pwc-archive/papers-with-abstracts huggingface dataset:
A Study of Actor and Action Semantic Retention in Video Supervoxel Segmentation
Authors: Chenliang Xu, Richard F. Doell, Stephen José Hanson, Catherine Hanson, Jason J. Corso
Date: 2013-11-13 00:00:00
Tasks: object-detection, Object Detection, Segmentation

Existing methods in the semantic computer vision community seem unable to
deal with the explosion and richness of modern, open-source and social video
content. Although sophisticated methods such as object detection or
bag-of-words models have been well studied, they typically operate on low level
features and ultimately suffer from either scalability issues or a lack of
semantic meaning. On the other hand, video supervoxel segmentation has recently
been established and applied to large scale data processing, which potentially
serves as an intermediate representation to high level video semantic
extraction. The supervoxels are rich decompositions of the video content: they
capture object shape and motion well. However, it is not yet known if the
supervoxel segmentation retains the semantics of the underlying video content.
In this paper, we conduct a systematic study of how well the actor and action
semantics are retained in video supervoxel segmentation. Our study has human
observers watching supervoxel segmentation videos and trying to discriminate
both actor (human or animal) and action (one of eight everyday actions). We
gather and analyze a large set of 640 human perceptions over 96 videos in 3
different supervoxel scales. Furthermore, we conduct machine recognition
experiments on a feature defined on supervoxel segmentation, called supervoxel
shape context, which is inspired by the higher order processes in human
perception. Our ultimate findings suggest that a significant amount of
semantics have been well retained in the video supervoxel segmentation and can
be used for further video analysis.
On a non-local spectrogram for denoising one-dimensional signals
Authors: Gonzalo Galiano, Julián Velasco
Date: 2013-11-13 00:00:00
Tasks: Denoising, Image Denoising

In previous works, we investigated the use of local filters based on partial
differential equations (PDE) to denoise one-dimensional signals through the
image processing of time-frequency representations, such as the spectrogram. In
this image denoising algorithms, the particularity of the image was hardly
taken into account. We turn, in this paper, to study the performance of
non-local filters, like Neighborhood or Yaroslavsky filters, in the same
problem. We show that, for certain iterative schemes involving the Neighborhood
filter, the computational time is drastically reduced with respect to
Yaroslavsky or nonlinear PDE based filters, while the outputs of the filtering
processes are similar. This is heuristically justified by the connection
between the (fast) Neighborhood filter applied to a spectrogram and the
corresponding Nonlocal Means filter (accurate) applied to the Wigner-Ville
distribution of the signal. This correspondence holds only for time-frequency
representations of one-dimensional signals, not to usual images, and in this
sense the particularity of the image is exploited. We compare though a series
of experiments on synthetic and biomedical signals the performance of local and
non-local filters.
Stochastic inference with deterministic spiking neurons
Authors: Mihai A. Petrovici, Johannes Bill, Ilja Bytschok, Johannes Schemmel, Karlheinz Meier
Date: 2013-11-13 00:00:00
Tasks: Bayesian Inference

The seemingly stochastic transient dynamics of neocortical circuits observed
in vivo have been hypothesized to represent a signature of ongoing stochastic
inference. In vitro neurons, on the other hand, exhibit a highly deterministic
response to various types of stimulation. We show that an ensemble of
deterministic leaky integrate-and-fire neurons embedded in a spiking noisy
environment can attain the correct firing statistics in order to sample from a
well-defined target distribution. We provide an analytical derivation of the
activation function on the single cell level; for recurrent networks, we
examine convergence towards stationarity in computer simulations and
demonstrate sample-based Bayesian inference in a mixed graphical model. This
establishes a rigorous link between deterministic neuron models and functional
stochastic dynamics on the network level.
Sound, Complete and Minimal UCQ-Rewriting for Existential Rules
Authors: Mélanie König, Michel Leclère, Marie-Laure Mugnier, Michaël Thomazo
Date: 2013-11-13 00:00:00

