pwc-archive/papers-with-abstracts huggingface dataset:
Optimal packing of attractor states in neural representations
Authors: John J. Vastola
Date: 2025-04-16 00:00:00

Animals' internal states reflect variables like their position in space, orientation, decisions, and motor actions -- but how should these internal states be arranged? Internal states which frequently transition between one another should be close enough that transitions can happen quickly, but not so close that neural noise significantly impacts the stability of those states, and how reliably they can be encoded and decoded. In this paper, we study the problem of striking a balance between these two concerns, which we call an `optimal packing' problem since it resembles mathematical problems like sphere packing. While this problem is generally extremely difficult, we show that symmetries in environmental transition statistics imply certain symmetries of the optimal neural representations, which allows us in some cases to exactly solve for the optimal state arrangement. We focus on two toy cases: uniform transition statistics, and cyclic transition statistics. Code is available at https://github.com/john-vastola/optimal-packing-neurreps23 .
In Pursuit of Total Reproducibility
Authors: Moritz E. Beber
Date: 2025-04-15 00:00:00

The vast majority of scientific contributions in the field of computational systems biology are based on mathematical models. These models can be broadly classified as either dynamic (kinetic) models or steady-state (constraint-based) models. They are often described in specific markup languages whose purpose is to aid in the distribution and standardization of models. Despite numerous established standards in the field, reproducibility remains problematic due to the substantial effort required for compliance, diversity of implementations, and the lack of proportionate rewards for researchers. This article explores the application of event sourcing - a software engineering technique where system state is derived from sequential recorded events - to address reproducibility challenges in computational systems biology. Event sourcing, exemplified by systems like git, offers a promising solution by maintaining complete, immutable records of all changes to a model. Through examples including leader and follower applications, local and remote computation, and contribution tracking, this work demonstrates how event-sourced systems can automate standards compliance, provide comprehensive audit trails, enable perfect replication of processes, facilitate collaboration, and generate multiple specialized read models from a single event log. An implementation of the outlined principles has the potential to transform computational systems biology by providing unprecedented transparency, reproducibility, and collaborative capabilities, ultimately accelerating research through more effective model reuse and integration. An event-sourced approach to modeling in computational systems biology may act as an example to related disciplines and contribute to ending the reproducibility crisis plaguing multiple major fields of science.
Optimum Contribution Selection for Honeybees
Authors: Manuel Du, Richard Bernstein, Andreas Hoppe
Date: 2025-04-17 00:00:00
Tasks: Articles

In 1997, T. H. E. Meuwissen published a groundbreaking article titled 'Maximizing the response of selection with a predefined rate of inbreeding', in which he provided an optimized solution for the trade-off between genetic response and inbreeding avoidance in animal breeding. Evidently, this issue is highly relevant for the honeybee with its small breeding population sizes. However, the genetic peculiarities of bees have thus far prevented an application of the theory to this species. The present manuscript intends to fill this desideratum. It develops the necessary bee-specific theory and introduces a small R script that implements Optimum Contribution Selection (OCS) for honeybees. While researching for this manuscript, we found it rather cumbersome that even though Meuwissen's theory is 28 years old and has sparked research in many new directions, to our knowledge, there is still no comprehensive textbook on the topic. Instead, all relevant information had to be extracted from several articles, leading to a steep learning curve. We anticipate that many honeybee breeding scientists with a putative interest in OCS for honeybees have little to no experience with classical OCS. Thus, we decided to embed our new derivations into a general introduction to OCS that then specializes more and more to the honeybee case. The result are these 121 pages, of which we hope that at least the first sections can also be of use for breeding theorists concerned with other species than honeybees.
Assessing the Spatial and Temporal Risk of HPAIV Transmission to Danish Cattle via Wild Birds
Authors: You Chang, Jose L. Gonzales, Mossa Merhi Reimert, Erik Rattenborg, Mart C. M. de jong, Beate Conrady
Date: 2025-04-16 00:00:00

