pwc-archive/papers-with-abstracts huggingface dataset:
Effect of Copper Chemical Form on The Growth of Pseudomonas aeruginosa Isolated from Burned Patients and on Its Cu Uptake
Authors: Amjad Al Tarawneh, Haitham Qaralleh, Muhamad O. Al-limoun, Khaled M. Khleifat
Date: 2019-11-23 00:00:00
Tasks: Form

The main object of this investigation was to shed light on the information on the level of Cu uptake by Pseudomonas aeruginosa that has been previously isolated from patients using Gram-negative E. aerogenes ans a Gram-positive Bacillus thuringiensis as a control for Cu uptake measurements. Cu uptake data showed that maximum Cu uptake was obtained by the Pseudomonas aeruginosa. The metal uptake was dependent on the type of biosorbent with different accumulation affinities toward the tested chemical form of the copper. Cells harvested at exponential growth phase showed slightly higher Cu uptake than at stationary phase, which reflect that the Cu uptake is metabolism dependent-process. The increasing order of affinity of the cupric chloride and cupric sulphate towards the three genera were almost constant. However, where cupric nitrate was used, the copper uptake behavior in Pseudomonas aeroginosa was not changed from those results with the other two chemical forms, whereas with E. aerogenes & B. thuringiensis, different Cu uptake behaviors were observed. In B. thuengenesis, the copper uptake rate as a function of initial Cu concentration were shown to increase with increasing the initial concentration of Cu, constantly above 320 ppm as compared with others. In Enterobacter with cupric nitrate, different bahavior was also shown in which the copper uptake decline sharply beyond the 320 ppm. These results reflect that the Cu uptake in P. aeruginosa is with different mechanisms as compared with other tested bacteria.
Generativity and Interactional Effects: an Overview
Date: 2019-11-23 00:00:00

We propose a means to relate properties of an interconnected system to its
separate component systems in the presence of cascade-like phenomena. Building
on a theory of interconnection reminiscent of the behavioral approach to
systems theory, we introduce the notion of generativity, and its byproduct,
generative effects. Cascade effects, enclosing contagion phenomena and
cascading failures, are seen as instances of generative effects. The latter are
precisely the instances where properties of interest are not preserved or
behave very badly when systems interact. The goal is to overcome that
obstruction. We will show how to extract mathematical objects from the systems,
that encode their generativity: their potential to generate new phenomena upon
interaction. Those objects may then be used to link the properties of the
interconnected system to its separate systems. Such a link will be executed
through the use of exact sequences from commutative algebra.
A singular stochastic control approach for optimal pairs trading with proportional transaction costs
Authors: Haipeng Xing
Date: 2019-11-24 00:00:00
Tasks: Portfolio Optimization

Optimal trading strategies for pairs trading have been studied by models that try to find either optimal shares of stocks by assuming no transaction costs or optimal timing of trading fixed numbers of shares of stocks with transaction costs. To find optimal strategies which determine optimally both trade times and number of shares in pairs trading process, we use a singular stochastic control approach to study an optimal pairs trading problem with proportional transaction costs. Assuming a cointegrated relationship for a pair of stock log-prices, we consider a portfolio optimization problem which involves dynamic trading strategies with proportional transaction costs. We show that the value function of the control problem is the unique viscosity solution of a nonlinear quasi-variational inequality, which is equivalent to a free boundary problem for the singular stochastic control value function. We then develop a discrete time dynamic programming algorithm to compute the transaction regions, and show the convergence of the discretization scheme. We illustrate our approach with numerical examples and discuss the impact of different parameters on transaction regions. We study the out-of-sample performance in an empirical study that consists of six pairs of U.S. stocks selected from different industry sectors, and demonstrate the efficiency of the optimal strategy.
Topologically Mapping the Macroeconomy
Date: 2019-11-24 00:00:00
Tasks: Decision Making, Topological Data Analysis