We address the issue of Ontology-Based Data Access, with ontologies
represented in the framework of existential rules, also known as Datalog+/-. A
well-known approach involves rewriting the query using ontological knowledge.
We focus here on the basic rewriting technique which consists of rewriting the
initial query into a union of conjunctive queries. First, we study a generic
breadth-first rewriting algorithm, which takes as input any rewriting operator,
and define properties of rewriting operators that ensure the correctness of the
algorithm. Then, we focus on piece-unifiers, which provide a rewriting operator
with the desired properties. Finally, we propose an implementation of this
framework and report some experiments.
Architecture of an Ontology-Based Domain-Specific Natural Language Question Answering System
Authors: Athira P. M., Sreeja M., P. C. Reghu Raj
Date: 2013-11-13 00:00:00
Tasks: Question Answering

Question answering (QA) system aims at retrieving precise information from a
large collection of documents against a query. This paper describes the
architecture of a Natural Language Question Answering (NLQA) system for a
specific domain based on the ontological information, a step towards semantic
web question answering. The proposed architecture defines four basic modules
suitable for enhancing current QA capabilities with the ability of processing
complex questions. The first module was the question processing, which analyses
and classifies the question and also reformulates the user query. The second
module allows the process of retrieving the relevant documents. The next module
processes the retrieved documents, and the last module performs the extraction
and generation of a response. Natural language processing techniques are used
for processing the question and documents and also for answer extraction.
Ontology and domain knowledge are used for reformulating queries and
identifying the relations. The aim of the system is to generate short and
specific answer to the question that is asked in the natural language in a
specific domain. We have achieved 94 % accuracy of natural language question
answering in our implementation.
smart application for AMS using Face Recognition
Authors: MuthuKalyani. K, VeeraMuthu. A
Date: 2013-11-13 00:00:00
Tasks: Face Recognition, Management

Attendance Management System (AMS) can be made into smarter way by using face recognition technique, where we use a CCTV camera to be fixed at the entry point of a classroom, which automatically captures the image of the person and checks the observed image with the face database using android enhanced smart phone. It is typically used for two purposes. Firstly, marking attendance for student by comparing the face images produced recently and secondly, recognition of human who are strange to the environment i.e. an unauthorized person For verification of image, a newly emerging trend 3D Face Recognition is used which claims to provide more accuracy in matching the image databases and has an ability to recognize a subject at different view angles.
An Efficient Method for Recognizing the Low Quality Fingerprint Verification by Means of Cross Correlation
Authors: V. Karthikeyan, V. J. Vijayalakshmi
Date: 2013-11-13 00:00:00
Tasks: fingerprint verification

In this paper, we propose an efficient method to provide personal
identification using fingerprint to get better accuracy even in noisy
condition. The fingerprint matching based on the number of corresponding
minutia pairings, has been in use for a long time, which is not very efficient
for recognizing the low quality fingerprints. To overcome this problem,
correlation technique is used. The correlation-based fingerprint verification
system is capable of dealing with low quality images from which no minutiae can
be extracted reliably and with fingerprints that suffer from non-uniform shape
distortions, also in case of damaged and partial images. Orientation Field
Methodology (OFM) has been used as a preprocessing module, and it converts the
images into a field pattern based on the direction of the ridges, loops and
bifurcations in the image of a fingerprint. The input image is then Cross
Correlated (CC) with all the images in the cluster and the highest correlated
image is taken as the output. The result gives a good recognition rate, as the
proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC)
for fingerprint identification.
Informed Source Separation: A Bayesian Tutorial
Authors: Kevin H. Knuth
Date: 2013-11-13 00:00:00

Source separation problems are ubiquitous in the physical sciences; any
situation where signals are superimposed calls for source separation to
estimate the original signals. In this tutorial I will discuss the Bayesian
approach to the source separation problem. This approach has a specific
advantage in that it requires the designer to explicitly describe the signal
model in addition to any other information or assumptions that go into the
problem description. This leads naturally to the idea of informed source
separation, where the algorithm design incorporates relevant information about
the specific problem. This approach promises to enable researchers to design
their own high-quality algorithms that are specifically tailored to the problem
at hand.
Learning Input and Recurrent Weight Matrices in Echo State Networks
Authors: Hamid Palangi, Li Deng, Rabab K. Ward
Date: 2013-11-13 00:00:00