A highly pathogenic avian influenza (HPAI) panzootic has severely impacted wild bird populations worldwide, with documented (zoonotic) transmission to mammals, including humans. Ongoing HPAI outbreaks on U.S. cattle farms have raised concerns about potential spillover of virus from birds to cattle in other countries, including Denmark. In the EU, the Bird Flu Radar tool, coordinated by EFSA, monitors the spatio-temporal risk of HPAIV infection in wild bird populations. A preparedness tool to assess the spillover risk to the cattle industry is currently lacking, despite its critical importance. This study aims to assess the temporal and spatial risk of HPAI virus (HPAIV) spillover from wild birds, particularly waterfowl, into cattle populations in Denmark. To support this assessment, a spillover transmission model is developed by integrating two well-established surveillance tools, eBird and Bird Flu Radar, in combination with global cattle density data. The generated quantitative risk maps reveal the heterogeneous temporal and spatial distribution of HPAIV spillover risk from wild birds to cattle across Denmark. The highest risk periods are observed during calendar weeks 50 to 10. The estimated total number of spillover cases nationwide is 1.93 (95% CI: 0.48, 4.98) in 2024, and 0.62 cases (95% CI: 0.15, 1.25) in 2025 (up to April). These risk estimates provide valuable insights to support veterinary contingency planning and enable targeted allocation of resources in highrisk areas for the early detection of HPAIV in cattle.
The role of wild birds in the global highly pathogenic avian influenza H5 panzootic
Authors: Manon Couty, Claire Guinat, Diletta Fornasiero, François-Xavier Briand, Pierre-Yves Henry, Béatrice Grasland, Loïc Palumbo, Guillaume Le Loc'h
Date: 2025-04-16 00:00:00

The highly pathogenic avian influenza (HPAI) H5 clade 2.3.4.4b has triggered an unprecedented global panzootic. As the frequency and scale of HPAI H5 outbreaks continue to rise, understanding how wild birds contribute to shape the global virus spread across regions, affecting poultry, domestic and wild mammals, is increasingly critical. In this review, we examine ecological and evolutionary studies to map the global transmission routes of HPAI H5 viruses, identify key wild bird species involved in viral dissemination, and explore infection patterns, including mortality and survival. We also highlight major remaining knowledge gaps that hinder a full understanding of wild birds role in viral dynamics, which must be addressed to enhance surveillance strategies and refine risk assessment models aimed at preventing future outbreaks in wildlife, domestic animals and safeguard public health.
How Much is Enough? An Empirical Test of the Resource Dispersion Hypothesis
Authors: Sourabh Biswas, Kalyan Ghosh, Sumedha Touhid, Srijaya Nandi, Arpan Bhattacharyya, Arunima Bhattacharyya, Milisha Das, Raktim Paul, Anindita Bhadra
Date: 2025-04-15 00:00:00
Tasks: Management

Free-ranging dogs (Canis familiaris) thrive in diverse landscapes, including those heavily modified by humans. This study investigated the influence of resource availability on their spatial ecology across 52 rural and 41 urban sites, comparing urban and rural environments. Census-based surveys were conducted to understand the distribution of dogs and resources, while territory-based observations were carried out across different seasons to capture temporal variability in dog populations and resource availability. Dog and resource density were significantly higher in urban areas, supporting the Resource Dispersion Hypothesis (RDH). Territory size (TS) varied seasonally, decreasing significantly (by 21%) post-mating, likely reflecting shifts in resource demands and distribution. TS was positively correlated with resource heterogeneity, dispersion, patch richness, and male-to-female ratio, but not with group size, which remained stable across seasons and resource gradients. This suggests that while resource availability and sex ratio influence space use, social factors play a key role in shaping group dynamics. These findings highlight the complex interplay between resource availability, social behaviour, and human influences in shaping the spatial ecology of free-ranging dogs and have important implications for their management and disease control, informing targeted interventions such as spay/neuter programs and responsible waste management in both urban and rural landscapes.
Single-shot Star-convex Polygon-based Instance Segmentation for Spatially-correlated Biomedical Objects
Authors: Trina De, Adrian Urbanski, Artur Yakimovich
Date: 2025-04-16 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation

