pwc-archive/papers-with-abstracts huggingface dataset:
A random planting model
Authors: Julian Talbot, Pascal Viot, David Colliaux
Date: 2024-09-24 00:00:00
Tasks: model

The adoption of agroecological practices will be crucial to address the challenges of climate change and biodiversity loss. Such practices favor the cultivation of plants in complex mixtures with layouts differing from the monoculture approach of conventional agriculture. Inspired by random sequential adsorption processes, we propose a one-dimensional model in which the plants are represented as line segments that start as points and grow at a constant rate until they reach length $\sigma$ after a time interval $\tau$. The planting positions and times are randomly chosen with the constraint that plant overlap is forbidden. We apply an exact, event-driven simulation to investigate the resulting spatiotemporal patterns and yields in both mono- and duocultures. After a transient period, with oscillations in the density and coverage, the field reaches a steady state in which the mean age of plants is one half of the time to maturity. The structure of the active plants is characterized by correlation functions between the fluctuation of the age of a plant and its $k$th neighbour. Nearest neighbours are negatively correlated, while next nearest neighbours tend to have similar ages. The steady state yield increases with the planting rate and approaches a maximum value of 4/3 plants per unit length per unit time. For two species with the same size at maturity but different growth rates, the more slowly growing species is enriched in the harvest compared to the seed mix composition. If two species have the same time to maturity but different sizes, the smaller one is enriched in the harvest and, at a sufficiently high planting rate, the larger species may be completely absent. For two species with the same ratio of $\sigma/\tau$ the selectivity is insensitive to the planting rate. The model may be extended to higher dimensions, more species and other planting strategies.
Signal inference in financial stock return correlations through phase-ordering kinetics in the quenched regime
Authors: Ixandra Achitouv, Vincent Lahoche, Dine Ousmane Samary
Date: 2024-09-29 00:00:00

Financial stock return correlations have been analyzed through the lens of random matrix theory to differentiate the underlying signal from spurious correlations. The continuous spectrum of the eigenvalue distribution derived from the stock return correlation matrix typically aligns with a rescaled Marchenko-Pastur distribution, indicating no detectable signal. In this study, we introduce a stochastic field theory model to establish a detection threshold for signals present in the limit where the eigenvalues are within the continuous spectrum, which itself closely resembles that of a random matrix where standard methods such as principal component analysis fail to infer a signal. We then apply our method to Standard & Poor's 500 financial stocks' return correlations, detecting the presence of a signal in the largest eigenvalues within the continuous spectrum.
How does liquidity shape the yield curve?
Authors: Victor Le Coz, Iacopo Mastromatteo, Michael Benzaquen
Date: 2024-09-18 00:00:00

The phenomenology of the forward rate curve (FRC) can be accurately understood by the fluctuations of a stiff elastic string (Le Coz and Bouchaud, 2024). By relating the exogenous shocks driving such fluctuations to the surprises in the order flows, we elevate the model from purely describing price variations to a microstructural model that incorporates the joint dynamics of prices and order flows, accounting for both impact and cross-impact effects. Remarkably, this framework allows for at least the same explanatory power as existing cross-impact models, while using significantly fewer parameters. In addition, our model generates liquidity-dependent correlations between the forward rate of one tenor and the order flow of another, consistent with recent empirical findings. We show that the model also account for the non-martingale behavior of prices at short timescales.
Quantifying wave propagation in a chain of FitzHugh-Nagumo neurons
Authors: L. Messee Goulefack, C. Masoller, R. Yamapi, C. Anteneodo
Date: 2024-09-24 00:00:00
Tasks: Time Series

