pwc-archive/papers-with-abstracts huggingface dataset:
Robust Bayesian Method for Simultaneous Block Sparse Signal Recovery with Applications to Face Recognition
Authors: Igor Fedorov, Ritwik Giri, Bhaskar D. Rao, Truong Q. Nguyen
Date: 2016-05-06 00:00:00
Tasks: Face Recognition

In this paper, we present a novel Bayesian approach to recover simultaneously
block sparse signals in the presence of outliers. The key advantage of our
proposed method is the ability to handle non-stationary outliers, i.e. outliers
which have time varying support. We validate our approach with empirical
results showing the superiority of the proposed method over competing
approaches in synthetic data experiments as well as the multiple measurement
face recognition problem.
Vocabulary Manipulation for Neural Machine Translation
Authors: Haitao Mi, Zhiguo Wang, Abe Ittycheriah
Date: 2016-05-10 00:00:00
Proceeding: ACL 2016 8
Tasks: Machine Translation, Sentence, Translation, Word Translation

In order to capture rich language phenomena, neural machine translation
models have to use a large vocabulary size, which requires high computing time
and large memory usage. In this paper, we alleviate this issue by introducing a
sentence-level or batch-level vocabulary, which is only a very small sub-set of
the full output vocabulary. For each sentence or batch, we only predict the
target words in its sentence-level or batch-level vocabulary. Thus, we reduce
both the computing time and the memory usage. Our method simply takes into
account the translation options of each word or phrase in the source sentence,
and picks a very small target vocabulary for each sentence based on a
word-to-word translation model or a bilingual phrase library learned from a
traditional machine translation model. Experimental results on the large-scale
English-to-French task show that our method achieves better translation
performance by 1 BLEU point over the large vocabulary neural machine
translation system of Jean et al. (2015).
Clustering subgaussian mixtures by semidefinite programming
Authors: Dustin G. Mixon, Soledad Villar, Rachel Ward
Date: 2016-02-22 00:00:00
Tasks: Clustering

We introduce a model-free relax-and-round algorithm for k-means clustering
based on a semidefinite relaxation due to Peng and Wei. The algorithm
interprets the SDP output as a denoised version of the original data and then
rounds this output to a hard clustering. We provide a generic method for
proving performance guarantees for this algorithm, and we analyze the algorithm
in the context of subgaussian mixture models. We also study the fundamental
limits of estimating Gaussian centers by k-means clustering in order to compare
our approximation guarantee to the theoretically optimal k-means clustering
solution.
Road Detection through Supervised Classification
Authors: Yasamin Alkhorshid, Kamelia Aryafar, Sven Bauer, Gerd Wanielik
Date: 2016-05-10 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, BIG-bench Machine Learning, Classification, General Classification

Autonomous driving is a rapidly evolving technology. Autonomous vehicles are
capable of sensing their environment and navigating without human input through
sensory information such as radar, lidar, GNSS, vehicle odometry, and computer
vision. This sensory input provides a rich dataset that can be used in
combination with machine learning models to tackle multiple problems in
supervised settings. In this paper we focus on road detection through
gray-scale images as the sole sensory input. Our contributions are twofold:
first, we introduce an annotated dataset of urban roads for machine learning
tasks; second, we introduce a road detection framework on this dataset through
supervised classification and hand-crafted feature vectors.
Avoiding Wireheading with Value Reinforcement Learning
Authors: Tom Everitt, Marcus Hutter
Date: 2016-05-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

How can we design good goals for arbitrarily intelligent agents?
Reinforcement learning (RL) is a natural approach. Unfortunately, RL does not
work well for generally intelligent agents, as RL agents are incentivised to
shortcut the reward sensor for maximum reward -- the so-called wireheading
problem. In this paper we suggest an alternative to RL called value
reinforcement learning (VRL). In VRL, agents use the reward signal to learn a
utility function. The VRL setup allows us to remove the incentive to wirehead
by placing a constraint on the agent's actions. The constraint is defined in
terms of the agent's belief distributions, and does not require an explicit
specification of which actions constitute wireheading.
Self-Modification of Policy and Utility Function in Rational Agents
Authors: Tom Everitt, Daniel Filan, Mayank Daswani, Marcus Hutter
Date: 2016-05-10 00:00:00
Tasks: General Reinforcement Learning, Reinforcement Learning

