pwc-archive/papers-with-abstracts huggingface dataset:
Disruption of the mitochondrial network in a mouse model of Huntington's disease visualized by in tissue multiscale 3D electron microscopy
Authors: Eva Martin Solana, Laura Casado Zueras, Teobaldo E. Torres, Gerardo F. Goya, Maria Rosario Fernandez Fernandez, Jose Jesus Fernandez
Date: 2024-06-23 00:00:00
Tasks: Electron Tomography

Huntington's disease (HD) is an inherited neurodegenerative disorder caused by an expanded CAG repeat in the coding sequence of the huntingtin protein. Initially, it predominantly affects medium-sized spiny neurons (MSSNs) of the corpus striatum. No effective treatment is available, thus urging the identification of potential therapeutic targets. While evidence of mitochondrial structural alterations in HD exists, previous studies mainly employed 2D approaches and were performed outside the strictly native brain context. In this study, we adopted a novel multiscale approach to conduct a comprehensive 3D in situ structural analysis of mitochondrial disturbances in a mouse model of HD. We investigated MSSNs within brain tissue under optimal structural conditions utilizing state-of-the-art 3D imaging technologies, specifically FIB/SEM for the complete imaging of neuronal somas and Electron Tomography for detailed morphological examination and image processing-based quantitative analysis. Our findings suggest a disruption of the mitochondrial network towards fragmentation in HD. The network of interlaced, slim, and long mitochondria observed in healthy conditions transforms into isolated, swollen, and short entities, with internal cristae disorganization, cavities, and abnormally large matrix granules.
Temporal Dynamics beyond the Exceptional Point in the Ikeda Map with Balanced Gain and Loss
Authors: Jyoti Prasad Deka, Amarendra K. Sarma
Date: 2024-05-13 00:00:00

We investigate the temporal dynamics of the Ikeda Map with Balanced Gain and Loss and in the presence of feedback loops with saturation nonlinearity. From the bifurcation analysis, we find that the temporal evolution of optical power undergoes period quadrupling at the exceptional point (EP) of the system and beyond that, chaotic dynamics emerge in the system and this has been further corroborated from the Largest Lyapunov Exponent (LLE) of the model. For a closer inspection, we analyzed the parameter basin of the system, which further leads to our inference that the Ikeda Map with Balanced Gain and Loss exhibits the emergence of chaotic dynamics beyond the exceptional point (EP). Furthermore, we find that the temporal dynamics beyond the EP regime leads to the onset of Extreme Events (EE) in this system via attractor merging crisis.
Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales
Authors: Peter Manshausen, Yair Cohen, Peter Harrington, Jaideep Pathak, Mike Pritchard, Piyush Garg, Morteza Mardani, Karthik Kashinath, Simon Byrne, Noah Brenowitz
Date: 2024-06-19 00:00:00

Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation have been proposed which allow for using new input data without retraining the model. They could also dramatically accelerate the costly data assimilation process used in operational regional weather models. Here, in a central US testbed, we demonstrate the viability of score-based data assimilation in the context of realistically complex km-scale weather. We train an unconditional diffusion model to generate snapshots of a state-of-the-art km-scale analysis product, the High Resolution Rapid Refresh. Then, using score-based data assimilation to incorporate sparse weather station data, the model produces maps of precipitation and surface winds. The generated fields display physically plausible structures, such as gust fronts, and sensitivity tests confirm learnt physics through multivariate relationships. Preliminary skill analysis shows the approach already outperforms a naive baseline of the High-Resolution Rapid Refresh system itself. By incorporating observations from 40 weather stations, 10% lower RMSEs on left-out stations are attained. Despite some lingering imperfections such as insufficiently disperse ensemble DA estimates, we find the results overall an encouraging proof of concept, and the first at km-scale. It is a ripe time to explore extensions that combine increasingly ambitious regional state generators with an increasing set of in situ, ground-based, and satellite remote sensing data streams.
Efficient approximations of transcriptional bursting effects on the dynamics of a gene regulatory network
Authors: Jochen Kursawe, Antoine Moneyron, Tobias Galla
Date: 2024-06-27 00:00:00

