pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Block Structures and High-resolution Model Selection in Large Networks
Authors: Tiago P. Peixoto
Date: 2013-10-16 00:00:00
Tasks: Community Detection, Model Selection, Vocal Bursts Intensity Prediction

Discovering and characterizing the large-scale topological features in
empirical networks are crucial steps in understanding how complex systems
function. However, most existing methods used to obtain the modular structure
of networks suffer from serious problems, such as being oblivious to the
statistical evidence supporting the discovered patterns, which results in the
inability to separate actual structure from noise. In addition to this, one
also observes a resolution limit on the size of communities, where smaller but
well-defined clusters are not detectable when the network becomes large. This
phenomenon occurs not only for the very popular approach of modularity
optimization, which lacks built-in statistical validation, but also for more
principled methods based on statistical inference and model selection, which do
incorporate statistical validation in a formally correct way. Here we construct
a nested generative model that, through a complete description of the entire
network hierarchy at multiple scales, is capable of avoiding this limitation,
and enables the detection of modular structure at levels far beyond those
possible with current approaches. Even with this increased resolution, the
method is based on the principle of parsimony, and is capable of separating
signal from noise, and thus will not lead to the identification of spurious
modules even on sparse networks. Furthermore, it fully generalizes other
approaches in that it is not restricted to purely assortative mixing patterns,
directed or undirected graphs, and ad hoc hierarchical structures such as
binary trees. Despite its general character, the approach is tractable, and can
be combined with advanced techniques of community detection to yield an
efficient algorithm that scales well for very large networks.
A Tiered Move-making Algorithm for General Non-submodular Pairwise Energies
Authors: Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr
Date: 2014-03-25 00:00:00
Tasks: Denoising, Image Denoising, Image Segmentation, Image Stitching, Segmentation, Semantic Segmentation

A large number of problems in computer vision can be modelled as energy
minimization problems in a Markov Random Field (MRF) or Conditional Random
Field (CRF) framework. Graph-cuts based $\alpha$-expansion is a standard
move-making method to minimize the energy functions with sub-modular pairwise
terms. However, certain problems require more complex pairwise terms where the
$\alpha$-expansion method is generally not applicable.
  In this paper, we propose an iterative {\em tiered move making algorithm}
which is able to handle general pairwise terms. Each move to the next
configuration is based on the current labeling and an optimal tiered move,
where each tiered move requires one application of the dynamic programming
based tiered labeling method introduced in Felzenszwalb et. al.
\cite{tiered_cvpr_felzenszwalbV10}. The algorithm converges to a local minimum
for any general pairwise potential, and we give a theoretical analysis of the
properties of the algorithm, characterizing the situations in which we can
expect good performance. We first evaluate our method on an object-class
segmentation problem using the Pascal VOC-11 segmentation dataset where we
learn general pairwise terms. Further we evaluate the algorithm on many other
benchmark labeling problems such as stereo, image segmentation, image stitching
and image denoising. Our method consistently gets better accuracy and energy
values than alpha-expansion, loopy belief propagation (LBP), quadratic
pseudo-boolean optimization (QPBO), and is competitive with TRWS.
Capturing and Recognizing Objects Appearance Employing Eigenspace
Authors: M. Ashrafuzzaman, M. M . Rahman, M. M. A. Hashem
Date: 2014-03-25 00:00:00

This paper presents a method of capturing objects appearances from its
environment and it also describes how to recognize unknown appearances creating
an eigenspace. This representation and recognition can be done automatically
taking objects various appearances by using robotic vision from a defined
environment. This technique also allows extracting objects from some sort of
complicated scenes. In this case, some of object appearances are taken with
defined occlusions and eigenspaces are created by accepting both of
non-occluded and occluded appearances together. Eigenspace is constructed
successfully every times when a new object appears, and various appearances
accumulated gradually. A sequence of appearances is generated from its
accumulated shapes, which is used for recognition of the unknown objects
appearances. Various objects environments are shown in the experiment to
capture objects appearances and experimental results show effectiveness of the
proposed approach.
Classroom Video Assessment and Retrieval via Multiple Instance Learning
Authors: Qifeng Qiao, Peter A. Beling
Date: 2014-03-25 00:00:00
Tasks: Multiple Instance Learning, Retrieval, Video Retrieval

