pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging statistical models to improve pre-season forecasting and in-season management of a recreational fishery
Authors: A. Challen Hyman, Chloe Ramsay, Tiffanie A. Cross, Beverly Sauls, Thomas K. Frazer
Date: 2025-03-21 00:00:00
Tasks: Management

Effective management of recreational fisheries requires accurate forecasting of future harvests and real-time monitoring of ongoing harvests. Traditional methods that rely on historical catch data to predict short-term harvests can be unreliable, particularly if changes in management regulations alter angler behavior. In contrast, statistical modeling approaches can provide faster, more flexible, and potentially more accurate predictions, enhancing management outcomes. In this study, we developed and tested models to improve predictions of Gulf of Mexico gag harvests for both pre-season planning and in-season monitoring. Our best-fitting model outperformed traditional methods (i.e., estimates derived from historical average harvest) for both cumulative pre-season projections and in-season monitoring. Notably, our modeling framework appeared to be more accurate in more recent, shorter seasons due to its ability to account for effort compression. A key advantage of our framework is its ability to explicitly quantify the probability of exceeding harvest quotas for any given season duration. This feature enables managers to evaluate trade-offs between season duration and conservation goals. This is especially critical for vulnerable, highly targeted stocks. Our findings also underscore the value of statistical models to complement and advance traditional fisheries management approaches.
Multi-Modality Representation Learning for Antibody-Antigen Interactions Prediction
Authors: Peijin Guo, Minghui Li, Hewen Pan, Ruixiang Huang, Lulu Xue, Shengqing Hu, Zikang Guo, Wei Wan, Shengshan Hu
Date: 2025-03-22 00:00:00
Tasks: Graph Attention, Prediction, Representation Learning

While deep learning models play a crucial role in predicting antibody-antigen interactions (AAI), the scarcity of publicly available sequence-structure pairings constrains their generalization. Current AAI methods often focus on residue-level static details, overlooking fine-grained structural representations of antibodies and their inter-antibody similarities. To tackle this challenge, we introduce a multi-modality representation approach that integates 3D structural and 1D sequence data to unravel intricate intra-antibody hierarchical relationships. By harnessing these representations, we present MuLAAIP, an AAI prediction framework that utilizes graph attention networks to illuminate graph-level structural features and normalized adaptive graph convolution networks to capture inter-antibody sequence associations. Furthermore, we have curated an AAI benchmark dataset comprising both structural and sequence information along with interaction labels. Through extensive experiments on this benchmark, our results demonstrate that MuLAAIP outperforms current state-of-the-art methods in terms of predictive performance. The implementation code and dataset are publicly available at https://github.com/trashTian/MuLAAIP for reproducibility.
NaFM: Pre-training a Foundation Model for Small-Molecule Natural Products
Authors: Yuheng Ding, Bo Qiang, Yiran Zhou, Jie Yu, Qi Li, Liangren Zhang, Yusong Wang, Zhenmin Liu
Date: 2025-03-22 00:00:00
Tasks: Contrastive Learning, Drug Discovery, Graph Learning

Natural products, as metabolites from microorganisms, animals, or plants, exhibit diverse biological activities, making them crucial for drug discovery. Nowadays, existing deep learning methods for natural products research primarily rely on supervised learning approaches designed for specific downstream tasks. However, such one-model-for-a-task paradigm often lacks generalizability and leaves significant room for performance improvement. Additionally, existing molecular characterization methods are not well-suited for the unique tasks associated with natural products. To address these limitations, we have pre-trained a foundation model for natural products based on their unique properties. Our approach employs a novel pretraining strategy that is especially tailored to natural products. By incorporating contrastive learning and masked graph learning objectives, we emphasize evolutional information from molecular scaffolds while capturing side-chain information. Our framework achieves state-of-the-art (SOTA) results in various downstream tasks related to natural product mining and drug discovery. We first compare taxonomy classification with synthesized molecule-focused baselines to demonstrate that current models are inadequate for understanding natural synthesis. Furthermore, by diving into a fine-grained analysis at both the gene and microbial levels, NaFM demonstrates the ability to capture evolutionary information. Eventually, our method is experimented with virtual screening, illustrating informative natural product representations that can lead to more effective identification of potential drug candidates.
An LLM-Powered Clinical Calculator Chatbot Backed by Verifiable Clinical Calculators and their Metadata
Authors: Niranjan Kumar, Farid Seifi, Marisa Conte, Allen Flynn
Date: 2025-03-21 00:00:00
Tasks: Chatbot, RAG, Retrieval-augmented Generation

