pwc-archive/papers-with-abstracts huggingface dataset:
A Dynamic Default Contagion Model: From Eisenberg-Noe to the Mean Field
Date: 2019-12-18 00:00:00

In this work we introduce a model of default contagion that combines the
approaches of Eisenberg-Noe interbank networks and dynamic mean field
interactions. The proposed contagion mechanism provides an endogenous rule for
early defaults in a network of financial institutions. The main result is to
demonstrate a mean field interaction that can be found as the limit of the
finite bank system generated from a finite Eisenberg-Noe style network. In this
way, we connect two previously disparate frameworks for systemic risk, and in
turn we provide a bridge for exploiting recent advances in mean field analysis
when modelling systemic risk. The mean field limit is shown to be well-posed
and is identified as a certain conditional McKean-Vlasov type problem that
respects the original network topology under suitable assumptions.
Four-factor model of Quanto CDS with jumps-at-default and stochastic recovery
Authors: Andrey Itkin, Fazlollah Soleymani
Date: 2019-12-17 00:00:00

In this paper we modify the model of Itkin, Shcherbakov and Veygman, (2019) (ISV2019), proposed for pricing Quanto Credit Default Swaps (CDS) and risky bonds, in several ways. First, it is known since the Lehman Brothers bankruptcy that the recovery rate could significantly vary right before or at default, therefore, in this paper we consider it to be stochastic. Second, to reduce complexity of the model, we treat the domestic interest rate as deterministic, because, as shown in ISV2019, volatility of the domestic interest rate does not contribute much to the value of the Quanto CDS spread. Finally, to solve the corresponding systems of 4D partial differential equations we use a different flavor of the Radial Basis Function (RBF) method which is a combination of localized RBF and finite-difference methods, and is known in the literature as RBF-FD. Results of our numerical experiments presented in the paper demonstrate that the influence of volatility of the recovery rate is significant if the correlation between the recovery rate and the log-intensity of the default is non-zero. Also, the impact of the recovery mean-reversion rate on the Quanto CDS spread could be comparable with the impact due to jump-at-default in the FX rate.
Econometrics For Decision Making: Building Foundations Sketched By Haavelmo And Wald
Authors: Charles F. Manski
Date: 2019-12-17 00:00:00
Tasks: Decision Making, Econometrics

Haavelmo (1944) proposed a probabilistic structure for econometric modeling, aiming to make econometrics useful for decision making. His fundamental contribution has become thoroughly embedded in subsequent econometric research, yet it could not answer all the deep issues that the author raised. Notably, Haavelmo struggled to formalize the implications for decision making of the fact that models can at most approximate actuality. In the same period, Wald (1939, 1945) initiated his own seminal development of statistical decision theory. Haavelmo favorably cited Wald, but econometrics did not embrace statistical decision theory. Instead, it focused on study of identification, estimation, and statistical inference. This paper proposes statistical decision theory as a framework for evaluation of the performance of models in decision making. I particularly consider the common practice of as-if optimization: specification of a model, point estimation of its parameters, and use of the point estimate to make a decision that would be optimal if the estimate were accurate. A central theme is that one should evaluate as-if optimization or any other model-based decision rule by its performance across the state space, listing all states of nature that one believes feasible, not across the model space. I apply the theme to prediction and treatment choice. Statistical decision theory is conceptually simple, but application is often challenging. Advancement of computation is the primary task to continue building the foundations sketched by Haavelmo and Wald.
The Hoare-fol Tool
Authors: Maxime Folschette
Date: 2019-12-17 00:00:00
Tasks: Translation

This document presents the tool named ''Application of Hoare Logic and Dijkstra's Weakest Proposition Calculus to Biological Regulatory Networks Using Path Programs with Branching First-Order Logic Operators'' or Hoare-fol for short. This tool consists in an implementation of the theoretical work developed in [Bernot et al., 2019] and contains the following features: (1) computation of the weakest precondition of a Hoare triple, (2) simplification of this weakest precondition using De Morgan laws and partial knowledge on the initial state, and (3) translation into Answer Set Programming to allow a solving of all compatible solutions.
Assessing Inference Methods
Authors: Bruno Ferman
Date: 2019-12-18 00:00:00

