pwc-archive/papers-with-abstracts huggingface dataset:
Control of Battery Storage Systems for the Simultaneous Provision of Multiple Services
Date: 2018-10-12 00:00:00

In this paper, we propose a control framework for a battery energy storage
system to provide simultaneously multiple services to the electrical grid. The
objective is to maximise the battery exploitation from these services in the
presence of uncertainty (load, stochastic distributed generation, grid
frequency). The framework is structured in two phases. In a period-ahead phase,
we solve an optimization problem that allocates the battery power and energy
budgets to the different services. In the subsequent real-time phase the
control set-points for the deployment of such services are calculated
separately and superimposed. The control framework is first formulated in a
general way and then casted in the problem of providing dispatchability of a
medium voltage feeder in conjunction to primary frequency control. The
performance of the proposed framework are validated by simulations and
real-scale experi- ments, performed with a grid-connected 560 kWh/720 kVA
Li-ion battery energy storage system.
Cognitive Radars: A Reality?
Date: 2018-02-28 00:00:00

This paper describes some key ideas and applications of cognitive radars,
highlighting the limits and the path forward. Cognitive radars are systems
based on the perception-action cycle of cognition that sense the environment,
learn from it relevant information about the target and the background, then
adapt the radar sensor to optimally satisfy the needs of their mission
according to a desired goal. The concept of cognitive radar was introduced
originally for active radar only. In this paper we describe how this paradigm
can be applied also to passive radar. In particular, we describe (i) cognitive
active radars that work in a spectrally dense environment and change the
transmitted waveform on-the-fly to avoid interference with the primary user of
the channel, such as broadcast or communication systems, (ii) cognitive active
radars that adjust transmit waveform parameters to achieve a specified level of
target tracking performance, and (iii) cognitive passive radars, that contrary
to the active radars cannot directly change the transmitted waveforms, but can
instead select the best source of opportunity to improve detection and tracking
performance.
Generalized Information Ratio
Date: 2018-04-23 00:00:00
Tasks: Management

Alpha-based performance evaluation may fail to capture correlated residuals
due to model errors. This paper proposes using the Generalized Information
Ratio (GIR) to measure performance under misspecified benchmarks. Motivated by
the theoretical link between abnormal returns and residual covariance matrix,
GIR is derived as alphas scaled by the inverse square root of residual
covariance matrix. GIR nests alphas and Information Ratio as special cases,
depending on the amount of information used in the residual covariance matrix.
We show that GIR is robust to various degrees of model misspecification and
produces stable out-of-sample returns. Incorporating residual correlations
leads to substantial gains that alleviate model error concerns of active
management.
An Note on Why Geographically Weighted Regression Overcomes Multidimensional-Kernel-Based Varying-Coefficient Model
Date: 2018-04-12 00:00:00
Tasks: regression

It is widely known that geographically weighted regression(GWR) is
essentially same as varying-coefficient model. In the former research about
varying-coefficient model, scholars tend to use multidimensional-kernel-based
locally weighted estimation(MLWE) so that information of both distance and
direction is considered. However, when we construct the local weight matrix of
geographically weighted estimation, distance among the locations in the
neighbor is the only factor controlling the value of entries of weight matrix.
In other word, estimation of GWR is distance-kernel-based. Thus, in this paper,
under stationary and limited dependent data with multidimensional subscripts,
we analyze the local mean squared properties of without any assumption of the
form of coefficient functions and compare it with MLWE. According to the
theoretical and simulation results, geographically-weighted locally linear
estimation(GWLE) is asymptotically more efficient than MLWE. Furthermore, a
relationship between optimal bandwith selection and design of scale parameters
is also obtained.
High-Precision Measurement of Sine and Pulse Reference Signals using Software-Defined Radio
Date: 2018-04-10 00:00:00

This paper addresses simultaneous, high-precision measurement and analysis of
generic reference signals by using inexpensive commercial off-the-shelf
Software Defined Radio hardware. Sine reference signals are digitally
down-converted to baseband for the analysis of phase deviations. Hereby, we
compare the precision of the fixed-point hardware Digital Signal Processing
chain with a custom Single Instruction Multiple Data (SIMD) x86 floating-point
implementation. Pulse reference signals are analyzed by a software trigger that
precisely locates the time where the slope passes a certain threshold. The
measurement system is implemented and verified using the Universal Software
Radio Peripheral (USRP) N210 by Ettus Research LLC. Applying standard 10 MHz
and 1 PPS reference signals for testing, a measurement precision (standard
deviation) of 0.36 ps and 16.6 ps is obtained, respectively. In connection with
standard PC hardware, the system allows long-term acquisition and storage of
measurement data over several weeks. A comparison is given to the Dual Mixer
Time Difference (DMTD) and Time Interval Counter (TIC), which are
state-of-the-art measurement methods for sine and pulse signal analysis,
respectively. Furthermore, we show that our proposed USRP-based approach
outperforms measurements with a high-grade Digital Sampling Oscilloscope.
Proximal Gradient Algorithms: Applications in Signal Processing
Date: 2020-01-27 00:00:00
Tasks: Denoising

