pwc-archive/papers-with-abstracts huggingface dataset:
Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak
Authors: Madhab Barman, Madhurima Panja, Nachiketa Mishra, Tanujit Chakraborty
Date: 2025-02-15 00:00:00
Tasks: Bayesian Inference, Conformal Prediction, Deep Learning, Uncertainty Quantification

Tuberculosis (TB) remains a formidable global health challenge, driven by complex spatiotemporal transmission dynamics and influenced by factors such as population mobility and behavioral changes. We propose an Epidemic-Guided Deep Learning (EGDL) approach that fuses mechanistic epidemiological principles with advanced deep learning techniques to enhance early warning systems and intervention strategies for TB outbreaks. Our framework is built upon a modified networked Susceptible-Infectious-Recovered (MN-SIR) model augmented with a saturated incidence rate and graph Laplacian diffusion, capturing both long-term transmission dynamics and region-specific population mobility patterns. Compartmental model parameters are rigorously estimated using Bayesian inference via the Markov Chain Monte Carlo approach. Theoretical analysis leveraging the comparison principle and Green's formula establishes global stability properties of the disease-free and endemic equilibria. Building on these epidemiological insights, we design two forecasting architectures, EGDL-Parallel and EGDL-Series, that integrate the mechanistic outputs of the MN-SIR model within deep neural networks. This integration mitigates the overfitting risks commonly encountered in data-driven methods and filters out noise inherent in surveillance data, resulting in reliable forecasts of real-world epidemic trends. Experiments conducted on TB incidence data from 47 prefectures in Japan and 31 provinces in mainland China demonstrate that our approach delivers robust and accurate predictions across multiple time horizons (short to medium-term forecasts), supporting its generalizability across regions with different population dynamics.
OptimOTU: Taxonomically aware OTU clustering with optimized thresholds and a bioinformatics workflow for metabarcoding data
Authors: Brendan Furneaux, Sten Anslan, Panu Somervuo, Jenni Hultman, Nerea Abrego, Tomas Roslin, Otso Ovaskainen
Date: 2025-02-14 00:00:00
Tasks: Clustering, Denoising

To turn environmentally derived metabarcoding data into community matrices for ecological analysis, sequences must first be clustered into operational taxonomic units (OTUs). This task is particularly complex for data including large numbers of taxa with incomplete reference libraries. OptimOTU offers a taxonomically aware approach to OTU clustering. It uses a set of taxonomically identified reference sequences to choose optimal genetic distance thresholds for grouping each ancestor taxon into clusters which most closely match its descendant taxa. Then, query sequences are clustered according to preliminary taxonomic identifications and the optimized thresholds for their ancestor taxon. The process follows the taxonomic hierarchy, resulting in a full taxonomic classification of all the query sequences into named taxonomic groups as well as placeholder "pseudotaxa" which accommodate the sequences that could not be classified to a named taxon at the corresponding rank. The OptimOTU clustering algorithm is implemented as an R package, with computationally intensive steps implemented in C++ for speed, and incorporating open-source libraries for pairwise sequence alignment. Distances may also be calculated externally, and may be read from a UNIX pipe, allowing clustering of large datasets where the full distance matrix would be inconveniently large to store in memory. The OptimOTU bioinformatics pipeline includes a full workflow for paired-end Illumina sequencing data that incorporates quality filtering, denoising, artifact removal, taxonomic classification, and OTU clustering with OptimOTU. The OptimOTU pipeline is developed for use on high performance computing clusters, and scales to datasets with millions of reads per sample, and tens of thousands of samples.
Automated Hypothesis Validation with Agentic Sequential Falsifications
Authors: Kexin Huang, Ying Jin, Ryan Li, Michael Y. Li, Emmanuel Candès, Jure Leskovec
Date: 2025-02-14 00:00:00
Tasks: Decision Making, Hallucination, Sociology

