pwc-archive/papers-with-abstracts huggingface dataset:
Host-feeding preferences and temperature shape the dynamics of West Nile virus: a mathematical model of assessing the abatement planning
Authors: Suman Bhowmick, Megan Fritz, Rebecca Lee Smith
Date: 2023-10-09 00:00:00

West Nile virus (WNV) is prevalent in the United States but it shows considerable divergence in transmission patterns and spatio-temporal intensity.It is to be noted that the mechanism that drives the transmission potential of WNV is described by the abilities of host species to maintain and disseminate the pathogens pertinent with different eco-epidemiological factors that have an influence on the contact rates amongst the interacting species.There is growing evidence that several vectors exhibit strong feeding preferences towards different host communities.We construct a process based weather driven ordinary differential equation (ODE) model to understand the impact of one vector species Culex pipiens, preferred avian and non-preferred human hosts and compared it surveillance data for the Culex pipiens complex collected in Cook County, Illinois, USA.In our mechanistic model, we also demonstrate that adulticide treatments produced significant reductions in the Culex pipiens population.We take into account the feeding index that can be described as the ratio between observed frequency of mosquitoes feeding on one host compared to another host, divided by the expected frequency of mosquitoes feeding on these two hosts based on the presence of the particular hosts to develop this transmission model for WNV. Our findings demonstrate that the interplay between the feeding index and mosquito abatement strategy is rather a complex phenomenon and it induces a heterogeneous contact rates that should be included while modelling multi-host, multi-vector transmission model.
A Symmetry-based Framework for Model Selection of Coral Reef Population Growth Models
Authors: Reemon Spector
Date: 2023-10-07 00:00:00
Tasks: Model Selection

The problem of selecting a model given a set of candidates remains a challenging one that pervades many scientific fields. We employ techniques from the theory of Lie groups to analyse the symmetries in differential equation models of population growth, with the aim of informing the model selection problem. To illustrate the use of Lie symmetries in model selection, we apply them to simulated data and to coral reef data from the Great Barrier Reef, demonstrating that the trivial symmetries can distinguish between candidate models. A method for finding locally optimal parameters for multi-parameter symmetries is presented, and the paper concludes with related results, some open problems, and avenues of further research.
Physics-tailored machine learning reveals unexpected physics in dusty plasmas
Authors: Wentao Yu, Eslam Abdelaleem, Ilya Nemenman, Justin C. Burton
Date: 2023-10-08 00:00:00
Tasks: Physical Intuition, scientific discovery

Dusty plasma is a mixture of ions, electrons, and macroscopic charged particles that is commonly found in space and planetary environments. The particles interact through Coulomb forces mediated by the surrounding plasma, and as a result, the effective forces between particles can be non-conservative and non-reciprocal. Machine learning (ML) models are a promising route to learn these complex forces, yet their structure should match the underlying physical constraints to provide useful insight. Here we demonstrate and experimentally validate an ML approach that incorporates physical intuition to infer force laws in a laboratory dusty plasma. Trained on 3D particle trajectories, the model accounts for inherent symmetries, non-identical particles, and learns the effective non-reciprocal forces between particles with exquisite accuracy (R^2>0.99). We validate the model by inferring particle masses in two independent yet consistent ways. The model's accuracy enables precise measurements of particle charge and screening length, discovering large deviations from common theoretical assumptions. Our ability to identify new physics from experimental data demonstrates how ML-powered approaches can guide new routes of scientific discovery in many-body systems. Furthermore, we anticipate our ML approach to be a starting point for inferring laws from dynamics in a wide range of many-body systems, from colloids to living organisms.
Joint inversion of Time-Lapse Surface Gravity and Seismic Data for Monitoring of 3D CO$_2$ Plumes via Deep Learning
Authors: Adrian Celaya, Mauricio Araya-Polo
Date: 2023-09-24 00:00:00
Tasks: Deep Learning