Advances in numerical optimization have supported breakthroughs in several
areas of signal processing. This paper focuses on the recent enhanced variants
of the proximal gradient numerical optimization algorithm, which combine
quasi-Newton methods with forward-adjoint oracles to tackle large-scale
problems and reduce the computational burden of many applications. These
proximal gradient algorithms are here described in an easy-to-understand way,
illustrating how they are able to address a wide variety of problems arising in
signal processing. A new high-level modeling language is presented which is
used to demonstrate the versatility of the presented algorithms in a series of
signal processing application examples such as sparse deconvolution, total
variation denoising, audio de-clipping and others.
When do we have the power to detect biological interactions in spatial point patterns?
Date: 2018-03-17 00:00:00

Determining the relative importance of environmental factors, biotic
interactions and stochasticity in assembling and maintaining species-rich
communities remains a major challenge in ecology. In plant communities,
interactions between individuals of different species are expected to leave a
spatial signature in the form of positive or negative spatial correlations over
distances relating to the spatial scale of interaction. Most studies using
spatial point process tools have found relatively little evidence for
interactions between pairs of species. More interactions tend to be detected in
communities with fewer species. However, there is currently no understanding of
how the power to detect spatial interactions may change with sample size, or
the scale and intensity of interactions.
  We use a simple 2-species model where the scale and intensity of interactions
are controlled to simulate point pattern data. In combination with an
approximation to the variance of the spatial summary statistics that we sample,
we investigate the power of current spatial point pattern methods to correctly
reject the null model of bivariate species independence.
  We show that the power to detect interactions is positively related to the
abundances of the species tested, and the intensity and scale of interactions.
Increasing imbalance in abundances has a negative effect on the power to detect
interactions. At population sizes typically found in currently available
datasets for species-rich plant communities we find only a very low power to
detect interactions. Differences in power may explain the increased frequency
of interactions in communities with fewer species. Furthermore, the
community-wide frequency of detected interactions is very sensitive to a
minimum abundance criterion for including species in the analyses.
A balance index for phylogenetic trees based on rooted quartets
Date: 2019-03-22 00:00:00

We define a new balance index for rooted phylogenetic trees based on the
symmetry of the evolutive history of every set of 4 leaves. This index makes
sense for multifurcating trees and it can be computed in time linear in the
number of leaves. We determine its maximum and minimum values for arbitrary and
bifurcating trees, and we provide exact formulas for its expected value and
variance on bifurcating trees under Ford's $\alpha$-model and Aldous'
$\beta$-model and on arbitrary trees under the $\alpha$-$\gamma$-model.
Frequency-modulated continuous-wave LiDAR compressive depth-mapping
Date: 2018-05-16 00:00:00
Tasks: Compressive Sensing

We present an inexpensive architecture for converting a frequency-modulated
continuous-wave LiDAR system into a compressive-sensing based depth-mapping
camera. Instead of raster scanning to obtain depth-maps, compressive sensing is
used to significantly reduce the number of measurements. Ideally, our approach
requires two difference detectors. % but can operate with only one at the cost
of doubling the number of measurments. Due to the large flux entering the
detectors, the signal amplification from heterodyne detection, and the effects
of background subtraction from compressive sensing, the system can obtain
higher signal-to-noise ratios over detector-array based schemes while scanning
a scene faster than is possible through raster-scanning. %Moreover, we show how
a single total-variation minimization and two fast least-squares minimizations,
instead of a single complex nonlinear minimization, can efficiently recover
high-resolution depth-maps with minimal computational overhead. Moreover, by
efficiently storing only $2m$ data points from $m<n$ measurements of an $n$
pixel scene, we can easily extract depths by solving only two linear equations
with efficient convex-optimization methods.
Mutation and selection in bacteria: modelling and calibration
Date: 2018-10-28 00:00:00

Temporal evolution of a clonal bacterial population is modelled taking into
account reversible mutation and selection mechanisms. For the mutation model,
an efficient algorithm is proposed to verify whether experimental data can be
explained by this model. The selection-mutation model has unobservable fitness
parameters and, to estimate them, we use an Approximate Bayesian Computation
(ABC) algorithm. The algorithms are illustrated using in vitro data for phase
variable genes of Campylobacter jejuni.
Optimal Sensor Collaboration for Parameter Tracking Using Energy Harvesting Sensors
Date: 2018-03-25 00:00:00

