pwc-archive/papers-with-abstracts huggingface dataset:
Risk-dependent centrality in economic and financial networks
Authors: Paolo Bartesaghi, Michele Benzi, Gian Paolo Clemente, Rosanna Grassi, Ernesto Estrada
Date: 2019-07-18 00:00:00

Node centrality is one of the most important and widely used concepts in the study of complex networks. Here, we extend the paradigm of node centrality in financial and economic networks to consider the changes of node "importance" produced not only by the variation of the topology of the system but also as a consequence of the external levels of risk to which the network as a whole is submitted. Starting from the "Susceptible-Infected" (SI) model of epidemics and its relation to the communicability functions of networks we develop a series of risk-dependent centralities for nodes in (financial and economic) networks. We analyze here some of the most important mathematical properties of these risk-dependent centrality measures. In particular, we study the newly observed phenomenon of ranking interlacement, by means of which two entities may interlace their ranking positions in terms of risk in the network as a consequence of the change in the external conditions only, i.e., without any change in the topology. We test the risk-dependent centralities by studying two real-world systems: the network generated by collecting assets of the S\&P 100 and the corporate board network of the US top companies, according to Forbes in 1999. We found that a high position in the ranking of the analyzed financial companies according to their risk-dependent centrality corresponds to companies more sensitive to the external market variations during the periods of crisis.
Joint Design of surveillance radar and MIMO communication in cluttered environments
Date: 2019-07-18 00:00:00

In this study, we consider a spectrum sharing architecture, wherein a
multiple-input multiple-output communication system cooperatively coexists with
a surveillance radar. The degrees of freedom for system design are the transmit
powers of both systems, the receive linear filters used for pulse compression
and interference mitigation at the radar receiver, and the space-time
communication codebook. The design criterion is the maximization of the mutual
information between the input and output symbols of the communication system,
subject to constraints aimed at safeguarding the radar performance. Unlike
previous studies, we do not require any time-synchronization between the two
systems, and we guarantee the radar performance on all of the range-azimuth
cells of the patrolled region under signal-dependent (endogenous) and
signal-independent (exogenous) interference. This leads to a non-convex
problem, and an approximate solution is thus introduced using a block
coordinate ascent method. A thorough analysis is provided to show the merits of
the proposed approach and emphasize the inherent tradeoff among the achievable
mutual information, the density of scatterers in the environment, and the
number of protected radar cells.
Linearizing Active Antenna Arrays: Method and Measurements
Date: 2019-07-18 00:00:00

In this paper, we provide a novel framework for efficient digital
predistortion (DPD) based linearization of active antenna arrays with multiple
and mutually different nonlinear power amplifiers. The proposed method builds
on the use of a combined feedback signal essentially characterizing the
observable nonlinear distortion at the receiving end. The proposed method is
validated with extensive over-the-air RF measurements on a 64-element active
antenna array transmitter operating at 28 GHz carrier frequency and
transmitting a 200 MHz wide 5G New Radio (NR) waveform. The obtained results
demonstrate the excellent linearization capabilities of the proposed solution,
which allows for a very efficient implementation in practical systems.
Parameter estimation for biochemical reaction networks using Wasserstein distances
Date: 2019-10-21 00:00:00
Tasks: Bayesian Optimization, parameter estimation

We present a method for estimating parameters in stochastic models of
biochemical reaction networks by fitting steady-state distributions using
Wasserstein distances. We simulate a reaction network at different parameter
settings and train a Gaussian process to learn the Wasserstein distance between
observations and the simulator output for all parameters. We then use Bayesian
optimization to find parameters minimizing this distance based on the trained
Gaussian process. The effectiveness of our method is demonstrated on the
three-stage model of gene expression and a genetic feedback loop for which
moment-based methods are known to perform poorly. Our method is applicable to
any simulator model of stochastic reaction networks, including Brownian
Dynamics.
Centralised and Distributed Optimization for Aggregated Flexibility Services Provision
Date: 2019-07-18 00:00:00
Tasks: Distributed Optimization

