pwc-archive/papers-with-abstracts huggingface dataset:
Optical images-based edge detection in Synthetic Aperture Radar images
Authors: Gilberto P. Silva Junior, Alejandro C. Frery, Sandra Sandri, Humberto Bustince, Edurne Barrenechea, Cédric Marco-Detchart
Date: 2015-08-24 00:00:00
Tasks: Edge Detection

We address the issue of adapting optical images-based edge detection
techniques for use in Polarimetric Synthetic Aperture Radar (PolSAR) imagery.
We modify the gravitational edge detection technique (inspired by the Law of
Universal Gravity) proposed by Lopez-Molina et al, using the non-standard
neighbourhood configuration proposed by Fu et al, to reduce the speckle noise
in polarimetric SAR imagery. We compare the modified and unmodified versions of
the gravitational edge detection technique with the well-established one
proposed by Canny, as well as with a recent multiscale fuzzy-based technique
proposed by Lopez-Molina et Alejandro We also address the issues of aggregation
of gray level images before and after edge detection and of filtering. All
techniques addressed here are applied to a mosaic built using class
distributions obtained from a real scene, as well as to the true PolSAR image;
the mosaic results are assessed using Baddeley's Delta Metric. Our experiments
show that modifying the gravitational edge detection technique with a
non-standard neighbourhood configuration produces better results than the
original technique, as well as the other techniques used for comparison. The
experiments show that adapting edge detection methods from Computational
Intelligence for use in PolSAR imagery is a new field worthy of exploration.
Stochastic Behavior of the Nonnegative Least Mean Fourth Algorithm for Stationary Gaussian Inputs and Slow Learning
Authors: Jingen Ni, Jian Yang, Jie Chen, Cédric Richard, José Carlos M. Bermudez
Date: 2015-08-24 00:00:00

Some system identification problems impose nonnegativity constraints on the
parameters to estimate due to inherent physical characteristics of the unknown
system. The nonnegative least-mean-square (NNLMS) algorithm and its variants
allow to address this problem in an online manner. A nonnegative least mean
fourth (NNLMF) algorithm has been recently proposed to improve the performance
of these algorithms in cases where the measurement noise is not Gaussian. This
paper provides a first theoretical analysis of the stochastic behavior of the
NNLMF algorithm for stationary Gaussian inputs and slow learning. Simulation
results illustrate the accuracy of the proposed analysis.
Echoes of Persuasion: The Effect of Euphony in Persuasive Communication
Authors: Marco Guerini, Gözde Özbal, Carlo Strapparava
Date: 2015-08-24 00:00:00
Proceeding: HLT 2015 5
Tasks: Persuasiveness

While the effect of various lexical, syntactic, semantic and stylistic
features have been addressed in persuasive language from a computational point
of view, the persuasive effect of phonetics has received little attention. By
modeling a notion of euphony and analyzing four datasets comprising persuasive
and non-persuasive sentences in different domains (political speeches, movie
quotes, slogans and tweets), we explore the impact of sounds on different forms
of persuasiveness. We conduct a series of analyses and prediction experiments
within and across datasets. Our results highlight the positive role of phonetic
devices on persuasion.
Searching for significant patterns in stratified data
Authors: Felipe Llinares-Lopez, Laetitia Papaxanthos, Dean Bodenham, Karsten Borgwardt
Date: 2015-08-24 00:00:00

Significant pattern mining, the problem of finding itemsets that are
significantly enriched in one class of objects, is statistically challenging,
as the large space of candidate patterns leads to an enormous multiple testing
problem. Recently, the concept of testability was proposed as one approach to
correct for multiple testing in pattern mining while retaining statistical
power. Still, these strategies based on testability do not allow one to
condition the test of significance on the observed covariates, which severely
limits its utility in biomedical applications. Here we propose a strategy and
an efficient algorithm to perform significant pattern mining in the presence of
categorical covariates with K states.
An evolutionary approach to the identification of Cellular Automata based on partial observations
Authors: Witold Bołt, Jan M. Baetens, Bernard De Baets
Date: 2015-08-24 00:00:00

