pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Virus Propagation: A Transition Model Representation of Stochastic Epidemiological Models
Date: 2020-06-18 00:00:00
Tasks: Vocal Bursts Type Prediction

The growing literature on the propagation of COVID-19 relies on various
dynamic SIR-type models (Susceptible-Infected-Recovered) which yield
model-dependent results. For transparency and ease of comparing the results, we
introduce a common representation of the SIR-type stochastic epidemiological
models. This representation is a discrete time transition model, which allows
us to classify the epidemiological models with respect to the number of states
(compartments) and their interpretation. Additionally, the transition model
eliminates several limitations of the deterministic continuous time
epidemiological models which are pointed out in the paper. We also show that
all SIR-type models have a nonlinear (pseudo) state space representation and
are easily estimable from an extended Kalman filter.
Improving Urban Traffic Throughput with Vehicle Platooning: Theory and Experiments
Date: 2020-07-28 00:00:00
Tasks: Model Predictive Control

In this paper we present a model-predictive control (MPC) based approach for
vehicle platooning in an urban traffic setting. Our primary goal is to
demonstrate that vehicle platooning has the potential to significantly increase
throughput at intersections, which can create bottlenecks in the traffic flow.
To do so, our approach relies on vehicle connectivity: vehicle-to-vehicle (V2V)
and vehicle-to-infrastructure (V2I) communication. In particular, we introduce
a customized V2V message set which features a velocity forecast, i.e. a
prediction on the future velocity trajectory, which enables platooning vehicles
to accurately maintain short following distances, thereby increasing
throughput. Furthermore, V2I communication allows platoons to react immediately
to changes in the state of nearby traffic lights, e.g. when the traffic phase
becomes green, enabling additional gains in traffic efficiency. We present our
design of the vehicle platooning system, and then evaluate performance by
estimating the potential gains in terms of throughput using our results from
simulation, as well as experiments conducted with real test vehicles on a
closed track. Lastly, we briefly overview our demonstration of vehicle
platooning on public roadways in Arcadia, CA.
Structured Massive Access for Scalable Cell-Free Massive MIMO Systems
Authors: Shuaifei Chen, Jiayi Zhang, Emil Björnson, Jing Zhang, Bo Ai
Date: 2020-06-18 00:00:00
Tasks: Fairness

How to meet the demand for increasing number of users, higher data rates, and stringent quality-of-service (QoS) in the beyond fifth-generation (B5G) networks? Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising solution, in which many wireless access points cooperate to jointly serve the users by exploiting coherent signal processing. However, there are still many unsolved practical issues in cell-free massive MIMO systems, whereof scalable massive access implementation is one of the most vital. In this paper, we propose a new framework for structured massive access in cell-free massive MIMO systems, which comprises one initial access algorithm, a partial large-scale fading decoding (P-LSFD) strategy, two pilot assignment schemes, and one fractional power control policy. New closed-form spectral efficiency (SE) expressions with maximum ratio (MR) combining are derived. The simulation results show that our proposed framework provides high SE when using local partial minimum mean-square error (LP-MMSE) and MR combining. Specifically, the proposed initial access algorithm and pilot assignment schemes outperform their corresponding benchmarks, P-LSFD achieves scalability with a negligible performance loss compared to the conventional optimal large-scale fading decoding (LSFD), and scalable fractional power control provides a controllable trade-off between user fairness and the average SE.
The topology of higher-order complexes associated with brain-function hubs in human connectomes
Date: 2020-06-18 00:00:00
Tasks: Hippocampus