Understanding how external stimuli propagate in neural systems is an important challenge in the fields of neuroscience and nonlinear dynamics. Despite extensive studies over several decades, this problem remains poorly understood. In this work, we examine a simple ``toy model'' of an excitable medium, a linear chain of diffusely coupled FitzHugh-Nagumo neurons, and analyze the transmission of a sinusoidal signal injected into one of the neurons at the ends of the chain. We measure to what extent the propagation of the wave reaching the opposite end is affected by the frequency and amplitude of the signal, the number of neurons in the chain and the strength of their mutual diffusive coupling. To quantify these effects, we measure the cross-correlation between the time-series of the membrane potentials of the end neurons. This measure allows us to detect the values of the parameters that delimit different propagation regimes.
Navigating Uncertainty: Risk-Averse vs. Risk-Prone Strategies in Populations Facing Demographic and Environmental Stochasticity
Authors: Rubén Calvo Ibáñez, Miguel Ángel Muñoz, Tobias Galla
Date: 2024-09-24 00:00:00

Strategies aimed at reducing the negative effects of long-term uncertainty and risk are common in biology, game theory, and finance, even if they entail a cost in terms of mean benefit. Here, we focus on the single mutant's invasion of a finite resident population subject to fluctuating environmental conditions. Thus, the game-theoretical model we analyze integrates environmental and demographic randomness, i.e., the two leading sources of stochasticity and uncertainty. We use simulations and mathematical analysis to study if and when strategies that either increase or reduce payoff variance across environmental states can enhance the mutant fixation probability. Variance aversion implies that the mutant pays insurance in terms of mean payoff to avoid worst-case scenarios. Variance-prone or gambling strategies, on the other hand, entail specialization, allowing the mutant to capitalize on transient favorable conditions, leading to a series of ``boom-and-bust'' cycles. Our analyses elucidate how the rate of change of environmental conditions and the shape of the probability distribution of possible states affect the possible most convenient strategies. We discuss how our results relate to the bet-hedging theory, which aims to reduce fitness variance rather than payoff variance. We also describe the analogies and differences between these similar yet distinct approaches.
Non-Markovian Rock-Paper-Scissors games
Authors: Ohad Vilk, Mauro Mobilia, Michael Assaf
Date: 2024-09-22 00:00:00

There is mounting evidence that species interactions often involve long-term memory, with highly-varying waiting times between successive events and long-range temporal correlations. Accounting for memory undermines the common Markovian assumption, and dramatically impacts key ingredients of population dynamics including birth, foraging, predation, and competition processes. Here, we study a critical aspect of population dynamics, namely non-Markovian multi-species competition. This is done in the realm of the zero-sum rock-paper-scissors (zRPS) model that is broadly used in the life sciences to metaphorically describe cyclic competition between three interacting species. We develop a general non-Markovian formalism for multi-species dynamics, allowing us to determine the regions of the parameter space where each species dominates. In particular, when the dynamics are Markovian, the waiting times are exponentially distributed and the fate of the zRPS model in large well-mixed populations is encoded in a remarkably simple condition, often referred to as the ``law of the weakest'' (LOW), stating that the species with the lowest growth rate is the most likely to prevail. We show that the survival behavior and LOW of the zRPS model are critically affected by non-exponential waiting times, and especially, by their coefficient of variation. Our findings provide key insight into the influence of long waiting times on non-Markovian evolutionary processes.
X-ray multiple-beam (n-baem) dynamical diffraction theories, numerical methods to solve them and experimental verification by using the synchrotron X-rays
Authors: Kouhei Okitsu
Date: 2024-09-25 00:00:00

Behavior of X-rays diffracted in a perfect or quasi-perfect crystal can be described by the dynamical theory of X-ray diffraction. Study on the two-beam cases in which only transmitted and one reflected X-ray beams are strong has a history of one hundred years. However, the population of researchers who study on the multiple-beam cases (n-beam cases) in which more than two beams are simultaneously strong is small. The present author has derived the Takagi-Taupin (T-T) dynamical theory that can be applied to the n-beam cases, coded the computer programs to solve it and experimentally verified them by using the synchrotron X-rays. The equivalence between the Ewald-Laue (E-L) and the T-T dynamical theories described by the Fourier transform also for the n-beam cases is explicitly verified in the present paper. Further, the methods of the computer simulations and the experiments are also described. Furthermore, a hypothesis concerning the too large values of R-factor in protein crystallography is also described. This might be extremely important in protein crystallography in the future.
ASPINN: An asymptotic strategy for solving singularly perturbed differential equations
Authors: Sen Wang, Peizhi Zhao, Tao Song
Date: 2024-09-20 00:00:00
Tasks: Kolmogorov-Arnold Networks