We consider different types of simulations that applied researchers may use to assess whether their inference methods are reliable. We show that different assessments vary in the set of problems they may detect, possibility of sequential-testing distortions, necessity of application-specific modifications, and implementation complexity. We present evidence that even the use of simple and easy-to-implement assessments can detect problems in a wide range of settings. While more complex assessments may detect problems that simpler assessments would not detect, they do not uniformly dominate simpler assessments in this dimension. Moreover, they may induce sequential-testing distortions, and may require application-specific modifications.
Learning the Globally Optimal Distributed LQ Regulator
Date: 2020-05-30 00:00:00
Proceeding: L4DC 2020 6
Tasks: valid

We study model-free learning methods for the output-feedback Linear Quadratic
(LQ) control problem in finite-horizon subject to subspace constraints on the
control policy. Subspace constraints naturally arise in the field of
distributed control and present a significant challenge in the sense that
standard model-based optimization and learning leads to intractable numerical
programs in general. Building upon recent results in zeroth-order optimization,
we establish model-free sample-complexity bounds for the class of distributed
LQ problems where a local gradient dominance constant exists on any sublevel
set of the cost function. %which admit a local gradient dominance constant
valid on the sublevel set of the cost function. We prove that a fundamental
class of distributed control problems - commonly referred to as Quadratically
Invariant (QI) problems - as well as others possess this property. To the best
of our knowledge, our result is the first sample-complexity bound guarantee on
learning globally optimal distributed output-feedback control policies.
Hybrid threats as an exogenous economic shock
Date: 2019-12-17 00:00:00
Tasks: regression

The aim of this study is to contribute to the theory of exogenous economic
shocks and their equivalents in an attempt to explain business cycle
fluctuations, which still do not have a clear explanation. To this end the
author has developed an econometric model based on a regression analysis.
Another objective is to tackle the issue of hybrid threats, which have not yet
been subjected to a cross-disciplinary research. These were reviewed in terms
of their economic characteristics in order to complement research in the fields
of defence and security.
Inefficiencies in Digital Advertising Markets
Date: 2020-02-22 00:00:00
Tasks: Blocking

Digital advertising markets are growing and attracting increased scrutiny.
This paper explores four market inefficiencies that remain poorly understood:
ad effect measurement, frictions between and within advertising channel
members, ad blocking and ad fraud. These topics are not unique to digital
advertising, but each manifests in new ways in markets for digital ads. We
identify relevant findings in the academic literature, recent developments in
practice, and promising topics for future research.
Optimal Experiment Design for AC Power Systems Admittance Estimation
Date: 2020-05-11 00:00:00
Tasks: Experimental Design, parameter estimation

The integration of renewables into electrical grids calls for the development
of tailored control schemes which in turn require reliable grid models. In many
cases, the grid topology is known but the actual parameters are not exactly
known. This paper proposes a new approach for online parameter estimation in
power systems based on optimal experimental design using multiple measurement
snapshots. In contrast to conventional methods, our method computes optimal
excitations extracting the maximum information in each estimation step to
accelerate convergence. The performance of the proposed method is illustrated
on a case study.
Digital Controllers in Discrete and Continuous Time Domains for a Robot Arm Manipulator
Date: 2019-12-19 00:00:00

This paper articulates design and performance analysis of digital controllers
in discrete and continuous time domains for a single-joint robot arm
manipulator. The investigated robot arm system is modeled as a single degree of
freedom (DOF) plant and there is a feedback sensor implying a closed-loop
system. The design approach incorporates discrete (z-plane) and continuous-time
(warped s-plane or w-plane) domain parameters. Four digital controllers -
phase-lag, phase-lead, proportional-integral (PI) and
proportional-integral-derivative (PID) are theoretically designed and
implemented to achieve a phase margin of 40 deg. for the compensated system.
For performance evaluations, Bode plots of the compensated open-loop systems
and step response characteristics of the closed-loop systems are determined.
Point Cloud Rendering after Coding: Impacts on Subjective and Objective Quality
Date: 2020-10-15 00:00:00

