pwc-archive/papers-with-abstracts huggingface dataset:
Predicted Trajectory Guidance Control Framework of Teleoperated Ground Vehicles Compensating for Delays
Authors: Qiang Zhang, Zhouli Xu, Yihang Wang, Lingfang Yang, Xiaolin Song, Zhi Huang
Date: 2022-12-06 00:00:00

Maneuverability and drivability of the teleoperated ground vehicle could be seriously degraded by large communication delays if the delays are not properly compensated. This paper proposes a predicted trajectory guidance control (PTGC) framework to compensate for such delays, thereby improving the performance of the teleoperation system. The novelty of this PTGC framework is that teleoperators intended trajectory is predicted at the vehicle side with their delayed historical control commands and the LiDAR 3D point cloud of the environment, and then the vehicle is guided by the predicted trajectory. By removing the teleoperator from the direct control loop, the presented method is less sensitive to delays, and delays are compensated as long as the prediction horizon exceeds the delays. Human-in-the-loop simulation experiments are designed to evaluate the teleoperation performance with the proposed method under five delay levels. Based on the repeated measurement analysis of variance, it is concluded that the PTGC method can significantly improve the performance of the teleoperated ground vehicles under large delays(>200ms), such as the task completion time (TCT), deviation to centerline (D2C) and steering effort (SE). In addition, the results also show that teleoperators can adapt to smaller delays, and the presented method is ineffective in such cases.
PigV$^2$: Monitoring Pig Vital Signs through Ground Vibrations Induced by Heartbeat and Respiration
Authors: Yiwen Dong, Jesse R Codling, Gary Rohrer, Jeremy Miles, Sudhendu Sharma, Tami Brown-Brandl, Pei Zhang, Hae Young Noh
Date: 2022-12-07 00:00:00

Pig vital sign monitoring (e.g., estimating the heart rate (HR) and respiratory rate (RR)) is essential to understand the stress level of the sow and detect the onset of parturition. It helps to maximize peri-natal survival and improve animal well-being in swine production. The existing approach mainly relies on manual measurement, which is labor-intensive and only provides a few points of information. Other sensing modalities such as wearables and cameras are developed to enable more continuous measurement, but are still limited due to animal discomfort, data transfer, and storage challenges. In this paper, we introduce PigV$^2$, the first system to monitor pig heart rate and respiratory rate through ground vibrations. Our approach leverages the insight that both heartbeat and respiration generate ground vibrations when the sow is lying on the floor. We infer vital information by sensing and analyzing these vibrations. The main challenge in developing PigV$^2$ is the overlap of vital- and non-vital-related information in the vibration signals, including pig movements, pig postures, pig-to-sensor distances, and so on. To address this issue, we first characterize their effects, extract their current status, and then reduce their impact by adaptively interpolating vital rates over multiple sensors. PigV$^2$ is evaluated through a real-world deployment with 30 pigs. It has 3.4% and 8.3% average errors in monitoring the HR and RR of the sows, respectively.
Systematic Analysis of Biomolecular Conformational Ensembles with PENSA
Authors: Martin Vögele, Neil J. Thomson, Sang T. Truong, Jasper McAvity, Ulrich Zachariae, Ron O. Dror
Date: 2022-12-06 00:00:00
Tasks: feature selection

