pwc-archive/papers-with-abstracts huggingface dataset:
A Spatial Stochastic SIR Model for Transmission Networks with Application to COVID-19 Epidemic in China
Date: 2020-08-17 00:00:00

Governments around the world have implemented preventive measures against the
spread of the coronavirus disease (COVID-19). In this study, we consider a
multivariate discrete-time Markov model to analyze the propagation of COVID-19
across 33 provincial regions in China. This approach enables us to evaluate the
effect of mobility restriction policies on the spread of the disease. We use
data on daily human mobility across regions and apply the Bayesian framework to
estimate the proposed model. The results show that the spread of the disease in
China was predominately driven by community transmission within regions and the
lockdown policy introduced by local governments curbed the spread of the
pandemic. Further, we document that Hubei was only the epicenter of the early
epidemic stage. Secondary epicenters, such as Beijing and Guangdong, had
already become established by late January 2020, and the disease spread out to
connected regions. The transmission from these epicenters substantially
declined following the introduction of human mobility restrictions across
regions.
Reconfigurable and Intelligent Ultra-Wideband Angular Sensing: Prototype Design and Validation
Authors: Himani Joshi, Sumit J. Darak, Mohammad Alaee-Kerahroodi, Bhavani Shankar Mysore Rama Rao
Date: 2020-08-13 00:00:00
Tasks: Direction of Arrival Estimation

The emergence of beyond-licensed spectrum sharing in FR1 (0.45-6 GHz) and FR2 (24 - 52 GHz) along with the multi-antenna narrow-beam based directional transmissions demand a wideband spectrum sensing in temporal as well as spatial domains. We referred to it as ultra-wideband angular spectrum sensing (UWAS), and it consists of digitization followed by characterization of the wideband spectrum. In this paper, we design and develop state-of-the-art UWAS prototype using USRPs and LabVIEW NXG for the validation in the real-radio environment. Since 5G is expected to co-exist with LTE, the transmitter generates the multi-directional multi-user wideband traffic via LTE specific single carrier frequency division multiple access (SC-FDMA) approach. At the receiver, the first step of wideband spectrum digitization is accomplished using a novel approach of integrating sparse antenna-array with reconfigurable sub-Nyquist sampling (SNS). The reconfigurable SNS allows the digitization of non-contiguous spectrum via low-rate analog-to-digital converters, but it needs intelligence to choose the frequency bands for digitization. We explore the multi-play multi-armed bandit based learning algorithm to embed intelligence. Compared to previous works, the proposed characterization (frequency band status and direction-of-arrival estimation) approach does not need prior knowledge of received signal distribution. The detailed experimental results for various spectrum statistics, power gains and antenna array arrangements along with lower complexity validate the functional correctness, superiority and feasibility of the proposed UWAS over state-of-the-art approaches.
A Technical Overview of AV1
Date: 2020-08-13 00:00:00
Tasks: Video Compression

The AV1 video compression format is developed by the Alliance for Open Media
consortium. It achieves more than 30% reduction in bit-rate compared to its
predecessor VP9 for the same decoded video quality. This paper provides a
technical overview of the AV1 codec design that enables the compression
performance gains with considerations for hardware feasibility.
Conditions for indexability of restless bandits and an O(K^3) algorithm to compute Whittle index
Authors: Nima Akbarzadeh, Aditya Mahajan
Date: 2020-08-13 00:00:00

Restless bandits are a class of sequential resource allocation problems concerned with allocating one or more resources among several alternative processes where the evolution of the process depends on the resource allocated to them. Such models capture the fundamental trade-offs between exploration and exploitation. In 1988, Whittle developed an index heuristic for restless bandit problems which has emerged as a popular solution approach due to its simplicity and strong empirical performance. The Whittle index heuristic is applicable if the model satisfies a technical condition known as indexability. In this paper, we present two general sufficient conditions for indexability and identify simpler to verify refinements of these conditions. We then revisit a previously proposed algorithm called adaptive greedy algorithm which is known to compute the Whittle index for a subclass of restless bandits. We show that a generalization of the adaptive greedy algorithm computes the Whittle index for all indexable restless bandits. We present an efficient implementation of this algorithm which can compute the Whittle index of a restless bandit with $K$ states in O(K^3) computations. Finally, we present a detailed numerical study which affirms the strong performance of the Whittle index heuristic.
Energy-Efficient Control Adaptation with Safety Guarantees for Learning-Enabled Cyber-Physical Systems
Date: 2020-10-15 00:00:00
Tasks: Deep Reinforcement Learning