Clinical calculators are widely used, and large language models (LLMs) make it possible to engage them using natural language. We demonstrate a purpose-built chatbot that leverages software implementations of verifiable clinical calculators via LLM tools and metadata about these calculators via retrieval augmented generation (RAG). We compare the chatbot's response accuracy to an unassisted off-the-shelf LLM on four natural language conversation workloads. Our chatbot achieves 100% accuracy on queries interrogating calculator metadata content and shows a significant increase in clinical calculation accuracy vs. the off-the-shelf LLM when prompted with complete sentences (86.4% vs. 61.8%) or with medical shorthand (79.2% vs. 62.0%). It eliminates calculation errors when prompted with complete sentences (0% vs. 16.8%) and greatly reduces them when prompted with medical shorthand (2.4% vs. 18%). While our chatbot is not ready for clinical use, these results show progress in minimizing incorrect calculation results.
Structural and Practical Identifiability of Phenomenological Growth Models for Epidemic Forecasting
Authors: Yuganthi R. Liyanage, Gerardo Chowell, Gleb Pogudin, Necibe Tuncer
Date: 2025-03-21 00:00:00
Tasks: Epidemiology, parameter estimation

Phenomenological models are highly effective tools for forecasting disease dynamics using real world data, particularly in scenarios where detailed knowledge of disease mechanisms is limited. However, their reliability depends on the model parameters' structural and practical identifiability. In this study, we systematically analyze the identifiability of six commonly used growth models in epidemiology:the generalized growth model, the generalized logistic model, the Richards model, the generalized Richards model, the Gompertz model, and a modified SEIR model with inhomogeneous mixing. To address challenges posed by non-integer power exponents in these models, we reformulate them by introducing additional state variables. This enables rigorous structural identifiability analysis using the StructuralIdentifiability.jl package in JULIA. We validate the structural identifiability results by performing parameter estimation and forecasting using the GrowthPredict MATLAB toolbox. This toolbox is designed to fit and forecast time series trajectories based on phenomenological growth models. We applied it to three epidemiological datasets: weekly incidence data for monkeypox, COVID 19, and Ebola. Additionally, we assess practical identifiability through Monte Carlo simulations to evaluate parameter estimation robustness under varying levels of observational noise. Our results confirm that all six models are structurally identifiable under the proposed reformulation. Furthermore, practical identifiability analyses demonstrate that parameter estimates remain robust across different noise levels, though sensitivity varies by model and dataset. These findings provide critical insights into the strengths and limitations of phenomenological models to characterize epidemic trajectories, emphasizing their adaptability to real world challenges and their role in informing public health interventions.
Ex vivo experiment on vertebral body with defect representing bone metastasis
Authors: W. Lokbani, V. Allard, T. Broussolle, CY. Barrey, C. B. Confavreux, K. Bruyere, JP. Roux, F. Bermond, H. Follet, D. Mitton
Date: 2025-03-21 00:00:00
Tasks: 3D Reconstruction

Osteolytic metastases located in the vertebrae reduce strength and enhance the risk of vertebral fractures. This risk can be predicted by means of validated finite element models, but their reproducibility needs to be assessed. For that purpose, experimental data are requested. The aim of this study was to conduct open-access experiments on vertebrae, with artificial defect representing lytic metastasis and using well-defined boundary conditions. Twelve lumbar vertebral bodies (L1) were prepared by removing the cortical endplates and creating defects that represent lytic metastases, by drilling the cancellous bone. Vertebral bodies were scanned using clinical High Resolution peripherical Quantitative Computed Tomography before and after defect creation for 3D reconstruction. The specimens were then tested under compression loading until failure. Surface Digital Image Correlation was used to assess strain fields on the anterior wall of the vertebral body. These data (biomechanics data and the tomographic images needed to build subject-specific models) are shared with the scientific community in order to assess different vertebral models on the same dataset.
Early Prediction of Alzheimer's and Related Dementias: A Machine Learning Approach Utilizing Social Determinants of Health Data
Authors: Bereket Kindo, Arjee Restar, Anh Tran
Date: 2025-03-20 00:00:00

Alzheimer's disease and related dementias (AD/ADRD) represent a growing healthcare crisis affecting over 6 million Americans. While genetic factors play a crucial role, emerging research reveals that social determinants of health (SDOH) significantly influence both the risk and progression of cognitive functioning, such as cognitive scores and cognitive decline. This report examines how these social, environmental, and structural factors impact cognitive health trajectories, with a particular focus on Hispanic populations, who face disproportionate risk for AD/ADRD. Using data from the Mexican Health and Aging Study (MHAS) and its cognitive assessment sub study (Mex-Cog), we employed ensemble of regression trees models to predict 4-year and 9-year cognitive scores and cognitive decline based on SDOH. This approach identified key predictive SDOH factors to inform potential multilevel interventions to address cognitive health disparities in this population.
Equilibrium with non-convex preferences: some insights
Authors: Cuong Le Van, Ngoc-Sang Pham
Date: 2025-03-21 00:00:00

