pwc-archive/papers-with-abstracts huggingface dataset:
Why Do Urban Legends Go Viral?
Authors: Marco Guerini, Carlo Strapparava
Date: 2016-01-22 00:00:00

Urban legends are a genre of modern folklore, consisting of stories about
rare and exceptional events, just plausible enough to be believed, which tend
to propagate inexorably across communities. In our view, while urban legends
represent a form of "sticky" deceptive text, they are marked by a tension
between the credible and incredible. They should be credible like a news
article and incredible like a fairy tale to go viral. In particular we will
focus on the idea that urban legends should mimic the details of news (who,
where, when) to be credible, while they should be emotional and readable like a
fairy tale to be catchy and memorable. Using NLP tools we will provide a
quantitative analysis of these prototypical characteristics. We also lay out
some machine learning experiments showing that it is possible to recognize an
urban legend using just these simple features.
Bitwise Neural Networks
Authors: Minje Kim, Paris Smaragdis
Date: 2016-01-22 00:00:00

Based on the assumption that there exists a neural network that efficiently
represents a set of Boolean functions between all binary inputs and outputs, we
propose a process for developing and deploying neural networks whose weight
parameters, bias terms, input, and intermediate hidden layer output signals,
are all binary-valued, and require only basic bit logic for the feedforward
pass. The proposed Bitwise Neural Network (BNN) is especially suitable for
resource-constrained environments, since it replaces either floating or
fixed-point arithmetic with significantly more efficient bitwise operations.
Hence, the BNN requires for less spatial complexity, less memory bandwidth, and
less power consumption in hardware. In order to design such networks, we
propose to add a few training schemes, such as weight compression and noisy
backpropagation, which result in a bitwise network that performs almost as well
as its corresponding real-valued network. We test the proposed network on the
MNIST dataset, represented using binary features, and show that BNNs result in
competitive performance while offering dramatic computational savings.
Coalition-based Planning of Military Operations: Adversarial Reasoning Algorithms in an Integrated Decision Aid
Authors: Larry Ground, Alexander Kott, Ray Budd
Date: 2016-01-22 00:00:00
Tasks: Scheduling

Use of knowledge-based planning tools can help alleviate the challenges of
planning a complex operation by a coalition of diverse parties in an
adversarial environment. We explore these challenges and potential
contributions of knowledge-based tools using as an example the CADET system, a
knowledge-based tool capable of producing automatically (or with human
guidance) battle plans with realistic degree of detail and complexity. In
ongoing experiments, it compared favorably with human planners. Interleaved
planning, scheduling, routing, attrition and consumption processes comprise the
computational approach of this tool. From the coalition operations perspective,
such tools offer an important aid in rapid synchronization of assets and
actions of heterogeneous assets belonging to multiple organizations,
potentially with distinct doctrine and rules of engagement. In this paper, we
discuss the functionality of the tool, provide a brief overview of the
technical approach and experimental results, and outline the potential value of
such tools.
3-D/2-D Registration of Cardiac Structures by 3-D Contrast Agent Distribution Estimation
Authors: Matthias Hoffmann, Christopher Kowalewski, Andreas Maier, Klaus Kurzidim, Norbert Strobel, Joachim Hornegger
Date: 2016-01-22 00:00:00
Tasks: Anatomy, GPU

For augmented fluoroscopy during cardiac catheter ablation procedures, a
preoperatively acquired 3-D model of the left atrium of the patient can be
registered to X-ray images. Therefore the 3D-model is matched with the contrast
agent based appearance of the left atrium. Commonly, only small amounts of
contrast agent (CA) are used to locate the left atrium. This is why we focus on
robust registration methods that work also if the structure of interest is only
partially contrasted. In particular, we propose two similarity measures for
CA-based registration: The first similarity measure, explicit apparent edges,
focuses on edges of the patient anatomy made visible by contrast agent and can
be computed quickly on the GPU. The second novel similarity measure computes a
contrast agent distribution estimate (CADE) inside the 3-D model and rates its
consistency with the CA seen in biplane fluoroscopic images. As the CADE
computation involves a reconstruction of CA in 3-D using the CA within the
fluoroscopic images, it is slower. Using a combination of both methods, our
evaluation on 11 well-contrasted clinical datasets yielded an error of
7.9+/-6.3 mm over all frames. For 10 datasets with little CA, we obtained an
error of 8.8+/-6.7 mm. Our new methods outperform a registration based on the
projected shadow significantly (p<0.05).
Topological descriptors for 3D surface analysis
Authors: Matthias Zeppelzauer, Bartosz Zieliński, Mateusz Juda, Markus Seidl
Date: 2016-01-22 00:00:00
Tasks: Classification, General Classification