Neural networks have been increasingly applied for control in
learning-enabled cyber-physical systems (LE-CPSs) and demonstrated great
promises in improving system performance and efficiency, as well as reducing
the need for complex physical models. However, the lack of safety guarantees
for such neural network based controllers has significantly impeded their
adoption in safety-critical CPSs. In this work, we propose a controller
adaptation approach that automatically switches among multiple controllers,
including neural network controllers, to guarantee system safety and improve
energy efficiency. Our approach includes two key components based on formal
methods and machine learning. First, we approximate each controller with a
Bernstein-polynomial based hybrid system model under bounded disturbance, and
compute a safe invariant set for each controller based on its corresponding
hybrid system. Intuitively, the invariant set of a controller defines the state
space where the system can always remain safe under its control. The union of
the controllers' invariants sets then define a safe adaptation space that is
larger than (or equal to) that of each controller. Second, we develop a deep
reinforcement learning method to learn a controller switching strategy for
reducing the control/actuation energy cost, while with the help of a safety
guard rule, ensuring that the system stays within the safe space. Experiments
on a linear adaptive cruise control system and a non-linear Van der Pol's
oscillator demonstrate the effectiveness of our approach on energy saving and
safety enhancement.
Bounding Infection Prevalence by Bounding Selectivity and Accuracy of Tests: With Application to Early COVID-19
Authors: Jörg Stoye
Date: 2020-08-14 00:00:00

I propose novel partial identification bounds on infection prevalence from information on test rate and test yield. The approach utilizes user-specified bounds on (i) test accuracy and (ii) the extent to which tests are targeted, formalized as restriction on the effect of true infection status on the odds ratio of getting tested and thereby embeddable in logit specifications. The motivating application is to the COVID-19 pandemic but the strategy may also be useful elsewhere. Evaluated on data from the pandemic's early stage, even the weakest of the novel bounds are reasonably informative. Notably, and in contrast to speculations that were widely reported at the time, they place the infection fatality rate for Italy well above the one of influenza by mid-April.
Generalized Radio Environment Monitoring for Next Generation Wireless Networks
Authors: Halise Turkmen, Muhammad Sohaib J. Solaija, Haji M. Furqan, Huseyin Arslan
Date: 2020-08-14 00:00:00
Tasks: Intelligent Communication

Enabling technologies of 5G and beyond wireless communication networks, such as millimeter-wave communication, beamforming, and multiple-input multiple-output (MIMO) antenna systems, are becoming increasingly dependent on accurate information of the physical environment for optimized communication performance. The acquisition of this information is a significant challenge, which can be eased by knowledge of the radio environment. Radio environment mapping (REM) has long been considered an enabler of cognitive radios (CRs). However, the limitations of radios effectively confined the application of REM to spectrum sensing and interference mapping. With the advent of more capable software-defined radios and advanced networks, the idea of a truly intelligent communication system empowered by enhanced REM can be realized. To this effect, we propose the generalized radio environment monitoring (G-REM) concept to include all aspects of the radio environment. We outline a general, self contained framework which enables radio environment monitoring in standalone devices. The presented G-REM framework comprises of different information sources, sensing methods, sensing modes, and mapping techniques to enable the realization of more reliable, secure, efficient, and faster future wireless networks. The accompanying challenges and future research direction are also provided.
Performance Evaluation of Reconfigurable Intelligent Surface Assisted D-band Wireless Communication
Authors: Evangelos N. Papasotiriou, Alexandros-Apostolos A. Boulogeorgos, Antigone Stratakou, Angeliki Alexiou
Date: 2020-08-14 00:00:00

