pwc-archive/papers-with-abstracts huggingface dataset:
Risk aversion as an evolutionary adaptation
Authors: Arend Hintze, Randal S. Olson, Christoph Adami, Ralph Hertwig
Date: 2013-10-23 00:00:00

Risk aversion is a common behavior universal to humans and animals alike.
Economists have traditionally defined risk preferences by the curvature of the
utility function. Psychologists and behavioral economists also make use of
concepts such as loss aversion and probability weighting to model risk
aversion. Neurophysiological evidence suggests that loss aversion has its
origins in relatively ancient neural circuitries (e.g., ventral striatum).
Could there thus be an evolutionary origin to risk avoidance? We study this
question by evolving strategies that adapt to play the equivalent mean payoff
gamble. We hypothesize that risk aversion in the equivalent mean payoff gamble
is beneficial as an adaptation to living in small groups, and find that a
preference for risk averse strategies only evolves in small populations of less
than 1,000 individuals, while agents exhibit no such strategy preference in
larger populations. Further, we discover that risk aversion can also evolve in
larger populations, but only when the population is segmented into small groups
of around 150 individuals. Finally, we observe that risk aversion only evolves
when the gamble is a rare event that has a large impact on the individual's
fitness. These findings align with earlier reports that humans lived in small
groups for a large portion of their evolutionary history. As such, we suggest
that rare, high-risk, high-payoff events such as mating and mate competition
could have driven the evolution of risk averse behavior in humans living in
small groups.
Logic in the Lab
Authors: Rineke Verbrugge
Date: 2013-10-23 00:00:00

This file summarizes the plenary talk on laboratory experiments on logic at
the TARK 2013 - 14th Conference on Theoretical Aspects of Rationality and
Knowledge.
Spatial-Spectral Boosting Analysis for Stroke Patients' Motor Imagery EEG in Rehabilitation Training
Authors: Hao Zhang, Liqing Zhang
Date: 2013-10-23 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Motor Imagery

Current studies about motor imagery based rehabilitation training systems for
stroke subjects lack an appropriate analytic method, which can achieve a
considerable classification accuracy, at the same time detects gradual changes
of imagery patterns during rehabilitation process and disinters potential
mechanisms about motor function recovery. In this study, we propose an adaptive
boosting algorithm based on the cortex plasticity and spectral band shifts.
This approach models the usually predetermined spatial-spectral configurations
in EEG study into variable preconditions, and introduces a new heuristic of
stochastic gradient boost for training base learners under these preconditions.
We compare our proposed algorithm with commonly used methods on datasets
collected from 2 months' clinical experiments. The simulation results
demonstrate the effectiveness of the method in detecting the variations of
stroke patients' EEG patterns. By chronologically reorganizing the weight
parameters of the learned additive model, we verify the spatial compensatory
mechanism on impaired cortex and detect the changes of accentuation bands in
spectral domain, which may contribute important prior knowledge for
rehabilitation practice.
Computing Preferred Extensions in Abstract Argumentation: a SAT-based Approach
Authors: Federico Cerutti, Paul E. Dunne, Massimiliano Giacomin, Mauro Vallati
Date: 2013-10-18 00:00:00
Tasks: Abstract Argumentation

This paper presents a novel SAT-based approach for the computation of
extensions in abstract argumentation, with focus on preferred semantics, and an
empirical evaluation of its performances. The approach is based on the idea of
reducing the problem of computing complete extensions to a SAT problem and then
using a depth-first search method to derive preferred extensions. The proposed
approach has been tested using two distinct SAT solvers and compared with three
state-of-the-art systems for preferred extension computation. It turns out that
the proposed approach delivers significantly better performances in the large
majority of the considered cases.
The model of quantum evolution
Authors: Konstantin P. Wishnevsky
Date: 2008-06-27 00:00:00
Tasks: model

