pwc-archive/papers-with-abstracts huggingface dataset:
Spatially disordered environments stabilize competitive metacommunities
Authors: Prajwal Padmanabha, Giorgio Nicoletti, Davide Bernardi, Samir Suweis, Sandro Azaele, Andrea Rinaldo, Amos Maritan
Date: 2024-04-15 00:00:00
Tasks: valid

Metapopulation models have been instrumental in demonstrating the ecological impact of landscape structure on the survival of a focal species in complex environments. However, extensions to multiple species with arbitrary dispersal networks often rely on phenomenological assumptions limiting their scope. Here, we develop a multilayer network model of competitive dispersing metacommunities to investigate how spatially structured environments impact species coexistence and ecosystem stability. We show that homogeneous environments always lead to monodominance unless all species' fitness parameters are in an exact trade-off. However, this precise fine-tuning does not guarantee coexistence in generic heterogeneous environments. By introducing general spatial disorder in the model, we solve it exactly in the mean-field limit, finding that stable coexistence becomes possible in the presence of strong disorder. Crucially, coexistence is supported by the spontaneous localization of species through the emergence of ecological niches. Our results remain qualitatively valid in arbitrary dispersal networks, where topological features can improve species coexistence. Finally, we employ our model to study how correlated disorder promotes spatial ecological patterns in realistic terrestrial and riverine landscapes. Our work provides a novel framework to understand how landscape structure enables coexistence in metacommunities by acting as the substrate for ecological interactions.
Heart Rate Variability Series is the Output of a non-Chaotic System driven by Dynamical Noise
Authors: M. Bianco, A. Scarciglia, C. Bonanno, G. Valenza
Date: 2024-04-17 00:00:00
Tasks: Heart Rate Variability

Heart rate variability (HRV) series reflects the dynamical variation of heartbeat-to-heartbeat intervals in time and is one of the outputs of the cardiovascular system. Over the years, this system has been recognized for generating nonlinear and complex heartbeat dynamics, with the latter referring to a high sensitivity to small -- theoretically infinitesimal -- input changes. While early research associated chaotic behavior with the cardiovascular system, evidence of stochastic inputs to the system, i.e., a physiological noise, invalidated those conclusions. To date, a comprehensive characterization of the cardiovascular system dynamics, accounting for dynamical noise input, has not been undertaken. In this study, we propose a novel methodological framework for evaluating the presence of regular or chaotic dynamics in noisy dynamical systems. The method relies on the estimation of asymptotic growth rate of noisy mean square displacement series in a two-dimensional phase space. We validated the proposed method using synthetic series comprising well-known regular and chaotic maps. We applied the method to real HRV series from healthy subjects, as well as patients with atrial fibrillation and congestive heart failure, during unstructured long-term activity. Results indicate that HRV series are consistently generated by a regular system driven by dynamical noise.
Physics and technology of Laser Lightning Control
Authors: Thomas Produit, Jérôme Kasparian, Farhad Rachidi, Marcos Rubinstein, Aurélien Houard, Jean-Pierre Wolf
Date: 2024-04-15 00:00:00

The recent development of high average, high peak power lasers has revived the effort of using lasers as a potential tool to influence natural lightning. Although impressive, the current progress in laser lightning control technology may only be the beginning of a new area involving a positive feedback between powerful laser development and atmospheric research. In this review paper, we critically evaluate the past, present and future of Laser Lightning Control (LLC), considering both its technological and scientific significance in atmospheric research.
Lightweight Geometric Deep Learning for Molecular Modelling in Catalyst Discovery
Authors: Patrick Geitner
Date: 2024-04-05 00:00:00
Tasks: Deep Learning, Robust Design