Solving Singularly Perturbed Differential Equations (SPDEs) presents challenges due to the rapid change of their solutions at the boundary layer. In this manuscript, We propose Asymptotic Physics-Informed Neural Networks (ASPINN), a generalization of Physics-Informed Neural Networks (PINN) and General-Kindred Physics-Informed Neural Networks (GKPINN) approaches. This is a decomposition method based on the idea of asymptotic analysis. Compared to PINN, the ASPINN method has a strong fitting ability for solving SPDEs due to the placement of exponential layers at the boundary layer. Unlike GKPINN, ASPINN lessens the number of fully connected layers, thereby reducing the training cost more effectively. Moreover, ASPINN theoretically approximates the solution at the boundary layer more accurately, which accuracy is also improved compared to GKPINN. We demonstrate the effect of ASPINN by solving diverse classes of SPDEs, which clearly shows that the ASPINN method is promising in boundary layer problems. Furthermore, we introduce Chebyshev Kolmogorov-Arnold Networks (Chebyshev-KAN) instead of MLP, achieving better performance in various experiments.
Dynamical stability of evolutionarily stable strategy in asymmetric games
Authors: Vikash Kumar Dubey, Suman Chakraborty, Arunava Patra, Sagar Chakraborty
Date: 2024-09-28 00:00:00

Evolutionarily stable strategy (ESS) is the defining concept of evolutionary game theory. It has a fairly unanimously accepted definition for the case of symmetric games which are played in a homogeneous population where all individuals are in same role. However, in asymmetric games, which are played in a population with multiple subpopulations (each of which has individuals in one particular role), situation is not as clear. Various generalizations of ESS defined for such cases differ in how they correspond to fixed points of replicator equation which models evolutionary dynamics of frequencies of strategies in the population. Moreover, some of the definitions may even be equivalent, and hence, redundant in the scheme of things. Along with reporting some new results, this paper is partly indented as a contextual mini-review of some of the most important definitions of ESS in asymmetric games. We present the definitions coherently and scrutinize them closely while establishing equivalences -- some of them hitherto unreported -- between them wherever possible. Since it is desirable that a definition of ESS should correspond to asymptotically stable fixed points of replicator dynamics, we bring forward the connections between various definitions and their dynamical stabilities. Furthermore, we find the use of principle of relative entropy to gain information-theoretic insights into the concept of ESS in asymmetric games, thereby establishing a three-fold connection between game theory, dynamical system theory, and information theory in this context. We discuss our conclusions also in the backdrop of asymmetric hypermatrix games where more than two individuals interact simultaneously in the course of getting payoffs.
Heterogeneous populations of quadratic integrate-and-fire neurons: on the generality of Lorentzian distributions
Authors: Bastian Pietras, Ernest Montbrió
Date: 2024-09-26 00:00:00

Over the last decade, next-generation neural mass models have become increasingly prominent in mathematical neuroscience. These models link microscopic dynamics with low-dimensional systems of so-called firing rate equations that exactly capture the collective dynamics of large populations of heterogeneous quadratic integrate-and-fire (QIF) neurons. A particularly tractable type of heterogeneity is the distribution of the QIF neurons' excitability parameters, or inputs, according to a Lorentzian. While other distributions -- such as those approximating Gaussian or uniform distributions -- admit to exact mean-field reductions, they result in more complex firing rate equations that are challenging to analyze, and it remains unclear whether they produce comparable collective dynamics. Here, we first demonstrate why Lorentzian heterogeneity is analytically favorable and, second, identify when it leads to qualitatively different collective dynamics compared to other types of heterogeneity. A stationary mean-field approach enables us to derive explicit expressions for the distributions of the neurons' firing rates and voltages in macroscopic stationary states with arbitrary heterogeneities. We also explicate the exclusive relationship between Lorentzian distributed inputs and Lorentzian distributed voltages, whose width happens to coincide with the population firing rate. A dynamic mean-field approach for unimodal heterogeneities further allows us to comprehensively analyze and compare collective dynamics. We find that different types of heterogeneity typically yield qualitatively similar dynamics. However, when gap junction coupling is present, Lorentzian heterogeneity induces nonuniversal behavior, obscuring a diversity-induced transition to synchrony.
Spatiotemporal Behaviour of SIR Models with Cross-Diffusion and Vital Dynamics
Authors: Maryam Ahmadpoortorkamani, Alexei Cheviakov
Date: 2024-09-28 00:00:00

