pwc-archive/papers-with-abstracts huggingface dataset:
A tau-leaping method for computing joint probability distributions of the first-passage time and position of a Brownian particle
Authors: Jaroslav Albert
Date: 2023-01-02 00:00:00
Tasks: Position

First passage time (FPT) is the time a particle, subject to some stochastic process, hits or crosses a closed surface for the very first time. $\tau$-leaping methods are a class of stochastic algorithms in which, instead of simulating every single reaction, many reactions are ``leaped" over in order to shorten the computing time. In this paper we developed a $\tau$-leaping method for computing the FPT and position in arbitrary volumes for a Brownian particle governed by the Langevin equation. The $\tau$-leaping method proposed here works as follows. A sphere is inscribed within the volume of interest (VOI) centered at the initial particle's location. On this sphere, the FPT is sampled, as well as the position, which becomes the new initial position. Then, another sphere, centered at this new location, is inscribed. This process continues until the sphere becomes smaller than some minimal radius $R_{\text{min}}$. When this occurs, the $\tau$-leaping switches to the conventional Monte Carlo, which runs until the particle either crosses the surface of the VOI or finds its way to a position where a sphere of radius $>R_{\text{min}}$ can be inscribed. The switching between $\tau$-leaping and MC continues until the particle crosses the surface of the VOI. The size of this radius depends on the system parameters and on one's notion of accuracy: the larger this radius the more accurate the $\tau$-leaping method, but also less efficient. This trade off between accuracy and efficiency is discussed. For two VOI, the $\tau$-leaping method is shown to be accurate and more efficient than MC by at least a factor of 10 and up to a factor of about 110. However, while MC becomes exponentially slower with increasing VOI, the efficiency of the $\tau$-leaping method remains relatively unchanged. Thus, the $\tau$-leaping method can potentially be many orders of magnitude more efficient than MC.
Tunable intracellular transport on converging microtubule morphologies
Authors: Niranjan Sarpangala, Brooke Randell, Ajay Gopinathan, Oleg Kogan
Date: 2023-01-03 00:00:00

A common type of cytoskeletal morphology involves multiple converging microbutubules with their minus ends collected and stabilized by a microtubule organizing center (MTOC) in the interior of the cell. This arrangement enables the ballistic transport of cargo bound to microtubules, both dynein mediated transport towards the MTOC and kinesin mediated transport away from it, interspersed with diffusion for unbound cargo-motor complexes. Spatial and temporal positioning of the MTOC allows for bidirectional transport towards and away from specific organelles and locations within the cell and also the sequestering and subsequent dispersal of dynein transported cargo. The general principles governing dynamics, efficiency and tunability of such transport in the MTOC vicinity is not fully understood. To address this, we develop a one-dimensional model that includes advective transport towards an attractor (such as the MTOC), and diffusive transport that allows particles to reach absorbing boundaries (such as cellular membranes). We calculated the mean first passage time (MFPT) for cargo to reach the boundaries as a measure of the effectiveness of sequestering (large MFPT) and diffusive dispersal (low MFPT). The MFPT experiences a dramatic growth in magnitude, transitioning from a low to high MFPT regime (dispersal to sequestering) over a window of cargo attachment/detachment rates that is close to in vivo values. We find that increasing either the attachment or detachment rate, while fixing the other, can result in optimal dispersal when the attractor is placed asymmetrically. Finally, we describe a rare event regime, where the escape location is exponentially sensitive to the attractor positioning. Our results suggest that structures such as the MTOC allow for the sensitive control of the spatial and temporal features of transport and corresponding function under physiological conditions.
Competition of alliances in a cyclically dominant eight-species population
Authors: Junpyo Park, Xiaojie Chen, Attila Szolnoki
Date: 2023-01-02 00:00:00
Tasks: valid

In a diverse population, where many species are present, competitors can fight for surviving at individual and collective levels. In particular, species, which would beat each other individually, may form a specific alliance that ensures them stable coexistence against the invasion of an external species. Our principal goal is to identify those general features of a formation which determine its vitality. Therefore, we here study a traditional Lotka-Volterra model of eight-species where two four-species cycles can fight for space. Beside these formations, there are other solutions which may emerge when invasion rates are varied. The complete range of parameters is explored and we find that in most of the cases those alliances prevail which are formed by equally strong members. Interestingly, there are regions where the symmetry is broken and the system is dominated by a solution formed by seven species. Our work also highlights that serious finite-size effects may emerge which prevent observing the valid solution in a small system.
Global excitability and network structure in the human brain
Authors: Youssef Kora, Salma Salhi, Jörn Davidsen, Christoph Simon
Date: 2023-01-03 00:00:00