New technology for energy storage is necessary for the large-scale adoption of renewable energy sources like wind and solar. The ability to discover suitable catalysts is crucial for making energy storage more cost-effective and scalable. The Open Catalyst Project aims to apply advances in graph neural networks (GNNs) to accelerate progress in catalyst discovery, replacing Density Functional Theory-based (DFT) approaches that are computationally burdensome. Current approaches involve scaling GNNs to over 1 billion parameters, pushing the problem out of reach for a vast majority of machine learning practitioner around the world. This study aims to evaluate the performance and insights gained from using more lightweight approaches for this task that are more approachable for smaller teams to encourage participation from individuals from diverse backgrounds. By implementing robust design patterns like geometric and symmetric message passing, we were able to train a GNN model that reached a MAE of 0.0748 in predicting the per-atom forces of adsorbate-surface interactions, rivaling established model architectures like SchNet and DimeNet++ while using only a fraction of trainable parameters.
A Strategy Transfer and Decision Support Approach for Epidemic Control in Experience Shortage Scenarios
Authors: X. Xiao, P. Chen, X. Cao, K. Liu, L. Deng, D. Zhao, Z. Chen, Q. Deng, F. Yu, H. Zhang
Date: 2024-04-10 00:00:00
Tasks: Collaborative Filtering, Decision Making

Epidemic outbreaks can cause critical health concerns and severe global economic crises. For countries or regions with new infectious disease outbreaks, it is essential to generate preventive strategies by learning lessons from others with similar risk profiles. A Strategy Transfer and Decision Support Approach (STDSA) is proposed based on the profile similarity evaluation. There are four steps in this method: (1) The similarity evaluation indicators are determined from three dimensions, i.e., the Basis of National Epidemic Prevention & Control, Social Resilience, and Infection Situation. (2) The data related to the indicators are collected and preprocessed. (3) The first round of screening on the preprocessed dataset is conducted through an improved collaborative filtering algorithm to calculate the preliminary similarity result from the perspective of the infection situation. (4) Finally, the K-Means model is used for the second round of screening to obtain the final similarity values. The approach will be applied to decision-making support in the context of COVID-19. Our results demonstrate that the recommendations generated by the STDSA model are more accurate and aligned better with the actual situation than those produced by pure K-means models. This study will provide new insights into preventing and controlling epidemics in regions that lack experience.
The mind-brain relationship and the perspective of meaning
Authors: Ranjan Mukhopadhyay
Date: 2024-04-17 00:00:00

We view the mind-body problem in terms of the two interconnected problems of phenomenal consciousness and mental causation, namely, how subjective conscious experience can arise from physical neurological processes and how conscious mental states can causally act upon the physical world. In order to address these problems, I develop here a non-physicalist framework that combines two apparently antithetical views: the materialist view of the mind as a product of the brain and the metaphysical view of consciousness rooted in an underlying hidden reality. I discuss how this framework resolves the problem of mental causation while being simultaneously consistent with fundamental physical principles. I will elucidate how the framework ties in to the perspective of meaning that acts as the bridge between physical neurological processes and the conscious mind. Moreover, we will see how both our awareness of the self and our representation of the external world are connected to this perspective.
Weighted-Average Least Squares for Negative Binomial Regression
Authors: Kevin Huynh
Date: 2024-04-17 00:00:00
Tasks: regression

Model averaging methods have become an increasingly popular tool for improving predictions and dealing with model uncertainty, especially in Bayesian settings. Recently, frequentist model averaging methods such as information theoretic and least squares model averaging have emerged. This work focuses on the issue of covariate uncertainty where managing the computational resources is key: The model space grows exponentially with the number of covariates such that averaged models must often be approximated. Weighted-average least squares (WALS), first introduced for (generalized) linear models in the econometric literature, combines Bayesian and frequentist aspects and additionally employs a semiorthogonal transformation of the regressors to reduce the computational burden. This paper extends WALS for generalized linear models to the negative binomial (NB) regression model for overdispersed count data. A simulation experiment and an empirical application using data on doctor visits were conducted to compare the predictive power of WALS for NB regression to traditional estimators. The results show that WALS for NB improves on the maximum likelihood estimator in sparse situations and is competitive with lasso while being computationally more efficient.
Bayesian Markov-Switching Vector Autoregressive Process
Authors: Battulga Gankhuu
Date: 2024-04-17 00:00:00

