pwc-archive/papers-with-abstracts huggingface dataset:
Numerical Solution of the Steady-State Network Flow Equations for a Non-Ideal Gas
Authors: Shriram Srinivasan, Kaarthik Sundar, Vitaliy Gyrya, Anatoly Zlotnik
Date: 2022-03-31 00:00:00

We formulate a steady-state network flow problem for non-ideal gas that relates injection rates and nodal pressures in the network to flows in pipes. For this problem, we present and prove a theorem on uniqueness of generalized solution for a broad class of non-ideal pressure-density relations that satisfy a monotonicity property. Further, we develop a Newton-Raphson algorithm for numerical solution of the steady-state problem, which is made possible by a systematic non-dimensionalization of the equations. The developed algorithm has been extensively tested on benchmark instances and shown to converge robustly to a generalized solution. Previous results indicate that the steady-state network flow equations for an ideal gas are difficult to solve by the Newton-Raphson method because of its extreme sensitivity to the initial guess. In contrast, we find that non-dimensionalization of the steady-state problem is key to robust convergence of the Newton-Raphson method. We identify criteria based on the uniqueness of solutions under which the existence of a non-physical generalized solution found by a non-linear solver implies non-existence of a physical solution, i.e., infeasibility of the problem. Finally, we compare pressure and flow solutions based on ideal and non-ideal equations of state to demonstrate the need to apply the latter in practice. The solver developed in this article is open-source and is made available for both the academic and research communities as well as the industry.
Lyapunov based Stochastic Stability of Human-Machine Interaction: A Quantum Decision System Approach
Authors: Luke Snow, Shashwat Jain, Vikram Krishnamurthy
Date: 2022-03-31 00:00:00
Tasks: Decision Making

In mathematical psychology, decision makers are modeled using the Lindbladian equations from quantum mechanics to capture important human-centric features such as order effects and violation of the sure thing principle. We consider human-machine interaction involving a quantum decision maker (human) and a controller (machine). Given a sequence of human decisions over time, how can the controller dynamically provide input messages to adapt these decisions so as to converge to a specific decision? We show via novel stochastic Lyapunov arguments how the Lindbladian dynamics of the quantum decision maker can be controlled to converge to a specific decision asymptotically. Our methodology yields a useful mathematical framework for human-sensor decision making. The stochastic Lyapunov results are also of independent interest as they generalize recent results in the literature.
Landmark and IMU Data Fusion: Systematic Convergence Geometric Nonlinear Observer for SLAM and Velocity Bias
Authors: Hashim A. Hashim, Abdelrahman E. E. Eltoukhy
Date: 2020-11-20 00:00:00
Tasks: Position, Simultaneous Localization and Mapping

Navigation solutions suitable for cases when both autonomous robot's pose (\textit{i.e}., attitude and position) and its environment are unknown are in great demand. Simultaneous Localization and Mapping (SLAM) fulfills this need by concurrently mapping the environment and observing robot's pose with respect to the map. This work proposes a nonlinear observer for SLAM posed on the manifold of the Lie group of $\mathbb{SLAM}_{n}\left(3\right)$, characterized by systematic convergence, and designed to mimic the nonlinear motion dynamics of the true SLAM problem. The system error is constrained to start within a known large set and decay systematically to settle within a known small set. The proposed estimator is guaranteed to achieve predefined transient and steady-state performance and eliminate the unknown bias inevitably present in velocity measurements by directly using measurements of angular and translational velocity, landmarks, and information collected by an inertial measurement unit (IMU). Experimental results obtained by testing the proposed solution on a real-world dataset collected by a quadrotor demonstrate the observer's ability to estimate the six-degrees-of-freedom (6 DoF) robot pose and to position unknown landmarks in three-dimensional (3D) space. Keywords: Simultaneous Localization and Mapping, Nonlinear filter for SLAM, Nonlinear filter for SLAM on Matrix Lie group, pose, asymptotic stability, prescribed performance, adaptive estimate, feature, inertial measurement unit, inertial vision unit, IMU, SE(3), SO(3), noise.
Leakage Localization in Water Distribution Networks: A Model-Based Approach
Authors: Ludvig Lindstrom, Sebin Gracy, Sindri Magnusson, Henrik Sandberg
Date: 2022-03-31 00:00:00

