pwc-archive/papers-with-abstracts huggingface dataset:
Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective
Date: 2020-04-14 00:00:00

We provide quantitative predictions of first order supply and demand shocks
for the U.S. economy associated with the COVID-19 pandemic at the level of
individual occupations and industries. To analyze the supply shock, we classify
industries as essential or non-essential and construct a Remote Labor Index,
which measures the ability of different occupations to work from home. Demand
shocks are based on a study of the likely effect of a severe influenza epidemic
developed by the US Congressional Budget Office. Compared to the pre-COVID
period, these shocks would threaten around 22% of the US economy's GDP,
jeopardise 24% of jobs and reduce total wage income by 17%. At the industry
level, sectors such as transport are likely to have output constrained by
demand shocks, while sectors relating to manufacturing, mining and services are
more likely to be constrained by supply shocks. Entertainment, restaurants and
tourism face large supply and demand shocks. At the occupation level, we show
that high-wage occupations are relatively immune from adverse supply and
demand-side shocks, while low-wage occupations are much more vulnerable. We
should emphasize that our results are only first-order shocks -- we expect them
to be substantially amplified by feedback effects in the production network.
Channel Hardening in Massive MIMO: Model Parameters and Experimental Assessment
Date: 2020-04-14 00:00:00

Reliability is becoming increasingly important for many applications
envisioned for future wireless systems. A technology that could improve
reliability in these systems is massive MIMO (Multiple-Input Multiple-Output).
One reason for this is a phenomenon called channel hardening, which means that
as the number of antennas in the system increases, the variations of channel
gain decrease in both the time- and frequency domain. Our analysis of channel
hardening is based on a joint comparison of theory, measurements and
simulations. Data from measurement campaigns including both indoor and outdoor
scenarios, as well as cylindrical and planar base station arrays, are analyzed.
The simulation analysis includes a comparison with the COST 2100 channel model
with its massive MIMO extension. The conclusion is that the COST 2100 model is
well suited to represent real scenarios, and provides a reasonable match to
actual measurements up to the uncertainty of antenna patterns and user
interaction. Also, the channel hardening effect in practical massive MIMO
channels is less pronounced than in complex independent and identically
distributed (i.i.d.) Gaussian channels, which are often considered in
theoretical work.
Exact Simulation of Variance Gamma related OU processes: Application to the Pricing of Energy Derivatives
Authors: Piergiacomo Sabino
Date: 2020-04-14 00:00:00

In this study we define a three-step procedure to relate the self-decomposability of the stationary law of a generalized Ornstein-Uhlenbeck process to the law of the increments of such processes. Based on this procedure and the results of Qu et al. (2019), we derive the exact simulation, without numerical inversion, of the skeleton of a Variance Gamma, and of a symmetric Variance Gamma driven Ornstein-Uhlenbeck process. Extensive numerical experiments are reported to demonstrate the accuracy and efficiency of our algorithms. These results are instrumental to simulate the spot price dynamics in energy markets and to price Asian options and gas storages by Monte Carlo simulations in a framework similar to the one discussed in Cummins et al. (2017, 2018).
Fast Variable Density Poisson-Disc Sample Generation with Directional Variation
Authors: Nicholas Dwork, Corey A. Baron, Ethan M. I. Johnson, Daniel O'Connor, John M. Pauly, Peder E. Z. Larson
Date: 2020-04-14 00:00:00
Tasks: compressed sensing

We present a fast method for generating random samples according to a variable density Poisson-disc distribution. A minimum threshold distance is used to create a background grid array for keeping track of those points that might affect any new candidate point; this reduces the number of conflicts that must be checked before acceptance of a new point, thus reducing the number of computations required. We demonstrate the algorithm's ability to generate variable density Poisson-disc sampling patterns according to a parameterized function, including patterns where the variations in density are a function of direction. We further show that these sampling patterns are appropriate for compressed sensing applications. Finally, we present a method to generate patterns with a specific acceleration rate.
Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data
Authors: Saúl Langarica, Felipe Núñez
Date: 2020-04-14 00:00:00
Tasks: Denoising, Self-Supervised Learning, Time Series, Time Series Analysis

In an industrial IoT setting, ensuring the quality of sensor data is a must when data-driven algorithms operate on the upper layers of the control system. Unfortunately, the common place in industrial facilities is to find sensor time series heavily corrupted by noise and outliers. In this work, a purely data-driven self-supervised learning-based approach based on a blind denoising autoencoder is proposed for real time denoising of industrial sensor data. The term \textit{blind} stresses that no prior knowledge about the noise is required for denoising, in contrast to typical denoising autoencoders where prior knowledge about the noise is required. Blind denoising is achieved by using a noise contrastive estimation (NCE) regularization on the latent space of the autoencoder, which not only helps to denoise but also induces a meaningful and smooth latent space. Experimental evaluation in both a simulated system and a real industrial process shows that the proposed technique outperforms classical denoising methods.
A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving
Date: 2020-04-15 00:00:00

