pwc-archive/papers-with-abstracts huggingface dataset:
Probabilistic modelling of gait for remote passive monitoring applications
Date: 2019-01-30 00:00:00

Passive and non-obtrusive health monitoring using wearables can potentially
bring new insights into the user's health status throughout the day and may
support clinical diagnosis and treatment. However, identifying segments of
free-living data that sufficiently reflect the user's health is challenging. In
this work we have studied the problem of modelling real-life gait which is a
very indicative behaviour for multiple movement disorders including Parkinson's
disease (PD). We have developed a probabilistic framework for unsupervised
analysis of the gait, clustering it into different types, which can be used to
evaluate gait abnormalities occurring in daily life. Using a unique dataset
which contains sensor and video recordings of people with and without PD in
their own living environment, we show that our model driven approach achieves
high accuracy gait detection and can capture clinical improvement after
medication intake.
UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization
Date: 2019-07-16 00:00:00
Tasks: Edge-computing, Scheduling

In this paper, we study an unmanned aerial vehicle (UAV)-assisted mobile edge
computing (MEC) architecture, in which a UAV roaming around the area may serve
as a computing server to help user equipment (UEs) compute their tasks or act
as a relay for further offloading their computation tasks to the access point
(AP). We aim to minimize the weighted sum energy consumption of the UAV and UEs
subject to the task constraints, the information-causality constraints, the
bandwidth allocation constraints and the UAV's trajectory constraints. The
required optimization is nonconvex, and an alternating optimization algorithm
is proposed to jointly optimize the computation resource scheduling, bandwidth
allocation, and the UAV's trajectory in an iterative fashion. Numerical results
demonstrate that significant performance gain is obtained over conventional
methods. Also, the advantages of the proposed algorithm are more prominent when
handling computation-intensive latency-critical tasks.
Structural Damage Detection Using Ensemble Empirical Mode Decomposition, Hilbert Transform and Artificial Neural Networks
Date: 2018-11-30 00:00:00
Tasks: Structural Health Monitoring

Civil structures are on the verge of changing which leads energy dissipation
capacity to decline. Structural Health Monitoring (SHM) as a process in order
to implement a damage detection strategy and assess the condition of structure
plays a key role in structural reliability. Earthquake is a recognized factor
in variation of structures condition, inasmuch as inelastic behavior of a
building subjected to design level earthquakes is plausible. In this study
Hilbert Huang Transformation (HHT) is superseded by Ensemble Empirical Mode
decomposition (EEMD) and Hilbert Transform (HT) together. Albeit analogous,
EEMD brings more appropriate Intrinsic Mode Functions (IMFs) than Empirical
Mode Decomposition (EMD). IMFs are employed to assess first mode frequency and
mode shape. Afterward, Artificial Neural Networks (ANN) is applied to predict
story acceleration based on acceleration of structure during previous moments.
ANN functions precisely. Therefore, any congruency between predicted and
measured acceleration provides onset of damage. Then another ANN method is
applied to estimate stiffness matrix. Though first mode shape and frequency is
calculated in advance, it essentially requires an inverse problem to be solved
in order to find stiffness matrix. This task is done by ANN. In other words,
these two ANN methods are exercised to forecast location and measure severity
of damage respectively. This algorithm is implemented on one nonlinear
moment-resisting steel frame and the results are acceptable.
Simulation of Stylized Facts in Agent-Based Computational Economic Market Models
Authors: Maximilian Beikirch, Simon Cramer, Martin Frank, Philipp Otte, Emma Pabich, Torsten Trimborn
Date: 2018-11-27 00:00:00

We study the qualitative and quantitative appearance of stylized facts in several agent-based computational economic market (ABCEM) models. We perform our simulations with the SABCEMM (Simulator for Agent-Based Computational Economic Market Models) tool recently introduced by the authors (Trimborn et al. 2019). Furthermore, we present novel ABCEM models created by recombining existing models and study them with respect to stylized facts as well. This can be efficiently performed by the SABCEMM tool thanks to its object-oriented software design. The code is available on GitHub (Trimborn et al. 2018), such that all results can be reproduced by the reader.
Roadside acoustic sensors to support vulnerable pedestrians via their smartphone
Date: 2018-11-26 00:00:00

