pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Layer SIS Model with an Infrastructure Network
Authors: Philip E. Pare, Axel Janson, Sebin Gracy, Ji Liu, Henrik Sandberg, Karl H. Johansson
Date: 2021-09-20 00:00:00
Tasks: model

This paper deals with the spread of diseases over both a population network and an infrastructure network. We develop a layered networked spread model for a susceptible-infected-susceptible (SIS) pathogen-borne disease spreading over a human contact network and an infrastructure network, and refer to it as a layered networked susceptible-infected-water-susceptible (SIWS) model. The SIWS network is in the healthy state (also referred to as the disease-free equilibrium) if none of the individuals in the population are infected nor is the infrastructure network contaminated; otherwise, we say that the network is in the endemic state (also referred to as the endemic equilibrium). First, we establish sufficient conditions for local exponential stability and global asymptotic stability (GAS) of the healthy state. Second, we provide sufficient conditions for existence, uniqueness, and GAS of the endemic state. Building off of these results, we provide a necessary, and sufficient, condition for the healthy state to be the unique equilibrium of our model. Third, we show that the endemic equilibrium of the SIWS model is worse than that of the networked SIS model without any infrastructure network, in the sense that at least one subpopulation has strictly larger infection proportion at the endemic equilibrium in the former model than that in the latter. Fourth, we study an observability problem, and, assuming that the measurements of the sickness-levels of the human contact network are available, provide a necessary and sufficient condition for estimation of the pathogen levels in the infrastructure network. Furthermore, we provide another sufficient, but not necessary, condition for estimation of pathogen levels in the infrastructure network.
Estimation Algorithm Non-Stationary Frequency of the Sinusoidal Signal
Authors: S. I. Nizovtsev, S. V. Shavetov, A. A. Pyrkin
Date: 2021-09-20 00:00:00
Tasks: LEMMA, regression

The article considers the problem of identifying the variable frequency of a sinusoidal signal. To obtain a regression model of the signal, an iterative differentiation of the original analytical expression is performed, and the swapping lemma is applied. The estimation of the parameters of the non-stationary frequency is implemented using the dynamic expansion of the regressor and mixing (DREM) procedure and the Luenberger observer. As a result of the numerical simulation, the efficiency of the proposed algorithm is demonstrated, showing the convergence of the frequency estimate to the true value.
Design, Simulation and Feasibility Analysis of Bifacial Solar PV System in Marine Drive Road, Cox's Bazar
Authors: Abdullah Al Mehadi, Mirza Muntasir Nishat, Fahim Faisal, Ahmed Raza Hasan Bhuiyan, Mohyeu Hussain, Md Ashraful Hoque
Date: 2021-09-20 00:00:00
Tasks: Financial Analysis

This paper proposes a design and simulation based investigative analysis of a vertically mounted bifacial solar photovoltaic model in Marine Drive Road, Cox's Bazar. Cox's bazar is a famous tourist destination which seems to be a flexible site for implementing such energy harvesting system without affecting the nearby eco-system and solves the existing land shortage problem. Moreover, the infrastructure will provide insulation to noise related problem faced by nearby residents, arising from traffic noises. A model road of 200 meters is reconnoitered for energy harvesting by solar power using three prominent software namely PVSOL, PVsyst, and SAM where a promising mean annual yield of 70492.9 kWh is obtained, and the bifacial gain is calculated to be 12.26%. In addition, a deviation analysis is performed among each of the software and it is found that PVSOL and PVsyst have shown less deviation. Furthermore, a comprehensive financial analysis shows total installation cost to be 84759.74$.
Energy-Efficient Design for IRS-Assisted MEC Networks with NOMA
Authors: Qun Wang, Fuhui Zhou, Han Hu, Rose Qingyang Hu
Date: 2021-09-19 00:00:00
Tasks: Edge-computing

