pwc-archive/papers-with-abstracts huggingface dataset:
Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling
Date: 2019-09-17 00:00:00
Tasks: Prediction

Seismic events, among many other natural hazards, reduce due functionality
and exacerbate vulnerability of in-service buildings. Accurate modeling and
prediction of building's response subjected to earthquakes makes possible to
evaluate building performance. To this end, we leverage the recent advances in
deep learning and develop a physics-guided convolutional neural network
(PhyCNN) framework for data-driven seismic response modeling and serviceability
assessment of buildings. The proposed PhyCNN approach is capable of accurately
predicting building's seismic response in a data-driven fashion without the
need of a physics-based analytical/numerical model. The basic concept is to
train a deep PhyCNN model based on available seismic input-output datasets
(e.g., from simulation or sensing) and physics constraints. The trained PhyCNN
can then used as a surrogate model for structural seismic response prediction.
Available physics (e.g., the law of dynamics) can provide constraints to the
network outputs, alleviate overfitting issues, reduce the need of big training
datasets, and thus improve the robustness of the trained model for more
reliable prediction. The trained surrogate model is then utilized for fragility
analysis given certain limit state criteria (e.g., the serviceability state).
In addition, an unsupervised learning algorithm based on K-means clustering is
also proposed to partition the limited number of datasets to training,
validation and prediction categories, so as to maximize the use of limited
datasets. The performance of the proposed approach is demonstrated through
three case studies including both numerical and experimental examples.
Convincing results illustrate that the proposed PhyCNN paradigm outperforms
conventional pure data-based neural networks.
Simple complex amplitude encoding of a phase-only hologram using binarized amplitude
Date: 2019-09-18 00:00:00

For this work, we introduced the use of binary amplitude for our proposed
complex amplitude encoding of a phase-only hologram. By principle, a complex
amplitude in a hologram plane can be represented by the amplitude and its
phase. However, a phase-only hologram contains only phase information of the
complex amplitude, which results in degradation of reconstruction quality from
the hologram. In our method, by approximating the amplitude in the hologram
plane using a binary amplitude, we can finally record the complex amplitude of
an original light in the phase-only hologram. We validated the effectiveness of
our method with two examples, hologram reconstruction and generation of
Hermite-Gaussian beams.
Poisson Noise Removal Using Multi-Frame 3D Block Matching
Date: 2019-09-18 00:00:00

The 3D block matching (BM3D) filter belongs to the state-of-the-art
techniques for eliminating additive white Gaussian noise from single-frame
images. There exist four multi-frame extensions of BM3D as of today. In this
work, we combine these extensions with a variance stabilising transformation
(VST) for eliminating Poisson noise. Our evaluation reveals that the extension
which retains the original noise model of the noisy images and additionally has
a comprehensive connectivity of 2D and temporal image information at both pixel
and patch levels, gives the best results. Additionally, we find a surprising
change in performance of one the four extensions due to the specific
application of the VST. Finally, we also introduce a simple low-pass filtering
as a preprocessing step for the best performing extension. This can give rise
to a significant additional improvement of 0.94 dB in the output according to
the peak signal to noise ratio.
LoRa Physical Layer Evaluation for Point-to-Point Links and Coverage Measurements in Diverse Environments
Date: 2019-09-18 00:00:00

This paper focuses on Point-to-Point LoRa connections. We report on coverage
measurements performed in three distinct environments, i.e., coastal, forest
and urban. Our field experiments demonstrate coverage up to 1 km with antennas
at only 1.5 m height in an urban scenario. In free Line-of-Sight (LoS) this
coverage is extended to 4 km. Based on these results, we are developing a path
loss model in future work. The developed hardware and software including
measurements are open-source.
Sensor Fusion for Magneto-Inductive Navigation
Date: 2019-09-18 00:00:00
Tasks: Position, Sensor Fusion

Magneto-inductive navigation is an inexpensive and easily deployable solution
to many of today's navigation problems. By utilizing very low frequency
magnetic fields, magneto-inductive technology circumvents the problems with
attenuation and multipath that often plague competing modalities. Using
triaxial transmitter and receiver coils, it is possible to compute position and
orientation estimates in three dimensions. However, in many situations,
additional information is available that constrains the set of possible
solutions. For example, the receiver may be known to be coplanar with the
transmitter, or orientation information may be available from inertial sensors.
We employ a maximum a posteriori estimator to fuse magneto-inductive signals
with such complementary information. Further, we derive the Cramer-Rao bound
for the position estimates and investigate the problem of detecting distortions
caused by ferrous material. The performance of the estimator is compared to the
Cramer-Rao bound and a state-of-the-art estimator using both simulations and
real-world data. By fusing magneto-inductive signals with accelerometer
measurements, the median position error is reduced almost by a factor of two.
Reduction and Hamiltonian aspects of a model for virus-tumour interaction in oncolytic virotherapy
Date: 2019-09-17 00:00:00