We propose a new warning system based on smartphones that evaluates the risk
of motor vehicle for vulnerable pedestrian (VP). The acoustic sensors are
embedded in roadside to receive vehicles sounds and they are classified into
heavy vehicle, light vehicle with low speed, light vehicle with high speed, and
no vehicle classes. For this aim, we extract new features by Mel-frequency
Cepstrum Coefficients (MFCC) and Linear Predictive Coefficients (LPC)
algorithms. We use different classification algorithms and show that MLP neural
network achieves at least 96.77% in accuracy criterion. To install this system,
directional microphones are embedded on roadside and the risk is classified
there. Then, for every microphone, a danger area is defined and the warning
alarms have been sent to every VPs smartphones covered in this danger area.
UAV Command and Control, Navigation and Surveillance: A Review of Potential 5G and Satellite Systems
Date: 2020-04-17 00:00:00

Drones, unmanned aerial vehicles (UAVs), or unmanned aerial systems (UAS) are
expected to be an important component of 5G/beyond 5G (B5G) communications.
This includes their use within cellular architectures (5G UAVs), in which they
can facilitate both wireless broadcast and point-to-point transmissions,
usually using small UAS (sUAS). Allowing UAS to operate within airspace along
with commercial, cargo, and other piloted aircraft will likely require
dedicated and protected aviation spectrum at least in the near term, while
regulatory authorities adapt to their use. The command and control (C2), or
control and non-payload communications (CNPC) link provides safety critical
information for the control of the UAV both in terrestrial-based line of sight
(LOS) conditions and in satellite communication links for so-called beyond LOS
(BLOS) conditions. In this paper, we provide an overview of these CNPC links as
they may be used in 5G and satellite systems by describing basic concepts and
challenges. We review new entrant technologies that might be used for UAV C2 as
well as for payload communication, such as millimeter wave (mmWave) systems,
and also review navigation and surveillance challenges. A brief discussion of
UAV-to-UAV communication and hardware issues are also provided.
Structural Damage Detection and Localization with Unknown Post-Damage Feature Distribution Using Sequential Change-Point Detection Method
Authors: Liao Yizheng, Kiremidjian Anne S., Rajagopal Ram, Loh Chin-Hsuing
Date: 2018-11-14 00:00:00
Tasks: Change Point Detection, Structural Health Monitoring, Time Series, Time Series Analysis

The high structural deficient rate poses serious risks to the operation of
many bridges and buildings. To prevent critical damage and structural collapse,
a quick structural health diagnosis tool is needed during normal operation or
immediately after extreme events. In structural health monitoring (SHM), many
existing works will have limited performance in the quick damage identification
process because 1) the damage event needs to be identified with short delay and
2) the post-damage information is usually unavailable. To address these
drawbacks, we propose a new damage detection and localization approach based on
stochastic time series analysis. Specifically, the damage sensitive features
are extracted from vibration signals and follow different distributions before
and after a damage event. Hence, we use the optimal change point detection
theory to find damage occurrence time. As the existing change point detectors
require the post-damage feature distribution, which is unavailable in SHM, we
propose a maximum likelihood method to learn the distribution parameters from
the time-series data. The proposed damage detection using estimated parameters
also achieves the optimal performance. Also, we utilize the detection results
to find damage location without any further computation. Validation results
show highly accurate damage identification in American Society of Civil
Engineers benchmark structure and two shake table experiments.
Machine Learning Classifications of Coronary Artery Disease
Authors: Nassif Ali Bou, Mahdi Omar, Nasir Qassim, Talib Manar Abu, Azzeh Mohammad
Date: 2018-11-26 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, Medical Diagnosis, Specificity

