pwc-archive/papers-with-abstracts huggingface dataset:
Analytical Modeling of Nonlinear Fiber Propagation for Four Dimensional Symmetric Constellations
Authors: Hami Rabbani, Mostafa Ayaz, Lotfollah Beygi, Gabriele Liga, Alex Alvarado, Erik Agrell, Magnus Karlsson
Date: 2020-09-22 00:00:00
Tasks: valid

Coherent optical transmission systems naturally lead to a four dimensional (4D) signal space, i.e., two polarizations each with two quadratures. In this paper we derive an analytical model to quantify the impact of Kerr nonlinearity on such 4Dspaces, taking the interpolarization dependency into account. This is in contrast to previous models such as the GN and EGN models, which are valid for polarization multiplexed (PM)formats, where the two polarizations are seen as independent channels on which data is multiplexed. The proposed model agrees with the EGN model in the special case of independent two-dimensional modulation in each polarization. The model accounts for the predominant nonlinear terms in a WDM system, namely self-phase modulation and and cross-phase modulation. Numerical results show that the EGN model may inaccurately estimate the nonlinear interference of 4D formats. This nonlinear interference discrepancy between the results of the proposed model and the EGN model could be up to 2.8 dB for a system with 80 WDM channels. The derived model is validated by split-step Fourier simulations, and it is shown to follow simulations very closely.
Output-Feedback Model Predictive Control with Online Identification
Date: 2020-09-22 00:00:00
Tasks: model, Model Predictive Control

Model predictive control (MPC) is a widely used modern control technique with
numerous successful application in diverse areas. Much of this success is due
to the ability of MPC to enforce state and control constraints, which are
crucial in many applications of control. In order to avoid the need for an
observer, output-feedback model predictive control with online identification
(OFMPCOI) uses the block observable canonical form whose state consists of past
values of the control inputs and measured outputs. Online identification is
performed using recursive least squares (RLS) with variable-rate forgetting.
The article describes the algorithmic details of OFMPCOI and numerically
investigates its performance through a collection of numerical examples that
highlight various control challenges, such as model order uncertainty, sensor
noise, prediction horizon, stabilization, magnitude and move-size saturation,
and stabilization. The numerical examples are used to probe the performance of
OFMPCOI in terms of persistency, consistency, and exigency. Since OFMPCOI does
not employ a separate control perturbation to enhance persistency, the focus is
on self-generated persistency during transient operation. For closed-loop
identification using RLS, sensor noise gives rise to bias in the identified
model, and the goal is to determine the effect of the lack of consistency.
Finally, the numerical examples reveal exigency, which is the extent to which
the online identification emphasizes model characteristics that are most
relevant to meeting performance objectives.
Channel Estimation for Distributed Intelligent Reflecting Surfaces Assisted Multi-User MISO Systems
Authors: Hibatallah Alwazani, Qurrat-Ul-Ain Nadeem, Anas Chaaban
Date: 2020-09-22 00:00:00

Intelligent reflecting surfaces (IRSs)-assisted wireless communication promises improved system performance, while posing new challenges in channel estimation (CE) due to the passive nature of the reflecting elements. Although a few CE protocols for IRS-assisted multiple-input single-output (MISO) systems have appeared, they either require long channel training times or are developed under channel sparsity assumptions. Moreover, existing works focus on a single IRS, whereas in practice multiple such surfaces should be installed to truly benefit from the concept of reconfiguring propagation environments. In light of these challenges, this paper tackles the CE problem for the distributed IRSs-assisted multi-user MISO system. An optimal CE protocol requiring relatively low training overhead is developed using Bayesian techniques under the practical assumption that the BS-IRSs channels are dominated by the line-of-sight (LoS) components. An optimal solution for the phase shifts vectors required at all IRSs during CE is determined and the minimum mean square error (MMSE) estimates of the BSusers direct channels and the IRSs-users channels are derived. Simulation results corroborate the normalized MSE (NMSE) analysis and establish the advantage of the proposed protocol as compared to benchmark scheme in terms of training overhead.
DiviK: Divisive intelligent K-Means for hands-free unsupervised clustering in big biological data
Authors: Grzegorz Mrukwa, Joanna Polanska
Date: 2020-09-22 00:00:00
Tasks: Clustering, Feature Engineering