This paper has been withdrawn by the author due to extremely unscientific
errors.
A Ray-based Approach for Boundary Estimation of Fiber Bundles Derived from Diffusion Tensor Imaging
Authors: Miriam H. A. Bauer, Sebastiano Barbieri, Jan Klein, Jan Egger, Daniela Kuhnt, Bernd Freisleben, Horst K. Hahn, Christopher Nimsky
Date: 2013-10-23 00:00:00

Diffusion Tensor Imaging (DTI) is a non-invasive imaging technique that
allows estimation of the location of white matter tracts in-vivo, based on the
measurement of water diffusion properties. For each voxel, a second-order
tensor can be calculated by using diffusion-weighted sequences (DWI) that are
sensitive to the random motion of water molecules. Given at least 6
diffusion-weighted images with different gradients and one unweighted image,
the coefficients of the symmetric diffusion tensor matrix can be calculated.
Deriving the eigensystem of the tensor, the eigenvectors and eigenvalues can be
calculated to describe the three main directions of diffusion and its
magnitude. Using DTI data, fiber bundles can be determined, to gain information
about eloquent brain structures. Especially in neurosurgery, information about
location and dimension of eloquent structures like the corticospinal tract or
the visual pathways is of major interest. Therefore, the fiber bundle boundary
has to be determined. In this paper, a novel ray-based approach for boundary
estimation of tubular structures is presented.
Inferring Fitness in Finite Populations with Moran-like dynamics
Authors: Marc Harper
Date: 2013-03-19 00:00:00
Tasks: Bayesian Inference

Biological fitness is not an observable quantity and must be inferred from
population dynamics. Bayesian inference applied to the Moran process and
variants yields a robust inference method that can infer fitness in populations
evolving via a Moran dynamic and generalizations. Information about fitness is
derived solely from birth-events in birth-death and death-birth processes in
which selection acts proportionally to fitness, which allows the method to be
applied to populations on a network where the network itself may be changing in
time. Populations may also be allowed to change size while still allowing
estimates for fitness to be inferred.
Optimally fuzzy temporal memory
Authors: Karthik H. Shankar, Marc W. Howard
Date: 2012-11-22 00:00:00
Tasks: Future prediction, Prediction, Time Series, Time Series Analysis, Time Series Forecasting

Any learner with the ability to predict the future of a structured
time-varying signal must maintain a memory of the recent past. If the signal
has a characteristic timescale relevant to future prediction, the memory can be
a simple shift register---a moving window extending into the past, requiring
storage resources that linearly grows with the timescale to be represented.
However, an independent general purpose learner cannot a priori know the
characteristic prediction-relevant timescale of the signal. Moreover, many
naturally occurring signals show scale-free long range correlations implying
that the natural prediction-relevant timescale is essentially unbounded. Hence
the learner should maintain information from the longest possible timescale
allowed by resource availability. Here we construct a fuzzy memory system that
optimally sacrifices the temporal accuracy of information in a scale-free
fashion in order to represent prediction-relevant information from
exponentially long timescales. Using several illustrative examples, we
demonstrate the advantage of the fuzzy memory system over a shift register in
time series forecasting of natural signals. When the available storage
resources are limited, we suggest that a general purpose learner would be
better off committing to such a fuzzy memory system.
Multiple Kernel Learning for Brain-Computer Interfacing
Authors: Wojciech Samek, Alexander Binder, Klaus-Robert Müller
Date: 2013-10-22 00:00:00
Tasks: General Classification

Combining information from different sources is a common way to improve
classification accuracy in Brain-Computer Interfacing (BCI). For instance, in
small sample settings it is useful to integrate data from other subjects or
sessions in order to improve the estimation quality of the spatial filters or
the classifier. Since data from different subjects may show large variability,
it is crucial to weight the contributions according to importance. Many
multi-subject learning algorithms determine the optimal weighting in a separate
step by using heuristics, however, without ensuring that the selected weights
are optimal with respect to classification. In this work we apply Multiple
Kernel Learning (MKL) to this problem. MKL has been widely used for feature
fusion in computer vision and allows to simultaneously learn the classifier and
the optimal weighting. We compare the MKL method to two baseline approaches and
investigate the reasons for performance improvement.
Skin Segmentation based Elastic Bunch Graph Matching for efficient multiple Face Recognition
Authors: Sayantan Sarkar
Date: 2013-10-22 00:00:00
Tasks: Face Detection, Face Recognition, Graph Matching, Segmentation, Template Matching