Hypotheses are central to information acquisition, decision-making, and discovery. However, many real-world hypotheses are abstract, high-level statements that are difficult to validate directly. This challenge is further intensified by the rise of hypothesis generation from Large Language Models (LLMs), which are prone to hallucination and produce hypotheses in volumes that make manual validation impractical. Here we propose Popper, an agentic framework for rigorous automated validation of free-form hypotheses. Guided by Karl Popper's principle of falsification, Popper validates a hypothesis using LLM agents that design and execute falsification experiments targeting its measurable implications. A novel sequential testing framework ensures strict Type-I error control while actively gathering evidence from diverse observations, whether drawn from existing data or newly conducted procedures. We demonstrate Popper on six domains including biology, economics, and sociology. Popper delivers robust error control, high power, and scalability. Furthermore, compared to human scientists, Popper achieved comparable performance in validating complex biological hypotheses while reducing time by 10 folds, providing a scalable, rigorous solution for hypothesis validation.
Advanced 3D-Printed Multiphasic Scaffold with Optimal PRP Dosage for Chondrogenesis of BM-MSCs in Osteochondral Tissue Engineering
Authors: Faezeh Ghobadi, Maryam Mohammadi, Rooja Kalantarzadeh, Ehsan Lotfi, Shokoufeh Borhan, Narendra Pal Singh Chauhan, Ghazaleh Salehi, Sara Simorgh
Date: 2025-02-16 00:00:00

In osteochondral tissue engineering (OCTE), simultaneously regenerating subchondral bone and cartilage tissue presents a significant challenge. Multiphasic scaffolds were created and manufactured using 3D printing to address this issue. Excellent interfacial mechanical properties and biocompatibility enhance the growth and chondrogenic differentiation of bone marrow mesenchymal stem cells (BM-MSCs). The subchondral bone bottom layer is mimicked by incorporating varying concentrations of graphene oxide (GO) (0%, 1%, and 2% w/v) into a bioink composed of alginate (Alg) and gelatin (Gel). Based on evaluations of mechanical and biocompatibility properties, 1% GO is selected for further studies. Subsequently, the GO concentration is kept constant while varying the platelet-rich plasma (PRP) dosage in the multiphasic scaffolds. Different PRP dosages (0%, 1%, 2%, and 3% w/v) are integrated into the Alg-Gel bioink to simulate cartilage tissues. Results indicate that 3D-printed scaffolds containing 1% or 2% PRP exhibit favorable biomechanical properties, with no significant differences observed. However, BM-MSCs exposed to 2% PRP demonstrate enhanced adhesion, growth, and viability. Additionally, real-time PCR and Alcian blue staining confirm increased chondrogenic expression and glycosaminoglycans (GAGs) synthesis. This work highlights the promising potential of 3D-printed multiphasic frameworks in the development of OCTE.
A deep BSDE approach for the simultaneous pricing and delta-gamma hedging of large portfolios consisting of high-dimensional multi-asset Bermudan options
Authors: Balint Negyesi, Cornelis W. Oosterlee
Date: 2025-02-17 00:00:00

A deep BSDE approach is presented for the pricing and delta-gamma hedging of high-dimensional Bermudan options, with applications in portfolio risk management. Large portfolios of a mixture of multi-asset European and Bermudan derivatives are cast into the framework of discretely reflected BSDEs. This system is discretized by the One Step Malliavin scheme (Negyesi et al. [2024, 2025]) of discretely reflected Markovian BSDEs, which involves a $\Gamma$ process, corresponding to second-order sensitivities of the associated option prices. The discretized system is solved by a neural network regression Monte Carlo method, efficiently for a large number of underlyings. The resulting option Deltas and Gammas are used to discretely rebalance the corresponding replicating strategies. Numerical experiments are presented on both high-dimensional basket options and large portfolios consisting of multiple options with varying early exercise rights, moneyness and volatility. These examples demonstrate the robustness and accuracy of the method up to $100$ risk factors. The resulting hedging strategies significantly outperform benchmark methods both in the case of standard delta- and delta-gamma hedging.
Transformer Based Time-Series Forecasting for Stock
Authors: Shuozhe Li, Zachery B Schulwol, Risto Miikkulainen
Date: 2025-01-29 00:00:00
Tasks: Time Series, Time Series Forecasting