Atomic-level simulations are widely used to study biomolecules and their dynamics. A common goal in such studies is to compare simulations of a molecular system under several conditions -- for example, with various mutations or bound ligands -- in order to identify differences between the molecular conformations adopted under these conditions. However, the large amount of data produced by simulations of ever larger and more complex systems often renders it difficult to identify the structural features that are relevant for a particular biochemical phenomenon. We present a flexible software package named PENSA that enables a comprehensive and thorough investigation into biomolecular conformational ensembles. It provides featurizations and feature transformations that allow for a complete representation of biomolecules like proteins and nucleic acids, including water and ion binding sites, thus avoiding bias that would come with manual feature selection. PENSA implements methods to systematically compare the distributions of molecular features across ensembles to find the significant differences between them and identify regions of interest. It also includes a novel approach to quantify the state-specific information between two regions of a biomolecule, which allows, e.g., tracing information flow to identify allosteric pathways. PENSA also comes with convenient tools for loading data and visualizing results, making them quick to process and easy to interpret. PENSA is an open-source Python library maintained at https://github.com/drorlab/pensa along with an example workflow and a tutorial. We demonstrate its usefulness in real-world examples by showing how it helps to determine molecular mechanisms efficiently.
Machine Learning Scoring Functions for Drug Discoveries from Experimental and Computer-Generated Protein-Ligand Structures: Towards Per-Target Scoring Functions
Authors: F. Pellicani, D. Dal Ben, A. Perali, S. Pilati
Date: 2022-12-06 00:00:00
Tasks: Drug Discovery

In recent years, machine learning has been proposed as a promising strategy to build accurate scoring functions for computational docking finalized to numerically empowered drug discovery. However, the latest studies have suggested that over-optimistic results had been reported due to the correlations present in the experimental databases used for training and testing. Here, we investigate the performance of an artificial neural network in binding affinity predictions, comparing results obtained using both experimental protein-ligand structures as well as larger sets of computer-generated structures created using commercial software. Interestingly, similar performances are obtained on both databases. We find a noticeable performance suppression when moving from random horizontal tests to vertical tests performed on target proteins not included in the training data. The possibility to train the network on relatively easily created computer-generated databases leads us to explore per-target scoring functions, trained and tested ad-hoc on complexes including only one target protein. Encouraging results are obtained, depending on the type of protein being addressed.
The unequal effects of the health-economy tradeoff during the COVID-19 pandemic
Authors: Marco Pangallo, Alberto Aleta, R. Maria del Rio Chanona, Anton Pichler, David Martín-Corral, Matteo Chinazzi, François Lafond, Marco Ajelli, Esteban Moro, Yamir Moreno, Alessandro Vespignani, J. Doyne Farmer
Date: 2022-12-07 00:00:00
Tasks: counterfactual

The potential tradeoff between health outcomes and economic impact has been a major challenge in the policy making process during the COVID-19 pandemic. Epidemic-economic models designed to address this issue are either too aggregate to consider heterogeneous outcomes across socio-economic groups, or, when sufficiently fine-grained, not well grounded by empirical data. To fill this gap, we introduce a data-driven, granular, agent-based model that simulates epidemic and economic outcomes across industries, occupations, and income levels with geographic realism. The key mechanism coupling the epidemic and economic modules is the reduction in consumption demand due to fear of infection. We calibrate the model to the first wave of COVID-19 in the New York metropolitan area, showing that it reproduces key epidemic and economic statistics, and then examine counterfactual scenarios. We find that: (a) both high fear of infection and strict restrictions similarly harm the economy but reduce infections; (b) low-income workers bear the brunt of both the economic and epidemic harm; (c) closing non-customer-facing industries such as manufacturing and construction only marginally reduces the death toll while considerably increasing unemployment; and (d) delaying the start of protective measures does little to help the economy and worsens epidemic outcomes in all scenarios. We anticipate that our model will help designing effective and equitable non-pharmaceutical interventions that minimize disruptions in the face of a novel pandemic.
Quantum spin models for numerosity perception
Authors: Jorge Yago Malo, Guido Marco Cicchini, Maria Concetta Morrone, Maria Luisa Chiofalo
Date: 2022-12-06 00:00:00