We utilize a model of Wilson-Cowan oscillators to investigate structure-function relationships in the human brain by means of simulations of the spontaneous dynamics of brain networks generated through human connectome data. This allows us to establish relationships between the global excitability of such networks and global structural network quantities for connectomes of two different sizes for a number of individual subjects. We compare the qualitative behavior of such correlations between biological networks and shuffled networks, the latter generated by shuffling the pairwise connectivities of the former while preserving their distribution. Our results point towards a remarkable propensity of the brain's to achieve a trade-off between low network wiring cost and strong functionality, and highlight the unique capacity of brain network topologies to exhibit a strong transition from an inactive state to a globally excited one.
Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data
Authors: Georgia Papacharalampous, Hristos Tyralis, Anastasios Doulamis, Nikolaos Doulamis
Date: 2022-12-17 00:00:00
Tasks: regression

Gridded satellite precipitation datasets are useful in hydrological applications as they cover large regions with high density. However, they are not accurate in the sense that they do not agree with ground-based measurements. An established means for improving their accuracy is to correct them by adopting machine learning algorithms. This correction takes the form of a regression problem, in which the ground-based measurements have the role of the dependent variable and the satellite data are the predictor variables, together with topography factors (e.g., elevation). Most studies of this kind involve a limited number of machine learning algorithms, and are conducted for a small region and for a limited time period. Thus, the results obtained through them are of local importance and do not provide more general guidance and best practices. To provide results that are generalizable and to contribute to the delivery of best practices, we here compare eight state-of-the-art machine learning algorithms in correcting satellite precipitation data for the entire contiguous United States and for a 15-year period. We use monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) gridded dataset, together with monthly earth-observed precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The results suggest that extreme gradient boosting (XGBoost) and random forests are the most accurate in terms of the squared error scoring function. The remaining algorithms can be ordered as follows from the best to the worst: Bayesian regularized feed-forward neural networks, multivariate adaptive polynomial splines (poly-MARS), gradient boosting machines (gbm), multivariate adaptive regression splines (MARS), feed-forward neural networks, linear regression.
Deep learning for bias-correcting CMIP6-class Earth system models
Authors: Philipp Hess, Stefan Lange, Christof Schötz, Niklas Boers
Date: 2022-12-16 00:00:00
Tasks: Deep Learning, Vocal Bursts Intensity Prediction

The accurate representation of precipitation in Earth system models (ESMs) is crucial for reliable projections of the ecological and socioeconomic impacts in response to anthropogenic global warming. The complex cross-scale interactions of processes that produce precipitation are challenging to model, however, inducing potentially strong biases in ESM fields, especially regarding extremes. State-of-the-art bias correction methods only address errors in the simulated frequency distributions locally at every individual grid cell. Improving unrealistic spatial patterns of the ESM output, which would require spatial context, has not been possible so far. Here, we show that a post-processing method based on physically constrained generative adversarial networks (cGANs) can correct biases of a state-of-the-art, CMIP6-class ESM both in local frequency distributions and in the spatial patterns at once. While our method improves local frequency distributions equally well as gold-standard bias-adjustment frameworks, it strongly outperforms any existing methods in the correction of spatial patterns, especially in terms of the characteristic spatial intermittency of precipitation extremes.
Neural criticality from effective latent variables
Authors: Mia C. Morrell, Ilya Nemenman, Audrey J. Sederberg
Date: 2023-01-02 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Observations of power laws in neural activity data have raised the intriguing notion that brains may operate in a critical state. One example of this critical state is "avalanche criticality," which has been observed in various systems, including cultured neurons, zebrafish, rodent cortex, and human EEG. More recently, power laws were also observed in neural populations in the mouse under an activity coarse-graining procedure, and they were explained as a consequence of the neural activity being coupled to multiple latent dynamical variables. An intriguing possibility is that avalanche criticality emerges due to a similar mechanism. Here, we determine the conditions under which latent dynamical variables give rise to avalanche criticality. We find that populations coupled to multiple latent variables produce critical behavior across a broader parameter range than those coupled to a single, quasi-static latent variable, but in both cases, avalanche criticality is observed without fine-tuning of model parameters. We identify two regimes of avalanches, both critical but differing in the amount of information carried about the latent variable. Our results suggest that avalanche criticality arises in neural systems in which activity is effectively modeled as a population driven by a few dynamical variables and these variables can be inferred from the population activity.
Gaussian Blur and Relative Edge Response
Authors: Austin C. Bergstrom, David Conran, David W. Messinger
Date: 2023-01-02 00:00:00
Tasks: Data Augmentation

