pwc-archive/papers-with-abstracts huggingface dataset:
Rational evaluation of various epidemic models based on the COVID-19 data of China
Authors: Wuyue Yang, Dongyan Zhang, Liangrong Peng, Changjing Zhuge, Liu Hong
Date: 2020-03-12 00:00:00

In this paper, based on the Akaike information criterion, root mean square error and robustness coefficient, a rational evaluation of various epidemic models/methods, including seven empirical functions, four statistical inference methods and five dynamical models, on their forecasting abilities is carried out. With respect to the outbreak data of COVID-19 epidemics in China, we find that before the inflection point, all models fail to make a reliable prediction. The Logistic function consistently underestimates the final epidemic size, while the Gompertz's function makes an overestimation in all cases. Towards statistical inference methods, the methods of sequential Bayesian and time-dependent reproduction number are more accurate at the late stage of an epidemic. And the transition-like behavior of exponential growth method from underestimation to overestimation with respect to the inflection point might be useful for constructing a more reliable forecast. Compared to ODE-based SIR, SEIR and SEIR-AHQ models, the SEIR-QD and SEIR-PO models generally show a better performance on studying the COVID-19 epidemics, whose success we believe could be attributed to a proper trade-off between model complexity and fitting accuracy. Our findings not only are crucial for the forecast of COVID-19 epidemics, but also may apply to other infectious diseases.
Energy-efficient Deployment of Multiple UAVs Using Ellipse Clustering to Establish Base Stations
Date: 2020-03-12 00:00:00
Tasks: Clustering, User Simulation

The demand for future wireless communication systems is being satisfied for
various circumstances through unmanned aerial vehicles (UAVs), which act as
flying base stations (BSs). In this letter, we propose an ellipse clustering
algorithm that maximizes the user coverage probability of UAV- BSs and avoids
inter-cell interference with minimal transmit power. We obtain the coverage of
each UAV by adjusting its antenna half-power beamwidth, orientation, and 3D
location by minimizing the path loss of the cell-edge user. Simulation results
confirm that the proposed algorithm achieves high system throughput and
coverage probability with lower transmit power compared to conventional
algorithms.
Characterizing Algal blooms in a shallow and a deep channel over a decade (2008-2018)
Date: 2020-03-16 00:00:00

The outbreaks of algal blooms occur in both shallow and deep-water bodies. To
compare the characteristics of algal blooms in the shallow and deep water, we
consider the Arabian Gulf and Sea of Oman as a case study. While the Arabian
Gulf is a shallow region dominated by advective features, the Sea of Oman is a
deep channel dominated by numerous eddies. Both these regions is have rarely
been studied due to lack of available data thus preventing the characterization
of phenomenon such as algal blooms in the regions. Nonetheless, a recent unique
and comprehensive dataset of the frequent algal blooms collected over the last
decade in the Arabian Gulf and Oman Sea is utilized in this study to analyze
the spatio-temporal variability of the blooms thereof. These data are also used
to characterize the algal bloom species and analyze the relationship between
the water properties and the algal blooms in the shallow and deep waters. There
is a general decreasing trend of the algal bloom events from 2010 to 2018 in
the Arabian Gulf while in the Sea of Oman there is an increasing trend. We
reveal a clear seasonality with the highest frequency of algal blooms during
winter and spring. We have noticed that algal blooms have the feature of the
annual cycle with initial blooms happening in November-December and
December-January in the Arabian Gulf and Sea of Oman, respectively. The
analysis further demonstrates that the algal blooms grow better at salinity
levels of 39-40 psu/37-37.5 psu, temperature of 23-24 oC, and pH of 8 in the
Arabian Gulf/Oman Sea. Findings of this study provide insight into the
relationship between water properties and algal bloom frequency, and a basis
for future research into the drivers behind these observed spatio-temporal
trends.
Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities
Authors: Christian Bayer, Chiheb Ben Hammouda, Raul Tempone
Date: 2020-03-12 00:00:00
Tasks: Density Estimation, Numerical Integration