Biomedical images often contain objects known to be spatially correlated or nested due to their inherent properties, leading to semantic relations. Examples include cell nuclei being nested within eukaryotic cells and colonies growing exclusively within their culture dishes. While these semantic relations bear key importance, detection tasks are often formulated independently, requiring multi-shot analysis pipelines. Importantly, spatial correlation could constitute a fundamental prior facilitating learning of more meaningful representations for tasks like instance segmentation. This knowledge has, thus far, not been utilised by the biomedical computer vision community. We argue that the instance segmentation of two or more categories of objects can be achieved in parallel. We achieve this via two architectures HydraStarDist (HSD) and the novel (HSD-WBR) based on the widely-used StarDist (SD), to take advantage of the star-convexity of our target objects. HSD and HSD-WBR are constructed to be capable of incorporating their interactions as constraints into account. HSD implicitly incorporates spatial correlation priors based on object interaction through a joint encoder. HSD-WBR further enforces the prior in a regularisation layer with the penalty we proposed named Within Boundary Regularisation Penalty (WBR). Both architectures achieve nested instance segmentation in a single shot. We demonstrate their competitiveness based on $IoU_R$ and AP and superiority in a new, task-relevant criteria, Joint TP rate (JTPR) compared to their baseline SD and Cellpose. Our approach can be further modified to capture partial-inclusion/-exclusion in multi-object interactions in fluorescent or brightfield microscopy or digital imaging. Finally, our strategy suggests gains by making this learning single-shot and computationally efficient.
Advances in Surrogate Modeling for Biological Agent-Based Simulations: Trends, Challenges, and Future Prospects
Authors: Kerri-Ann Norton, Daniel Bergman, Harsh Vardhan Jain, Trachette Jackson
Date: 2025-04-15 00:00:00
Tasks: parameter estimation, Uncertainty Quantification

Agent-based modeling (ABM) is a powerful computational approach for studying complex biological and biomedical systems, yet its widespread use remains limited by significant computational demands. As models become increasingly sophisticated, the number of parameters and interactions rises rapidly, exacerbating the so-called curse of dimensionality and making comprehensive parameter exploration and uncertainty analyses computationally prohibitive. Surrogate modeling provides a promising solution by approximating ABM behavior through computationally efficient alternatives, greatly reducing the runtime needed for parameter estimation, sensitivity analysis, and uncertainty quantification. In this review, we examine traditional approaches for performing these tasks directly within ABMs -- providing a baseline for comparison -- and then synthesize recent developments in surrogate-assisted methodologies for biological and biomedical applications. We cover statistical, mechanistic, and machine-learning-based approaches, emphasizing emerging hybrid strategies that integrate mechanistic insights with machine learning to balance interpretability and scalability. Finally, we discuss current challenges and outline directions for future research, including the development of standardized benchmarks to enhance methodological rigor and facilitate the broad adoption of surrogate-assisted ABMs in biology and medicine.
Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations
Authors: Tianjian Yang, Wei Vivian Li
Date: 2025-04-15 00:00:00
Tasks: Clustering, Data Integration, Dimensionality Reduction, Missing Values