We investigate topological descriptors for 3D surface analysis, i.e. the
classification of surfaces according to their geometric fine structure. On a
dataset of high-resolution 3D surface reconstructions we compute persistence
diagrams for a 2D cubical filtration. In the next step we investigate different
topological descriptors and measure their ability to discriminate structurally
different 3D surface patches. We evaluate their sensitivity to different
parameters and compare the performance of the resulting topological descriptors
to alternative (non-topological) descriptors. We present a comprehensive
evaluation that shows that topological descriptors are (i) robust, (ii) yield
state-of-the-art performance for the task of 3D surface analysis and (iii)
improve classification performance when combined with non-topological
descriptors.
Geometric-Algebra LMS Adaptive Filter and its Application to Rotation Estimation
Authors: Wilder B. Lopes, Anas Al-Nuaimi, Cassio G. Lopes
Date: 2016-01-22 00:00:00

This paper exploits Geometric (Clifford) Algebra (GA) theory in order to
devise and introduce a new adaptive filtering strategy. From a least-squares
cost function, the gradient is calculated following results from Geometric
Calculus (GC), the extension of GA to handle differential and integral
calculus. The novel GA least-mean-squares (GA-LMS) adaptive filter, which
inherits properties from standard adaptive filters and from GA, is developed to
recursively estimate a rotor (multivector), a hypercomplex quantity able to
describe rotations in any dimension. The adaptive filter (AF) performance is
assessed via a 3D point-clouds registration problem, which contains a rotation
estimation step. Calculating the AF computational complexity suggests that it
can contribute to reduce the cost of a full-blown 3D registration algorithm,
especially when the number of points to be processed grows. Moreover, the
employed GA/GC framework allows for easily applying the resulting filter to
estimating rotors in higher dimensions.
Pushing the Boundaries of Boundary Detection using Deep Learning
Authors: Iasonas Kokkinos
Date: 2015-11-23 00:00:00
Tasks: Boundary Detection, Deep Learning, Segmentation, Semantic Segmentation

In this work we show that adapting Deep Convolutional Neural Network training
to the task of boundary detection can result in substantial improvements over
the current state-of-the-art in boundary detection.
  Our contributions consist firstly in combining a careful design of the loss
for boundary detection training, a multi-resolution architecture and training
with external data to improve the detection accuracy of the current state of
the art. When measured on the standard Berkeley Segmentation Dataset, we
improve theoptimal dataset scale F-measure from 0.780 to 0.808 - while human
performance is at 0.803. We further improve performance to 0.813 by combining
deep learning with grouping, integrating the Normalized Cuts technique within a
deep network.
  We also examine the potential of our boundary detector in conjunction with
the task of semantic segmentation and demonstrate clear improvements over
state-of-the-art systems. Our detector is fully integrated in the popular Caffe
framework and processes a 320x420 image in less than a second.
Online Event Recognition from Moving Vessel Trajectories
Authors: Kostas Patroumpas, Elias Alevizos, Alexander Artikis, Marios Vodas, Nikos Pelekis, Yannis Theodoridis
Date: 2016-01-22 00:00:00