We introduce a fully 3D, deep learning-based approach for the joint inversion of time-lapse surface gravity and seismic data for reconstructing subsurface density and velocity models. The target application of this proposed inversion approach is the prediction of subsurface CO2 plumes as a complementary tool for monitoring CO2 sequestration deployments. Our joint inversion technique outperforms deep learning-based gravity-only and seismic-only inversion models, achieving improved density and velocity reconstruction, accurate segmentation, and higher R-squared coefficients. These results indicate that deep learning-based joint inversion is an effective tool for CO$_2$ storage monitoring. Future work will focus on validating our approach with larger datasets, simulations with other geological storage sites, and ultimately field data.
Investigating the Correlation between Force Output, Strains, and Pressure for Active Skeletal Muscle Contractions
Authors: Karan Taneja, Xiaolong He, John Hodgson, Usha Sinha, Shantanu Sinha, J. S. Chen
Date: 2023-10-09 00:00:00

Experimental observations suggest that the force output of the skeletal muscle tissue can be correlated to the intra-muscular pressure generated by the muscle belly. However, pressure often proves difficult to measure through in-vivo tests. Simulations on the other hand, offer a tool to model muscle contractions and analyze the relationship between muscle force generation and deformations as well as pressure outputs, enabling us to gain insight into correlations among experimentally measurable quantities such as principal and volumetric strains, and the force output. In this work, a correlation study is performed using Pearson's and Spearman's correlation coefficients on the force output of the skeletal muscle, the principal and volumetric strains experienced by the muscle and the pressure developed within the muscle belly as the muscle tissue undergoes isometric contractions due to varying activation profiles. The study reveals strong correlations between force output and the strains at all locations of the belly, irrespective of the type of activation profile used. This observation enables estimation on the contribution of various muscle groups to the total force by the experimentally measurable principal and volumetric strains in the muscle belly. It is also observed that pressure does not correlate well with force output due to stress relaxation near the boundary of muscle belly.
Longitudinal Volumetric Study for the Progression of Alzheimer's Disease from Structural MRI
Authors: Prayas Sanyal, Srinjay Mukherjee, Arkapravo Das, Anindya Sen
Date: 2023-10-09 00:00:00
Tasks: Alzheimer's Detection, Image Registration, Prognosis, Skull Stripping

Alzheimer's Disease (AD) is an irreversible neurodegenerative disorder affecting millions of individuals today. The prognosis of the disease solely depends on treating symptoms as they arise and proper caregiving, as there are no current medical preventative treatments apart from newly developing drugs which can, at most, slow the progression. Thus, early detection of the disease at its most premature state is of paramount importance. This work aims to survey imaging biomarkers corresponding to the progression of AD and also reviews some of the existing feature extraction methods. A longitudinal study of structural MR images was performed for given temporal test subjects with AD selected randomly from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. A pipeline was implemented to study the data, including modern pre-processing techniques such as spatial image registration, skull stripping, inhomogeneity correction and tissue segmentation using an unsupervised learning approach using intensity histogram information. The temporal data across multiple visits is used to study the structural change in volumes of these tissue classes, namely, cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) as the patients progressed further into the disease. To detect changes in volume trends, we also analyse the data with a modified Mann-Kendall statistic. The segmented features thus extracted and the subsequent trend analysis provide insights such as atrophy, increase or intolerable shifting of GM, WM and CSF and should help in future research for automated analysis of Alzheimer's detection with clinical domain explainability.
Distortion-Aware Phase Retrieval Receiver for High-Order QAM Transmission with Carrierless Intensity-Only Measurements
Authors: Hanzi Huang, Haoshuo Chen, Qi Gao, Yetian Huang, Nicolas K. Fontaine, Mikael Mazur, Lauren Dallachiesa, Roland Ryf, Zhengxuan Li, Yingxiong Song
Date: 2023-10-09 00:00:00
Tasks: Retrieval

