authorship Identification Model
Authors: Sarwat Nizamani, Nasrullah Memon
Date: 2013-12-06 00:00:00
Tasks: feature selection, model

In this paper we present a model for email authorship identification (EAI) by
employing a Cluster-based Classification (CCM) technique. Traditionally,
stylometric features have been successfully employed in various authorship
analysis tasks; we extend the traditional feature-set to include some more
interesting and effective features for email authorship identification (e.g.
the last punctuation mark used in an email, the tendency of an author to use
capitalization at the start of an email, or the punctuation after a greeting or
farewell). We also included Info Gain feature selection based content features.
It is observed that the use of such features in the authorship identification
process has a positive impact on the accuracy of the authorship identification
task. We performed experiments to justify our arguments and compared the
results with other base line models. Experimental results reveal that the
proposed CCM-based email authorship identification model, along with the
proposed feature set, outperforms the state-of-the-art support vector machine
(SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The
proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25
authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5%
accuracy has been achieved on authors' constructed real email dataset. The
results on Enron dataset have been achieved on quite a large number of authors
as compared to the models proposed by Iqbal et al. [1, 2].
Deep and Wide Multiscale Recursive Networks for Robust Image Labeling
Authors: Gary B. Huang, Viren Jain
Date: 2013-10-01 00:00:00
Tasks: Scene Parsing

Feedforward multilayer networks trained by supervised learning have recently
demonstrated state of the art performance on image labeling problems such as
boundary prediction and scene parsing. As even very low error rates can limit
practical usage of such systems, methods that perform closer to human accuracy
remain desirable. In this work, we propose a new type of network with the
following properties that address what we hypothesize to be limiting aspects of
existing methods: (1) a `wide' structure with thousands of features, (2) a
large field of view, (3) recursive iterations that exploit statistical
dependencies in label space, and (4) a parallelizable architecture that can be
trained in a fraction of the time compared to benchmark multilayer
convolutional networks. For the specific image labeling problem of boundary
prediction, we also introduce a novel example weighting algorithm that improves
segmentation accuracy. Experiments in the challenging domain of connectomic
reconstruction of neural circuity from 3d electron microscopy data show that
these "Deep And Wide Multiscale Recursive" (DAWMR) networks lead to new levels
of image labeling performance. The highest performing architecture has twelve
layers, interwoven supervised and unsupervised stages, and uses an input field
of view of 157,464 voxels ($54^3$) to make a prediction at each image location.
We present an associated open source software package that enables the simple
and flexible creation of DAWMR networks.
Constraints on the search space of argumentation
Authors: Julio Lemos
Date: 2013-12-06 00:00:00

Drawing from research on computational models of argumentation (particularly
the Carneades Argumentation System), we explore the graphical representation of
arguments in a dispute; then, comparing two different traditions on the limits
of the justification of decisions, and devising an intermediate, semi-formal,
model, we also show that it can shed light on the theory of dispute resolution.
  We conclude our paper with an observation on the usefulness of highly
constrained reasoning for Online Dispute Resolution systems. Restricting the
search space of arguments exclusively to reasons proposed by the parties
(vetoing the introduction of new arguments by the human or artificial
arbitrator) is the only way to introduce some kind of decidability -- together
with foreseeability -- in the argumentation system.
Object-oriented Bayesian networks for a decision support system for antitrust enforcement
Authors: Julia Mortera, Paola Vicard, Cecilia Vergari
Date: 2013-01-08 00:00:00

We study an economic decision problem where the actors are two firms and the
Antitrust Authority whose main task is to monitor and prevent firms' potential
anti-competitive behaviour and its effect on the market. The Antitrust
Authority's decision process is modelled using a Bayesian network where both
the relational structure and the parameters of the model are estimated from a
data set provided by the Authority itself. A number of economic variables that
influence this decision process are also included in the model. We analyse how
monitoring by the Antitrust Authority affects firms' strategies about
cooperation. Firms' strategies are modelled as a repeated prisoner's dilemma
using object-oriented Bayesian networks. We show how the integration of firms'
decision process and external market information can be modelled in this way.
Various decision scenarios and strategies are illustrated.
Towards Normalizing the Edit Distance Using a Genetic Algorithms Based Scheme
Authors: Muhammad Marwan Muhammad Fuad
Date: 2013-12-06 00:00:00

