pwc-archive/papers-with-abstracts huggingface dataset:
Relative wealth concerns with partial information and heterogeneous priors
Authors: Chao Deng, Xizhi Su, Chao Zhou
Date: 2020-07-23 00:00:00

We establish a Nash equilibrium in a market with $ N $ agents with the performance criteria of relative wealth level when the market return is unobservable. Each investor has a random prior belief on the return rate of the risky asset. The investors can be heterogeneous in both the mean and variance of the prior. By a separation result and a martingale argument, we show that the optimal investment strategy under a stochastic return rate model can be characterized by a fully-coupled linear FBSDE. Two sets of deep neural networks are used for the numerical computation to first find each investor's estimate of the mean return rate and then solve the FBSDEs. We establish the existence and uniqueness result for the class of FBSDEs with stochastic coefficients and solve the utility game under partial information using deep neural network function approximators. We demonstrate the efficiency and accuracy by a base-case comparison with the solution from the finite difference scheme in the linear case and apply the algorithm to the general case of nonlinear hidden variable process. Simulations of investment strategies show a herd effect that investors trade more aggressively under relativeness concerns. Statistical properties of the investment strategies and the portfolio performance, including the Sharpe ratios and the Variance Risk ratios (VRRs) are examed. We observe that the agent with the most accurate prior estimate is likely to lead the herd, and the effect of competition on heterogeneous agents varies more with market characteristics compared to the homogeneous case.
Adaptive Control of Time-Varying Parameter Systems with Asymptotic Tracking
Date: 2020-07-23 00:00:00

A continuous adaptive control design is developed for nonlinear dynamical
systems with linearly parameterizable uncertainty involving time-varying
uncertain parameters. The key feature of this design is a robust integral of
the sign of the error (RISE)-like term in the adaptation law which compensates
for potentially destabilizing terms in the closed-loop error system arising
from the time-varying nature of uncertain parameters. A Lyapunov-based
stability analysis ensures asymptotic tracking, and boundedness of the
closed-loop signals.
A structure-preserving numerical approach for simulating algae blooms in marine water bodies of western Patagonia
Date: 2020-07-23 00:00:00

Patagonian fjords' area is one of the largest estuarine regions in the world.
Every one of its water bodies displays a unique hydrodynamic behavior with
enormous effects on the biogeochemical characteristics of the ecosystems. In
this context, algal blooms are ecological phenomena of major relevance.
Numerical simulation has proved to be a promising tool to understand their
impacts. It has not been used for studying algal blooms in this zone. This
article focuses on proposing a novel numerical model for simulating brief algal
blooms occurring in water bodies of western Patagonia. The proposed model
presents a trade-off between complexity and applicability since field-data
sparsity in the zone discourages using more sophisticated approaches. The model
is based on a two-layer description of the water column. The first layer
represents the euphotic zone where an embedded biogeochemical model of
NPZD-type is used to model a mass-conserving trophic web. High intensity wind
drives the water column mixing, introducing an upward flux of nutrients that
boosts high rates of primary production. A time-dependent Gaussian pulse is
used to describe this process. Mass losses due to detritus sinking are also
included. Then, the ecosystem's dynamics is represented by means of an
externally forced, non-autonomous system of ordinary differential equations
which is characterized by strictly positive trajectories but that it is not
longer mass-conserving. A structure-preserving time integrator based on a
splitting-composition technique is designed for solving the system's equations.
It is cast as a three-steps algorithm and provides an exact estimations of
biomass fluxes. Additionally, a genetic algorithm-based tool is used to
calibrate the model's parameters in realistic scenarios. The proposed model is
applied im a detailed study of a winter bloom in an austral fjord.
Proposal for a Comprehensive (Crypto) Asset Taxonomy
Authors: Thomas Ankenbrand, Denis Bieri, Roland Cortivo, Johannes Hoehener, Thomas Hardjono
Date: 2020-07-23 00:00:00
Tasks: Classification, General Classification