Higher-order connectivity in complex systems described by simplexes of
different orders provides a geometry for simplex-based dynamical variables and
interactions. Simplicial complexes that constitute a functional geometry of the
human connectome can be crucial for the brain complex dynamics. In this
context, the best-connected brain areas, designated as hub nodes, play a
central role in supporting integrated brain function. Here, we study the
structure of simplicial complexes attached to eight global hubs in the female
and male connectomes and identify the core networks among the affected brain
regions. These eight hubs (Putamen, Caudate, Hippocampus and Thalamus-Proper in
the left and right cerebral hemisphere) are the highest-ranking according to
their topological dimension, defined as the number of simplexes of all orders
in which the node participates. Furthermore, we analyse the weight-dependent
heterogeneity of simplexes. We demonstrate changes in the structure of
identified core networks and topological entropy when the threshold weight is
gradually increased. These results highlight the role of higher-order
interactions in human brain networks and provide additional evidence for
(dis)similarity between the female and male connectomes.
Cell-Free Massive MIMO with Nonorthogonal Pilots for Internet of Things
Authors: Shilpa Rao, Alexei Ashikhmin, Hong Yang
Date: 2020-06-18 00:00:00

We consider Internet of Things (IoT) organized on the principles of cell-free massive MIMO. Since the number of things is very large, orthogonal pilots cannot be assigned to all of them even if the things are stationary. This results in an unavoidable pilot contamination problem, worsened by the fact that, for IoT, since the things are operating at very low transmit power. To mitigate this problem and achieve a high throughput, we use cell-free systems with optimal linear minimum mean squared error (LMMSE) channel estimation, while traditionally simple suboptimal estimators have been used in such systems. We further derive the analytical uplink and downlink signal-to-interference-plus-noise ratio (SINR) expressions for this scenario, which depends only on large scale fading coefficients. This allows us to design new power control algorithms that require only infrequent transmit power adaptation. Simulation results show a 40% improvement in uplink and downlink throughputs and 95% in energy efficiency over existing cell-free wireless systems and at least a three-fold uplink improvement over known IoT systems based on small-cell systems.
Some useful approximations for calculation of directivities of multibeam power patterns of large planar arrays
Authors: Javad Shabanpour, Homayoon Oraizi
Date: 2020-06-18 00:00:00
Tasks: Quantization

In this paper, by adopting the superposition of terms with unequal coefficients, some useful closed-form formulas for the class of large planar arrays are presented for predicting the directivities of radiated beams. Despite the applied simplifying assumptions, the provided formulas may be used for designing a large multiple-beam planar array with a specified power pattern. Several illustrative examples are presented which are numerically demonstrate by the MATLAB software. The applicability of the proposed synthesis method verified by the achieved conformity between the simulations and theoretical predictions. Furthermore, the impact of quantization and array dimensions on the performance of power controlling as well as the limitations on the maximum scan angle are investigated. It is believed that the proposed straightforward approach for array synthesis provides a new opportunity for various applications such as multiple-target radar systems and beamforming array antenna.
Body Travel Performance Improvement of Space Vehicle Electromagnetic Suspension System using LQG and LQI Control Methods
Authors: Mustefa Jibril, Messay Tadese, Eliyas Alemayehu Tadese
Date: 2020-06-18 00:00:00

Electromagnetic suspension system (EMS) is mostly used in the field of high-speed vehicle. In this paper, a space exploring vehicle quarter electromagnetic suspension system is modelled, designed and simulated using linear quadratic optimal control problem. Linear quadratic Gaussian and linear quadratic integral controllers are designed to improve the body travel of the vehicle using bump road profile. Comparison between the proposed controllers is done and a promising simulation result have been analyzed.
Design and Control of Steam Flow in Cement Production Process using Neural Network Based Controllers
Authors: Mustefa Jibril, Messay Tadese, Eliyas Alemayehu Tadese
Date: 2020-06-18 00:00:00