This paper is aimed at developing and combining different algorithms for face detection and face recognition to generate an efficient mechanism that can detect and recognize the facial regions of input image. For the detection of face from complex region, skin segmentation isolates the face-like regions in a complex image and following operations of morphology and template matching rejects false matches to extract facial region. For the recognition of the face, the image database is now converted into a database of facial segments. Hence, implementing the technique of Elastic Bunch Graph matching (EBGM) after skin segmentation generates Face Bunch Graphs that acutely represents the features of an individual face enhances the quality of the training set. This increases the matching probability significantly.
Word Spotting in Cursive Handwritten Documents using Modified Character Shape Codes
Authors: Sayantan Sarkar
Date: 2013-10-22 00:00:00

There is a large collection of Handwritten English paper documents of
Historical and Scientific importance. But paper documents are not recognized
directly by computer. Hence the closest way of indexing these documents is by
storing their document digital image. Hence a large database of document images
can replace the paper documents. But the document and data corresponding to
each image cannot be directly recognized by the computer.
  This paper applies the technique of word spotting using Modified Character
Shape Code to Handwritten English document images for quick and efficient query
search of words on a database of document images. It is different from other
Word Spotting techniques as it implements two level of selection for word
segments to match search query. First based on word size and then based on
character shape code of query. It makes the process faster and more efficient
and reduces the need of multiple pre-processing.
Combined l_1 and greedy l_0 penalized least squares for linear model selection
Authors: Piotr Pokarowski, Jan Mielniczuk
Date: 2013-10-22 00:00:00
Tasks: Model Selection

We introduce a computationally effective algorithm for a linear model
selection consisting of three steps: screening--ordering--selection (SOS).
Screening of predictors is based on the thresholded Lasso that is l_1 penalized
least squares. The screened predictors are then fitted using least squares (LS)
and ordered with respect to their t statistics. Finally, a model is selected
using greedy generalized information criterion (GIC) that is l_0 penalized LS
in a nested family induced by the ordering. We give non-asymptotic upper bounds
on error probability of each step of the SOS algorithm in terms of both
penalties. Then we obtain selection consistency for different (n, p) scenarios
under conditions which are needed for screening consistency of the Lasso. For
the traditional setting (n >p) we give Sanov-type bounds on the error
probabilities of the ordering--selection algorithm. Its surprising consequence
is that the selection error of greedy GIC is asymptotically not larger than of
exhaustive GIC. We also obtain new bounds on prediction and estimation errors
for the Lasso which are proved in parallel for the algorithm used in practice
and its formal version.
Improvement of Automatic Hemorrhages Detection Methods Using Shapes Recognition
Authors: Nidhal Khdhair El Abbadi, Enas Hamood Al Saadi
Date: 2013-10-22 00:00:00

Diabetic Retinopathy is a medical condition where the retina is damaged
because fluid leaks from blood vessels into the retina. The presence of
hemorrhages in the retina is the earliest symptom of diabetic retinopathy. The
number and shape of hemorrhages is used to indicate the severity of the
disease. Early automated hemorrhage detection can help reduce the incidence of
blindness. This paper introduced new method depending on the hemorrhage shape
to detect the dot hemorrhage (DH), its number, and size at early stage, this
can be achieved by reducing the retinal image details. Detection and recognize
the DH by following three sequential steps, removing the fovea, removing the
vasculature and recognize DH by determining the circularity for all the objects
in the image, finally determine the shape factor which is related to DH
recognition, this stage strengthens the recognition process. The proposed
method recognizes and separates all the DH.
Fusion of Hyperspectral and Panchromatic Images using Spectral Uumixing Results
Authors: Roozbeh Rajabi, Hassan Ghassemian
Date: 2013-10-22 00:00:00