The multilevel Monte Carlo (MLMC) method is highly efficient for estimating expectations of a functional of a solution to a stochastic differential equation (SDE). However, MLMC estimators may be unstable and have a poor (noncanonical) complexity in the case of low regularity of the functional. To overcome this issue, we extend our previously introduced idea of numerical smoothing in (Quantitative Finance, 23(2), 209-227, 2023), in the context of deterministic quadrature methods to the MLMC setting. The numerical smoothing technique is based on root-finding methods combined with one-dimensional numerical integration with respect to a single well-chosen variable. This study is motivated by the computation of probabilities of events, pricing options with a discontinuous payoff, and density estimation problems for dynamics where the discretization of the underlying stochastic processes is necessary. The analysis and numerical experiments reveal that the numerical smoothing significantly improves the strong convergence, and consequently, the complexity and robustness (by making the kurtosis at deep levels bounded) of the MLMC method. In particular, we show that numerical smoothing enables recovering the MLMC complexities obtained for Lipschitz functionals due to the optimal variance decay rate when using the Euler--Maruyama scheme. For the Milstein scheme, numerical smoothing recovers the canonical MLMC complexity even for the nonsmooth integrand mentioned above. Finally, our approach efficiently estimates univariate and multivariate density functions.
Indemnity Payments in Agricultural Insurance: Risk Exposure of EU States
Date: 2020-03-12 00:00:00

This study estimates the risk contributions of individual European countries
regarding the indemnity payments in agricultural insurance. We model the total
risk exposure as an insurance portfolio where each country is unique in terms
of its risk characteristics. The data has been collected from the recent
surveys conducted by the European Commission and the World Bank. Farm
Accountancy Data Network is used as well. 22 out of 26 member states are
included in the study. The results suggest that the EuroMediterranean countries
are the major risk contributors. These countries not only have the highest
expected loss but also high volatility of indemnity payments. Nordic countries
have the lowest indemnity payments and risk exposure.
Fixed-order strong H-infinity control of interconnected systems with time-delays
Date: 2020-03-10 00:00:00

We design fixed-order strong H-infinity controllers for general time-delay
systems. The designer chooses the controller order and may introduce constant
time-delays in the controller. We represent the closed-loop system of the plant
and the controller as delay differential algebraic equations (DDAEs). This
representation deals with any interconnection of systems with time-delays
without any elimination techniques. We present a numerical algorithm to compute
the strong H-infinity norm for DDAEs which is robust to arbitrarily small delay
perturbations, unlike the standard H-infinity norm. We optimize the strong
H-infinity norm of the closed-loop system based on non-smooth, non-convex
optimization methods using this algorithm and the computation of the gradient
of the strong H-infinity norm with respect to the controller parameters. We
tune the controller parameters and design H-infinity controllers with a
prescribed order or structure.
Multidimensional Analysis of Monthly Stock Market Returns
Date: 2020-03-12 00:00:00

This study examines the monthly returns in Turkish and American stock market
indices to investigate whether these markets experience abnormal returns during
some months of the calendar year. The data used in this research includes 212
observations between January 1996 and August 2014. I apply statistical summary
analysis, decomposition technique, dummy variable estimation, and binary
logistic regression to check for the monthly market anomalies. The
multidimensional methods used in this article suggest weak evidence against the
efficient market hypothesis on monthly returns. While some months tend to show
abnormal returns, there is no absolute unanimity in the applied approaches.
Nevertheless, there is a strikingly negative May effect on the Turkish stocks
following a positive return in April. Stocks tend to be bullish in December in
both markets, yet we do not observe anya significant January effect is not
observed.
Actuator Security Index for Structured Systems
Date: 2020-03-12 00:00:00

Given a network with the set of vulnerable actuators (and sensors), the
security index of an actuator equals the minimum number of sensors and
actuators that needs to be compromised so as to conduct a perfectly
undetectable attack using the said actuator. This paper deals with the problem
of computing actuator security indices for discrete-time LTI network systems.
Firstly, we show that, under a structured systems framework, the actuator
security index is generic. Thereafter, we provide graph-theoretic conditions
for computing the structural actuator security index. The said conditions are
in terms of existence of linkings on appropriately-defined directed
(sub)graphs. Based on these conditions, we present an algorithm for computing
the structural index.
Inf-convolution and optimal risk sharing with countable sets of risk measures
Authors: Marcelo Brutti Righi, Marlon Ruoso Moresco
Date: 2020-03-12 00:00:00

