pwc-archive/papers-with-abstracts huggingface dataset:
Semiparametric Slepian-Bangs Formula for Complex Elliptically Symmetric Distributions
Date: 2019-02-25 00:00:00

This letter aims at deriving a Semiparametric Slepian-Bangs (SSB) formula for
Complex Elliptically Symmetric (CES) distributed data vectors. The
Semiparametric Cram\'{e}r-Rao Bound (SCRB), related to the proposed SSB
formula, provides a lower bound on the Mean Square Error (MSE) of \textit{any}
robust estimator of a parameter vector parameterizing the mean vector and the
scatter matrix of the given CES-distributed vector in the presence of an
unknown, nuisance, density generator.
Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliability
Date: 2018-07-24 00:00:00
Tasks: Bayesian Inference

Neural responses in the cortex change over time both systematically, due to
ongoing plasticity and learning, and seemingly randomly, due to various sources
of noise and variability. Most previous work considered each of these
processes, learning and variability, in isolation -- here we study neural
networks exhibiting both and show that their interaction leads to the emergence
of powerful computational properties. We trained neural networks on classical
unsupervised learning tasks, in which the objective was to represent their
inputs in an efficient, easily decodable form, with an additional cost for
neural reliability which we derived from basic biophysical considerations. This
cost on reliability introduced a tradeoff between energetically cheap but
inaccurate representations and energetically costly but accurate ones. Despite
the learning tasks being non-probabilistic, the networks solved this tradeoff
by developing a probabilistic representation: neural variability represented
samples from statistically appropriate posterior distributions that would
result from performing probabilistic inference over their inputs. We provide an
analytical understanding of this result by revealing a connection between the
cost of reliability, and the objective for a state-of-the-art Bayesian
inference strategy: variational autoencoders. We show that the same cost leads
to the emergence of increasingly accurate probabilistic representations as
networks become more complex, from single-layer feed-forward, through
multi-layer feed-forward, to recurrent architectures. Our results provide
insights into why neural responses in sensory areas show signatures of
sampling-based probabilistic representations, and may inform future deep
learning algorithms and their implementation in stochastic low-precision
computing systems.
Rule Based Metadata Extraction Framework from Academic Articles
Authors: Azimjonov Jahongir, Alikhanov Jumabek
Date: 2018-07-24 00:00:00
Tasks: Articles

Metadata of scientific articles such as title, abstract, keywords or index
terms, body text, conclusion, reference and others play a decisive role in
collecting, managing and storing academic data in scientific databases,
academic journals and digital libraries. An accurate extraction of these kinds
of data from scientific papers is crucial to organize and retrieve important
scientific information for researchers as well as librarians. Research social
network systems and academic digital library systems provide academic data
extracting, organizing and retrieving services. Mostly these types of services
are not free or open source. They also have some performance problems and
extracting limitations in the number of PDF (Portable Document Format) files
that you can upload to the extraction systems. In this paper, a completely free
and open source Java based high performance metadata extraction framework is
proposed. This frameworks extraction speed is 9-10 times faster than existing
metadata extraction systems. It is also flexible in that it allows uploading of
unlimited number of PDF files. In this approach, titles of papers are extracted
using layout features, font and size characteristics of text. Other metadata
fields such as abstracts, body text, keywords, conclusions and references are
extracted from PDF files using fixed rule sets. Extracted metadata are stored
in both Oracle database and XML (Extensible Markup Language) file. This
framework can be used to make scientific collections in digital libraries,
online journals, online and offline scientific databases, government research
agencies and research centers.
Mitigation of Human RF Exposure in 5G Downlink
Date: 2018-10-24 00:00:00

While research on communications at frequencies above 6 gigahertz (GHz) has
been primarily confined to performance improvement, their potentially harmful
impacts on human health are not studied as significantly. Most of the existing
studies that paid attention to the health impacts above 6 GHz focused only on
the uplink due to closer contact with a transmitter to a human body. In this
letter, we present the human electromagnetic field (EMF) exposure in the
downlink of Fifth-Generation Wireless Systems (5G). Moreover, we propose a
downlink protocol that guarantees the EMF exposure under a threshold while
keeping the data rate above the 5G requirements.
Analysis of bacterial population growth using extended logistic growth model with distributed delay
Date: 2018-07-21 00:00:00

In the present work, we develop a delayed Logistic growth model to study the
effects of decontamination on the bacterial population in the ambient
environment. Using the linear stability analysis, we study different case
scenarios, where bacterial population may establish at the positive equilibrium
or go extinct due to increased decontamination. The results are verified using
numerical simulation of the model.
Noncoherent Multi-User MIMO Communications using Covariance CSIT
Date: 2018-07-24 00:00:00