We present a system for online monitoring of maritime activity over streaming
positions from numerous vessels sailing at sea. It employs an online tracking
module for detecting important changes in the evolving trajectory of each
vessel across time, and thus can incrementally retain concise, yet reliable
summaries of its recent movement. In addition, thanks to its complex event
recognition module, this system can also offer instant notification to marine
authorities regarding emergency situations, such as risk of collisions,
suspicious moves in protected zones, or package picking at open sea. Not only
did our extensive tests validate the performance, efficiency, and robustness of
the system against scalable volumes of real-world and synthetically enlarged
datasets, but its deployment against online feeds from vessels has also
confirmed its capabilities for effective, real-time maritime surveillance.
Recommender systems inspired by the structure of quantum theory
Authors: Cyril Stark
Date: 2016-01-22 00:00:00
Tasks: Quantization, Recommendation Systems

Physicists use quantum models to describe the behavior of physical systems.
Quantum models owe their success to their interpretability, to their relation
to probabilistic models (quantization of classical models) and to their high
predictive power. Beyond physics, these properties are valuable in general data
science. This motivates the use of quantum models to analyze general
nonphysical datasets. Here we provide both empirical and theoretical insights
into the application of quantum models in data science. In the theoretical part
of this paper, we firstly show that quantum models can be exponentially more
efficient than probabilistic models because there exist datasets that admit
low-dimensional quantum models and only exponentially high-dimensional
probabilistic models. Secondly, we explain in what sense quantum models realize
a useful relaxation of compressed probabilistic models. Thirdly, we show that
sparse datasets admit low-dimensional quantum models and finally, we introduce
a method to compute hierarchical orderings of properties of users (e.g.,
personality traits) and items (e.g., genres of movies). In the empirical part
of the paper, we evaluate quantum models in item recommendation and observe
that the predictive power of quantum-inspired recommender systems can compete
with state-of-the-art recommender systems like SVD++ and PureSVD. Furthermore,
we make use of the interpretability of quantum models by computing hierarchical
orderings of properties of users and items. This work establishes a connection
between data science (item recommendation), information theory (communication
complexity), mathematical programming (positive semidefinite factorizations)
and physics (quantum models).
Learning Support Correlation Filters for Visual Tracking
Authors: Wangmeng Zuo, Xiaohe Wu, Liang Lin, Lei Zhang, Ming-Hsuan Yang
Date: 2016-01-22 00:00:00
Tasks: Visual Tracking

Sampling and budgeting training examples are two essential factors in
tracking algorithms based on support vector machines (SVMs) as a trade-off
between accuracy and efficiency. Recently, the circulant matrix formed by dense
sampling of translated image patches has been utilized in correlation filters
for fast tracking. In this paper, we derive an equivalent formulation of a SVM
model with circulant matrix expression and present an efficient alternating
optimization method for visual tracking. We incorporate the discrete Fourier
transform with the proposed alternating optimization process, and pose the
tracking problem as an iterative learning of support correlation filters (SCFs)
which find the global optimal solution with real-time performance. For a given
circulant data matrix with n^2 samples of size n*n, the computational
complexity of the proposed algorithm is O(n^2*logn) whereas that of the
standard SVM-based approaches is at least O(n^4). In addition, we extend the
SCF-based tracking algorithm with multi-channel features, kernel functions, and
scale-adaptive approaches to further improve the tracking performance.
Experimental results on a large benchmark dataset show that the proposed
SCF-based algorithms perform favorably against the state-of-the-art tracking
methods in terms of accuracy and speed.
A Robust Frame-based Nonlinear Prediction System for Automatic Speech Coding
Authors: Mahmood Yousefi-Azar, Farbod Razzazi
Date: 2016-01-22 00:00:00

In this paper, we propose a neural-based coding scheme in which an artificial
neural network is exploited to automatically compress and decompress speech
signals by a trainable approach. Having a two-stage training phase, the system
can be fully specified to each speech frame and have robust performance across
different speakers and wide range of spoken utterances. Indeed, Frame-based
nonlinear predictive coding (FNPC) would code a frame in the procedure of
training to predict the frame samples. The motivating objective is to analyze
the system behavior in regenerating not only the envelope of spectra, but also
the spectra phase. This scheme has been evaluated in time and discrete cosine
transform (DCT) domains and the output of predicted phonemes show the
potentiality of the FNPC to reconstruct complicated signals. The experiments
were conducted on three voiced plosive phonemes, b/d/g/ in time and DCT domains
versus the number of neurons in the hidden layer. Experiments approve the FNPC
capability as an automatic coding system by which /b/d/g/ phonemes have been
reproduced with a good accuracy. Evaluations revealed that the performance of
FNPC system, trained to predict DCT coefficients is more desirable,
particularly for frames with the wider distribution of energy, compared to time
samples.
The Latent Structure of Dictionaries
Authors: Philippe Vincent-Lamarre, Alexandre Blondin Massé, Marcos Lopes, Mélanie Lord, Odile Marcotte, Stevan Harnad
Date: 2014-11-01 00:00:00
Tasks: All