The inf-convolution of risk measures is directly related to risk sharing and general equilibrium, and it has attracted considerable attention in mathematical finance and insurance problems. However, the theory is restricted to finite sets of risk measures. This study extends the inf-convolution of risk measures in its convex-combination form to a countable (not necessarily finite) set of alternatives. The intuitive meaning of this approach is to represent a generalization of the current finite convex weights to the countable case. Subsequently, we extensively generalize known properties and results to this framework. Specifically, we investigate the preservation of properties, dual representations, optimal allocations, and self-convolution.
Covariance matrix filtering with bootstrapped hierarchies
Date: 2020-03-12 00:00:00

Statistical inference of the dependence between objects often relies on
covariance matrices. Unless the number of features (e.g. data points) is much
larger than the number of objects, covariance matrix cleaning is necessary to
reduce estimation noise. We propose a method that is robust yet flexible enough
to account for fine details of the structure covariance matrix. Robustness
comes from using a hierarchical ansatz and dependence averaging between
clusters; flexibility comes from a bootstrap procedure. This method finds
several possible hierarchical structures in DNA microarray gene expression
data, and leads to lower realized risk in global minimum variance portfolios
than current filtering methods when the number of data points is relatively
small.
UAV-to-Device Underlay Communications: Age of Information Minimization by Multi-agent Deep Reinforcement Learning
Date: 2020-03-12 00:00:00
Tasks: Deep Reinforcement Learning

In recent years, unmanned aerial vehicles (UAVs) have found numerous sensing
applications, which are expected to add billions of dollars to the world
economy in the next decade. To further improve the Quality-of-Service (QoS) in
such applications, the 3rd Generation Partnership Project (3GPP) has considered
the adoption of terrestrial cellular networks to support UAV sensing services,
also known as the cellular Internet of UAVs. In this paper, we consider a
cellular Internet of UAVs, where the sensory data can be transmitted either to
base station (BS) via cellular links, or to mobile devices by underlay
UAV-to-Device (U2D) communications. To evaluate the freshness of data, the age
of information (AoI) is adopted, in which a lower AoI implies fresher data.
Since UAVs' AoIs are determined by their trajectories during sensing and
transmission, we investigate the AoI minimization problem for UAVs by designing
their trajectories. This problem is a Markov decision problem (MDP) with an
infinite state-action space, and thus we utilize multi-agent deep reinforcement
learning (DRL) to approximate the state-action space. Then, we propose a
multi-UAV trajectory design algorithm to solve this problem. Simulation results
show that our algorithm achieves a lower AoI than greedy algorithm and policy
gradient algorithm.
The effect of pulsed electromagnetic field exposure on osteoinduction of human mesenchymal stem cells cultured on nano-$TiO_{2}$ surfaces
Authors: Nora Bloise, Loredana Petecchia, Gabriele Ceccarelli, Lorenzo Fassina, Cesare Usai, Federico Bertoglio, Martina Balli, Massimo Vassalli, Maria Gabriella Cusella De Angelis, Paola Gavazzo, Marcello Imbriani, Livia Visai
Date: 2020-03-10 00:00:00