An understanding of the economic landscape in a world of ever increasing data
necessitates representations of data that can inform policy, deepen
understanding and guide future research. Topological Data Analysis offers a set
of tools which deliver on all three calls. Abstract two-dimensional snapshots
of multi-dimensional space readily capture non-monotonic relationships, inform
of similarity between points of interest in parameter space, mapping such to
outcomes. Specific examples show how some, but not all, countries have returned
to Great Depression levels, and reappraise the links between real private
capital growth and the performance of the economy. Theoretical and empirical
expositions alike remind on the dangers of assuming monotonic relationships and
discounting combinations of factors as determinants of outcomes; both dangers
Topological Data Analysis addresses. Policy-makers can look at outcomes and
target areas of the input space where such are not satisfactory, academics may
additionally find evidence to motivate theoretical development, and
practitioners can gain a rapid and robust base for decision making.
High-Dimensional Forecasting in the Presence of Unit Roots and Cointegration
Date: 2019-11-24 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

We investigate how the possible presence of unit roots and cointegration
affects forecasting with Big Data. As most macroeoconomic time series are very
persistent and may contain unit roots, a proper handling of unit roots and
cointegration is of paramount importance for macroeconomic forecasting. The
high-dimensional nature of Big Data complicates the analysis of unit roots and
cointegration in two ways. First, transformations to stationarity require
performing many unit root tests, increasing room for errors in the
classification. Second, modelling unit roots and cointegration directly is more
difficult, as standard high-dimensional techniques such as factor models and
penalized regression are not directly applicable to (co)integrated data and
need to be adapted. We provide an overview of both issues and review methods
proposed to address these issues. These methods are also illustrated with two
empirical applications.
Effects of Adopting Ultra-Fast Charging Stations in the San Francisco Bay Area
Date: 2019-11-25 00:00:00

Ultra-Fast Charging (UFC) is a rising technology that can shorten the time of
charging an Electric Vehicle (EV) from hours to minutes. However, the power
consumption characteristics of UFC bring new challenges to the existing power
system, and its pros and cons are yet to be studied. This project aims to set
up a framework for studying the different aspects of substituting the normal
non-residential EV chargers within the San Francisco Bay Area with Ultra-Fast
Charging (UFC) stations. Three objectives were defined for three stakeholders
involved in this simulation, namely: the EV user, the station owner, and the
grid operator. The results show that, UFCs will significantly contribute to
increase of peak load and power consumption during the peak demand period,
which is an undesirable outcome from grid operation perspective. Total
electricity and operations and maintenance costs for station owner would
increase subsequently, while this can be justified by analyzing the value of
time (VOT) from an EV-user perspective. Additionally, peak-shaving using
battery storage facilities is studied for complementing the applied technology
change and mitigating the impacts of higher power consumption on the grid.
A More Refined Mobile Edge Cache Replacement Scheme for Adaptive Video Streaming with Mutual Cooperation in Multi-MEC Servers
Date: 2019-11-25 00:00:00
Tasks: Edge-computing

In this paper, we propose a more refined video segment based Mobile Edge
Computing (MEC) enhanced cache update strategy, which takes into account the
client's playback status and transmission state, MEC cache capacity and the
popularity of each segment, to improve the quality of experience (QoE) of
clients and the use ratio of MEC cache. In each cache update period, the
segments which cannot bring significant QoE improvement will be deleted and
meanwhile more suitable representations of the segments will be cahed instead.
First, we divide the MEC cache space into three parts based on segment
popularity and segment importance level. In addition, the size of different
cache parts can be transformed to each other to further exploit the MEC cache
space. For different parts, the corresponding deleted strategy and caching
strategy are formulated, where the deleted strategy combines the requested
times of segment and transmission capability of clients, and the caching
strategy utilizes the playback state of clients to calculate the caching
priority. Furthermore, we formulate the cache update problem to maximize the
utility function of clients subject to the constraint of MEC cache size and
transmission capacity. The brand and branch method is emplyed to obatain the
optimal solution. Simulation results show that our proposed algorithm can
improve system throughput and hit ratio of video segment, and at the same time
decrease playback frozen time and system backhaul traffic compared with other
existing algorithms.
A Neural Network Architecture to Learn Explicit MPC Controllers from Data
Date: 2019-11-25 00:00:00
Tasks: Model Predictive Control