Coronary Artery Disease (CAD) is one of the leading causes of death
worldwide, and so it is very important to correctly diagnose patients with the
disease. For medical diagnosis, machine learning is a useful tool, however
features and algorithms must be carefully selected to get accurate
classification. To this effect, three feature selection methods have been used
on 13 input features from the Cleveland dataset with 297 entries, and 7 were
selected. The selected features were used to train three different classifiers,
which are SVM, Na\"ive Bayes and KNN using 10-fold cross-validation. The
resulting models evaluated using Accuracy, Recall, Specificity and Precision.
It is found that the Na\"ive Bayes classifier performs the best on this dataset
and features, outperforming or matching SVM and KNN in all the four evaluation
parameters used and achieving an accuracy of 84%.
Playing with and against Hedge
Authors: Anagnostou Miltiades E., Lambrou Maria A.
Date: 2018-11-20 00:00:00

Hedge has been proposed as an adaptive scheme, which guides an agent's
decision in resource selection and distribution problems that can be modeled as
a multi-armed bandit full information game. Such problems are encountered in
the areas of computer and communication networks, e.g. network path selection,
load distribution, network interdiction, and also in problems in the area of
transportation. We study Hedge under the assumption that the total loss that
can be suffered by the player in each round is upper bounded. In this paper, we
study the worst performance of Hedge.
The lure of misleading causal statements in functional connectivity research
Date: 2020-10-23 00:00:00
Tasks: Functional Connectivity

As neuroscientists we want to understand how causal interactions or
mechanisms within the brain give rise to perception, cognition, and behavior.
It is typical to estimate interaction effects from measured activity using
statistical techniques such as functional connectivity, Granger Causality, or
information flow, whose outcomes are often falsely treated as revealing
mechanistic insight. Since these statistical techniques fit models to
low-dimensional measurements from brains, they ignore the fact that brain
activity is high-dimensional. Here we focus on the obvious confound of common
inputs: the countless unobserved variables likely have more influence than the
few observed ones. Any given observed correlation can be explained by an
infinite set of causal models that take into account the unobserved variables.
Therefore, correlations within massively undersampled measurements tell us
little about mechanisms. We argue that these mis-inferences of causality from
correlation are augmented by an implicit redefinition of words that suggest
mechanisms, such as connectivity, causality, and flow.
Asymptotic Filter Behavior for High-Frequency Expert Opinions in a Market with Gaussian Drift
Authors: Abdelali Gabih, Hakam Kondakji, Ralf Wunderlich
Date: 2018-12-09 00:00:00

This paper investigates a financial market where stock returns depend on a hidden Gaussian mean reverting drift process. Information on the drift is obtained from returns and expert opinions in the form of noisy signals about the current state of the drift arriving at the jump times of a homogeneous Poisson process. Drift estimates are based on Kalman filter techniques and described by the conditional mean and covariance matrix of the drift given the observations. We study the filter asymptotics for increasing arrival intensity of expert opinions and prove that the conditional mean is a consistent drift estimator, it converges in the mean-square sense to the hidden drift. Thus, in the limit as the arrival intensity goes to infinity investors have full information about the drift.
Digital Cancellation of Passive Intermodulation in FDD Transceivers
Date: 2018-12-10 00:00:00

Modern radio systems and transceivers utilize carrier aggregation (CA) to
meet the demands for higher and higher data rates. However, the adoption of CA
in the existing Long Term Evolution (LTE)-Advanced and emerging 5G New Radio
(NR) mobile networks, in case of frequency division duplexing (FDD), may incur
self-interference challenges with certain band combinations. More specifically,
the nonlinear distortion products of the transmit signals or component carriers
(CCs), stemming from the passive radio frequency (RF) front-end components of
the transceiver, can appear in one or more of the configured receiver bands,
potentially leading to the receiver desensitization. In this paper, we present
advanced baseband equivalent signal models for such passive intermodulation
(PIM) distortion viewed from the RX point of view, considering also potential
memory effects in the PIM generation. Then, building on these signal models, a
digital self-interference cancellation technique operating in the transceiver
digital front-end is presented. The performance of the proposed solution is
evaluated with real-life RF measurements for LTE-Advanced type user equipment
(UE) with dual CC inter-band CA, demonstrating excellent suppression
properties. The findings in this work indicate that digital cancellation is a
feasible approach for improving the receiver sensitivity of mobile devices that
may be prone to RF front-end induced PIM challenges.
Shattering the glass ceiling? How the institutional context mitigates the gender gap in entrepreneurship
Date: 2018-12-10 00:00:00