Energy-efficient design is of crucial importance in wireless internet of things (IoT) networks. In order to serve massive users while achieving an energy-efficient operation, an intelligent reflecting surface (IRS) assisted mobile edge computing (MEC) network with non-orthogonal multiple access (NOMA) is studied in this paper. The energy efficiency (EE) is maximized by jointly optimizing the offloading power, local computing frequency, receiving beamforming, and IRS phase-shift matrix. The problem is challenging to solve due to the non-convex fractional objective functions and the coupling among the variables. A semidefinite programming relaxation (SDR) based alternating algorithm is developed. Simulation results demonstrate that the proposed design outperforms the benchmark schemes in terms of EE. Applying IRS and NOMA can effectively improve the performance of the MEC network.
Autonomous orbit determination for satelite formations using relative sensing: observability analysis and optimization
Authors: Pedro Rocha Cachim, João Gomes, Rodrigo Ventura
Date: 2021-09-19 00:00:00

Orbit determination of spacecraft in orbit has been mostly dependent on either GNSS satellite signals or ground station telemetry. Both methods present their limitations, however: GNSS signals can only be used effectively in earth orbit, and ground-based orbit determination presents an inherent latency that increases with the Earth-spacecraft distance. For spacecraft flying formations, an alternative method of orbit determination, independent of external signals, consists in the observation of the spacecraft's position with respect to the central body through the relative positioning history of the spacecraft within the formation. In this paper, the potential of the relative positioning method is demonstrated in the context of the SunRISE mission, and compared with the mission's previously proposed orbit determination methods. An optimization study is then made to find the optimal placement of a new spacecraft in the formation so as to maximize the positioning accuracy of the system. Finally, the possibility of removing part of the system's relative bearing measurements while maintaing its observability is also studied. The resulting system is found to be observable, but ill-conditioned.
Relaxed Conditions for Parameterized Linear Matrix Inequality in the Form of Double Sum
Authors: Do Wan Kim, Dong Hwan Lee
Date: 2021-09-19 00:00:00
Tasks: Form

The aim of this study is to investigate less conservative conditions for a parameterized linear matrix inequality (PLMI) expressed in the form of a double convex sum. This type of PLMI frequently appears in T-S fuzzy control system analysis and design problems. In this letter, we derive new, less conservative linear matrix inequalities (LMIs) for the PLMI by employing the proposed sum relaxation method based on Young's inequality. The derived LMIs are proven to be less conservative than the existing conditions related to this topic in the literature. The proposed technique is applicable to various stability analysis and control design problems for T-S fuzzy systems, which are formulated as solving the PLMIs in the form of a double convex sum. Furthermore, examples is provided to illustrate the reduced conservatism of the derived LMIs.
Minimum-fuel Spacecraft Rendezvous based on Sparsity Promoting Optimization
Authors: Vrushabh Zinage, Efstathios Bakolas
Date: 2021-09-17 00:00:00
Tasks: Compressive Sensing

In this paper, we consider the classical spacecraft rendezvous problem in which the so-called active spacecraft has to approach the target spacecraft which is moving in an elliptical orbit around a planet by using the minimum possible amount of fuel. Instead of using standard convex optimization tools which can be computationally expensive, we use modified versions of the Iteratively Reweighted Least Squares (IRLS) algorithm from compressive sensing to compute sparse optimal control sequences which minimize the fuel consumption for both thrust vectoring and orthogonal vectoring (active) spacecraft. Numerical simulations are performed to verify the efficacy of our approach.
Non-equilibrium time-dependent solution to discrete choice with social interactions
Authors: James Holehouse, Hector Pollitt
Date: 2021-09-20 00:00:00