In this paper we consider the identification problem of Cellular Automata
(CAs). The problem is defined and solved in the context of partial observations
with time gaps of unknown length, i.e. pre-recorded, partial configurations of
the system at certain, unknown time steps. A solution method based on a
modified variant of a Genetic Algorithm (GA) is proposed and illustrated with
brief experimental results.
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning
Authors: Maxime Gasse, Alex Aussem, Haytham Elghazel
Date: 2015-05-19 00:00:00

We present a novel hybrid algorithm for Bayesian network structure learning,
called Hybrid HPC (H2PC). It first reconstructs the skeleton of a Bayesian
network and then performs a Bayesian-scoring greedy hill-climbing search to
orient the edges. It is based on a subroutine called HPC, that combines ideas
from incremental and divide-and-conquer constraint-based methods to learn the
parents and children of a target variable. We conduct an experimental
comparison of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the
most powerful state-of-the-art algorithm for Bayesian network structure
learning, on several benchmarks with various data sizes. Our extensive
experiments show that H2PC outperforms MMHC both in terms of goodness of fit to
new data and in terms of the quality of the network structure itself, which is
closer to the true dependence structure of the data. The source code (in R) of
H2PC as well as all data sets used for the empirical tests are publicly
available.
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction
Authors: Victor Campos, Amaia Salvador, Brendan Jou, Xavier Giró-i-Nieto
Date: 2015-08-20 00:00:00
Tasks: Sentiment Analysis, Visual Sentiment Prediction

Visual media are powerful means of expressing emotions and sentiments. The
constant generation of new content in social networks highlights the need of
automated visual sentiment analysis tools. While Convolutional Neural Networks
(CNNs) have established a new state-of-the-art in several vision problems,
their application to the task of sentiment analysis is mostly unexplored and
there are few studies regarding how to design CNNs for this purpose. In this
work, we study the suitability of fine-tuning a CNN for visual sentiment
prediction as well as explore performance boosting techniques within this deep
learning setting. Finally, we provide a deep-dive analysis into a benchmark,
state-of-the-art network architecture to gain insight about how to design
patterns for CNNs on the task of visual sentiment prediction.
Fast Asynchronous Parallel Stochastic Gradient Decent
Authors: Shen-Yi Zhao, Wu-Jun Li
Date: 2015-08-24 00:00:00

Stochastic gradient descent~(SGD) and its variants have become more and more
popular in machine learning due to their efficiency and effectiveness. To
handle large-scale problems, researchers have recently proposed several
parallel SGD methods for multicore systems. However, existing parallel SGD
methods cannot achieve satisfactory performance in real applications. In this
paper, we propose a fast asynchronous parallel SGD method, called AsySVRG, by
designing an asynchronous strategy to parallelize the recently proposed SGD
variant called stochastic variance reduced gradient~(SVRG). Both theoretical
and empirical results show that AsySVRG can outperform existing
state-of-the-art parallel SGD methods like Hogwild! in terms of convergence
rate and computation cost.
Iterative Thresholded Bi-Histogram Equalization for Medical Image Enhancement
Authors: Muhammad Ali Qadar, Yan Zhaowen, Li Hua
Date: 2015-08-24 00:00:00
Tasks: Image Enhancement, Medical Image Enhancement

Enhancement of human vision to get an insight to information content is of
vital importance. The traditional histogram equalization methods have been
suffering from amplified contrast with the addition of artifacts and a
surprising unnatural visibility of the processed images. In order to overcome
these drawbacks, this paper proposes interative, mean, and multi-threshold
selection criterion with plateau limits, which consist of histogram
segmentation, clipping and transformation modules. The histogram partition
consists of multiple thresholding processes that divide the histogram into two
parts, whereas the clipping process nicely enhances the contrast by having a
check on the rate of enhancement that could be tuned. Histogram equalization to
each segmented sub-histogram provides the output image with preserved
brightness and enhanced contrast. Results of the present study showed that the
proposed method efficiently handles the noise amplification. Further, it also
preserves the brightness by retaining natural look of targeted image.
Robust Visual Tracking via Convolutional Networks
Authors: Kaihua Zhang, Qingshan Liu, Yi Wu, Ming-Hsuan Yang
Date: 2015-01-19 00:00:00
Tasks: Visual Tracking