Echo State Networks (ESNs) are a special type of the temporally deep network
model, the Recurrent Neural Network (RNN), where the recurrent matrix is
carefully designed and both the recurrent and input matrices are fixed. An ESN
uses the linearity of the activation function of the output units to simplify
the learning of the output matrix. In this paper, we devise a special technique
that take advantage of this linearity in the output units of an ESN, to learn
the input and recurrent matrices. This has not been done in earlier ESNs due to
their well known difficulty in learning those matrices. Compared to the
technique of BackPropagation Through Time (BPTT) in learning general RNNs, our
proposed method exploits linearity of activation function in the output units
to formulate the relationships amongst the various matrices in an RNN. These
relationships results in the gradient of the cost function having an analytical
form and being more accurate. This would enable us to compute the gradients
instead of obtaining them by recursion as in BPTT. Experimental results on
phone state classification show that learning one or both the input and
recurrent matrices in an ESN yields superior results compared to traditional
ESNs that do not learn these matrices, especially when longer time steps are
used.
Authorship Attribution Using Word Network Features
Authors: Shibamouli Lahiri, Rada Mihalcea
Date: 2013-11-12 00:00:00
Tasks: Authorship Attribution, BIG-bench Machine Learning

In this paper, we explore a set of novel features for authorship attribution
of documents. These features are derived from a word network representation of
natural language text. As has been noted in previous studies, natural language
tends to show complex network structure at word level, with low degrees of
separation and scale-free (power law) degree distribution. There has also been
work on authorship attribution that incorporates ideas from complex networks.
The goal of our paper is to explore properties of these complex networks that
are suitable as features for machine-learning-based authorship attribution of
documents. We performed experiments on three different datasets, and obtained
promising results.
Approximate Inference in Continuous Determinantal Point Processes
Authors: Raja Hafiz Affandi, Emily B. Fox, Ben Taskar
Date: 2013-11-12 00:00:00
Tasks: Point Processes

Determinantal point processes (DPPs) are random point processes well-suited
for modeling repulsion. In machine learning, the focus of DPP-based models has
been on diverse subset selection from a discrete and finite base set. This
discrete setting admits an efficient sampling algorithm based on the
eigendecomposition of the defining kernel matrix. Recently, there has been
growing interest in using DPPs defined on continuous spaces. While the
discrete-DPP sampler extends formally to the continuous case, computationally,
the steps required are not tractable in general. In this paper, we present two
efficient DPP sampling schemes that apply to a wide range of kernel functions:
one based on low rank approximations via Nystrom and random Fourier feature
techniques and another based on Gibbs sampling. We demonstrate the utility of
continuous DPPs in repulsive mixture modeling and synthesizing human poses
spanning activity spaces.
Structural Learning for Template-free Protein Folding
Authors: Feng Zhao
Date: 2013-11-06 00:00:00
Tasks: Protein Folding

The thesis is aimed to solve the template-free protein folding problem by
tackling two important components: efficient sampling in vast conformation
space, and design of knowledge-based potentials with high accuracy. We have
proposed the first-order and second-order CRF-Sampler to sample structures from
the continuous local dihedral angles space by modeling the lower and higher
order conditional dependency between neighboring dihedral angles given the
primary sequence information. A framework combining the Conditional Random
Fields and the energy function is introduced to guide the local conformation
sampling using long range constraints with the energy function.
  The relationship between the sequence profile and the local dihedral angle
distribution is nonlinear. Hence we proposed the CNF-Folder to model this
complex relationship by applying a novel machine learning model Conditional
Neural Fields which utilizes the structural graphical model with the neural
network. CRF-Samplers and CNF-Folder perform very well in CASP8 and CASP9.
  Further, a novel pairwise distance statistical potential (EPAD) is designed
to capture the dependency of the energy profile on the positions of the
interacting amino acids as well as the types of those amino acids, opposing the
common assumption that this energy profile depends only on the types of amino
acids. EPAD has also been successfully applied in the CASP 10 Free Modeling
experiment with CNF-Folder, especially outstanding on some uncommon structured
targets.
Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of Classification Error in the Gaussian Model
Authors: Amin Zollanvari, Edward R. Dougherty
Date: 2013-10-05 00:00:00
Tasks: General Classification