We present a methodology to learn explicit Model Predictive Control (eMPC)
laws from sample data points with tunable complexity. The learning process is
cast in a special Neural Network setting where the coefficients of two linear
layers and a parametric quadratic program (pQP) implicit layer are optimized to
fit the training data. Thanks to this formulation, powerful tools from the
machine learning community can be exploited to speed up the off-line
computations through high parallelization. The final controller can be deployed
via low-complexity eMPC and the resulting closed-loop system can be certified
for stability using existing tools available in the literature. A numerical
example on the voltage-current regulation of a multicell DC-DC converter is
provided, where the storage and on-line computational demands of the initial
controller are drastically reduced with negligible performance impact.
Spatial self-organisation enables species coexistence in a model for savanna ecosystems
Date: 2019-11-25 00:00:00

The savanna biome is characterised by a continuous vegetation cover,
comprised of herbaceous and woody plants. The coexistence of species in arid
savannas, where water availability is the main limiting resource for plant
growth, provides an apparent contradiction to the classical principle of
competitive exclusion. Previous theoretical work using nonspatial models has
focussed on the development of an understanding of coexistence mechanisms
through the consideration of resource niche separation and ecosystem
disturbances. In this paper, we propose that a spatial self-organisation
principle, caused by a positive feedback between local vegetation growth and
water redistribution, is sufficient for species coexistence in savanna
ecosystems. We propose a spatiotemporal ecohydrological model of partial
differential equations, based on the Klausmeier reaction-advection-diffusion
model for vegetation patterns, to investigate the effects of spatial
interactions on species coexistence on sloped terrain. Our results suggest that
species coexistence is a possible model outcome, if a balance is kept between
the species' average fitness (a measure of a species' competitive abilities in
a spatially uniform setting) and their colonisation abilities. Spatial
heterogeneities in resource availability are utilised by the superior coloniser
(grasses), before it is outcompeted by the species of higher average fitness
(trees). A stability analysis of the spatially nonuniform coexistence solutions
further suggests that grasses act as ecosystem engineers and facilitate the
formation of a continuous tree cover for precipitation levels unable to support
a uniform tree density in the absence of a grass species.
An Iterative Interference Cancellation Algorithm for Large Intelligent Surfaces
Date: 2019-11-25 00:00:00
Tasks: CPU

The Large Intelligent Surface (LIS) concept is a promising technology aiming
to revolutionize wireless communication by exploiting spatial multiplexing at
its fullest. Despite of its potential, due to the size of the LIS and the large
number of antenna elements involved there is a need of decentralized
architectures together with distributed algorithms which can reduce the
inter-connection data-rate and computational requirement in the Central
Processing Unit (CPU). In this article we address the uplink detection problem
in the LIS system and propose a decentralize architecture based on panels,
which perform local linear processing. We also provide the sum-rate capacity
for such architecture and derive an algorithm to obtain the equalizer, which
aims to maximize the sum-rate capacity. A performance analysis is also
presented, including a comparison to a naive approach based on a reduced form
of the matched filter (MF) method. The results shows the superiority of the
proposed algorithm.
Optimal Model-Based Sensor Placement & Adaptive Monitoring Of An Oil Spill
Date: 2019-11-25 00:00:00