The Multi-User downlink, particularly in a Multi-Cell Massive MIMO setting,
requires enormous amounts of instantaneous CSIT (Channel State Information at
the Transmitter(s)), iCSIT. Here we focus on exploiting channel covariance CSIT
(coCSIT) only. In particular multipath induced structured low rank covariances
are considered that arise in Massive MIMO and mmWave settings, which we call
pathwise CSIT (pwCSIT). The resulting non-Kronecker MIMO channel covariance
structures lead to a split between the roles of transmitters and receivers in
MIMO systems. For the beamforming optimization, we consider a minorization
approach applied to the Massive MIMO limit of the Expected Weighted Sum Rate.
Simulations indicate that the pwCSIT based designs may lead to limited spectral
efficiency loss compared to iCSIT based designs, while trading fast fading CSIT
for slow fading CSIT. We also point out that the pathwise approach may lead to
distributed designs with only local pwCSIT, and analyze the sum rates for iCSIT
and pwCSIT in the low and high SNR limits.
A Cognitive Sub-Nyquist MIMO Radar Prototype
Date: 2019-06-13 00:00:00

We present a cognitive prototype that demonstrates a colocated,
frequency-division-multiplexed, multiple-input multiple-output (MIMO) radar
which implements both temporal and spatial sub-Nyquist sampling. The signal is
sampled and recovered via the Xampling framework. Cognition is due to the fact
that the transmitter adapts its signal spectrum by emitting only those subbands
that the receiver samples and processes. Real-time experiments demonstrate
sub-Nyquist MIMO recovery of target scenes with 87.5% spatio-temporal bandwidth
reduction and signal-to-noise-ratio of -10 dB.
Evolution of female choice under intralocus sexual conflict and genotype-by-environment interactions
Date: 2018-07-24 00:00:00

In many species, females are hypothesised to obtain 'good genes' for their
offspring by mating with males in good condition. However, female preferences
might deplete genetic variance and make choice redundant. Additionally,
high-condition males sometime produce low-fitness offspring, for example
because of environmental turnover and gene-by-environment interactions (GEIs)
for fitness, or because fit males carry sexually-antagonistic alleles causing
them to produce unfit daughters. Here, we extend previous theory by
investigating the evolution of female mate choice in a spatially explicit
evolutionary simulation implementing both GEIs and intralocus sexual conflict
(IASC), under sex-specific hard or soft selection. We show that IASC can weaken
female preferences for high-condition males or even cause a preference for
males in low condition, depending on the relative benefits of producing
well-adapted sons versus daughters, which in turn depends on the relative
hardness of selection on males and females. We discuss the relevance of our
results to conservation genetics and empirical evolutionary biology.
On the computational analysis of the genetic algorithm for attitude control of a carrier system
Authors: Jahanshahi Hadi, Sari Naeimeh Najafizadeh
Date: 2018-06-27 00:00:00

This paper intends to cover three main topics. First, a fuzzy-PID controller
is designed to control the thrust vector of a launch vehicle, accommodating a
CanSat. Then, the genetic algorithm (GA) is employed to optimize the controller
performance. Finally, through adjusting the algorithm parameters, their impact
on the optimization process is examined. In this regard, the motion vector
control is programmed based on the governing dynamic equations of motion for
payload delivery in the desired altitude and flight-path angle. This utilizes
one single input and one preferential fuzzy inference engine, where the latter
acts to avoid the system instability in large angles for the thrust vector. The
optimization objective functions include the deviations of the thrust vector
and the system from the equilibrium state, which must be met simultaneously.
Sensitivity analysis of the parameters of the genetic algorithm involves
examining nine different cases and discussing their impact on the optimization
results.
Chromatic transitions in the emergence of syntax networks
Date: 2018-07-24 00:00:00
Tasks: Language Acquisition

The emergence of syntax during childhood is a remarkable example of how
complex correlations unfold in nonlinear ways through development. In
particular, rapid transitions seem to occur as children reach the age of two,
which seems to separate a two-word, tree-like network of syntactic relations
among words from a scale-free graphs associated to the adult, complex grammar.
Here we explore the evolution of syntax networks through language acquisition
using the {\em chromatic number}, which captures the transition and provides a
natural link to standard theories on syntactic structures. The data analysis is
compared to a null model of network growth dynamics which is shown to display
nontrivial and sensible differences. In a more general level, we observe that
the chromatic classes define independent regions of the graph, and thus, can be
interpreted as the footprints of incompatibility relations, somewhat as opposed
to modularity considerations.
A Convex Formulation for Binary Tomography
Authors: Ajinkya Kadu, Tristan van Leeuwen
Date: 2018-07-24 00:00:00

