pwc-archive/papers-with-abstracts huggingface dataset:
Mathematical Modeling of Soil-Transmitted Helminth Infection: Human-Animal Dynamics with Environmental Reservoirs
Authors: Rafiatu Imoro, Maica Krizna Gavina, Vachel Gay Paller, Jomar Rabajante, Mark Jayson Cortez, Editha Jose
Date: 2025-04-04 00:00:00
Tasks: Sensitivity

Soil-transmitted helminth (STH) infections, one of the most prevalent neglected tropical diseases, pose a significant threat to public health in tropical and subtropical areas. These parasites infect humans and animals through direct contact with contaminated soil or accidental ingestion. This study examines the dynamics of STH transmission using a deterministic compartmental model and nonlinear ordinary differential equations. Our model incorporates the roles humans, animals, and the environment play as reservoirs for spreading STH. We derived the basic reproduction number and demonstrate that the disease-free and endemic equilibrium points are asymptotically stable under specific thresholds. We also performed a sensitivity analysis to determine how each parameter affects the model's output. The sensitivity analysis identifies key parameters influencing infection rates, such as ingestion rate, disease progression rate, and shedding rate, all of which increase infection. Conversely, higher clearance and recovery rates decrease infection. The study also highlights the potential for cross-species transmission of STH infections between humans and animals, underscoring the One Health concept, which acknowledges the interdependence of human, animal, and environmental health.
Stochastic Optimization with Optimal Importance Sampling
Authors: Liviu Aolaritei, Bart P. G. Van Parys, Henry Lam, Michael I. Jordan
Date: 2025-04-04 00:00:00
Tasks: Stochastic Optimization

Importance Sampling (IS) is a widely used variance reduction technique for enhancing the efficiency of Monte Carlo methods, particularly in rare-event simulation and related applications. Despite its power, the performance of IS is often highly sensitive to the choice of the proposal distribution and frequently requires stochastic calibration techniques. While the design and analysis of IS have been extensively studied in estimation settings, applying IS within stochastic optimization introduces a unique challenge: the decision and the IS distribution are mutually dependent, creating a circular optimization structure. This interdependence complicates both the analysis of convergence for decision iterates and the efficiency of the IS scheme. In this paper, we propose an iterative gradient-based algorithm that jointly updates the decision variable and the IS distribution without requiring time-scale separation between the two. Our method achieves the lowest possible asymptotic variance and guarantees global convergence under convexity of the objective and mild assumptions on the IS distribution family. Furthermore, we show that these properties are preserved under linear constraints by incorporating a recent variant of Nesterov's dual averaging method.
Online Traffic Density Estimation using Physics-Informed Neural Networks
Authors: Dennis Wilkman, Kateryna Morozovska, Karl Henrik Johansson, Matthieu Barreau
Date: 2025-04-04 00:00:00
Tasks: Density Estimation

Recent works on the application of Physics-Informed Neural Networks to traffic density estimation have shown to be promising for future developments due to their robustness to model errors and noisy data. In this paper, we introduce a methodology for online approximation of the traffic density using measurements from probe vehicles in two settings: one using the Greenshield model and the other considering a high-fidelity traffic simulation. The proposed method continuously estimates the real-time traffic density in space and performs model identification with each new set of measurements. The density estimate is updated in almost real-time using gradient descent and adaptive weights. In the case of full model knowledge, the resulting algorithm has similar performance to the classical open-loop one. However, in the case of model mismatch, the iterative solution behaves as a closed-loop observer and outperforms the baseline method. Similarly, in the high-fidelity setting, the proposed algorithm correctly reproduces the traffic characteristics.
Conditioning Diffusions Using Malliavin Calculus
Authors: Jakiw Pidstrigach, Elizabeth Baker, Carles Domingo-Enrich, George Deligiannidis, Nikolas Nüsken
Date: 2025-04-04 00:00:00

