pwc-archive/papers-with-abstracts huggingface dataset:
Coronavirus: Case for Digital Money?
Authors: Zura Kakushadze, Jim Kyung-Soo Liew
Date: 2020-05-17 00:00:00

We discuss the pros of adopting government-issued digital currencies as well as a supranational digital iCurrency. One such pro is to get rid of paper money (and coinage), a ubiquitous medium for spreading germs, as highlighted by the recent coronavirus outbreak. We set forth three policy recommendations for adapting mobile devices as new digital wallets, regulatory oversight of sovereign digital currencies and user data protection, and a supranational digital iCurrency for facilitating international digital monetary linkages.
Applying the Nash Bargaining Solution for a Reasonable Royalty
Authors: David M. Kryskowski, David Kryskowski
Date: 2020-05-16 00:00:00

There has been limited success applying the Nash Bargaining Solution (NBS) in assigning intellectual property damages due to the difficulty of relating it to the specific facts of the case. Because of this, parties are not taking advantage of Georgia-Pacific factor fifteen. This paper intends to bring clarity to the NBS so it can be applied to the facts of a case. This paper normalizes the NBS and provides a methodology for determining the bargaining weight in Nash's solution. Several examples demonstrate this normalized form, and a nomograph is added for computational ease.
Line Failure Localization of Power Networks Part I: Non-cut Outages
Authors: Linqi Guo, Chen Liang, Alessandro Zocca, Steven H. Low, Adam Wierman
Date: 2020-05-20 00:00:00

Transmission line failures in power systems propagate non-locally, making the control of the resulting outages extremely difficult. In this work, we establish a mathematical theory that characterizes the patterns of line failure propagation and localization in terms of network graph structure. It provides a novel perspective on distribution factors that precisely captures Kirchhoff's Law in terms of topological structures. Our results show that the distribution of specific collections of subtrees of the transmission network plays a critical role on the patterns of power redistribution, and motivates the block decomposition of the transmission network as a structure to understand long-distance propagation of disturbances. In Part I of this paper, we present the case when the post-contingency network remains connected after an initial set of lines are disconnected simultaneously. In Part II, we present the case when an outage separates the network into multiple islands.
Statistical Issues and Recommendations for Clinical Trials Conducted During the COVID-19 Pandemic
Authors: R. Daniel Meyer, Bohdana Ratitch, Marcel Wolbers, Olga Marchenko, Hui Quan, Daniel Li, Chrissie Fletcher, Xin Li, David Wright, Yue Shentu, Stefan Englert, Wei Shen, Jyotirmoy Dey, Thomas Liu, Ming Zhou, Norman Bohidar, Peng-Liang Zhao, Michael Hale
Date: 2020-05-21 00:00:00

The COVID-19 pandemic has had and continues to have major impacts on planned and ongoing clinical trials. Its effects on trial data create multiple potential statistical issues. The scale of impact is unprecedented, but when viewed individually, many of the issues are well defined and feasible to address. A number of strategies and recommendations are put forward to assess and address issues related to estimands, missing data, validity and modifications of statistical analysis methods, need for additional analyses, ability to meet objectives and overall trial interpretability.
Sensor Networks TDOA Self-Calibration: 2D Complexity Analysis and Solutions
Authors: Luca Ferranti, Kalle Åström, Magnus Oskarsson, Jani Boutellier, Juho Kannala
Date: 2020-05-20 00:00:00

Given a network of receivers and transmitters, the process of determining their positions from measured pseudoranges is known as network self-calibration. In this paper we consider 2D networks with synchronized receivers but unsynchronized transmitters and the corresponding calibration techniques, known as Time-Difference-Of-Arrival (TDOA) techniques. Despite previous work, TDOA self-calibration is computationally challenging. Iterative algorithms are very sensitive to the initialization, causing convergence issues. In this paper, we present a novel approach, which gives an algebraic solution to two previously unsolved scenarios. We also demonstrate that our solvers produce an excellent initial value for non-linear optimisation algorithms, leading to a full pipeline robust to noise.
Repeat Contacts and the Spread of Disease: An Agent Model with Compartmental Solution
Date: 2020-06-12 00:00:00

