pwc-archive/papers-with-abstracts huggingface dataset:
Delayed closed-loop neurostimulation for the treatment of pathological brain rhythms in mental disorders
Authors: Thomas Wahl, Joséphine Riedinger, Michel Duprez, Axel Hutt
Date: 2023-03-20 00:00:00

Mental disorders (MD) are among the top most demanding challenges in world-wide health. According to the World Health Organization, the burden of MDs continues to grow with significant impact on health and major social and human rights. A large number of MDs exhibit pathological rhythms, which serve as the disorders characteristic biomarkers. These rhythms are the targets for neurostimulation techniques. Open-loop neurostimulation employs stimulation protocols, which are rather independent of the patients health and brain state in the moment of treatment. Most alternative closed-loop stimulation protocols consider real-time brain activity observations but appear as adaptive open-loop protocols, where e.g. pre-defined stimulation sets in if observations fulfil pre-defined criteria. The present theoretical work proposes a fully-adaptive closed-loop neurostimulation setup, that tunes the brain activities power spectral density (PSD) according to a user-defined PSD. The utilized brain model is non-parametric and estimated from the observations via magnitude fitting in a pre-stimulus setup phase. Moreover, the algorithm takes into account possible conduction delays in the feedback connection between observation and stimulation electrode. All involved features are illustrated on pathological alpha- and gamma-rhythms known from psychosis. To this end, we simulate numerically a linear neural population brain model and a non-linear cortico-thalamic feedback loop model recently derived to explain brain activity in psychosis.
Mobility restrictions in response to local epidemic outbreaks in rock-paper-scissors models
Authors: J. Menezes
Date: 2023-03-19 00:00:00

We study a three-species cyclic model whose organisms are vulnerable to contamination with an infectious disease which propagates person-to-person. We consider that individuals of one species perform an evolutionary self-preservation strategy by reducing the mobility rate to minimise infection risk whenever an epidemic outbreak reaches the neighbourhood. Running stochastic simulations, we quantify the changes in spatial patterns induced by unevenness in the cyclic game introduced by the mobility restriction strategy of organisms of one out of the species. Our findings show that variations in disease virulence impact the benefits of dispersal limitation reaction, with the relative reduction of the organisms' infection risk accentuating in surges of less contagious or deadlier diseases. The effectiveness of the mobility restriction tactic depends on the tolerable fraction of infected neighbours used as a trigger of the defensive strategy and the deceleration level. If each organism promptly reacts to the arrival of the first viral vectors in its surroundings with strict mobility reduction, contamination risk decreases significantly. Our conclusions may help biologists understand the impact of evolutionary defensive strategies in ecosystems during an epidemic.
DNA supercoiling in bacteria: state of play and challenges from a viewpoint of physics based modeling
Authors: Ivan Junier, Elham Ghobadpour, Olivier Espeli, Ralf Everaers
Date: 2023-03-20 00:00:00

DNA supercoiling is central to many fundamental processes of living organisms. Its average level along the chromosome and over time reflects the dynamic equilibrium of opposite activities of topoisomerases, which are required to relax mechanical stresses that are inevitably produced during DNA replication and gene transcription. Supercoiling affects all scales of the spatio-temporal organization of bacterial DNA, from the base pair to the large scale chromosome conformation. Highlighted in vitro and in vivo in the 1960s and 1970s, respectively, the first physical models were proposed concomitantly in order to predict the deformation properties of the double helix. About fifteen years later, polymer physics models demonstrated on larger scales the plectonemic nature and the tree-like organization of supercoiled DNA. Since then, many works have tried to establish a better understanding of the multiple structuring and physiological properties of bacterial DNA in thermodynamic equilibrium and far from equilibrium. The purpose of this essay is to address upcoming challenges by thoroughly exploring the relevance, predictive capacity, and limitations of current physical models, with a specific focus on structural properties beyond the scale of the double helix. We discuss more particularly the problem of DNA conformations, the interplay between DNA supercoiling with gene transcription and DNA replication, its role on nucleoid formation and, finally, the problem of scaling up models. Our primary objective is to foster increased collaboration between physicists and biologists. To achieve this, we have reduced the respective jargon to a minimum and we provide some explanatory background material for the two communities.
Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields
Authors: Chapin E. Cavender, David A. Case, Julian C. -H. Chen, Lillian T. Chong, Daniel A. Keedy, Kresten Lindorff-Larsen, David L. Mobley, O. H. Samuli Ollila, Chris Oostenbrink, Paul Robustelli, Vincent A. Voelz, Michael E. Wall, David C. Wych, Michael K. Gilson
Date: 2023-03-02 00:00:00
Tasks: Benchmarking

