pwc-archive/papers-with-abstracts huggingface dataset:
Tracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption
Date: 2019-10-16 00:00:00

People are living longer than ever due to advances in healthcare, and this
has prompted many healthcare providers to look towards remote patient care as a
means to meet the needs of the future. It is now a priority to enable people to
reside in their own homes rather than in overburdened facilities whenever
possible. The increasing maturity of IoT technologies and the falling costs of
connected sensors has made the deployment of remote healthcare at scale an
increasingly attractive prospect. In this work we demonstrate that we can
measure the consistency and regularity of the behaviour of a household using
sensor readings generated from interaction with the home environment. We show
that we can track changes in this behaviour regularity longitudinally and
detect changes that may be related to significant life events or trends that
may be medically significant. We achieve this using periodicity analysis on
water usage readings sampled from the main household water meter every 15
minutes for over 8 months. We utilise an IoT Application Enablement Platform in
conjunction with low cost LoRa-enabled sensors and a Low Power Wide Area
Network in order to validate a data collection methodology that could be
deployed at large scale in future. We envision the statistical methods
described here being applied to data streams from the homes of elderly and
at-risk groups, both as a means of early illness detection and for monitoring
the well-being of those with known illnesses.
Asset Pricing with General Transaction Costs: Theory and Numerics
Date: 2020-04-15 00:00:00
Tasks: Time Series, Time Series Analysis

We study risk-sharing equilibria with general convex costs on the agents'
trading rates. For an infinite-horizon model with linear state dynamics and
exogenous volatilities, we prove that the equilibrium returns mean-revert
around their frictionless counterparts - the deviation has Ornstein-Uhlenbeck
dynamics for quadratic costs whereas it follows a doubly-reflected Brownian
motion if costs are proportional. More general models with arbitrary state
dynamics and endogenous volatilities lead to multidimensional systems of
nonlinear, fully-coupled forward-backward SDEs. These fall outside the scope of
known wellposedness results, but can be solved numerically using the
simulation-based deep-learning approach of Han, Jentzen and E (2018). In a
calibration to time series of prices and trading volume, realistic liquidity
premia are accompanied by a moderate increase in volatility. The effects of
different cost specifications are rather similar, justifying the use of
quadratic costs as a proxy for other less tractable specifications.
Dynamical strategies for obstacle avoidance during Dictyostelium discoideum aggregation: a Multi-agent system model
Authors: Daniele Proverbio, Marco Maggiora
Date: 2019-05-13 00:00:00
Tasks: Blocking

Chemotaxis, the movement of an organism in response to chemical stimuli, is a typical feature of many microbiological systems. In particular, the social amoeba \textit{Disctyostelium discoideum} is widely used as a model organism, but it is not still clear how it behaves in heterogeneous environments. A few models focusing on mechanical features have already addressed the question; however, we suggest that phenomenological models focusing on the population dynamics may provide new meaningful data. Consequently, by means of a specific Multi-agent system model, we study the dynamical features emerging from complex social interactions among individuals belonging to amoeba colonies.\\ After defining an appropriate metric to quantitatively estimate the gathering process, we find that: a) obstacles play the role of local topological perturbation, as they alter the flux of chemical signals; b) physical obstacles (blocking the cellular motion and the chemical flux) and purely chemical obstacles (only interfering with chemical flux) elicit similar dynamical behaviors; c) a minimal program for robustly gathering simulated cells does not involve mechanisms for obstacle sensing and avoidance; d) fluctuations of the dynamics concur in preventing multiple stable clusters. Comparing those findings with previous results, we speculate about the fact that chemotactic cells can avoid obstacles by simply following the altered chemical gradient. Social interactions are sufficient to guarantee the aggregation of the whole colony past numerous obstacles.
Physical Layer Security for NOMA: Requirements, Merits, Challenges, and Recommendations
Authors: Haji M. Furqan, Jehad. M. Hamamreh, Huseyin Arslan
Date: 2019-05-13 00:00:00