Mathematical models of gene regulatory networks are widely used to study cell fate changes and transcriptional regulation. When designing such models, it is important to accurately account for sources of stochasticity. However, doing so can be computationally expensive and analytically untractable, posing limits on the extent of our explorations and on parameter inference. Here, we explore this challenge using the example of a simple auto-negative feedback motif, in which we incorporate stochastic variation due to transcriptional bursting and noise from finite copy numbers. We find that transcriptional bursting may change the qualitative dynamics of the system by inducing oscillations when they would not otherwise be present, or by magnifying existing oscillations. We describe multiple levels of approximation for the model in the form of differential equations, piecewise deterministic processes, and stochastic differential equations. Importantly, we derive how the classical chemical Langevin equation can be extended to include a noise term representing transcriptional bursting. This approximation drastically decreases computation times and allows us to analytically calculate properties of the dynamics, such as their power spectrum. We explore when these approximations break down and provide recommendations for their use. Our analysis illustrates the importance of accounting for transcriptional bursting when simulating gene regulatory network dynamics and provides recommendations to do so with computationally efficient methods.
Data-Driven Computing Methods for Nonlinear Physics Systems with Geometric Constraints
Authors: Yunjin Tong
Date: 2024-06-20 00:00:00
Tasks: scientific discovery

In a landscape where scientific discovery is increasingly driven by data, the integration of machine learning (ML) with traditional scientific methodologies has emerged as a transformative approach. This paper introduces a novel, data-driven framework that synergizes physics-based priors with advanced ML techniques to address the computational and practical limitations inherent in first-principle-based methods and brute-force machine learning methods. Our framework showcases four algorithms, each embedding a specific physics-based prior tailored to a particular class of nonlinear systems, including separable and nonseparable Hamiltonian systems, hyperbolic partial differential equations, and incompressible fluid dynamics. The intrinsic incorporation of physical laws preserves the system's intrinsic symmetries and conservation laws, ensuring solutions are physically plausible and computationally efficient. The integration of these priors also enhances the expressive power of neural networks, enabling them to capture complex patterns typical in physical phenomena that conventional methods often miss. As a result, our models outperform existing data-driven techniques in terms of prediction accuracy, robustness, and predictive capability, particularly in recognizing features absent from the training set, despite relying on small datasets, short training periods, and small sample sizes.
The myth of declining competitive balance in the UEFA Champions League group stage
Authors: László Csató, Dóra Gréta Petróczy
Date: 2024-06-27 00:00:00

The degree of competitive balance is a crucial issue in the analysis of sports competitions. According to previous studies, competitive balance has significantly declined in the UEFA Champions League group stage over the recent decades. Our paper introduces six alternative indices in order to explore the robustness of these results. The ex ante measures are based on Elo ratings, while our ex post measures compare the group ranking to reasonable benchmarks. We find no evidence of any trend in the competitive balance of the UEFA Champions League group stage between the 2003/04 and 2023/24 seasons.
Derivation of a Schrödinger Equation for Single Neurons Through Stochastic Neural Dynamics
Authors: Partha Ghose
Date: 2024-06-24 00:00:00

Despite the prevalent view that quantum mechanics is irrelevant to macroscopic biological systems because of inherent noise and decoherence, this paper demonstrates that the electrical noise (Brownian motion) in neuron membranes gives rise to an `emergent' Schr\"{o}dinger equation involving a new neuronal constant $\hat{\hbar}$, fundamentally challenging the standard view of neuronal behaviour. This result could provide new insights into the underlying mechanisms of brain function, thus challenging existing paradigms in both quantum physics and neuroscience. A possible empirical test of this emergent quantum behaviour would be to look for quantum fluctuations in subthreshold neural oscillations.
Factor multivariate stochastic volatility models of high dimension
Authors: Benjamin Poignard, Manabu Asai
Date: 2024-06-27 00:00:00

Building upon the pertinence of the factor decomposition to break the curse of dimensionality inherent to multivariate volatility processes, we develop a factor model-based multivariate stochastic volatility (fMSV) framework that relies on two viewpoints: sparse approximate factor model and sparse factor loading matrix. We propose a two-stage estimation procedure for the fMSV model: the first stage obtains the estimators of the factor model, and the second stage estimates the MSV part using the estimated common factor variables. We derive the asymptotic properties of the estimators. Simulated experiments are performed to assess the forecasting performances of the covariance matrices. The empirical analysis based on vectors of asset returns illustrates that the forecasting performances of the fMSV models outperforms competing conditional covariance models.
A Note on Identification of Match Fixed Effects as Interpretable Unobserved Match Affinity
Authors: Suguru Otani, Tohya Sugano
Date: 2024-06-27 00:00:00

We highlight that match fixed effects, represented by the coefficients of interaction terms involving dummy variables for two elements, lack identification without specific restrictions on parameters. Consequently, the coefficients typically reported as relative match fixed effects by statistical software are not interpretable. To address this, we establish normalization conditions that enable identification of match fixed effect parameters as interpretable indicators of unobserved match affinity, facilitating comparisons among observed matches. Using data from middle school students in the 2007 Trends in International Mathematics and Science Study (TIMSS), we highlight the distribution of comparable match fixed effects within a specific school.
Forecast Relative Error Decomposition
Authors: Christian Gourieroux, Quinlan Lee
Date: 2024-06-25 00:00:00