This study introduces marginal density functions of the general Bayesian Markov-Switching Vector Autoregressive (MS-VAR) process. In the case of the Bayesian MS-VAR process, we provide closed-form density functions and Monte-Carlo simulation algorithms, including the importance sampling method. The Monte-Carlo simulation method departs from the previous simulation methods because it removes the duplication in a regime vector.
Forecasting with panel data: Estimation uncertainty versus parameter heterogeneity
Authors: M. Hashem Pesaran, Andreas Pick, Allan Timmermann
Date: 2024-04-17 00:00:00

We provide a comprehensive examination of the predictive performance of panel forecasting methods based on individual, pooling, fixed effects, and empirical Bayes estimation, and propose optimal weights for forecast combination schemes. We consider linear panel data models, allowing for weakly exogenous regressors and correlated heterogeneity. We quantify the gains from exploiting panel data and demonstrate how forecasting performance depends on the degree of parameter heterogeneity, whether such heterogeneity is correlated with the regressors, the goodness of fit of the model, and the dimensions of the data. Monte Carlo simulations and empirical applications to house prices and CPI inflation show that empirical Bayes and forecast combination methods perform best overall and rarely produce the least accurate forecasts for individual series.
Estimation for conditional moment models based on martingale difference divergence
Authors: Kunyang Song, Feiyu Jiang, Ke Zhu
Date: 2024-04-17 00:00:00
Tasks: Time Series

We provide a new estimation method for conditional moment models via the martingale difference divergence (MDD).Our MDD-based estimation method is formed in the framework of a continuum of unconditional moment restrictions. Unlike the existing estimation methods in this framework, the MDD-based estimation method adopts a non-integrable weighting function, which could grab more information from unconditional moment restrictions than the integrable weighting function to enhance the estimation efficiency. Due to the nature of shift-invariance in MDD, our MDD-based estimation method can not identify the intercept parameters. To overcome this identification issue, we further provide a two-step estimation procedure for the model with intercept parameters. Under regularity conditions, we establish the asymptotics of the proposed estimators, which are not only easy-to-implement with analytic asymptotic variances, but also applicable to time series data with an unspecified form of conditional heteroskedasticity. Finally, we illustrate the usefulness of the proposed estimators by simulations and two real examples.
Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference
Authors: Helmut Lütkepohl, Fei Shang, Luis Uzeda, Tomasz Woźniak
Date: 2024-04-17 00:00:00
Tasks: Bayesian Inference

We consider structural vector autoregressions identified through stochastic volatility. Our focus is on whether a particular structural shock is identified by heteroskedasticity without the need to impose any sign or exclusion restrictions. Three contributions emerge from our exercise: (i) a set of conditions under which the matrix containing structural parameters is partially or globally unique; (ii) a statistical procedure to assess the validity of the conditions mentioned above; and (iii) a shrinkage prior distribution for conditional variances centred on a hypothesis of homoskedasticity. Such a prior ensures that the evidence for identifying a structural shock comes only from the data and is not favoured by the prior. We illustrate our new methods using a U.S. fiscal structural model.
From Predictive Algorithms to Automatic Generation of Anomalies
Authors: Sendhil Mullainathan, Ashesh Rambachan
Date: 2024-04-15 00:00:00