In generative modelling and stochastic optimal control, a central computational task is to modify a reference diffusion process to maximise a given terminal-time reward. Most existing methods require this reward to be differentiable, using gradients to steer the diffusion towards favourable outcomes. However, in many practical settings, like diffusion bridges, the reward is singular, taking an infinite value if the target is hit and zero otherwise. We introduce a novel framework, based on Malliavin calculus and centred around a generalisation of the Tweedie score formula to nonlinear stochastic differential equations, that enables the development of methods robust to such singularities. This allows our approach to handle a broad range of applications, like diffusion bridges, or adding conditional controls to an already trained diffusion model. We demonstrate that our approach offers stable and reliable training, outperforming existing techniques. As a byproduct, we also introduce a novel score matching objective. Our loss functions are formulated such that they could readily be extended to manifold-valued and infinite dimensional diffusions.
A stochastic volatility approximation for a tick-by-tick price model with mean-field interaction
Authors: Paolo Dai Pra, Paolo Pigato
Date: 2025-04-04 00:00:00
Tasks: Point Processes

We consider a tick-by-tick model of price formation, in which buy and sell orders are modeled as self-exciting point processes (Hawkes process), similar to the one in [El Euch, Fukasawa, Rosenbaum, The microstructural foundations of leverage effect and rough volatility, Finance and Stochastics, 2018]. We adopt an agent based approach by studying the aggregation of a large number of these point processes, mutually interacting in a mean-field sense. The financial interpretation is that of an asset on which several labeled agents place buy and sell orders following these point processes, influencing the price. The mean-field interaction introduces positive correlations between order volumes coming from different agents that reflect features of real markets such as herd behavior and contagion. When the large scale limit of the aggregated asset price is computed, if parameters are set to a critical value, a singular phenomenon occurs: the aggregated model converges to a stochastic volatility model with leverage effect and faster-than-linear mean reversion of the volatility process. The faster-than-linear mean reversion of the volatility process is supported by econometric evidence, and we have linked it in [Dai Pra, Pigato, Multi-scaling of moments in stochastic volatility models, Stochastic Processes and their Applications, 2015] to the observed multifractal behavior of assets prices and market indices. This seems connected to the Statistical Physics perspective that expects anomalous scaling properties to arise in the critical regime.
A computational transition for detecting multivariate shuffled linear regression by low-degree polynomials
Authors: Zhangsong Li
Date: 2025-04-04 00:00:00

In this paper, we study the problem of multivariate shuffled linear regression, where the correspondence between predictors and responses in a linear model is obfuscated by a latent permutation. Specifically, we investigate the model $Y=\tfrac{1}{\sqrt{1+\sigma^2}}(\Pi_* X Q_* + \sigma Z)$, where $X$ is an $n*d$ standard Gaussian design matrix, $Z$ is an $n*m$ Gaussian noise matrix, $\Pi_*$ is an unknown $n*n$ permutation matrix, and $Q_*$ is an unknown $d*m$ on the Grassmanian manifold satisfying $Q_*^{\top} Q_* = \mathbb I_m$. Consider the hypothesis testing problem of distinguishing this model from the case where $X$ and $Y$ are independent Gaussian random matrices of sizes $n*d$ and $n*m$, respectively. Our results reveal a phase transition phenomenon in the performance of low-degree polynomial algorithms for this task. (1) When $m=o(d)$, we show that all degree-$D$ polynomials fail to distinguish these two models even when $\sigma=0$, provided with $D^4=o\big( \tfrac{d}{m} \big)$. (2) When $m=d$ and $\sigma=\omega(1)$, we show that all degree-$D$ polynomials fail to distinguish these two models provided with $D=o(\sigma)$. (3) When $m=d$ and $\sigma=o(1)$, we show that there exists a constant-degree polynomial that strongly distinguish these two models. These results establish a smooth transition in the effectiveness of low-degree polynomial algorithms for this problem, highlighting the interplay between the dimensions $m$ and $d$, the noise level $\sigma$, and the computational complexity of the testing task.
High-dimensional ridge regression with random features for non-identically distributed data with a variance profile
Authors: Issa-Mbenard Dabo, Jérémie Bigot
Date: 2025-04-03 00:00:00