Deep networks have been successfully applied to visual tracking by learning a
generic representation offline from numerous training images. However the
offline training is time-consuming and the learned generic representation may
be less discriminative for tracking specific objects. In this paper we present
that, even without offline training with a large amount of auxiliary data,
simple two-layer convolutional networks can be powerful enough to develop a
robust representation for visual tracking. In the first frame, we employ the
k-means algorithm to extract a set of normalized patches from the target region
as fixed filters, which integrate a series of adaptive contextual filters
surrounding the target to define a set of feature maps in the subsequent
frames. These maps measure similarities between each filter and the useful
local intensity patterns across the target, thereby encoding its local
structural information. Furthermore, all the maps form together a global
representation, which is built on mid-level features, thereby remaining close
to image-level information, and hence the inner geometric layout of the target
is also well preserved. A simple soft shrinkage method with an adaptive
threshold is employed to de-noise the global representation, resulting in a
robust sparse representation. The representation is updated via a simple and
effective online strategy, allowing it to robustly adapt to target appearance
variations. Our convolution networks have surprisingly lightweight structure,
yet perform favorably against several state-of-the-art methods on the CVPR2013
tracking benchmark dataset with 50 challenging videos.
Morphometry-Based Longitudinal Neurodegeneration Simulation with MR Imaging
Authors: Si-Qi Liu, Sidong Liu, Sonia Pujol, Ron Kikinis, Dagan Feng, Michael Fulham, Weidong Cai
Date: 2015-08-24 00:00:00

We present a longitudinal MR simulation framework which simulates the future
neurodegenerative progression by outputting the predicted follow-up MR image
and the voxel-based morphometry (VBM) map. This framework expects the patients
to have at least 2 historical MR images available. The longitudinal and
cross-sectional VBM maps are extracted to measure the affinity between the
target subject and the template subjects collected for simulation. Then the
follow-up simulation is performed by resampling the latest available target MR
image with a weighted sum of non-linear transformations derived from the
best-matched templates. The leave-one-out strategy was used to compare
different simulation methods. Compared to the state-of-the-art voxel-based
method, our proposed morphometry-based simulation achieves better accuracy in
most cases.
Convex Calibration Dimension for Multiclass Loss Matrices
Authors: Harish G. Ramaswamy, Shivani Agarwal
Date: 2014-08-12 00:00:00
Tasks: General Classification

We study consistency properties of surrogate loss functions for general
multiclass learning problems, defined by a general multiclass loss matrix. We
extend the notion of classification calibration, which has been studied for
binary and multiclass 0-1 classification problems (and for certain other
specific learning problems), to the general multiclass setting, and derive
necessary and sufficient conditions for a surrogate loss to be calibrated with
respect to a loss matrix in this setting. We then introduce the notion of
convex calibration dimension of a multiclass loss matrix, which measures the
smallest `size' of a prediction space in which it is possible to design a
convex surrogate that is calibrated with respect to the loss matrix. We derive
both upper and lower bounds on this quantity, and use these results to analyze
various loss matrices. In particular, we apply our framework to study various
subset ranking losses, and use the convex calibration dimension as a tool to
show both the existence and non-existence of various types of convex calibrated
surrogates for these losses. Our results strengthen recent results of Duchi et
al. (2010) and Calauzenes et al. (2012) on the non-existence of certain types
of convex calibrated surrogates in subset ranking. We anticipate the convex
calibration dimension may prove to be a useful tool in the study and design of
surrogate losses for general multiclass learning problems.
The Max $K$-Armed Bandit: A PAC Lower Bound and tighter Algorithms
Authors: Yahel David, Nahum Shimkin
Date: 2015-08-23 00:00:00