We introduce a class of relative error decomposition measures that are well-suited for the analysis of shocks in nonlinear dynamic models. They include the Forecast Relative Error Decomposition (FRED), Forecast Error Kullback Decomposition (FEKD) and Forecast Error Laplace Decomposition (FELD). These measures are favourable over the traditional Forecast Error Variance Decomposition (FEVD) because they account for nonlinear dependence in both a serial and cross-sectional sense. This is illustrated by applications to dynamic models for qualitative data, count data, stochastic volatility and cyberrisk.
Efficient two-sample instrumental variable estimators with change points and near-weak identification
Authors: Bertille Antoine, Otilia Boldea, Niccolo Zaccaria
Date: 2024-06-24 00:00:00

We consider estimation and inference in a linear model with endogenous regressors where the parameters of interest change across two samples. If the first-stage is common, we show how to use this information to obtain more efficient two-sample GMM estimators than the standard split-sample GMM, even in the presence of near-weak instruments. We also propose two tests to detect change points in the parameters of interest, depending on whether the first-stage is common or not. We derive the limiting distribution of these tests and show that they have non-trivial power even under weaker and possibly time-varying identification patterns. The finite sample properties of our proposed estimators and testing procedures are illustrated in a series of Monte-Carlo experiments, and in an application to the open-economy New Keynesian Phillips curve. Our empirical analysis using US data provides strong support for a New Keynesian Phillips curve with incomplete pass-through and reveals important time variation in the relationship between inflation and exchange rate pass-through.
Credit Ratings: Heterogeneous Effect on Capital Structure
Authors: Helmut Wasserbacher, Martin Spindler
Date: 2024-06-27 00:00:00
Tasks: Causal Inference, Variable Selection

Why do companies choose particular capital structures? A compelling answer to this question remains elusive despite extensive research. In this article, we use double machine learning to examine the heterogeneous causal effect of credit ratings on leverage. Taking advantage of the flexibility of random forests within the double machine learning framework, we model the relationship between variables associated with leverage and credit ratings without imposing strong assumptions about their functional form. This approach also allows for data-driven variable selection from a large set of individual company characteristics, supporting valid causal inference. We report three findings: First, credit ratings causally affect the leverage ratio. Having a rating, as opposed to having none, increases leverage by approximately 7 to 9 percentage points, or 30\% to 40\% relative to the sample mean leverage. However, this result comes with an important caveat, captured in our second finding: the effect is highly heterogeneous and varies depending on the specific rating. For AAA and AA ratings, the effect is negative, reducing leverage by about 5 percentage points. For A and BBB ratings, the effect is approximately zero. From BB ratings onwards, the effect becomes positive, exceeding 10 percentage points. Third, contrary to what the second finding might imply at first glance, the change from no effect to a positive effect does not occur abruptly at the boundary between investment and speculative grade ratings. Rather, it is gradual, taking place across the granular rating notches ("+/-") within the BBB and BB categories.
Costly Signalling in DAOs
Authors: Darcy W. E. Allen, Jason Potts, Julian Waters-Lynch, Max Parasol
Date: 2024-06-26 00:00:00

Decentralised Autonomous Organisations (DAOs) are a new type of digital organisation that uses blockchain infrastructure (e.g. smart contracts, tokens) to coordinate a group of people around a shared mission. Like all organisations, DAOs must attract sources of funding and other resources, and discover and retain a talented community and workforce. To do this, they must signal their true quality. Yet the characteristics of the environment that DAOs operate in (pseudonymous actors, global scale, permissionless entry and exit) makes this difficult. We apply costly signalling theory to explore the information asymmetry problem in DAOs and some of the strategies (behaviours and investments) and institutional solutions (including better signalling mechanisms) that have evolved to solve this problem.
Revisiting the capitalist road to communism: unconditional basic income and the post-labor world
Authors: Robert van der Veen, Loek Groot
Date: 2024-06-26 00:00:00

The thesis of a capitalist road to communism (van der Veen and Van Parijs, 1986) asserts that Marx realm of freedom can be reached from within welfare capitalism, skipping socialism, by using a tax-financed unconditional basic income until it is close to disposable income per head, so that the very distinction between paid work and free time is cancelled as a result. We revisit and update this thesis for two reasons: the recent prospect of a post-labor society following the automation revolution in technology, and that welfare capitalism has become more inegalitarian and less hospitable to basic income. We use a simple economic model which incorporates an upward adjustment of basic income to labor-saving technical change and distinguishes between capital that complements labor and capital that is fully substitutable with labor. A baseline simulation of the model shows the economic feasibility of a capitalist transition to communism. Two versions of a scenario incorporating interplay between technical change and market socialist institutional reforms are set out which make the transition politically viable to some extent, depending on the social distribution of power over technology. The most promising version is one in which the productivity of labor and automation capital grow at similar rates. We show in which respects it approximates the ideal of communism. One finding is that communism does not require reaching the final stage of a post-labor society. We conclude with a reflection on the relevance of our present update for the more immediate future of unconditional basic income.
Is the logistical engagement of stakeholders in short food chains a crucible of alternativity?
Authors: Camille Horvath, Céline Raimbert, Gwenaëlle Raton
Date: 2024-06-26 00:00:00
Tasks: Marketing