In this paper, we design an optimal sensor collaboration strategy among
neighboring nodes while tracking a time-varying parameter using wireless sensor
networks in the presence of imperfect communication channels. The sensor
network is assumed to be self-powered, where sensors are equipped with energy
harvesters that replenish energy from the environment. In order to minimize the
mean square estimation error of parameter tracking, we propose an online sensor
collaboration policy subject to real-time energy harvesting constraints. The
proposed energy allocation strategy is computationally light and only relies on
the second-order statistics of the system parameters. For this, we first
consider an offline non-convex optimization problem, which is solved exactly
using semidefinite programming. Based on the offline solution, we design an
online power allocation policy that requires minimal online computation and
satisfies the dynamics of energy flow at each sensor. We prove that the
proposed online policy is asymptotically equivalent to the optimal offline
solution and show its convergence rate and robustness. We empirically show that
the estimation performance of the proposed online scheme is better than that of
the online scheme when channel state information about the dynamical system is
available in the low SNR regime. Numerical results are conducted to demonstrate
the effectiveness of our approach.
Precoding and Spatial Modulation in the Downlink of MU-MIMO Systems
Date: 2018-03-19 00:00:00

This work focuses on the downlink communication of a multiuser MIMO system
where the base station antennas and the users' receiving antennas are all
active, but at each transmission, only a subset of the receive antennas is
selected by the base station to receive the information symbols, and the
particular chosen subset (pattern) represents part of the information conveyed
to the user. In this paper we present a mathematical model for the system and
develop expressions that are fairly general and adequate for its analysis.
Based on these expressions we propose a procedure to optimize the choice by the
ERB of the sets of antenna patterns to be used in the transmissions to the
different users, aiming at the maximization of the detection signal-to-noise
ratio. Performance results, with and without the optimization procedure, are
presented for different scenarios.
Adaptive Matching Pursuit based Online Identification and Control Scheme for Nonlinear Systems
Date: 2018-07-22 00:00:00

The complexity of adaptive control of nonlinear time-varying systems requires
the use of novel methods that have lower computational complexity as well as
ensuring good performance under time-varying parameter changes. In this study,
we use adaptive matching pursuit algorithm with wavelet bases for an online
identification and control of the nonlinear system with time-varying
parameters. We apply the proposed online identification and control scheme to
two different benchmark examples of nonlinear system identification and
control. Simulation results show that the proposed algorithm, using adaptive
matching pursuit with wavelet bases, can effectively identify and control the
nonlinear system even in presence of time-varying parameters.
Pro-arrhythmogenic effects of heterogeneous tissue curvature: A suggestion for role of left atrial appendage in atrial fibrillation
Date: 2018-09-14 00:00:00

Background: The arrhythmogenic role of atrial complex morphology has not yet
been clearly elucidated. We hypothesized that bumpy tissue geometry can induce
action potential duration (APD) dispersion and wavebreak in atrial fibrillation
(AF).
  Methods and Results: We simulated 2D-bumpy atrial model by varying the degree
of bumpiness, and 3D-left atrial (LA) models integrated by LA computed
tomographic (CT) images taken from 14 patients with persistent AF. We also
analyzed wave-dynamic parameters with bipolar electrograms during AF and
compared them with LA-CT geometry in 30 patients with persistent AF. In
2D-bumpy model, APD dispersion increased (p<0.001) and wavebreak occurred
spontaneously when the surface bumpiness was higher, showing phase
transition-like behavior (p<0.001). Bumpiness gradient 2D-model showed that
spiral wave drifted in the direction of higher bumpiness, and phase singularity
(PS) points were mostly located in areas with higher bumpiness. In 3D-LA model,
PS density was higher in LA appendage (LAA) compared to other LA parts
(p<0.05). In 30 persistent AF patients, the surface bumpiness of LAA was
5.8-times that of other LA parts (p<0.001), and exceeded critical bumpiness to
induce wavebreak. Wave dynamics complexity parameters were consistently
dominant in LAA (p<0.001).
  Conclusion: The bumpy tissue geometry promotes APD dispersion, wavebreak, and
spiral wave drift in in silico human atrial tissue, and corresponds to clinical
electro-anatomical maps.
Modelling stock correlations with expected returns from investors
Date: 2018-03-23 00:00:00
Tasks: Decision Making

Stock correlations is crucial to asset pricing, investor decision-making, and
financial risk regulations. However, microscopic explanation based on
agent-based modeling is still lacking. We here propose a model derived from
minority game for modeling stock correlations, in which an agent's expected
return for one stock is influenced by the historical return of the other stock.
Each agent makes a decision based on his expected return with reference to
information dissemination and the historical return of the stock. We find that
the returns of the stocks are positively (negatively) correlated when agents'
expected returns for one stock are positively (negatively) correlated with the
historical return of the other. We provide both numerical simulations and
analytical studies and give explanations to stock correlations for cases with
agents having either homogeneous or heterogeneous expected returns. The result
still holds when other factors such as holding decisions and external events
are included which broadens the practicability of the model.
Limit distribution of the quartet balance index for Aldous's b>=0-model
Date: 2019-08-30 00:00:00