Developments in the distributed ledger technology have led to new types of assets with a broad range of purposes. Although some classification frameworks for common instruments from traditional finance and some for these new, so called cryptographic assets already exist and are used, a holistic approach to integrate both worlds is missing. The present paper fills this research gap by identifying 14 attributes, each of which is assigned different characteristics, that can be used to classify all types of assets in a structured manner. Our proposed taxonomy which is an extension of existing classification frameworks, summarises these findings in a morphological box and is tested for practicability by classifying exemplary assets like cash and bitcoin. The final classification framework can help to ensure that the various stakeholders, such as investors or supervisors, have a consistent view of the different types of assets, and in particular of their characteristics, and also helps to establish standardised terminology.
Simulation of Blockchain based Power Trading with Solar Power Prediction in Prosumer Consortium Model
Authors: Kaung Si Thu, Weerakorn Ongsakul
Date: 2020-07-23 00:00:00
Tasks: Decision Making

Prosumer consortium energy transactive models can be one of the solutions for energy costs, increasing performance and for providing reliable electricity utilizing distributed power generation, to a local group or community, like a university. This research study demonstrates the simulation of blockchain based power trading, supplemented by the solar power prediction using MLFF neural network training in two prosumer nodes. This study can be the initial step in the implementation of a power trading market model based on a decentralized blockchain system, with distributed generations in a university grid system. This system can balance the electricity demand and supply within the institute, enable secure and rapid transactions, and the local market system can be reinforced by forecasting solar generation. The performance of the MLFF training can predict almost 90% accuracy of the model as short term ahead forecasting. Because of it, the prosumer bodies can complete the decision making before trading to their benefit.
Age structured SIR model for the spread of infectious diseases through indirect contacts
Date: 2020-07-23 00:00:00

In this article, we discuss an age-structured SIR model in which disease not
only spread through direct person to person contacts for e.g. infection due to
surface contamination but it can also spread through indirect contacts. It is
evident that age also plays a crucial role in SARS virus infection including
COVID-19 infection. We formulate our model as an abstract semilinear Cauchy
problem in an appropriate Banach space to show the existence of solution and
also show the existence of steady states. It is assumed in this work that the
population is in a demographic stationary state and show that there is no
disease-free equilibrium point as long as there is a transmission of infection
due to the indirect contacts in the environment.
Sub-Nyquist Co-Prime Sensing with Compressed Inter-Element Spacing -- Low Latency Approach
Authors: Usham V. Dias
Date: 2020-07-23 00:00:00

Co-prime arrays with compressed inter-element spacing (CACIS) is one of the generalizations of the co-prime array. The inter-element spacing can be varied in this case. The prototype co-prime arrays and nested arrays are a special case of the CACIS scheme. The problems that were not addressed previously are considered in this paper. The fundamentals of the difference set for the CACIS configuration are developed for low latency. In addition, the closed-form expressions for the weight function (number of samples that contribute to estimate the autocorrelation) and bias window of the correlogram estimate, which were previously unknown, are derived. Ideally, the bias window should be an impulse. Several examples are provided along with simulations to verify the claims made. All possible sample pairs are used for estimation, which provides for low latency. As an application, temporal spectrum is considered for simulations.
Dynamic residential load scheduling based on an adaptive consumption level pricing scheme
Date: 2020-07-23 00:00:00
Tasks: Scheduling

Demand response (DR) for smart grids, which intends to balance the required
power demand with the available supply resources, has been gaining widespread
attention. The growing demand for electricity has presented new opportunities
for residential load scheduling systems to improve energy consumption by
shifting or curtailing the demand required with respect to price change or
emergency cases. In this paper, a dynamic residential load scheduling system
(DRLS) is proposed for optimal scheduling of household appliances on the basis
of an adaptive consumption level (CL) pricing scheme (ACLPS). The proposed load
scheduling system encourages customers to manage their energy consumption
within the allowable consumption allowance (CA) of the proposed DR pricing
scheme to achieve lower energy bills. Simulation results show that employing
the proposed DRLS system benefits the customers by reducing their energy bill
and the utility companies by decreasing the peak load of the aggregated load
demand. For a given case study, the proposed residential load scheduling system
based on ACLPS allows customers to reduce their energy bills by up to 53% and
to decrease the peak load by up to 35%.
Using modern motion estimation algorithms in existing video codecs
Date: 2020-07-23 00:00:00
Tasks: Motion Estimation, Video Compression