Non-Orthogonal Multiple Access (NOMA) has been recognized as one of the most significant enabling technologies for future wireless systems due to its eminent spectral efficiency, ability to provide an additional degree of freedom for Ultra Reliable Low Latency Communications (URLLC) and grant free random access. Meanwhile, Physical Layer Security (PLS) has got much attention for future wireless communication systems due to its capability to provide security without relying on traditional cryptography based algorithms. In this article, security design requirements for NOMA and solutions provided by PLS to fulfill these requirements are discussed. The merits and challenges arising from employing PLS to NOMA are identified. Finally, future recommendations and prospective solutions are also presented.
Mixtures of Mean-Preserving Contractions
Date: 2020-09-20 00:00:00

Given a purely atomic probability measure with support on n points, P, any
mean-preserving contraction (mpc) of P, Q, with support on m > n points is a
mixture of mpcs of P, each with support on most n points. We illustrate an
application of this result in economics.
Reconfigurable On-chip Photoconductive Switches
Date: 2019-05-13 00:00:00

Microwave photonics uses light to carry and process microwave signals over a
photonic link. However, light can instead be used as a stimulus to microwave
devices that directly control microwave signals. Such optically controlled
amplitude and phase-shift switches are investigated for use in reconfigurable
microwave systems, but they suffer from large footprint, high optical power
level required for switching, lack of scalability and complex integration
requirements, restricting their implementation in practical microwave systems.
Here, we report Monolithic Optically Reconfigurable Integrated Microwave
Switches (MORIMSs) built on a CMOS compatible silicon photonic chip that
addresses all of the stringent requirements. Our scalable micrometer-scale
switches provide higher switching efficiency and require optical power orders
of magnitude lower than the state-of-the-art. Also, it opens a new research
direction on silicon photonic platforms integrating microwave circuitry. This
work has important implications in reconfigurable microwave and millimeter wave
devices for future communication networks.
Sustainable Investing and the Cross-Section of Returns and Maximum Drawdown
Authors: Lisa R. Goldberg, Saad Mouti
Date: 2019-05-13 00:00:00

We use supervised learning to identify factors that predict the cross-section of returns and maximum drawdown for stocks in the US equity market. Our data run from January 1970 to December 2019 and our analysis includes ordinary least squares, penalized linear regressions, tree-based models, and neural networks. We find that the most important predictors tended to be consistent across models, and that non-linear models had better predictive power than linear models. Predictive power was higher in calm periods than in stressed periods. Environmental, social, and governance indicators marginally impacted the predictive power of non-linear models in our data, despite their negative correlation with maximum drawdown and positive correlation with returns. Upon exploring whether ESG variables are captured by some models, we find that ESG data contribute to the prediction nonetheless.
Aphids, Ants and Ladybirds: a mathematical model predicting their population dynamics
Date: 2019-05-13 00:00:00
Tasks: Time Series Analysis

The interaction between aphids, ants and ladybirds has been investigated from
an ecological point of view since many decades, while there are no attempts to
describe it from a mathematical point of view. This paper introduces a new
mathematical model to describe the within-season population dynamics in an
ecological patch of a system composed by aphids, ants and ladybirds, through a
set of four differential equations. The proposed model is based on the
Kindlmann and Dixon set of differential equations, focused on the prediction of
the aphids-ladybirds population densities, that share a prey-predator
relationship. The population of ants, in mutualistic relationship with aphids
and in interspecific competition with ladybirds, is described according to the
Holland and De Angelis mathematical model, in which the authors faced the
problem of mutualistic interactions in general terms. The set of differential
equations proposed here is discretized by means the Nonstandard Finite
Difference scheme, successfully applied by Gabbriellini to the mutualistic
model. The constructed finite-difference scheme is positivity-preserving and
characterized by four nonhyperbolic steady-states, as highlighted by the
phase-space and time-series analyses. Particular attention is dedicated to the
steady-state most interesting from an ecological point of view, whose
asymptotic stability is demonstrated via the Centre Manifold Theory. The model
allows to numerically confirm that mutualistic relationship effectively
influences the population dynamic, by increasing the peaks of the aphids and
ants population densities. Nonetheless, it is showed that the asymptotical
populations of aphids and ladybirds collapse for any initial condition, unlike
that of ants that, after the peak, settle on a constant asymptotic value.
Merton's portfolio problem under Volterra Heston model
Authors: Bingyan Han, Hoi Ying Wong
Date: 2019-05-14 00:00:00
Tasks: model, Portfolio Optimization

