pwc-archive/papers-with-abstracts huggingface dataset:
Segmentized quarantine policy for managing a tradeoff between containment of infectious disease and social cost of quarantine
Authors: Jungwoo Kim, Taesik Lee
Date: 2024-11-09 00:00:00

By the end of 2021, COVID-19 had spread to over 230 countries, with over 5.4 million deaths. To contain its spread, many countries implemented non-pharmaceutical interventions, notably contact tracing and self-quarantine policies. However, these measures came with significant social costs, highlighting the need for more sustainable approaches that minimize disruptions to economic and societal activities. This research explores a segmentized quarantine policy, applying different quarantine measures for various population segments to better balance the benefits and costs of containment. Different groups, like students versus working adults, have distinct societal activity patterns, posing varied risks for disease spread. We define segmentized quarantine policy across two dimensions-contact tracing range and quarantine period-and optimize these parameters for each segment to minimize total infection cases and quarantine days. Using an Agent-Based Epidemic Simulation and an Evolutionary Algorithm to derive the Pareto front, we demonstrate that segmentized policies can be more effective than uniform policies, with specific segments benefiting from tailored measures. The findings support segmentized quarantine as a viable, efficient, and sustainable approach, offering a valuable framework for public health policy in future pandemics.
MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration
Authors: Ziqi Ni, Yahao Li, Kaijia Hu, Kunyuan Han, Ming Xu, Xingyu Chen, Fengqi Liu, Yicong Ye, Shuxin Bai
Date: 2024-11-10 00:00:00

The rapid evolution of artificial intelligence, particularly large language models, presents unprecedented opportunities for materials science research. We proposed and developed an AI materials scientist named MatPilot, which has shown encouraging abilities in the discovery of new materials. The core strength of MatPilot is its natural language interactive human-machine collaboration, which augments the research capabilities of human scientist teams through a multi-agent system. MatPilot integrates unique cognitive abilities, extensive accumulated experience, and ongoing curiosity of human-beings with the AI agents' capabilities of advanced abstraction, complex knowledge storage and high-dimensional information processing. It could generate scientific hypotheses and experimental schemes, and employ predictive models and optimization algorithms to drive an automated experimental platform for experiments. It turns out that our system demonstrates capabilities for efficient validation, continuous learning, and iterative optimization.
From noisy cell size control to population growth: when variability can be beneficial
Authors: Arthur Genthon
Date: 2024-11-13 00:00:00

Single-cell experiments revealed substantial variability in generation times, growth rates but also in birth and division sizes between genetically identical cells. Understanding how these fluctuations determine the fitness of the population, i.e. its growth rate, is necessary in any quantitative theory of evolution. Here, we develop a biologically-relevant model which accounts for the stochasticity in single-cell growth rates, birth sizes and division sizes. We derive expressions for the population growth rate and for the mean birth size in the population in terms of the single-cell fluctuations. Allowing division sizes to fluctuate reveals how the mechanism of cell size control (timer, sizer, adder) influences population growth. Surprisingly, we find that fluctuations in single-cell growth rates can be beneficial for population growth when slow-growing cells tend to divide at smaller sizes than fast-growing cells. Our framework is not limited to exponentially-growing cells like $\textit{Escherichia coli}$, and we derive similar expressions for cells with linear and bi-linear growth laws, such as $\textit{Mycobacterium tuberculosis}$ and fission yeast $\textit{Schizosaccharomyces pombe}$, respectively.
An age-structured diffusive model for epidemic modelling: Lie symmetries and exact solutions
Authors: Roman Cherniha, Vasyl' Davydovych
Date: 2024-11-12 00:00:00