The recent deployment of distributed battery units in prosumer premises offer
new opportunities for providing aggregated flexibility services to both
distribution system operators and balance responsible parties. The optimization
problem presented in this paper is formulated with an objective of cost
minimization which includes energy and battery degradation cost to provide
flexibility services. A decomposed solution approach with the alternating
direction method of multipliers (ADMM) is used instead of commonly adopted
centralised optimization to reduce the computational burden and time, and then
reduce scalability limitations. In this work we apply a modified version of
ADMM that includes two new features with respect to the original algorithm:
first, the primal variables are updated concurrently, which reduces
significantly the computational cost when we have a large number of involved
prosumers; second, it includes a regularization term named Proximal Jacobian
(PJ) that ensures the stability of the solution. A case study is presented for
optimal battery operation of 100 prosumer sites with real-life data. The
proposed method finds a solution which is equivalent to the centralised
optimization problem and is computed between 5 and 12 times faster. Thus,
aggregators or large-scale energy communities can use this scalable algorithm
to provide flexibility services.
Estimating regional cerebral blood flow using resting-state functional MRI via machine learning
Date: 2019-07-18 00:00:00
Tasks: BIG-bench Machine Learning

Perfusion MRI is an important modality in many brain imaging protocols, since
it probes cerebrovascular changes in aging and many diseases; however, it may
not be always available. Here we introduce a method that seeks to estimate
regional perfusion properties using spectral information of resting-state
functional MRI (rsfMRI) via machine learning. We used pairs of rsfMRI and
arterial spin labeling (ASL) images from the same elderly individuals with
normal cognition (NC; n = 45) and mild cognitive impairment (MCI; n = 26), and
built support vector machine models aiming to estimate regional cerebral blood
flow (CBF) from the rsfMRI signal alone. This method demonstrated higher
associations between the estimated CBF and actual CBF (ASL-CBF) at the total
lobar gray matter (r = 0.40; FDR-p = 1.9e-03), parietal lobe (r = 0.46, FDR-p =
8e-04), and occipital lobe (r = 0.35; FDR-p = 0.01) using rsfMRI signals of
frequencies [0.01-0.15] Hertz compared to frequencies [0.01-0.10] Hertz and
[0.01-0.20] Hertz, respectively. We further observed significant associations
between the estimated CBF and actual CBF in 24 regions of interest (p < 0.05),
with the highest association observed in the superior parietal lobule (r =
0.50, FDR-p = 0.002). Moreover, the estimated CBF at superior parietal lobule
showed significant correlation with the mini-mental state exam (MMSE) score (r
= 0.27; FDR-p = 0.04) and decreased in MCI with lower MMSE score compared to NC
group (FDR-p = 0.04). Overall, these results suggest that the proposed
framework can obtain estimates of regional perfusion from rsfMRI, which can
serve as surrogate perfusion measures in the absence of ASL.
How many different clonotypes do immune repertoires contain?
Date: 2019-07-18 00:00:00
Tasks: Diversity

Immune repertoires rely on diversity of T-cell and B-cell receptors to
protect us against foreign threats. The ability to recognize a wide variety of
pathogens is linked to the number of different clonotypes expressed by an
individual. Out of the estimated $\sim 10^{12}$ different B and T cells in
humans, how many of them express distinct receptors? We review current and past
estimates for these numbers. We point out a fundamental limitation of current
methods, which ignore the tail of small clones in the distribution of clone
sizes. We show that this tail strongly affects the total number of clones, but
it is impractical to access experimentally. We propose that combining
statistical models with mechanistic models of lymphocyte clonal dynamics offers
possible new strategies for estimating the number of clones.
Analyses of protein cores reveal fundamental differences between solution and crystal structures
Date: 2019-07-18 00:00:00

