pwc-archive/papers-with-abstracts huggingface dataset:
Spectrum Sensing and Resource Allocation for 5G Heterogeneous Cloud Radio Access Networks
Date: 2019-07-12 00:00:00

In this paper, the problem of opportunistic spectrum sharing for the next
generation of wireless systems empowered by the cloud radio access network
(C-RAN) is studied. More precisely, low-priority users employ cooperative
spectrum sensing to detect a vacant portion of the spectrum that is not
currently used by high-priority users. The design of the scheme is to maximize
the overall throughput of the low-priority users while guaranteeing the quality
of service of the high-priority users. This objective is attained by optimally
adjusting spectrum sensing time with respect to imposed target probabilities of
detection and false alarm as well as dynamically allocating and assigning C-RAN
resources, i.e., transmit powers, sub-carriers, remote radio heads (RRHs), and
base-band units. The presented optimization problem is non-convex and NP-hard
that is extremely hard to tackle directly. To solve the problem, a low-complex
iterative approach is proposed in which sensing time, user association
parameters and transmit powers of RRHs are alternatively assigned and optimized
at every step. Numerical results are then provided to demonstrate the necessity
of performing sensing time adjustment in such systems as well as balancing the
sensing-throughput tradeoff.
Location and portfolio selection problems: A unified framework
Authors: Justo Puerto, Moises Rodríguez-Madrena, Andrea Scozzari
Date: 2019-07-15 00:00:00
Tasks: Clustering, Portfolio Optimization

Given a set of assets and an investment capital, the classical portfolio selection problem consists in determining the amount of capital to be invested in each asset in order to build the most profitable portfolio. The portfolio optimization problem is naturally modeled as a mean-risk bi-criteria optimization problem where the mean rate of return of the portfolio must be maximized whereas a given risk measure must be minimized. Several mathematical programming models and techniques have been presented in the literature in order to efficiently solve the portfolio problem. A relatively recent promising line of research is to exploit clustering information of an assets network in order to develop new portfolio optimization paradigms. In this paper we endow the assets network with a metric based on correlation coefficients between assets' returns, and show how classical location problems on networks can be used for clustering assets. In particular, by adding a new criterion to the portfolio selection problem based on an objective function of a classical location problem, we are able to measure the effect of clustering on the selected assets with respect to the non-selected ones. Most papers dealing with clustering and portfolio selection models solve these problems in two distinct steps: cluster first and then selection. The innovative contribution of this paper is that we propose a Mixed-Integer Linear Programming formulation for dealing with this problem in a unified phase. The effectiveness of our approach is validated reporting some preliminary computational experiments on some real financial dataset.
Acoustic Scene Classification Using Fusion of Attentive Convolutional Neural Networks for DCASE2019 Challenge
Date: 2019-07-13 00:00:00
Tasks: Acoustic Scene Classification, Scene Classification, Speaker Verification

In this report, the Brno University of Technology (BUT) team submissions for
Task 1 (Acoustic Scene Classification, ASC) of the DCASE-2019 challenge are
described. Also, the analysis of different methods is provided. The proposed
approach is a fusion of three different Convolutional Neural Network (CNN)
topologies. The first one is a VGG like two-dimensional CNNs. The second one is
again a two-dimensional CNN network which uses Max-Feature-Map activation and
called Light-CNN (LCNN). The third network is a one-dimensional CNN which
mainly used for speaker verification and called x-vector topology. All proposed
networks use self-attention mechanism for statistic pooling. As a feature, we
use a 256-dimensional log Mel-spectrogram. Our submissions are a fusion of
several networks trained on 4-folds generated evaluation setup using different
fusion strategies.
Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems
Date: 2019-07-16 00:00:00
Tasks: Computational Efficiency, Super-Resolution

This paper develops efficient channel estimation techniques for
millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems
under practical hardware limitations, including an arbitrary array geometry and
a hybrid hardware structure. Taking on an angle-based approach, this work
adopts a generalized array manifold separation approach via Jacobi-Anger
approximation, which transforms a non-ideal, non-uniform array manifold into a
virtual array domain with a desired uniform geometric structure to facilitate
super-resolution angle estimation and channel acquisition. Accordingly,
structure-based optimization techniques are developed to effectively estimate
both the channel covariance and the instantaneous channel state information
(CSI) within a short sensing time. In particular, the difference in
time-variation of channel path angles and path gains is capitalized to design a
two-step CSI estimation scheme that can quickly sense fading channels.
Theoretical results are provided on the fundamental limits of the proposed
technique in terms of sample efficiency. For computational efficiency, a fast
iterative algorithm is developed via the alternating direction method of
multipliers. Other related issues such as spurious-peak cancellation in
nonuniform linear arrays and extensions to higher-dimensional cases are also
discussed. Simulations testify the effectiveness of the proposed approaches in
hybrid mmWave massive MIMO systems with arbitrary arrays.
Connected Vehicle Supported Adaptive Traffic Control for Near-congested Condition in a Mixed Traffic Stream
Date: 2019-07-14 00:00:00
Tasks: Traffic Signal Control