We consider the Max $K$-Armed Bandit problem, where a learning agent is faced
with several sources (arms) of items (rewards), and interested in finding the
best item overall. At each time step the agent chooses an arm, and obtains a
random real valued reward. The rewards of each arm are assumed to be i.i.d.,
with an unknown probability distribution that generally differs among the arms.
Under the PAC framework, we provide lower bounds on the sample complexity of
any $(\epsilon,\delta)$-correct algorithm, and propose algorithms that attain
this bound up to logarithmic factors. We compare the performance of this
multi-arm algorithms to the variant in which the arms are not distinguishable
by the agent and are chosen randomly at each stage. Interestingly, when the
maximal rewards of the arms happen to be similar, the latter approach may
provide better performance.
Emphatic TD Bellman Operator is a Contraction
Authors: Assaf Hallak, Aviv Tamar, Shie Mannor
Date: 2015-08-14 00:00:00
Tasks: Off-policy evaluation

Recently, \citet{SuttonMW15} introduced the emphatic temporal differences
(ETD) algorithm for off-policy evaluation in Markov decision processes. In this
short note, we show that the projected fixed-point equation that underlies ETD
involves a contraction operator, with a $\sqrt{\gamma}$-contraction modulus
(where $\gamma$ is the discount factor). This allows us to provide error bounds
on the approximation error of ETD. To our knowledge, these are the first error
bounds for an off-policy evaluation algorithm under general target and behavior
policies.
Light Efficient Flutter Shutter
Authors: Moshe Ben-Ezra
Date: 2015-08-23 00:00:00
Tasks: compressed sensing, Deblurring

Flutter shutter is a technique in which the exposure is chopped into segments
and light is only integrated part of the time. By carefully selecting the
chopping sequence it is possible to better condition the data for
reconstruction problems such as motion deblurring, focal sweeping, and
compressed sensing. The partial exposure trades better conditioning for less
energy. In problems such as motion deblurring the available energy is what
caused the problem in the first place (as strong illumination allows short
exposure thus eliminates motion blur). It is still beneficial because the
benefit from the better conditioning outweighs the cost in energy.
  This documents is focused on light efficient flutter shutter that provides
better conditioning and better energy utilization than conventional flutter
shutter.
Implementing an intelligent version of the classical sliding-puzzle game for unix terminals using Golang's concurrency primitives
Authors: Pravendra Singh
Date: 2015-03-28 00:00:00

An intelligent version of the sliding-puzzle game is developed using the new
Go programming language, which uses a concurrent version of the A* Informed
Search Algorithm to power solver-bot that runs in the background. The game runs
in computer system's terminals. Mainly, it was developed for UNIX-type systems
but it works pretty well in nearly all the operating systems because of
cross-platform compatibility of the programming language used. The game uses
language's concurrency primitives to simplify most of the hefty parts of the
game. A real-time notification delivery architecture is developed using
language's built-in concurrency support, which performs similar to event based
context aware invocations like we see on the web platform.
Confounds and Consequences in Geotagged Twitter Data
Authors: Umashanthi Pavalanathan, Jacob Eisenstein
Date: 2015-06-07 00:00:00
Proceeding: EMNLP 2015 9

Twitter is often used in quantitative studies that identify
geographically-preferred topics, writing styles, and entities. These studies
rely on either GPS coordinates attached to individual messages, or on the
user-supplied location field in each profile. In this paper, we compare these
data acquisition techniques and quantify the biases that they introduce; we
also measure their effects on linguistic analysis and text-based geolocation.
GPS-tagging and self-reported locations yield measurably different corpora, and
these linguistic differences are partially attributable to differences in
dataset composition by age and gender. Using a latent variable model to induce
age and gender, we show how these demographic variables interact with geography
to affect language use. We also show that the accuracy of text-based
geolocation varies with population demographics, giving the best results for
men above the age of 40.
Leveraging Monolingual Data for Crosslingual Compositional Word Representations
Authors: Hubert Soyer, Pontus Stenetorp, Akiko Aizawa
Date: 2014-12-19 00:00:00
Tasks: Document Classification