There have been several studies suggesting that protein structures solved by
NMR spectroscopy and x-ray crystallography show significant differences. To
understand the origin of these differences, we assembled a database of
high-quality protein structures solved by both methods. We also find
significant differences between NMR and crystal structures---in the
root-mean-square deviations of the C$_{\alpha}$ atomic positions, identities of
core amino acids, backbone and sidechain dihedral angles, and packing fraction
of core residues. In contrast to prior studies, we identify the physical basis
for these differences by modelling protein cores as jammed packings of
amino-acid-shaped particles. We find that we can tune the jammed packing
fraction by varying the degree of thermalization used to generate the packings.
For an athermal protocol, we find that the average jammed packing fraction is
identical to that observed in the cores of protein structures solved by x-ray
crystallography. In contrast, highly thermalized packing-generation protocols
yield jammed packing fractions that are even higher than those observed in NMR
structures. These results indicate that thermalized systems can pack more
densely than athermal systems, which suggests a physical basis for the
structural differences between protein structures solved by NMR and x-ray
crystallography.
Statistical Clear Sky Fitting Algorithm
Date: 2019-07-18 00:00:00

We present an algorithm that estimates a clear sky performance signal from
the measured power of a PV system. The algorithm uses only observed power
output, and assumes no knowledge of weather, irradiance data, or system
configuration metadata. This is a novel approach to understanding the clear sky
behavior of an installed PV system, that does not rely on traditional
atmospheric and geometric modeling techniques.
Data-Driven Wide-Area Control Design of Power System Using the Passivity Shortage Framework
Date: 2019-07-18 00:00:00

A novel wide-area control design is presented to mitigate inter-area power
frequency oscillations. A large-scale power system is decomposed into a network
of passivity-short subsystems whose nonlinear interconnections have a
state-dependent affine form, and by utilizing the passivity shortage framework
a two-level design procedure is developed. At the lower level, any generator
control can be viewed as one that makes the generator passivity-short and $L_2$
stable, and the stability impact of the lower-level control on the overall
system can be characterized in terms of two parameters. While the system is
nonlinear, the impact parameters can be optimized by solving a data-driven
matrix inequality (DMI), and the high-level wide-area control is then designed
by solving another Lyapunov matrix inequality in terms of the design
parameters. The proposed methodology makes the design modular, and the
resulting control is adaptive with respect to operating conditions of the power
system. A test system is used to illustrate the proposed design, including DMI
and the wide-area control, and simulation results demonstrate effectiveness in
damping out inter-area oscillations.
Joint Coverage and Power Control in Highly Dynamic and Massive UAV Networks: An Aggregative Game-theoretic Learning Approach
Authors: Zhuoying Li, Pan Zhou, Yanru Zhang, Lin Gao
Date: 2019-07-19 00:00:00

Unmanned aerial vehicles (UAV) ad-hoc network is a significant contingency plan for communication after a natural disaster, such as typhoon and earthquake. To achieve efficient and rapid networks deployment, we employ noncooperative game theory and amended binary log-linear algorithm (BLLA) seeking for the Nash equilibrium which achieves the optimal network performance. We not only take channel overlap and power control into account but also consider coverage and the complexity of interference. However, extensive UAV game theoretical models show limitations in post-disaster scenarios which require large-scale UAV network deployments. Besides, the highly dynamic post-disaster scenarios cause strategies updating constraint and strategy-deciding error on UAV ad-hoc networks. To handle these problems, we employ aggregative game which could capture and cover those characteristics. Moreover, we propose a novel synchronous payoff-based binary log-linear learning algorithm (SPBLLA) to lessen information exchange and reduce time consumption. Ultimately, the experiments indicate that, under the same strategy-deciding error rate, SPBLLA's learning rate is manifestly faster than that of the revised BLLA. Hence, the new model and algorithm are more suitable and promising for large-scale highly dynamic scenarios.
Accuracy of position determination in Ca$^{2+}$ signaling
Authors: Vaibhav H. Wasnik, Peter Lipp, Karsten Kruse
Date: 2019-07-19 00:00:00
Tasks: Position