Human bone marrow-derived mesenchymal stem cells (hBM-MSCs) are considered a great promise in the repair and regeneration of bone. Considerable efforts have been oriented towards uncovering the best strategy to promote stem cells osteogenic differentiation. In previous studies, hBM-MSCs exposed to physical stimuli such as pulsed electromagnetic fields (PEMFs) or directly seeded on nanostructured titanium surfaces ($TiO_{2}$) were shown to improve their differentiation to osteoblasts in osteogenic condition. In the present study, the effect of a daily PEMF-exposure on osteogenic differentiation of hBM-MSCs seeded onto nanostructured $TiO_{2}$ (with clusters under 100 nm of dimension) was investigated. $TiO_{2}$-seeded cells were exposed to PEMF (magnetic field intensity: 2 mT; intensity of induced electric field: 5 mV; frequency: 75 Hz) and examined in terms of cell physiology modifications and osteogenic differentiation. Results showed that PEMF exposure affected $TiO_{2}$-seeded cells osteogenesis by interfering with selective calcium-related osteogenic pathways, and greatly enhanced hBM-MSCs osteogenic features such as the expression of early/late osteogenic genes and protein production (e.g., ALP, COL-I, osteocalcin and osteopontin) and ALP activity. Finally, PEMF-treated cells resulted to secrete into conditioned media higher amounts of BMP-2, DCN and COL-I than untreated cell cultures. These findings confirm once more the osteoinductive potential of PEMF, suggesting that its combination with $TiO_{2}$ nanostructured surface might be a great option in bone tissue engineering applications.
Bringing in the outliers: A sparse subspace clustering approach to learn a dictionary of mouse ultrasonic vocalizations
Date: 2020-03-12 00:00:00
Tasks: Clustering

Mice vocalize in the ultrasonic range during social interactions. These
vocalizations are used in neuroscience and clinical studies to tap into complex
behaviors and states. The analysis of these ultrasonic vocalizations (USVs) has
been traditionally a manual process, which is prone to errors and human bias,
and is not scalable to large scale analysis. We propose a new method to
automatically create a dictionary of USVs based on a two-step spectral
clustering approach, where we split the set of USVs into inlier and outlier
data sets. This approach is motivated by the known degrading performance of
sparse subspace clustering with outliers. We apply spectral clustering to the
inlier data set and later find the clusters for the outliers. We propose
quantitative and qualitative performance measures to evaluate our method in
this setting, where there is no ground truth. Our approach outperforms two
baselines based on k-means and spectral clustering in all of the proposed
performance measures, showing greater distances between clusters and more
variability between clusters.
Computing Robustly Forward Invariant Sets for Mixed-Monotone Systems
Date: 2020-08-21 00:00:00

This work presents new tools for studying reachability and set invariance for
continuous-time mixed-monotone dynamical systems subject to a disturbance
input. The vector field of a mixed-monotone system is decomposable via a
decomposition function into increasing and decreasing components, and this
decomposition enables embedding the original dynamics in a higher-dimensional
embedding system. While the original system is subject to an unknown
disturbance input, the embedding system has no disturbances and its
trajectories provide bounds for finite-time reachable sets of the original
dynamics. Our main contribution is to show how one can efficiently identify
robustly forward invariant and attractive sets for mixed-monotone systems by
studying certain equilibria of this embedding system. We show also how this
approach, when applied to the backward-time dynamics, establishes different
robustly forward invariant sets for the original dynamics. Lastly, we present
an independent result for computing decomposition functions for systems with
polynomial dynamics. These tools and results are demonstrated through several
examples and a case study.
Optimal market making with persistent order flow
Authors: Paul Jusselin
Date: 2020-03-12 00:00:00
Tasks: Clustering

\noindent We address the issue of market making on electronic markets when taking into account the clustering and long memory properties of market order flows. We consider a market model with one market maker and order flows driven by general Hawkes processes. We formulate the market maker's objective as a stochastic control problem. We characterize an optimal control by proving existence and uniqueness of a viscosity solution to the associated Hamilton-Jacobi-Bellman equation. Finally we propose a fully consistent numerical method allowing to implement this optimal strategy in practice.
Comments on `Design and Implementation of Model-Predictive Control With Friction Compensation on an Omnidirectional Mobile Robot'
Date: 2020-03-12 00:00:00
Tasks: Friction, Model Predictive Control