This paper builds up on T. Martinez-Coronado, A. Mir, F. Rossello and G.
Valiente's work "A balance index for phylogenetic trees based on quartets",
introducing a new balance index for trees. We show here that this balance
index, in the case of Aldous's b>=0-model, convergences weakly to a
distribution that can be characterized as the fixed point of a contraction
operator on a class of distributions.
A Term Structure Model for Dividends and Interest Rates
Authors: Damir Filipović, Sander Willems
Date: 2018-03-06 00:00:00

Over the last decade, dividends have become a standalone asset class instead of a mere side product of an equity investment. We introduce a framework based on polynomial jump-diffusions to jointly price the term structures of dividends and interest rates. Prices for dividend futures, bonds, and the dividend paying stock are given in closed form. We present an efficient moment based approximation method for option pricing. In a calibration exercise we show that a parsimonious model specification has a good fit with Euribor interest rate swaps and swaptions, Euro Stoxx 50 index dividend futures and dividend options, and Euro Stoxx 50 index options.
A Nonparametric Approach to Measure the Heterogeneous Spatial Association: Under Spatial Temporal Data
Date: 2018-03-22 00:00:00

Spatial association and heterogeneity are two critical areas in the research
about spatial analysis, geography, statistics and so on. Though large amounts
of outstanding methods has been proposed and studied, there are few of them
tend to study spatial association under heterogeneous environment.
Additionally, most of the traditional methods are based on distance statistic
and spatial weighted matrix. However, in some abstract spatial situations,
distance statistic can not be applied since we can not even observe the
geographical locations directly. Meanwhile, under these circumstances, due to
invisibility of spatial positions, designing of weight matrix can not
absolutely avoid subjectivity. In this paper, a new entropy-based method, which
is data-driven and distribution-free, has been proposed to help us investigate
spatial association while fully taking the fact that heterogeneity widely
exist. Specifically, this method is not bounded with distance statistic or
weight matrix. Asymmetrical dependence is adopted to reflect the heterogeneity
in spatial association for each individual and the whole discussion in this
paper is performed on spatio-temporal data with only assuming stationary
m-dependent over time.
Almost Sure Uniqueness of a Global Minimum Without Convexity
Date: 2019-02-19 00:00:00
Tasks: regression

This paper establishes the argmin of a random objective function to be unique
almost surely. This paper first formulates a general result that proves almost
sure uniqueness without convexity of the objective function. The general result
is then applied to a variety of applications in statistics. Four applications
are discussed, including uniqueness of M-estimators, both classical likelihood
and penalized likelihood estimators, and two applications of the argmin
theorem, threshold regression and weak identification.
An ontological investigation of unimaginable events
Date: 2018-06-23 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

We show that, under mild assumptions, some unimaginable events - which we
refer to as Black Swan events - must necessarily occur. It follows as a
corollary of our theorem that any computational model of decision-making under
uncertainty is incomplete in the sense that not all events that occur can be
taken into account. In the context of decision theory we argue that this
constitutes a stronger sense of uncertainty than Knightian uncertainty.
The Altes Family of Log-Periodic Chirplets and the Hyperbolic Chirplet Transform
Date: 2018-11-12 00:00:00
Tasks: Fault Detection, Time Series Analysis

This work revisits a class of biomimetically inspired log-periodic waveforms
first introduced by R.A. Altes in the 1970s for generalized target description.
It was later observed that there is a close connection between such sonar
techniques and wavelet decomposition for multiresolution analysis. Motivated by
this, we formalize the original Altes waveforms as a family of hyperbolic
chirplets suitable for the detection of accelerating time-series oscillations.
The formalism results in a remarkably flexible set of wavelets with desirable
properties of admissibility, regularity, vanishing moments, and time-frequency
localization. These "Altes wavelets" also facilitate efficient implementation
of the scale invariant hyperbolic chirplet transform (HCT).
  From a practical perspective, log-periodic oscillations with an acceleration
towards criticality can serve as indicators of an incipient bifurcation. Such
signals abound in nature, often as precursors to phase transitions in the
non-linear dynamics of complex systems. For example, the authors' interest lies
in automatic detection of the well documented phenomenon of log-periodic price
dynamics during financial bubbles and preceding market crashes. However, the
methodology presented here is more widely applicable in such diverse domains as
prediction of critical failures in mechanical systems, and fault detection in
electrical networks. Examples beyond failure diagnostics include animal species
identification via call recordings, commercial \& military radar, and there are
many more. A synthetic application is presented in this report for illustrative
purposes.