This review article provides an overview of structurally oriented experimental datasets that can be used to benchmark protein force fields, focusing on data generated by nuclear magnetic resonance (NMR) spectroscopy and room temperature (RT) protein crystallography. We discuss what the observables are, what they tell us about structure and dynamics, what makes them useful for assessing force field accuracy, and how they can be connected to molecular dynamics simulations carried out using the force field one wishes to benchmark. We also touch on statistical issues that arise when comparing simulations with experiment. We hope this article will be particularly useful to computational researchers and trainees who develop, benchmark, or use protein force fields for molecular simulations.
Standard errors when a regressor is randomly assigned
Authors: Denis Chetverikov, Jinyong Hahn, Zhipeng Liao, Andres Santos
Date: 2023-03-18 00:00:00
Tasks: valid

We examine asymptotic properties of the OLS estimator when the values of the regressor of interest are assigned randomly and independently of other regressors. We find that the OLS variance formula in this case is often simplified, sometimes substantially. In particular, when the regressor of interest is independent not only of other regressors but also of the error term, the textbook homoskedastic variance formula is valid even if the error term and auxiliary regressors exhibit a general dependence structure. In the context of randomized controlled trials, this conclusion holds in completely randomized experiments with constant treatment effects. When the error term is heteroscedastic with respect to the regressor of interest, the variance formula has to be adjusted not only for heteroscedasticity but also for correlation structure of the error term. However, even in the latter case, some simplifications are possible as only a part of the correlation structure of the error term should be taken into account. In the context of randomized control trials, this implies that the textbook homoscedastic variance formula is typically not valid if treatment effects are heterogenous but heteroscedasticity-robust variance formulas are valid if treatment effects are independent across units, even if the error term exhibits a general dependence structure. In addition, we extend the results to the case when the regressor of interest is assigned randomly at a group level, such as in randomized control trials with treatment assignment determined at a group (e.g., school/village) level.
Bootstrap based asymptotic refinements for high-dimensional nonlinear models
Authors: Joel L. Horowitz, Ahnaf Rafi
Date: 2023-03-16 00:00:00
Tasks: Model Selection, Vocal Bursts Intensity Prediction

We consider penalized extremum estimation of a high-dimensional, possibly nonlinear model that is sparse in the sense that most of its parameters are zero but some are not. We use the SCAD penalty function, which provides model selection consistent and oracle efficient estimates under suitable conditions. However, asymptotic approximations based on the oracle model can be inaccurate with the sample sizes found in many applications. This paper gives conditions under which the bootstrap, based on estimates obtained through SCAD penalization with thresholding, provides asymptotic refinements of size \(O \left( n^{- 2} \right)\) for the error in the rejection (coverage) probability of a symmetric hypothesis test (confidence interval) and \(O \left( n^{- 1} \right)\) for the error in the rejection (coverage) probability of a one-sided or equal tailed test (confidence interval). The results of Monte Carlo experiments show that the bootstrap can provide large reductions in errors in rejection and coverage probabilities. The bootstrap is consistent, though it does not necessarily provide asymptotic refinements, even if some parameters are close but not equal to zero. Random-coefficients logit and probit models and nonlinear moment models are examples of models to which the procedure applies.
Tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets
Authors: Yun-Shi Dai, Peng-Fei Dai, Wei-Xing Zhou
Date: 2023-03-20 00:00:00
Tasks: Management, Portfolio Optimization