Investigating molecular heterogeneity provides insights about tumor origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible - therefore, automated unsupervised learning approaches are utilized for discovering heterogeneity. However, automated unsupervised analyses require a lot of experience with setting their hyperparameters and usually an upfront knowledge about the number of expected substructures. Moreover, numerous measured molecules require an additional step of feature engineering to provide valuable results. In this work, we propose DiviK: a scalable stepwise algorithm with local data-driven feature space adaptation for the segmentation of high-dimensional datasets. The combination of three quality indices: Dice Index, Rand Index and EXIMS score are used to assess the quality of unsupervised analyses in 3D space. DiviK was validated on two separate high-throughput datasets acquired by Mass Spectrometry Imaging in 2D and 3D. DiviK could be one of the default choices to consider during the initial exploration of Mass Spectrometry Imaging data. It provides a trade-off between absolute heterogeneity detection and focus on biologically plausible structures, and does not require specifying the number of expected structures before the analysis. With its unique local feature space adaptation, it is robust against dominating global patterns when focusing on the detail. Finally, due to its simplicity, DiviK is easily generalizable to an even more flexible framework, useful for other '-omics' data, or tabular data in general (including medical images after appropriate embedding). A generic implementation is freely available under Apache 2.0 license at https://github.com/gmrukwa/divik.
CoVaR with volatility clustering, heavy tails and non-linear dependence
Authors: Michele Leonardo Bianchi, Giovanni De Luca, Giorgia Rivieccio
Date: 2020-09-22 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

In this paper we estimate the conditional value-at-risk by fitting different multivariate parametric models capturing some stylized facts about multivariate financial time series of equity returns: heavy tails, negative skew, asymmetric dependence, and volatility clustering. While the volatility clustering effect is got by AR-GARCH dynamics of the GJR type, the other stylized facts are captured through non-Gaussian multivariate models and copula functions. The CoVaR$^{\leq}$ is computed on the basis on the multivariate normal model, the multivariate normal tempered stable (MNTS) model, the multivariate generalized hyperbolic model (MGH) and four possible copula functions. These risk measure estimates are compared to the CoVaR$^{=}$ based on the multivariate normal GARCH model. The comparison is conducted by backtesting the competitor models over the time span from January 2007 to March 2020. In the empirical study we consider a sample of listed banks of the euro area belonging to the main or to the additional global systemically important banks (GSIBs) assessment sample.
Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation
Authors: Arindam Chowdhury, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra
Date: 2020-09-22 00:00:00

We study the problem of optimal power allocation in a single-hop ad hoc wireless network. In solving this problem, we depart from classical purely model-based approaches and propose a hybrid method that retains key modeling elements in conjunction with data-driven components. More precisely, we put forth a neural network architecture inspired by the algorithmic unfolding of the iterative weighted minimum mean squared error (WMMSE) method, that we denote by unfolded WMMSE (UWMMSE). The learnable weights within UWMMSE are parameterized using graph neural networks (GNNs), where the time-varying underlying graphs are given by the fading interference coefficients in the wireless network. These GNNs are trained through a gradient descent approach based on multiple instances of the power allocation problem. We show that the proposed architecture is permutation equivariant, thus facilitating generalizability across network topologies. Comprehensive numerical experiments illustrate the performance attained by UWMMSE along with its robustness to hyper-parameter selection and generalizability to unseen scenarios such as different network densities and network sizes.
Identifying and Analyzing Sepsis States: A Retrospective Study on Patients with Sepsis in ICUs
Authors: Chih-Hao Fang, Vikram Ravindra, Salma Akhter, Mohammad Adibuzzaman, Paul Griffin, Shankar Subramaniam, Ananth Grama
Date: 2020-09-21 00:00:00

Sepsis accounts for more than 50% of hospital deaths, and the associated cost ranks the highest among hospital admissions in the US. Improved understanding of disease states, severity, and clinical markers has the potential to significantly improve patient outcomes and reduce cost. We develop a computational framework that identifies disease states in sepsis using clinical variables and samples in the MIMIC-III database. We identify six distinct patient states in sepsis, each associated with different manifestations of organ dysfunction. We find that patients in different sepsis states are statistically significantly composed of distinct populations with disparate demographic and comorbidity profiles. Collectively, our framework provides a holistic view of sepsis, and our findings provide the basis for future development of clinical trials and therapeutic strategies for sepsis.
Efficient Portfolios
Authors: Keith A. Lewis
Date: 2020-09-22 00:00:00

Given two random realized returns on an investment, which is to be preferred? This is a fundamental problem in finance that has no definitive solution except in the case one investment always returns more than the other. In 1952 Markowitz and Roy introduced the following criterion for risk vs. return in portfolio selection: if two portfolios have the same expected realized return then prefer the one with smaller variance. An efficient portfolio has the least variance among all portfolios having the same expected realized return. The primary contribution of this short note is observation that the CAPM formula holds for realized returns as random variables, not just their expectations. This follows directly from writing down a mathematical model for one period investments.
Joint routing and pricing control in congested mixed autonomy networks
Authors: Mohammadhadi Mansourianfar, Ziyuan Gu, S. Travis Waller, Meead Saberi
Date: 2020-09-23 00:00:00
Tasks: Autonomous Vehicles