We examine how the institutional context affects the relationship between
gender and opportunity entrepreneurship. To do this, we develop a multi-level
model that connects feminist theory at the micro-level to institutional theory
at the macro-level. It is hypothesized that the gender gap in opportunity
entrepreneurship is more pronounced in low-quality institutional contexts and
less pronounced in high-quality institutional contexts. Using data from the
Global Entrepreneurship Monitor (GEM) and regulation data from the economic
freedom of the world index (EFW), we test our predictions and find evidence in
support of our model. Our findings suggest that, while there is a gender gap in
entrepreneurship, these disparities are reduced as the quality of the
institutional context improves.
The seed bank coalescent with simultaneous switching
Date: 2018-12-21 00:00:00

We introduce a new Wright-Fisher type model for seed banks incorporating
"simultaneous switching", which is motivated by recent work on microbial
dormancy. We show that the simultaneous switching mechanism leads to a new
jump-diffusion limit for the scaled frequency processes, extending the
classical Wright-Fisher and seed bank diffusion limits. We further establish a
new dual coalescent structure with multiple activation and deactivation events
of lineages. While this seems reminiscent of multiple merger events in general
exchangeable coalescents, it actually leads to an entirely new class of
coalescent processes with unique qualitative and quantitative behaviour. To
illustrate this, we provide a novel kind of condition for coming down from
infinity for these coalescents using recent results of Griffiths.
Intelligent optical performance monitor using multi-task learning based artificial neural network
Date: 2018-12-10 00:00:00
Tasks: Multi-Task Learning

An intelligent optical performance monitor using multi-task learning based
artificial neural network (MTL-ANN) is designed for simultaneous OSNR
monitoring and modulation format identification (MFI). Signals' amplitude
histograms (AHs) after constant module algorithm are selected as the input
features for MTL-ANN. The experimental results of 20-Gbaud NRZ-OOK, PAM4 and
PAM8 signals demonstrate that MTL-ANN could achieve OSNR monitoring and MFI
simultaneously with higher accuracy and stability compared with single-task
learning based ANNs (STL-ANNs). The results show an MFI accuracy of 100% and
OSNR monitoring root-mean-square error of 0.63 dB for the three modulation
formats under consideration. Furthermore, the number of neuron needed for the
single MTL-ANN is almost the half of STL-ANN, which enables reduced-complexity
optical performance monitoring devices for real-time performance monitoring.
Stability of spontaneous, correlated activity in mouse auditory cortex
Date: 2018-12-10 00:00:00

Neural systems can be modeled as networks of functionally connected neural
elements. The resulting network can be analyzed using mathematical tools from
network science and graph theory to quantify the system's topological
organization and to better understand its function. While the network-based
approach is common in the analysis of large-scale neural systems probed by
non-invasive neuroimaging, few studies have used network science to study the
organization of networks reconstructed at the cellular level, and thus many
very basic and fundamental questions remain unanswered. Here, we used
two-photon calcium imaging to record spontaneous activity from the same set of
cells in mouse auditory cortex over the course of several weeks. We reconstruct
functional networks in which cells are linked to one another by edges weighted
according to the correlation of their fluorescence traces. We show that the
networks exhibit modular structure across multiple topological scales and that
these multi-scale modules unfold as part of a hierarchy. We also show that, on
average, network architecture becomes increasingly dissimilar over time, with
similarity decaying monotonically with the distance (in time) between sessions.
Finally, we show that a small fraction of cells maintain strongly-correlated
activity over multiple days, forming a stable temporal core surrounded by a
fluctuating and variable periphery. Our work provides a careful methodological
blueprint for future studies of spontaneous activity measured by two-photon
calcium imaging using cutting-edge computational methods and machine learning
algorithms informed by explicit graphical models from network science. The
methods are easily extended to additional datasets, opening the possibility of
studying cellular level network organization of neural systems and how that
organization is modulated by stimuli or altered in models of disease.
Examples of usage of nearfield acoustic holography methods for far field estimations: Part 1. CW signals
Date: 2018-11-27 00:00:00

