pwc-archive/papers-with-abstracts huggingface dataset:
Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models
Authors: Peter L. Spirtes
Date: 2013-09-17 00:00:00

The Trek Separation Theorem (Sullivant et al. 2010) states necessary and
sufficient conditions for a linear directed acyclic graphical model to entail
for all possible values of its linear coefficients that the rank of various
sub-matrices of the covariance matrix is less than or equal to n, for any given
n. In this paper, I extend the Trek Separation Theorem in two ways: I prove
that the same necessary and sufficient conditions apply even when the
generating model is partially non-linear and contains some cycles. This
justifies application of constraint-based causal search algorithms such as the
BuildPureClusters algorithm (Silva et al. 2006) for discovering the causal
structure of latent variable models to data generated by a wider class of
causal models that may contain non-linear and cyclic relations among the latent
variables.
Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes
Authors: Roger Frigola, Carl Edward Rasmussen
Date: 2013-03-12 00:00:00
Tasks: Gaussian Processes, regression

We introduce GP-FNARX: a new model for nonlinear system identification based
on a nonlinear autoregressive exogenous model (NARX) with filtered regressors
(F) where the nonlinear regression problem is tackled using sparse Gaussian
processes (GP). We integrate data pre-processing with system identification
into a fully automated procedure that goes from raw data to an identified
model. Both pre-processing parameters and GP hyper-parameters are tuned by
maximizing the marginal likelihood of the probabilistic model. We obtain a
Bayesian model of the system's dynamics which is able to report its uncertainty
in regions where the data is scarce. The automated approach, the modeling of
uncertainty and its relatively low computational cost make of GP-FNARX a good
candidate for applications in robotics and adaptive control.
A notion of continuity in discrete spaces and applications
Authors: Valerio Capraro
Date: 2012-10-08 00:00:00

We propose a notion of continuous path for locally finite metric spaces,
taking inspiration from the recent development of A-theory for locally finite
connected graphs. We use this notion of continuity to derive an analogue in Z^2
of the Jordan curve theorem and to extend to a quite large class of locally
finite metric spaces (containing all finite metric spaces) an inequality for
the \ell^p-distortion of a metric space that has been recently proved by
Pierre-Nicolas Jolissaint and Alain Valette for finite connected graphs.
Exploiting Similarities among Languages for Machine Translation
Authors: Tomas Mikolov, Quoc V. Le, Ilya Sutskever
Date: 2013-09-17 00:00:00
Tasks: Machine Translation, Translation

Dictionaries and phrase tables are the basis of modern statistical machine
translation systems. This paper develops a method that can automate the process
of generating and extending dictionaries and phrase tables. Our method can
translate missing word and phrase entries by learning language structures based
on large monolingual data and mapping between languages from small bilingual
data. It uses distributed representation of words and learns a linear mapping
between vector spaces of languages. Despite its simplicity, our method is
surprisingly effective: we can achieve almost 90% precision@5 for translation
of words between English and Spanish. This method makes little assumption about
the languages, so it can be used to extend and refine dictionaries and
translation tables for any language pairs.
A Non-Local Means Filter for Removing the Poisson Noise
Authors: Qiyu Jin, Ion Grama, Quansheng Liu
Date: 2013-09-17 00:00:00
Tasks: Denoising, Image Denoising

A new image denoising algorithm to deal with the Poisson noise model is
given, which is based on the idea of Non-Local Mean. By using the "Oracle"
concept, we establish a theorem to show that the Non-Local Means Filter can
effectively deal with Poisson noise with some modification. Under the
theoretical result, we construct our new algorithm called Non-Local Means
Poisson Filter and demonstrate in theory that the filter converges at the usual
optimal rate. The filter is as simple as the classic Non-Local Means and the
simulation results show that our filter is very competitive.
On Convergent Finite Difference Schemes for Variational - PDE Based Image Processing
Authors: V. B. S. Prasath, Juan C. Moreno
Date: 2013-09-16 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration

We study an adaptive anisotropic Huber functional based image restoration
scheme. By using a combination of L2-L1 regularization functions, an adaptive
Huber functional based energy minimization model provides denoising with edge
preservation in noisy digital images. We study a convergent finite difference
scheme based on continuous piecewise linear functions and use a variable
splitting scheme, namely the Split Bregman, to obtain the discrete minimizer.
Experimental results are given in image denoising and comparison with additive
operator splitting, dual fixed point, and projected gradient schemes illustrate
that the best convergence rates are obtained for our algorithm.
Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel
Authors: Tai Qin, Karl Rohe
Date: 2013-09-16 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Clustering

Spectral clustering is a fast and popular algorithm for finding clusters in
networks. Recently, Chaudhuri et al. (2012) and Amini et al.(2012) proposed
inspired variations on the algorithm that artificially inflate the node degrees
for improved statistical performance. The current paper extends the previous
statistical estimation results to the more canonical spectral clustering
algorithm in a way that removes any assumption on the minimum degree and
provides guidance on the choice of the tuning parameter. Moreover, our results
show how the "star shape" in the eigenvectors--a common feature of empirical
networks--can be explained by the Degree-Corrected Stochastic Blockmodel and
the Extended Planted Partition model, two statistical models that allow for
highly heterogeneous degrees. Throughout, the paper characterizes and justifies
several of the variations of the spectral clustering algorithm in terms of
these models.
Learning a Loopy Model For Semantic Segmentation Exactly
Authors: Andreas Christian Mueller, Sven Behnke
Date: 2013-09-16 00:00:00
Tasks: model, Semantic Segmentation

Learning structured models using maximum margin techniques has become an
indispensable tool for com- puter vision researchers, as many computer vision
applications can be cast naturally as an image labeling problem. Pixel-based or
superpixel-based conditional random fields are particularly popular examples.
Typ- ically, neighborhood graphs, which contain a large number of cycles, are
used. As exact inference in loopy graphs is NP-hard in general, learning these
models without approximations is usually deemed infeasible. In this work we
show that, despite the theoretical hardness, it is possible to learn loopy
models exactly in practical applications. To this end, we analyze the use of
multiple approximate inference techniques together with cutting plane training
of structural SVMs. We show that our proposed method yields exact solutions
with an optimality guarantees in a computer vision application, for little
additional computational cost. We also propose a dynamic caching scheme to
accelerate training further, yielding runtimes that are comparable with
approximate methods. We hope that this insight can lead to a reconsideration of
the tractability of loopy models in computer vision.
Why SOV might be initially preferred and then lost or recovered? A theoretical framework
Authors: Ramon Ferrer-i-Cancho
Date: 2013-09-16 00:00:00
Tasks: Diversity

Little is known about why SOV order is initially preferred and then discarded
or recovered. Here we present a framework for understanding these and many
related word order phenomena: the diversity of dominant orders, the existence
of free words orders, the need of alternative word orders and word order
reversions and cycles in evolution. Under that framework, word order is
regarded as a multiconstraint satisfaction problem in which at least two
constraints are in conflict: online memory minimization and maximum
predictability.
Domain and Function: A Dual-Space Model of Semantic Relations and Compositions
Authors: Peter D. Turney
Date: 2013-09-16 00:00:00
Tasks: Semantic Composition