A living cell senses its environment and responds to external signals. In this work, we study theoretically, the precision at which cells can determine the position of a spatially localized transient extracellular signal. To this end, we focus on the case, where the stimulus is converted into the release of a small molecule that acts as a second messenger, for example, Ca$^{2+}$, and activates kinases that change the activity of enzymes by phosphorylating them. We analyze the spatial distribution of phosphorylation events using stochastic simulations as well as a mean-field approach. Kinases that need to bind to the cell membrane for getting activated provide more accurate estimates than cytosolic kinases. Our results could explain why the rate of Ca$^{2+}$ detachment from the membrane-binding conventional Protein Kinase C$\alpha$ is larger than its phosphorylation rate.
Stochastic Spread Pairs Trading in the Indian Commodity Market
Date: 2019-07-19 00:00:00

In this study, we applied a stochastic spread pairs trading strategy on the
Indian commodity market. The complete set of commodities were taken whose spot
price was available for the period of January 1st 2010 to December 31st 2018
including energy, metals and the agricultural commodity sector. Spot data was
taken from the MCX pooled spot prices for 17 commodities. The data was split
into training period (January 1st 2010 to 14th March 2017) and testing
period(15th Match 2017 to 31st December 2018). The splitting was done using a
80:20 split.Johanssen Cointegration tests were done on training data for pairs
of commodities to check for long-run relationship and the cointegrated
commodities were selected for formation of the trading process. We found a
total of 12 cointegrated pairs out of 136 possible pairs. Cointegration was
assumed for the testing period. A single-factor stochastic trading approach was
applied on the logarithmic spread of the cointegrated pairs for both the
training and testing period.The parameters of stochastic spread model were
estimated using differential evolution algorithm. Also parameters for the
trading rule were optimized by backtesting on the training period and assumed
for the testing period. The results show a sharpe ratio of above 1.4 for all
the commodity cointegrated pairs in the backtesing period.
Rate Splitting with Finite Constellations: The Benefits of Interference Exploitation vs Suppression
Date: 2019-07-19 00:00:00

Rate-Splitting (RS) has been proposed recently to enhance the performance of
multi-user multiple-input multiple-output (MU-MIMO) systems. In RS, a user
message is split into a common and a private part, where the common part is
decoded by all users, while the private part is decoded only by the intended
user. In this paper, we study RS under a phase-shift keying (PSK) input
alphabet for multi-user multi-antenna system and propose a constructive
interference (CI) exploitation approach to further enhance the sum-rate
achieved by RS under PSK signaling. To that end, new analytical expressions for
the ergodic sum-rate are derived for two precoding techniques of the private
messages, namely, 1) a traditional interference suppression zero-forcing (ZF)
precoding approach, 2) a closed-form CI precoding approach. Our analysis is
presented for perfect channel state information at the transmitter (CSIT), and
is extended to imperfect CSIT knowledge. A novel power allocation strategy,
specifically suited for the finite alphabet setup, is derived and shown to lead
to superior performance for RS over conventional linear precoding not relying
on RS (NoRS). The results in this work validate the significant sum-rate gain
of RS with CI over the conventional RS with ZF and NoRS.
Lévy-Ito Models in Finance
Authors: George Bouzianis, Lane P. Hughston, Sebastian Jaimungal, Leandro Sánchez-Betancourt
Date: 2019-07-19 00:00:00

We present an overview of the broad class of financial models in which the prices of assets are L\'evy-Ito processes driven by an $n$-dimensional Brownian motion and an independent Poisson random measure. The Poisson random measure is associated with an $n$-dimensional L\'evy process. Each model consists of a pricing kernel, a money market account, and one or more risky assets. We show how the excess rate of return above the interest rate can be calculated for risky assets in such models, thus showing the relationship between risk and return when asset prices have jumps. The framework is applied to a variety of asset classes, allowing one to construct new models as well as interesting generalizations of familiar models.
A Vine-copula extension for the HAR model
Date: 2019-07-19 00:00:00