The paper studies the problem of leakage localization in water distribution networks. For the case of a single pipe that suffers from a single leak, by taking recourse to pressure and flow measurements, and assuming those are noiseless, we provide a closed-form expression for leak localization, leak exponent and leak constant. For the aforementioned setting, but with noisy pressure and flow measurements, an expression for estimating the location of the leak is provided. Finally, assuming the existence of a single leak, for a network comprising of more than one pipe and assuming that the network has a tree structure, we provide a systematic procedure for determining the leak location, the leak exponent, and the leak constant
Optimal Management of a Smart Port with Shore-Connection and Hydrogen Supplying by Stochastic Model Predictive Control
Authors: Francesco Conte, Fabio D'Agostino, Daniele Kaza, Stefano Massucco, Gianluca Natrella, Federico Silvestro
Date: 2022-03-31 00:00:00
Tasks: Management, Model Predictive Control, Stochastic Optimization

The paper proposes an optimal management strategy for a Smart Port equipped with renewable generation and composed by an electrified quay, operating Cold-Ironing, and a Hydrogen-based quay, supplying Zero-Emission Ships. One Battery Energy Storage System and one Hydrogen Energy Storage System are used to manage renewable energy sources and to supply electric and hydrogen-fueled ships. A model predictive control based algorithm is designed to define the best economic strategy to be followed during operations. The control algorithm takes into account the uncertainties of renewable energy generation using stochastic optimization. The performance of the approach is tested on a potential future Smart Port equipped with wind and photovoltaic generation.
Computation of Optical Refractive Index Structure Parameter from its Statistical Definition Using Radiosonde Data
Authors: Florian Quatresooz, Danielle Vanhoenacker-Janvier, Claude Oestges
Date: 2022-04-01 00:00:00
Tasks: Astronomy

Knowledge of the optical refractive index structure parameter $C_n^2$ is of interest for Free Space Optics (FSO) and ground-based optical astronomy, as it depicts the strength of the expected scintillation on the received optical waves. Focus is given here to models using meteorological quantities coming from radiosonde measurements as inputs to estimate the $C_n^2$ profile in the atmosphere. A model relying on the $C_n^2$ statistical definition is presented and applied to recent high-density radiosonde profiles at Trappes (France) and Hilo, HI (USA). It is also compared to thermosonde measurements coming from the T-REX campaign. This model enables to obtain site-specific average profiles and to identify isolated turbulent layers using only pressure and temperature measurements, paving the way for optical site selection. It offers similar performance when compared to a Tatarskii-based model inspired by the literature.
Inverse Design and Experimental Verification of a Bianisotropic Metasurface Using Optimization and Machine Learning
Authors: Stewart Pearson, Parinaz Naseri, Sean V. Hum
Date: 2022-03-28 00:00:00

Electromagnetic metasurfaces have attracted significant interest recently due to their low profile and advantageous applications. Practically, many metasurface designs start with a set of constraints for the radiated far-field, such as main-beam direction(s) and side lobe levels, and end with a non-uniform physical structure for the surface. This problem is quite challenging, since the required tangential field transformations are not completely known when only constraints are placed on the scattered fields. Hence, the required surface properties cannot be solved for analytically. Moreover, the translation of the desired surface properties to the physical unit cells can be time-consuming and difficult, as it is often a one-to-many mapping in a large solution space. Here, we divide the inverse design process into two steps: a macroscopic and microscopic design step. In the former, we use an iterative optimization process to find the surface properties that radiate a far-field pattern that complies with specified constraints. This iterative process exploits non-radiating currents to ensure a passive and lossless design. In the microscopic step, these optimized surface properties are realized with physical unit cells using machine learning surrogate models. The effectiveness of this end-to-end synthesis process is demonstrated through measurement results of a beam-splitting prototype.
Beyond Wilson-Cowan dynamics: oscillations and chaos without inhibition
Authors: Vincent Painchaud, Nicolas Doyon, Patrick Desrosiers
Date: 2022-04-01 00:00:00