The behavior of the random feature model in the high-dimensional regression framework has become a popular issue of interest in the machine learning literature}. This model is generally considered for feature vectors $x_i = \Sigma^{1/2} x_i'$, where $x_i'$ is a random vector made of independent and identically distributed (iid) entries, and $\Sigma$ is a positive definite matrix representing the covariance of the features. In this paper, we move beyond {\CB this standard assumption by studying the performances of the random features model in the setting of non-iid feature vectors}. Our approach is related to the analysis of the spectrum of large random matrices through random matrix theory (RMT) {\CB and free probability} results. We turn to the analysis of non-iid data by using the notion of variance profile {\CB which} is {\CB well studied in RMT.} Our main contribution is then the study of the limits of the training and {\CB prediction} risks associated to the ridge estimator in the random features model when its dimensions grow. We provide asymptotic equivalents of these risks that capture the behavior of ridge regression with random features in a {\CB high-dimensional} framework. These asymptotic equivalents, {\CB which prove to be sharp in numerical experiments}, are retrieved by adapting, to our setting, established results from operator-valued free probability theory. Moreover, {\CB for various classes of random feature vectors that have not been considered so far in the literature}, our approach allows to show the appearance of the double descent phenomenon when the ridge regularization parameter is small enough.
Adaptive sparse variational approximations for Gaussian process regression
Authors: Dennis Nieman, Botond Szabó
Date: 2025-04-04 00:00:00
Tasks: regression

Accurate tuning of hyperparameters is crucial to ensure that models can generalise effectively across different settings. In this paper, we present theoretical guarantees for hyperparameter selection using variational Bayes in the nonparametric regression model. We construct a variational approximation to a hierarchical Bayes procedure, and derive upper bounds for the contraction rate of the variational posterior in an abstract setting. The theory is applied to various Gaussian process priors and variational classes, resulting in minimax optimal rates. Our theoretical results are accompanied with numerical analysis both on synthetic and real world data sets.
Towards a Unified Framework for Determining Conformational Ensembles of Disordered Proteins
Authors: Hamidreza Ghafouri, Pavel Kadeřávek, Ana M Melo, Maria Cristina Aspromonte, Pau Bernadó, Juan Cortes, Zsuzsanna Dosztányi, Gabor Erdos, Michael Feig, Giacomo Janson, Kresten Lindorff-Larsen, Frans A. A. Mulder, Peter Nagy, Richard Pestell, Damiano Piovesan, Marco Schiavina, Benjamin Schuler, Nathalie Sibille, Giulio Tesei, Peter Tompa, Michele Vendruscolo, Jiri Vondrasek, Wim Vranken, Lukas Zidek, Silvio C. E. Tosatto, Alexander Miguel Monzon
Date: 2025-04-04 00:00:00
Tasks: Benchmarking

Disordered proteins play essential roles in myriad cellular processes, yet their structural characterization remains a major challenge due to their dynamic and heterogeneous nature. We here present a community-driven initiative to address this problem by advocating a unified framework for determining conformational ensembles of disordered proteins. Our aim is to integrate state-of-the-art experimental techniques with advanced computational methods, including knowledge-based sampling, enhanced molecular dynamics, and machine learning models. The modular framework comprises three interconnected components: experimental data acquisition, computational ensemble generation, and validation. The systematic development of this framework will ensure the accurate and reproducible determination of conformational ensembles of disordered proteins. We highlight the open challenges necessary to achieve this goal, including force field accuracy, efficient sampling, and environmental dependency, advocating for collaborative benchmarking and standardized protocols.
PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks
Authors: Valentin Lombard, Sergei Grudinin, Elodie Laine
Date: 2025-03-19 00:00:00
Tasks: Graph Neural Network, Protein Structure Prediction, Transfer Learning