We propose a multiple instance learning approach to content-based retrieval
of classroom video for the purpose of supporting human assessing the learning
environment. The key element of our approach is a mapping between the semantic
concepts of the assessment system and features of the video that can be
measured using techniques from the fields of computer vision and speech
analysis. We report on a formative experiment in content-based video retrieval
involving trained experts in the Classroom Assessment Scoring System, a widely
used framework for assessment and improvement of learning environments. The
results of this experiment suggest that our approach has potential application
to productivity enhancement in assessment and to broader retrieval tasks.
Random design analysis of ridge regression
Authors: Daniel Hsu, Sham M. Kakade, Tong Zhang
Date: 2011-06-13 00:00:00
Tasks: LEMMA, regression

This work gives a simultaneous analysis of both the ordinary least squares
estimator and the ridge regression estimator in the random design setting under
mild assumptions on the covariate/response distributions. In particular, the
analysis provides sharp results on the ``out-of-sample'' prediction error, as
opposed to the ``in-sample'' (fixed design) error. The analysis also reveals
the effect of errors in the estimated covariance structure, as well as the
effect of modeling errors, neither of which effects are present in the fixed
design setting. The proofs of the main results are based on a simple
decomposition lemma combined with concentration inequalities for random vectors
and matrices.
Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine
Authors: Takanori Watanabe, Daniel Kessler, Clayton Scott, Michael Angstadt, Chandra Sripada
Date: 2013-10-21 00:00:00
Tasks: Data Augmentation, Disease Prediction, feature selection

Substantial evidence indicates that major psychiatric disorders are
associated with distributed neural dysconnectivity, leading to strong interest
in using neuroimaging methods to accurately predict disorder status. In this
work, we are specifically interested in a multivariate approach that uses
features derived from whole-brain resting state functional connectomes.
However, functional connectomes reside in a high dimensional space, which
complicates model interpretation and introduces numerous statistical and
computational challenges. Traditional feature selection techniques are used to
reduce data dimensionality, but are blind to the spatial structure of the
connectomes. We propose a regularization framework where the 6-D structure of
the functional connectome is explicitly taken into account via the fused Lasso
or the GraphNet regularizer. Our method only restricts the loss function to be
convex and margin-based, allowing non-differentiable loss functions such as the
hinge-loss to be used. Using the fused Lasso or GraphNet regularizer with the
hinge-loss leads to a structured sparse support vector machine (SVM) with
embedded feature selection. We introduce a novel efficient optimization
algorithm based on the augmented Lagrangian and the classical alternating
direction method, which can solve both fused Lasso and GraphNet regularized SVM
with very little modification. We also demonstrate that the inner subproblems
of the algorithm can be solved efficiently in analytic form by coupling the
variable splitting strategy with a data augmentation scheme. Experiments on
simulated data and resting state scans from a large schizophrenia dataset show
that our proposed approach can identify predictive regions that are spatially
contiguous in the 6-D "connectome space," offering an additional layer of
interpretability that could provide new insights about various disease
processes.
Development and evaluation of a 3D model observer with nonlinear spatiotemporal contrast sensitivity
Authors: Ali R. N. Avanaki, Kathryn S. Espig, Andrew D. A. Maidment, Cedric Marchessoux, Predrag R. Bakic, Tom R. L. Kimpe
Date: 2014-03-24 00:00:00
Tasks: Sensitivity, Unity