How many words (and which ones) are sufficient to define all other words?
When dictionaries are analyzed as directed graphs with links from defining
words to defined words, they reveal a latent structure. Recursively removing
all words that are reachable by definition but that do not define any further
words reduces the dictionary to a Kernel of about 10%. This is still not the
smallest number of words that can define all the rest. About 75% of the Kernel
turns out to be its Core, a Strongly Connected Subset of words with a
definitional path to and from any pair of its words and no word's definition
depending on a word outside the set. But the Core cannot define all the rest of
the dictionary. The 25% of the Kernel surrounding the Core consists of small
strongly connected subsets of words: the Satellites. The size of the smallest
set of words that can define all the rest (the graph's Minimum Feedback Vertex
Set or MinSet) is about 1% of the dictionary, 15% of the Kernel, and half-Core,
half-Satellite. But every dictionary has a huge number of MinSets. The Core
words are learned earlier, more frequent, and less concrete than the
Satellites, which in turn are learned earlier and more frequent but more
concrete than the rest of the Dictionary. In principle, only one MinSet's words
would need to be grounded through the sensorimotor capacity to recognize and
categorize their referents. In a dual-code sensorimotor-symbolic model of the
mental lexicon, the symbolic code could do all the rest via re-combinatory
definition.
Depth and Reflection Total Variation for Single Image Dehazing
Authors: Wei Wang, Chuanjiang He
Date: 2016-01-22 00:00:00
Tasks: Image Dehazing, Single Image Dehazing

Haze removal has been a very challenging problem due to its ill-posedness,
which is more ill-posed if the input data is only a single hazy image. In this
paper, we present a new approach for removing haze from a single input image.
The proposed method combines the model widely used to describe the formation of
a haze image with the assumption in Retinex that an image is the product of the
illumination and the reflection. We assume that the depth and reflection
functions are spatially piecewise smooth in the model, where the total
variation is used for the regularization. The proposed model is defined as a
constrained optimization problem, which is solved by an alternating
minimization scheme and the fast gradient projection algorithm. Some theoretic
analyses are given for the proposed model and algorithm. Finally, numerical
examples are presented to demonstrate that our method can restore vivid and
contrastive hazy images effectively.
Exploiting Low-dimensional Structures to Enhance DNN Based Acoustic Modeling in Speech Recognition
Authors: Pranay Dighe, Gil Luyet, Afsaneh Asaei, Herve Bourlard
Date: 2016-01-22 00:00:00
Tasks: Dictionary Learning, speech-recognition, Speech Recognition

We propose to model the acoustic space of deep neural network (DNN)
class-conditional posterior probabilities as a union of low-dimensional
subspaces. To that end, the training posteriors are used for dictionary
learning and sparse coding. Sparse representation of the test posteriors using
this dictionary enables projection to the space of training data. Relying on
the fact that the intrinsic dimensions of the posterior subspaces are indeed
very small and the matrix of all posteriors belonging to a class has a very low
rank, we demonstrate how low-dimensional structures enable further enhancement
of the posteriors and rectify the spurious errors due to mismatch conditions.
The enhanced acoustic modeling method leads to improvements in continuous
speech recognition task using hybrid DNN-HMM (hidden Markov model) framework in
both clean and noisy conditions, where upto 15.4% relative reduction in word
error rate (WER) is achieved.
When is Clustering Perturbation Robust?
Authors: Margareta Ackerman, Jarrod Moore
Date: 2016-01-22 00:00:00
Tasks: Clustering