Given appropriate representations of the semantic relations between carpenter
and wood and between mason and stone (for example, vectors in a vector space
model), a suitable algorithm should be able to recognize that these relations
are highly similar (carpenter is to wood as mason is to stone; the relations
are analogous). Likewise, with representations of dog, house, and kennel, an
algorithm should be able to recognize that the semantic composition of dog and
house, dog house, is highly similar to kennel (dog house and kennel are
synonymous). It seems that these two tasks, recognizing relations and
compositions, are closely connected. However, up to now, the best models for
relations are significantly different from the best models for compositions. In
this paper, we introduce a dual-space model that unifies these two tasks. This
model matches the performance of the best previous models for relations and
compositions. The dual-space model consists of a space for measuring domain
similarity and a space for measuring function similarity. Carpenter and wood
share the same domain, the domain of carpentry. Mason and stone share the same
domain, the domain of masonry. Carpenter and mason share the same function, the
function of artisans. Wood and stone share the same function, the function of
materials. In the composition dog house, kennel has some domain overlap with
both dog and house (the domains of pets and buildings). The function of kennel
is similar to the function of house (the function of shelters). By combining
domain and function similarities in various ways, we can model relations,
compositions, and other aspects of semantics.
The Cyborg Astrobiologist: Matching of Prior Textures by Image Compression for Geological Mapping and Novelty Detection
Authors: P. C. McGuire, A. Bonnici, K. R. Bruner, C. Gross, J. Ormö, R. A. Smosna, S. Walter, L. Wendt
Date: 2013-09-16 00:00:00
Tasks: Image Compression, Novelty Detection

(abridged) We describe an image-comparison technique of Heidemann and Ritter
that uses image compression, and is capable of: (i) detecting novel textures in
a series of images, as well as of: (ii) alerting the user to the similarity of
a new image to a previously-observed texture. This image-comparison technique
has been implemented and tested using our Astrobiology Phone-cam system, which
employs Bluetooth communication to send images to a local laptop server in the
field for the image-compression analysis. We tested the system in a field site
displaying a heterogeneous suite of sandstones, limestones, mudstones and
coalbeds. Some of the rocks are partly covered with lichen. The image-matching
procedure of this system performed very well with data obtained through our
field test, grouping all images of yellow lichens together and grouping all
images of a coal bed together, and giving a 91% accuracy for similarity
detection. Such similarity detection could be employed to make maps of
different geological units. The novelty-detection performance of our system was
also rather good (a 64% accuracy). Such novelty detection may become valuable
in searching for new geological units, which could be of astrobiological
interest. The image-comparison technique is an unsupervised technique that is
not capable of directly classifying an image as containing a particular
geological feature; labeling of such geological features is done post facto by
human geologists associated with this study, for the purpose of analyzing the
system's performance. By providing more advanced capabilities for similarity
detection and novelty detection, this image-compression technique could be
useful in giving more scientific autonomy to robotic planetary rovers, and in
assisting human astronauts in their geological exploration and assessment.
Performance Investigation of Feature Selection Methods
Authors: Anuj sharma, Shubhamoy Dey
Date: 2013-09-16 00:00:00
Tasks: feature selection, General Classification, Opinion Mining, Sentiment Analysis, text-classification, Text Classification

Sentiment analysis or opinion mining has become an open research domain after
proliferation of Internet and Web 2.0 social media. People express their
attitudes and opinions on social media including blogs, discussion forums,
tweets, etc. and, sentiment analysis concerns about detecting and extracting
sentiment or opinion from online text. Sentiment based text classification is
different from topical text classification since it involves discrimination
based on expressed opinion on a topic. Feature selection is significant for
sentiment analysis as the opinionated text may have high dimensions, which can
adversely affect the performance of sentiment analysis classifier. This paper
explores applicability of feature selection methods for sentiment analysis and
investigates their performance for classification in term of recall, precision
and accuracy. Five feature selection methods (Document Frequency, Information
Gain, Gain Ratio, Chi Squared, and Relief-F) and three popular sentiment
feature lexicons (HM, GI and Opinion Lexicon) are investigated on movie reviews
corpus with a size of 2000 documents. The experimental results show that
Information Gain gave consistent results and Gain Ratio performs overall best
for sentimental feature selection while sentiment lexicons gave poor
performance. Furthermore, we found that performance of the classifier depends
on appropriate number of representative feature selected from text.
Using Self-Organizing Maps for Sentiment Analysis
Authors: Anuj Sharma, Shubhamoy Dey
Date: 2013-09-16 00:00:00
Tasks: Classification, General Classification, Opinion Mining, Quantization, Sentiment Analysis