Routing controllability of connected and autonomous vehicles (CAVs) has been shown to reduce the adverse effects of selfish routing on the network efficiency. However, the assumption that CAV owners would readily allow themselves to be controlled externally by a central agency for the good of the system is unrealistic. In this paper, we propose a joint routing and pricing control scheme that aims to incentivize CAVs to seek centrally controlled system-optimal (SO) routing by saving on tolls while user equilibrium (UE) seeking human-driven vehicles (HVs) are subject to a congestion charge. The problem is formulated as a bi-level optimization program where the upper level optimizes the dynamic toll rates using the network fundamental diagram (NFD) and the lower level is a mixed equilibrium simulation-based dynamic traffic assignment model (SBDTA) considering different combinations of SO-seeking CAVs. We apply a feedback-based controller to solve for the optimal spatially differentiated distance-based congestion charge from which SO-seeking CAVs are exempt; but UE-seeking HVs are subject to the charge for entering the city center. To capture the distinct microscopic behavior of CAVs in the mixed autonomy traffic, we also implement an adaptive link fundamental diagram (FD) within the SBDTA model. The proposed joint control scheme encourages CAV owners to seek SO routing resulting in less total system travel time. It also discourages UE-seeking HVs from congesting the city center. We demonstrate the performance of the proposed scheme in both a small network and a large-scale network of Melbourne, Australia.
Pricing Cryptocurrency Options
Authors: Ai Jun Hou, Weining Wang, Cathy Y. H. Chen, Wolfgang Karl Härdle
Date: 2020-09-23 00:00:00

Cryptocurrencies, especially Bitcoin (BTC), which comprise a new digital asset class, have drawn extraordinary worldwide attention. The characteristics of the cryptocurrency/BTC include a high level of speculation, extreme volatility and price discontinuity. We propose a pricing mechanism based on a stochastic volatility with a correlated jump (SVCJ) model and compare it to a flexible co-jump model by Bandi and Ren\`o (2016). The estimation results of both models confirm the impact of jumps and co-jumps on options obtained via simulation and an analysis of the implied volatility curve. We show that a sizeable proportion of price jumps are significantly and contemporaneously anti-correlated with jumps in volatility. Our study comprises pioneering research on pricing BTC options. We show how the proposed pricing mechanism underlines the importance of jumps in cryptocurrency markets.
Zero-inertia Offshore Grids: N-1 Security and Active Power Sharing
Authors: Georgios S. Misyris, Andrea Tosatto, Spyros Chatzivasileiadis, Tilman Weckesser
Date: 2020-09-23 00:00:00

With Denmark dedicated to maintaining its leading position in the integration of massive shares of wind energy, the construction of new offshore energy islands has been recently approved by the Danish government. These new islands will be zero-inertia systems, meaning that no synchronous generation will be installed in the island and that power imbalances will be shared only among converters. To this end, this paper proposes a methodology to calculate and update the frequency droops gains of the offshore converters in compliance with the N-1 security criterion in case of converter outage. The frequency droop gains are calculated solving an optimization problem which takes into consideration the power limitations of the converters as well as the stability of the system. As a consequence, the proposed controller ensures safe operation of off-shore systems in the event of any power imbalance and allows for greater loadability at pre-fault state, as confirmed by the simulation results.
Simulation-based optimisation of the timing of loan recovery across different portfolios
Authors: Arno Botha, Conrad Beyers, Pieter de Villiers
Date: 2020-09-21 00:00:00

A novel procedure is presented for the objective comparison and evaluation of a bank's decision rules in optimising the timing of loan recovery. This procedure is based on finding a delinquency threshold at which the financial loss of a loan portfolio (or segment therein) is minimised. Our procedure is an expert system that incorporates the time value of money, costs, and the fundamental trade-off between accumulating arrears versus forsaking future interest revenue. Moreover, the procedure can be used with different delinquency measures (other than payments in arrears), thereby allowing an indirect comparison of these measures. We demonstrate the system across a range of credit risk scenarios and portfolio compositions. The computational results show that threshold optima can exist across all reasonable values of both the payment probability (default risk) and the loss rate (loan collateral). In addition, the procedure reacts positively to portfolios afflicted by either systematic defaults (such as during an economic downturn) or episodic delinquency (i.e., cycles of curing and re-defaulting). In optimising a portfolio's recovery decision, our procedure can better inform the quantitative aspects of a bank's collection policy than relying on arbitrary discretion alone.
Stochastic output feedback MPC with intermittent observations
Authors: Shuhao Yan, Mark Cannon, Paul J. Goulart
Date: 2020-09-22 00:00:00
Tasks: Model Predictive Control