In this paper, we develop a multivariate evolutionary generalised linear
model (GLM) framework for claims reserving, which allows for dynamic features
of claims activity in conjunction with dependency across business lines to
accurately assess claims reserves. We extend the traditional GLM reserving
framework on two fronts: GLM fixed factors are allowed to evolve in a recursive
manner, and dependence is incorporated in the specification of these factors
using a common shock approach.
  We consider factors that evolve across accident years in conjunction with
factors that evolve across calendar years. This two-dimensional evolution of
factors is unconventional as a traditional evolutionary model typically
considers the evolution in one single time dimension. This creates challenges
for the estimation process, which we tackle in this paper. We develop the
formulation of a particle filtering algorithm with parameter learning
procedure. This is an adaptive estimation approach which updates evolving
factors of the framework recursively over time.
  We implement and illustrate our model with a simulated data set, as well as a
set of real data from a Canadian insurer.
A Model-Driven Deep Learning Method for Massive MIMO Detection
Date: 2020-04-15 00:00:00
Tasks: Deep Learning

In this paper, an efficient massive multiple-input multiple-output (MIMO)
detector is proposed by employing a deep neural network (DNN). Specifically, we
first unfold an existing iterative detection algorithm into the DNN structure,
such that the detection task can be implemented by deep learning (DL) approach.
We then introduce two auxiliary parameters at each layer to better cancel
multiuser interference (MUI). The first parameter is to generate the residual
error vector while the second one is to adjust the relationship among previous
layers. We further design the training procedure to optimize the auxiliary
parameters with pre-processed inputs. The so derived MIMO detector falls into
the category of model-driven DL. The simulation results show that the proposed
MIMO detector can achieve preferable detection performance compared to the
existing detectors for massive MIMO systems.
Probabilistic reachable and invariant sets for linear systems with correlated disturbance
Date: 2020-04-15 00:00:00

In this paper a constructive method to determine and compute probabilistic
reachable and invariant sets for linear discrete-time systems, excited by a
stochastic disturbance, is presented. The samples of the disturbance signal are
not assumed to be uncorrelated, only a bound on the correlation matrices is
supposed to be known. The concept of correlation bound is introduced and
employed to determine probabilistic reachable sets and probabilistic invariant
sets. Constructive methods for their computation, based on convex optimization,
are given.
An analytical study of participating policies with minimum rate guarantee and surrender option
Authors: Maria B. Chiarolla, Tiziano De Angelis, Gabriele Stabile
Date: 2020-04-15 00:00:00

We perform a detailed theoretical study of the value of a class of participating policies with four key features: $(i)$ the policyholder is guaranteed a minimum interest rate on the policy reserve; $(ii)$ the contract can be terminated by the holder at any time until maturity (surrender option); $(iii)$ at the maturity (or upon surrender) a bonus can be credited to the holder if the portfolio backing the policy outperforms the current policy reserve; $(iv)$ due to solvency requirements the contract ends if the value of the underlying portfolio of assets falls below the policy reserve. Our analysis is probabilistic and it relies on optimal stopping and free boundary theory. We find a structure of the optimal surrender strategy which was undetected by previous (mostly numerical) studies on the same topic. Optimal surrender of the contract is triggered by two `stop-loss' boundaries and by a `too-good-to-persist' boundary (in the language of \cite{EV20}). Financial implications of this strategy are discussed in detail and supported by extensive numerical experiments.
Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making
Authors: Jonathan Sadighian
Date: 2020-04-15 00:00:00
Tasks: Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

There has been a recent surge in interest in the application of artificial intelligence to automated trading. Reinforcement learning has been applied to single- and multi-instrument use cases, such as market making or portfolio management. This paper proposes a new approach to framing cryptocurrency market making as a reinforcement learning challenge by introducing an event-based environment wherein an event is defined as a change in price greater or less than a given threshold, as opposed to by tick or time-based events (e.g., every minute, hour, day, etc.). Two policy-based agents are trained to learn a market making trading strategy using eight days of training data and evaluate their performance using 30 days of testing data. Limit order book data recorded from Bitmex exchange is used to validate this approach, which demonstrates improved profit and stability compared to a time-based approach for both agents when using a simple multi-layer perceptron neural network for function approximation and seven different reward functions.
Latency-Aware Offloading in Integrated Satellite Terrestrial Networks
Date: 2020-04-15 00:00:00