We experimentally investigate transmitting high-order quadrature amplitude modulation (QAM) signals with carrierless and intensity-only measurements with phase retrieval (PR) receiving techniques. The intensity errors during measurement, including noise and distortions, are found to be a limiting factor for the precise convergence of the PR algorithm. To improve the PR reconstruction accuracy, we propose a distortion-aware PR scheme comprising both training and reconstruction stages. By estimating and emulating the distortion caused by various channel impairments, the proposed scheme enables enhanced agreement between the estimated and measured amplitudes throughout the PR iteration, thus resulting in improved reconstruction performance to support high-order QAM transmission. With the aid of proposed techniques, we experimentally demonstrate 50-GBaud 16QAM and 32QAM signals transmitting through a standard single-mode optical fiber (SSMF) span of 40 and 80 km, and achieve bit error rates (BERs) below the 6.25% hard decision (HD)-forward error correction (FEC) and 25% soft decision (SD)-FEC thresholds for the two modulation formats, respectively. By tuning the pilot symbol ratio and applying concatenated coding, we also demonstrate that a post-FEC data rate of up to 140 Gb/s can be achieved for both distances at an optimal pilot symbol ratio of 20%.
Treatment Choice, Mean Square Regret and Partial Identification
Authors: Toru Kitagawa, Sokbae Lee, Chen Qiu
Date: 2023-10-10 00:00:00

We consider a decision maker who faces a binary treatment choice when their welfare is only partially identified from data. We contribute to the literature by anchoring our finite-sample analysis on mean square regret, a decision criterion advocated by Kitagawa, Lee, and Qiu (2022). We find that optimal rules are always fractional, irrespective of the width of the identified set and precision of its estimate. The optimal treatment fraction is a simple logistic transformation of the commonly used t-statistic multiplied by a factor calculated by a simple constrained optimization. This treatment fraction gets closer to 0.5 as the width of the identified set becomes wider, implying the decision maker becomes more cautious against the adversarial Nature.
Robust Minimum Distance Inference in Structural Models
Authors: Joan Alegre, Juan Carlos Escanciano
Date: 2023-10-09 00:00:00

This paper proposes minimum distance inference for a structural parameter of interest, which is robust to the lack of identification of other structural nuisance parameters. Some choices of the weighting matrix lead to asymptotic chi-squared distributions with degrees of freedom that can be consistently estimated from the data, even under partial identification. In any case, knowledge of the level of under-identification is not required. We study the power of our robust test. Several examples show the wide applicability of the procedure and a Monte Carlo investigates its finite sample performance. Our identification-robust inference method can be applied to make inferences on both calibrated (fixed) parameters and any other structural parameter of interest. We illustrate the method's usefulness by applying it to a structural model on the non-neutrality of monetary policy, as in \cite{nakamura2018high}, where we empirically evaluate the validity of the calibrated parameters and we carry out robust inference on the slope of the Phillips curve and the information effect.
Identification and Estimation in a Class of Potential Outcomes Models
Authors: Manu Navjeevan, Rodrigo Pinto, Andres Santos
Date: 2023-10-08 00:00:00
Tasks: valid

This paper develops a class of potential outcomes models characterized by three main features: (i) Unobserved heterogeneity can be represented by a vector of potential outcomes and a type describing the manner in which an instrument determines the choice of treatment; (ii) The availability of an instrumental variable that is conditionally independent of unobserved heterogeneity; and (iii) The imposition of convex restrictions on the distribution of unobserved heterogeneity. The proposed class of models encompasses multiple classical and novel research designs, yet possesses a common structure that permits a unifying analysis of identification and estimation. In particular, we establish that these models share a common necessary and sufficient condition for identifying certain causal parameters. Our identification results are constructive in that they yield estimating moment conditions for the parameters of interest. Focusing on a leading special case of our framework, we further show how these estimating moment conditions may be modified to be doubly robust. The corresponding double robust estimators are shown to be asymptotically normally distributed, bootstrap based inference is shown to be asymptotically valid, and the semi-parametric efficiency bound is derived for those parameters that are root-n estimable. We illustrate the usefulness of our results for developing, identifying, and estimating causal models through an empirical evaluation of the role of mental health as a mediating variable in the Moving To Opportunity experiment.
Challenges in Statistically Rejecting the Perfect Competition Hypothesis Using Imperfect Competition Data
Authors: Yuri Matsumura, Suguru Otani
Date: 2023-10-06 00:00:00