This paper combines the Copula-CoVaR approach with the ARMA-GARCH-skewed Student-t model to investigate the tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets, taking four main agricultural commodities, namely soybean, maize, wheat, and rice as examples. The empirical results indicate that the tail dependence structures for the four futures-spot pairs are quite different, and each of them exhibits a certain degree of asymmetry. In addition, the futures market for each agricultural commodity has significant and robust extreme downside and upside risk spillover effects on the spot market, and the downside risk spillover effects for both soybeans and maize are significantly stronger than their corresponding upside risk spillover effects, while there is no significant strength difference between the two risk spillover effects for wheat, and rice. This study provides a theoretical basis for strengthening global food cooperation and maintaining global food security, and has practical significance for investors to use agricultural commodities for risk management and portfolio optimization.
The Economic Value of User Tracking for Publishers
Authors: Rene Laub, Klaus M. Miller, Bernd Skiera
Date: 2023-03-20 00:00:00

Regulators and browsers increasingly restrict user tracking to protect users' privacy online. In two large-scale empirical studies, we study the economic implications for publishers relying on selling advertising space to finance their content. In our first study, we draw on 42 million ad impressions from 111 publishers covering EU desktop browsing traffic in 2016. In our second study, we use 218 million ad impressions from 10,526 publishers (i.e., apps) covering EU and US mobile in-app browsing traffic in 2023. The two studies differ in the share of trackable users (Study 1: 85%; Study 2: Apple: 17%, Android: 91%). Still, we find similar average ad impression price decreases (Study 1: 18% and Study 2: 23%) when user tracking is unavailable. More than 90% of the publishers realize lower prices when selling ad impressions for untrackable users. Publishers offering content on sports, cars, lifestyle & shopping, and news & information suffer the most. Premium publishers with high-quality edited content and strong reputations, thematic-focused (niche) publishers, and smaller publishers suffer less from the unavailability of user tracking. In contrast, non-premium publishers with non-edited or user-generated content, thematic-broad (general news) publishers, and larger publishers suffer more. The availability of a user ID generates the highest value for publishers, whereas collecting a user's browsing history, perceived as intrusive by most users, generates only a small value for publishers. These results affirm that ensuring user privacy online has substantial costs for online publishers, but those costs differ across publishers and the type of collected data. This article offers suggestions to reduce these costs.
Bifurcation analysis of the Keynesian cross model
Authors: Xinyu Li
Date: 2023-03-20 00:00:00
Tasks: model

This study rigorously investigates the Keynesian cross model of a national economy with a focus on the dynamic relationship between government spending and economic equilibrium. The model consists of two ordinary differential equations regarding the rate of change of national income and the rate of consumer spending. Three dynamic relationships between national income and government spending are studied. This study aims to classify the stabilities of equilibrium states for the economy by discussing different cases of government spending. Furthermore, the implication of government spending on the national economy is investigated based on phase portraits and bifurcation analysis of the dynamical system in each scenario.
A Re-Examination of the Foundations of Cost of Capital for Regulatory Purposes
Authors: Darryl Biggar
Date: 2023-03-20 00:00:00

In regulatory proceedings, few issues are more hotly debated than the cost of capital. This article formalises the theoretical foundation of cost of capital estimation for regulatory purposes. Several common regulatory practices lack a solid foundation in the theory. For example, the common practice of estimating a single cost of capital for the regulated firm suffers from a circularity problem, especially in the context of a multi-year regulatory period. In addition, the relevant cost of debt cannot be estimated using the yield-to-maturity on a corporate bond. We suggest possible directions for reform of cost of capital practices in regulatory proceedings.
School-based malaria chemoprevention as a cost-effective approach to improve cognitive and educational outcomes: a meta-analysis
Authors: Noam Angrist, Matthew C. H. Jukes, Sian Clarke, R. Matthew Chico, Charles Opondo, Donald Bundy, Lauren M. Cohee
Date: 2023-03-19 00:00:00
Tasks: Metric Learning