Fifty years ago, Wilson and Cowan developed a mathematical model to describe the activity of neural populations. In this seminal work, they divided the cells in three groups: active, sensitive and refractory, and obtained a dynamical system to describe the evolution of the average firing rates of the populations. In the present work, we investigate the impact of the often neglected refractory state and show that taking it into account can introduce new dynamics. Starting from a continuous-time Markov chain, we perform a rigorous derivation of a mean-field model that includes the refractory fractions of populations as dynamical variables. Then, we perform bifurcation analysis to explain the occurance of periodic solutions in cases where the classical Wilson-Cowan does not predict oscillations. We also show that our mean-field model is able to predict chaotic behavior in the dynamics of networks with as little as two populations.
Fluctuation-dissipation relations for spiking neurons
Authors: Benjamin Lindner
Date: 2022-04-01 00:00:00

Spontaneous fluctuations and stimulus response are essential features of neural functioning but how they are connected is poorly understood. I derive fluctuation-dissipation relations (FDR) between the spontaneous spike and voltage correlations and the firing rate susceptibility for i) the leaky integrate-and-fire (IF) model with white noise; ii) an IF model with arbitrary voltage dependence, an adaptation current, and correlated noise. The FDRs can be used to derive correlation statistics or to infer the system's response from observations of its spontaneous activity.
Should alterations in water viscosity be addressed in soil carbon models?
Authors: Newton La Scala Jr., Alexandre Souto Martinez, Kurt Arnold Spokas, Daniel Ruiz Potma Gonçalves, Rafael Mazer Etto
Date: 2022-04-01 00:00:00

Despite all the efforts, there is no agreement on how temperature affects soil carbon decay and consequently soil CO2 emission, due to overlapping of environmental constraints. To gain further insight into the driving forces of soil microbial processes, we herein examine the abiotic physical environment and its potential influence on microbial activity. In this work we discuss a mechanism which is related to temperature sensitivity of soil carbon stability following a first-order kinetic theory. Soil carbon decomposition is linked to diffusion and consequently to water viscosity, splitting the effects of temperature from viscosity, here we suggest that viscosity could be a controlling factor on bacterial mobility and nutrient diffusion. As a result, viscosity effect on the potential soil carbon losses is demonstrated and could be an important influence in the feedbacks of climate change on soil carbon cycling kinetics.
Support-vector-machine with Bayesian optimization for lithofacies classification using elastic properties
Authors: Yohei Nishitsuji, Jalil Nasseri
Date: 2022-03-14 00:00:00
Tasks: Bayesian Optimization

We investigate an applicability of Bayesian-optimization (BO) to optimize hyperparameters associated with support-vector-machine (SVM) in order to classify facies using elastic properties derived from well data in the East Central Graben, UKCS. The cross-plot products of the field dataset appear to be successfully classified with non-linear boundaries. Although there are a few factors to be predetermined in the BO scheme such as an iteration number to deal with a trade-off between the prediction accuracy and the computational cost, this approach effectively reduces possible human subjectivity connected to the architecture of the SVM. Our proposed workflow might be beneficial in resource-exploration and development in terms of subsurface objective technical evaluations.
Pose correction scheme for camera-scanning Fourier ptychography based on camera calibration and homography transform
Authors: Baiqi Cui, Shaohui Zhang, Yechao Wang, Yao Hu, Qun Hao
Date: 2022-04-01 00:00:00
Tasks: Camera Calibration, Translation

Fourier ptychography (FP), as a computational imaging method, is a powerful tool to improve imaging resolution. Camera-scanning Fourier ptychography extends the application of FP from micro to macro creatively. Due to the non-ideal scanning of the camera driven by the mechanical translation stage, the pose error of the camera occurs, greatly degrading the reconstruction quality, while a precise translation stage is expensive and not suitable for wide-range imaging. Here, to improve the imaging performance of camera-scanning Fourier ptychography, we propose a pose correction scheme based on camera calibration and homography transform approaches. The scheme realizes the accurate alignment of data set and location error correction in the frequency domain. Simulation and experimental results demonstrate this method can optimize the reconstruction results and realize high-quality imaging effectively. Combined with the feature recognition algorithm, the scheme provides the possibility for applying FP in remote sensing imaging and space imaging.
Decomposition of Differences in Distribution under Sample Selection and the Gender Wage Gap
Authors: Santiago Pereda-Fernández
Date: 2022-04-01 00:00:00
Tasks: quantile regression, regression, valid