Background: The integration and analysis of multi-modal data are increasingly essential across various domains including bioinformatics. As the volume and complexity of such data grow, there is a pressing need for computational models that not only integrate diverse modalities but also leverage their complementary information to improve clustering accuracy and insights, especially when dealing with partial observations with missing data. Results: We propose Generalized Probabilistic Canonical Correlation Analysis (GPCCA), an unsupervised method for the integration and joint dimensionality reduction of multi-modal data. GPCCA addresses key challenges in multi-modal data analysis by handling missing values within the model, enabling the integration of more than two modalities, and identifying informative features while accounting for correlations within individual modalities. The model demonstrates robustness to various missing data patterns and provides low-dimensional embeddings that facilitate downstream clustering and analysis. In a range of simulation settings, GPCCA outperforms existing methods in capturing essential patterns across modalities. Additionally, we demonstrate its applicability to multi-omics data from TCGA cancer datasets and a multi-view image dataset. Conclusion: GPCCA offers a useful framework for multi-modal data integration, effectively handling missing data and providing informative low-dimensional embeddings. Its performance across cancer genomics and multi-view image data highlights its robustness and potential for broad application. To make the method accessible to the wider research community, we have released an R package, GPCCA, which is available at https://github.com/Kaversoniano/GPCCA.
Investigation of Cross-border Banking Activities
Authors: Hoang-Anh Le, Xuan Vinh Vo
Date: 2025-04-16 00:00:00

This paper investigates cross-border lending behavior from the Group of Seven (G7) during the 2001-2013 period. We employ gravity model to consider how bilateral factors, global factors, and other determinants of pull factors affect cross -border lending . The empirical results demonstrate that driving factors for cross-border lending have been changing since the 2008 Global Financial Crisis . Particularly , continent variable has more significant correlation with cross-border claims during the post-crisis period, while distance variable becomes less important during that time than it was in the pre-crisis period. Additionally , higher lending claims is more likely related to common language after the financial crisis. Moreover, the role of pull factors, except the size of borrowing economies, is not significant in explaining cross-border banking activities since GFC.
Optimal Capital Structure for Life Insurance Companies Offering Surplus Participation
Authors: Felix Fießinger, Mitja Stadje
Date: 2025-04-17 00:00:00
Tasks: Sensitivity

We adapt Leland's dynamic capital structure model to the context of an insurance company selling participating life insurance contracts explaining the existence of life insurance contracts which provide both a guaranteed payment and surplus participation to the policyholders. Our derivation of the optimal participation rate reveals its pronounced sensitivity to the contract duration and the associated tax rate. Moreover, the asset substitution effect, which describes the tendency of equity holders to increase the riskiness of a company's investment decisions, decreases when adding surplus participation.
Universal portfolios in continuous time: an approach in pathwise Itô calculus
Authors: Xiyue Han, Alexander Schied
Date: 2025-04-16 00:00:00

We provide a simple and straightforward approach to a continuous-time version of Cover's universal portfolio strategies within the model-free context of F\"ollmer's pathwise It\^o calculus. We establish the existence of the universal portfolio strategy and prove that its portfolio value process is the average of all values of constant rebalanced strategies. This result relies on a systematic comparison between two alternative descriptions of self-financing trading strategies within pathwise It\^o calculus. We moreover provide a comparison result for the performance and the realized volatility and variance of constant rebalanced portfolio strategies
Semiparametric Dynamic Copula Models for Portfolio Optimization
Authors: Savita Pareek, Sujit K. Ghosh
Date: 2025-04-16 00:00:00
Tasks: Portfolio Optimization

The mean-variance portfolio model, based on the risk-return trade-off for optimal asset allocation, remains foundational in portfolio optimization. However, its reliance on restrictive assumptions about asset return distributions limits its applicability to real-world data. Parametric copula structures provide a novel way to overcome these limitations by accounting for asymmetry, heavy tails, and time-varying dependencies. Existing methods have been shown to rely on fixed or static dependence structures, thus overlooking the dynamic nature of the financial market. In this study, a semiparametric model is proposed that combines non-parametrically estimated copulas with parametrically estimated marginals to allow all parameters to dynamically evolve over time. A novel framework was developed that integrates time-varying dependence modeling with flexible empirical beta copula structures. Marginal distributions were modeled using the Skewed Generalized T family. This effectively captures asymmetry and heavy tails and makes the model suitable for predictive inferences in real world scenarios. Furthermore, the model was applied to rolling windows of financial returns from the USA, India and Hong Kong economies to understand the influence of dynamic market conditions. The approach addresses the limitations of models that rely on parametric assumptions. By accounting for asymmetry, heavy tails, and cross-correlated asset prices, the proposed method offers a robust solution for optimizing diverse portfolios in an interconnected financial market. Through adaptive modeling, it allows for better management of risk and return across varying economic conditions, leading to more efficient asset allocation and improved portfolio performance.
Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks
Authors: Yu Zhang, Zelin Wu, Claudio Tessone
Date: 2025-04-17 00:00:00
Tasks: Time Series