There is limited evidence of health interventions impact on cognitive function and educational outcomes. We build on two prior systematic reviews to conduct a meta-analysis, exploring the effects of one of the most consequential health interventions, malaria chemoprevention, on education outcomes. We pool data from nine study treatment groups (N=4,075) and outcomes across four countries. We find evidence of a positive effect (Cohen's d = 0.12, 95% CI [0.08, 0.16]) on student cognitive function, achieved at low cost. These results show that malaria chemoprevention can be highly cost effective in improving some cognitive skills, such as sustained attention. Moreover, we conduct simulations using a new common metric (learning-adjusted years of development) to compare cost-effectiveness across diverse interventions. While we might expect that traditional education interventions provide an immediate learning gain, health interventions such as malaria prevention can have surprisingly cost-effective education benefits, enabling children to achieve their full human capital potential.
Minimum Wage Pass-through to Wholesale and Retail Prices: Evidence from Cannabis Scanner Data
Authors: Carl Hase
Date: 2023-03-18 00:00:00

A growing empirical literature finds that firms pass the cost of minimum wage hikes onto consumers via higher retail prices. Yet, little is known about minimum wage effects on wholesale prices and whether retailers face a wholesale cost shock in addition to the labor cost shock. I exploit the vertically disintegrated market structure of Washington state's legal recreational cannabis industry to investigate minimum wage pass-through to wholesale and retail prices. In a difference-in-differences with continuous treatment framework, I utilize scanner data on $6 billion of transactions across the supply chain and leverage geographic variation in firms' minimum wage exposure across six minimum wage hikes between 2018 and 2021. When ignoring wholesale cost effects, I find retail pass-through elasticities consistent with existing literature -- yet retail pass-through elasticities more than double once wholesale cost effects are accounted for. Retail markups do not adjust to the wholesale cost shock, indicating a full pass-through of the wholesale cost shock to retail prices. The results highlight the importance of analyzing the entire supply chain when evaluating the product market effects of minimum wage hikes.
On Using Proportional Representation Methods as Alternatives to Pro-Rata Based Order Matching Algorithms in Stock Exchanges
Authors: Sanjay Bhattacherjee, Palash Sarkar
Date: 2023-03-16 00:00:00

The first observation of the paper is that methods for determining proportional representation in electoral systems may be suitable as alternatives to the pro-rata order matching algorithm used in stock exchanges. The main part of our work is to comprehensively consider various well known proportional representation methods and analyse in details their suitability for replacing the pro-rata algorithm. Our analysis consists of a theoretical study as well as simulation studies based on data sampled from a distribution which has been suggested in the literature as models of limit orders. Based on our analysis, we put forward the suggestion that the well known Hamilton's method is a superior alternative to the pro-rata algorithm for order matching applications.
Decomposability and Strategy-proofness in Multidimensional Models
Authors: Shurojit Chatterji, Huaxia Zeng
Date: 2023-03-20 00:00:00

We introduce the notion of a multidimensional hybrid preference domain on a (finite) set of alternatives that is a Cartesian product of finitely many components. We demonstrate that in a model of public goods provision, multidimensional hybrid preferences arise naturally through assembling marginal preferences under the condition of semi-separability - a weakening of separability. The main result shows that under a suitable "richness" condition, every strategy-proof rule on this domain can be decomposed into component-wise strategy-proof rules, and more importantly every domain of preferences that reconciles decomposability of rules with strategy-proofness must be a multidimensional hybrid domain.
Efficient Public Good Provision Between and Within Groups
Authors: Chowdhury Mohammad Sakib Anwar, Jorge Bruno, Renaud Foucart, Sonali SenGupta
Date: 2023-03-18 00:00:00
Tasks: Position