The paper is devoted to the usage of nearfield acoustic holography methods
for estimating far field of the object. An experiment was carried out in
anechoic chamber. First, acoustic filed was recorded in a plane that was close
to source. This signals records were used to reconstruct the far field by
computation routines. Second, the signal in the far field is measured and the
results are compared. Several methods are tested and research on possible
reduction of the microphone array size is carried out. The most significant
reduction of the measurement facility complexity is usage a linear array in
stead of the planar array that is made possible due to introduced computation
routines
Fast Node Cardinality Estimation and Cognitive MAC Protocol Design for Heterogeneous Machine-to-Machine Networks
Date: 2018-12-10 00:00:00

Machine-to-Machine (M2M) networks are an emerging technology with
applications in numerous areas including smart grids, smart cities, vehicular
telematics, and healthcare. In this paper, we design two estimation protocols
for rapidly obtaining separate estimates of the number of active nodes of each
traffic type in a heterogeneous M2M network with $T$ types of M2M nodes (e.g.,
those that send emergency, periodic, normal type data etc), where $T \geq 2$ is
an arbitrary integer. One of these protocols, Method I, is a simple scheme, and
the other, Method II, is more sophisticated and performs better than Method I.
Also, we design a medium access control (MAC) protocol that supports
multi-channel operation for a heterogeneous M2M network with an arbitrary
number of types of M2M nodes, operating as a secondary network using Cognitive
Radio technology. Our Cognitive MAC protocol uses the proposed node cardinality
estimation protocols to rapidly estimate the number of active nodes of each
type in every time frame; these estimates are used to find the optimal
contention probabilities to be used in the MAC protocol. We compute a closed
form expression for the expected number of time slots required by Method I to
execute as well as a simple upper bound on it. Also, we mathematically analyze
the performance of the Cognitive MAC protocol and obtain expressions for the
expected number of successful contentions per frame and the expected amount of
energy consumed. Finally, we evaluate the performances of our proposed
estimation protocols and Cognitive MAC protocol using simulations.
Alternative states in plant communities driven by a life-history tradeoff and demographic stochasticity
Date: 2020-10-29 00:00:00

Life-history tradeoffs among species are major drivers of community assembly.
Most studies investigate how tradeoffs promote deterministic coexistence of
species. It remains unclear how tradeoffs may instead promote historically
contingent exclusion of species, where species dominance is affected by initial
abundances, causing alternative community states. Focusing on the
establishment-longevity tradeoff, we study the transient dynamics and
equilibrium outcomes of competitive interactions in a simulation model of plant
community assembly. We show that, in this model, the establishment-longevity
tradeoff is a necessary but not sufficient condition for alternative stable
equilibria that require also low fecundity for both species. An analytical
approximation of our simulation model demonstrates that alternative stable
equilibria are driven by demographic stochasticity in the number of seeds
arriving at each establishment site. This site-scale stochasticity is only
affected by fecundity and therefore occurs even in infinitely large
communities. In many cases where the establishment-longevity tradeoff does not
cause alternative stable equilibria, it still decreases the rate of convergence
toward the single equilibrium, resulting in decades of transient dynamics that
can appear indistinguishable from alternative stable equilibria in empirical
studies.