In the recent years, the proliferation of wireless data traffic has led the scientific community to explore the use of higher unallocated frequency bands, such as the millimeter wave and terahertz (0.1-10 THz) bands. However, they are prone to blockages from obstacles laid in the transceiver path. To address this, in this work, the use of a reconfigurable-intelligent-surface (RIS) to restore the link between a transmitter (TX) and a receiver (RX), operating in the D-band (110-170 GHz) is investigated. The system performance is evaluated in terms of pathgain and capacity considering the RIS design parameters, the TX/RX-RIS distance and the elevation angles from the center of the RIS to the transceivers.
Secret Key Generation for Intelligent Reflecting Surface Assisted Wireless Communication Networks
Authors: Zijie Ji, Phee Lep Yeoh, Deyou Zhang, Gaojie Chen, Yan Zhang, Zunwen He, Hao Yin, Yonghui Li
Date: 2020-08-14 00:00:00

We propose and analyze secret key generation using intelligent reflecting surface (IRS) assisted wireless communication networks. To this end, we first formulate the minimum achievable secret key capacity for an IRS acting as a passive beamformer in the presence of multiple eavesdroppers. Next, we develop an optimization framework for the IRS reflecting coefficients based on the secret key capacity lower bound. To derive a tractable and efficient solution, we design and analyze a semidefinite relaxation (SDR) and successive convex approximation (SCA) based algorithm for the proposed optimization. Simulation results show that employing our IRS-based algorithm can significantly improve the secret key generation capacity for a wide-range of wireless channel parameters.
Resilient Abstraction-Based Controller Design
Date: 2020-08-14 00:00:00

We consider the computation of resilient controllers for perturbed non-linear
dynamical systems w.r.t. linear-time temporal logic specifications. We address
this problem through the paradigm of Abstraction-Based Controller Design (ABCD)
where a finite state abstraction of the perturbed system dynamics is
constructed and utilized for controller synthesis. In this context, our
contribution is twofold: (I) We construct abstractions which model the impact
of occasional high disturbance spikes on the system via so called disturbance
edges. (II) We show that the application of resilient reactive synthesis
techniques to these abstract models results in closed loop systems which are
optimally resilient to these occasional high disturbance spikes. We have
implemented this resilient ABCD workflow on top of SCOTS and showcase our
method through multiple robot planning examples.
Generalized Coordinated Multipoint Framework for 5G and Beyond
Authors: Muhammad Sohaib J. Solaija, Hanadi Salman, Abuu B. Kihero, Mehmet Izzet Saglam, Huseyin Arslan
Date: 2020-08-14 00:00:00
Tasks: Diversity

The characteristic feature of 5G is the diversity of its services for different user needs. However, the requirements for these services are competing in nature, which impresses the necessity of a coordinated and flexible network architecture. Although coordinated multipoint (CoMP) systems were primarily proposed to improve the cell edge performance in 4G, their collaborative nature can be leveraged to support the diverse requirements and enabling technologies of 5G and beyond networks. To this end, we propose generalization of CoMP to a proactive and efficient resource utilization framework capable of supporting different user requirements such as reliability, latency, throughput, and security while considering network constraints. This article elaborates on the multiple aspects, inputs, and outputs of the generalized CoMP (GCoMP) framework. Apart from user requirements, the GCoMP decision mechanism also considers the CoMP scenario and network architecture to decide upon outputs such as CoMP technique or appropriate coordinating clusters. To enable easier understanding of the concept, popular use cases, such as vehicle-to-everything (V2X) communication and eHealth, are studied. Additionally, interesting challenges and open areas in GCoMP are discussed.
An empirical model on the dynamics of Covid-19 spread in human population
Date: 2020-08-13 00:00:00

We propose a mathematical model to analyze the time evolution of the total
number of infected population with Covid-19 disease at a region in the ongoing
pandemic. Using the available data of Covid-19 infected population on various
countries we formulate a model which can successfully track the time evolution
from early days to the saturation period in a given wave of this infectious
disease. It involves a set of effective parameters which can be extracted from
the available data. Using those parameters the future trajectories of the
disease spread can also be projected. A set of differential equations is also
proposed whose solutions are these time evolution trajectories. Using such a
formalism we project the future time evolution trajectories of infection spread
for a number of countries where the Covid-19 infection is still rapidly rising.
A Data-Driven Control-Theoretic Paradigm for Pandemic Mitigation with Application to Covid-19
Date: 2020-08-14 00:00:00
Tasks: Epidemiology