Motion estimation is a key component of any modern video codec. Our
understanding of motion and the estimation of motion from video has come a very
long way since 2000. More than 135 different algorithms have been recently
reviewed by Scharstein et al http://vision.middlebury.edu/flow/. These new
algorithms differ markedly from Block Matching which has been the mainstay of
video compression for some time. This paper presents comparisons of H.264 and
MP4 compression using different motion estimation methods. In so doing we
present as well methods for adapting pre-computed motion fields for use within
a codec. We do not observe significant gains to be had with the methods chosen
w.r.t. Rate Distortion tradeoffs but the results reflect a significantly more
complex interrelationship between motion and compression than would be
expected. There remains much more to be done to improve the coverage of this
comparison to the emerging standards but these initial results show that there
is value in these explorations.
Dominant Resource Fairness with Meta-Types
Authors: Steven Yin, Shatian Wang, Lingyi Zhang, Christian Kroer
Date: 2020-07-21 00:00:00
Tasks: Fairness

Inspired by the recent COVID-19 pandemic, we study a generalization of the multi-resource allocation problem with heterogeneous demands and Leontief utilities. Unlike existing settings, we allow each agent to specify requirements to only accept allocations from a subset of the total supply for each resource. These requirements can take form in location constraints (e.g. A hospital can only accept volunteers who live nearby due to commute limitations). This can also model a type of substitution effect where some agents need 1 unit of resource A \emph{or} B, both belonging to the same meta-type. But some agents specifically want A, and others specifically want B. We propose a new mechanism called Dominant Resource Fairness with Meta Types which determines the allocations by solving a small number of linear programs. The proposed method satisfies Pareto optimality, envy-freeness, strategy-proofness, and a notion of sharing incentive for our setting. To the best of our knowledge, we are the first to study this problem formulation, which improved upon existing work by capturing more constraints that often arise in real life situations. Finally, we show numerically that our method scales better to large problems than alternative approaches.
Type III Responses to Transient Inputs in Hybrid Nonlinear Neuron Models
Date: 2020-07-23 00:00:00
Tasks: Vocal Bursts Type Prediction

Experimental characterization of neuronal dynamics involves recording both of
spontaneous activity patterns and of responses to transient and sustained
inputs. While much theoretical attention has been devoted to the spontaneous
activity of neurons, less is known about the dynamic mechanisms shaping their
responses to transient inputs, although these bear significant physiological
relevance. Here, we study responses to transient inputs in a widely used class
of neuron models (nonlinear adaptive hybrid models) well-known to reproduce a
number of biologically realistic behaviors. We focus on responses to transient
inputs that have been previously associated with Type III neurons, arguably the
least studied category in Hodgkin's classification, which are those neurons
that never exhibit continuous firing in response to sustained excitatory
currents. The two phenomena that we study are post-inhibitory facilitation, in
which an otherwise subthreshold excitatory input can induce a spike if it is
applied with proper timing after an inhibitory pulse, and slope detection, in
which a neuron spikes to a transient input only when the input's rate of change
is in a specific, bounded range. We analyze the origin of these phenomena in
nonlinear hybrid models and provide a geometric characterization of dynamical
structures associated with PIF in the system and an analytical study of slope
detection for tent inputs. While the necessary and sufficient conditions for
these behaviors are easily satisfied in neurons with Type III excitability, our
proofs are quite general and valid for neurons that do not exhibit Type III
excitability as well. This study therefore provides a framework for the
mathematical analysis of these responses to transient inputs associated with
Type III neurons in other systems and for advancing our understanding of these
systems' computational properties.
The Relationship between the Economic and Financial Crises and Unemployment Rate in the European Union -- How Institutions Affected Their Linkage
Date: 2020-07-20 00:00:00