We theoretically prove why statistically rejecting the null hypothesis of perfect competition is challenging, known as a common problem in the literature. We also assess the finite sample performance of the conduct parameter test in homogeneous goods markets, showing that statistical power increases with the number of markets, a larger conduct parameter, and a stronger demand rotation instrument. However, even with a moderate number of markets and five firms, rejecting the null hypothesis of perfect competition remains difficult, irrespective of instrument strength or the use of optimal instruments. Our findings suggest that empirical results failing to reject perfect competition are due to the limited number of markets rather than methodological shortcomings.
Life cycle insurance, bequest motives and annuity loads
Authors: Aleksandar Arandjelović, Geoffrey Kingston, Pavel V. Shevchenko
Date: 2023-10-10 00:00:00

We investigate insurance purchases when bequest motives are age-varying and life insurance and life annuities both carry loads. The existing life cycle literature assumes bequests are normal goods without being either necessities or luxuries. Much of the literature also assumes implicitly that life annuity loads are negative. A key finding of the literature is that the demand for life insurance and the demand for life annuities are symmetrical. It is optimal to buy life-contingent insurance throughout life, even under loads. A life annuity phase backs directly onto a life insurance phase. We find that realistic examples with positive loads on both products reveal up to two distinct periods of non-participation, one in midlife and the other adjoining the maximum age. We highlight examples with necessity bequests during child-rearing years and luxury bequests thereafter. This set of assumptions explains why a substantial demand for life insurance during child-rearing years can co-exist with negligible demand for life annuities later on. A realistic 18% load on both products generates this outcome.
Did Private Election Administration Funding Advantage Democrats in 2020?
Authors: Apoorva Lal, Daniel M Thompson
Date: 2023-10-08 00:00:00

Private donors contributed more than $350 million to local election officials to support the administration of the 2020 election. Supporters argue these grants were neutral and necessary to maintain normal election operations during the pandemic, while critics worry these grants mostly went to Democratic strongholds and tilted election outcomes. These concerns have led twenty-four states to restrict private election grants. How much did these grants shape the 2020 presidential election? To answer this question, we collect administrative data on private election administration grants and election outcomes. We then use new advances in synthetic control methods to compare presidential election results and turnout in counties that received grants to counties with identical average presidential election results and turnout before 2020. While counties that favor Democrats were much more likely to apply for a grant, we find that the grants did not have a noticeable effect on the presidential election. Our estimates of the average effect of receiving a grant on Democratic vote share range from 0.02 percentage points to 0.36 percentage points. Our estimates of the average effect of receiving a grant on turnout range from -0.03 percentage points to 0.13 percentage points. Across specifications, our 95% confidence intervals typically include negative effects, and our confidence intervals from all specifications fail to include effects on Democratic vote share larger than 0.58 percentage points and effects on turnout larger than 0.40 percentage points. We characterize the magnitude of our effects by asking how large they are compared to the margin by which Biden won the 2020 election. In simple bench-marking exercises, we find that the effects of the grants were likely too small to have changed the outcome of the 2020 presidential election.
A time-varying finance-led model for U.S. business cycles
Authors: Marcio Santetti
Date: 2023-10-08 00:00:00
Tasks: Bayesian Inference