Connected Vehicles (CVs) have the potential to significantly increase the
safety, mobility, and environmental benefits of transportation applications. In
this research, we have developed a real time adaptive traffic signal control
algorithm that utilizes only CV data to compute the signal timing parameters
for an urban arterial in the near congested condition. We have used a machine
learning based short term traffic forecasting model to predict the overall
traffic counts in CV based platoons. Using a multi objective optimization
technique, we compute the green interval time for each intersection using CV
based platoons. Later, we dynamically adjust intersection offsets in real time,
so the vehicles in the major street can experience improved operational
conditions compared to loop detector based actuated coordinated signal control.
Using a 3 mile long simulated corridor of US 29 in Greenville, SC, we have
evaluated the performance of our CV based adaptive signal control. For the next
time interval, using only 5% CV data, the Root Mean Square Error of the machine
learning based prediction is 10 vehicles. Our analysis reveals that the CV
based adaptive signal control improves operational conditions in the major
street compared to the actuated coordinated scenario. Also, using only CV data,
the operational performance improves even for a low CV penetration (5% CV), and
the benefit increases with increasing CV penetration. We can provide
operational benefits to both CVs and non CVs with the limited data from 5% CVs,
with 5.6% average speed increase, and 66.7% and 32.4% reduction in average
maximum queue length and stopped delay, respectively, in major street
coordinated direction compared to the actuated coordinated scenario in the same
direction.
On the inconsistency of matching without replacement
Authors: Fredrik Sävje
Date: 2019-07-16 00:00:00

The paper shows that matching without replacement on propensity scores produces estimators that generally are inconsistent for the average treatment effect of the treated. To achieve consistency, practitioners must either assume that no units exist with propensity scores greater than one-half or assume that there is no confounding among such units. The result is not driven by the use of propensity scores, and similar artifacts arise when matching on other scores as long as it is without replacement.
A model-free backward and forward nonlinear PDEs for implied volatility
Authors: Peter Carr, Andrey Itkin, SASHA STOIKOV
Date: 2019-07-17 00:00:00

We derive a backward and forward nonlinear PDEs that govern the implied volatility of a contingent claim whenever the latter is well-defined. This would include at least any contingent claim written on a positive stock price whose payoff at a possibly random time is convex. We also discuss suitable initial and boundary conditions for those PDEs. Finally, we demonstrate how to solve them numerically by using an iterative finite-difference approach.
Existence and Uniqueness of Solutions to the Stochastic Bellman Equation with Unbounded Shock
Date: 2019-07-17 00:00:00

In this paper we develop a general framework to analyze stochastic dynamic
problems with unbounded utility functions and correlated and unbounded shocks.
We obtain new results of the existence and uniqueness of solutions to the
Bellman equation through a general fixed point theorem that generalizes known
results for Banach contractions and local contractions. We study an endogenous
growth model as well as the Lucas asset pricing model in an exchange economy,
significantly expanding their range of applicability.
Krasovskii and Shifted Passivity Based Control
Date: 2020-09-28 00:00:00

In this paper, our objective is to develop novel passivity based control
techniques by introducing a new passivity concept named Krasovskii passivity.
As a preliminary step, we investigate properties of Krasovskii passive systems
and establish relations among four relevant passivity concepts including
Krasovskii passivity. Then, we develop novel dynamic controllers based on
Krasovskii passivity and based on extended shifted passivity.
The cyclicality of loan loss provisions under three different accounting models: the United Kingdom, Spain, and Brazil
Date: 2019-07-17 00:00:00
Tasks: Management

A controversy involving loan loss provisions in banks concerns their
relationship with the business cycle. While international accounting standards
for recognizing provisions (incurred loss model) would presumably be
pro-cyclical, accentuating the effects of the current economic cycle, an
alternative model, the expected loss model, has countercyclical
characteristics, acting as a buffer against economic imbalances caused by
expansionary or contractionary phases in the economy. In Brazil, a mixed
accounting model exists, whose behavior is not known to be pro-cyclical or
countercyclical. The aim of this research is to analyze the behavior of these
accounting models in relation to the business cycle, using an econometric model
consisting of financial and macroeconomic variables. The study allowed us to
identify the impact of credit risk behavior, earnings management, capital
management, Gross Domestic Product (GDP) behavior, and the behavior of the
unemployment rate on provisions in countries that use different accounting
models. Data from commercial banks in the United Kingdom (incurred loss), in
Spain (expected loss), and in Brazil (mixed model) were used, covering the
period from 2001 to 2012. Despite the accounting models of the three countries
being formed by very different rules regarding possible effects on the business
cycles, the results revealed a pro-cyclical behavior of provisions in each
country, indicating that when GDP grows, provisions tend to fall and vice
versa. The results also revealed other factors influencing the behavior of loan
loss provisions, such as earning management.
Combining traffic counts and Bluetooth data for link-origin-destination matrix estimation in large urban networks: The Brisbane case study
Date: 2019-07-17 00:00:00
Tasks: User Identification