Recently, point clouds have shown to be a promising way to represent 3D
visual data for a wide range of immersive applications, from augmented reality
to autonomous cars. Emerging imaging sensors have made easier to perform richer
and denser point cloud acquisition, notably with millions of points, thus
raising the need for efficient point cloud coding solutions. In such a
scenario, it is important to evaluate the impact and performance of several
processing steps in a point cloud communication system, notably the quality
degradations associated to point cloud coding solutions. Moreover, since point
clouds are not directly visualized but rather processed with a rendering
algorithm before shown on any display, the perceived quality of point cloud
data highly depends on the rendering solution. In this context, the main
objective of this paper is to study the impact of several coding and rendering
solutions on the perceived user quality and in the performance of available
objective quality assessment metrics. Another contribution regards the
assessment of recent MPEG point cloud coding solutions for several popular
rendering methods which were never presented before. The conclusions regard the
visibility of three types of coding artifacts for the three considered
rendering approaches as well as the strengths and weakness of objective quality
metrics when point clouds are rendered after coding.
FliI6-FliJ molecular motor assists with unfolding in the type III secretion export apparatus
Authors: Jiri Kucera, Eugene M. Terentjev
Date: 2019-12-19 00:00:00

The role of rotational molecular motors of the ATP synthase class is integral to the metabolism of cells. Yet the function of FliI6-FliJ complex - homologous to the F1 ATPase motor - within the flagellar export apparatus remains unclear. We use a simple two-state model adapted from studies of linear molecular motors to identify key features of this motor. The two states are the 'locked' ground state where the FliJ coiled coil filament experiences fluctuations in an asymmetric torsional potential, and a 'free' excited state in which FliJ undergoes rotational diffusion. Michaelis-Menten kinetics was used to treat transitions between these two states, and obtain the average angular velocity of the FliJ filament within the FliI6 stator: ~9.0 rps. The motor was then studied under external counter torque conditions in order to ascertain its maximal power output: Pmax ~42 kBT/s, and the stall torque: ~3 kBT/rad. Two modes of action within the flagellar export apparatus are proposed, in which the motor performs useful work either by continuously 'grinding' through the resistive environment, or by exerting equal and opposite stall force on it. In both cases, the resistance is provided by flagellin subunits entering the flagellar export channel prior to their unfolding. We therefore propose that the function of the FliI6-FliJ complex is to lower the energy barrier and therefore assist in unfolding of the flagellar proteins before feeding them into the transport channel.
Reconfigurable Intelligent Surfaces based RF Sensing: Design, Optimization, and Implementation
Date: 2019-12-22 00:00:00

Using radio-frequency (RF) sensing techniques for human posture recognition
has attracted growing interest due to its advantages of pervasiveness,
contact-free observation, and privacy protection. Conventional RF sensing
techniques are constrained by their radio environments, which limit the number
of transmission channels to carry multi-dimensional information about human
postures. Instead of passively adapting to the environment, in this paper, we
design an RF sensing system for posture recognition based on reconfigurable
intelligent surfaces (RISs). The proposed system can actively customize the
environments to provide the desirable propagation properties and diverse
transmission channels. However, achieving high recognition accuracy requires
the optimization of RIS configuration, which is a challenging problem. To
tackle this challenge, we formulate the optimization problem, decompose it into
two subproblems and propose algorithms to solve them. Based on the developed
algorithms, we implement the system and carry out practical experiments. Both
simulation and experimental results verify the effectiveness of the designed
algorithms and system. Compared to the random configuration and
non-configurable environment cases, the designed system can greatly improve the
recognition accuracy.
Evolution of cooperation in networked heterogeneous fluctuating environments
Date: 2019-12-19 00:00:00