A new age-structured diffusive model for the mathematical modelling of epidemics is suggested. The model can be considered as a generalization of two models suggested earlier for the same purposes. The Lie symmetry classification of the model is derived. It is shown that the model admits an infinite-dimensional Lie algebra of invariance. Using the Lie symmetries, exact solutions, in particular those of the travelling wave types and in terms of special functions, are constructed. An example of application of the correctly-specified exact solution for calculation of total numbers of infected individuals during an epidemic is presented.
We must re-evaluate assumptions about carbon trading for effective climate change mitigation
Authors: Alyssa R. Pfadt-Trilling, Marie-Odile P. Fortier
Date: 2024-11-06 00:00:00

Effective climate action depends on dismantling the assumptions and oversimplifications that have become the basis of climate policy. The assumption that greenhouse gases (GHG) are fungible and the use of single-point values in normalizing GHG species to CO2-equivalents can propagate inaccuracies in carbon accounting and have already led to failures of carbon offset systems. Separate emission reduction targets and tracking by GHG species are recommended to achieve long-term climate stabilization.
Pheromone-Guided Navigation of Potential Mates: A Distinct Exploration Strategy
Authors: Nick Dashti, M. N. Najafi, Debra J. Searles
Date: 2024-11-13 00:00:00
Tasks: Navigate

Animals, especially insects, navigate their environments using complex strategies that integrate responses to chemical signals such as pheromones. The relative importance an insect places on foraging, finding a mate or other tasks can significantly influence its movement patterns. This study uncovers the distinct exploration strategies that emerge when these priorities vary. A new model is introduced that explicitly treats a pair of insects and their response to each other's pheromones. The strategy results in dynamics that exhibit characteristic anomalous scaling in displacement and non-Gaussian distributions of position; specifically, compressed exponential distributions for the number of encounters and the total duration of encounters. Our model not only elucidates these emergent behaviours but also provides insight into optimizing encounter frequencies. These strategies can be inverted to design pheromone-based traps for luring target species to specific areas for removal. The model is also applicable more broadly to pairs of agents that both respond to the paths traced by themselves and each other.
A Message Passing Neural Network Surrogate Model for Bond-Associated Peridynamic Material Correspondence Formulation
Authors: Xuan Hu, Qijun Chen, Nicholas H. Luo, Richy J. Zheng, Shaofan Li
Date: 2024-10-29 00:00:00
Tasks: GPU

Peridynamics is a non-local continuum mechanics theory that offers unique advantages for modeling problems involving discontinuities and complex deformations. Within the peridynamic framework, various formulations exist, among which the material correspondence formulation stands out for its ability to directly incorporate traditional continuum material models, making it highly applicable to a range of engineering challenges. A notable advancement in this area is the bond-associated correspondence model, which not only resolves issues of material instability but also achieves high computational accuracy. However, the bond-associated model typically requires higher computational costs than FEA, which can limit its practical application. To address this computational challenge, we propose a novel surrogate model based on a message-passing neural network (MPNN) specifically designed for the bond-associated peridynamic material correspondence formulation. Leveraging the similarities between graph structure and the neighborhood connectivity inherent to peridynamics, we construct an MPNN that can transfers domain knowledge from peridynamics into a computational graph and shorten the computation time via GPU acceleration. Unlike conventional graph neural networks that focus on node features, our model emphasizes edge-based features, capturing the essential material point interactions in the formulation. A key advantage of this neural network approach is its flexibility: it does not require fixed neighborhood connectivity, making it adaptable across diverse configurations and scalable for complex systems. Furthermore, the model inherently possesses translational and rotational invariance, enabling it to maintain physical objectivity: a critical requirement for accurate mechanical modeling.
Turkey's Earthquakes: Damage Prediction and Feature Significance Using A Multivariate Analysis
Authors: Shrey Shah, Alex Lin, Scott Lin, Josh Patel, Michael Lam, Kevin Zhu
Date: 2024-10-29 00:00:00