Clustering is a fundamental data mining tool that aims to divide data into
groups of similar items. Generally, intuition about clustering reflects the
ideal case -- exact data sets endowed with flawless dissimilarity between
individual instances.
  In practice however, these cases are in the minority, and clustering
applications are typically characterized by noisy data sets with approximate
pairwise dissimilarities. As such, the efficacy of clustering methods in
practical applications necessitates robustness to perturbations.
  In this paper, we perform a formal analysis of perturbation robustness,
revealing that the extent to which algorithms can exhibit this desirable
characteristic is inherently limited, and identifying the types of structures
that allow popular clustering paradigms to discover meaningful clusters in
spite of faulty data.
GeoTextTagger: High-Precision Location Tagging of Textual Documents using a Natural Language Processing Approach
Authors: Shawn Brunsting, Hans De Sterck, Remco Dolman, Teun van Sprundel
Date: 2016-01-22 00:00:00
Tasks: Articles, General Classification, TAG

Location tagging, also known as geotagging or geolocation, is the process of
assigning geographical coordinates to input data. In this paper we present an
algorithm for location tagging of textual documents. Our approach makes use of
previous work in natural language processing by using a state-of-the-art
part-of-speech tagger and named entity recognizer to find blocks of text which
may refer to locations. A knowledge base (OpenStreatMap) is then used to find a
list of possible locations for each block. Finally, one location is chosen for
each block by assigning distance-based scores to each location and repeatedly
selecting the location and block with the best score. We tested our geolocation
algorithm with Wikipedia articles about topics with a well-defined geographical
location that are geotagged by the articles' authors, where classification
approaches have achieved median errors as low as 11 km, with attainable
accuracy limited by the class size. Our approach achieved a 10th percentile
error of 490 metres and median error of 54 kilometres on the Wikipedia dataset
we used. When considering the five location tags with the greatest scores, 50%
of articles were assigned at least one tag within 8.5 kilometres of the
article's author-assigned true location. We also tested our approach on Twitter
messages that are tagged with the location from which the message was sent.
Twitter texts are challenging because they are short and unstructured and often
do not contain words referring to the location they were sent from, but we
obtain potentially useful results. We explain how we use the Spark framework
for data analytics to collect and process our test data. In general,
classification-based approaches for location tagging may be reaching their
upper accuracy limit, but our precision-focused approach has high accuracy for
some texts and shows significant potential for improvement overall.
Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model
Authors: Shi Feng, Shujie Liu, Mu Li, Ming Zhou
Date: 2016-01-13 00:00:00
Tasks: Attribute, Decoder, Image Captioning, Machine Translation, NMT, speech-recognition, Speech Recognition, Translation

Neural machine translation has shown very promising results lately. Most NMT
models follow the encoder-decoder framework. To make encoder-decoder models
more flexible, attention mechanism was introduced to machine translation and
also other tasks like speech recognition and image captioning. We observe that
the quality of translation by attention-based encoder-decoder can be
significantly damaged when the alignment is incorrect. We attribute these
problems to the lack of distortion and fertility models. Aiming to resolve
these problems, we propose new variations of attention-based encoder-decoder
and compare them with other models on machine translation. Our proposed method
achieved an improvement of 2 BLEU points over the original attention-based
encoder-decoder.
Manifold-Kernels Comparison in MKPLS for Visual Speech Recognition
Authors: Amr Bakry, Ahmed Elgammal
Date: 2016-01-22 00:00:00
Tasks: speech-recognition, Speech Recognition, Visual Speech Recognition

Speech recognition is a challenging problem. Due to the acoustic limitations,
using visual information is essential for improving the recognition accuracy in
real-life unconstraint situations. One common approach is to model the visual
recognition as nonlinear optimization problem. Measuring the distances between
visual units is essential for solving this problem. Embedding the visual units
on a manifold and using manifold kernels is one way to measure these distances.
This work is intended to evaluate the performance of several manifold kernels
for solving the problem of visual speech recognition. We show the theory behind
each kernel. We apply manifold kernel partial least squares framework to OuluVs
and AvLetters databases, and show empirical comparison between all kernels.
This framework provides convenient way to explore different kernels.