Any agent that is part of the environment it interacts with and has versatile
actuators (such as arms and fingers), will in principle have the ability to
self-modify -- for example by changing its own source code. As we continue to
create more and more intelligent agents, chances increase that they will learn
about this ability. The question is: will they want to use it? For example,
highly intelligent systems may find ways to change their goals to something
more easily achievable, thereby `escaping' the control of their designers. In
an important paper, Omohundro (2008) argued that goal preservation is a
fundamental drive of any intelligent system, since a goal is more likely to be
achieved if future versions of the agent strive towards the same goal. In this
paper, we formalise this argument in general reinforcement learning, and
explore situations where it fails. Our conclusion is that the self-modification
possibility is harmless if and only if the value function of the agent
anticipates the consequences of self-modifications and use the current utility
function when evaluating the future.
PARAPH: Presentation Attack Rejection by Analyzing Polarization Hypotheses
Authors: Ethan M. Rudd, Manuel Gunther, Terrance E. Boult
Date: 2016-05-10 00:00:00

For applications such as airport border control, biometric technologies that
can process many capture subjects quickly, efficiently, with weak supervision,
and with minimal discomfort are desirable. Facial recognition is particularly
appealing because it is minimally invasive yet offers relatively good
recognition performance. Unfortunately, the combination of weak supervision and
minimal invasiveness makes even highly accurate facial recognition systems
susceptible to spoofing via presentation attacks. Thus, there is great demand
for an effective and low cost system capable of rejecting such attacks.To this
end we introduce PARAPH -- a novel hardware extension that exploits different
measurements of light polarization to yield an image space in which
presentation media are readily discernible from Bona Fide facial
characteristics. The PARAPH system is inexpensive with an added cost of less
than 10 US dollars. The system makes two polarization measurements in rapid
succession, allowing them to be approximately pixel-aligned, with a frame rate
limited by the camera, not the system. There are no moving parts above the
molecular level, due to the efficient use of twisted nematic liquid crystals.
We present evaluation images using three presentation attack media next to an
actual face -- high quality photos on glossy and matte paper and a video of the
face on an LCD. In each case, the actual face in the image generated by PARAPH
is structurally discernible from the presentations, which appear either as
noise (print attacks) or saturated images (replay attacks).
Spatial database implementation of fuzzy region connection calculus for analysing the relationship of diseases
Authors: Somaye Davari, Nasser Ghadiri
Date: 2015-05-18 00:00:00
Tasks: Decision Making

Analyzing huge amounts of spatial data plays an important role in many
emerging analysis and decision-making domains such as healthcare, urban
planning, agriculture and so on. For extracting meaningful knowledge from
geographical data, the relationships between spatial data objects need to be
analyzed. An important class of such relationships are topological relations
like the connectedness or overlap between regions. While real-world
geographical regions such as lakes or forests do not have exact boundaries and
are fuzzy, most of the existing analysis methods neglect this inherent feature
of topological relations. In this paper, we propose a method for handling the
topological relations in spatial databases based on fuzzy region connection
calculus (RCC). The proposed method is implemented in PostGIS spatial database
and evaluated in analyzing the relationship of diseases as an important
application domain. We also used our fuzzy RCC implementation for fuzzification
of the skyline operator in spatial databases. The results of the evaluation
show that our method provides a more realistic view of spatial relationships
and gives more flexibility to the data analyst to extract meaningful and
accurate results in comparison with the existing methods.
Kernel-Based Structural Equation Models for Topology Identification of Directed Networks
Authors: Yanning Shen, Brian Baingana, Georgios B. Giannakis
Date: 2016-05-10 00:00:00
Tasks: Edge Detection