Accurate damage prediction is crucial for disaster preparedness and response strategies, particularly given the frequent earthquakes in Turkey. Utilizing datasets on earthquake data, infrastructural quality metrics, and contemporary socioeconomic factors, we tested various machine-learning architectures to forecast death tolls and fatalities per affected population. Our findings indicate that the Random Forest model provides the most reliable predictions. The model highlights earthquake magnitude and building stability as the primary determinants of damage. This research contributes to the reduction of fatalities in future seismic events in Turkey.
Bilateral Signal Warping for Left Ventricular Hypertrophy Diagnosis
Authors: Wei Tang, Kangning Cui, Raymond H. Chan, Jean-Michel Morel
Date: 2024-11-13 00:00:00
Tasks: Electrocardiography (ECG)

Left Ventricular Hypertrophy (LVH) is a major cardiovascular risk factor, linked to heart failure, arrhythmia, and sudden cardiac death, often resulting from chronic stress like hypertension. Electrocardiography (ECG), while varying in sensitivity, is widely accessible and cost-effective for detecting LVH-related morphological changes. This work introduces a bilateral signal warping (BSW) approach to improve ECG-based LVH diagnosis. Our method creates a library of heartbeat prototypes from patients with consistent ECG patterns. After preprocessing to eliminate baseline wander and detect R peaks, we apply BSW to cluster heartbeats, generating prototypes for both normal and LVH classes. We compare each new record to these references to support diagnosis. Experimental results show promising potential for practical application in clinical settings.
Steady-State and Dynamical Behavior of a PDE Model of Multilevel Selection with Pairwise Group-Level Competition
Authors: Konstantinos Alexiou, Daniel B. Cooney
Date: 2024-11-13 00:00:00

Evolutionary competition often occurs simultaneously at multiple levels of organization, in which traits or behaviors that are costly for an individual can provide collective benefits to groups to which the individual belongs. Building off of recent work that has used ideas from game theory to study evolutionary competition within and among groups, we study a PDE model for multilevel selection that considers group-level evolutionary dynamics through a pairwise conflict depending on the strategic composition of the competing groups. This model allows for incorporation of group-level frequency dependence, facilitating the exploration for how the form of probabilities for victory in a group-level conflict can impact the long-time support for cooperation via multilevel selection. We characterize well-posedness properties for measure-valued solutions of our PDE model and apply these properties to show that the population will converge to a delta-function at the all-defector equilibrium when between-group selection is sufficiently weak. We further provide necessary conditions for the existence of bounded steady state densities for the multilevel dynamics of Prisoners' Dilemma and Hawk-Dove scenarios, using a mix of analytical and numerical techniques to characterize the relative strength of between-group selection required to ensure the long-time survival of cooperation via multilevel selection. We also see that the average payoff at steady state appears to be limited by the average payoff of the all-cooperator group, even for games in which groups achieve maximal average payoff at intermediate levels of cooperation, generalizing behavior that has previously been observed in PDE models of multilevel selection with frequency-indepdent group-level competition.
Sparse Interval-valued Time Series Modeling with Machine Learning
Authors: Haowen Bao, Yongmiao Hong, Yuying Sun, Shouyang Wang
Date: 2024-11-14 00:00:00
Tasks: Management, Time Series, Time Series Analysis

By treating intervals as inseparable sets, this paper proposes sparse machine learning regressions for high-dimensional interval-valued time series. With LASSO or adaptive LASSO techniques, we develop a penalized minimum distance estimation, which covers point-based estimators are special cases. We establish the consistency and oracle properties of the proposed penalized estimator, regardless of whether the number of predictors is diverging with the sample size. Monte Carlo simulations demonstrate the favorable finite sample properties of the proposed estimation. Empirical applications to interval-valued crude oil price forecasting and sparse index-tracking portfolio construction illustrate the robustness and effectiveness of our method against competing approaches, including random forest and multilayer perceptron for interval-valued data. Our findings highlight the potential of machine learning techniques in interval-valued time series analysis, offering new insights for financial forecasting and portfolio management.
Difference-in-Differences with Sample Selection
Authors: Gayani Rathnayake, Akanksha Negi, Otavio Bartalotti, Xueyan Zhao
Date: 2024-11-14 00:00:00
Tasks: Causal Inference, counterfactual