Humans share with animals, both vertebrates and invertebrates, the capacity to sense the number of items in their environment already at birth. The pervasiveness of this skill across the animal kingdom suggests that it should emerge in very simple populations of neurons. Current modelling literature, however, has struggled to suggest a simple architecture carrying out this task, with most proposals suggesting the emergence of number sense in multi-layered complex neural networks, and typically requiring supervised learning. We present a simple quantum spin model with all-to-all connectivity, where numerosity is encoded in the spectrum after stimulation with a number of transient signals occurring in a random or orderly temporal sequence. We use a paradigmatic simulational approach borrowed from the theory and methods of open quantum systems out of equilibrium, as a possible way to describe information processing in neural systems. Our method is able to capture many of the perceptual characteristics of numerosity in such systems. The frequency components of the magnetization spectra at harmonics of the system's tunneling frequency increase with the number of stimuli presented. The amplitude decoding of each spectrum, performed with an ideal-observer model, reveals that the system follows Weber's law, one of the hallmarks of numerosity perception across the animal kingdom. This contrasts with the well-known failure to reproduce Weber's law with linear system or accumulators models.
Semiparametric Distribution Regression with Instruments and Monotonicity
Authors: Dominik Wied
Date: 2022-12-07 00:00:00
Tasks: regression

This paper proposes IV-based estimators for the semiparametric distribution regression model in the presence of an endogenous regressor, which are based on an extension of IV probit estimators. We discuss the causal interpretation of the estimators and two methods (monotone rearrangement and isotonic regression) to ensure a monotonically increasing distribution function. Asymptotic properties and simulation evidence are provided. An application to wage equations reveals statistically significant and heterogeneous differences to the inconsistent OLS-based estimator.
Bayesian Forecasting in Economics and Finance: A Modern Review
Authors: Gael M. Martin, David T. Frazier, Worapree Maneesoonthorn, Ruben Loaiza-Maya, Florian Huber, Gary Koop, John Maheu, Didier Nibbering, Anastasios Panagiotelis
Date: 2022-12-07 00:00:00

The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem -- model, parameters, latent states -- is able to be quantified explicitly, and factored into the forecast distribution via the process of integration or averaging. Allied with the elegance of the method, Bayesian forecasting is now underpinned by the burgeoning field of Bayesian computation, which enables Bayesian forecasts to be produced for virtually any problem, no matter how large, or complex. The current state of play in Bayesian forecasting in economics and finance is the subject of this review. The aim is to provide the reader with an overview of modern approaches to the field, set in some historical context; and with sufficient computational detail given to assist the reader with implementation.
The long-term effect of childhood exposure to technology using surrogates
Authors: Sylvia Klosin, Nicolaj Søndergaard Mühlbach
Date: 2022-12-06 00:00:00

We study how childhood exposure to technology at ages 5-15 via the occupation of the parents affects the ability to climb the social ladder in terms of income at ages 45-49 using the Danish micro data from years 1961-2019. Our measure of technology exposure covers the degree to which using computers (hardware and software) is required to perform an occupation, and it is created by merging occupational codes with detailed data from O*NET. The challenge in estimating this effect is that long-term outcome is observed over a different time horizon than our treatment of interest. We therefore adapt the surrogate index methodology, linking the effect of our childhood treatment on intermediate surrogates, such as income and education at ages 25-29, to the effect on adulthood income. We estimate that a one standard error increase in exposure to technology increases the income rank by 2\%-points, which is economically and statistically significant and robust to cluster-correlation within families. The derived policy recommendation is to update the educational curriculum to expose children to computers to a higher degree, which may then act as a social leveler.
The impact of CAP subsidies on the productivity of cereal farms in six European countries
Authors: Luigi Biagini, Federico Antonioli, Simone Severini
Date: 2022-12-07 00:00:00