This paper investigates Merton's portfolio problem in a rough stochastic environment described by Volterra Heston model. The model has a non-Markovian and non-semimartingale structure. By considering an auxiliary random process, we solve the portfolio optimization problem with the martingale optimality principle. Optimal strategies for power and exponential utilities are derived in semi-closed form solutions depending on the respective Riccati-Volterra equations. We numerically examine the relationship between investment demand and volatility roughness.
A Solvable Two-dimensional Optimal Stopping Problem in the Presence of Ambiguity
Date: 2019-05-14 00:00:00

According to conventional wisdom, ambiguity accelerates optimal timing by
decreasing the value of waiting in comparison with the unambiguous benchmark
case. We study this mechanism in a multidimensional setting and show that in a
multifactor model ambiguity does not only influence the rate at which the
underlying processes are expected to grow, it also affects the rate at which
the problem is discounted. This mechanism where nature also selects the rate at
which the problem is discounted cannot appear in a one-dimensional setting and
as such we identify an indirect way of how ambiguity affects optimal timing.
Modeling Interference-Free Neuron Spikes with Optogenetic Stimulation
Date: 2019-12-29 00:00:00

This paper predicts the ability to externally control the firing times of a
cortical neuron whose behavior follows the Izhikevich neuron model. The
Izhikevich neuron model provides an efficient and biologically plausible method
to track a cortical neuron's membrane potential and its firing times. The
external control is a simple optogenetic model represented by an illumination
source that stimulates a saturating or decaying membrane current. This paper
considers firing frequencies that are sufficiently low for the membrane
potential to return to its resting potential after it fires. The time required
for the neuron to charge and for the neuron to recover to the resting potential
are numerically fitted to functions of the Izhikevich neuron model parameters
and the peak input current. Results show that simple functions of the model
parameters and maximum input current can be used to predict the charging and
recovery times, even when there are deviations in the actual parameter values.
Furthermore, the predictions lead to lower bounds on the firing frequency that
can be achieved without significant distortion.
Asset Pricing with Heterogeneous Beliefs and Illiquidity
Authors: Johannes Muhle-Karbe, Marcel Nutz, Xiaowei Tan
Date: 2019-05-14 00:00:00

This paper studies the equilibrium price of an asset that is traded in continuous time between N agents who have heterogeneous beliefs about the state process underlying the asset's payoff. We propose a tractable model where agents maximize expected returns under quadratic costs on inventories and trading rates. The unique equilibrium price is characterized by a weakly coupled system of linear parabolic equations which shows that holding and liquidity costs play dual roles. We derive the leading-order asymptotics for small transaction and holding costs which give further insight into the equilibrium and the consequences of illiquidity.
Robust Precoding Design for Coarsely Quantized MU-MIMO Under Channel Uncertainties
Date: 2019-05-14 00:00:00
Tasks: LEMMA

Recently, multi-user multiple input multiple output (MU-MIMO) systems with
low-resolution digital-to-analog converters (DACs) has received considerable
attention, owing to the capability of dramatically reducing the hardware cost.
Besides, it has been shown that the use of low-resolution DACs enable great
reduction in power consumption while maintain the performance loss within
acceptable margin, under the assumption of perfect knowledge of channel state
information (CSI). In this paper, we investigate the precoding problem for the
coarsely quantized MU-MIMO system without such an assumption. The channel
uncertainties are modeled to be a random matrix with finite second-order
statistics. By leveraging a favorable relation between the multi-bit DACs
outputs and the single-bit ones, we first reformulate the original complex
precoding problem into a nonconvex binary optimization problem. Then, using the
S-procedure lemma, the nonconvex problem is recast into a tractable formulation
with convex constraints and finally solved by the semidefinite relaxation (SDR)
method. Compared with existing representative methods, the proposed precoder is
robust to various channel uncertainties and is able to support a MUMIMO system
with higher-order modulations, e.g., 16QAM.
The connection between multiple prices of an Option at a given time with single prices defined at different times: The concept of weak-value in quantum finance
Authors: Ivan Arraut, Alan Au, Alan Ching-biu Tse, Carlos Segovia
Date: 2019-05-14 00:00:00