Proteins move and deform to ensure their biological functions. Despite significant progress in protein structure prediction, approximating conformational ensembles at physiological conditions remains a fundamental open problem. This paper presents a novel perspective on the problem by directly targeting continuous compact representations of protein motions inferred from sparse experimental observations. We develop a task-specific loss function enforcing data symmetries, including scaling and permutation operations. Our method PETIMOT (Protein sEquence and sTructure-based Inference of MOTions) leverages transfer learning from pre-trained protein language models through an SE(3)-equivariant graph neural network. When trained and evaluated on the Protein Data Bank, PETIMOT shows superior performance in time and accuracy, capturing protein dynamics, particularly large/slow conformational changes, compared to state-of-the-art flow-matching approaches and traditional physics-based models.
Dimensionality reduction for k-means clustering of large-scale influenza mutation datasets
Authors: Emilee Walden, Jiahui Chen, Guo-Wei Wei
Date: 2025-04-04 00:00:00
Tasks: Clustering, Dimensionality Reduction, Diversity

Viral mutations pose significant threats to public health by increasing infectivity, strengthening vaccine resistance, and altering disease severity. To track these evolving patterns, agencies like the CDC annually evaluate thousands of virus strains, underscoring the urgent need to understand viral mutagenesis and evolution in depth. In this study, we integrate genomic analysis, clustering, and three leading dimensionality reduction approaches, namely, principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP)-to investigate the effects of COVID-19 on influenza virus propagation. By applying these methods to extensive pre- and post-pandemic influenza datasets, we reveal how selective pressures during the pandemic have influenced the diversity of influenza genetics. Our findings indicate that combining robust dimension reduction with clustering yields critical insights into the complex dynamics of viral mutation, informing both future research directions and strategies for public health intervention.
Building functional and mechanistic models of cortical computation based on canonical cell type connectivity
Authors: Arno Granier, Katharina A Wilmes, Mihai A Petrovici, Walter Senn
Date: 2025-04-03 00:00:00

Neuronal circuits of the cerebral cortex are the structural basis of mammalian cognition. The same qualitative components and connectivity motifs are repeated across functionally specialized cortical areas and mammalian species, suggesting a single underlying algorithmic motif. Here, we propose a perspective on current knowledge of the cortical structure, from which we extract two core principles for computational modeling. The first principle is that cortical cell types fulfill distinct computational roles. The second principle is that cortical connectivity can be efficiently characterized by only a few canonical blueprints of connectivity between cell types. Starting with these two foundational principles, we outline a general framework for building functional and mechanistic models of cortical circuits.
Spatially explicit population modeling with habitat preferential movement of Glis glis (Edible dormouse)
Authors: Jeehye Choi, KyoungEun Lee, Do-Hun Lee, Byungjoon Min, Tae-Soo Chon
Date: 2025-04-04 00:00:00

In this study, we present a population dispersal model for Glis glis, a rodent species with a strong preference for forest habitats. The model addresses dispersal processes by incorporating the ecological traits, including habitat preferences of the species, potential growth rate, home range, and carrying capacity. In this model, the landscape is divided into spatial units based on the home range, and the probability of individuals moving from one spatial unit to another is calculated using habitat preference and population density. The movement probability between different spatial locations is determined by the product of two factors: the escape probability based on the relative ratio of population density to carrying capacity and the relative difference in habitat preferences. Both probabilities are calculated using logistic functions. The results indicate that the combination of habitat preference and local density dependence plays a role in shaping the dispersal patterns of G. glis. This highlights the importance of considering habitat preference and density-dependent effects concurrently when forecasting population dispersal under field conditions.
Large deformation diffeomorphic cardiac strain mapping
Authors: Beatrice Moscoloni, Patrick Segers, Mathias Peirlinck
Date: 2025-04-04 00:00:00