This paper presents a model based adaptive monitoring method for the
estimation of flow tracers, with application to mapping, prediction and
observation of oil spills in the immediate aftermath of an incident. Autonomous
agents are guided to optimal sensing locations via the solution of a PDE
constrained optimisation problem, obtained using the adjoint method. The
proposed method employs a dynamic model of the combined ocean and oil dynamics,
with states that are updated in real-time using a Kalman filter that fuses
agent-based measurements with a reduced-order model of the ocean circulation
dynamics. In turn, the updated predictions from the fluid model are used to
identify and update the reduced order model, in a process of continuous
feedback. The proposed method exhibits a 30% oil presence mapping and
prediction improvement compared to standard industrial oil observation sensor
guidance and model use.
Sound event detection via dilated convolutional recurrent neural networks
Date: 2020-07-20 00:00:00
Tasks: Event Detection, Sound Event Detection

Convolutional recurrent neural networks (CRNNs) have achieved
state-of-the-art performance for sound event detection (SED). In this paper, we
propose to use a dilated CRNN, namely a CRNN with a dilated convolutional
kernel, as the classifier for the task of SED. We investigate the effectiveness
of dilation operations which provide a CRNN with expanded receptive fields to
capture long temporal context without increasing the amount of CRNN's
parameters. Compared to the classifier of the baseline CRNN, the classifier of
the dilated CRNN obtains a maximum increase of 1.9%, 6.3% and 2.5% at F1 score
and a maximum decrease of 1.7%, 4.1% and 3.9% at error rate (ER), on the
publicly available audio corpora of the TUT-SED Synthetic 2016, the TUT Sound
Event 2016 and the TUT Sound Event 2017, respectively.
Experimental realization of Fermi-Pasta-Ulam-Tsingou recurrence in a long-haul optical fiber transmission system
Date: 2019-11-25 00:00:00

The integrable nonlinear Schr\"odinger equation (NLSE) is a fundamental model
of nonlinear science which also has important consequences in engineering. The
powerful framework of the periodic inverse scattering transform (IST) provides
a description of the nonlinear phenomena modulational instability and
Fermi-Pasta-Ulam-Tsingou (FPUT) recurrence in terms of exact solutions. It
associates the complex nonlinear dynamics with invariant nonlinear spectral
degrees of freedom that may be used to encode information. While optical fiber
is an ideal testing ground of its predictions, maintaining integrability over
sufficiently long distances to observe recurrence, as well as synthesizing and
measuring the field in both amplitude and phase on the picosecond timescales of
typical experiments is challenging. Here we report on the experimental
realization of FPUT recurrence in terms of an exact space-time-periodic
solution of the integrable NLSE in a testbed for optical communication
experiments. The complex-valued initial condition is constructed by means of
the finite-gap integration method, modulated onto the optical carrier driven by
an arbitrary waveform generator and launched into a recirculating fiber loop
with periodic amplification. The measurement with an intradyne coherent
receiver after a predetermined number of revolutions provides a non-invasive
full-field characterization of the space-time dynamics. The recurrent
space-time evolution is in close agreement with theoretical predictions over a
distance of 9000 km. Nonlinear spectral analysis reveals an invariant nonlinear
spectrum. The space-time scale exceeds that of previous experiments on FPUT
recurrence in fiber by three orders of magnitude.
Decision making under uncertainty in energy systems: state of the art
Date: 2019-11-25 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

The energy system studies include a wide range of issues from short term
(e.g. real-time, hourly, daily and weekly operating decisions) to long term
horizons (e.g. planning or policy making). The decision making chain is fed by
input parameters which are usually subject to uncertainties. The art of dealing
with uncertainties has been developed in various directions and has recently
become a focal point of interest. In this paper, a new standard classification
of uncertainty modeling techniques for decision making process is proposed.
These methods are introduced and compared along with demonstrating their
strengths and weaknesses. The promising lines of future researches are explored
in the shadow of a comprehensive overview of the past and present applications.
The possibility of using the novel concept of Z-numbers is introduced for the
first time.
Predicting crashes in oil prices during the COVID-19 pandemic with mixed causal-noncausal models
Authors: Alain Hecq, Elisa Voisin
Date: 2019-11-25 00:00:00