In response to the negative environmental, social, and economic externalities of the dominant food model, a consensus is emerging on the need to develop alternative food systems capable of facilitating a transition to a more sustainable model. Among these alternatives, short food supply chains (SFSCs) are identified as potential crucibles of alternativity. Defined as distribution channels with a maximum of one intermediary between producer and consumer, SFSCs can contribute to the reterritorialization of food flows and the creation of relational and geographical proximities among food chain actors. This study examines how logistical engagement translates into the choice of SFSC points of sale by consumers and farmers. It evaluates this engagement through the travel times farmers and consumers are willing to endure to sell or buy at an SFSC point of sale, and the prices they are willing to accept or pay for SFSC products. A discrete choice experiment conducted in 2022 with 154 farm managers and 1022 consumers analyzed preferences regarding types of points of sale, sales prices, travel times, and relational proximity. The results show that consumers engaged in SFSCs favour alternative points of sale such as farms and associations, even for longer travel times. Farmers also value these points of sale, with logistical engagement more pronounced among those already marketing through SFSCs. Supermarkets appear less attractive to alternative profiles but serve as entry points for new participants in SFSCs. Relational proximity between farmers and consumers is important, with a preference for direct exchanges during sales. This study highlights the importance of actor engagement in SFSCs and offers perspectives for developing alternative logistics by valuing relational proximity and using conventional points of sale as entry points for new actors in the SFSCs.
Convex Choice
Authors: Navin Kartik, Andreas Kleiner
Date: 2024-06-27 00:00:00

For multidimensional Euclidean type spaces, we study convex choice: from any choice set, the set of types that make the same choice is convex. We establish that, in a suitable sense, this property characterizes the sufficiency of local incentive constraints. Convex choice is also of interest more broadly. We tie convex choice to a notion of directional single-crossing differences (DSCD). For an expected-utility agent choosing among lotteries, DSCD implies that preferences are either one-dimensional or must take the affine form that has been tractable in multidimensional mechanism design.
Pareto-Nash Reversion Strategies: Three Period Dynamic Co-operative Signalling with Sticky Efficiency Wages
Authors: Alfred A. B. Mayaki
Date: 2024-06-26 00:00:00

This paper uses Nash equilibrium reversion as an optimal tool for clearing dynamic prices and wages. Various exogenous competitive rigidities determine the balanced growth path of the efficiency wage and the outcome of repeated household/firm wage bargaining decisions. A location model is used to explore the extent to which a downstream spatial cooperation agreement might affect the price equilibrium. There is also an endogenous hiring function and a knowledge base that is increasing in output as is the real wage. As the article demonstrates after accounting for real rigidities in the baseline model the effect of wage growth on household utility through staggered bargaining can be best captured by adopting a policy of point scoring on the mobility of skilled labor against the model's key rigidities. Mobility point scores which serve to encourage mobility from skilled labor within the model not only increase the knowledge base but also place upward pressure on nominal wage growth.
Complexity Aversion
Authors: Yuan Gu, Chao Hung Chan
Date: 2024-06-26 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

This paper proposes a model of decision-making under uncertainty in which an agent is constrained in her cognitive ability to consider complex acts. We identify the complexity of an act according to the corresponding partition of state space. The agent ranks acts according to the expected utility net of complexity cost. A key feature of this model is that the agent is able to update her complexity cost function after the arrival of new information. The main result characterizes axiomatically an updating rule for complexity cost function, the Minimal Complexity Aversion representation. According to this rule, the agent measures the complexity cost of an act conditional on the new information by using the cost of another act that gives exactly the same partition of the event but with the lowest ex-ante cost.
Information Revelation and Pandering in Elections
Authors: Navin Kartik, Francesco Squintani, Katrin Tinn
Date: 2024-06-24 00:00:00

Do elections efficiently aggregate politicians' policy-relevant private information? This paper argues that politicians' office motivation is an obstacle. In a two-candidate Hotelling-Downs model in which each candidate has socially-valuable policy information, we establish that equilibrium welfare is at best what can be obtained by disregarding one politician's information. We also find that for canonical information structures, politicians have an incentive to ``anti-pander'', i.e., to overreact to their information. Some degree of pandering -- underreacting to information -- would be socially beneficial.