In this paper a NARMA L2, model reference and neural network predictive controller is utilized in order to control the output flow rate of the steam in furnace by controlling the steam flow valve. The steam flow control system is basically a feedback control system which is mostly used in cement production industries. The design of the system with the proposed controllers is done with Matlab/Simulink toolbox. The system is designed for the actual steam flow output to track the desired steam that is given to the system as input for two desired steam input signals (step and sine wave). In order to analyze the performance of the system, comparison of the proposed controllers is done by simulating the system for the two reference signals for the system with and without sensor noise disturbance. Finally the comparison results prove the effectiveness of the presented process control system with model reference controller.
Investment Disputes and Abnormal Volatility of Stocks
Authors: Jozef Barunik, Zdenek Drabek, Matej Nevrla
Date: 2020-06-18 00:00:00

Dramatic growth of investment disputes between foreign investors and host states rises serious questions about the impact of those disputes on investors. This paper is the first to explain increased uncertainty of investors about the outcome of arbitration, which may or may not lead to compensation for damages claimed by the investor. We find robust evidence that investment disputes lead to abnormal share fluctuations of companies involved in disputes with host countries. Importantly, while a positive outcome for an investor decreases uncertainty back to original levels, we document strong increase in the volatility of companies with negative outcome for the investor. We find that several variables including size of the award, political instability, location of arbitration, country of origin of investor or public policy considerations in host country explain large portion of the investor's uncertainty.
Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate
Date: 2020-07-30 00:00:00

In this paper, we estimate the time-varying COVID-19 contact rate of a
Susceptible-Infected-Recovered (SIR) model. Our measurement of the contact rate
is constructed using data on actively infected, recovered and deceased cases.
We propose a new trend filtering method that is a variant of the
Hodrick-Prescott (HP) filter, constrained by the number of possible kinks. We
term it the $\textit{sparse HP filter}$ and apply it to daily data from five
countries: Canada, China, South Korea, the UK and the US. Our new method yields
the kinks that are well aligned with actual events in each country. We find
that the sparse HP filter provides a fewer kinks than the $\ell_1$ trend
filter, while both methods fitting data equally well. Theoretically, we
establish risk consistency of both the sparse HP and $\ell_1$ trend filters.
Ultimately, we propose to use time-varying $\textit{contact growth rates}$ to
document and monitor outbreaks of COVID-19.
Converting Biomechanical Models from OpenSim to MuJoCo
Authors: Aleksi Ikkala, Perttu Hämäläinen
Date: 2020-06-17 00:00:00
Tasks: MuJoCo, reinforcement-learning, Reinforcement Learning (RL)

OpenSim is a widely used biomechanics simulator with several anatomically accurate human musculo-skeletal models. While OpenSim provides useful tools to analyse human movement, it is not fast enough to be routinely used for emerging research directions, e.g., learning and simulating motor control through deep neural networks and Reinforcement Learning (RL). We propose a framework for converting OpenSim models to MuJoCo, the de facto simulator in machine learning research, which itself lacks accurate musculo-skeletal human models. We show that with a few simple approximations of anatomical details, an OpenSim model can be automatically converted to a MuJoCo version that runs up to 600 times faster. We also demonstrate an approach to computationally optimize MuJoCo model parameters so that forward simulations of both simulators produce similar results.
A SIR model assumption for the spread of COVID-19 in different communities
Date: 2020-06-18 00:00:00

In this paper, we study the effectiveness of the modelling approach on the
pandemic due to the spreading of the novel COVID-19 disease and develop a
susceptible-infected-removed (SIR) model that provides a theoretical framework
to investigate its spread within a community. Here, the model is based upon the
well-known susceptible-infected-removed (SIR) model with the difference that a
total population is not defined or kept constant per se and the number of
susceptible individuals does not decline monotonically. To the contrary, as we
show herein, it can be increased in surge periods! In particular, we
investigate the time evolution of different populations and monitor diverse
significant parameters for the spread of the disease in various communities,
represented by countries and the state of Texas in the USA. The SIR model can
provide us with insights and predictions of the spread of the virus in
communities that the recorded data alone cannot. Our work shows the importance
of modelling the spread of COVID-19 by the SIR model that we propose here, as
it can help to assess the impact of the disease by offering valuable
predictions. Our analysis takes into account data from January to June, 2020,
the period that contains the data before and during the implementation of
strict and control measures. We propose predictions on various parameters
related to the spread of COVID-19 and on the number of susceptible, infected
and removed populations until September 2020. By comparing the recorded data
with the data from our modelling approaches, we deduce that the spread of
COVID-19 can be under control in all communities considered, if proper
restrictions and strong policies are implemented to control the infection rates
early from the spread of the disease.
Conflict in Africa during COVID-19: social distancing, food vulnerability and welfare response
Date: 2020-06-18 00:00:00