In this paper, we introduce a new control-theoretic paradigm for mitigating
the spread of a virus. To this end, our discrete-time controller, aims to
reduce the number of new daily deaths, and consequently, the cumulative number
of deaths. In contrast to much of the existing literature, we do not rely on a
potentially complex virus transmission model whose equations must be customized
to the "particulars" of the pandemic at hand. For new viruses such as Covid-19,
the epidemiology driving the modelling process may not be well known and model
estimation with limited data may be unreliable. With this motivation in mind,
the new paradigm described here is data-driven and, to a large extent, we avoid
modelling difficulties by concentrating on just two key quantities which are
common to pandemics: the doubling time, denoted by $d(k)$ and the peak day
denoted by $\theta(k)$. Our numerical studies to date suggest that our
appealingly simple model can provide a reasonable fit to real data. Given that
time is of the essence during the ongoing global health crisis, the intent of
this paper is to introduce this new paradigm to control practitioners and
describe a number of new research directions suggested by our current results.
A study of uncompensated latency in ADS-B reports
Authors: Michelle George, Saurav Tuladhar, Siva Sivananthan
Date: 2020-08-14 00:00:00
Tasks: Position

The total latency (TL) in ADS-B is the difference between the true TOA of the original position measured by the onboard GPS and the TOA in the ADS-B report at the GS. We processed the ADS-B reports extracted from live-data recorded at the Chicago to produce the results in this report. We observed that 99% of aircraft in our data set were broadcasting in non-UTC coupled mode. For these aircraft, STARS exclusively used ADS-B reports to update tracks when available. The calculated ULs for the non-UTC coupled ADS-B reports were well within the budget (-200 ms to +400 ms) allowed by the DO-260B MOPS. Moreover, the mean latencies from both methods were less than +/-50 ms. The latencies obtained from the MTPES method were observed to be in close agreement with the means from the ATPE method. About 1% of aircraft were broadcasting in UTC coupled mode. The reports from these aircraft were identified to have a non-compliant link version, causing STARS to reject these reports for tracking. Moreover, the reported position and TOA were found to be unsynchronized, which conflicts with the MOPS requirement for UTC coupled operation.
Gait complexity assessed by detrended fluctuation analysis is sensitive to inconsistencies in stride time series: A modeling study
Date: 2020-08-14 00:00:00
Tasks: Time Series, Time Series Analysis

Background: Human gait exhibits complex fractal fluctuations among
consecutive strides. The time series of gait parameters are long-range
correlated (statistical persistence). In contrast, when gait is synchronized
with external rhythmic cues, the fluctuation regime is modified to stochastic
oscillations around the target frequency (statistical anti-persistence). To
highlight these two fluctuation modes, the prevalent methodology is the
detrended fluctuation analysis (DFA). The DFA outcome is the scaling exponent,
which lies between 0.5 and 1 if the time series exhibit long-range
correlations, and below 0.5 if the time series is anti-correlated. A
fundamental assumption for applying DFA is that the analyzed time series
results from a time-invariant generating process. However, a gait time series
may be constituted by an ensemble of sub-segments with distinct fluctuation
regimes (e.g., correlated and anti-correlated). Methods: Several proportions of
correlated and anti-correlated time series were mixed together and then
analyzed through DFA. The original (before mixing) time series were generated
via autoregressive fractionally integrated moving average (ARFIMA) modelling or
actual gait data. Results: Results evidenced a nonlinear sensitivity of DFA to
the mix of correlated and anti-correlated series. Notably, adding a small
proportion of correlated segments into an anti-correlated time series had
stronger effects than the reverse. Significance: In case of changes in gait
control during a walking trial, the resulting time series may be a patchy
ensemble of several fluctuation regimes. When applying DFA, the scaling
exponent may be misinterpreted. Cued walking studies may be most at risk of
suffering this issue in cases of sporadic synchronization with external cues.
A Centralized and Scalable Uplink Power Control Algorithm in Low SINR Scenarios
Authors: Xuesong Cai, István Kovács, Jeroen Wigard, Preben Mogensen
Date: 2020-08-14 00:00:00