Binary tomography is concerned with the recovery of binary images from a few of their projections (i.e., sums of the pixel values along various directions). To reconstruct an image from noisy projection data, one can pose it as a constrained least-squares problem. As the constraints are non-convex, many approaches for solving it rely on either relaxing the constraints or heuristics. In this paper we propose a novel convex formulation, based on the Lagrange dual of the constrained least-squares problem. The resulting problem is a generalized LASSO problem which can be solved efficiently. It is a relaxation in the sense that it can only be guaranteed to give a feasible solution; not necessarily the optimal one. In exhaustive experiments on small images (2x2, 3x3, 4x4) we find, however, that if the problem has a unique solution, our dual approach finds it. In case of multiple solutions, our approach finds the commonalities between the solutions. Further experiments on realistic numerical phantoms and an experiment on X-ray dataset show that our method compares favourably to Total Variation and DART.
End-to-End Deep Imitation Learning: Robot Soccer Case Study
Authors: Aşık Okan, Görer Binnur, Akın H. Levent
Date: 2018-06-28 00:00:00
Tasks: Imitation Learning

In imitation learning, behavior learning is generally done using the features
extracted from the demonstration data. Recent deep learning algorithms enable
the development of machine learning methods that can get high dimensional data
as an input. In this work, we use imitation learning to teach the robot to
dribble the ball to the goal. We use B-Human robot software to collect
demonstration data and a deep convolutional network to represent the policies.
We use top and bottom camera images of the robot as input and speed commands as
outputs. The CNN policy learns the mapping between the series of images and
speed commands. In 3D realistic robotics simulator experiments, we show that
the robot is able to learn to search the ball and dribble the ball, but it
struggles to align to the goal. The best-proposed policy model learns to score
4 goals out of 20 test episodes.
Hardware-In-The-Loop Vulnerability Analysis of a Single-Machine Infinite-Bus Power System
Date: 2018-07-24 00:00:00
Tasks: Management

The dynamic performance of the generators is a critical factor for the safe
operation of the power grid. To this extent, the stability of the frequency of
generators is the target of cyber attacks since its instability may lead to
sizable cascade failures in the whole network. In this paper, we perform the
vulnerability analysis in a developed power grid Hardware-In-The-Loop (HITL)
testbed with a Wago 750-881 PLC sending control commands to the generators and
a 750 Feeder Management Relay connected to a local load. A process-aware
coordinated attack is demonstrated by spoofing control commands sent by the PLC
and the relay to the simulated power system which is modeled as a
single-machine infinite-bus (SMIB). Based on the reachability analysis, the
attacker can find the optimal attack signal to drive the system state out of
their safe set of values. Thereafter, it is experimentally demonstrated that
the attacker does not need to send attack signal continuously if he implements
a carefully designed coordinated attack on the PLC and the relay. The presented
assessments provide information about the best time to launch an attack in
order to destabilize the power system.
Parallel Whale Optimization Algorithm for Solving Constrained and Unconstrained Optimization Problems
Authors: Sauber Amr M., Nasef Mohammed M., Houssein Essam H., Hassanien Aboul Ella
Date: 2018-06-21 00:00:00

Recently the engineering optimization problems require large computational
demands and long solution time even on high multi-processors computational
devices. In this paper, an OpenMP inspired parallel version of the whale
optimization algorithm (PWOA) to obtain enhanced computational throughput and
global search capability is presented. It automatically detects the number of
available processors and divides the workload among them to accomplish the
effective utilization of the available resources. PWOA is applied on twenty
unconstrained optimization functions on multiple dimensions and five
constrained optimization engineering functions. The proposed parallelism PWOA
algorithms performance is evaluated using parallel metrics such as speedup,
efficiency. The comparison illustrates that the proposed PWOA algorithm has
obtained the same results while exceeding the sequential version in
performance. Furthermore, PWOA algorithm in the term of computational time and
speed of parallel metric was achieved better results over the sequential
processing compared to the standard WOA.
Structural health monitoring with distributed wireless sensor networks
Date: 2018-07-10 00:00:00
Tasks: Structural Health Monitoring

Wireless Sensor Networks(WSN) are a today technology with great
practicability in the real world. We focus on describing WSN architecture,
regarding usefulness in constructions like structural health monitoring and
importance, and advantages of using WSN in this domain
Sensor Networks Architecture for Vehicles and Pedestrians Traffic Control
Date: 2018-07-11 00:00:00
Tasks: Management

In this paper we present a sensor network based architecture for urban
traffic management, hierarchically structured on three layers: sensing,
processing& aggregation and control. On proposed architecture we define traffic
decongestion methods for vehicles and also for pedestrians. Finally, we
presented a case study on how traffic control can be implemented in a concrete
situation, based on the proposed architecture, pointing future directions of
development.
HyperNets and their application to learning spatial transformations
Authors: Potapov Alexey, Shcherbakov Oleg, Zhdanov Innokentii, Rodionov Sergey, Skorobogatko Nikolai
Date: 2018-07-12 00:00:00