We study the effect of social distancing, food vulnerability, welfare and
labour COVID-19 policy responses on riots, violence against civilians and
food-related conflicts. Our analysis uses georeferenced data for 24 African
countries with monthly local prices and real-time conflict data reported in the
Armed Conflict Location and Event Data Project (ACLED) from January 2015 until
early May 2020. Lockdowns and recent welfare policies have been implemented in
light of COVID-19, but in some contexts also likely in response to ongoing
conflicts. To mitigate the potential risk of endogeneity, we use instrumental
variables. We exploit the exogeneity of global commodity prices, and three
variables that increase the risk of COVID-19 and efficiency in response such as
countries colonial heritage, male mortality rate attributed to air pollution
and prevalence of diabetes in adults. We find that the probability of
experiencing riots, violence against civilians, food-related conflicts and food
looting has increased since lockdowns. Food vulnerability has been a
contributing factor. A 10% increase in the local price index is associated with
an increase of 0.7 percentage points in violence against civilians.
Nonetheless, for every additional anti-poverty measure implemented in response
to COVID-19 the probability of experiencing violence against civilians, riots
and food-related conflicts declines by approximately 0.2 percentage points.
These anti-poverty measures also reduce the number of fatalities associated
with these conflicts. Overall, our findings reveal that food vulnerability has
increased conflict risks, but also offer an optimistic view of the importance
of the state in providing an extensive welfare safety net.
Bounded confidence dynamics and graph control: enforcing consensus
Date: 2020-06-18 00:00:00

A generic feature of bounded confidence type models is the formation of
clusters of agents. We propose and study a variant of bounded confidence
dynamics with the goal of inducing unconditional convergence to a consensus.
The defining feature of these dynamics, which we name the No one left behind
dynamics, is the introduction of a local control on the agents which preserves
the connectivity of the interaction network. We rigorously demonstrate that
these dynamics result in unconditional convergence to a consensus. The
qualitative nature of our argument prevents us quantifying how fast a consensus
emerges, however we present numerical evidence that sharp convergence rates
would be challenging to obtain for such dynamics. Finally, we propose a relaxed
version of the control. The dynamics that result maintain many of the
qualitative features of the bounded confidence dynamics yet ultimately still
converge to a consensus as the control still maintains connectivity of the
interaction network.
A Blind Beam Tracking Scheme for Millimeter Wave Systems
Authors: Steve Blandino, Thibault Bertrand, Claude Desset, Andre Bourdoux, Sofie Pollin
Date: 2020-06-19 00:00:00
Tasks: Position

Millimeter-wave is one of the technologies powering the new generation of wireless communication systems. To compensate the high path-loss, millimeter-wave devices need to use highly directional antennas. Consequently, beam misalignment causes strong performance degradation reducing the link throughput or even provoking a complete outage. Conventional solutions, e.g. IEEE 802.11ad, propose the usage of additional training sequences to track beam misalignment. These methods however introduce significant overhead especially in dynamic scenarios. In this paper we propose a beamforming scheme that can reduce this overhead. First, we propose an algorithm to design a codebook suitable for mobile scenarios. Secondly, we propose a blind beam tracking algorithm based on particle filter, which describes the angular position of the devices with a posterior density function constructed by particles. The proposed scheme reduces by more than 80% the overhead caused by additional training sequences.
Optimization of Wireless Relaying With Flexible UAV-Borne Reflecting Surfaces
Authors: Taniya Shafique, Hina Tabassum, Ekram Hossain
Date: 2020-06-19 00:00:00