Using a probability of novel encounter derived from a physical model, we
augment the SIR compartmental model for disease spread. Scenarios with the same
initial trajectories and identical $R_0$ values can diverge greatly depending
on the speed at which our circles of acquaintances grow stale - leading to
order of magnitude differences in final case counts. A momentum effect arises
from variation in the mean time since infection, and this feeds back into new
infection rate and faster decline in the late stages of an outbreak. Rapid
extinction of an outbreak can occur in the early stages, but once this
opportunity is missed the effect is diminished and then, only herd immunity can
help.
From learning gait signatures of many individuals to reconstructing gait dynamics of one single individual
Authors: Fushing Hsieh, Xiaodong Wang
Date: 2020-05-21 00:00:00
Tasks: Time Series Analysis

Based on the same databases, we computationally address two seemingly highly related, in fact drastically distinct, questions via computational data-driven algorithms: 1) how to precisely achieve the big task of differentiating gait signatures of many individuals? 2) how to reconstruct an individual's complex gait dynamics in full? Our brains can "effortlessly" resolve the first question, but will definitely fail in the second one. Since many fine temporal scale gait patterns surely escape our eyes. Based on accelerometers' 3D gait time series databases, we link the answers toward both questions via multiscale structural dependency within gait dynamics of our musculoskeletal system. Two types of dependency manifestations are explored. We first develop simple algorithmic computing called Principle System-State Analysis (PSSA) for the coarse dependency in implicit forms. PSSA is shown to be able to efficiently classifying among many subjects. We then develop a multiscale Local-1st-Global-2nd (L1G2) Coding Algorithm and a landmark computing algorithm. With both algorithms, we can precisely dissect rhythmic gait cycles, and then decompose each cycle into a series of cyclic gait phases. With proper color-coding and stacking, we reconstruct and represent an individual's gait dynamics via a 3D cylinder to collectively reveal universal deterministic and stochastic structural patterns on centisecond (10 milliseconds) scale across all rhythmic cycles. This 3D cylinder can serve as "passtensor" for authentication purposes related to clinical diagnoses and cybersecurity.
End-to-End Far-Field Speech Recognition with Unified Dereverberation and Beamforming
Date: 2020-10-27 00:00:00
Tasks: speech-recognition, Speech Recognition

Despite successful applications of end-to-end approaches in multi-channel
speech recognition, the performance still degrades severely when the speech is
corrupted by reverberation. In this paper, we integrate the dereverberation
module into the end-to-end multi-channel speech recognition system and explore
two different frontend architectures. First, a multi-source mask-based weighted
prediction error (WPE) module is incorporated in the frontend for
dereverberation. Second, another novel frontend architecture is proposed, which
extends the weighted power minimization distortionless response (WPD)
convolutional beamformer to perform simultaneous separation and
dereverberation. We derive a new formulation from the original WPD, which can
handle multi-source input, and replace eigenvalue decomposition with the matrix
inverse operation to make the back-propagation algorithm more stable. The above
two architectures are optimized in a fully end-to-end manner, only using the
speech recognition criterion. Experiments on both spatialized wsj1-2mix corpus
and REVERB show that our proposed model outperformed the conventional methods
in reverberant scenarios.
Does an artificial intelligence perform market manipulation with its own discretion? -- A genetic algorithm learns in an artificial market simulation
Authors: Takanobu Mizuta
Date: 2020-05-21 00:00:00

Who should be charged with responsibility for an artificial intelligence performing market manipulation have been discussed. In this study, I constructed an artificial intelligence using a genetic algorithm that learns in an artificial market simulation, and investigated whether the artificial intelligence discovers market manipulation through learning with an artificial market simulation despite a builder of artificial intelligence has no intention of market manipulation. As a result, the artificial intelligence discovered market manipulation as an optimal investment strategy. This result suggests necessity of regulation, such as obligating builders of artificial intelligence to prevent artificial intelligence from performing market manipulation.
On stabilizability of switched linear systems under restricted switching
Date: 2020-05-21 00:00:00