Origin-Destination matrix estimation is a keystone for traffic representation
and analysis. Traditionally estimated thanks to traffic counts, surveys and
socio-economic models, recent technological advances permit to rethink the
estimation problem. Road user identification technologies, such as connected
GPS, Bluetooth or Wifi detectors bring additional information, that is, for a
fraction of the users, the origin, the destination and to some extend the
itinerary taken. In the present work, this additional information is used for
the estimation of a more comprehensive traffic representation tool: the
link-origin-destination matrix. Such three-dimensional matrices extend the
concept of traditional origin-destination matrices by also giving information
on the traffic assignment. Their estimation is solved as an inverse problem
whose objective function represents a trade-off between important properties
the traffic has to satisfy. This article presents the theory and how to
implement such method on real dataset. With the case study of Brisbane City
where over 600 hundreds Bluetooth detectors have been installed it also
illustrates the opportunities such link-origin-destination matrices create for
traffic analysis.
The Marginal Stability of Proteins: How Jiggling and Wiggling of Atoms are Connected to Neutral Evolution
Date: 2020-03-19 00:00:00

Here we propose that the upper bound marginal stability of proteins (7.4
kcal/mol) is a universal property that includes macro-molecular complexes and
is not affected by molecular changes such as mutations and Post-Translational
Modifications. Its existence is, essentially, a consequence of the Anfinsen
thermodynamic hypothesis rather than a result of an evolutive process. This
result enables us to conjecture that neutral evolution should also be, with
respect to protein stability, a universal phenomenon.
Optimal Secure Control with Linear Temporal Logic Constraints
Date: 2019-07-17 00:00:00

Prior work on automatic control synthesis for cyber-physical systems under
logical constraints has primarily focused on environmental disturbances or
modeling uncertainties, however, the impact of deliberate and malicious attacks
has been less studied. In this paper, we consider a discrete-time dynamical
system with a linear temporal logic (LTL) constraint in the presence of an
adversary, which is modeled as a stochastic game. We assume that the adversary
observes the control policy before choosing an attack strategy. We investigate
two problems. In the first problem, we synthesize a robust control policy for
the stochastic game that maximizes the probability of satisfying the LTL
constraint. A value iteration based algorithm is proposed to compute the
optimal control policy. In the second problem, we focus on a subclass of LTL
constraints, which consist of an arbitrary LTL formula and an invariant
constraint. We then investigate the problem of computing a control policy that
minimizes the expected number of invariant constraint violations while
maximizing the probability of satisfying the arbitrary LTL constraint. We
characterize the optimality condition for the desired control policy. A policy
iteration based algorithm is proposed to compute the control policy. We
illustrate the proposed approaches using two numerical case studies.
Contract Design with Costly Convex Self-Control
Date: 2019-07-17 00:00:00

In this note, we consider the pricing problem of a profit-maximizing
monopolist who faces naive consumers with convex self-control preferences.
Connectivity-Preserving Swarm Teleoperation Over A Tree Network With Time-Varying Delays
Date: 2019-07-16 00:00:00

A teleoperated swarm must follow the unpredictable commands of its human
operator while remaining connected. When the swarm communications are limited
by distance and affected by delays, both the user input and the transmission
delays endanger the connectivity of the swarm. This paper presents a
constructive control strategy that overcomes both threats. The strategy
modulates the intra-swarm couplings and the damping injected to each slave in
the swarm based on a customized potential. Lyapunov-based set invariance
analysis proves that the proposed explicit gain updating law limits the impact
of the operator input and preserves the initial tree connectivity of a
delay-free swarm. Further augmentation with stricter selection of control gains
robustifies the design to time-varying delays in intra-swarm communications.
The paper also establishes the input-to-state stability of a teleoperated
time-delay swarm under the proposed dynamic control. Experiments validate
connectivity maintenance and synchronization during time-delay swarm
teleoperation with the proposed control.
Assessment of the Value of Frequency Response Times in Power Systems
Date: 2019-07-17 00:00:00