We investigate improvements to our 3D model observer with the goal of better
matching human observer performance as a function of viewing distance,
effective contrast, maximum luminance, and browsing speed. Two nonlinear
methods of applying the human contrast sensitivity function (CSF) to a 3D model
observer are proposed, namely the Probability Map (PM) and Monte Carlo (MC)
methods. In the PM method, the visibility probability for each frequency
component of the image stack, p, is calculated taking into account Barten's
spatiotemporal CSF, the component modulation, and the human psychometric
function. The probability p is considered to be equal to the perceived
amplitude of the frequency component and thus can be used by a traditional
model observer (e.g., LG-msCHO) in the space-time domain. In the MC method,
each component is randomly kept with probability p or discarded with 1-p. The
amplitude of the retained components is normalized to unity. The methods were
tested using DBT stacks of an anthropomorphic breast phantom processed in a
comprehensive simulation pipeline. Our experiments indicate that both the PM
and MC methods yield results that match human observer performance better than
the linear filtering method as a function of viewing distance, effective
contrast, maximum luminance, and browsing speed.
Coherent Multi-Sentence Video Description with Variable Level of Detail
Authors: Anna Senina, Marcus Rohrbach, Wei Qiu, Annemarie Friedrich, Sikandar Amin, Mykhaylo Andriluka, Manfred Pinkal, Bernt Schiele
Date: 2014-03-24 00:00:00
Tasks: Sentence, Video Description

Humans can easily describe what they see in a coherent way and at varying
level of detail. However, existing approaches for automatic video description
are mainly focused on single sentence generation and produce descriptions at a
fixed level of detail. In this paper, we address both of these limitations: for
a variable level of detail we produce coherent multi-sentence descriptions of
complex videos. We follow a two-step approach where we first learn to predict a
semantic representation (SR) from video and then generate natural language
descriptions from the SR. To produce consistent multi-sentence descriptions, we
model across-sentence consistency at the level of the SR by enforcing a
consistent topic. We also contribute both to the visual recognition of objects
proposing a hand-centric approach as well as to the robust generation of
sentences using a word lattice. Human judges rate our multi-sentence
descriptions as more readable, correct, and relevant than related work. To
understand the difference between more detailed and shorter descriptions, we
collect and analyze a video description corpus of three levels of detail.
New Algorithmic Approaches to Point Constellation Recognition
Authors: Thomas Bourgeat, Julien Bringer, Herve Chabanne, Robin Champenois, Jeremie Clement, Houda Ferradi, Marc Heinrich, Paul Melotti, David Naccache, Antoine Voizard
Date: 2014-03-24 00:00:00
Tasks: Translation

Point constellation recognition is a common problem with many pattern
matching applications. Whilst useful in many contexts, this work is mainly
motivated by fingerprint matching. Fingerprints are traditionally modelled as
constellations of oriented points called minutiae. The fingerprint verifier's
task consists in comparing two point constellations. The compared
constellations may differ by rotation and translation or by much more involved
transforms such as distortion or occlusion. This paper presents three new
constellation matching algorithms. The first two methods generalize an
algorithm by Bringer and Despiegel. Our third proposal creates a very
interesting analogy between mechanical system simulation and the constellation
recognition problem.
Adaptive MCMC-Based Inference in Probabilistic Logic Programs
Authors: Arun Nampally, C. R. Ramakrishnan
Date: 2014-03-24 00:00:00
Tasks: Reinforcement Learning

Probabilistic Logic Programming (PLP) languages enable programmers to specify
systems that combine logical models with statistical knowledge. The inference
problem, to determine the probability of query answers in PLP, is intractable
in general, thereby motivating the need for approximate techniques. In this
paper, we present a technique for approximate inference of conditional
probabilities for PLP queries. It is an Adaptive Markov Chain Monte Carlo
(MCMC) technique, where the distribution from which samples are drawn is
modified as the Markov Chain is explored. In particular, the distribution is
progressively modified to increase the likelihood that a generated sample is
consistent with evidence. In our context, each sample is uniquely characterized
by the outcomes of a set of random variables. Inspired by reinforcement
learning, our technique propagates rewards to random variable/outcome pairs
used in a sample based on whether the sample was consistent or not. The
cumulative rewards of each outcome is used to derive a new "adapted
distribution" for each random variable. For a sequence of samples, the
distributions are progressively adapted after each sample. For a query with
"Markovian evaluation structure", we show that the adapted distribution of
samples converges to the query's conditional probability distribution. For
Markovian queries, we present a modified adaptation process that can be used in
adaptive MCMC as well as adaptive independent sampling. We empirically evaluate
the effectiveness of the adaptive sampling methods for queries with and without
Markovian evaluation structure.
Ensemble Detection of Single & Multiple Events at Sentence-Level
Authors: Luís Marujo, Anatole Gershman, Jaime Carbonell, João P. Neto, David Martins de Matos
Date: 2014-03-24 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Sentence