Structural equation models (SEMs) have been widely adopted for inference of
causal interactions in complex networks. Recent examples include unveiling
topologies of hidden causal networks over which processes such as spreading
diseases, or rumors propagate. The appeal of SEMs in these settings stems from
their simplicity and tractability, since they typically assume linear
dependencies among observable variables. Acknowledging the limitations inherent
to adopting linear models, the present paper advocates nonlinear SEMs, which
account for (possible) nonlinear dependencies among network nodes. The
advocated approach leverages kernels as a powerful encompassing framework for
nonlinear modeling, and an efficient estimator with affordable tradeoffs is put
forth. Interestingly, pursuit of the novel kernel-based approach yields a
convex regularized estimator that promotes edge sparsity, and is amenable to
proximal-splitting optimization methods. To this end, solvers with
complementary merits are developed by leveraging the alternating direction
method of multipliers, and proximal gradient iterations. Experiments conducted
on simulated data demonstrate that the novel approach outperforms linear SEMs
with respect to edge detection errors. Furthermore, tests on a real gene
expression dataset unveil interesting new edges that were not revealed by
linear SEMs, which could shed more light on regulatory behavior of human genes.
A note on the statistical view of matrix completion
Authors: Tianxi Li
Date: 2016-05-10 00:00:00
Tasks: Matrix Completion, valid

A very simple interpretation of matrix completion problem is introduced based
on statistical models. Combined with the well-known results from missing data
analysis, such interpretation indicates that matrix completion is still a valid
and principled estimation procedure even without the missing completely at
random (MCAR) assumption, which almost all of the current theoretical studies
of matrix completion assume.
Context-Aware Adaptive Framework for e-Health Monitoring
Authors: Haider Mshali, Tayeb Lemlouma, Damien Magoni
Date: 2016-05-10 00:00:00

For improving e-health services, we propose a context-aware framework to
monitor the activities of daily living of dependent persons. We define a
strategy for generating long-term realistic scenarios and a framework
containing an adaptive monitoring algorithm based on three approaches for
optimizing resource usage. The used approaches provide a deep knowledge about
the person's context by considering: the person's profile, the activities and
the relationships between activities. We evaluate the performances of our
framework and show its adaptability and significant reduction in network,
energy and processing usage over a traditional monitoring implementation.
Destination Prediction by Trajectory Distribution Based Model
Authors: Philippe C. Besse, Brendan Guillouet, Jean-Michel Loubes, Francois Royer
Date: 2016-05-10 00:00:00
Tasks: Clustering, model, Prediction

In this paper we propose a new method to predict the final destination of
vehicle trips based on their initial partial trajectories. We first review how
we obtained clustering of trajectories that describes user behaviour. Then, we
explain how we model main traffic flow patterns by a mixture of 2d Gaussian
distributions. This yielded a density based clustering of locations, which
produces a data driven grid of similar points within each pattern. We present
how this model can be used to predict the final destination of a new trajectory
based on their first locations using a two step procedure: We first assign the
new trajectory to the clusters it mot likely belongs. Secondly, we use
characteristics from trajectories inside these clusters to predict the final
destination. Finally, we present experimental results of our methods for
classification of trajectories and final destination prediction on datasets of
timestamped GPS-Location of taxi trips. We test our methods on two different
datasets, to assess the capacity of our method to adapt automatically to
different subsets.
Automatic 3D liver location and segmentation via convolutional neural networks and graph cut
Authors: Fang Lu, Fa Wu, Peijun Hu, Zhiyi Peng, Dexing Kong
Date: 2016-05-10 00:00:00
Tasks: Computed Tomography (CT), Segmentation