Contemporary epidemiological models often involve spatial variation, providing an avenue to investigate the averaged dynamics of individual movements. In this work, we extend a recent model by Vaziry, Kolokolnikov, and Kevrekidis [Royal Society Open Science 9 (10), 2022] that included, in both infected and susceptible population dynamics equations, a cross-diffusion term with the second spatial derivative of the infected population density. Diffusion terms of this type occur, for example, in the Keller-Siegel chemotaxis model. The presented model corresponds to local orderly commute of susceptible and infected individuals, and is shown to arise in two dimensions as a limit of a discrete process. The present contribution identifies and studies specific features of the new model's dynamics, including various types of infection waves and buffer zones protected from the infection. The model with vital dynamics additionally exhibits complex spatiotemporal behaviour that involves the generation of quasiperiodic infection waves and emergence of transient strongly heterogeneous patterns.
WeatherFormer: Empowering Global Numerical Weather Forecasting with Space-Time Transformer
Authors: Junchao Gong, Tao Han, Kang Chen, Lei Bai
Date: 2024-09-21 00:00:00
Tasks: Data Augmentation, Weather Forecasting

Numerical Weather Prediction (NWP) system is an infrastructure that exerts considerable impacts on modern society.Traditional NWP system, however, resolves it by solving complex partial differential equations with a huge computing cluster, resulting in tons of carbon emission. Exploring efficient and eco-friendly solutions for NWP attracts interest from Artificial Intelligence (AI) and earth science communities. To narrow the performance gap between the AI-based methods and physic predictor, this work proposes a new transformer-based NWP framework, termed as WeatherFormer, to model the complex spatio-temporal atmosphere dynamics and empowering the capability of data-driven NWP. WeatherFormer innovatively introduces the space-time factorized transformer blocks to decrease the parameters and memory consumption, in which Position-aware Adaptive Fourier Neural Operator (PAFNO) is proposed for location sensible token mixing. Besides, two data augmentation strategies are utilized to boost the performance and decrease training consumption. Extensive experiments on WeatherBench dataset show WeatherFormer achieves superior performance over existing deep learning methods and further approaches the most advanced physical model.
Developing a Thailand solar irradiance map using Himawari-8 satellite imageries and deep learning models
Authors: Suwichaya Suwanwimolkul, Natanon Tongamrak, Nuttamon Thungka, Naebboon Hoonchareon, Jitkomut Songsiri
Date: 2024-09-21 00:00:00