Web 2.0 services have enabled people to express their opinions, experience
and feelings in the form of user-generated content. Sentiment analysis or
opinion mining involves identifying, classifying and aggregating opinions as
per their positive or negative polarity. This paper investigates the efficacy
of different implementations of Self-Organizing Maps (SOM) for sentiment based
visualization and classification of online reviews. Specifically, this paper
implements the SOM algorithm for both supervised and unsupervised learning from
text documents. The unsupervised SOM algorithm is implemented for sentiment
based visualization and classification tasks. For supervised sentiment
analysis, a competitive learning algorithm known as Learning Vector
Quantization is used. Both algorithms are also compared with their respective
multi-pass implementations where a quick rough ordering pass is followed by a
fine tuning pass. The experimental results on the online movie review data set
show that SOMs are well suited for sentiment based classification and sentiment
polarity visualization.
A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services
Authors: Anuj Sharma, Dr. Prabin Kumar Panigrahi
Date: 2013-09-16 00:00:00
Tasks: Management, Marketing

Marketing literature states that it is more costly to engage a new customer
than to retain an existing loyal customer. Churn prediction models are
developed by academics and practitioners to effectively manage and control
customer churn in order to retain existing customers. As churn management is an
important activity for companies to retain loyal customers, the ability to
correctly predict customer churn is necessary. As the cellular network services
market becoming more competitive, customer churn management has become a
crucial task for mobile communication operators. This paper proposes a neural
network based approach to predict customer churn in subscription of cellular
wireless services. The results of experiments indicate that neural network
based approach can predict customer churn.
Hidden Structure and Function in the Lexicon
Authors: Olivier Picard, Mélanie Lord, Alexandre Blondin-Massé, Odile Marcotte, Marcos Lopes, Stevan Harnad
Date: 2013-08-11 00:00:00
Tasks: All

How many words are needed to define all the words in a dictionary?
Graph-theoretic analysis reveals that about 10% of a dictionary is a unique
Kernel of words that define one another and all the rest, but this is not the
smallest such subset. The Kernel consists of one huge strongly connected
component (SCC), about half its size, the Core, surrounded by many small SCCs,
the Satellites. Core words can define one another but not the rest of the
dictionary. The Kernel also contains many overlapping Minimal Grounding Sets
(MGSs), each about the same size as the Core, each part-Core, part-Satellite.
MGS words can define all the rest of the dictionary. They are learned earlier,
more concrete and more frequent than the rest of the dictionary. Satellite
words, not correlated with age or frequency, are less concrete (more abstract)
words that are also needed for full lexical power.
Computational Methods for Probabilistic Inference of Sector Congestion in Air Traffic Management
Authors: Gaétan Marceau, Pierre Savéant, Marc Schoenauer
Date: 2013-09-16 00:00:00
Tasks: Management

This article addresses the issue of computing the expected cost functions
from a probabilistic model of the air traffic flow and capacity management. The
Clenshaw-Curtis quadrature is compared to Monte-Carlo algorithms defined
specifically for this problem. By tailoring the algorithms to this model, we
reduce the computational burden in order to simulate real instances. The study
shows that the Monte-Carlo algorithm is more sensible to the amount of
uncertainty in the system, but has the advantage to return a result with the
associated accuracy on demand. The performances for both approaches are
comparable for the computation of the expected cost of delay and the expected
cost of congestion. Finally, this study shows some evidences that the
simulation of the proposed probabilistic model is tractable for realistic
instances.
Multiobjective Tactical Planning under Uncertainty for Air Traffic Flow and Capacity Management
Authors: Gaétan Marceau, Pierre Savéant, Marc Schoenauer
Date: 2013-09-16 00:00:00
Tasks: Management, Trajectory Prediction