We introduce a new tool for predicting the evolution of an option for the cases where at some specific time, there is a high-degree of uncertainty for identifying its price. We work over the special case where we can predict the evolution of the system by joining a single price for the Option, defined at some specific time with a pair of prices defined at another instant. This is achieved by describing the evolution of the system through a financial Hamiltonian. The extension to the case of multiple prices at a given instant is straightforward. We also explain how to apply these results in real situations.
The paradox of monotone structural QRE
Date: 2019-07-29 00:00:00

McKelvey and Palfrey (1995)'s monotone structural Quantal Response
Equilibrium theory may be misspecified for the study of monotone behavior.
Efficient computation of mean reverting portfolios using cyclical coordinate descent
Date: 2019-05-14 00:00:00

The econometric challenge of finding sparse mean reverting portfolios based
on a subset of a large number of assets is well known. Many current
state-of-the-art approaches fall into the field of co-integration theory, where
the problem is phrased in terms of an eigenvector problem with sparsity
constraint. Although a number of approximate solutions have been proposed to
solve this NP-hard problem, all are based on relatively simple models and are
limited in their scalability. In this paper we leverage information obtained
from a heterogeneous simultaneous graphical dynamic linear model (H-SGDLM) and
propose a novel formulation of the mean reversion problem, which is phrased in
terms of a quasi-convex minimisation with a normalisation constraint. This new
formulation allows us to employ a cyclical coordinate descent algorithm for
efficiently computing an exact sparse solution, even in a large universe of
assets, while the use of H-SGDLM data allows us to easily control the required
level of sparsity. We demonstrate the flexibility, speed and scalability of the
proposed approach on S\&P$500$, FX and ETF futures data.
Reduced Form Capital Optimization
Authors: Yadong Li, Dimitri Offengenden, Jan Burgy
Date: 2019-05-15 00:00:00
Tasks: Form

We formulate banks' capital optimization problem as a classic mean variance optimization, by leveraging an accurate linear approximation to the Shapely or Constrained Aumann-Shapley (CAS) allocation of max or nested max cost functions. This reduced form formulation admits an analytical solution, to the optimal leveraged balance sheet (LBS) and risk weighted assets (RWA) target of banks' business units for achieving the best return on capital.
What is the Minimal Systemic Risk in Financial Exposure Networks?
Date: 2019-05-15 00:00:00
Tasks: Management

Management of systemic risk in financial markets is traditionally associated
with setting (higher) capital requirements for market participants. There are
indications that while equity ratios have been increased massively since the
financial crisis, systemic risk levels might not have lowered, but even
increased. It has been shown that systemic risk is to a large extent related to
the underlying network topology of financial exposures. A natural question
arising is how much systemic risk can be eliminated by optimally rearranging
these networks and without increasing capital requirements. Overlapping
portfolios with minimized systemic risk which provide the same market
functionality as empirical ones have been studied by [pichler2018]. Here we
propose a similar method for direct exposure networks, and apply it to
cross-sectional interbank loan networks, consisting of 10 quarterly
observations of the Austrian interbank market. We show that the suggested
framework rearranges the network topology, such that systemic risk is reduced
by a factor of approximately 3.5, and leaves the relevant economic features of
the optimized network and its agents unchanged. The presented optimization
procedure is not intended to actually re-configure interbank markets, but to
demonstrate the huge potential for systemic risk management through rearranging
exposure networks, in contrast to increasing capital requirements that were
shown to have only marginal effects on systemic risk [poledna2017]. Ways to
actually incentivize a self-organized formation toward optimal network
configurations were introduced in [thurner2013] and [poledna2016]. For
regulatory policies concerning financial market stability the knowledge of
minimal systemic risk for a given economic environment can serve as a benchmark
for monitoring actual systemic risk in markets.
Integration of SCADA services in cross-infrastructure holistic tests of cyber-physical energy systems
Date: 2019-05-15 00:00:00