I address the decomposition of the differences between the distribution of outcomes of two groups when individuals self-select themselves into participation. I differentiate between the decomposition for participants and the entire population, highlighting how the primitive components of the model affect each of the distributions of outcomes. Additionally, I introduce two ancillary decompositions that help uncover the sources of differences in the distribution of unobservables and participation between the two groups. The estimation is done using existing quantile regression methods, for which I show how to perform uniformly valid inference. I illustrate these methods by revisiting the gender wage gap, finding that changes in female participation and self-selection have been the main drivers for reducing the gap.
Finite Sample Inference in Incomplete Models
Authors: Lixiong Li, Marc Henry
Date: 2022-04-01 00:00:00

We propose confidence regions for the parameters of incomplete models with exact coverage of the true parameter in finite samples. Our confidence region inverts a test, which generalizes Monte Carlo tests to incomplete models. The test statistic is a discrete analogue of a new optimal transport characterization of the sharp identified region. Both test statistic and critical values rely on simulation drawn from the distribution of latent variables and are computed using solutions to discrete optimal transport, hence linear programming problems. We also propose a fast preliminary search in the parameter space with an alternative, more conservative yet consistent test, based on a parameter free critical value.
Estimating Separable Matching Models
Authors: Alfred Galichon, Bernard Salanié
Date: 2022-04-01 00:00:00

In this paper we propose two simple methods to estimate models of matching with transferable and separable utility introduced in Galichon and Salani\'e (2022). The first method is a minimum distance estimator that relies on the generalized entropy of matching. The second relies on a reformulation of the more special but popular Choo and Siow (2006) model; it uses generalized linear models (GLMs) with two-way fixed effects.
Female Agency and its Implications on Mental and Physical Health: Evidence from the city of Dhaka
Authors: Upasak Das, Gindo Tampubolon
Date: 2022-04-01 00:00:00

Women agency defined as the ability to conceive of purposeful plan and to carry out action consistent with such a plan can play an important role in determining health status. Using data from female respondents conducted in a survey in the city of Dhaka in Bangladesh, this paper explores how women agency relates to their physical and mental health. The findings indicate women with high agency to experience significantly lesser mental distress on average. Counterintuitively, these women are more likely to report poor physical health. As an explanation, we propose purposeful action among women with high agency as a potential reason, wherein they conceive purpose in the future and formulate action that is feasible today. Hence, these women prefer to report illness and get the required treatment to ensure better future health. This illuminates our understanding of sustainable development and emphasises the critical role of women agency for sustainable human development.
Same environment, stratified impacts? Air pollution, extreme temperatures, and birth weight in south China
Authors: Xiaoying Liu, Jere R. Behrman, Emily Hannum, Fan Wang, Qingguo Zhao
Date: 2022-04-01 00:00:00

This paper investigates whether associations between birth weight and prenatal ambient environmental conditions--pollution and extreme temperatures--differ by 1) maternal education; 2) children's innate health; and 3) interactions between these two. We link birth records from Guangzhou, China, during a period of high pollution, to ambient air pollution (PM10 and a composite measure) and extreme temperature data. We first use mean regressions to test whether, overall, maternal education is an "effect modifier" in the relationships between ambient air pollution, extreme temperature, and birth weight. We then use conditional quantile regressions to test for effect heterogeneity according to the unobserved innate vulnerability of babies after conditioning on other confounders. Results show that 1) the negative association between ambient exposures and birth weight is twice as large at lower conditional quantiles of birth weights as at the median; 2) the protection associated with college-educated mothers with respect to pollution and extreme heat is heterogeneous and potentially substantial: between 0.02 and 0.34 standard deviations of birth weights, depending on the conditional quantiles; 3) this protection is amplified under more extreme ambient conditions and for infants with greater unobserved innate vulnerabilities.
The spillover effect of neighboring port on regional industrial diversification and regional economic resilience
Authors: Jung-In Yeon, Sojung Hwang, Bogang Jun
Date: 2022-04-01 00:00:00