Power control is becoming increasingly essential for the fifth-generation (5G) and beyond systems. An example use-case, among others, is the unmanned-aerial-vehicle (UAV) communications where the nearly line-of-sight (LoS) radio channels may result in very low signal-to-interference-plus-noise ratios (SINRs). Investigations in [1] proposed to efficiently and reliably solve this kind of non-convex problem via a series of geometrical programmings (GPs) using condensation approximation. However, it is only applicable for a small-scale network with several communication pairs and practically infeasible with more (e.g. tens of) nodes to be jointly optimized. We therefore in this paper aim to provide new insights into this problem. By properly introducing auxiliary variables, the problem is transformed to an equivalent form which is simpler and more intuitive for condensation. A novel condensation method with linear complexity is also proposed based on the form. The enhancements make the GP-based power control feasible for both small-and especially large-scale networks that are common in 5G and beyond. The algorithm is verified via simulations. A preliminary case study of uplink UAV communications also shows the potential of the algorithm.
Pulsed Waveforms and Intermittently Nonlinear Filtering in Synthesis of Low-SNR and Covert Communications
Authors: Alexei V. Nikitin, Ruslan L. Davidchack
Date: 2020-08-13 00:00:00

In traditional spread-spectrum techniques, a wideband transmit signal is obtained by modulating a wideband carrier by a narrowband signal containing a relatively low-rate message. In the receiver, the respective demodulation/despreading restores the information-carrying narrowband signal. In this paper, we introduce an alternative approach, where the low-rate information is encoded directly into a wideband waveform of a given bandwidth, without physical "spreading" of the carrier's frequency. The main advantages of this approach lie in extended options for encoding the information, and in retaining a reversible control over the temporal and amplitude structures of the modulating wideband waveforms. Significant "excess bandwidth" (over that needed to carry the information) enables us to use allpass filters to manage statistical properties and time-domain appearances of these waveforms without changing their spectral composition. For example, a mixture of transmitted waveforms can be shaped as a low-crest-factor signal (e.g. to reduce the burden on the power amplifier), and/or made statistically indistinguishable from Gaussian noise (e.g. for covert transmissions and physical layer steganography), while the selected components of the received waveform can be transformed into high-crest-factor pulse trains suitable for multiplexing and/or low-SNR communications. Further, control over the temporal and amplitude structures of wideband waveforms carrying low-rate information enables effective use of nonlinear filtering techniques. Such techniques can be employed for robust real-time asynchronous extraction of the information, as well as for separation of wideband signal components with identical spectral content from each other. This can facilitate development of a large variety of low-SNR and covert communication configurations.
Short Term Stress of Covid-19 On World Major Stock Indices
Authors: Muhammad Rehan, Jahanzaib Alvi, Suleyman Serdar Karaca
Date: 2020-08-14 00:00:00
Tasks: Clustering

The main objective of this study is to check short term stress of COVID-19 on the American, European, Asian, and Pacific stock market indices, furthermore, the correlation between all the stock markets during the pandemic. Secondary data of 41 stock exchange from 32 countries have been collected from investing.com website from 1st July 2019 to 14th May 2020 for the stock market and the COVID-19 data has been collected according to the first cases reported in the country, stocks market are classified either developed or emerging economy, further divided according to the subcontinent i.e. America, Europe, and Pacific/Asia, the main focus in the data is the report of first COVID-19 cases. The study reveals that there is volatility in the all the 41 stock market (American, Europe, Asia, and Pacific) after reporting of the first case and volatility increase with the increase of COVID-19 cases, moreover, there is a significant negative relationship between the number of COVID-19 cases and 41 major stock indices of American, Europe, Asia and Pacific, European subcontinent market found more effected from the COVID-19 than another subcontinent, there is Clustering effect of COVID-19 on all the stock market except American's stock market due to smart capital investing.