Machine learning algorithms can find predictive signals that researchers fail to notice; yet they are notoriously hard-to-interpret. How can we extract theoretical insights from these black boxes? History provides a clue. Facing a similar problem -- how to extract theoretical insights from their intuitions -- researchers often turned to ``anomalies:'' constructed examples that highlight flaws in an existing theory and spur the development of new ones. Canonical examples include the Allais paradox and the Kahneman-Tversky choice experiments for expected utility theory. We suggest anomalies can extract theoretical insights from black box predictive algorithms. We develop procedures to automatically generate anomalies for an existing theory when given a predictive algorithm. We cast anomaly generation as an adversarial game between a theory and a falsifier, the solutions to which are anomalies: instances where the black box algorithm predicts - were we to collect data - we would likely observe violations of the theory. As an illustration, we generate anomalies for expected utility theory using a large, publicly available dataset on real lottery choices. Based on an estimated neural network that predicts lottery choices, our procedures recover known anomalies and discover new ones for expected utility theory. In incentivized experiments, subjects violate expected utility theory on these algorithmically generated anomalies; moreover, the violation rates are similar to observed rates for the Allais paradox and Common ratio effect.
Attitudinal Loyalty Manifestation in Banking CSR: Cross-Buying Behavior and Customer Advocacy
Authors: Muhamad Bhayuta Yudhi Putera, Melia Famiola
Date: 2024-04-17 00:00:00

This study in the banking industry examines the influence of attitudinal loyalty on customer advocacy and cross buying behavior, alongside the moderating roles of Quality of Life and Corporate Social Responsibility support in the CSR fit and loyalty relationship. Employing Structural Equation Modeling, it reveals that higher attitudinal loyalty significantly boosts customer advocacy and propensity for cross buying. The findings highlight the importance of nurturing customer loyalty through valuable and relevant offerings, as CSR fit alone does not define the loyalty of the banking customer. Banks are advised to target customers with a high Quality of Life and engage with those who support CSR initiatives aligning with the banks objectives, to enhance loyalty and deepen customer relationships.
The dynamics of diversity on corporate boards
Authors: Matthias Raddant, Fariba Karimi
Date: 2024-04-17 00:00:00
Tasks: Decision Making, Diversity

Diversity in leadership positions, including corporate boards, is an important aspect of equality. It is important because it is the key to better decision-making and innovation, and above all, it paves the way for future generations to participate and shape our society. Many studies emphasize the importance of the visibility of role models and the effect that connectivity has on the success of minorities in leadership. However, the connectivity of firms, the dynamics of the adoption of minorities into leadership positions, and the long-term effects in terms of group dynamics and visibility are not well understood. Here, we present a model that shows how these effects work together in a dynamic model that is calibrated with empirical data of firm and board networks. We show that homophily -- the appointment of minorities is influenced by the presence of minorities in a board and its neighboring entities -- is an important effect shaping the trajectory towards equality. We further show how perception biases and feedback related to the visibility of minority members influence the dynamic. We find that reaching equality can be sped up or slowed down depending on the distribution of minorities in central firms. These insights bear significant implications for policy-making geared towards fostering equality and diversity within corporate boards.
The Relationship between Consumer Theories with and without Utility Maximization
Authors: Yuhki Hosoya
Date: 2024-04-16 00:00:00

To study the assumption that the utility maximization hypothesis implicitly adds to consumer theory, we consider a mathematical representation of pre-marginal revolution consumer theory based on subjective exchange ratios. We introduce two axioms on subjective exchange ratio, and show that both axioms hold if and only if consumer behavior is consistent with the utility maximization hypothesis. Moreover, we express the process for a consumer to find the transaction stopping point in terms of differential equations, and prove that the conditions for its stability are equal to the two axioms introduced in the above argument. Therefore, the consumer can find his/her transaction stopping point if and only if his/her behavior is consistent with the utility maximization hypothesis. In addition to these results, we discuss equivalence conditions for axioms to evaluate their mathematical strength, and methods for expressing the theory of subjective exchange ratios in terms of binary relations.
Heterogeneity, trade integration and spatial inequality
Authors: José M. Gaspar
Date: 2024-04-15 00:00:00