Cryptocurrencies are digital tokens built on blockchain technology, with thousands actively traded on centralized exchanges (CEXs). Unlike stocks, which are backed by real businesses, cryptocurrencies are recognized as a distinct class of assets by researchers. How do investors treat this new category of asset in trading? Are they similar to stocks as an investment tool for investors? We answer these questions by investigating cryptocurrencies' and stocks' price time series which can reflect investors' attitudes towards the targeted assets. Concretely, we use different machine learning models to classify cryptocurrencies' and stocks' price time series in the same period and get an extremely high accuracy rate, which reflects that cryptocurrency investors behave differently in trading from stock investors. We then extract features from these price time series to explain the price pattern difference, including mean, variance, maximum, minimum, kurtosis, skewness, and first to third-order autocorrelation, etc., and then use machine learning methods including logistic regression (LR), random forest (RF), support vector machine (SVM), etc. for classification. The classification results show that these extracted features can help to explain the price time series pattern difference between cryptocurrencies and stocks.
Bayesian Density-Density Regression with Application to Cell-Cell Communications
Authors: Khai Nguyen, Yang Ni, Peter Mueller
Date: 2025-04-17 00:00:00
Tasks: regression

We introduce a scalable framework for regressing multivariate distributions onto multivariate distributions, motivated by the application of inferring cell-cell communication from population-scale single-cell data. The observed data consist of pairs of multivariate distributions for ligands from one cell type and corresponding receptors from another. For each ordered pair $e=(l,r)$ of cell types $(l \neq r)$ and each sample $i = 1, \ldots, n$, we observe a pair of distributions $(F_{ei}, G_{ei})$ of gene expressions for ligands and receptors of cell types $l$ and $r$, respectively. The aim is to set up a regression of receptor distributions $G_{ei}$ given ligand distributions $F_{ei}$. A key challenge is that these distributions reside in distinct spaces of differing dimensions. We formulate the regression of multivariate densities on multivariate densities using a generalized Bayes framework with the sliced Wasserstein distance between fitted and observed distributions. Finally, we use inference under such regressions to define a directed graph for cell-cell communications.
Trend Filtered Mixture of Experts for Automated Gating of High-Frequency Flow Cytometry Data
Authors: Sangwon Hyun, Tim Coleman, Francois Ribalet, Jacob Bien
Date: 2025-04-16 00:00:00
Tasks: Mixture-of-Experts

Ocean microbes are critical to both ocean ecosystems and the global climate. Flow cytometry, which measures cell optical properties in fluid samples, is routinely used in oceanographic research. Despite decades of accumulated data, identifying key microbial populations (a process known as ``gating'') remains a significant analytical challenge. To address this, we focus on gating multidimensional, high-frequency flow cytometry data collected {\it continuously} on board oceanographic research vessels, capturing time- and space-wise variations in the dynamic ocean. Our paper proposes a novel mixture-of-experts model in which both the gating function and the experts are given by trend filtering. The model leverages two key assumptions: (1) Each snapshot of flow cytometry data is a mixture of multivariate Gaussians and (2) the parameters of these Gaussians vary smoothly over time. Our method uses regularization and a constraint to ensure smoothness and that cluster means match biologically distinct microbe types. We demonstrate, using flow cytometry data from the North Pacific Ocean, that our proposed model accurately matches human-annotated gating and corrects significant errors.