This paper deals with stability of discrete-time switched linear systems
whose all subsystems are unstable and the set of admissible switching signals
obeys pre-specified restrictions on switches between the subsystems and dwell
times on the subsystems. We derive sufficient conditions on the subsystems
matrices such that a switched system is globally exponentially stable under a
set of purely time-dependent switching signals that obeys the given
restrictions. The main apparatuses for our analysis are (matrix) commutation
relations between certain products of the subsystems matrices and
graph-theoretic arguments.
A Computational Approach to Hedging Credit Valuation Adjustment in a Jump-Diffusion Setting
Authors: T. van der Zwaard, L. A. Grzelak, C. W. Oosterlee
Date: 2020-05-21 00:00:00

This study contributes to understanding Valuation Adjustments (xVA) by focussing on the dynamic hedging of Credit Valuation Adjustment (CVA), corresponding Profit & Loss (P&L) and the P&L explain. This is done in a Monte Carlo simulation setting, based on a theoretical hedging framework discussed in existing literature. We look at hedging CVA market risk for a portfolio with European options on a stock, first in a Black-Scholes setting, then in a Merton jump-diffusion setting. Furthermore, we analyze the trading business at a bank after including xVAs in pricing. We provide insights into the hedging of derivatives and their xVAs by analyzing and visualizing the cash-flows of a portfolio from a desk structure perspective. The case study shows that not charging CVA at trade inception results in an expected loss. Furthermore, hedging CVA market risk is crucial to end up with a stable trading strategy. In the Black-Scholes setting this can be done using the underlying stock, whereas in the Merton jump-diffusion setting we need to add extra options to the hedge portfolio to properly hedge the jump risk. In addition to the simulation, we derive analytical results that explain our observations from the numerical experiments. Understanding the hedging of CVA helps to deal with xVAs in a practical setting.
Generalized half-center oscillators with short-term synaptic plasticity
Date: 2020-05-21 00:00:00

How can we develop simple yet realistic models of the small neural circuits
known as central pattern generators (CPGs), which contribute to generate
complex multi-phase locomotion in living animals? In this paper we introduce a
new model (with design criteria) of a generalized half-center oscillator
(gHCO), (pools of) neurons reciprocally coupled by fast/slow inhibitory and
excitatory synapses, to produce either alternating bursting or synchronous
patterns depending on the sensory or other external input. We also show how to
calibrate its parameters, based on both physiological and functional criteria
and on bifurcation analysis. This model accounts for short-term neuromodulation
in a bio-physically plausible way and is a building block to develop more
realistic and functionally accurate CPG models. Examples and counterexamples
are used to point out the generality and effectiveness of our design approach.
Self-calibration and antenna grouping for bistatic oceanographic High-Frequency Radars
Authors: Dylan Dumas, Charles-Antoine Guérin
Date: 2020-05-21 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We propose two concepts for the significant improvement of surface current mapping with bistatic oceanographic High-Frequency Radars. These ameliorations pertain to the azimuthal processing of radar data with linear or quasi-linear antenna arrays. The first idea is to take advantage of the remote transmitter to perform an automatic correction of the complex gains of the receiving antennas based on the analysis of the signal received in the direct path. This direct signal can be found at the zero-Doppler and minimal range cell in the Range-Doppler representation. We term this adjustment as "self-calibration" of the receiving array, as it can be performed in real-time without any specific action from the operator. The second idea consists in applying a Direction Finding technique (instead of traditional Beam Forming) not only to the full array of antenna but also to subarrays made of a smaller number of sequential antennas, a method which we refer to as "antenna grouping". The combination of self-calibration and antenna grouping makes it possible to obtain high-resolution maps with full coverage, thereby combining the respective merits of Direction Finding and Beam Forming techniques. In addition the method is found robust to missing antennas in the array. These techniques are applied to and illustrated with the multistatic High-Frequency Radar network in Toulon.
Predictive Control of Rural Microgrids with Temperature-dependent Battery Degradation Cost
Date: 2020-05-22 00:00:00