This paper aims to estimate the impact of economic and financial crises on
the unemployment rate in the European Union, taking also into consideration the
institutional specificities, since unemployment was the main channel through
which the economic and financial crisis influenced the social developments.. In
this context, I performed two institutional clusters depending on their
inclusive or extractive institutional features and, in each cases, I computed
the crisis effect on unemployment rate over the 2003-2017 period. Both models
were estimated by using Panel Estimated Generalized Least Squares method, and
are weighted by Period SUR option in order to remove, in advance the possible
inconveniences of the models. The institutions proved to be a relevant
criterion that drives the impact of economic and financial crises on the
unemployment rate, highlighting that countries with inclusive institutions are
less vulnerable to economic shocks and are more resilient than countries with
extractive institutions. The quality of institutions was also found to have a
significant effect on the response of unemployment rate to the dynamic of its
drivers.
Exclusion of the fittest predicts microbial community diversity in fluctuating environments
Authors: Shota Shibasaki, Mauro Mobilia, Sara Mitri
Date: 2020-07-23 00:00:00
Tasks: Diversity

Microorganisms live in environments that inevitably fluctuate between mild and harsh conditions. As harsh conditions may cause extinctions, the rate at which fluctuations occur can shape microbial communities and their diversity, but we still lack an intuition on how. Here, we build a mathematical model describing two microbial species living in an environment where substrate supplies randomly switch between abundant and scarce. We then vary the rate of switching as well as different properties of the interacting species, and measure the probability of the weaker species driving the stronger one extinct. We find that this probability increases with the strength of demographic noise under harsh conditions and peaks at either low, high, or intermediate switching rates depending on both species' ability to withstand the harsh environment. This complex relationship shows why finding patterns between environmental fluctuations and diversity has historically been difficult. In parameter ranges where the fittest species was most likely to be excluded, however, the beta diversity in larger communities also peaked. In sum, how environmental fluctuations affect interactions between a few species pairs predicts their effect on the beta diversity of the whole community.
T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions
Date: 2020-07-23 00:00:00
Tasks: Super-Resolution

Relaxometry studies in preterm and at-term newborns have provided insight
into brain microstructure, thus opening new avenues for studying normal brain
development and supporting diagnosis in equivocal neurological situations.
However, such quantitative techniques require long acquisition times and
therefore cannot be straightforwardly translated to in utero brain
developmental studies. In clinical fetal brain magnetic resonance imaging
routine, 2D low-resolution T2-weighted fast spin echo sequences are used to
minimize the effects of unpredictable fetal motion during acquisition. As
super-resolution techniques make it possible to reconstruct a 3D
high-resolution volume of the fetal brain from clinical low-resolution images,
their combination with quantitative acquisition schemes could provide fast and
accurate T2 measurements. In this context, the present work demonstrates the
feasibility of using super-resolution reconstruction from conventional
T2-weighted fast spin echo sequences for 3D isotropic T2 mapping. A
quantitative magnetic resonance phantom was imaged using a clinical T2-weighted
fast spin echo sequence at variable echo time to allow for super-resolution
reconstruction at every echo time and subsequent T2 mapping of samples whose
relaxometric properties are close to those of fetal brain tissue. We
demonstrate that this approach is highly repeatable, accurate and robust when
using six echo times (total acquisition time under 9 minutes) as compared to
gold-standard single-echo spin echo sequences (several hours for one single 2D
slice).
Mathematical modeling of glioma invasion: acid- and vasculature mediated go-or-grow dichotomy and the influence of tissue anisotropy
Authors: Martina Conte, Christina Surulescu
Date: 2020-07-23 00:00:00