Cyber-Physical Energy System, due to its multi-domain nature, requires a
holistic validation methodology, which may involve the integration of assets
and expertise from various research infrastructures. In this paper, the
integration of Supervisory Control and Data Acquisition services to
cross-infrastructure experiment is proposed. The method requires a high degree
of interoperability among the participating partners and can be applied to
extend the capacity as well as the degree of realism of advanced validation
method such as co-simulation, remote hardware-in-the-loop or hybrid simulation.
The proposed method is applied to a case study of multi-agent system based
control for islanded microgrid where real devices from one platform is
integrated to real-time simulation and control platform in a distanced
infrastructure, in a holistic experimental implementation.
Blur resolved OCT: full-range interferometric synthetic aperture microscopy through dispersion encoding
Date: 2020-01-31 00:00:00

We present a computational method for full-range interferometric synthetic
aperture microscopy (ISAM) under dispersion encoding. With this, one can
effectively double the depth range of optical coherence tomography (OCT),
whilst dramatically enhancing the spatial resolution away from the focal plane.
To this end, we propose a model-based iterative reconstruction (MBIR) method,
where ISAM is directly considered in an optimization approach, and we make the
discovery that sparsity promoting regularization effectively recovers the
full-range signal. Within this work, we adopt an optimal nonuniform discrete
fast Fourier transform (NUFFT) implementation of ISAM, which is both fast and
numerically stable throughout iterations. We validate our method with several
complex samples, scanned with a commercial SD-OCT system with no hardware
modification. With this, we both demonstrate full-range ISAM imaging, and
significantly outperform combinations of existing methods.
Tunability of the Dual Feedback Genetic Oscillator Modeled by the Asymmetry in Transcription and Translation
Authors: Yash Joshi, Yash Kiran Jawale, Chaitanya Anil Athale
Date: 2019-05-15 00:00:00
Tasks: Translation

Oscillatory gene circuits are ubiquitous to biology and are involved in fundamental processes of cell cycle, circadian rhythms and developmental systems. The synthesis of small, non-natural oscillatory genetic circuits have been increasingly used to test fundamental principles of genetic network dynamics. A recently developed fast, tunable genetic oscillator by Stricker et al.[23] has demonstrated robustness and tunability of oscillatory behavior by combining positive and negative feedback loops. This oscillator combining lacI (negative) and araC (positive) feedback loops, was however modeled using multiple layers of differential equations to capture the molecular complexity of regulation, in order to explain the experimentally measured oscillations. We have developed a reduced model based on delay differential equations (DDEs) of this dual feedback loop oscillator, that reproduces the tunability of oscillator period and amplitude based on the concentration of the two inducers isopropyl b-D-1-thiogalactopyranoside (IPTG) and arabinose. Previous work had predicted a need for an asymmetry in copy numbers of activator (araC) and repressor (lacI) genes encoded on plasmids. We use our reduced model to redesign the network by comparing the effect of asymmetry in gene expression at the level of (a) DNA copy numbers and the rates of (b) mRNA translation and (c) degradation. We find the minimal period of the oscillator is sensitive to DNA copy number asymmetry, but translation rate asymmetry has an identical effect as plasmid copy numbers, while modulating the asymmetry in mRNA degradation can improve the tunability of period of the oscillator, together with increased robustness to replication 'noise' and influence of the host cell cycle. Thus, our model predicts experimentally testable principles to redesign a potentially more robust oscillatory genetic network.