Next-generation communication networks are expected to integrate newly-used
technologies in a smart way to ensure continuous connectivity in rural areas
and to alleviate the traffic load in dense regions. The prospective access
network in 6G should hinge on satellite systems to take advantage of their wide
coverage and high capacity. However, adopting satellites in 6G could be
hindered because of the additional latency introduced, which is not tolerable
by all traffic types. Therefore, we propose a traffic offloading scheme that
integrates both the satellite and terrestrial networks to smartly allocate the
traffic between them while satisfying different traffic requirements.
Specifically, the proposed scheme offloads the Ultra-Reliable Low Latency
Communication (URLLC) traffic to the terrestrial backhaul to satisfy its
stringent latency requirement. However, it offloads the enhanced Mobile
Broadband (eMBB) traffic to the satellite since eMBB needs high data rates but
is not always sensitive to delay. Our scheme is shown to reduce the
transmission delay of URLLC packets, decrease the number of dropped eMBB
packets, and hence improve the network's availability. Our findings highlight
that the inter-working between satellite and terrestrial networks is crucial to
mitigate the expected high load on the limited terrestrial capacity.
Relative Positioning of Autonomous Systems using Signals of Opportunity
Authors: Nicolas Souli, Panayiotis Kolios, Georgios Ellinas
Date: 2020-04-15 00:00:00

For reliable operation, next generation autonomous agents will need enhanced situational perception as well as precise navigation capabilities. The global navigation satellite system (GNSS) signals that are utilized by practically all modern positioning systems cannot satisfy this requirement for heighten autonomy levels and positioning is becoming a decisive factor for their proliferation. This work investigates how relative positioning can be achieved using signals that are already accessible in the environment, and derives an online procedure for the exploitation of these signals for localization in GNSS-challenged areas. The proposed relative positioning system (RPS) explores the signal properties over a large spectrum of frequency bands, and derives a vehicle tracking algorithm to accurately estimate the vehicle's trajectory in space and time using an arbitrary set of unknown reference positions. Experimental results demonstrate the applicability of RPS and investigate its performance over the different parameter values.
HLA predictions from the bronchoalveolar lavage fluid samples of five patients at the early stage of the Wuhan seafood market COVID-19 outbreak
Authors: Rene L Warren, Inanc Birol
Date: 2020-04-15 00:00:00

We are in the midst of a global viral pandemic, one with no cure and a high mortality rate. The Human Leukocyte Antigen (HLA) gene complex plays a critical role in host immunity. We predicted HLA class I and II alleles from the transcriptome sequencing data prepared from the bronchoalveolar lavage fluid samples of five patients at the early stage of the COVID-19 outbreak. We identified the HLA-I allele A*24:02 in four out of five patients, which is higher than the expected frequency (17.2%) in the South Han Chinese population. The difference is statistically significant with a p-value less than $10^{-4}$. Our analysis results may help provide future insights on disease susceptibility.
The Road to 6G: Ten Physical Layer Challenges for Communications Engineers
Authors: Michail Matthaiou, Okan Yurduseven, Hien Quoc Ngo, David Morales-Jimenez, Simon L. Cotton, Vincent F. Fusco
Date: 2020-04-15 00:00:00

While the deployment of 5G cellular systems will continue well in to the next decade, much interest is already being generated towards technologies that will underlie its successor, 6G. Undeniably, 5G will have transformative impact on the way we live and communicate, yet, it is still far away from supporting the Internet-of-Everything (IoE), where upwards of a million devices per $\textrm{km}^3$ (both terrestrial and aerial) will require ubiquitous, reliable, low-latency connectivity. This article looks at some of the fundamental problems that pertain to key physical layer enablers for 6G. This includes highlighting challenges related to intelligent reflecting surfaces, cell-free massive MIMO and THz communications. Our analysis covers theoretical modeling challenges, hardware implementation issues and scalability among others. The article concludes by delineating the critical role of signal processing in the new era for wireless communications.
From code to market: Network of developers and correlated returns of cryptocurrencies
Authors: Lorenzo Lucchini, Laura Alessandretti, Bruno Lepri, Angela Gallo, Andrea Baronchelli
Date: 2020-04-15 00:00:00