This paper presents an online platform showing Thailand solar irradiance map every 30 minutes, available at https://www.cusolarforecast.com. The methodology for estimating global horizontal irradiance (GHI) across Thailand relies on cloud index extracted from Himawari-8 satellite imagery, Ineichen clear-sky model with locally-tuned Linke turbidity, and machine learning models. The methods take clear-sky irradiance, cloud index, re-analyzed GHI and temperature data from the MERRA-2 database, and date-time as inputs for GHI estimation models, including LightGBM, LSTM, Informer, and Transformer. These are benchmarked with the estimate from a commercial service X by evaluation of 15-minute ground GHI data from 53 ground stations over 1.5 years during 2022-2023. The results show that the four models exhibit comparable overall MAE performance to the service X. The best model is LightGBM with an overall MAE of 78.58 W/sqm and RMSE of 118.97 W/sqm, while the service X achieves the lowest MAE, RMSE, and MBE in cloudy condition. Obtaining re-analyzed MERRA-2 data for the whole Thailand region is not economically feasible for deployment. When removing these features, the Informer model has a winning performance in MAE of 78.67 W/sqm. The obtained performance aligns with existing literature by taking the climate zone and time granularity of data into consideration. As the map shows an estimate of GHI over 93,000 grids with a frequent update, the paper also describes a computational framework for displaying the entire map. It tests the runtime performance of deep learning models in the GHI estimation process.
Prithvi WxC: Foundation Model for Weather and Climate
Authors: Johannes Schmude, Sujit Roy, Will Trojak, Johannes Jakubik, Daniel Salles Civitarese, Shraddha Singh, Julian Kuehnert, Kumar Ankur, Aman Gupta, Christopher E Phillips, Romeo Kienzler, Daniela Szwarcman, Vishal Gaur, Rajat Shinde, Rohit Lal, Arlindo Da Silva, Jorge Luis Guevara Diaz, Anne Jones, Simon Pfreundschuh, Amy Lin, Aditi Sheshadri, Udaysankar Nair, Valentine Anantharaj, Hendrik Hamann, Campbell Watson, Manil Maskey, Tsengdar J Lee, Juan Bernabe Moreno, Rahul Ramachandran
Date: 2024-09-20 00:00:00
Tasks: model

Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting. While the parallel developments in the AI literature focus on foundation models -- models that can be effectively tuned to address multiple, different use cases -- the developments on the weather and climate side largely focus on single-use cases with particular emphasis on mid-range forecasting. We close this gap by introducing Prithvi WxC, a 2.3 billion parameter foundation model developed using 160 variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Prithvi WxC employs an encoder-decoder-based architecture, incorporating concepts from various recent transformer models to effectively capture both regional and global dependencies in the input data. The model has been designed to accommodate large token counts to model weather phenomena in different topologies at fine resolutions. Furthermore, it is trained with a mixed objective that combines the paradigms of masked reconstruction with forecasting. We test the model on a set of challenging downstream tasks namely: Autoregressive rollout forecasting, Downscaling, Gravity wave flux parameterization, and Extreme events estimation. The pretrained model with 2.3 billion parameters, along with the associated fine-tuning workflows, has been publicly released as an open-source contribution via Hugging Face.
Surface solar radiation: AI satellite retrieval can outperform Heliosat and generalizes well to other climate zones
Authors: K. R. Schuurman, A. Meyer
Date: 2024-09-16 00:00:00
Tasks: Retrieval

Accurate estimates of surface solar irradiance (SSI) are essential for solar resource assessments and solar energy forecasts in grid integration and building control applications. SSI estimates for spatially extended regions can be retrieved from geostationary satellites such as Meteosat. Traditional SSI satellite retrievals like Heliosat rely on physical radiative transfer modelling. We introduce the first machine-learning-based satellite retrieval for instantaneous SSI and demonstrate its capability to provide accurate and generalizable SSI estimates across Europe. Our deep learning retrieval provides near real-time SSI estimates based on data-driven emulation of Heliosat and fine-tuning on pyranometer networks. By including SSI from ground stations, our SSI retrieval model can outperform Heliosat accuracy and generalize well to regions with other climates and surface albedos in cloudy conditions (clear-sky index < 0.8). We also show that the SSI retrieved from Heliosat exhibits large biases in mountain regions, and that training and fine-tuning our retrieval models on SSI data from ground stations strongly reduces these biases, outperforming Heliosat. Furthermore, we quantify the relative importance of the Meteosat channels and other predictor variables like solar zenith angle for the accuracy of our deep learning SSI retrieval model in different cloud conditions. We find that in cloudy conditions multiple near-infrared and infrared channels enhance the performance. Our results can facilitate the development of more accurate satellite retrieval models of surface solar irradiance.