To the naked eye, stock prices are considered chaotic, dynamic, and unpredictable. Indeed, it is one of the most difficult forecasting tasks that hundreds of millions of retail traders and professional traders around the world try to do every second even before the market opens. With recent advances in the development of machine learning and the amount of data the market generated over years, applying machine learning techniques such as deep learning neural networks is unavoidable. In this work, we modeled the task as a multivariate forecasting problem, instead of a naive autoregression problem. The multivariate analysis is done using the attention mechanism via applying a mutated version of the Transformer, "Stockformer", which we created.
A Cholesky decomposition-based asset selection heuristic for sparse tangent portfolio optimization
Authors: Hyunglip Bae, Haeun Jeon, Minsu Park, YongJae lee, Woo Chang Kim
Date: 2025-02-17 00:00:00
Tasks: Management, Portfolio Optimization

In practice, including large number of assets in mean-variance portfolios can lead to higher transaction costs and management fees. To address this, one common approach is to select a smaller subset of assets from the larger pool, constructing more efficient portfolios. As a solution, we propose a new asset selection heuristic which generates a pre-defined list of asset candidates using a surrogate formulation and re-optimizes the cardinality-constrained tangent portfolio with these selected assets. This method enables faster optimization and effectively constructs portfolios with fewer assets, as demonstrated by numerical analyses on historical stock returns. Finally, we discuss a quantitative metric that can provide a initial assessment of the performance of the proposed heuristic based on asset covariance.
Heterogenous Macro-Finance Model: A Mean-field Game Approach
Authors: Hoang Vu, Tomoyuki Ichiba
Date: 2025-02-15 00:00:00
Tasks: Friction

We investigate the full dynamics of capital allocation and wealth distribution of heterogeneous agents in a frictional economy during booms and busts using tools from mean-field games. Two groups in our models, namely the expert and the household, are interconnected within and between their classes through the law of capital processes and are bound by financial constraints. Such a mean-field interaction explains why experts accumulate a lot of capital in the good times and reverse their behavior quickly in the bad times even in the absence of aggregate macro-shocks. When common noises from the market are involved, financial friction amplifies the mean-field effect and leads to capital fire sales by experts. In addition, the implicit interlink between and within heterogeneous groups demonstrates the slow economic recovery and characterizes the deviating and fear-of-missing-out (FOMO) behaviors of households compared to their counterparts. Our model also gives a fairly explicit representation of the equilibrium solution without exploiting complicated numerical approaches.
A Distillation-based Future-aware Graph Neural Network for Stock Trend Prediction
Authors: Zhipeng Liu, Peibo Duan, Mingyang Geng, Bin Zhang
Date: 2025-02-15 00:00:00
Tasks: Graph Neural Network, Prediction, Stock Prediction, Stock Trend Prediction

Stock trend prediction involves forecasting the future price movements by analyzing historical data and various market indicators. With the advancement of machine learning, graph neural networks (GNNs) have been extensively employed in stock prediction due to their powerful capability to capture spatiotemporal dependencies of stocks. However, despite the efforts of various GNN stock predictors to enhance predictive performance, the improvements remain limited, as they focus solely on analyzing historical spatiotemporal dependencies, overlooking the correlation between historical and future patterns. In this study, we propose a novel distillation-based future-aware GNN framework (DishFT-GNN) for stock trend prediction. Specifically, DishFT-GNN trains a teacher model and a student model, iteratively. The teacher model learns to capture the correlation between distribution shifts of historical and future data, which is then utilized as intermediate supervision to guide the student model to learn future-aware spatiotemporal embeddings for accurate prediction. Through extensive experiments on two real-world datasets, we verify the state-of-the-art performance of DishFT-GNN.
Robust Pricing of Equity-Indexed Annuities under Uncertain Volatility and Stochastic Interest Rate
Authors: Ludovic Goudenège, Andrea Molent, Antonino Zanette
Date: 2025-02-14 00:00:00