We study the existence of equilibrium when agents' preferences may not beconvex. For some specific utility functions, we provide a necessary and sufficientcondition under which there exists an equilibrium. The standard approach cannot be directly applied to our examples because the demand correspondence of some agents is neither single-valued nor convex-valued.
Heterogeneity of household stock portfolios in a national market
Authors: Matteo Milazzo, Federico Musciotto, Jyrki Piilo, Rosario N. Mantegna
Date: 2025-03-22 00:00:00

We study the long term dynamics of the stock portfolios owned by single Finnish legal entities in the Helsinki venue of the Nasdaq Nordic between 2001 and 2021. Using the Herfindahl-Hirschman index as a measure of concentration for the composition of stock portfolios, we investigate the concentration of Finnish household portfolios both at the level of each individual household and tracking the time evolution of an aggregated Finnish household portfolio. We also consider aggregated portfolios of two other macro categories of investors one comprising Finnish institutional investors and the other comprising foreign investors. Different macro categories of investors present a different degree of concentration of aggregated stock portfolios with highest concentration observed for foreign investors. For individual Finnish retail investors, portfolio concentration estimated by the Herfindahl-Hirschman index presents high values for more than half of the total number of retail investors. In spite of the observation that retail stock portfolios are often composed by just a few stocks, the concentration of the aggregated stock portfolio for Finnish retail investors has a portfolio concentration comparable with the one of Finnish institutional investors. Within retail investors, stock portfolios of women present a similar pattern of portfolios of men but with a systematic higher level of concentration observed for women both at individual and at aggregated level.
Agent-Based Models for Two Stocks with Superhedging
Authors: Dario Crisci, Sebastian E. Ferrando, Konrad Gajewski
Date: 2025-03-23 00:00:00

An agent-based modelling methodology for the joint price evolution of two stocks is put forward. The method models future multidimensional price trajectories reflecting how a class of agents rebalance their portfolios in an operational way by reacting to how stocks' charts unfold. Prices are expressed in units of a third stock that acts as numeraire. The methodology is robust, in particular, it does not depend on any prior probability or analytical assumptions and it is based on constructing scenarios/trajectories. A main ingredient is a superhedging interpretation that provides relative superhedging prices between the two modelled stocks. The operational nature of the methodology gives objective conditions for the validity of the model and so implies realistic risk-rewards profiles for the agent's operations. Superhedging computations are performed with a dynamic programming algorithm deployed on a graph data structure. Null subsets of the trajectory space are directly related to arbitrage opportunities (i.e. there is no need for probabilistic considerations) that may emerge during the trajectory set construction. It follows that the superhedging algorithm handles null sets in a rigorous and intuitive way. Superhedging and underhedging bounds are kept relevant to the investor by means of a worst case pruning method and, as an alternative, a theory supported pruning that relies on a new notion of small arbitrage.
Clearing Sections of Lattice Liability Networks
Authors: Robert Ghrist, Julian Gould, Miguel Lopez, Hans Riess
Date: 2025-03-22 00:00:00
Tasks: Descriptive

Modern financial networks involve complex obligations that transcend simple monetary debts: multiple currencies, prioritized claims, supply chain dependencies, and more. We present a mathematical framework that unifies and extends these scenarios by recasting the classical Eisenberg-Noe model of financial clearing in terms of lattice liability networks. Each node in the network carries a complete lattice of possible states, while edges encode nominal liabilities. Our framework generalizes the scalar-valued clearing vectors of the classical model to lattice-valued clearing sections, preserving the elegant fixed-point structure while dramatically expanding its descriptive power. Our main theorem establishes that such networks possess clearing sections that themselves form a complete lattice under the product order. This structure theorem enables tractable analysis of equilibria in diverse domains, including multi-currency financial systems, decentralized finance with automated market makers, supply chains with resource transformation, and permission networks with complex authorization structures. We further extend our framework to chain-complete lattices for term structure models and multivalued mappings for complex negotiation systems. Our results demonstrate how lattice theory provides a natural language for understanding complex network dynamics across multiple domains, creating a unified mathematical foundation for analyzing systemic risk, resource allocation, and network stability.
Asset pre-selection for a cardinality constrained index tracking portfolio with optional enhancement
Authors: N. Meade, C. A. Valle, J. E. Beasley
Date: 2025-03-24 00:00:00