The most important aspect of any classifier is its error rate, because this
quantifies its predictive capacity. Thus, the accuracy of error estimation is
critical. Error estimation is problematic in small-sample classifier design
because the error must be estimated using the same data from which the
classifier has been designed. Use of prior knowledge, in the form of a prior
distribution on an uncertainty class of feature-label distributions to which
the true, but unknown, feature-distribution belongs, can facilitate accurate
error estimation (in the mean-square sense) in circumstances where accurate
completely model-free error estimation is impossible. This paper provides
analytic asymptotically exact finite-sample approximations for various
performance metrics of the resulting Bayesian Minimum Mean-Square-Error (MMSE)
error estimator in the case of linear discriminant analysis (LDA) in the
multivariate Gaussian model. These performance metrics include the first,
second, and cross moments of the Bayesian MMSE error estimator with the true
error of LDA, and therefore, the Root-Mean-Square (RMS) error of the estimator.
We lay down the theoretical groundwork for Kolmogorov double-asymptotics in a
Bayesian setting, which enables us to derive asymptotic expressions of the
desired performance metrics. From these we produce analytic finite-sample
approximations and demonstrate their accuracy via numerical examples. Various
examples illustrate the behavior of these approximations and their use in
determining the necessary sample size to achieve a desired RMS. The
Supplementary Material contains derivations for some equations and added
figures.
Multiple Closed-Form Local Metric Learning for K-Nearest Neighbor Classifier
Authors: Jianbo Ye
Date: 2013-11-12 00:00:00
Tasks: Form, General Classification, Metric Learning

Many researches have been devoted to learn a Mahalanobis distance metric,
which can effectively improve the performance of kNN classification. Most
approaches are iterative and computational expensive and linear rigidity still
critically limits metric learning algorithm to perform better. We proposed a
computational economical framework to learn multiple metrics in closed-form.
Aggregation of Affine Estimators
Authors: Dong Dai, Philippe Rigollet, Lucy Xia, Tong Zhang
Date: 2013-11-12 00:00:00
Tasks: Model Selection

We consider the problem of aggregating a general collection of affine
estimators for fixed design regression. Relevant examples include some commonly
used statistical estimators such as least squares, ridge and robust least
squares estimators. Dalalyan and Salmon (2012) have established that, for this
problem, exponentially weighted (EW) model selection aggregation leads to sharp
oracle inequalities in expectation, but similar bounds in deviation were not
previously known. While results indicate that the same aggregation scheme may
not satisfy sharp oracle inequalities with high probability, we prove that a
weaker notion of oracle inequality for EW that holds with high probability.
Moreover, using a generalization of the newly introduced $Q$-aggregation scheme
we also prove sharp oracle inequalities that hold with high probability.
Finally, we apply our results to universal aggregation and show that our
proposed estimator leads simultaneously to all the best known bounds for
aggregation, including $\ell_q$-aggregation, $q \in (0,1)$, with high
probability.
When Does More Regularization Imply Fewer Degrees of Freedom? Sufficient Conditions and Counter Examples from Lasso and Ridge Regression
Authors: Shachar Kaufman, Saharon Rosset
Date: 2013-11-12 00:00:00
Tasks: regression