It is often convenient to use Gaussian blur in studying image quality or in data augmentation pipelines for training convoluional neural networks. Because of their convenience, Guassians are sometimes used as first order approximations of optical point spread functions. Here, we derive and evaluate closed form relationships between Gaussian blur parameters and relative edge response, finding good agreement with measured results. Additionally, we evaluate the extent to which Gaussian approximations of optical point spread functions can be used to predict relative edge response, finding that Gaussian relationships provide a reasonable approximation in limited circumstances but not across a wide range of optical parameters.
Fitting mixed logit random regret minimization models using maximum simulated likelihood
Authors: Ziyue Zhu, Álvaro A. Gutiérrez-Vargas, Martina Vandebroek
Date: 2023-01-03 00:00:00
Tasks: Numerical Integration

This article describes the mixrandregret command, which extends the randregret command introduced in Guti\'errez-Vargas et al. (2021, The Stata Journal 21: 626-658) incorporating random coefficients for Random Regret Minimization models. The newly developed command mixrandregret allows the inclusion of random coefficients in the regret function of the classical RRM model introduced in Chorus (2010, European Journal of Transport and Infrastructure Research 10: 181-196). The command allows the user to specify a combination of fixed and random coefficients. In addition, the user can specify normal and log-normal distributions for the random coefficients using the commands' options. The models are fitted using simulated maximum likelihood using numerical integration to approximate the choice probabilities.
The Chained Difference-in-Differences
Authors: Christophe Bellégo, David Benatia, Vincent Dortet-Bernardet
Date: 2023-01-03 00:00:00

This paper studies the identification, estimation, and inference of long-term (binary) treatment effect parameters when balanced panel data is not available, or consists of only a subset of the available data. We develop a new estimator: the chained difference-in-differences, which leverages the overlapping structure of many unbalanced panel data sets. This approach consists in aggregating a collection of short-term treatment effects estimated on multiple incomplete panels. Our estimator accommodates (1) multiple time periods, (2) variation in treatment timing, (3) treatment effect heterogeneity, (4) general missing data patterns, and (5) sample selection on observables. We establish the asymptotic properties of the proposed estimator and discuss identification and efficiency gains in comparison to existing methods. Finally, we illustrate its relevance through (i) numerical simulations, and (ii) an application about the effects of an innovation policy in France.
Time-Varying Coefficient DAR Model and Stability Measures for Stablecoin Prices: An Application to Tether
Authors: Antoine Djobenou, Emre Inan, Joann Jasiak
Date: 2023-01-02 00:00:00

This paper examines the dynamics of Tether, the stablecoin with the largest market capitalization. We show that the distributional and dynamic properties of Tether/USD rates have been evolving from 2017 to 2021. We use local analysis methods to detect and describe the local patterns, such as short-lived trends, time-varying volatility and persistence. To accommodate these patterns, we consider a time varying parameter Double Autoregressive tvDAR(1) model under the assumption of local stationarity of Tether/USD rates. We estimate the tvDAR model non-parametrically and test hypotheses on the functional parameters. In the application to Tether, the model provides a good fit and reliable out-of-sample forecasts at short horizons, while being robust to time-varying persistence and volatility. In addition, the model yields a simple plug-in measure of stability for Tether and other stablecoins for assessing and comparing their stability.
Inference for Large Panel Data with Many Covariates
Authors: Markus Pelger, Jiacheng Zou
Date: 2022-12-31 00:00:00
Tasks: valid

This paper proposes a novel testing procedure for selecting a sparse set of covariates that explains a large dimensional panel. Our selection method provides correct false detection control while having higher power than existing approaches. We develop the inferential theory for large panels with many covariates by combining post-selection inference with a novel multiple testing adjustment. Our data-driven hypotheses are conditional on the sparse covariate selection. We control for family-wise error rates for covariate discovery for large cross-sections. As an easy-to-use and practically relevant procedure, we propose Panel-PoSI, which combines the data-driven adjustment for panel multiple testing with valid post-selection p-values of a generalized LASSO, that allows us to incorporate priors. In an empirical study, we select a small number of asset pricing factors that explain a large cross-section of investment strategies. Our method dominates the benchmarks out-of-sample due to its better size and power.
Higher-order Refinements of Small Bandwidth Asymptotics for Density-Weighted Average Derivative Estimators
Authors: Matias D. Cattaneo, Max H. Farrell, Michael Jansson, Ricardo Masini
Date: 2022-12-31 00:00:00