Fluctuating environments are situations where the spatio-temporal stochasticity plays a significant role in the evolutionary dynamics. The study of the evolution of cooperation in these environments typically assumes a homogeneous, well mixed population, whose constituents are endowed with identical capabilities. In this paper, we generalize these results by developing a systematic study for the cooperation dynamics in fluctuating environments under the consideration of structured, heterogeneous populations with individual entities subjected to general behavioral rules. Considering complex network topologies, and a behavioral rule based on generalized reciprocity, we perform a detailed analysis of the effect of the underlying interaction structure on the evolutionary stability of cooperation. We find that, in the presence of environmental fluctuations, the cooperation dynamics can lead to the creation of multiple network components, each with distinct evolutionary properties. This is paralleled to the freezing state in the Random Energy Model. We utilize this result to examine the applicability of our generalized reciprocity behavioral rule in a variety of settings. We thereby show that the introduced rule leads to steady state cooperative behavior that is always greater than or equal to the one predicted by the evolutionary stability analysis of unconditional cooperation. As a consequence, the implementation of our results may go beyond explaining the evolution of cooperation. In particular, they can be directly applied in domains that deal with the development of artificial systems able to adequately mimic reality, such as reinforcement learning.
Quantitative and qualitative analysis of asynchronous neural activity
Date: 2019-12-19 00:00:00

The activity of a sparse network of leaky integrate-and-fire neurons is
carefully revisited with reference to a regime of a bona-fide asynchronous
dynamics. The study is preceded by a finite-size scaling analysis, carried out
to identify a setup where collective synchronization is negligible. The
comparison between quenched and annealed networks reveals the emergence of
substantial differences when the coupling strength is increased, via a scenario
somehow reminiscent of a phase transition. For sufficiently strong synaptic
coupling, quenched networks exhibit a highly bursting neural activity, well
reproduced by a self-consistent approach, based on the assumption that the
input synaptic current is the superposition of independent renewal processes.
The distribution of interspike intervals turns out to be relatively
long-tailed; a crucial feature required for the self-sustainment of the
bursting activity in a regime where neurons operate on average (much) below
threshold. A semi-quantitative analogy with Ornstein-Uhlenbeck processes helps
validating this interpretation. Finally, an alternative explanation in terms of
Poisson processes is offered under the additional assumption of mutual
correlations among excitatory and inhibitory spikes.
Short-time detection of QRS complexes using dual channels 1 based on U-Net and bidirectional long short-term memory
Date: 2019-12-18 00:00:00

Cardiovascular disease is associated with high rates of morbidity and
mortality, and can be reflected by 19 abnormal features of electrocardiogram
(ECG). Detecting changes in the QRS complexes in ECG 20 signals is regarded as
a straightforward, noninvasive, inexpensive, and preliminary diagnosis approach
21 for evaluating the cardiac health of patients. Therefore, detecting QRS
complexes in ECG signals must 22 be accurate over short times. However, the
reliability of automatic QRS detection is restricted by all 23 kinds of noise
and complex signal morphologies. In this study, we proposed a new algorithm for
24 short-time detection of QRS complexes using dual channels based on U-Net and
bidirectional long 25 short-term memory. First, a proposed preprocessor with
mean filtering and discrete wavelet transform 26 was initially applied to
remove different types of noise. Next the signal was transformed and 27
annotations were relabeled. Finally, a method combining U-Net and bidirectional
long short-term 28 memory with dual channels was used for the short-time
detection of QRS complexes. The proposed 29 algorithm was trained and tested
using 44 ECG records from the MIT-BIH arrhythmia database. It 30 achieved, on
average, high values for detection sensitivity (99.56%), positive predictivity
(99.72%), 31 and accuracy (99.28%) on the test set, indicating an improvement
compared to algorithms reported in 32 the literature.
Pay-As-You-Drive Insurance Pricing Model
Date: 2019-12-17 00:00:00
Tasks: model

Every time drivers take to the road, and with each mile that they drive,
exposes themselves and others to the risk of an accident. Insurance premiums
are only weakly linked to mileage, however, and have lump-sum characteristics
largely. The result is too much driving, and too many accidents. In this paper,
we introduce some useful theoretical results for Pay-As-You-Drive in Automobile
insurances. We consider a counting process and also find the distribution of
discounted collective risk model when the counting process is non-homogeneous
Poisson.
Sum Rate Maximization for Multiuser MISO Downlink with Intelligent Reflecting Surface
Authors: Silei Wang, Qiang Li, Sissi Xiaoxiao Wu, Jingran Lin
Date: 2019-12-19 00:00:00