Off-grid systems have emerged as a sustainable and cost-effective solution
for rural electrification. In sub-Sarahan Africa (SSA), a great number of
solar-hybrid microgrids have been installed or planned, operating stand-alone
or grid-tied to a weak grid. Presence of intermittent energy sources
necessitates the provision of energy storage for system balancing. Reliability
and economic performance of those rural microgrids strongly depend on specific
control strategies. This work develops a predictive control framework dedicated
to rural microgrids incorporating a temperature-dependent battery degradation
model. Based on a scalable DC PV-battery microgrid, the realistic simulation
shows its superior performance in the reliability improvement and cost
reduction. Compared with the day-ahead control without the
temperature-dependent battery degradation model, this control strategy can
improve the reliability by 5.5% and extend the lead-acid battery life time by
26%, equivalent to lowering the levelised cost of electricity (LCOE) by 13%.
Production networks and epidemic spreading: How to restart the UK economy?
Date: 2020-05-21 00:00:00
Tasks: Epidemiology

We analyse the economics and epidemiology of different scenarios for a phased
restart of the UK economy. Our economic model is designed to address the unique
features of the COVID-19 pandemic. Social distancing measures affect both
supply and demand, and input-output constraints play a key role in restricting
economic output. Standard models for production functions are not adequate to
model the short-term effects of lockdown. A survey of industry analysts
conducted by IHS Markit allows us to evaluate which inputs for each industry
are absolutely necessary for production over a two month period. Our model also
includes inventory dynamics and feedback between unemployment and consumption.
We demonstrate that economic outcomes are very sensitive to the choice of
production function, show how supply constraints cause strong network effects,
and find some counter-intuitive effects, such as that reopening only a few
industries can actually lower aggregate output. Occupation-specific data and
contact surveys allow us to estimate how different industries affect the
transmission rate of the disease. We investigate six different re-opening
scenarios, presenting our best estimates for the increase in R0 and the
increase in GDP. Our results suggest that there is a reasonable compromise that
yields a relatively small increase in R0 and delivers a substantial boost in
economic output. This corresponds to a situation in which all non-consumer
facing industries reopen, schools are open only for workers who need childcare,
and everyone who can work from home continues to work from home.
Fast and Accurate Langevin Simulations of Stochastic Hodgkin-Huxley Dynamics
Date: 2020-05-21 00:00:00

Fox and Lu introduced a Langevin framework for discrete-time stochastic
models of randomly gated ion channels such as the Hodgkin-Huxley (HH) system.
They derived a Fokker-Planck equation with state-dependent diffusion tensor $D$
and suggested a Langevin formulation with noise coefficient matrix $S$ such
that $SS^\intercal=D$. Subsequently, several authors introduced a variety of
Langevin equations for the HH system. In this paper, we present a natural
14-dimensional dynamics for the HH system in which each \emph{directed} edge in
the ion channel state transition graph acts as an independent noise source,
leading to a $14\times 28$ noise coefficient matrix $S$. We show that (i) the
corresponding 14D system of ordinary differential \rev{equations} is consistent
with the classical 4D representation of the HH system; (ii) the 14D
representation leads to a noise coefficient matrix $S$ that can be obtained
cheaply on each timestep, without requiring a matrix decomposition; (iii)
sample trajectories of the 14D representation are pathwise equivalent to
trajectories of Fox and Lu's system, as well as trajectories of several
existing Langevin models; (iv) our 14D representation (and those equivalent to
it) give the most accurate interspike-interval distribution, not only with
respect to moments but under both the $L_1$ and $L_\infty$ metric-space norms;
and (v) the 14D representation gives an approximation to exact Markov chain
simulations that are as fast and as efficient as all equivalent models. Our
approach goes beyond existing models, in that it supports a stochastic
shielding decomposition that dramatically simplifies $S$ with minimal loss of
accuracy under both voltage- and current-clamp conditions.
Detecting and explaining changes in various assets' relationships in financial markets
Authors: Makoto Naraoka, Teruaki Hayashi, Takaaki Yoshino, Toshiaki Sugie, Kota Takano, Yukio Ohsawa
Date: 2020-05-21 00:00:00
Tasks: Decision Making, Time Series, Time Series Analysis