There are errors in the dynamics model in \cite{b1}. In addition, some
details of the derivations and assumptions are missing in the paper. This
letter was submitted to the IEEE Transactions on Mechatronics and although
reviewers acknowledged the merits of the paper, but was not finally approved to
be published and suggested to be submitted as a conference paper. I still think
this work is worth disseminating and it is potentially very useful for students
or practitioners. In this letter, (i) the assumptions made are presented and
the governing dynamics with details are derived, and (ii) the correct equations
followed by the correct component of the state-space model ($\boldsymbol{A}$)
are given.
Optimal Sensing Precision in Ensemble and Unscented Kalman Filtering
Date: 2020-03-12 00:00:00

We consider the problem of selecting an optimal set of sensor precisions to
estimate the states of a non-linear dynamical system using an Ensemble Kalman
filter and an Unscented Kalman filter, which uses random and deterministic
ensembles respectively. Specifically, the goal is to choose at run-time, a
sparse set of sensor precisions for active-sensing that satisfies certain
constraints on the estimated state covariance. In this paper, we show that this
sensor precision selection problem is a semidefinite programming problem when
we use l1 norm over precision vector as the surrogate measure to induce
sparsity. We formulate a sensor selection scheme over multiple time steps, for
certain constraints on the terminal estimated state covariance.
Isochronous Architecture-Based Voltage-Active Power Droop for Multi-Inverter Systems
Date: 2020-03-12 00:00:00

Advanced microgrids consisting of distributed energy resources interfaced
with multi-inverter systems are becoming more common. Consequently, the
effectiveness of voltage and frequency regulation in microgrids using
conventional droop-based methodologies is challenged by uncertainty in the
sizeand schedule of loads. This article proposes an isochronous architecture of
parallel inverters with only voltage-active power droop (VP-D) control for
improving active power sharing as well as plug-and-play of multi-inverter based
distributed energyresources (DERs). In spite of not employing explicit control
for frequency regulation, this architecture allows even sharing of reactive
power while maintaining reduced circulating currents between inverters. The
performance is achieved even when there are mismatches between commanded
reference and power demanded from the actual load in the network. The
isochronous architecture is implemented by employing a global positioning
system (GPS) to disseminate timing signals that enable the microgrid to
maintain nominal system frequency in the entire network. This enables direct
control of active power through voltage source inverter (VSI) output voltage
regulation, even in the presence of system disturbances. A small signal
eigenvalue analysis of a multi-inverter system near the steady-state operating
point is presented to evaluate the stability of the multi-inverter system with
the proposed VP-D control. Simulation studies and hardware experiments on an
1.2 kVA prototype are conducted. The effectiveness of the proposed architecture
towards active and reactive power sharing between inverters with load scenarios
are demonstrated. Results of the hardware experiments corroborate the viability
of the proposed VP-D control architecture.
Eigen Value Analysis in Lower Bounding Uncertainty of Kalman Filter Estimates
Authors: Niladri Das, Raktim Bhattacharya
Date: 2020-03-12 00:00:00
Tasks: State Estimation

In this paper we are concerned with the error-covariance lower-bounding problem in Kalman filtering: a sensor releases a set of measurements to the data fusion/estimation center, which has a perfect knowledge of the dynamic model, to allow it to estimate the states, while preventing it to estimate the states beyond a given accuracy. We propose a measurement noise manipulation scheme to ensure lower-bound on the estimation accuracy of states. Our proposed method ensures lower-bound on the steady state estimation error of Kalman filter, using mathematical tools from eigen value analysis.
Analysis and forecast of COVID-19 spreading in China, Italy and France
Date: 2020-03-12 00:00:00

In this note we analyze the temporal dynamics of the coronavirus disease 2019
outbreak in China, Italy and France in the time window $22/01-09/03/2020$. A
first analysis of simple day-lag maps points to some universality in the
epidemic spreading, suggesting that simple mean-field models can be
meaningfully used to gather a quantitative picture of the epidemic spreading,
and notably the height and time of the peak of confirmed infected individuals.
  The analysis of the same data within a simple susceptible-(confirmed)
infected-recovered-deaths model indicates that the kinetic parameter that
describes the rate of recovery seems to be the same, irrespective of the
country, while the infection and death rates appear to be more variable. The
model places the peak in Italy around March 20$^{\rm th}$ 2020, with a maximum
number of confirmed infected individuals of about 16,000.