The intelligent reflecting surface (IRS) is a planar array with massive reconfigurable passive elements, which can align the reflecting signals at the receivers via controlling the phase shifts at each element independently. Since IRS can be implemented without RF chain, it is seen as a cost-effective solution for boosting the spectral efficiency for the future communication system. This work considers the IRS-aided multiuser multi-input single-output downlink transmission, and our goal is to maximize the sum rate by jointly optimizing the transmit beamformer at the base station and the phase shifts, which can be either continuous or discrete, at the IRS. This sum rate maximization (SRM) problem is challenging, especially for the discrete-phase case. To tackle it, we first derive a more tractable equivalent formulation of the SRM problem with structured convex constraints and a smooth objective. From there, a custom-derived block-coordinated accelerated projected gradient algorithm is developed. Simulation results demonstrate that the proposed design outperforms state-of-the-art design in the sum rate performance.
Radio Localization and Mapping with Reconfigurable Intelligent Surfaces
Authors: Henk Wymeersch, Jiguang He, Benoît Denis, Antonio Clemente, Markku Juntti
Date: 2019-12-19 00:00:00

5G radio at millimeter wave (mmWave) and beyond 5G concepts at 0.1-1 THz can exploit angle and delay measurements for localization, by the virtue of increased bandwidth and large antenna arrays but are limited in terms of blockage caused by obstacles. Reconfigurable intelligent surfaces (RISs) are seen as a transformative technology that can control the physical propagation environment in which they are embedded by passively reflecting EM waves in preferred directions. Whereas such RISs have been mainly intended for communication purposes, RISs can have great benefits in terms of performance, energy consumption, and cost for localization and mapping. These benefits as well as associated challenges are the main topics of this paper.
A Comprehensive Survey on Hybrid Communication for Internet of Nano-Things in Context of Body-Centric Communications
Date: 2019-12-16 00:00:00

With the huge advancement of nanotechnology over the past years, the devices
are shrinking into micro-scale, even nano-scale. Additionally, the Internet of
nano-things (IoNTs) are generally regarded as the ultimate formation of the
current sensor networks and the development of nanonetworks would be of great
help to its fulfilment, which would be ubiquitous with numerous applications in
all domains of life. However, the communication between the devices in such
nanonetworks is still an open problem. Body-centric nanonetworks are believed
to play an essential role in the practical application of IoNTs. BCNNs are also
considered as domain specific like wireless sensor networks and always deployed
on purpose to support a particular application. In these networks,
electromagnetic and molecular communications are widely considered as two main
promising paradigms and both follow their own development process. In this
survey, the recent developments of these two paradigms are first illustrated in
the aspects of applications, network structures, modulation techniques, coding
techniques and security to then investigate the potential of hybrid
communication paradigms. Meanwhile, the enabling technologies have been
presented to apprehend the state-of-art with the discussion on the possibility
of the hybrid technologies. Additionally, the inter-connectivity of
electromagnetic and molecular body-centric nanonetworks is discussed.
Afterwards, the related security issues of the proposed networks are discussed.
Finally, the challenges and open research directions are presented.
A Spatially and Temporally Attentive Joint Trajectory Prediction Framework for Modeling Vessel Intent
Date: 2019-12-18 00:00:00
Proceeding: L4DC 2020 6
Tasks: Trajectory Prediction

Ships, or vessels, often sail in and out of cluttered environments over the
course of their trajectories. Safe navigation in such cluttered scenarios
requires an accurate estimation of the intent of neighboring vessels and their
effect on the self and vice-versa well into the future. In manned vessels, this
is achieved by constant communication between people on board, nautical
experience, and audio and visual signals. In this paper we propose a deep
neural network based architecture to predict intent of neighboring vessels into
the future for an unmanned vessel solely based on positional data.