We examine the spillover effect of neighboring ports on regional industrial diversification and their economic resilience using the export data of South Korea from 2006 to 2020. First, we build two distinct product spaces of ports and port regions, and provide direct estimates of the role of neighboring ports as spillover channels spatially linked. This is in contrast to the previous literature that mainly regarded ports as transport infrastructure per se. Second, we confirm that the knowledge spillover effect from neighboring ports had a non-negligible role in sustaining regional economies during the recovery after the economic crisis but its power has weakened recently due to a loosened global value chain.
Artificial Intelligence and work: a critical review of recent research from the social sciences
Authors: Jean-Philippe Deranty, Thomas Corbin
Date: 2022-02-25 00:00:00
Tasks: Management

This review seeks to present a comprehensive picture of recent discussions in the social sciences of the anticipated impact of AI on the world of work. Issues covered include technological unemployment, algorithmic management, platform work an the politics of AI work. The review identifies the major disciplinary and methodological perspectives on AI's impact on work, and the obstacles they face in making predictions. Two parameters influencing the development and deployment of AI in the economy are highlighted, the capitalist imperative and nationalistic pressures.
Consumption-investment decisions with endogenous reference point and drawdown constraint
Authors: Zongxia Liang, Xiaodong Luo, Fengyi Yuan
Date: 2022-04-01 00:00:00

We propose a consumption-investment decision model where past consumption peak $h$ plays a crucial role. There are two important consumption levels: the lowest constrained level and a reference level, at which the risk aversion in terms of consumption rate is changed. We solve this stochastic control problem and derive the value function, optimal consumption plan, and optimal investment strategy in semi-explicit forms. We find five important thresholds of wealth, all as functions of $h$, and most of them are nonlinear functions. As can be seen from numerical results and theoretical analysis, this intuitive and simple model has significant economic implications, and there are at least three important predictions: the marginal propensity to consume out of wealth is generally decreasing but can be increasing for intermediate wealth levels, and it jumps inversely proportional to the risk aversion at the reference point; the implied relative risk aversion is roughly a smile in wealth; the welfare of the poor is more vulnerable to wealth shocks than the wealthy. Moreover, locally changing the risk aversion influences the optimal strategies globally, revealing some risk allocation behaviors.
Insuring uninsurable income
Authors: Michiko Ogaku
Date: 2022-04-01 00:00:00

This paper presents a method to avoid the ominous prediction of the previous work: if insured incomes of infinitely lived individuals are unobservable, then efficient allocations lead to permanent welfare inequality between individuals and immiseration of society. This paper proposes a mechanism that (i) promises within-period full insurance by postponing risks ; (ii) does not lead to social ranking among the same age groups; and (iii) could be sustainable.
Multiple Confidence Gates For Joint Training Of SE And ASR
Authors: Tianrui Wang, Weibin Zhu, Yingying Gao, Junlan Feng, Shilei Zhang
Date: 2022-04-01 00:00:00
Tasks: Robust Speech Recognition, Speech Enhancement, speech-recognition, Speech Recognition

Joint training of speech enhancement model (SE) and speech recognition model (ASR) is a common solution for robust ASR in noisy environments. SE focuses on improving the auditory quality of speech, but the enhanced feature distribution is changed, which is uncertain and detrimental to the ASR. To tackle this challenge, an approach with multiple confidence gates for jointly training of SE and ASR is proposed. A speech confidence gates prediction module is designed to replace the former SE module in joint training. The noisy speech is filtered by gates to obtain features that are easier to be fitting by the ASR network. The experimental results show that the proposed method has better performance than the traditional robust speech recognition system on test sets of clean speech, synthesized noisy speech, and real noisy speech.