We consider identification of average treatment effects on the treated (ATT) within the difference-in-differences (DiD) framework in the presence of endogenous sample selection. First, we establish that the usual DiD estimand fails to recover meaningful treatment effects, even if selection and treatment assignment are independent. Next, we partially identify the ATT for individuals who are always observed post-treatment regardless of their treatment status, and derive bounds on this parameter under different sets of assumptions about the relationship between sample selection and treatment assignment. Extensions to the repeated cross-section and two-by-two comparisons in the staggered adoption case are explored. Furthermore, we provide identification results for the ATT of three additional empirically relevant latent groups by incorporating outcome mean dominance assumptions which have intuitive appeal in applications. Finally, two empirical illustrations demonstrate the approach's usefulness by revisiting (i) the effect of a job training program on earnings(Calonico & Smith, 2017) and (ii) the effect of a working-from-home policy on employee performance (Bloom, Liang, Roberts, & Ying, 2015).
On the (Mis)Use of Machine Learning with Panel Data
Authors: Augusto Cerqua, Marco Letta, Gabriele Pinto
Date: 2024-11-14 00:00:00

We provide the first systematic assessment of data leakage issues in the use of machine learning on panel data. Our organizing framework clarifies why neglecting the cross-sectional and longitudinal structure of these data leads to hard-to-detect data leakage, inflated out-of-sample performance, and an inadvertent overestimation of the real-world usefulness and applicability of machine learning models. We then offer empirical guidelines for practitioners to ensure the correct implementation of supervised machine learning in panel data environments. An empirical application, using data from over 3,000 U.S. counties spanning 2000-2019 and focused on income prediction, illustrates the practical relevance of these points across nearly 500 models for both classification and regression tasks.
Commodity Booms, Local State Capacity, and Development
Authors: Dafne Murillo, Sebastian Sardon
Date: 2024-11-14 00:00:00

State capacity may shape whether natural resources generate prosperity, as it determines if windfalls are effectively turned into useful projects or wasted. We test this hypothesis studying the 2004-2011 mining boom in Peru, where mines' profits are redistributed as windfall transfers to local governments. Our empirical strategy combines geological data with the central government's mining windfalls allocation formula to identify the windfalls' effects on household incomes and other measures of economic development. Proxying local state capacity with the ability to tax and relying on a triple difference strategy we uncover significant variation in treatment response, with positive effects of windfalls limited to high state capacity localities. We find suggestive evidence that only localities with high state capacity succeed at transforming windfalls into infrastructure stocks, which in turns contributes to structural transformation and market integration. Lastly, social unrest increases in low state capacity localities that receive windfalls but fail to perceive their benefits. Our findings underscore important complementarities between investments in extractive industries and in state capacity.
The Structure of the U.S. Income Distribution
Authors: Conrad Kosowsky
Date: 2024-11-13 00:00:00

I show that U.S. incomes follow a one-parameter family of probability distributions over more than fifty years of data. I compare statistical models of income, and I highlight the inverse-gamma distribution as a parsimonious model that matches data particularly well and has straightforward theoretical interpretations. However, despite having relatively few parameters, the inverse-gamma distribution still overfits income data. I establish a linear relationship between parameter estimates, and a one-dimensional model emerges naturally when I exploit this relationship. I conclude with theoretical remarks about the model.
Does the Gini index represent people's views on inequality?
Authors: Gaëlle Aymeric, Brice Magdalou
Date: 2024-11-13 00:00:00