Cardiac deformation is a crucial biomarker for the evaluation of cardiac function. Current methods for estimating cardiac strain might underestimate local deformation due to through-plane motion and segmental averaging. Mesh-based mapping methods are gaining interest for localized analysis of cardiac motion and strain, yet they often do not consider important properties of cardiac tissue. In this work, we propose an extension of the large deformation diffeomorphic metric mapping framework to incorporate near incompressibility into the loss function that guides the mapping. As such, our mechanically regularized mLDDMM allows for accurate and mechanically coherent estimation of volume displacement and strain tensors from time-resolved three-dimensional meshes. We benchmark our method against the results of a finite element simulation of cardiac contraction and find a very good agreement between our estimation and the simulated ground truth. Our method forms a promising technique to extract volume displacement and strain tensors from time-resolved meshes while accounting for the incompressibility of cardiac tissue.
Information Leakages in the Green Bond Market
Authors: Darren Shannon, Jin Gong, Barry Sheehan
Date: 2025-04-04 00:00:00

Public announcement dates are used in the green bond literature to measure equity market reactions to upcoming green bond issues. We find a sizeable number of green bond announcements were pre-dated by anonymous information leakages on the Bloomberg Terminal. From a candidate set of 2,036 'Bloomberg News' and 'Bloomberg First Word' headlines gathered between 2016 and 2022, we identify 259 instances of green bond-related information being released before being publicly announced by the issuing firm. These pre-announcement leaks significantly alter the equity trading dynamics of the issuing firms over intraday and daily event windows. Significant negative abnormal returns and increased trading volumes are observed following news leaks about upcoming green bond issues. These negative investor reactions are concentrated amongst financial firms, and leaks that arrive pre-market or early in market trading. We find equity price movements following news leaks can be explained to a greater degree than following public announcements. Sectoral differences are also observed in the key drivers behind investor reactions to green bond leaks by non-financials (Tobin's Q and free cash flow) and financials (ROA). Our results suggest that information leakages have a strong impact on market behaviour, and should be accounted for in green bond literature. Our findings also have broader ramifications for financial literature going forward. Privileged access to financially material information, courtesy of the ubiquitous use of Bloomberg Terminals by professional investors, highlights the need for event studies to consider wider sets of communication channels to confirm the date at which information first becomes available.
Dynamic Investment Strategies Through Market Classification and Volatility: A Machine Learning Approach
Authors: Jinhui Li, Wenjia Xie, Luis Seco
Date: 2025-03-19 00:00:00
Tasks: Management

This study introduces a dynamic investment framework to enhance portfolio management in volatile markets, offering clear advantages over traditional static strategies. Evaluates four conventional approaches : equal weighted, minimum variance, maximum diversification, and equal risk contribution under dynamic conditions. Using K means clustering, the market is segmented into ten volatility-based states, with transitions forecasted by a Bayesian Markov switching model employing Dirichlet priors and Gibbs sampling. This enables real-time asset allocation adjustments. Tested across two asset sets, the dynamic portfolio consistently achieves significantly higher risk-adjusted returns and substantially higher total returns, outperforming most static methods. By integrating classical optimization with machine learning and Bayesian techniques, this research provides a robust strategy for optimizing investment outcomes in unpredictable market environments.
Statistical applications of the 20/60/20 rule in risk management and portfolio optimization
Authors: Kewin Pączek, Damian Jelito, Marcin Pitera, Agnieszka Wyłomańska
Date: 2025-03-19 00:00:00
Tasks: Management, Portfolio Optimization, Spatial Reasoning

This paper explores the applications of the 20/60/20 rule-a heuristic method that segments data into top-performing, average-performing, and underperforming groups-in mathematical finance. We review the statistical foundations of this rule and demonstrate its usefulness in risk management and portfolio optimization. Our study highlights three key applications. First, we apply the rule to stock market data, showing that it enables effective population clustering. Second, we introduce a novel, easy-to-implement method for extracting heavy-tail characteristics in risk management. Third, we integrate spatial reasoning based on the 20/60/20 rule into portfolio optimization, enhancing robustness and improving performance. To support our findings, we develop a new measure for quantifying tail heaviness and employ conditional statistics to reconstruct the unconditional distribution from the core data segment. This reconstructed distribution is tested on real financial data to evaluate whether the 20/60/20 segmentation effectively balances capturing extreme risks with maintaining the stability of central returns. Our results offer insights into financial data behavior under heavy-tailed conditions and demonstrate the potential of the 20/60/20 rule as a complementary tool for decision-making in finance.