This paper empirically assesses predictions of Goodwin's model of cyclical growth regarding demand and distributive regimes when integrating the real and financial sectors. In addition, it evaluates how financial and employment shocks affect the labor market and monetary policy variables over six different U.S. business-cycle peaks. It identifies a parsimonious Time-Varying Vector Autoregressive model with Stochastic Volatility (TVP-VAR-SV) with the labor share of income, the employment rate, residential investment, and the interest rate spread as endogenous variables. Using Bayesian inference methods, key results suggest (i) a combination of profit-led demand and profit-squeeze distribution; (ii) weakening of these regimes during the Great Moderation; and (iii) significant connections between the standard Goodwinian variables and residential investment as well as term spreads. Findings presented here broadly conform to the transition to increasingly deregulated financial and labor markets initiated in the 1980s.
Minimum wage and manufacturing labor share: Evidence from North Macedonia
Authors: Marjan Petreski, Jaakko Pehkonen
Date: 2023-10-08 00:00:00

The objective of the paper is to understand if the minimum wage plays a role for the labor share of manufacturing workers in North Macedonia. We decompose labor share movements on those along a share-capital curve, shifts of this locus, and deviations from it. We use the capital-output ratio, total factor productivity and prices of inputs to capture these factors, while the minimum wage is introduced as an element that moves the curve off. We estimate a panel of 20 manufacturing branches over the 2012-2019 period with FE, IV and system-GMM estimators. We find that the role of the minimum wage for the labor share is industry-specific. For industrial branches which are labor-intensive and low-pay, it increases workers' labor share, along a complementarity between capital and labor. For capital-intensive branches, it reduces labor share, likely through the job loss channel and along a substitutability between labor and capital. This applies to both branches where foreign investment and heavy industry are nested.
Poverty during Covid-19 in North Macedonia: Analysis of the distributional impact of the crisis and government response
Authors: Marjan Petreski
Date: 2023-10-08 00:00:00
Tasks: counterfactual

In this paper we simulate the poverty effect of the Covid-19 pandemic in North Macedonia and we analyze the income-saving power of three key government measures: the employment-retention scheme, the relaxed Guaranteed Minimum Income support, and one-off cash allowances. In this attempt, the counterfactual scenarios are simulated by using MK-MOD, the Macedonian Tax and Benefit Microsimulation Model, incorporating actual data on the shock-s magnitude from the second quarter of 2020. The results suggest that without the government interventions, of the country-s two million citizens, an additional 120,000 people would have been pushed into poverty by COVID-19, where 340,000 were already poor before the pandemic. Of the 120,000 newly poor about 16,000 would have been pushed into destitute poverty. The government-s automatic stabilizers worked to shield the poorest people, though these were clearly pro-feminine. In all, the analyzed government measures recovered more than half of the income loss, which curbed the poverty-increasing effect and pulled an additional 34,000 people out of extreme poverty. The employment-retention measure was regressive and pro-masculine; the Guaranteed Minimum Income relaxation (including automatic stabilizers) was progressive and pro-feminine; and the one-off support has been pro-youth.
The impact of the pandemic of Covid-19 on child poverty in North Macedonia: Simulation-based estimates
Authors: Marjan Petreski
Date: 2023-10-08 00:00:00
Tasks: Survey

The objective of this paper is to estimate the expected effects of the pandemic of Covid-19 for child poverty in North Macedonia. We rely on MK-MOD Tax & Benefit Microsimulation Model for North Macedonia based on the Survey on Income and Living Conditions 2019. The simulation takes into account the development of income, as per the observed developments in the first three quarters of 2020, derived from the Labor Force Survey, which incorporates the raw effect of the pandemic and the government response. In North Macedonia, almost no government measure directly aimed the income of children, however, three key and largest measures addressed household income: the wage subsidy of 14.500 MKD per worker in the hardest hit companies, relaxation of the criteria for obtaining the guaranteed minimum income, and one-off support to vulnerable groups of the population in two occasions. Results suggest that the relative child poverty rate is estimated to increase from 27.8 percent before the pandemic to 32.4 percent during the pandemic. This increase puts additional 19,000 children below the relative poverty threshold. Results further suggest that absolute poverty is likely to reduce primarily because of the automatic stabilizers in the case of social assistance and because of the one-time cash assistance.