This paper presents a theoretical framework to analyze the performance of integrated unmanned aerial vehicle (UAV)-intelligent reflecting surface (IRS) relaying system in which IRS provides an additional degree of freedom combined with the flexible deployment of full-duplex UAV to enhance communication between ground nodes. Our framework considers three different transmission modes: {\bf (i)} UAV-only mode, {\bf (ii)} IRS-only mode, and {\bf (iii)} integrated UAV-IRS mode to achieve spectral and energy-efficient relaying. For the proposed modes, we provide exact and approximate expressions for the end-to-end outage probability, ergodic capacity, and energy efficiency (EE) in closed-form. We use the derived expressions to optimize key system parameters such as the UAV altitude and the number of elements on the IRS considering different modes. We formulate the problems in the form of fractional programming (e.g. single ratio, sum of multiple ratios or maximization-minimization of ratios) and devise optimal algorithms using quadratic transformations. Furthermore, we derive an analytic criterion to optimally select different transmission modes to maximize ergodic capacity and EE for a given number of IRS elements. Numerical results validate the derived expressions with Monte-Carlo simulations and the proposed optimization algorithms with the solutions obtained through exhaustive search. Insights are drawn related to the different communication modes, optimal number of IRS elements, and optimal UAV height.
Cooling-Aware Resource Allocation and Load Management for Mobile Edge Computing Systems
Authors: Xiaojing Chen, Zhouyu Lu, Wei Ni, Xin Wang, Feng Wang, Shunqing Zhang, Shugong Xu
Date: 2020-06-19 00:00:00
Tasks: Edge-computing, Management

Driven by explosive computation demands of Internet of Things (IoT), mobile edge computing (MEC) provides a promising technique to enhance the computation capability for mobile users. In this paper, we propose a joint resource allocation and load management mechanism in an MEC system with wireless power transfer (WPT), by jointly optimizing the transmit power for WPT, the local/edge computing load, the offloading time, and the frequencies of the central processing units (CPUs) at the access point (AP) and the users. To achieve an energy-efficient and sustainable WPT-MEC system, we minimize the total energy consumption of the AP, while meeting computation latency requirements. Cooling energy which is non-negligible, is taken into account in minimizing the energy consumption of the MEC system. By rigorously orchestrating the state-of-the-art optimization techniques, we design an iterative algorithm and obtain the optimal solution in a semi-closed form. Based on the solution, interesting properties and insights are summarized. Extensive numerical tests show that the proposed algorithm can save up to 90.4% the energy of existing benchmarks.
Information-Driven Path Planning for UAV with Limited Autonomy in Large-scale Field Monitoring
Date: 2020-11-17 00:00:00
Tasks: State Estimation

This paper presents a novel information-based mission planner for a drone
tasked to monitor a spatially distributed dynamical phenomenon. For the sake of
simplicity, the area to be monitored is discretized. The insight behind the
proposed approach is that, thanks to the spatio-temporal dependencies of the
observed phenomenon, one does not need to collect data on the entire area. In
fact, unmeasured states can be estimated using an estimator, such as a Kalman
filter. In this context the planning problem becomes the one of generating a
flight path that maximizes the quality of the state estimation while satisfying
the flight constraints (e.g. flight time). The first result of this paper is to
formulate this problem as a special Orienteering Problem where the cost
function is a measure of the quality of the estimation. This approach provides
a Mixed-Integer Semi-Definite formulation to the problem which can be optimally
solved for small instances. For larger instances, two heuristics are proposed
which provide good sub-optimal results. To conclude, numerical simulations are
shown to prove the capabilities and efficiency of the proposed path planning
strategy. We believe this approach has the potential to increase dramatically
the area that a drone can monitor, thus increasing the number of applications
where monitoring with drones can become economically convenient.