An index tracker is a passive investment reproducing the return and risk of a market index, an enhanced index tracker offers a return greater than the index. We consider the selection of a portfolio of given cardinality to track an index, both without and with enhancement. We divide the problem into two steps - (1) pre-selection of assets; (2) estimation of weights on the assets chosen. The eight pre-selection procedures considered use: forward selection (FS) or backward elimination (BE); implemented using ordinary least squares (OLS) or least absolute deviation (LAD) regression; with a regression constant (c) or without (n). The two-step approach avoids the NP-hard problem arising when asset selection and asset weight computation are combined, leading to the selection of a cardinality constrained index tracking portfolio by computer intensive heuristic procedures with many examples in the literature solving for portfolios of 10 or fewer assets. Avoiding these restrictions, we show that out-of-sample tracking errors are roughly proportional to 1/sqrt(cardinality). We find OLS more effective than LAD; BE marginally more effective than FS; (n) marginally more effective than (c). For index tracking, both without and with enhancement, we use BE-OLS(n) in sensitivity analyses on the periods used for selection and evaluation. For a S&P 500 index tracker, we find that out-of-sample tracking error, transaction volume and return-risk ratios all improve as cardinality increases. By contrast for enhanced returns, cardinalities of the order 10 to 20 are most effective. The S&P 500 data used from 3/1/2005 to 29/12/2023 is available to researchers.
Generating realistic metaorders from public data
Authors: Guillaume Maitrier, Grégoire Loeper, Jean-Philippe Bouchaud
Date: 2025-03-23 00:00:00

This paper introduces a novel algorithm for generating realistic metaorders from public trade data, addressing a longstanding challenge in price impact research that has traditionally relied on proprietary datasets. Our method effectively recovers all established stylized facts of metaorders impact, such as the Square Root Law, the concave profile during metaorder execution, and the post-execution decay. This algorithm not only overcomes the dependence on proprietary data, a major barrier to research reproducibility, but also enables the creation of larger and more robust datasets that may increase the quality of empirical studies. Our findings strongly suggest that average realized short-term price impact is not due to information revelation (as in the Kyle framework) but has a mechanical origin which could explain the universality of the Square Root Law.
Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data
Authors: Filip Stefaniuk, Robert Ślepaczuk
Date: 2025-03-23 00:00:00

The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Mean Absolute Directional Loss (GMADL) and Quantile loss, are proposed and evaluated against the Buy and Hold benchmark and two benchmark strategies based on technical indicators. The evaluation is conducted using data of various frequencies: 5 minute, 15 minute, and 30 minute intervals, over the 6 different periods. Although the Informer-based model with Quantile loss did not outperform the benchmark, two other models achieved better results. The performance of the model using RMSE loss worsens when used with higher frequency data while the model that uses novel GMADL loss function is benefiting from higher frequency data and when trained on 5 minute interval it beat all the other strategies on most of the testing periods. The primary contribution of this study is the application and assessment of the RMSE, GMADL, and Quantile loss functions with the Informer model to forecast future returns, subsequently using these forecasts to develop automated trading strategies. The research provides evidence that employing an Informer model trained with the GMADL loss function can result in superior trading outcomes compared to the buy-and-hold approach.
A Simple Strategy to Deal with Toxic Flow
Authors: Álvaro Cartea, Leandro Sánchez-Betancourt
Date: 2025-03-23 00:00:00

We model the trading activity between a broker and her clients (informed and uninformed traders) as an infinite-horizon stochastic control problem. We derive the broker's optimal dealing strategy in closed form and use this to introduce an algorithm that bypasses the need to calibrate individual parameters, so the dealing strategy can be executed in real-world trading environments. Finally, we characterise the discount in the price of liquidity a broker offers clients. The discount strikes the optimal balance between maximising the order flow from the broker's clients and minimising adverse selection losses to the informed traders.
China and G7 in the Current Context of the World Trading
Authors: N. S. Gonchar, O. P. Dovzhyk, A. S. Zhokhin, W. H. Kozyrski, A. P. Makhort
Date: 2025-03-21 00:00:00

The paper analyses trade between the most developed economies of the world. The analysis is based on the previously proposed model of international trade. This model of international trade is based on the theory of general economic equilibrium. The demand for goods in this model is built on the import of goods by each of the countries participating in the trade. The structure of supply of goods in this model is determined by the structure of exports of each country. It is proved that in such a model, given a certain structure of supply and demand, there exists a so-called ideal equilibrium state in which the trade balance of each country is zero. Under certain conditions on the structure of supply and demand, there is an equilibrium state in which each country have a strictly positive trade balance. Among the equilibrium states under a certain structure of supply and demand, there are some that differ from the ones described above. Such states are characterized by the fact that there is an inequitable distribution of income between the participants in the trade. Such states are called degenerate. In this paper, based on the previously proposed model of international trade, an analysis of the dynamics of international trade of 8 of the world's most developed economies is made. It is shown that trade between these countries was not in a state of economic equilibrium. The found relative equilibrium price vector turned out to be very degenerate, which indicates the unequal exchange of goods on the market of the 8 studied countries. An analysis of the dynamics of supply to the market of the world's most developed economies showed an increase in China's share. The same applies to the share of demand.