"Code is law" is the funding principle of cryptocurrencies. The security, transferability, availability and other properties of a crypto-asset are determined by the code through which it is created. If code is open source, as it happens for most cryptocurrencies, this principle would prevent manipulations and grant transparency to users and traders. However, this approach considers cryptocurrencies as isolated entities thus neglecting possible connections between them. Here, we show that 4% of developers contribute to the code of more than one cryptocurrency and that the market reflects these cross-asset dependencies. In particular, we reveal that the first coding event linking two cryptocurrencies through a common developer leads to the synchronisation of their returns in the following months. Our results identify a clear link between the collaborative development of cryptocurrencies and their market behaviour. More broadly, our work reveals a so-far overlooked systemic dimension for the transparency of code-based ecosystems and we anticipate it will be of interest to researchers, investors and regulators.
Edge Computing For Smart Health: Context-aware Approaches, Opportunities, and Challenges
Date: 2020-04-15 00:00:00
Tasks: Data Compression, Edge-computing, Event Detection

Improving efficiency of healthcare systems is a top national interest
worldwide. However, the need of delivering scalable healthcare services to the
patients while reducing costs is a challenging issue. Among the most promising
approaches for enabling smart healthcare (s-health) are edge-computing
capabilities and next-generation wireless networking technologies that can
provide real-time and cost-effective patient remote monitoring. In this paper,
we present our vision of exploiting multi-access edge computing (MEC) for
s-health applications. We envision a MEC-based architecture and discuss the
benefits that it can bring to realize in-network and context-aware processing
so that the s-health requirements are met. We then present two main
functionalities that can be implemented leveraging such an architecture to
provide efficient data delivery, namely, multimodal data compression and
edge-based feature extraction for event detection. The former allows efficient
and low distortion compression, while the latter ensures high-reliability and
fast response in case of emergency applications. Finally, we discuss the main
challenges and opportunities that edge computing could provide and possible
directions for future research.
Visual explanation of country specific differences in Covid-19 dynamics
Date: 2020-04-15 00:00:00

This report provides a visual examination of Covid-19 case and death data. In
particular, it shows that country specific differences can too a large extend
be explained by two easily interpreted parameters. Namely, the delay between
reported cases and deaths and the fraction of cases observed. Furthermore, this
allows to lower bound the actual total number of people already infected.
Mathematical modeling of blood flow through arterial bifurcation
Authors: Tahmineh Azizi, Robert Mugabi
Date: 2020-03-31 00:00:00

The blood vascular system consists of blood vessels such as arteries, arterioles, capillaries and veins that convey blood throughout the body. The pressure difference which exists between the ends of the vessels provides the living force required to the flow of blood. In this study, we have used a model that is appropriate for use with blood flow simulation applications. We study the effect of decomposition of plaques and or tumour cells on the velocity profile, pressure of the incompressible, Newtonian fluid. We consider two different cases; vessels with bifurcation and without bifurcations and we display all the results through using Commercial software COMSOL Multiphysics 5.2.
Nucleus I: Adjunction spectra in recommender systems and descent
Authors: Dusko Pavlovic, Dominic J. D. Hughes
Date: 2020-04-15 00:00:00
Tasks: Recommendation Systems

Recommender systems build user profiles using concept analysis of usage matrices. The concepts are mined as spectra and form Galois connections. Descent is a general method for spectral decomposition in algebraic geometry and topology which also leads to generalized Galois connections. Both recommender systems and descent theory are vast research areas, separated by a technical gap so large that trying to establish a link would seem foolish. Yet a formal link emerged, all on its own, bottom-up, against authors' intentions and better judgment. Familiar problems of data analysis led to a novel solution in category theory. The present paper arose from a series of earlier efforts to provide a top-down account of these developments.
Decentralized Gaussian Filters for Cooperative Self-localization and Multi-target Tracking
Date: 2020-04-15 00:00:00

Scalable and decentralized algorithms for Cooperative Self-localization (CS)
of agents, and Multi-Target Tracking (MTT) are important in many applications.
In this work, we address the problem of Simultaneous Cooperative
Self-localization and Multi-Target Tracking (SCS-MTT) under target data
association uncertainty, i.e., the associations between measurements and target
tracks are unknown. Existing CS and tracking algorithms either make the
assumption of no data association uncertainty or employ a hard-decision rule
for measurement-to-target associations. We propose a novel decentralized
SCS-MTT method for an unknown and time-varying number of targets under
association uncertainty. Marginal posterior densities for agents and targets
are obtained by an efficient belief propagation (BP) based scheme while data
association is handled by marginalizing over all target-to-measurement
association probabilities. Decentralized single Gaussian and Gaussian mixture
implementations are provided based on average consensus schemes, which require
communication only with one-hop neighbors. An additional novelty is a
decentralized Gibbs mechanism for efficient evaluation of the product of
Gaussian mixtures. Numerical experiments show the improved CS and MTT
performance compared to the conventional approach of separate localization and
target tracking.