Starting from kinetic transport equations and subcellular dynamics we deduce a multiscale model for glioma invasion relying on the go-or-grow dichotomy and the influence of vasculature, acidity, and brain tissue anisotropy. Numerical simulations are performed for this model with multiple taxis, in order to assess the solution behavior under several scenarios of taxis and growth for tumor and endothelial cells. An extension of the model to incorporate the macroscopic evolution of normal tissue and necrotic matter allows us to perform tumor grading.
On the performance of optical phased array technology for beam steering
Authors: Antonio Cala' Lesina, Dominic Goodwill, Eric Bernier, Lora Ramunno, Pierre Berini
Date: 2020-07-20 00:00:00

Optical phased arrays are of strong interest for beam steering in telecom and LIDAR applications. A phased array ideally requires that the field produced by each element in the array (a pixel) is fully controllable in phase and amplitude (ideally constant). This is needed to realize a phase gradient along a direction in the array, and thus beam steering in that direction. In practice, grating lobes appear if the pixel size is not sub-wavelength, which is an issue for many optical technologies. Furthermore, the phase performance of an optical pixel may not span the required $2\pi$ phase range, or may not produce a constant amplitude over its phase range. These limitations result in imperfections in the phase gradient, which in turn introduce undesirable secondary lobes. We discuss the effects of non-ideal pixels on beam formation, in a general and technology-agnostic manner. By examining the strength of secondary lobes with respect to the main lobe, we quantify beam steering quality, and make recommendations on the pixel performance required for beam steering within prescribed specifications. By applying appropriate compensation strategies, we show that it is possible to realize high-quality beam steering even when the pixel performance is non-ideal, with intensity of the secondary lobes be two orders of magnitude smaller than the main lobe.
Ordering and Inequalities for Mixtures on Risk Aggregation
Authors: Yuyu Chen, Peng Liu, Yang Liu, Ruodu Wang
Date: 2020-07-24 00:00:00

Aggregation sets, which represent model uncertainty due to unknown dependence, are an important object in the study of robust risk aggregation. In this paper, we investigate ordering relations between two aggregation sets for which the sets of marginals are related by two simple operations: distribution mixtures and quantile mixtures. Intuitively, these operations ``homogenize" marginal distributions by making them similar. As a general conclusion from our results, more ``homogeneous" marginals lead to a larger aggregation set, and thus more severe model uncertainty, although the situation for quantile mixtures is much more complicated than that for distribution mixtures. We proceed to study inequalities on the worst-case values of risk measures in risk aggregation, which represent conservative calculation of regulatory capital. Among other results, we obtain an order relation on VaR under quantile mixture for marginal distributions with monotone densities. Numerical results are presented to visualize the theoretical results and further inspire some conjectures. Finally, we provide applications on portfolio diversification under dependence uncertainty and merging p-values in multiple hypothesis testing, and discuss the connection of our results to joint mixability.
New approaches in modeling belt-flesh-pelvis interaction using obese GHBMC models
Authors: Zhaonan Sun, Bronislaw Gepner, Jason Kerrigan
Date: 2020-07-24 00:00:00

Obesity is associated with higher fatality risk and altered distribution of occupant injuries in automotive collisions partially because of the increased depth of abdominal soft tissue, which results in limited or delayed engagement of the lap belt with the pelvis and increases the risk of pelvis submarining under the lap belt exposing occupant abdomen to belt loading. Previous modeling studies have shown that pelvis submarining could not be replicated using existing human body models. The goal of this study is to perform model modifications and investigate whether they could lead to model submarining. By detaching the connections between the pelvis and surrounding flesh, submarining like belt kinematics were observed. By remeshing the flesh parts of the model, similar belt kinematics was observed but the pelvic wings were fractured. Finally, large shear deformation on the flesh together with submarining like kinematics were observed in the model with its flesh modeled using the meshless Smooth Particle Galerkin Method SPG method. The results of this study showed that SPG method has potential to simulate large deformations in soft tissue which may be necessary to improve the biofidelity of belt/pelvis interaction.