Given the increasing penetration in renewable generation, the UK power system
is experiencing a decline in system inertia and an increase in frequency
response (FR) requirements. Faster FR products are a mitigating solution that
can cost-effectively meet the system balancing requirements. Thus, this paper
proposes a mixed integer linear programming (MILP) unit commitment model which
can simultaneously schedule inertial response, mandatory FR, as well as a
sub-second FR product - enhanced frequency response (EFR). The model quantifies
the value of providing faster reacting FR products in comparison with other
response times from typical FR products. The performance and value of EFR are
determined in a series of future energy scenarios with respect to the UK market
and system conditions.
Cylinder-Specific Model-Based Control of Combustion Phasing for Multiple-Cylinder Diesel Engines Operating with High Dilution and Boost Levels
Date: 2019-07-17 00:00:00

Accurate control of combustion phasing is indispensable for diesel engines
due to the strong impact of combustion timing on efficiency. In this work, a
non-linear combustion phasing model is developed and integrated with a
cylinder-specific model of intake gas. The combustion phasing model uses a
knock integral model, a burn duration model and a Wiebe function to predict
CA50 (the crank angle at which 50% of the mass of fuel has burned). Meanwhile,
the intake gas property model predicts the EGR fraction and the in-cylinder
pressure and temperature at intake valve closing (IVC) for different cylinders.
As such, cylinder-to-cylinder variation of the pressure and temperature at
intake valves closing is also considered in this model. This combined model is
simplified for controller design and validated. Based on these models, two
combustion phasing control strategies are explored. The first is an adaptive
controller that is designed for closed-loop control and the second is a
feedforward model-based control strategy for open-loop control. These two
control approaches were tested in simulations for all six cylinders and the
results demonstrate that the CA50 can reach steady state conditions within 10
cycles. In addition, the steady state errors are less than +/-0.1 crank angle
degree (CAD) with the adaptive control approach, and less than +/-1.3 CAD with
feedforward model-based control. The impact of errors on the control algorithms
is also discussed in the paper.
Behavioural Macroeconomic Policy: New perspectives on time inconsistency
Date: 2019-07-18 00:00:00

This paper brings together divergent approaches to time inconsistency from
macroeconomic policy and behavioural economics. Behavioural discount functions
from behavioural microeconomics are embedded into a game-theoretic analysis of
temptation versus enforcement to construct an encompassing model, nesting
combinations of time consistent and time inconsistent preferences. The analysis
presented in this paper shows that, with hyperbolic/quasihyperbolic
discounting, the enforceable range of inflation targets is narrowed. This
suggests limits to the effectiveness of monetary targets, under certain
conditions. The paper concludes with a discussion of monetary policy
implications, explored specifically in the light of current macroeconomic
policy debates.
Nucleation of antagonistic organisms and cellular competitions on curved, inflating substrates
Date: 2019-10-24 00:00:00

We consider the dynamics of spatially-distributed, diffusing populations of
organisms with antagonistic interactions. These interactions are found on many
length scales, ranging from kilometer-scale animal range dynamics with
selection against hybrids to micron-scale interactions between poison-secreting
microbial populations. We find that the dynamical line tension at the interface
between antagonistic organisms suppresses survival probabilities of small
clonal clusters: the line tension introduces a critical cluster size that an
organism with a selective advantage must achieve before deterministically
spreading through the population. We calculate the survival probability as a
function of selective advantage $\delta$ and antagonistic interaction strength
$\sigma$. Unlike a simple Darwinian selective advantage, the survival
probability depends strongly on the spatial diffusion constant $D_s$ of the
strains when $\sigma>0$, with suppressed survival when both species are more
motile. Finally, we study the survival probability of a single mutant cell at
the frontier of a growing spherical cluster of cells, such as the surface of an
avascular spherical tumor. Both the inflation and curvature of the frontier
significantly enhance the survival probability by changing the critical size of
the nucleating cell cluster.
Connecting metapopulation heterogeneity to aggregated lifetime statistics
Date: 2019-07-18 00:00:00

Aggregated metapopulation lifetime statistics has been used to access
stylized facts that might help identify the underlying patch-level dynamics.
For instance, the emergence of scaling laws in the aggregated probability
distribution of patch lifetimes can be associated to critical phenomena, in
which the correlation length among system units tends to diverge. Nevertheless,
an aggregated approach is biased by patch-level variability, a fact that can
blur the interpretation of the data. Here, I propose a weakly-coupled
metapopulation model to show how patch variability can solely trigger
qualitatively different lifetime probability distribution at the aggregated
level. In a generalized approach, I obtain a two-way connection between the
variability of a certain patch property (e.g. carrying capacity, environment
condition or connectivity) and the aggregated lifetime probability
distribution. Furthermore, for a particular case, assuming that scaling laws
are observed at the aggregated-level, I speculate the heterogeneity that could
be behind it, relating the qualitative features the variability (mean, variance
and concentration) to the scaling exponents. In this perspective, the
application points to the possibility of equivalence between heterogeneous
weakly-coupled metapopulations and homogeneous ones that exhibit critical
behavior.