Total factor productivity (TFP) is a key determinant of farm development, a sector that receives substantial public support. The issue has taken on great importance today, where the conflict in Ukraine has led to repercussions on the cereal markets. This paper investigates the effects of different subsidies on the productivity of cereal farms, accounting that farms differ according to the level of TFP. We relied on a three-step estimation strategy: i) estimation of production functions, ii) evaluation of TFP, and iii) assessment of the relationship between CAP subsidies and TFP. To overcome multiple endogeneity problems, the System-GMM estimator is adopted. The investigation embraces farms in France, Germany, Italy, Poland, Spain and the United Kingdom using the FADN samples from 2008 to 2018. Adding to previous analyses, we compare results from different countries and investigate three subsets of farms with varying levels of TFP. The outcomes confirm how CAP negatively impacts farm TFP, but the extent differs according to the type of subsidies, the six countries and, within these, among farms with different productivity groups. Therefore there is room for policy improvements in order to foster the productivity of cereal farms.
The Rise of Age-Friendly Jobs
Authors: Daron Acemoglu, Nicolaj Søndergaard Mühlbach, Andrew J. Scott
Date: 2022-12-06 00:00:00

In 1990, one in five U.S. workers were aged over 50 years whereas today it is one in three. One possible explanation for this is that occupations have become more accommodating to the preferences of older workers. We explore this by constructing an "age-friendliness" index for occupations. We use Natural Language Processing to measure the degree of overlap between textual descriptions of occupations and characteristics which define age-friendliness. Our index provides an approximation to rankings produced by survey participants and has predictive power for the occupational share of older workers. We find that between 1990 and 2020 around three quarters of occupations have seen their age-friendliness increase and employment in above-average age-friendly occupations has risen by 49 million. However, older workers have not benefited disproportionately from this rise, with substantial gains going to younger females and college graduates and with male non-college educated workers losing out the most. These findings point to the need to frame the rise of age-friendly jobs in the context of other labour market trends and imperfections. Purely age-based policies are insufficient given both heterogeneity amongst older workers as well as similarities between groups of older and younger workers. The latter is especially apparent in the overlapping appeal of specific occupational characteristics.
Applications of Machine Learning for the Ratemaking in Agricultural Insurances
Authors: Luigi Biagini
Date: 2022-12-06 00:00:00
Tasks: Variable Selection

This paper evaluates Machine Learning (ML) in establishing ratemaking for new insurance schemes. To make the evaluation feasible, we established expected indemnities as premiums. Then, we use ML to forecast indemnities using a minimum set of variables. The analysis simulates the introduction of an income insurance scheme, the so-called Income Stabilization Tool (IST), in Italy as a case study using farm-level data from the FADN from 2008-2018. We predicted the expected IST indemnities using three ML tools, LASSO, Elastic Net, and Boosting, that perform variable selection, comparing with the Generalized Linear Model (baseline) usually adopted in insurance investigations. Furthermore, Tweedie distribution is implemented to consider the peculiarity shape of the indemnities function, characterized by zero-inflated, no-negative value, and asymmetric fat-tail. The robustness of the results was evaluated by comparing the econometric and economic performance of the models. Specifically, ML has obtained the best goodness-of-fit than baseline, using a small and stable selection of regressors and significantly reducing the gathering cost of information. However, Boosting enabled it to obtain the best economic performance, balancing the most and most minor risky subjects optimally and achieving good economic sustainability. These findings suggest how machine learning can be successfully applied in agricultural insurance.This study represents one of the first to use ML and Tweedie distribution in agricultural insurance, demonstrating its potential to overcome multiple issues.
Can Machine Learning discover the determining factors in participation in insurance schemes? A comparative analysis
Authors: Luigi Biagini, Simone Severini
Date: 2022-12-06 00:00:00
Tasks: Variable Selection