We solve the binary decision model of Brock and Durlauf in time using a method reliant on the resolvent of the master operator of the stochastic process. Our solution is valid when not at equilibrium and can be used to exemplify path-dependent behaviours of the binary decision model. The solution is computationally fast and is indistinguishable from Monte Carlo simulation. Well-known metastable effects are observed in regions of the model's parameter space where agent rationality is above a critical value, and we calculate the time scale at which equilibrium is reached from first passage time theory to a much greater approximation than has been previously conducted. In addition to considering selfish agents, who only care to maximise their own utility, we consider altruistic agents who make decisions on the basis of maximising global utility. Curiously, we find that although altruistic agents coalesce more strongly on a particular decision, thereby increasing their utility in the short-term, they are also more prone to being subject to non-optimal metastable regimes as compared to selfish agents. The method used for this solution can be easily extended to other binary decision models, including Kirman's ant model, and under reinterpretation also provides a time-dependent solution to the mean-field Ising model. Finally, we use our time-dependent solution to construct a likelihood function that can be used on non-equilibrium data for model calibration. This is a rare finding, since often calibration in economic agent based models must be done without an explicit likelihood function. From simulated data, we show that even with a well-defined likelihood function, model calibration is difficult unless one has access to data representative of the underlying model.
Impact of Surface and Pore Characteristics on Fatigue Life of Laser Powder Bed Fusion Ti-6Al-4V Alloy Described by Neural Network Models
Authors: Seunghyun Moon, Ruimin Ma, Ross Attardo, Charles Tomonto, Mark Nordin, Paul Wheelock, Michael Glavicic, Maxwell Layman, Richard Billo, Tengfei Luo
Date: 2021-08-28 00:00:00

In this study, the effects of surface roughness and pore characteristics on fatigue lives of laser powder bed fusion (LPBF) Ti-6Al-4V parts were investigated. The 197 fatigue bars were printed using the same laser power but with varied scanning speeds. These actions led to variations in the geometries of microscale pores, and such variations were characterized using micro-computed tomography. To generate differences in surface roughness in fatigue bars, half of the samples were grit-blasted and the other half machined. Fatigue behaviors were analyzed with respect to surface roughness and statistics of the pores. For the grit-blasted samples, the contour laser scan in the LPBF strategy led to a pore-depletion zone isolating surface and internal pores with different features. For the machined samples, where surface pores resemble internal pores, the fatigue life was highly correlated with the average pore size and projected pore area in the plane perpendicular to the stress direction. Finally, a machine learning model using a drop-out neural network (DONN) was employed to establish a link between surface and pore features to the fatigue data (logN), and good prediction accuracy was demonstrated. Besides predicting fatigue lives, the DONN can also estimate the prediction uncertainty.
Tests for Group-Specific Heterogeneity in High-Dimensional Factor Models
Authors: Antoine Djogbenou, Razvan Sufana
Date: 2021-09-19 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Standard high-dimensional factor models assume that the comovements in a large set of variables could be modeled using a small number of latent factors that affect all variables. In many relevant applications in economics and finance, heterogenous comovements specific to some known groups of variables naturally arise, and reflect distinct cyclical movements within those groups. This paper develops two new statistical tests that can be used to investigate whether there is evidence supporting group-specific heterogeneity in the data. The first test statistic is designed for the alternative hypothesis of group-specific heterogeneity appearing in at least one pair of groups; the second is for the alternative of group-specific heterogeneity appearing in all pairs of groups. We show that the second moment of factor loadings changes across groups when heterogeneity is present, and use this feature to establish the theoretical validity of the tests. We also propose and prove the validity of a permutation approach for approximating the asymptotic distributions of the two test statistics. The simulations and the empirical financial application indicate that the proposed tests are useful for detecting group-specific heterogeneity.
Composite Likelihood for Stochastic Migration Model with Unobserved Factor
Authors: Antoine Djogbenou, Christian Gouriéroux, Joann Jasiak, Maygol Bandehali
Date: 2021-09-19 00:00:00