In this paper, we propose a novel methodology for pricing equity-indexed annuities featuring cliquet-style payoff structures and early surrender risk, using advanced financial modeling techniques. Specifically, the market is modeled by an equity index that follows an uncertain volatility framework, while the dynamics of the interest rate are captured by the Hull-White model. Due to the inherent complexity of the market dynamics under consideration, we develop a numerical algorithm that employs a tree-based framework to discretize both the interest rate and the underlying equity index, enhanced with local volatility optimization. The proposed algorithm is compared with a machine learning-based algorithm. Extensive numerical experiments demonstrate its high effectiveness. Furthermore, the numerical framework is employed to analyze key features of the insurance contract, including the delineation of the optimal exercise region when early surrender risk is incorporated.
An Integrated Model for Financial Risk Assessment of Grid-ignited Wildfires
Authors: Saeed Nematshahi, Amin Khodaei, Ali Arabnya
Date: 2025-01-30 00:00:00

In recent years, the frequency and intensity of grid-ignited wildfires have increased significantly, leading to an elevated level of risk exposure to public safety and financial repercussions for electric utilities threatening their solvency. It is, therefore, imperative for electric utilities to accurately assess the financial impact of potential wildfires ignited by their power infrastructure. This is a critical step toward developing risk-informed strategies to mitigate grid-ignited wildfires from both operational and financial perspectives. This paper proposes and develops an integrated model to evaluate the damage costs associated with potential grid-ignited wildfires to allow assessing financial risk with greater precision than existing literature. The proposed model is tailored to assess the financial risk associated with grid-ignited wildfires, including environmental damages, destroyed structures, and damage to the power grid assets. We quantify the risk associated with each power line, thereby identifying areas that require immediate preemptive actions. To visually represent the risk levels associated with the transmission grid topology, we implement a color-coded risk heatmap. The heatmap categorizes risk levels as follows: low-risk areas are denoted in white, moderate-low risk regions in green, medium-risk areas in yellow, and high-risk zones in red...
Generalized Factor Neural Network Model for High-dimensional Regression
Authors: Zichuan Guo, Mihai Cucuringu, Alexander Y. Shestopaloff
Date: 2025-02-16 00:00:00
Tasks: regression

We tackle the challenges of modeling high-dimensional data sets, particularly those with latent low-dimensional structures hidden within complex, non-linear, and noisy relationships. Our approach enables a seamless integration of concepts from non-parametric regression, factor models, and neural networks for high-dimensional regression. Our approach introduces PCA and Soft PCA layers, which can be embedded at any stage of a neural network architecture, allowing the model to alternate between factor modeling and non-linear transformations. This flexibility makes our method especially effective for processing hierarchical compositional data. We explore ours and other techniques for imposing low-rank structures on neural networks and examine how architectural design impacts model performance. The effectiveness of our method is demonstrated through simulation studies, as well as applications to forecasting future price movements of equity ETF indices and nowcasting with macroeconomic data.
Generative Adversarial Networks for High-Dimensional Item Factor Analysis: A Deep Adversarial Learning Algorithm
Authors: Nanyu Luo, Feng Ji
Date: 2025-02-15 00:00:00
Tasks: parameter estimation, Representation Learning