In this paper we propose a conceptual framework for higher-order artificial
neural networks. The idea of higher-order networks arises naturally when a
model is required to learn some group of transformations, every element of
which is well-approximated by a traditional feedforward network. Thus the group
as a whole can be represented as a hyper network. One of typical examples of
such groups is spatial transformations. We show that the proposed framework,
which we call HyperNets, is able to deal with at least two basic spatial
transformations of images: rotation and affine transformation. We show that
HyperNets are able not only to generalize rotation and affine transformation,
but also to compensate the rotation of images bringing them into canonical
forms.
Synthesizing dynamic MRI using long-term recurrent convolutional networks
Authors: Frank Preiswerk, Cheng-Chieh Cheng, Jie Luo, Bruno Madore
Date: 2018-07-24 00:00:00

A method is proposed for converting raw ultrasound signals of respiratory organ motion into high frame rate dynamic MRI using a long-term recurrent convolutional neural network. Ultrasound signals were acquired using a single-element transducer, referred to here as `organ-configuration motion' (OCM) sensor, while sagittal MR images were simultaneously acquired. Both streams of data were used for training a cascade of convolutional layers, to extract relevant features from raw ultrasound, followed by a recurrent neural network, to learn its temporal dynamics. The network was trained with MR images on the output, and was employed to predict MR images at a temporal resolution of 100 frames per second, based on ultrasound input alone, without any further MR scanner input. The method was validated on 7 subjects.
A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data
Date: 2018-07-24 00:00:00
Tasks: Dimensionality Reduction, regression

We propose a matrix factorization technique that decomposes the resting state
fMRI (rs-fMRI) correlation matrices for a patient population into a sparse set
of representative subnetworks, as modeled by rank one outer products. The
subnetworks are combined using patient specific non-negative coefficients;
these coefficients are also used to model, and subsequently predict the
clinical severity of a given patient via a linear regression. Our
generative-discriminative framework is able to exploit the structure of rs-fMRI
correlation matrices to capture group level effects, while simultaneously
accounting for patient variability. We employ ten fold cross validation to
demonstrate the predictive power of our model on a cohort of fifty eight
patients diagnosed with Autism Spectrum Disorder. Our method outperforms
classical semi-supervised frameworks, which perform dimensionality reduction on
the correlation features followed by non-linear regression to predict the
clinical scores.
C-V2X Security Requirements and Procedures: Survey and Research Directions
Date: 2018-07-24 00:00:00
Tasks: Scheduling, Survey

The 3rd Generation Partnership Project (3GPP) defines the first
specifications for cellular-based vehicle-to-everything (C-V2X) communications
in Release 14. C-V2X extends LTE's device-to-device communication modes by
adding two new modes of operation for vehicular systems in coverage and out of
coverage of an LTE base station, or eNB. The vehicle-to-vehicle (V2V)
communication mode does not rely on the cellular infrastructure and the C-V2X
devices employ a distributed and sensing-based semi-persistent scheduling to
schedule their packet transmissions. As a promising alternative to dedicated
short range communications, the security aspects of C-V2X need to be carefully
designed and evaluated to ensure the availability and integrity of the service
and data. This paper discusses possible threat scenarios, reviews the 3GPP
specifications and finds that, despite the safety-critical nature of C-V2X,
only few mechanisms and procedures have be specified to secure the system. We
discuss emerging technologies and provide research directions to improve C-V2X
system security and reliability and ensure their widespread adoption in
civilian and mission-critical communication contexts.
Investigating the configurations in cross-shareholding: a joint copula-entropy approach
Date: 2018-06-14 00:00:00
Tasks: Fairness

--- the companies populating a Stock market, along with their connections,
can be effectively modeled through a directed network, where the nodes
represent the companies, and the links indicate the ownership. This paper deals
with this theme and discusses the concentration of a market. A
cross-shareholding matrix is considered, along with two key factors: the node
out-degree distribution which represents the diversification of investments in
terms of the number of involved companies, and the node in-degree distribution
which reports the integration of a company due to the sales of its own shares
to other companies. While diversification is widely explored in the literature,
integration is most present in literature on contagions. This paper captures
such quantities of interest in the two frameworks and studies the stochastic
dependence of diversification and integration through a copula approach. We
adopt entropies as measures for assessing the concentration in the market. The
main question is to assess the dependence structure leading to a better
description of the data or to market polarization (minimal entropy) or market
fairness (maximal entropy). In so doing, we derive information on the way in
which the in- and out-degrees should be connected in order to shape the market.
The question is of interest to regulators bodies, as witnessed by specific
alert threshold published on the US mergers guidelines for limiting the
possibility of acquisitions and the prevalence of a single company on the
market. Indeed, all countries and the EU have also rules or guidelines in order
to limit concentrations, in a country or across borders, respectively. The
calibration of copulas and model parameters on the basis of real data serves as
an illustrative application of the theoretical proposal.