Hyperspectral imaging, due to providing high spectral resolution images, is
one of the most important tools in the remote sensing field. Because of
technological restrictions hyperspectral sensors has a limited spatial
resolution. On the other hand panchromatic image has a better spatial
resolution. Combining this information together can provide a better
understanding of the target scene. Spectral unmixing of mixed pixels in
hyperspectral images results in spectral signature and abundance fractions of
endmembers but gives no information about their location in a mixed pixel. In
this paper we have used spectral unmixing results of hyperspectral images and
segmentation results of panchromatic image for data fusion. The proposed method
has been applied on simulated data using AVRIS Indian Pines datasets. Results
show that this method can effectively combine information in hyperspectral and
panchromatic images.
Improving the methods of email classification based on words ontology
Authors: Foruzan Kiamarzpour, Rouhollah Dianat, Mohammad Bahrani, Mehdi Sadeghzadeh
Date: 2013-10-22 00:00:00
Tasks: Classification, General Classification

The Internet has dramatically changed the relationship among people and their
relationships with others people and made the valuable information available
for the users. Email is the service, which the Internet provides today for its
own users; this service has attracted most of the users' attention due to the
low cost. Along with the numerous benefits of Email, one of the weaknesses of
this service is that the number of received emails is continually being
enhanced, thus the ways are needed to automatically filter these disturbing
letters. Most of these filters utilize a combination of several techniques such
as the Black or white List, using the keywords and so on in order to identify
the spam more accurately In this paper, we introduce a new method to classify
the spam. We are seeking to increase the accuracy of Email classification by
combining the output of several decision trees and the concept of ontology.
Generic identification of binary-valued hidden Markov processes
Authors: Alexander Schönhuth
Date: 2011-01-19 00:00:00

The generic identification problem is to decide whether a stochastic process
$(X_t)$ is a hidden Markov process and if yes to infer its parameters for all
but a subset of parametrizations that form a lower-dimensional subvariety in
parameter space. Partial answers so far available depend on extra assumptions
on the processes, which are usually centered around stationarity. Here we
present a general solution for binary-valued hidden Markov processes. Our
approach is rooted in algebraic statistics hence it is geometric in nature. We
find that the algebraic varieties associated with the probability distributions
of binary-valued hidden Markov processes are zero sets of determinantal
equations which draws a connection to well-studied objects from algebra. As a
consequence, our solution allows for algorithmic implementation based on
elementary (linear) algebraic routines.
Inducing Honest Reporting Without Observing Outcomes: An Application to the Peer-Review Process
Authors: Arthur Carvalho, Stanko Dimitrov, Kate Larson
Date: 2013-09-12 00:00:00

When eliciting opinions from a group of experts, traditional devices used to
promote honest reporting assume that there is an observable future outcome. In
practice, however, this assumption is not always reasonable. In this paper, we
propose a scoring method built on strictly proper scoring rules to induce
honest reporting without assuming observable outcomes. Our method provides
scores based on pairwise comparisons between the reports made by each pair of
experts in the group. For ease of exposition, we introduce our scoring method
by illustrating its application to the peer-review process. In order to do so,
we start by modeling the peer-review process using a Bayesian model where the
uncertainty regarding the quality of the manuscript is taken into account.
Thereafter, we introduce our scoring method to evaluate the reported reviews.
Under the assumptions that reviewers are Bayesian decision-makers and that they
cannot influence the reviews of other reviewers, we show that risk-neutral
reviewers strictly maximize their expected scores by honestly disclosing their
reviews. We also show how the group's scores can be used to find a consensual
review. Experimental results show that encouraging honest reporting through the
proposed scoring method creates more accurate reviews than the traditional
peer-review process.
PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification
Authors: Emilie Morvant, Sokol Koço, Liva Ralaivola
Date: 2012-02-28 00:00:00
Tasks: General Classification, Multi-class Classification