We study the method for detecting relationship changes in financial markets and providing human-interpretable network visualization to support the decision-making of fund managers dealing with multi-assets. First, we construct co-occurrence networks with each asset as a node and a pair with a strong relationship in price change as an edge at each time step. Second, we calculate Graph-Based Entropy to represent the variety of price changes based on the network. Third, we apply the Differential Network to finance, which is traditionally used in the field of bioinformatics. By the method described above, we can visualize when and what kind of changes are occurring in the financial market, and which assets play a central role in changes in financial markets. Experiments with multi-asset time-series data showed results that were well fit with actual events while maintaining high interpretability. It is suggested that this approach is useful for fund managers to use as a new option for decision making.
Deep neural networks for efficient phase demodulation in wavelength shifting interferometry
Authors: Jacob Black, Shichao Chen, Joseph G. Thomas, Yizheng Zhu
Date: 2020-05-21 00:00:00
Tasks: Sensitivity

Analytical phase demodulation algorithms in optical interferometry typically fail to reach the theoretical sensitivity limit set by the Cram\'er-Rao bound (CRB). We show that deep neural networks (DNNs) can perform efficient phase demodulation by achieving or exceeding the CRB by significant margins when trained with new information that is not utilized by conventional algorithms, such as noise statistics and parameter constraints. As an example, we developed and applied DNNs to wavelength shifting interferometry. When trained with noise statistics, the DNNs outperform the conventional algorithm in terms of phase sensitivity and achieve the traditional three parameter CRB. Further, by incorporating parameter constraints into the training sets, they can exceed the traditional CRB. For well confined parameters, the phase sensitivity of the DNNs can even approach a fundamental limit we refer to as the single parameter CRB. Such sensitivity improvement can translate into significant increase in single-to-noise ratio without hardware modification, or be used to relax hardware requirements.
The optimal investment strategy of a DC pension plan under deposit loan spread and the O-U process
Authors: Xiao Xu
Date: 2020-05-20 00:00:00

This paper is devoted to invest an optimal investment strategy for a defined-contribution (DC) pension plan under the Ornstein-Uhlenbeck (O-U) process and the loan. By considering risk-free asset, a risky asset driven by O-U process and a loan in the financial market, we firstly set up the dynamic equation and the asset market model which are instrumental in achieving the expected utility of ultimate wealth at retirement. Secondly, the corresponding Hamilton-Jacobi-Bellman(HJB) equation is derived by means of dynamic programming principle. The explicit expression for the optimal investment strategy is obtained by Legendre transform method. Finally, different parameters are selected to simulate the explicit solution and the financial interpretation of the optimal investment strategy is given.
Tissue failure propagation as mediated by circulatory flow
Authors: Gurdip Uppal, Gokhan Bahcecioglu, Pinar Zorlutuna, Dervis Can Vural
Date: 2020-05-21 00:00:00

Aging is driven by subcellular processes that are relatively well-understood. However the qualitative mechanisms and quantitative dynamics of how these micro-level failures cascade to a macro-level catastrophe in a tissue or organs remain largely unexplored. Here we experimentally and theoretically study how cell failure propagates in a synthetic tissue in the presence of advective flow. We argue that cells secrete cooperative factors, thereby forming a network of interdependence governed by diffusion and flow, which fails with a propagating front parallel to advective circulation.