The heterogeneous autoregressive (HAR) model is revised by modeling the joint
distribution of the four partial-volatility terms therein involved. Namely,
today's, yesterday's, last week's and last month's volatility components. The
joint distribution relies on a (C-) Vine copula construction, allowing to
conveniently extract volatility forecasts based on the conditional expectation
of today's volatility given its past terms. The proposed empirical application
involves more than seven years of high-frequency transaction prices for ten
stocks and evaluates the in-sample, out-of-sample and one-step-ahead forecast
performance of our model for daily realized-kernel measures. The model proposed
in this paper is shown to outperform the HAR counterpart under different models
for marginal distributions, copula construction methods, and forecasting
settings.
An optimization-based cooperative path-following framework for multiple robotic vehicles
Date: 2019-07-19 00:00:00
Tasks: Model Predictive Control

This paper addresses the design of an optimization-based cooperative
path-following control law for multiple robotic vehicles that optimally
balances the transient trade-off between coordination and path-following
errors. To this end, we formulate a more general multi-agent framework where
each agent is associated with (i) a continuous-time dynamical model, which
governs the evolution of its state, and (ii) an output equation that is a
function of both the state of the agent and a coordination vector. According to
a given network topology, each agent can access its state and coordination
vector, as well as the coordination vectors of the neighboring agents. In this
setup, the goal is to design a distributed control law that steers the output
signals to the origin, while simultaneously driving the coordination vectors of
the agents of the network to consensus. To solve this, we propose a model
predictive control scheme that builds on a pre-existing auxiliary consensus
control law to design a performance index that combines the output regulation
objective with the consensus objective. Convergence guarantees under which one
can solve this coordinated output regulation problem are provided. Numerical
simulations display the effectiveness of the proposed scheme applied to a
cooperative path following control problem of a network of 3D nonholonomic
robotic vehicles.
Attitude Control of a Novel Tailsitter: Swiveling Biplane-Quadrotor
Date: 2019-07-19 00:00:00
Tasks: valid

This paper proposes a solution to the attitude tracking problem for a novel
quadrotor tailsitter unmanned aerial vehicle called swiveling biplane
quadrotor. The proposed vehicle design addresses the lack of yaw control
authority in conventional biplane quadrotor tailsitters by proposing a new
design wherein two wings with two attached propellers are joined together with
a rod through a swivel mechanism. The yaw torque is generated by relative
rotation of the thrust vector of each wing. The unique design of this
configuration having two rigid bodies interconnected through a rod with zero
torsional rigidity makes the vehicle underactuated in the attitude
configuration manifold. An output tracking problem is posed which results in a
single equivalent rigid body attitude tracking problem with second-order moment
dynamics. The proposed controller is uniformly valid for all attitudes and is
based on dynamic feedback linearization in a geometric control framework.
Almost-global asymptotic stability of the desired equilibrium of the tracking
error dynamics is shown. The efficacy of the controller is shown with numerical
simulation and flight tests.
Modeling Morality
Date: 2019-07-19 00:00:00

Unlike any other field, the science of morality has drawn attention from an
extraordinarily diverse set of disciplines. An interdisciplinary research
program has formed in which economists, biologists, neuroscientists,
psychologists, and even philosophers have been eager to provide answers to
puzzling questions raised by the existence of human morality. Models and
simulations, for a variety of reasons, have played various important roles in
this endeavor. Their use, however, has sometimes been deemed as useless,
trivial and inadequate. The role of models in the science of morality has been
vastly underappreciated. This omission shall be remedied here, offering a much
more positive picture on the contributions modelers made to our understanding
of morality.
Replacement Policy of Systems with Dependent Components via Integration of Dynamic Programming and Simulated Annealing
Date: 2019-07-24 00:00:00

In a dependent multi-component system, increasing the deterioration of a part
leads to the increased deterioration rate of other parts as well. In these
systems, a deterioration limit is usually pre-determined for each part and the
considered part is replaced while reaching this limit. In this paper,
replacement conditions of these parts were examined according to the
replacement times in the past. Using dynamic programming, for every
deterioration rate of part 1, there is a deterioration limit for part 2, after
which either part 2 or both parts should be replaced. The only available system
data are the replacement time of the parts in the past according to the
replacement policy at the time of reaching deterioration limit. Therefore,
simulated annealing optimization method was used for estimating deterioration
rates. Finally, two examples were presented for comparing the proposed method
with the special limit replacement method, which showed the significance
superiority of the former.