Identifying factors that affect participation is key to a successful insurance scheme. This study's challenges involve using many factors that could affect insurance participation to make a better forecast.Huge numbers of factors affect participation, making evaluation difficult. These interrelated factors can mask the influence on adhesion predictions, making them misleading.This study evaluated how 66 common characteristics affect insurance participation choices. We relied on individual farm data from FADN from 2016 to 2019 with type 1 (Fieldcrops) farming with 10,926 observations.We use three Machine Learning (ML) approaches (LASSO, Boosting, Random Forest) compare them to the GLM model used in insurance modelling. ML methodologies can use a large set of information efficiently by performing the variable selection. A highly accurate parsimonious model helps us understand the factors affecting insurance participation and design better products.ML predicts fairly well despite the complexity of insurance participation problem. Our results suggest Boosting performs better than the other two ML tools using a smaller set of regressors. The proposed ML tools identify which variables explain participation choice. This information includes the number of cases in which single variables are selected and their relative importance in affecting participation.Focusing on the subset of information that best explains insurance participation could reduce the cost of designing insurance schemes.
Structural transformation and environmental externalities
Authors: Teevrat Garg, Maulik Jagnani, Hemant K. Pullabhotla
Date: 2022-12-05 00:00:00
Tasks: regression

Even as policymakers seek to encourage economic development by addressing misallocation due to frictions in labor markets, the associated production externalities - such as air pollution - remain unexplored. Using a regression discontinuity design, we show access to rural roads increases agricultural fires and particulate emissions. Farm labor exits are a likely mechanism responsible for the increase in agricultural fires: rural roads cause movement of workers out of agriculture and induce farmers to use fire - a labor-saving but polluting technology - to clear agricultural residue or to make harvesting less labor-intensive. Overall, the adoption of fires due to rural roads increases infant mortality rate by 5.5% in downwind locations.
Matching with Incomplete Preferences
Authors: Aditya Kuvalekar
Date: 2022-12-05 00:00:00

I study a two-sided marriage market in which agents have incomplete preferences -- i.e., they find some alternatives incomparable. The strong (weak) core consists of matchings wherein no coalition wants to form a new match between themselves, leaving some (all) agents better off without harming anyone. The strong core may be empty, while the weak core can be too large. I propose the concept of the "compromise core" -- a nonempty set that sits between the weak and the strong cores. Similarly, I define the men-(women-) optimal core and illustrate its benefit in an application to India's engineering college admissions system.
An Ellsberg paradox for ambiguity aversion
Authors: Christoph Kuzmics, Brian W. Rogers, Xiannong Zhang
Date: 2022-12-07 00:00:00

The 1961 Ellsberg paradox is typically seen as an empirical challenge to the subjective expected utility framework. Experiments based on Ellsberg's design have spawned a variety of new approaches, culminating in a new paradigm represented by, now classical, models of ambiguity aversion. We design and implement a decision-theoretic lab experiment that is extremely close to the original Ellsberg design and in which, empirically, subjects make choices very similar to those in the Ellsberg experiments. In our environment, however, these choices cannot be rationalized by any of the classical models of ambiguity aversion.
Screening with Persuasion
Authors: Dirk Bergemann, Tibor Heumann, Stephen Morris
Date: 2022-12-06 00:00:00

We analyze a nonlinear pricing model where the seller controls both product pricing (screening) and buyer information about their own values (persuasion). We prove that the optimal mechanism always consists of finitely many signals and items, even with a continuum of buyer values. The seller optimally pools buyer values and reduces product variety to minimize informational rents. We show that value pooling is optimal even for finite value distributions if their entropy exceeds a critical threshold. We also provide sufficient conditions under which the optimal menu restricts offering to a single item.
Respecting priorities versus respecting preferences in school choice: When is there a trade-off?
Authors: Estelle Cantillon, Li Chen, Juan S. Pereyra
Date: 2022-12-06 00:00:00

A classic trade-off that school districts face when deciding which matching algorithm to use is that it is not possible to always respect both priorities and preferences. The student-proposing deferred acceptance algorithm (DA) respects priorities but can lead to inefficient allocations. We identify a new condition on school choice markets under which DA is efficient. Our condition generalizes earlier conditions by placing restrictions on how preferences and priorities relate to one another only on the parts that are relevant for the assignment. Whenever there is a unique allocation that respects priorities, our condition captures all the environments for which DA is efficient. We show through stylized examples and simulations that our condition significantly expands the range of known environments for which DA is efficient. We also discuss how our condition sheds light on existing empirical findings.