We study the impact of economic integration on agglomeration in a model where all consumers are inter-regionally mobile and have heterogeneous preferences regarding their residential location choices. This heterogeneity is the unique dispersion force in the model. We show that, under reasonable values for the elasticity of substitution among varieties of consumption goods, a higher trade integration always promotes more symmetric spatial patterns, reducing the spatial inequality between regions, irrespective of the functional form of the dispersion force. We also show that an increase in the degree of heterogeneity in preferences for location leads to a more even spatial distribution of economic activities and thus also reduces the spatial inequality between regions.
Virtual Lung Screening Trial (VLST): An In Silico Study Inspired by the National Lung Screening Trial for Lung Cancer Detection
Authors: Fakrul Islam Tushar, Liesbeth Vancoillie, Cindy McCabe, Amareswararao Kavuri, Lavsen Dahal, Brian Harrawood, Milo Fryling, Mojtaba Zarei, Saman Sotoudeh-Paima, Fong Chi Ho, Dhrubajyoti Ghosh, Michael R. Harowicz, Tina D. Tailor, Sheng Luo, W. Paul Segars, Ehsan Abadi, Kyle J. Lafata, Joseph Y. Lo, Ehsan Samei
Date: 2024-04-17 00:00:00
Tasks: Computed Tomography (CT), Diagnostic, Lesion Detection, Specificity

Clinical imaging trials play a crucial role in advancing medical innovation but are often costly, inefficient, and ethically constrained. Virtual Imaging Trials (VITs) present a solution by simulating clinical trial components in a controlled, risk-free environment. The Virtual Lung Screening Trial (VLST), an in silico study inspired by the National Lung Screening Trial (NLST), illustrates the potential of VITs to expedite clinical trials, minimize risks to participants, and promote optimal use of imaging technologies in healthcare. This study aimed to show that a virtual imaging trial platform could investigate some key elements of a major clinical trial, specifically the NLST, which compared Computed tomography (CT) and chest radiography (CXR) for lung cancer screening. With simulated cancerous lung nodules, a virtual patient cohort of 294 subjects was created using XCAT human models. Each virtual patient underwent both CT and CXR imaging, with deep learning models, the AI CT-Reader and AI CXR-Reader, acting as virtual readers to perform recall patients with suspicion of lung cancer. The primary outcome was the difference in diagnostic performance between CT and CXR, measured by the Area Under the Curve (AUC). The AI CT-Reader showed superior diagnostic accuracy, achieving an AUC of 0.92 (95% CI: 0.90-0.95) compared to the AI CXR-Reader's AUC of 0.72 (95% CI: 0.67-0.77). Furthermore, at the same 94% CT sensitivity reported by the NLST, the VLST specificity of 73% was similar to the NLST specificity of 73.4%. This CT performance highlights the potential of VITs to replicate certain aspects of clinical trials effectively, paving the way toward a safe and efficient method for advancing imaging-based diagnostics.
Multimodal Fusion of Echocardiography and Electronic Health Records for the Detection of Cardiac Amyloidosis
Authors: Zishun Feng, Joseph A. Sivak, Ashok K. Krishnamurthy
Date: 2024-04-17 00:00:00
Tasks: Diagnostic

Cardiac amyloidosis, a rare and highly morbid condition, presents significant challenges for detection through echocardiography. Recently, there has been a surge in proposing machine-learning algorithms to identify cardiac amyloidosis, with the majority being imaging-based deep-learning approaches that require extensive data. In this study, we introduce a novel transformer-based multimodal fusion algorithm that leverages information from both imaging and electronic health records. Specifically, our approach utilizes echocardiography videos from both the parasternal long-axis (PLAX) view and the apical 4-chamber (A4C) view along with patients' demographic data, laboratory tests, and cardiac metrics to predict the probability of cardiac amyloidosis. We evaluated our method using 5-fold cross-validation on a dataset comprising 41 patients and achieved an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.94. The experimental results demonstrate that our approach can achieve competitive results with a significantly smaller dataset compared to prior imaging-based methods that required data from thousands of patients. This underscores the potential of leveraging multimodal data to enhance diagnostic accuracy in the identification of complex cardiac conditions such as cardiac amyloidosis.