We analyse the Hamiltonian structure of a system of first-order ordinary
differential equations used for modeling the interaction of an oncolytic virus
with a tumour cell population. The analysis is based on the existence of a
Jacobi Last Multiplier for the system and a time dependent first integral. For
suitable conditions on the model parameters this allows for the reduction of
the problem to a planar system of equations for which the time dependent
Hamiltonian flows are described. The geometry of the Hamiltonian flows are
finally investigated using the symplectic and cosymplectic methods.
Nonparametric Estimation of the Random Coefficients Model: An Elastic Net Approach
Date: 2019-09-19 00:00:00

This paper investigates and extends the computationally attractive
nonparametric random coefficients estimator of Fox, Kim, Ryan, and Bajari
(2011). We show that their estimator is a special case of the nonnegative
LASSO, explaining its sparse nature observed in many applications. Recognizing
this link, we extend the estimator, transforming it to a special case of the
nonnegative elastic net. The extension improves the estimator's recovery of the
true support and allows for more accurate estimates of the random coefficients'
distribution. Our estimator is a generalization of the original estimator and
therefore, is guaranteed to have a model fit at least as good as the original
one. A theoretical analysis of both estimators' properties shows that, under
conditions, our generalized estimator approximates the true distribution more
accurately. Two Monte Carlo experiments and an application to a travel mode
data set illustrate the improved performance of the generalized estimator.
Overconfidence and Prejudice
Date: 2019-09-18 00:00:00

We explore conclusions a person draws from observing society when he allows
for the possibility that individuals' outcomes are affected by group-level
discrimination. Injecting a single non-classical assumption, that the agent is
overconfident about himself, we explain key observed patterns in social
beliefs, and make a number of additional predictions. First, the agent believes
in discrimination against any group he is in more than an outsider does,
capturing widely observed self-centered views of discrimination. Second, the
more group memberships the agent shares with an individual, the more positively
he evaluates the individual. This explains one of the most basic facts about
social judgments, in-group bias, as well as "legitimizing myths" that justify
an arbitrary social hierarchy through the perceived superiority of the
privileged group. Third, biases are sensitive to how the agent divides society
into groups when evaluating outcomes. This provides a reason why some
ethnically charged questions should not be asked, as well as a potential
channel for why nation-building policies might be effective. Fourth, giving the
agent more accurate information about himself increases all his biases. Fifth,
the agent is prone to substitute biases, implying that the introduction of a
new outsider group to focus on creates biases against the new group but lowers
biases vis a vis other groups. Sixth, there is a tendency for the agent to
agree more with those in the same groups. As a microfoundation for our model,
we provide an explanation for why an overconfident agent might allow for
potential discrimination in evaluating outcomes, even when he initially did not
conceive of this possibility.
Emotion Filtering at the Edge
Date: 2019-09-18 00:00:00
Tasks: Privacy Preserving, Raspberry Pi 4, speech-recognition, Speech Recognition

Voice controlled devices and services have become very popular in the
consumer IoT. Cloud-based speech analysis services extract information from
voice inputs using speech recognition techniques. Services providers can thus
build very accurate profiles of users' demographic categories, personal
preferences, emotional states, etc., and may therefore significantly compromise
their privacy. To address this problem, we have developed a privacy-preserving
intermediate layer between users and cloud services to sanitize voice input
directly at edge devices. We use CycleGAN-based speech conversion to remove
sensitive information from raw voice input signals before regenerating
neutralized signals for forwarding. We implement and evaluate our emotion
filtering approach using a relatively cheap Raspberry Pi 4, and show that
performance accuracy is not compromised at the edge. In fact, signals generated
at the edge differ only slightly (~0.16%) from cloud-based approaches for
speech recognition. Experimental evaluation of generated signals show that
identification of the emotional state of a speaker can be reduced by ~91%.
Dynamic causal modelling of phase-amplitude interactions
Date: 2019-09-17 00:00:00

Models of coupled oscillators are used to describe a wide variety of
phenomena in neuroimaging. These models typically rest on the premise that
oscillator dynamics do not evolve beyond their respective limit cycles, and
hence that interactions can be described purely in terms of phase differences.
Whilst mathematically convenient, the restrictive nature of phase-only models
can limit their explanatory power. We therefore propose a generalisation of
dynamic causal modelling that incorporates both phase and amplitude. This
allows for the separate quantifications of phase and amplitude contributions to
the connectivity between neural regions. We establish, using model-generated
data and simulations of coupled pendula, that phase-only models perform well
only under weak coupling conditions. We also show that, despite their higher
complexity, phase-amplitude models can describe strongly coupled systems more
effectively than their phase-only counterparts. We relate our findings to four
metrics commonly used in neuroimaging: the Kuramoto order parameter,
cross-correlation, phase-lag index, and spectral entropy. We find that, with
the exception of spectral entropy, the phase-amplitude model is able to capture
all metrics more effectively than the phase-only model. We then demonstrate,
using local field potential recordings in rodents and functional magnetic
resonance imaging in macaque monkeys, that amplitudes in oscillator models play
an important role in describing neural dynamics in anaesthetised brain states.
Toward Understanding Crowd Mobility in Smart Cities through the Internet of Things
Date: 2019-09-17 00:00:00
Tasks: Crowd Counting, Management