This paper presents findings from a web-experiment on a representative sample of the French population. It examines the acceptability of the Pigou-Dalton principle of transfers, which posits that transferring income from an individual to a relatively poorer one, reduces overall inequality. While up to 60% of respondents reject standard transfers, the three alternative transfers we test receive more approval, especially those promoting solidarity among lower-income recipients. The study then models respondents' preferences with two types of social welfare functions, utilitarian and Extended Gini. The Extended Gini model aligns better with individual preferences. Nevertheless, Extended Gini-type social welfare functions that adhere to the principle of transfers (including the one underlying the Gini index) poorly capture preferences of each individual. However, quite surprisingly, the preferences of the median individual align almost perfectly with the Gini-based function, using either parametric or non-parametric estimates.
Complementing Carbon Credits from Forest-Related Activities with Biodiversity Insurance and Resilience Value
Authors: Hanna Fiegenbaum
Date: 2024-11-13 00:00:00

Carbon credits are a key component of most national and organizational climate strategies. Financing and delivering carbon credits from forest-related activities faces multiple risks at the project and asset levels. Financial mechanisms are employed to mitigate risks for investors and project developers, complemented by non-financial measures such as environmental and social safeguards and physical risk mitigation. Despite these efforts, academic research highlights that safeguards and climate risk mitigation measures are not efficiently implemented in some carbon projects and that specification of environmental safeguards remains underdeveloped. Further, environmental and social risk mitigation capacities may not be integrated into financial mechanisms. This text examines how ecosystem capacities can be leveraged and valued for mitigation of and adaptation to physical risks by complementing carbon credits with biodiversity insurance and resilience value.
How Transit Countries Become Refugee Destinations: Insights from Central and Eastern Europe
Authors: Liliana Harding, Ciprian Panzaru
Date: 2024-11-13 00:00:00
Tasks: Decision Making

This study explores how refugees' destination preferences evolve during transit, with a focus on Central and Eastern Europe, particularly Romania. Using a mixed-methods approach, we analyse data from the International Organization for Migration's (IOM) Flow Monitoring Surveys and complement it with qualitative insights from focus group discussions with refugees. The quantitative analysis reveals that refugees' preferences for destination countries often change during transit, influenced by factors such as safety concerns, asylum conditions, education, and the presence of relatives at the destination. Our results support the application of bounded rationality and human capital theory, showing that while economic opportunities are important, safety becomes the dominant concern during transit. The qualitative analysis adds depth to these findings, highlighting the role of political instability, social networks, and economic hardships as initial migration drivers. Additionally, the study reveals how refugees reassess their destination choices based on their experiences in transit countries, with Romania emerging as a viable settlement destination due to its relative stability and access to asylum procedures. This research contributes to migration studies by challenging the traditional view of transit countries and offering new insights into the fluid nature of refugee decision-making.
Estimating Variability in Hospital Charges: The Case of Cesarean Section
Authors: Anna Perfilyeva, Vittal Raghavendra Miskin, Ryan Aven, Craig Drohan, Huthaifa I. Ashqar
Date: 2024-11-12 00:00:00

This study sought to better understand the causes of price disparity in cesarean sections, using newly released hospital data. Beginning January 1, 2021, Centers for Medicare and Medicaid Services (CMS) requires hospitals functioning in the United States to publish online pricing information for items and services these hospitals provide in a machine-readable format and a consumer friendly shoppable format. Initial analyses of these data have shown that the price for a given procedure can differ in a hospital and across hospitals. The cesarean section (C-section) is one of the most common inpatient procedures performed across all hospitals in the United States as of 2018. This preliminary study found that for C-section procedures, pricing varied from as little as \$162 to as high as \$115,483 for a single procedure. Overall, indicators for quality and whether or not the hospital was a teaching hospital were found to be significantly significant, while variables including median income and the gini coefficient for wealth inequality were not shown to be statistically significant.
A note on the Cobb-Douglas function
Authors: Richard Vale
Date: 2024-11-11 00:00:00

This note observes that the Cobb-Douglas function is uniquely characterized by the property that, if the labour share of cost for a constant-returns-to-scale firm remains constant when the firm minimizes its cost for any given output level, then the firm's production function must be Cobb-Douglas.