Regularization aims to improve prediction performance of a given statistical
modeling approach by moving to a second approach which achieves worse training
error but is expected to have fewer degrees of freedom, i.e., better agreement
between training and prediction error. We show here, however, that this
expected behavior does not hold in general. In fact, counter examples are given
that show regularization can increase the degrees of freedom in simple
situations, including lasso and ridge regression, which are the most common
regularization approaches in use. In such situations, the regularization
increases both training error and degrees of freedom, and is thus inherently
without merit. On the other hand, two important regularization scenarios are
described where the expected reduction in degrees of freedom is indeed
guaranteed: (a) all symmetric linear smoothers, and (b) linear regression
versus convex constrained linear regression (as in the constrained variant of
ridge regression and lasso).
Equivalence of distance-based and RKHS-based statistics in hypothesis testing
Authors: Dino Sejdinovic, Bharath Sriperumbudur, Arthur Gretton, Kenji Fukumizu
Date: 2012-07-25 00:00:00
Tasks: Two-sample testing

We provide a unifying framework linking two classes of statistics used in
two-sample and independence testing: on the one hand, the energy distances and
distance covariances from the statistics literature; on the other, maximum mean
discrepancies (MMD), that is, distances between embeddings of distributions to
reproducing kernel Hilbert spaces (RKHS), as established in machine learning.
In the case where the energy distance is computed with a semimetric of negative
type, a positive definite kernel, termed distance kernel, may be defined such
that the MMD corresponds exactly to the energy distance. Conversely, for any
positive definite kernel, we can interpret the MMD as energy distance with
respect to some negative-type semimetric. This equivalence readily extends to
distance covariance using kernels on the product space. We determine the class
of probability distributions for which the test statistics are consistent
against all alternatives. Finally, we investigate the performance of the family
of distance kernels in two-sample and independence tests: we show in particular
that the energy distance most commonly employed in statistics is just one
member of a parametric family of kernels, and that other choices from this
family can yield more powerful tests.
Deep neural networks for single channel source separation
Authors: Emad M. Grais, Mehmet Umut Sen, Hakan Erdogan
Date: 2013-11-12 00:00:00

In this paper, a novel approach for single channel source separation (SCSS)
using a deep neural network (DNN) architecture is introduced. Unlike previous
studies in which DNN and other classifiers were used for classifying
time-frequency bins to obtain hard masks for each source, we use the DNN to
classify estimated source spectra to check for their validity during
separation. In the training stage, the training data for the source signals are
used to train a DNN. In the separation stage, the trained DNN is utilized to
aid in estimation of each source in the mixed signal. Single channel source
separation problem is formulated as an energy minimization problem where each
source spectra estimate is encouraged to fit the trained DNN model and the
mixed signal spectrum is encouraged to be written as a weighted sum of the
estimated source spectra. The proposed approach works regardless of the energy
scale differences between the source signals in the training and separation
stages. Nonnegative matrix factorization (NMF) is used to initialize the DNN
estimate for each source. The experimental results show that using DNN
initialized by NMF for source separation improves the quality of the separated
signal compared with using NMF for source separation.
Multivariate Bernoulli distribution
Authors: Bin Dai, Shilin Ding, Grace Wahba
Date: 2012-06-08 00:00:00
Tasks: Variable Selection

In this paper, we consider the multivariate Bernoulli distribution as a model
to estimate the structure of graphs with binary nodes. This distribution is
discussed in the framework of the exponential family, and its statistical
properties regarding independence of the nodes are demonstrated. Importantly
the model can estimate not only the main effects and pairwise interactions
among the nodes but also is capable of modeling higher order interactions,
allowing for the existence of complex clique effects. We compare the
multivariate Bernoulli model with existing graphical inference models - the
Ising model and the multivariate Gaussian model, where only the pairwise
interactions are considered. On the other hand, the multivariate Bernoulli
distribution has an interesting property in that independence and
uncorrelatedness of the component random variables are equivalent. Both the
marginal and conditional distributions of a subset of variables in the
multivariate Bernoulli distribution still follow the multivariate Bernoulli
distribution. Furthermore, the multivariate Bernoulli logistic model is
developed under generalized linear model theory by utilizing the canonical link
function in order to include covariate information on the nodes, edges and
cliques. We also consider variable selection techniques such as LASSO in the
logistic model to impose sparsity structure on the graph. Finally, we discuss
extending the smoothing spline ANOVA approach to the multivariate Bernoulli
logistic model to enable estimation of non-linear effects of the predictor
variables.