This paper designs a model predictive control (MPC) law for constrained linear systems with stochastic additive disturbances and noisy measurements, minimising a discounted cost subject to a discounted expectation constraint. It is assumed that sensor data is lost with a known probability. Taking into account the data losses modelled by a Bernoulli process, we parameterise the predicted control policy as an affine function of future observations and obtain a convex linear-quadratic optimal control problem. Constraint satisfaction and a discounted cost bound are ensured without imposing bounds on the distributions of the disturbance and noise inputs. In addition, the average long-run undiscounted closed loop cost is shown to be finite if the discount factor takes appropriate values. We analyse robustness of the proposed control law with respect to possible uncertainties in the arrival probability of sensor data and we bound the impact of these uncertainties on constraint satisfaction and the discounted cost. Numerical simulations are provided to illustrate these results.
A Deep Learning Approach for Dynamic Balance Sheet Stress Testing
Authors: Anastasios Petropoulos, Vassilis Siakoulis, Konstantinos P. Panousis, Loukas Papadoulas, Sotirios Chatzis
Date: 2020-09-23 00:00:00
Tasks: Deep Learning, Management

In the aftermath of the financial crisis, supervisory authorities have considerably altered the mode of operation of financial stress testing. Despite these efforts, significant concerns and extensive criticism have been raised by market participants regarding the considered unrealistic methodological assumptions and simplifications. Current stress testing methodologies attempt to simulate the risks underlying a financial institution's balance sheet by using several satellite models. This renders their integration a really challenging task, leading to significant estimation errors. Moreover, advanced statistical techniques that could potentially capture the non-linear nature of adverse shocks are still ignored. This work aims to address these criticisms and shortcomings by proposing a novel approach based on recent advances in Deep Learning towards a principled method for Dynamic Balance Sheet Stress Testing. Experimental results on a newly collected financial/supervisory dataset, provide strong empirical evidence that our paradigm significantly outperforms traditional approaches; thus, it is capable of more accurately and efficiently simulating real world scenarios.
Estimation of Motion Parameters for Ultrasound Images Using Motion Blur Invariants
Date: 2020-09-23 00:00:00

The quality of fetal ultrasound images is significantly affected by motion
blur while the imaging system requires low motion quality in order to capture
accurate data. This can be achieved with a mathematical model of motion blur in
time or frequency domain. We propose a new model of linear motion blur in both
frequency and moment domain to analyse the invariant features of blur
convolution for ultrasound images. Moreover, the model also helps to provide an
estimation of motion parameters for blur length and angle. These outcomes might
imply great potential of this invariant method in ultrasound imaging
application.
An algorithm to represent inbreeding trees
Date: 2020-09-21 00:00:00
Tasks: Math

Recent work has proven the existence of extreme inbreeding in a European ancestry sample taken from the contemporary UK population \cite{nature_01}. This result brings our attention again to a math problem related to inbreeding family trees and diversity. Groups with a finite number of individuals could give a variety of genetic relationships. { In previous works \cite{PhysRevE.92.052132, PhysRevE.90.022125, JARNE20191}, we have addressed the issue of building inbreeding trees for biparental reproduction using Markovian models. Here, we extend these studies by presenting an algorithm to generate and represent inbreeding trees with no overlapping generations. We explicitly assume a two-gender reproductory scheme, and we pay particular attention to the links between nodes. We show that even for a simple case with a relatively small number of nodes in the tree, there are a large number of possible ways to rearrange the links between generations. We present an open-source python code to generate the tree graph, the adjacency matrix, and the histogram of the links for each different tree representation. We show how this mapping reflects the difference between tree realizations, and how valuable information may be extracted upon inspection of these matrices. The algorithm includes a feature to average several tree realizations, obtain the connectivity distribution, and calculate the average and mean value. We used this feature to compare trees with a different number of generations and nodes. The code presented here, available in Git-Hub, may be easily modified to be applied to other areas of interest involving connections between individuals, extend the study to add more characteristics of the different nodes, etc.
Fractional-Order Single State Reset Element
Date: 2020-09-23 00:00:00
Tasks: Unity

This paper proposes a fractional-order reset element whose architecture
allows for the suppression of nonlinear effects for a range of frequencies.
Suppressing the nonlinear effects of a reset element for the desired frequency
range while maintaining it for the rest is beneficial, especially when it is
used in the framework of a "Constant in gain, Lead in phase" (CgLp) filter.
CgLp is a newly introduced nonlinear filter, bound to circumvent the well-known
linear control limitation -- the waterbed effect. The ideal behaviour of such a
filter in the frequency domain is unity gain while providing a phase lead for a
broad range of frequencies. However, CgLp's ideal behaviour is based on the
describing function, which is a first-order approximation that neglects the
effects of the higher-order harmonics in the output of the filter. Although
CgLp is fundamentally a nonlinear filter, its nonlinearity is not required for
all frequencies. Thus, it is shown in this paper that using the proposed reset
element architecture, CgLp gets closer to its ideal behaviour for a range of
frequencies, and its performance will be improved accordingly.