The normalized edit distance is one of the distances derived from the edit
distance. It is useful in some applications because it takes into account the
lengths of the two strings compared. The normalized edit distance is not
defined in terms of edit operations but rather in terms of the edit path. In
this paper we propose a new derivative of the edit distance that also takes
into consideration the lengths of the two strings, but the new distance is
related directly to the edit distance. The particularity of the new distance is
that it uses the genetic algorithms to set the values of the parameters it
uses. We conduct experiments to test the new distance and we obtain promising
results.
Particle Swarm Optimization of Information-Content Weighting of Symbolic Aggregate Approximation
Authors: Muhammad Marwan Muhammad Fuad
Date: 2013-12-06 00:00:00
Tasks: Position, Time Series, Time Series Analysis

Bio-inspired optimization algorithms have been gaining more popularity
recently. One of the most important of these algorithms is particle swarm
optimization (PSO). PSO is based on the collective intelligence of a swam of
particles. Each particle explores a part of the search space looking for the
optimal position and adjusts its position according to two factors; the first
is its own experience and the second is the collective experience of the whole
swarm. PSO has been successfully used to solve many optimization problems. In
this work we use PSO to improve the performance of a well-known representation
method of time series data which is the symbolic aggregate approximation (SAX).
As with other time series representation methods, SAX results in loss of
information when applied to represent time series. In this paper we use PSO to
propose a new minimum distance WMD for SAX to remedy this problem. Unlike the
original minimum distance, the new distance sets different weights to different
segments of the time series according to their information content. This
weighted minimum distance enhances the performance of SAX as we show through
experiments using different time series datasets.
Non-Asymptotic Analysis of Tangent Space Perturbation
Authors: Daniel N. Kaslovsky, Francois G. Meyer
Date: 2011-11-20 00:00:00

Constructing an efficient parameterization of a large, noisy data set of
points lying close to a smooth manifold in high dimension remains a fundamental
problem. One approach consists in recovering a local parameterization using the
local tangent plane. Principal component analysis (PCA) is often the tool of
choice, as it returns an optimal basis in the case of noise-free samples from a
linear subspace. To process noisy data samples from a nonlinear manifold, PCA
must be applied locally, at a scale small enough such that the manifold is
approximately linear, but at a scale large enough such that structure may be
discerned from noise. Using eigenspace perturbation theory and non-asymptotic
random matrix theory, we study the stability of the subspace estimated by PCA
as a function of scale, and bound (with high probability) the angle it forms
with the true tangent space. By adaptively selecting the scale that minimizes
this bound, our analysis reveals an appropriate scale for local tangent plane
recovery. We also introduce a geometric uncertainty principle quantifying the
limits of noise-curvature perturbation for stable recovery. With the purpose of
providing perturbation bounds that can be used in practice, we propose plug-in
estimates that make it possible to directly apply the theoretical results to
real data sets.
Persistence, Change, and the Integration of Objects and Processes in the Framework of the General Formal Ontology
Authors: Heinrich Herre
Date: 2013-11-07 00:00:00

In this paper we discuss various problems, associated to temporal phenomena.
These problems include persistence and change, the integration of objects and
processes, and truth-makers for temporal propositions. We propose an approach
which interprets persistence as a phenomenon emanating from the activity of the
mind, and which, additionally, postulates that persistence, finally, rests on
personal identity. The General Formal Ontology (GFO) is a top level ontology
being developed at the University of Leipzig. Top level ontologies can be
roughly divided into 3D-ontologies, and 4D-ontologies. GFO is the only top
level ontology, used in applications, which is a 4D-ontology admitting
additionally 3D objects. Objects and processes are integrated in a natural way.
Curriculum Learning for Handwritten Text Line Recognition
Authors: Jérôme Louradour, Christopher Kermorvant
Date: 2013-12-05 00:00:00