Understanding crowd mobility behaviors would be a key enabler for crowd
management in smart cities, benefiting various sectors such as public safety,
tourism and transportation. This article discusses the existing challenges and
the recent advances to overcome them and allow sharing information across
stakeholders of crowd management through Internet of Things (IoT) technologies.
The article proposes the usage of the new federated interoperable semantic IoT
platform (FIESTA-IoT), which is considered as "a system of systems". The
platform can support various IoT applications for crowd management in smart
cities. In particular, the article discusses two integrated IoT systems for
crowd mobility: 1) Crowd Mobility Analytics System, 2) Crowd Counting and
Location System (from the SmartSantander testbed). Pilot studies are conducted
in Gold Coast, Australia and Santander, Spain to fulfill various requirements
such as providing online and offline crowd mobility analyses with various
sensors in different regions. The analyses provided by these systems are shared
across applications in order to provide insights and support crowd management
in smart city environments.
Field-based mechanical phenotyping of cereal crops to assess lodging resistance
Authors: Lindsay Erndwein, Douglas D. Cook, Daniel J. Robertson, Erin E. Sparks
Date: 2019-09-18 00:00:00

Plant mechanical failure, also known as lodging, is the cause of significant and unpredictable yield losses in cereal crops. Lodging occurs in two distinct failure modes - stalk lodging and root lodging. Despite the prevalence and detrimental impact of lodging on crop yields, there is little consensus on how to phenotype plants in the field for lodging resistance and thus breed for mechanically resilient plants. This review provides an overview of field-based mechanical testing approaches to assess stalk and root lodging resistance. These approaches are placed in the context of future perspectives. Best practices and recommendations for acquiring field-based mechanical phenotypes of plants are also presented.
Decoupling stochastic optimal control problems for efficient solution: insights from experiments across a wide range of noise regimes
Date: 2019-09-18 00:00:00

We consider the problem of robotic planning under uncertainty in this paper.
This problem may be posed as a stochastic optimal control problem, a solution
to which is fundamentally intractable owing to the infamous "curse of
dimensionality". Hence, we consider the extension of a "decoupling principle"
that was recently proposed by some of the authors, wherein a nominal open-loop
problem is solved followed by a linear feedback design around the open-loop,
and which was shown to be near-optimal to second order in terms of a "small
noise" parameter, to a much wider range of noise levels. Our empirical evidence
suggests that this allows for tractable planning over a wide range of
uncertainty conditions without unduly sacrificing performance.
New Policy Design for Food Accessibility to the People in Need
Date: 2019-09-18 00:00:00
Tasks: Nutrition

Food insecurity is a term used to measure hunger and food deprivation of a
large population. As per the 2015 statistics provided by Feeding America - one
of the largest domestic hunger-relief organizations in the United States, 42.2
million Americans live in food insecure households, including 29.1 million
adults and 13.1 million children. This constitutes about 13.1% of households
that are food insecure. Food Banks have been developed to improve food security
for the needy. We have developed a novel food distribution policy using
suitable welfare and poverty indices and functions. In this work, we propose an
equitable and fair distribution of donated foods as per the demands and
requirements of the people, thus ensuring minimum wastage of food (perishable
and non-perishable) with focus towards nutrition. We present results and
analysis based on the application of the proposed policy using the information
of a local food bank as a case study. The results show that the new policy
performs better than the current methods in terms of population being covered
and reduction of food wastage obtaining suitable levels of nutrition.
Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
Date: 2019-09-19 00:00:00

In this paper, we provide for the first time a systematic comparison of
distribution matching (DM) and sphere shaping (SpSh) algorithms for short
blocklength probabilistic amplitude shaping. For asymptotically large
blocklengths, constant composition distribution matching (CCDM) is known to
generate the target capacity-achieving distribution. As the blocklength
decreases, however, the resulting rate loss diminishes the efficiency of CCDM.
We claim that for such short blocklengths and over the additive white Gaussian
channel (AWGN), the objective of shaping should be reformulated as obtaining
the most energy-efficient signal space for a given rate (rather than matching
distributions). In light of this interpretation, multiset-partition DM (MPDM),
enumerative sphere shaping (ESS) and shell mapping (SM), are reviewed as
energy-efficient shaping techniques. Numerical results show that MPDM and SpSh
have smaller rate losses than CCDM. SpSh--whose sole objective is to maximize
the energy efficiency--is shown to have the minimum rate loss amongst all. We
provide simulation results of the end-to-end decoding performance showing that
up to 1 dB improvement in power efficiency over uniform signaling can be
obtained with MPDM and SpSh at blocklengths around 200. Finally, we present a
discussion on the complexity of these algorithms from the perspective of
latency, storage and computations.
On the observability of relative positions in left-invariant multi-agent control systems and its application to formation control
Date: 2019-09-19 00:00:00