Purpose Segmentation of the liver from abdominal computed tomography (CT)
image is an essential step in some computer assisted clinical interventions,
such as surgery planning for living donor liver transplant (LDLT), radiotherapy
and volume measurement. In this work, we develop a deep learning algorithm with
graph cut refinement to automatically segment liver in CT scans. Methods The
proposed method consists of two main steps: (i) simultaneously liver detection
and probabilistic segmentation using 3D convolutional neural networks (CNNs);
(ii) accuracy refinement of initial segmentation with graph cut and the
previously learned probability map. Results The proposed approach was validated
on forty CT volumes taken from two public databases MICCAI-Sliver07 and
3Dircadb. For the MICCAI-Sliver07 test set, the calculated mean ratios of
volumetric overlap error (VOE), relative volume difference (RVD), average
symmetric surface distance (ASD), root mean square symmetric surface distance
(RMSD) and maximum symmetric surface distance (MSD) are 5.9%, 2.7%, 0.91%, 1.88
mm, and 18.94 mm, respectively. In the case of 20 3Dircadb data, the calculated
mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36%, 0.97%, 1.89%, 4.15 mm and
33.14 mm, respectively. Conclusion The proposed method is fully automatic
without any user interaction. Quantitative results reveal that the proposed
approach is efficient and accurate for hepatic volume estimation in a clinical
setup. The high correlation between the automatic and manual references shows
that the proposed method can be good enough to replace the time-consuming and
non-reproducible manual segmentation method.
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction
Authors: Zeming Lin, Jack Lanchantin, Yanjun Qi
Date: 2016-05-10 00:00:00
Tasks: General Classification, image-classification, Image Classification, Position, Property Prediction, Protein Structure Prediction

Predicting protein properties such as solvent accessibility and secondary
structure from its primary amino acid sequence is an important task in
bioinformatics. Recently, a few deep learning models have surpassed the
traditional window based multilayer perceptron. Taking inspiration from the
image classification domain we propose a deep convolutional neural network
architecture, MUST-CNN, to predict protein properties. This architecture uses a
novel multilayer shift-and-stitch (MUST) technique to generate fully dense
per-position predictions on protein sequences. Our model is significantly
simpler than the state-of-the-art, yet achieves better results. By combining
MUST and the efficient convolution operation, we can consider far more
parameters while retaining very fast prediction speeds. We beat the
state-of-the-art performance on two large protein property prediction datasets.
An efficient K-means algorithm for Massive Data
Authors: Marco Capó, Aritz Pérez, José Antonio Lozano
Date: 2016-05-10 00:00:00
Tasks: Clustering

Due to the progressive growth of the amount of data available in a wide
variety of scientific fields, it has become more difficult to ma- nipulate and
analyze such information. Even though datasets have grown in size, the K-means
algorithm remains as one of the most popular clustering methods, in spite of
its dependency on the initial settings and high computational cost, especially
in terms of distance computations. In this work, we propose an efficient
approximation to the K-means problem intended for massive data. Our approach
recursively partitions the entire dataset into a small number of sub- sets,
each of which is characterized by its representative (center of mass) and
weight (cardinality), afterwards a weighted version of the K-means algorithm is
applied over such local representation, which can drastically reduce the number
of distances computed. In addition to some theoretical properties, experimental
results indicate that our method outperforms well-known approaches, such as the
K-means++ and the minibatch K-means, in terms of the relation between number of
distance computations and the quality of the approximation.
Function-Described Graphs for Structural Pattern Recognition
Authors: Francesc Serratosa
Date: 2016-05-10 00:00:00
Tasks: Attribute

We present in this article the model Function-described graph (FDG), which is
a type of compact representation of a set of attributed graphs (AGs) that
borrow from Random Graphs the capability of probabilistic modelling of
structural and attribute information. We define the FDGs, their features and
two distance measures between AGs (unclassified patterns) and FDGs (models or
classes) and we also explain an efficient matching algorithm. Two applications
of FDGs are presented: in the former, FDGs are used for modelling and matching
3D-objects described by multiple views, whereas in the latter, they are used
for representing and recognising human faces, described also by several views.
Web Spam Detection Using Multiple Kernels in Twin Support Vector Machine
Authors: Seyed Hamid Reza Mohammadi, Mohammad Ali Zare Chahooki
Date: 2016-05-10 00:00:00
Tasks: BIG-bench Machine Learning, Spam detection