In this work, we propose a PAC-Bayes bound for the generalization risk of the
Gibbs classifier in the multi-class classification framework. The novelty of
our work is the critical use of the confusion matrix of a classifier as an
error measure; this puts our contribution in the line of work aiming at dealing
with performance measure that are richer than mere scalar criterion such as the
misclassification rate. Thanks to very recent and beautiful results on matrix
concentration inequalities, we derive two bounds showing that the true
confusion risk of the Gibbs classifier is upper-bounded by its empirical risk
plus a term depending on the number of training examples in each class. To the
best of our knowledge, this is the first PAC-Bayes bounds based on confusion
matrices.
Intelligent City Traffic Management and Public Transportation System
Authors: Snehal Mulay, Chinmay Dhekne, Rucha Bapat, Tanmay Budukh, Soham Gadgil
Date: 2013-10-22 00:00:00
Tasks: Management

Intelligent Transportation System in case of cities is controlling traffic
congestion and regulating the traffic flow. This paper presents three modules
that will help in managing city traffic issues and ultimately gives advanced
development in transportation system. First module, Congestion Detection and
Management will provide user real time information about congestion on the road
towards his destination, Second module, Intelligent Public Transport System
will provide user real time public transport information,i.e, local buses, and
the third module, Signal Synchronization will help in controlling congestion at
signals, with real time adjustments of signal timers according to the
congestion. All the information that user is getting about the traffic or
public transportation will be provided on users day to day device that is
mobile through Android application or SMS. Moreover, communication can also be
done via Website for Clients having internet access. And all these modules will
be fully automated without any human intervention at server side.
Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-rank Matrices
Authors: T. Tony Cai, Anru Zhang
Date: 2013-06-05 00:00:00
Tasks: compressed sensing

This paper considers compressed sensing and affine rank minimization in both
noiseless and noisy cases and establishes sharp restricted isometry conditions
for sparse signal and low-rank matrix recovery. The analysis relies on a key
technical tool which represents points in a polytope by convex combinations of
sparse vectors. The technique is elementary while leads to sharp results.
  It is shown that for any given constant $t\ge {4/3}$, in compressed sensing
$\delta_{tk}^A < \sqrt{(t-1)/t}$ guarantees the exact recovery of all $k$
sparse signals in the noiseless case through the constrained $\ell_1$
minimization, and similarly in affine rank minimization
$\delta_{tr}^\mathcal{M}< \sqrt{(t-1)/t}$ ensures the exact reconstruction of
all matrices with rank at most $r$ in the noiseless case via the constrained
nuclear norm minimization. Moreover, for any $\epsilon>0$,
$\delta_{tk}^A<\sqrt{\frac{t-1}{t}}+\epsilon$ is not sufficient to guarantee
the exact recovery of all $k$-sparse signals for large $k$. Similar result also
holds for matrix recovery. In addition, the conditions $\delta_{tk}^A <
\sqrt{(t-1)/t}$ and $\delta_{tr}^\mathcal{M}< \sqrt{(t-1)/t}$ are also shown to
be sufficient respectively for stable recovery of approximately sparse signals
and low-rank matrices in the noisy case.
RANSAC: Identification of Higher-Order Geometric Features and Applications in Humanoid Robot Soccer
Authors: Madison Flannery, Shannon Fenn, David Budden
Date: 2013-10-22 00:00:00
Tasks: General Classification

The ability for an autonomous agent to self-localise is directly proportional
to the accuracy and precision with which it can perceive salient features
within its local environment. The identification of such features by
recognising geometric profile allows robustness against lighting variations,
which is necessary in most industrial robotics applications. This paper details
a framework by which the random sample consensus (RANSAC) algorithm, often
applied to parameter fitting in linear models, can be extended to identify
higher-order geometric features. Goalpost identification within humanoid robot
soccer is investigated as an application, with the developed system yielding an
order-of-magnitude improvement in classification performance relative to a
traditional histogramming methodology.