This paper aims at shedding light upon how transforming or detrending a series can substantially impact predictions of mixed causal-noncausal (MAR) models, namely dynamic processes that depend not only on their lags but also on their leads. MAR models have been successfully implemented on commodity prices as they allow to generate nonlinear features such as locally explosive episodes (denoted here as bubbles) in a strictly stationary setting. We consider multiple detrending methods and investigate, using Monte Carlo simulations, to what extent they preserve the bubble patterns observed in the raw data. MAR models relies on the dynamics observed in the series alone and does not require economical background to construct a structural model, which can sometimes be intricate to specify or which may lack parsimony. We investigate oil prices and estimate probabilities of crashes before and during the first 2020 wave of the COVID-19 pandemic. We consider three different mechanical detrending methods and compare them to a detrending performed using the level of strategic petroleum reserves.
Denting the FRTB IMA computational challenge via Orthogonal Chebyshev Sliding Technique
Date: 2019-11-25 00:00:00

In this paper we introduce a new technique based on high-dimensional Chebyshev Tensors that we call \emph{Orthogonal Chebyshev Sliding Technique}. We implemented this technique inside the systems of a tier-one bank, and used it to approximate Front Office pricing functions in order to reduce the substantial computational burden associated with the capital calculation as specified by FRTB IMA. In all cases, the computational burden reductions obtained were of more than $90\%$, while keeping high degrees of accuracy, the latter obtained as a result of the mathematical properties enjoyed by Chebyshev Tensors.
A Stabilizing Control Algorithm for Asynchronous Parallel Quadratic Programming via Dual Decomposition
Date: 2019-11-21 00:00:00

This paper proposes a control algorithm for stable implementation of
asynchronous parallel quadratic programming (PQP) through dual decomposition
technique. In general, distributed and parallel optimization requires
synchronization of data at each iteration step due to the interdependency of
data. The synchronization latency may incur a large amount of waiting time
caused by an idle process during computation. We aim to mitigate this
synchronization penalty in PQP problems by implementing asynchronous updates of
the dual variable. The price to pay for adopting asynchronous computing
algorithms is the unpredictability of the solution, resulting in a tradeoff
between speedup and accuracy. In the worst case, the state of interest may
become unstable owing to the stochastic behavior of asynchrony. We investigate
the stability condition of asynchronous PQP problems by employing the switched
system framework. A formal algorithm is provided to ensure the asymptotic
stability of dual variables. Further, it is shown that the implementation of
the proposed algorithm guarantees the uniqueness of optimal solutions,
irrespective of asynchronous behavior. To verify the validity of the proposed
methods, simulation results are presented.
Analysis of a model for banded vegetation patterns in semi-arid environments with nonlocal dispersal
Date: 2019-11-25 00:00:00
Tasks: valid

Vegetation patterns are a characteristic feature of semi-arid regions. On
hillsides these patterns occur as stripes running parallel to the contours. The
Klausmeier model, a coupled reaction-advection-diffusion system, is a
deliberately simple model describing the phenomenon. In this paper, we replace
the diffusion term describing plant dispersal by a more realistic nonlocal
convolution integral to account for the possibility of long-range dispersal of
seeds. Our analysis focuses on the rainfall level at which there is a
transition between uniform vegetation and pattern formation. We obtain results,
valid to leading order in the large parameter comparing the rate of water flow
downhill to the rate of plant dispersal, for a negative exponential dispersal
kernel. Our results indicate that both a wider dispersal of seeds and an
increase in dispersal rate inhibit the formation of patterns. Assuming an
evolutionary trade-off between these two quantities, mathematically motivated
by the limiting behaviour of the convolution term, allows us to make
comparisons to existing results for the original reaction-advection-diffusion
system. These comparisons show that the nonlocal model always predicts a larger
parameter region supporting pattern formation. We then numerically extend the
results to other dispersal kernels, showing that the tendency to form patterns
depends on the type of decay of the kernel.