Search engines are the most important tools for web data acquisition. Web
pages are crawled and indexed by search Engines. Users typically locate useful
web pages by querying a search engine. One of the challenges in search engines
administration is spam pages which waste search engine resources. These pages
by deception of search engine ranking algorithms try to be showed in the first
page of results. There are many approaches to web spam pages detection such as
measurement of HTML code style similarity, pages linguistic pattern analysis
and machine learning algorithm on page content features. One of the famous
algorithms has been used in machine learning approach is Support Vector Machine
(SVM) classifier. Recently basic structure of SVM has been changed by new
extensions to increase robustness and classification accuracy. In this paper we
improved accuracy of web spam detection by using two nonlinear kernels into
Twin SVM (TSVM) as an improved extension of SVM. The classifier ability to data
separation has been increased by using two separated kernels for each class of
data. Effectiveness of new proposed method has been experimented with two
publicly used spam datasets called UK-2007 and UK-2006. Results show the
effectiveness of proposed kernelized version of TSVM in web spam page
detection.
Grammatical Case Based IS-A Relation Extraction with Boosting for Polish
Authors: Paweł Łoziński, Dariusz Czerski, Mieczysław A. Kłopotek
Date: 2016-05-10 00:00:00
Tasks: Relation, Relation Extraction

Pattern-based methods of IS-A relation extraction rely heavily on so called
Hearst patterns. These are ways of expressing instance enumerations of a class
in natural language. While these lexico-syntactic patterns prove quite useful,
they may not capture all taxonomical relations expressed in text. Therefore in
this paper we describe a novel method of IS-A relation extraction from
patterns, which uses morpho-syntactical annotations along with grammatical case
of noun phrases that constitute entities participating in IS-A relation. We
also describe a method for increasing the number of extracted relations that we
call pseudo-subclass boosting which has potential application in any
pattern-based relation extraction method. Experiments were conducted on a
corpus of about 0.5 billion web documents in Polish language.
Compact Hash Codes for Efficient Visual Descriptors Retrieval in Large Scale Databases
Authors: Simone Ercoli, Marco Bertini, Alberto del Bimbo
Date: 2016-05-10 00:00:00
Tasks: Retrieval

In this paper we present an efficient method for visual descriptors retrieval
based on compact hash codes computed using a multiple k-means assignment. The
method has been applied to the problem of approximate nearest neighbor (ANN)
search of local and global visual content descriptors, and it has been tested
on different datasets: three large scale public datasets of up to one billion
descriptors (BIGANN) and, supported by recent progress in convolutional neural
networks (CNNs), also on the CIFAR-10 and MNIST datasets. Experimental results
show that, despite its simplicity, the proposed method obtains a very high
performance that makes it superior to more complex state-of-the-art methods.
Learning theory estimates with observations from general stationary stochastic processes
Authors: Hanyuan Hang, Yunlong Feng, Ingo Steinwart, Johan A. K. Suykens
Date: 2016-05-10 00:00:00
Tasks: Learning Theory, quantile regression

This paper investigates the supervised learning problem with observations
drawn from certain general stationary stochastic processes. Here by
\emph{general}, we mean that many stationary stochastic processes can be
included. We show that when the stochastic processes satisfy a generalized
Bernstein-type inequality, a unified treatment on analyzing the learning
schemes with various mixing processes can be conducted and a sharp oracle
inequality for generic regularized empirical risk minimization schemes can be
established. The obtained oracle inequality is then applied to derive
convergence rates for several learning schemes such as empirical risk
minimization (ERM), least squares support vector machines (LS-SVMs) using given
generic kernels, and SVMs using Gaussian kernels for both least squares and
quantile regression. It turns out that for i.i.d.~processes, our learning rates
for ERM recover the optimal rates. On the other hand, for non-i.i.d.~processes
including geometrically $\alpha$-mixing Markov processes, geometrically
$\alpha$-mixing processes with restricted decay, $\phi$-mixing processes, and
(time-reversed) geometrically $\mathcal{C}$-mixing processes, our learning
rates for SVMs with Gaussian kernels match, up to some arbitrarily small extra
term in the exponent, the optimal rates. For the remaining cases, our rates are
at least close to the optimal rates. As a by-product, the assumed generalized
Bernstein-type inequality also provides an interpretation of the so-called
"effective number of observations" for various mixing processes.