Advances in deep learning and representation learning have transformed item factor analysis (IFA) in the item response theory (IRT) literature by enabling more efficient and accurate parameter estimation. Variational Autoencoders (VAEs) have been one of the most impactful techniques in modeling high-dimensional latent variables in this context. However, the limited expressiveness of the inference model based on traditional VAEs can still hinder the estimation performance. We introduce Adversarial Variational Bayes (AVB) algorithms as an improvement to VAEs for IFA with improved flexibility and accuracy. By bridging the strengths of VAEs and Generative Adversarial Networks (GANs), AVB incorporates an auxiliary discriminator network to reframe the estimation process as a two-player adversarial game and removes the restrictive assumption of standard normal distributions in the inference model. Theoretically, AVB can achieve similar or higher likelihood compared to VAEs. A further enhanced algorithm, Importance-weighted Adversarial Variational Bayes (IWAVB) is proposed and compared with Importance-weighted Autoencoders (IWAE). In an exploratory analysis of empirical data, IWAVB demonstrated superior expressiveness by achieving a higher likelihood compared to IWAE. In confirmatory analysis with simulated data, IWAVB achieved similar mean-square error results to IWAE while consistently achieving higher likelihoods. When latent variables followed a multimodal distribution, IWAVB outperformed IWAE. With its innovative use of GANs, IWAVB is shown to have the potential to extend IFA to handle large-scale data, facilitating the potential integration of psychometrics and multimodal data analysis.
A Novel Hybrid Approach to Contraceptive Demand Forecasting: Integrating Point Predictions with Probabilistic Distributions
Authors: Harsha Chamara Hewage, Bahman Rostami-Tabar, Aris Syntetos, Federico Liberatore, Glenn Milano
Date: 2025-02-13 00:00:00
Tasks: Demand Forecasting, Humanitarian, Time Series

Accurate demand forecasting is vital for ensuring reliable access to contraceptive products, supporting key processes like procurement, inventory, and distribution. However, forecasting contraceptive demand in developing countries presents challenges, including incomplete data, poor data quality, and the need to account for multiple geographical and product factors. Current methods often rely on simple forecasting techniques, which fail to capture demand uncertainties arising from these factors, warranting expert involvement. Our study aims to improve contraceptive demand forecasting by combining probabilistic forecasting methods with expert knowledge. We developed a hybrid model that combines point forecasts from domain-specific model with probabilistic distributions from statistical and machine learning approaches, enabling human input to fine-tune and enhance the system-generated forecasts. This approach helps address the uncertainties in demand and is particularly useful in resource-limited settings. We evaluate different forecasting methods, including time series, Bayesian, machine learning, and foundational time series methods alongside our new hybrid approach. By comparing these methods, we provide insights into their strengths, weaknesses, and computational requirements. Our research fills a gap in forecasting contraceptive demand and offers a practical framework that combines algorithmic and human expertise. Our proposed model can also be generalized to other humanitarian contexts with similar data patterns.
LEAPS: A discrete neural sampler via locally equivariant networks
Authors: Peter Holderrieth, Michael S. Albergo, Tommi Jaakkola
Date: 2025-02-15 00:00:00

We propose LEAPS, an algorithm to sample from discrete distributions known up to normalization by learning a rate matrix of a continuous-time Markov chain (CTMC). LEAPS can be seen as a continuous-time formulation of annealed importance sampling and sequential Monte Carlo methods, extended so that the variance of the importance weights is offset by the inclusion of the CTMC. To derive these importance weights, we introduce a set of Radon-Nikodym derivatives of CTMCs over their path measures. Because the computation of these weights is intractable with standard neural network parameterizations of rate matrices, we devise a new compact representation for rate matrices via what we call locally equivariant functions. To parameterize them, we introduce a family of locally equivariant multilayer perceptrons, attention layers, and convolutional networks, and provide an approach to make deep networks that preserve the local equivariance. This property allows us to propose a scalable training algorithm for the rate matrix such that the variance of the importance weights associated to the CTMC are minimal. We demonstrate the efficacy of LEAPS on problems in statistical physics.
Variational empirical Bayes variable selection in high-dimensional logistic regression
Authors: Yiqi Tang, Ryan Martin
Date: 2025-02-14 00:00:00
Tasks: regression, Variable Selection

Logistic regression involving high-dimensional covariates is a practically important problem. Often the goal is variable selection, i.e., determining which few of the many covariates are associated with the binary response. Unfortunately, the usual Bayesian computations can be quite challenging and expensive. Here we start with a recently proposed empirical Bayes solution, with strong theoretical convergence properties, and develop a novel and computationally efficient variational approximation thereof. One such novelty is that we develop this approximation directly for the marginal distribution on the model space, rather than on the regression coefficients themselves. We demonstrate the method's strong performance in simulations, and prove that our variational approximation inherits the strong selection consistency property satisfied by the posterior distribution that it is approximating.