In this work, we present a novel neural network based architecture for
inducing compositional crosslingual word representations. Unlike previously
proposed methods, our method fulfills the following three criteria; it
constrains the word-level representations to be compositional, it is capable of
leveraging both bilingual and monolingual data, and it is scalable to large
vocabularies and large quantities of data. The key component of our approach is
what we refer to as a monolingual inclusion criterion, that exploits the
observation that phrases are more closely semantically related to their
sub-phrases than to other randomly sampled phrases. We evaluate our method on a
well-established crosslingual document classification task and achieve results
that are either comparable, or greatly improve upon previous state-of-the-art
methods. Concretely, our method reaches a level of 92.7% and 84.4% accuracy for
the English to German and German to English sub-tasks respectively. The former
advances the state of the art by 0.9% points of accuracy, the latter is an
absolute improvement upon the previous state of the art by 7.7% points of
accuracy and an improvement of 33.0% in error reduction.
Gaussian Mixture Reduction Using Reverse Kullback-Leibler Divergence
Authors: Tohid Ardeshiri, Umut Orguner, Emre Özkan
Date: 2015-08-22 00:00:00

We propose a greedy mixture reduction algorithm which is capable of pruning
mixture components as well as merging them based on the Kullback-Leibler
divergence (KLD). The algorithm is distinct from the well-known Runnalls' KLD
based method since it is not restricted to merging operations. The capability
of pruning (in addition to merging) gives the algorithm the ability of
preserving the peaks of the original mixture during the reduction. Analytical
approximations are derived to circumvent the computational intractability of
the KLD which results in a computationally efficient method. The proposed
algorithm is compared with Runnalls' and Williams' methods in two numerical
examples, using both simulated and real world data. The results indicate that
the performance and computational complexity of the proposed approach make it
an efficient alternative to existing mixture reduction methods.
Towards Neural Network-based Reasoning
Authors: Baolin Peng, Zhengdong Lu, Hang Li, Kam-Fai Wong
Date: 2015-08-22 00:00:00

We propose Neural Reasoner, a framework for neural network-based reasoning
over natural language sentences. Given a question, Neural Reasoner can infer
over multiple supporting facts and find an answer to the question in specific
forms. Neural Reasoner has 1) a specific interaction-pooling mechanism,
allowing it to examine multiple facts, and 2) a deep architecture, allowing it
to model the complicated logical relations in reasoning tasks. Assuming no
particular structure exists in the question and facts, Neural Reasoner is able
to accommodate different types of reasoning and different forms of language
expressions. Despite the model complexity, Neural Reasoner can still be trained
effectively in an end-to-end manner. Our empirical studies show that Neural
Reasoner can outperform existing neural reasoning systems with remarkable
margins on two difficult artificial tasks (Positional Reasoning and Path
Finding) proposed in [8]. For example, it improves the accuracy on Path
Finding(10K) from 33.4% [6] to over 98%.
Sparse and spurious: dictionary learning with noise and outliers
Authors: Rémi Gribonval, Rodolphe Jenatton, Francis Bach
Date: 2014-07-19 00:00:00
Tasks: Dictionary Learning

A popular approach within the signal processing and machine learning
communities consists in modelling signals as sparse linear combinations of
atoms selected from a learned dictionary. While this paradigm has led to
numerous empirical successes in various fields ranging from image to audio
processing, there have only been a few theoretical arguments supporting these
evidences. In particular, sparse coding, or sparse dictionary learning, relies
on a non-convex procedure whose local minima have not been fully analyzed yet.
In this paper, we consider a probabilistic model of sparse signals, and show
that, with high probability, sparse coding admits a local minimum around the
reference dictionary generating the signals. Our study takes into account the
case of over-complete dictionaries, noisy signals, and possible outliers, thus
extending previous work limited to noiseless settings and/or under-complete
dictionaries. The analysis we conduct is non-asymptotic and makes it possible
to understand how the key quantities of the problem, such as the coherence or
the level of noise, can scale with respect to the dimension of the signals, the
number of atoms, the sparsity and the number of observations.