Event classification at sentence level is an important Information Extraction
task with applications in several NLP, IR, and personalization systems.
Multi-label binary relevance (BR) are the state-of-art methods. In this work,
we explored new multi-label methods known for capturing relations between event
types. These new methods, such as the ensemble Chain of Classifiers, improve
the F1 on average across the 6 labels by 2.8% over the Binary Relevance. The
low occurrence of multi-label sentences motivated the reduction of the hard
imbalanced multi-label classification problem with low number of occurrences of
multiple labels per instance to an more tractable imbalanced multiclass problem
with better results (+ 4.6%). We report the results of adding new features,
such as sentiment strength, rhetorical signals, domain-id (source-id and date),
and key-phrases in both single-label and multi-label event classification
scenarios.
An Efficient Feature Selection in Classification of Audio Files
Authors: Jayita Mitra, Diganta Saha
Date: 2014-03-24 00:00:00
Tasks: Attribute, Classification, Dimensionality Reduction, feature selection, General Classification, Genre classification, Music Genre Classification

In this paper we have focused on an efficient feature selection method in
classification of audio files. The main objective is feature selection and
extraction. We have selected a set of features for further analysis, which
represents the elements in feature vector. By extraction method we can compute
a numerical representation that can be used to characterize the audio using the
existing toolbox. In this study Gain Ratio (GR) is used as a feature selection
measure. GR is used to select splitting attribute which will separate the
tuples into different classes. The pulse clarity is considered as a subjective
measure and it is used to calculate the gain of features of audio files. The
splitting criterion is employed in the application to identify the class or the
music genre of a specific audio file from testing database. Experimental
results indicate that by using GR the application can produce a satisfactory
result for music genre classification. After dimensionality reduction best
three features have been selected out of various features of audio file and in
this technique we will get more than 90% successful classification result.
Brain Tumor Detection Based On Mathematical Analysis and Symmetry Information
Authors: Narkhede Sachin G., Vaishali Khairnar, Sujata Kadu
Date: 2014-03-24 00:00:00
Tasks: Brain Tumor Segmentation, Image Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Image segmentation some of the challenging issues on brain magnetic resonance
image tumor segmentation caused by the weak correlation between magnetic
resonance imaging intensity and anatomical meaning.With the objective of
utilizing more meaningful information to improve brain tumor segmentation,an
approach which employs bilateral symmetry information as an additional feature
for segmentation is proposed.This is motivated by potential performance
improvement in the general automatic brain tumor segmentation systems which are
important for many medical and scientific applications.Brain Magnetic Resonance
Imaging segmentation is a complex problem in the field of medical imaging
despite various presented methods.MR image of human brain can be divided into
several sub-regions especially soft tissues such as gray matter,white matter
and cerebra spinal fluid.Although edge information is the main clue in image
segmentation,it cannot get a better result in analysis the content of images
without combining other information.Our goal is to detect the position and
boundary of tumors automatically.Experiments were conducted on real
pictures,and the results show that the algorithm is flexible and convenient.
First Order Methods for Robust Non-negative Matrix Factorization for Large Scale Noisy Data
Authors: Jason Gejie Liu, Shuchin Aeron
Date: 2014-03-24 00:00:00