We generalize the model of Gallice and Monzon (2019) to incorporate a public goods game with groups, position uncertainty, and observational learning. Contributions are simultaneous within groups, but groups play sequentially based on their observation of an incomplete sample of past contributions. We show that full cooperation between and within groups is possible with self-interested players on a fixed horizon. Position uncertainty implies the existence of an equilibrium where groups of players conditionally cooperate in the hope of influencing further groups. Conditional cooperation implies that each group member is pivotal, so that efficient simultaneous provision within groups is an equilibrium.
Dynamic Information Provision: Rewarding the Past and Guiding the Future
Authors: Ian Ball
Date: 2023-03-16 00:00:00

I study the optimal provision of information in a long-term relationship between a sender and a receiver. The sender observes a persistent, evolving state and commits to send signals over time to the receiver, who sequentially chooses public actions that affect the welfare of both players. I solve for the sender's optimal policy in closed form: the sender reports the value of the state with a delay that shrinks over time and eventually vanishes. Even when the receiver knows the current state, the sender retains leverage by threatening to conceal the future evolution of the state.
Bronchoscopic video synchronization for interactive multimodal inspection of bronchial lesions
Authors: Qi Chang, Patrick D. Byrnes, Danish Ahmad, Jennifer Toth, Rebecca Bascom, William E. Higgins
Date: 2023-03-20 00:00:00
Tasks: Computed Tomography (CT), Video Synchronization

With lung cancer being the most fatal cancer worldwide, it is important to detect the disease early. A potentially effective way of detecting early cancer lesions developing along the airway walls (epithelium) is bronchoscopy. To this end, developments in bronchoscopy offer three promising noninvasive modalities for imaging bronchial lesions: white-light bronchoscopy (WLB), autofluorescence bronchoscopy (AFB), and narrow-band imaging (NBI). While these modalities give complementary views of the airway epithelium, the physician must manually inspect each video stream produced by a given modality to locate the suspect cancer lesions. Unfortunately, no effort has been made to rectify this situation by providing efficient quantitative and visual tools for analyzing these video streams. This makes the lesion search process extremely time-consuming and error-prone, thereby making it impractical to utilize these rich data sources effectively. We propose a framework for synchronizing multiple bronchoscopic videos to enable an interactive multimodal analysis of bronchial lesions. Our methods first register the video streams to a reference 3D chest computed-tomography (CT) scan to produce multimodal linkages to the airway tree. Our methods then temporally correlate the videos to one another to enable synchronous visualization of the resulting multimodal data set. Pictorial and quantitative results illustrate the potential of the methods.
Physics-Aware Motion Simulation for T2*-Weighted Brain MRI
Authors: Hannah Eichhorn, Kerstin Hammernik, Veronika Spieker, Samira M. Epp, Daniel Rueckert, Christine Preibisch, Julia A. Schnabel
Date: 2023-03-20 00:00:00
Tasks: Line Detection

In this work, we propose a realistic, physics-aware motion simulation procedure for T2*-weighted magnetic resonance imaging (MRI) to improve learning-based motion correction. As T2*-weighted MRI is highly sensitive to motion-related changes in magnetic field inhomogeneities, it is of utmost importance to include physics information in the simulation. Additionally, current motion simulations often only assume simplified motion patterns. Our simulations, on the other hand, include real recorded subject motion and realistic effects of motion-induced magnetic field inhomogeneity changes. We demonstrate the use of such simulated data by training a convolutional neural network to detect the presence of motion in affected k-space lines. The network accurately detects motion-affected k-space lines for simulated displacements down to $\geq$ 0.5mm (accuracy on test set: 92.5%). Finally, our results demonstrate exciting opportunities of simulation-based k-space line detection combined with more powerful reconstruction methods.