We introduce the conditional Maximum Composite Likelihood (MCL) estimation method for the stochastic factor ordered Probit model of credit rating transitions of firms. This model is recommended for internal credit risk assessment procedures in banks and financial institutions under the Basel III regulations. Its exact likelihood function involves a high-dimensional integral, which can be approximated numerically before maximization. However, the estimated migration risk and required capital tend to be sensitive to the quality of this approximation, potentially leading to statistical regulatory arbitrage. The proposed conditional MCL estimator circumvents this problem and maximizes the composite log-likelihood of the factor ordered Probit model. We present three conditional MCL estimators of different complexity and examine their consistency and asymptotic normality when n and T tend to infinity. The performance of these estimators at finite T is examined and compared with a granularity-based approach in a simulation study. The use of the MCL estimator is also illustrated in an empirical application.
She Innovates- Female owner and firm innovation in India
Authors: Shreya Biswas
Date: 2021-09-20 00:00:00

Using data from World Bank Enterprises Survey 2014, we find that having a female owner in India increases firm innovation probability using both input and output indicators of innovation. We account for possible endogeneity of female owner variable using a two stage instrumental variable probit model. We find that the positive effect of female owner variable is observed in the sub-samples of firms with more access to internal funding, young firms and firms located in regions with no or less crime This study highlights the need to promote female entrepreneurship as a potential channel for promoting firm innovation in India.
Economic Crises in a Model with Capital Scarcity and Self-Reflexive Confidence
Authors: Federico Guglielmo Morelli, Karl Naumann-Woleske, Michael Benzaquen, Marco Tarzia, Jean-Philippe Bouchaud
Date: 2021-09-20 00:00:00

In the General Theory, Keynes remarked that the economy's state depends on expectations, and that these expectations can be subject to sudden swings. In this work, we develop a multiple equilibria behavioural business cycle model that can account for demand or supply collapses due to abrupt drops in consumer confidence, which affect both consumption propensity and investment. We show that, depending on the model parameters, four qualitatively different outcomes can emerge, characterised by the frequency of capital scarcity and/or demand crises. In the absence of policy measures, the duration of such crises can increase by orders of magnitude when parameters are varied, as a result of the "paradox of thrift". Our model suggests policy recommendations that prevent the economy from getting trapped in extended stretches of low output, low investment and high unemployment.
Deep-Learning based Motion Correction for Myocardial T1 Mapping
Authors: Dar Arava, Mohammad Masarwy, Samah Khawaled, Moti Freiman
Date: 2021-09-19 00:00:00
Tasks: Deep Learning, regression

Myocardial T1 mapping is a cardiac MRI technique, used to assess myocardial fibrosis. In this technique, a series of T1-weighted MRI images are acquired with different saturation or inversion times. These images are fitted to the T1 model to estimate the model parameters and construct the desired T1 maps. In the presence of motion, the different T1-weighted images are not aligned. This, in turn, will cause errors and inaccuracies in the final estimation of the T1 maps. Therefore, motion correction is a necessary process for myocardial T1 mapping. We present a deep-learning (DL) based system for cardiac T1-weighted MRI images motion correction. When applying our DL-based motion correction system we achieve a statistically significant improved performance by means of R2 of the model fitting regression, in compared to the model fitting regression without motion correction (0.52 vs 0.29, p<0.05).
Hybrid Transceiver Design for Tera-Hertz MIMO Systems Relying on Bayesian Learning Aided Sparse Channel Estimation
Authors: Suraj Srivastava, Ajeet Tripathi, Neeraj Varshney, Aditya K. Jagannatham, Lajos Hanzo
Date: 2021-09-20 00:00:00
Tasks: Benchmarking