We investigate a method to deal with congestion of sectors and delays in the
tactical phase of air traffic flow and capacity management. It relies on
temporal objectives given for every point of the flight plans and shared among
the controllers in order to create a collaborative environment. This would
enhance the transition from the network view of the flow management to the
local view of air traffic control. Uncertainty is modeled at the trajectory
level with temporal information on the boundary points of the crossed sectors
and then, we infer the probabilistic occupancy count. Therefore, we can model
the accuracy of the trajectory prediction in the optimization process in order
to fix some safety margins. On the one hand, more accurate is our prediction;
more efficient will be the proposed solutions, because of the tighter safety
margins. On the other hand, when uncertainty is not negligible, the proposed
solutions will be more robust to disruptions. Furthermore, a multiobjective
algorithm is used to find the tradeoff between the delays and congestion, which
are antagonist in airspace with high traffic density. The flow management
position can choose manually, or automatically with a preference-based
algorithm, the adequate solution. This method is tested against two instances,
one with 10 flights and 5 sectors and one with 300 flights and 16 sectors.
Strategic Planning in Air Traffic Control as a Multi-objective Stochastic Optimization Problem
Authors: Gaétan Marceau, Pierre Savéant, Marc Schoenauer
Date: 2013-09-16 00:00:00
Tasks: Stochastic Optimization

With the objective of handling the airspace sector congestion subject to
continuously growing air traffic, we suggest to create a collaborative working
plan during the strategic phase of air traffic control. The plan obtained via a
new decision support tool presented in this article consists in a schedule for
controllers, which specifies time of overflight on the different waypoints of
the flight plans. In order to do it, we believe that the decision-support tool
shall model directly the uncertainty at a trajectory level in order to
propagate the uncertainty to the sector level. Then, the probability of
congestion for any sector in the airspace can be computed. Since air traffic
regulations and sector congestion are antagonist, we designed and implemented a
multi-objective optimization algorithm for determining the best trade-off
between these two criteria. The solution comes up as a set of alternatives for
the multi-sector planner where the severity of the congestion cost is
adjustable. In this paper, the Non-dominated Sorting Genetic Algorithm
(NSGA-II) was used to solve an artificial benchmark problem involving 24
aircraft and 11 sectors, and is able to provide a good approximation of the
Pareto front.
A Metric-learning based framework for Support Vector Machines and Multiple Kernel Learning
Authors: Huyen Do, Alexandros Kalousis
Date: 2013-09-16 00:00:00
Tasks: Metric Learning

Most metric learning algorithms, as well as Fisher's Discriminant Analysis
(FDA), optimize some cost function of different measures of within-and
between-class distances. On the other hand, Support Vector Machines(SVMs) and
several Multiple Kernel Learning (MKL) algorithms are based on the SVM large
margin theory. Recently, SVMs have been analyzed from SVM and metric learning,
and to develop new algorithms that build on the strengths of each. Inspired by
the metric learning interpretation of SVM, we develop here a new
metric-learning based SVM framework in which we incorporate metric learning
concepts within SVM. We extend the optimization problem of SVM to include some
measure of the within-class distance and along the way we develop a new
within-class distance measure which is appropriate for SVM. In addition, we
adopt the same approach for MKL and show that it can be also formulated as a
Mahalanobis metric learning problem. Our end result is a number of SVM/MKL
algorithms that incorporate metric learning concepts. We experiment with them
on a set of benchmark datasets and observe important predictive performance
improvements.
Evaluation the efficiency of artificial bee colony and the firefly algorithm in solving the continuous optimization problem
Authors: Seyyed Reza Khaze, Isa maleki, Sohrab Hojjatkhah, Ali Bagherinia
Date: 2013-09-16 00:00:00

Now the Meta-Heuristic algorithms have been used vastly in solving the
problem of continuous optimization. In this paper the Artificial Bee Colony
(ABC) algorithm and the Firefly Algorithm (FA) are valuated. And for presenting
the efficiency of the algorithms and also for more analysis of them, the
continuous optimization problems which are of the type of the problems of vast
limit of answer and the close optimized points are tested. So, in this paper
the efficiency of the ABC algorithm and FA are presented for solving the
continuous optimization problems and also the said algorithms are studied from
the accuracy in reaching the optimized solution and the resulting time and the
reliability of the optimized answer points of view.