Nonnegative matrix factorization (NMF) has been shown to be identifiable
under the separability assumption, under which all the columns(or rows) of the
input data matrix belong to the convex cone generated by only a few of these
columns(or rows) [1]. In real applications, however, such separability
assumption is hard to satisfy. Following [4] and [5], in this paper, we look at
the Linear Programming (LP) based reformulation to locate the extreme rays of
the convex cone but in a noisy setting. Furthermore, in order to deal with the
large scale data, we employ First-Order Methods (FOM) to mitigate the
computational complexity of LP, which primarily results from a large number of
constraints. We show the performance of the algorithm on real and synthetic
data sets.
Automatic recognition and tagging of topologically different regimes in dynamical systems
Authors: Jesse Berwald, Marian Gidea, Mikael Vejdemo-Johansson
Date: 2013-12-09 00:00:00
Tasks: TAG

Complex systems are commonly modeled using nonlinear dynamical systems. These
models are often high-dimensional and chaotic. An important goal in studying
physical systems through the lens of mathematical models is to determine when
the system undergoes changes in qualitative behavior. A detailed description of
the dynamics can be difficult or impossible to obtain for high-dimensional and
chaotic systems. Therefore, a more sensible goal is to recognize and mark
transitions of a system between qualitatively different regimes of behavior. In
practice, one is interested in developing techniques for detection of such
transitions from sparse observations, possibly contaminated by noise. In this
paper we develop a framework to accurately tag different regimes of complex
systems based on topological features. In particular, our framework works with
a high degree of success in picking out a cyclically orbiting regime from a
stationary equilibrium regime in high-dimensional stochastic dynamical systems.
AIS-INMACA: A Novel Integrated MACA Based Clonal Classifier for Protein Coding and Promoter Region Prediction
Authors: Pokkuluri Kiran Sree, Inampudi Ramesh Babu
Date: 2014-03-24 00:00:00
Tasks: Protein Structure Prediction

Most of the problems in bioinformatics are now the challenges in computing.
This paper aims at building a classifier based on Multiple Attractor Cellular
Automata (MACA) which uses fuzzy logic. It is strengthened with an artificial
Immune System Technique (AIS), Clonal algorithm for identifying a protein
coding and promoter region in a given DNA sequence. The proposed classifier is
named as AIS-INMACA introduces a novel concept to combine CA with artificial
immune system to produce a better classifier which can address major problems
in bioinformatics. This will be the first integrated algorithm which can
predict both promoter and protein coding regions. To obtain good fitness rules
the basic concept of Clonal selection algorithm was used. The proposed
classifier can handle DNA sequences of lengths 54,108,162,252,354. This
classifier gives the exact boundaries of both protein and promoter regions with
an average accuracy of 89.6%. This classifier was tested with 97,000 data
components which were taken from Fickett & Toung, MPromDb, and other sequences
from a renowned medical university. This proposed classifier can handle huge
data sets and can find protein and promoter regions even in mixed and
overlapped DNA sequences. This work also aims at identifying the logicality
between the major problems in bioinformatics and tries to obtaining a common
frame work for addressing major problems in bioinformatics like protein
structure prediction, RNA structure prediction, predicting the splicing pattern
of any primary transcript and analysis of information content in DNA, RNA,
protein sequences and structure. This work will attract more researchers
towards application of CA as a potential pattern classifier to many important
problems in bioinformatics
SRA: Fast Removal of General Multipath for ToF Sensors
Authors: Daniel Freedman, Eyal Krupka, Yoni Smolin, Ido Leichter, Mirko Schmidt
Date: 2014-03-24 00:00:00
Tasks: Dimensionality Reduction

A major issue with Time of Flight sensors is the presence of multipath
interference. We present Sparse Reflections Analysis (SRA), an algorithm for
removing this interference which has two main advantages. First, it allows for
very general forms of multipath, including interference with three or more
paths, diffuse multipath resulting from Lambertian surfaces, and combinations
thereof. SRA removes this general multipath with robust techniques based on
$L_1$ optimization. Second, due to a novel dimension reduction, we are able to
produce a very fast version of SRA, which is able to run at frame rate.
Experimental results on both synthetic data with ground truth, as well as real
images of challenging scenes, validate the approach.