Hybrid transceiver design in multiple-input multiple-output (MIMO) Tera-Hertz (THz) systems relying on sparse channel state information (CSI) estimation techniques is conceived. To begin with, a practical MIMO channel model is developed for the THz band that incorporates its molecular absorption and reflection losses, as well as its non-line-of-sight (NLoS) rays associated with its diffused components. Subsequently, a novel CSI estimation model is derived by exploiting the angular-sparsity of the THz MIMO channel. Then an orthogonal matching pursuit (OMP)-based framework is conceived, followed by designing a sophisticated Bayesian learning (BL)-based approach for efficient estimation of the sparse THz MIMO channel. The Bayesian Cramer-Rao Lower Bound (BCRLB) is also determined for benchmarking the performance of the CSI estimation techniques developed. Finally, an optimal hybrid transmit precoder and receiver combiner pair is designed, which directly relies on the beamspace domain CSI estimates and only requires limited feedback. Finally, simulation results are provided for quantifying the improved mean square error (MSE), spectral-efficiency (SE) and bit-error rate (BER) performance for transmission on practical THz MIMO channel obtained from the HIgh resolution TRANsmission (HITRAN)-database.
Nonsmooth convex optimization to estimate the Covid-19 reproduction number space-time evolution with robustness against low quality data
Authors: Barbara Pascal, Patrice Abry, Nelly Pustelnik, Stéphane G. Roux, Rémi Gribonval, Patrick Flandrin
Date: 2021-09-20 00:00:00
Tasks: Epidemiology

Daily pandemic surveillance, often achieved through the estimation of the reproduction number, constitutes a critical challenge for national health authorities to design countermeasures. In an earlier work, we proposed to formulate the estimation of the reproduction number as an optimization problem, combining data-model fidelity and space-time regularity constraints, solved by nonsmooth convex proximal minimizations. Though promising, that first formulation significantly lacks robustness against the Covid-19 data low quality (irrelevant or missing counts, pseudo-seasonalities,.. .) stemming from the emergency and crisis context, which significantly impairs accurate pandemic evolution assessments. The present work aims to overcome these limitations by carefully crafting a functional permitting to estimate jointly, in a single step, the reproduction number and outliers defined to model low quality data. This functional also enforces epidemiology-driven regularity properties for the reproduction number estimates, while preserving convexity, thus permitting the design of efficient minimization algorithms, based on proximity operators that are derived analytically. The explicit convergence of the proposed algorithm is proven theoretically. Its relevance is quantified on real Covid-19 data, consisting of daily new infection counts for 200+ countries and for the 96 metropolitan France counties, publicly available at Johns Hopkins University and Sant{\'e}-Publique-France. The procedure permits automated daily updates of these estimates, reported via animated and interactive maps. Open-source estimation procedures will be made publicly available.
Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems
Authors: Konstantin Usevich, Philippe Dreesen, Mariya Ishteva
Date: 2021-09-20 00:00:00

We consider the problem of identifying a parallel Wiener-Hammerstein structure from Volterra kernels. Methods based on Volterra kernels typically resort to coupled tensor decompositions of the kernels. However, in the case of parallel Wiener-Hammerstein systems, such methods require nontrivial constraints on the factors of the decompositions. In this paper, we propose an entirely different approach: by using special sampling (operating) points for the Jacobian of the nonlinear map from past inputs to the output, we can show that the Jacobian matrix becomes a linear projection of a tensor whose rank is equal to the number of branches. This representation allows us to solve the identification problem as a tensor recovery problem.
Double-RIS Versus Single-RIS Aided Systems: Tensor-Based MIMO Channel Estimation and Design Perspectives
Authors: Khaled Ardah, Sepideh Gherekhloo, André L. F. de Almeida, Martin Haardt
Date: 2021-09-19 00:00:00

Reconfigurable intelligent surfaces (RISs) have been proposed recently as new technology to tune the wireless propagation channels in real-time. However, most of the current works assume single-RIS (S-RIS)-aided systems, which can be limited in some application scenarios where a transmitter might need a multi-RIS-aided channel to communicate with a receiver. In this paper, we consider a double-RIS (D-RIS)-aided MIMO system and propose an alternating least-squared-based channel estimation method by exploiting the Tucker2 tensor structure of the received signals. Using the proposed method, the cascaded MIMO channel parts can be estimated separately, up to trivial scaling factors. Compared with the S-RIS systems, we show that if the RIS elements of a S-RIS system are distributed carefully between the two RISs in a D-RIS system, the training overhead can be reduced and the estimation accuracy can also be increased. Therefore, D-RIS systems can be seen as an appealing approach to further increase the coverage, capacity, and efficiency of future wireless networks compared to S-RIS systems.