The density weighted average derivative (DWAD) of a regression function is a canonical parameter of interest in economics. Classical first-order large sample distribution theory for kernel-based DWAD estimators relies on tuning parameter restrictions and model assumptions that imply an asymptotic linear representation of the point estimator. These conditions can be restrictive, and the resulting distributional approximation may not be representative of the actual sampling distribution of the statistic of interest. In particular, the approximation is not robust to bandwidth choice. Small bandwidth asymptotics offers an alternative, more general distributional approximation for kernel-based DWAD estimators that allows for, but does not require, asymptotic linearity. The resulting inference procedures based on small bandwidth asymptotics were found to exhibit superior finite sample performance in simulations, but no formal theory justifying that empirical success is available in the literature. Employing Edgeworth expansions, this paper shows that small bandwidth asymptotic approximations lead to inference procedures with higher-order distributional properties that are demonstrably superior to those of procedures based on asymptotic linear approximations.
Feature Selection for Personalized Policy Analysis
Authors: Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
Date: 2022-12-31 00:00:00
Tasks: feature selection

In this paper, we propose Forest-PLS, a feature selection method for analyzing policy effect heterogeneity in a more flexible and comprehensive manner than is typically available with conventional methods. In particular, our method is able to capture policy effect heterogeneity both within and across subgroups of the population defined by observable characteristics. To achieve this, we employ partial least squares to identify target components of the population and causal forests to estimate personalized policy effects across these components. We show that the method is consistent and leads to asymptotically normally distributed policy effects. To demonstrate the efficacy of our approach, we apply it to the data from the Pennsylvania Reemployment Bonus Experiments, which were conducted in 1988-1989. The analysis reveals that financial incentives can motivate some young non-white individuals to enter the labor market. However, these incentives may also provide a temporary financial cushion for others, dissuading them from actively seeking employment. Our findings highlight the need for targeted, personalized measures for young non-white male participants.
Gender Diversity in Ownership and Firm Innovativeness in Emerging Markets. The Mediating Roles of R&D Investments and External Capital
Authors: Vartuhi Tonoyan, Christopher Boudreaux
Date: 2023-01-03 00:00:00
Tasks: Diversity, Marketing

Despite recent evidence linking gender diversity in the firm with firm innovativeness, we know little about the underlying mechanisms. Building on and extending the Upper Echelon and entrepreneurship literature, we address two lingering questions: why and how does gender diversity in firm ownership affect firm innovativeness? We use survey data collected from 7,848 owner-managers of SMEs across 29 emerging markets to test our hypotheses. Our findings demonstrate that firms with higher gender diversity in ownership are more likely to invest in R&D and rely upon a breadth of external capital, with such differentials explaining sizeable proportions of the higher likelihood of overall firm innovativeness, product and process, as well as organizational and marketing innovations exhibited by their firms. Our findings are robust to corrections for alternative measurement of focal variables, sensitivity to outliers and subsamples, and endogenous self-selection concerns.
Lying Aversion and Vague Communication: An Experimental Study
Authors: Keh-Kuan Sun, Stella Papadokonstantaki
Date: 2023-01-01 00:00:00
Tasks: Experimental Design

An agent may strategically employ a vague message to mislead an audience's belief about the state of the world, but this may cause the agent to feel guilt or negatively impact how the audience perceives the agent. Using a novel experimental design that allows participants to be vague while at the same time isolating the internal cost of lying from the social identity cost of appearing dishonest, we explore the extent to which these two types of lying costs affect communication. We find that participants exploit vagueness to be consistent with the truth, while at the same time leveraging the imprecision to their own benefit. More participants use vague messages in treatments where concern with social identity is relevant. In addition, we find that social identity concerns substantially affect the length and patterns of vague messages used across the treatments.
Large-Scale 3D Printing -- Market Analysis
Authors: Razan Abdelazim Idris Alzain
Date: 2022-12-28 00:00:00

The aim of this research is to get a better understanding of the future of large-scale 3D printing. By developing the market analysis, it will be clear whether large-scale 3D printing is becoming more of a preferred way of printing custom-made parts for production companies. Companies can then choose whether to change their ways, for a more profitable less costly method, or stay on the route they are on. By getting deep into this topic, a new world of technology is then being discovered and familiarized. With a mix of theoretical and practical relevance, a complete coverage could be made on large-scale 3D printing. This paper could then cover all aspects of this topic, and the reader could then make their own judgment if large-scale 3D printing would be the best option.
The relationship between content marketing and the traditional marketing communication tools
Authors: Szabolcs Nagy, Gergo Hajdu
Date: 2022-12-26 00:00:00
Tasks: Marketing

Digitalization is making a significant impact on marketing. New marketing approaches and tools are emerging which are not always clearly categorised. This article seeks to investigate the relationship between one of the novel marketing tools, content marketing, and the five elements of the traditional marketing communication mix. Based on an extensive literature review, this paper analyses the main differences and similarities between them. This article aims to generate a debate on the status of content marketing. According to the authors' opinion, content marketing can be considered as the sixth marketing communication mix element. However, further research is needed to fill in the existing knowledge gap.