Recurrent Neural Networks (RNN) have recently achieved the best performance
in off-line Handwriting Text Recognition. At the same time, learning RNN by
gradient descent leads to slow convergence, and training times are particularly
long when the training database consists of full lines of text. In this paper,
we propose an easy way to accelerate stochastic gradient descent in this
set-up, and in the general context of learning to recognize sequences. The
principle is called Curriculum Learning, or shaping. The idea is to first learn
to recognize short sequences before training on all available training
sequences. Experiments on three different handwritten text databases (Rimes,
IAM, OpenHaRT) show that a simple implementation of this strategy can
significantly speed up the training of RNN for Text Recognition, and even
significantly improve performance in some cases.
Book embeddings of Reeb graphs
Authors: Vitaliy Kurlin
Date: 2013-12-05 00:00:00
Tasks: ARC

Let $X$ be a simplicial complex with a piecewise linear function
$f:X\to\mathbb{R}$. The Reeb graph $Reeb(f,X)$ is the quotient of $X$, where we
collapse each connected component of $f^{-1}(t)$ to a single point. Let the
nodes of $Reeb(f,X)$ be all homologically critical points where any homology of
the corresponding component of the level set $f^{-1}(t)$ changes. Then we can
label every arc of $Reeb(f,X)$ with the Betti numbers
$(\beta_1,\beta_2,\dots,\beta_d)$ of the corresponding $d$-dimensional
component of a level set. The homology labels give more information about the
original complex $X$ than the classical Reeb graph. We describe a canonical
embedding of a Reeb graph into a multi-page book (a star cross a line) and give
a unique linear code of this book embedding.
Max-Min Distance Nonnegative Matrix Factorization
Authors: Jim Jing-Yan Wang
Date: 2013-12-05 00:00:00

Nonnegative Matrix Factorization (NMF) has been a popular representation
method for pattern classification problem. It tries to decompose a nonnegative
matrix of data samples as the product of a nonnegative basic matrix and a
nonnegative coefficient matrix, and the coefficient matrix is used as the new
representation. However, traditional NMF methods ignore the class labels of the
data samples. In this paper, we proposed a supervised novel NMF algorithm to
improve the discriminative ability of the new representation. Using the class
labels, we separate all the data sample pairs into within-class pairs and
between-class pairs. To improve the discriminate ability of the new NMF
representations, we hope that the maximum distance of the within-class pairs in
the new NMF space could be minimized, while the minimum distance of the
between-class pairs pairs could be maximized. With this criterion, we construct
an objective function and optimize it with regard to basic and coefficient
matrices and slack variables alternatively, resulting in a iterative algorithm.
Task swapping networks in distributed systems
Authors: Dohan Kim
Date: 2011-07-10 00:00:00

In this paper we propose task swapping networks for task reassignments by
using task swappings in distributed systems. Some classes of task reassignments
are achieved by using iterative local task swappings between software agents in
distributed systems. We use group-theoretic methods to find a minimum-length
sequence of adjacent task swappings needed from a source task assignment to a
target task assignment in a task swapping network of several well-known
topologies.
Iterative Log Thresholding
Authors: Dmitry Malioutov, Aleksandr Aravkin
Date: 2013-12-05 00:00:00

Sparse reconstruction approaches using the re-weighted l1-penalty have been
shown, both empirically and theoretically, to provide a significant improvement
in recovering sparse signals in comparison to the l1-relaxation. However,
numerical optimization of such penalties involves solving problems with
l1-norms in the objective many times. Using the direct link of reweighted
l1-penalties to the concave log-regularizer for sparsity, we derive a simple
prox-like algorithm for the log-regularized formulation. The proximal splitting
step of the algorithm has a closed form solution, and we call the algorithm
'log-thresholding' in analogy to soft thresholding for the l1-penalty.
  We establish convergence results, and demonstrate that log-thresholding
provides more accurate sparse reconstructions compared to both soft and hard
thresholding. Furthermore, the approach can be directly extended to
optimization over matrices with penalty for rank (i.e. the nuclear norm penalty
and its re-weigthed version), where we suggest a singular-value
log-thresholding approach.