We consider the localization problem between agents while they run a
formation control algorithm. These algorithms typically demand from the agents
the information about their relative positions with respect to their neighbors.
We assume that this information is not available. Therefore, the agents need to
solve the observability problem of reconstructing their relative positions
based on other measurements between them. We first model the relative
kinematics between the agents as a left-invariant control system so that we can
exploit its appealing properties to solve the observability problem. Then, as a
particular application, we will focus on agents running a distance-based
control algorithm where their relative positions are not accessible but the
distances between them are.
Synthetic CT Generation from MRI Using Improved DualGAN
Date: 2019-09-19 00:00:00
Tasks: Image Generation, Image-to-Image Translation, Translation

Synthetic CT image generation from MRI scan is necessary to create
radiotherapy plans without the need of co-registered MRI and CT scans. The
chosen baseline adversarial model with cycle consistency permits unpaired
image-to-image translation. Perceptual loss function term and coordinate
convolutional layer were added to improve the quality of translated images. The
proposed architecture was tested on paired MRI-CT dataset, where the synthetic
CTs were compared to corresponding original CT images. The MAE between the
synthetic CT images and the real CT scans is 61 HU computed inside of the true
CTs body shape.
Approximately symbolic models for a class of continuous-time nonlinear systems
Date: 2019-09-19 00:00:00

Discrete abstractions have become a standard approach to assist control
synthesis under complex specifications. Most techniques for the construction of
discrete abstractions are based on sampling of both the state and time spaces,
which may not be able to guarantee safety for continuous-time systems. In this
work, we aim at addressing this problem by considering only state-space
abstraction. Firstly, we connect the continuous-time concrete system with its
discrete (state-space) abstraction with a control interface. Then, a novel
stability notion called controlled globally asymptotic/practical stability with
respect to a set is proposed. It is shown that every system, under the
condition that there exists an admissible control interface such that the
augmented system (composed of the concrete system and its abstraction) can be
made controlled globally practically stable with respect to the given set, is
approximately simulated by its discrete abstraction. The effectiveness of the
proposed results is illustrated by a simulation example.
The Effect of Oil Price on United Arab Emirates Goods Trade Deficit with the United States
Date: 2019-09-19 00:00:00

We seek to investigate the effect of oil price on UAE goods trade deficit
with the U.S. The current increase in the price of oil and the absence of
significant studies in the UAE economy are the main motives behind the current
study. Our paper focuses on a small portion of UAE trade, which is 11% of the
UAE foreign trade, however, it is a significant part since the U.S. is a major
trade partner with the UAE. The current paper concludes that oil price has a
significant positive influence on real imports. At the same time, oil price
does not have a significant effect on real exports. As a result, any increase
in the price of oil increases goods trade deficit of the UAE economy. The
policy implication of the current paper is that the revenue of oil sales is not
used to encourage UAE real exports.
Core Network Management Procedures for Self-Organized and Sustainable 5G Cellular Networks
Date: 2019-09-19 00:00:00
Tasks: Edge-computing, Management, Time Series, Time Series Analysis, Time Series Forecasting

Future Mobile Networks (MNs), 5G and beyond 5G, will require a paradigm shift
from traditional resource allocation mechanisms as Base Stations (BSs) will be
empowered with computation capabilities (i.e., offloading and computation is
performed closer to mobile users). This is motivated by the expected data
explosion in the volume, variety, and velocity, generated by pervasive mobile
and Internet of Things (IoT) devices at the network edge. Towards efficient
resource management, within the Multi-access Edge Computing (MEC) paradigm, we
make use of the Long Short-Term Memory (LSTM) neural network for time series
forecasting and control-theoretic techniques for foresighted optimization, thus
bringing intelligent mechanisms for handling network resources within the
network edge. Here, we propose online algorithms for autoscaling and
reconfiguring the computing-plus-communication resources within the virtualized
computing platform, and also enable dynamic switching on/off BSs by taking into
account the forecasted traffic load and harvested energy. The main goal is to
minimize the overall energy consumption, with a guarantee of Quality of Service
(QoS). Our numerical results, obtained through trace-driven simulations, show
that the proposed optimization strategies lead to a considerable reduction in
the energy consumed by the edge computing and communication facilities,
promoting energy self-sustainability within the MN through the use of green
energy.