pwc-archive/papers-with-abstracts huggingface dataset:
Integrating Human Knowledge Through Action Masking in Reinforcement Learning for Operations Research
Authors: Mirko Stappert, Bernhard Lutz, Niklas Goby, Dirk Neumann
Date: 2025-04-03 00:00:00
Tasks: Management, Reinforcement Learning (RL), Scheduling

Reinforcement learning (RL) provides a powerful method to address problems in operations research. However, its real-world application often fails due to a lack of user acceptance and trust. A possible remedy is to provide managers with the possibility of altering the RL policy by incorporating human expert knowledge. In this study, we analyze the benefits and caveats of including human knowledge via action masking. While action masking has so far been used to exclude invalid actions, its ability to integrate human expertise remains underexplored. Human knowledge is often encapsulated in heuristics, which suggest reasonable, near-optimal actions in certain situations. Enforcing such actions should hence increase trust among the human workforce to rely on the model's decisions. Yet, a strict enforcement of heuristic actions may also restrict the policy from exploring superior actions, thereby leading to overall lower performance. We analyze the effects of action masking based on three problems with different characteristics, namely, paint shop scheduling, peak load management, and inventory management. Our findings demonstrate that incorporating human knowledge through action masking can achieve substantial improvements over policies trained without action masking. In addition, we find that action masking is crucial for learning effective policies in constrained action spaces, where certain actions can only be performed a limited number of times. Finally, we highlight the potential for suboptimal outcomes when action masks are overly restrictive.
Solving the Paint Shop Problem with Flexible Management of Multi-Lane Buffers Using Reinforcement Learning and Action Masking
Authors: Mirko Stappert, Bernhard Lutz, Janis Brammer, Dirk Neumann
Date: 2025-04-03 00:00:00
Tasks: Management

In the paint shop problem, an unordered incoming sequence of cars assigned to different colors has to be reshuffled with the objective of minimizing the number of color changes. To reshuffle the incoming sequence, manufacturers can employ a first-in-first-out multi-lane buffer system allowing store and retrieve operations. So far, prior studies primarily focused on simple decision heuristics like greedy or simplified problem variants that do not allow full flexibility when performing store and retrieve operations. In this study, we propose a reinforcement learning approach to minimize color changes for the flexible problem variant, where store and retrieve operations can be performed in an arbitrary order. After proving that greedy retrieval is optimal, we incorporate this finding into the model using action masking. Our evaluation, based on 170 problem instances with 2-8 buffer lanes and 5-15 colors, shows that our approach reduces color changes compared to existing methods by considerable margins depending on the problem size. Furthermore, we demonstrate the robustness of our approach towards different buffer sizes and imbalanced color distributions.
On the Geometry of Receiver Operating Characteristic and Precision-Recall Curves
Authors: Reza Sameni
Date: 2025-04-02 00:00:00
Tasks: Binary Classification

We study the geometry of Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves in binary classification problems. The key finding is that many of the most commonly used binary classification metrics are merely functions of the composition function $G := F_p \circ F_n^{-1}$, where $F_p(\cdot)$ and $F_n(\cdot)$ are the class-conditional cumulative distribution functions of the classifier scores in the positive and negative classes, respectively. This geometric perspective facilitates the selection of operating points, understanding the effect of decision thresholds, and comparison between classifiers. It also helps explain how the shapes and geometry of ROC/PR curves reflect classifier behavior, providing objective tools for building classifiers optimized for specific applications with context-specific constraints. We further explore the conditions for classifier dominance, present analytical and numerical examples demonstrating the effects of class separability and variance on ROC and PR geometries, and derive a link between the positive-to-negative class leakage function $G(\cdot)$ and the Kullback--Leibler divergence. The framework highlights practical considerations, such as model calibration, cost-sensitive optimization, and operating point selection under real-world capacity constraints, enabling more informed approaches to classifier deployment and decision-making.
HCAF-DTA: drug-target binding affinity prediction with cross-attention fused hypergraph neural networks
Authors: Jiannuo Li, Lan Yao
Date: 2025-04-02 00:00:00
Tasks: Drug Design, Prediction, Tree Decomposition

Accurate prediction of the binding affinity between drugs and target proteins is a core task in computer-aided drug design. Existing deep learning methods tend to ignore the information of internal sub-structural features of drug molecules and drug-target interactions, resulting in limited prediction performance. In this paper, we propose a drug-target association prediction model HCAF-DTA based on cross-attention fusion hypergraph neural network. The model innovatively introduces hypergraph representation in the feature extraction stage: drug molecule hypergraphs are constructed based on the tree decomposition algorithm, and the sub-structural and global features extracted by fusing the hypergraph neural network with the graphical neural network through hopping connections, in which the hyper edges can efficiently characterise the functional functional groups and other key chemical features; for the protein feature extraction, a weighted graph is constructed based on the residues predicted by the ESM model contact maps to construct weighted graphs, and multilayer graph neural networks were used to capture spatial dependencies. In the prediction stage, a bidirectional multi-head cross-attention mechanism is designed to model intermolecular interactions from the dual viewpoints of atoms and amino acids, and cross-modal features with correlated information are fused by attention. Experiments on benchmark datasets such as Davis and KIBA show that HCAF-DTA outperforms state of the arts in all three performance evaluation metrics, with the MSE metrics reaching 0.198 and 0.122, respectively, with an improvement of up to 4% from the optimal baseline.
Computation of Minimum Numbers of Tile and Bond-Edge Types for DNA Self-Assembly of Select Archimedean Graphs
Authors: Tabitha Merrithew, Jessica Sorrells
Date: 2025-03-19 00:00:00

This project mathematically models the self-assembly of DNA nanostructures in the shape of select Archimedean graphs using the flexible tile model. Under three different sets of restrictions called scenarios, we employ principles of linear algebra and graph theory to determine the minimum number of different DNA branched molecules and bond types needed to construct the desired shapes, theoretically reducing laboratory costs and the waste of biomaterials. We determine exact values for $T_3(G)$, the minimum number of molecule (or ``tile") types needed for all six order 12 and 24 Archimedean graphs. We also determine exact values for $B_3(G)$, the minimum number of strand (or ``bond-edge") types, for three of the six graphs and establish bounds for the remaining three. Two algorithms, implemented as Python scripts, are used to analyze proposed design strategies for the graphs.
Effect of new healthy and live food supplement on Anemia disease in Wistar rats
Authors: Azam Bayat, Aref Khalkhali, Ali Reza Mahjoub
Date: 2025-04-03 00:00:00

Anemia is a decrease in hemoglobin and red blood cells and due to a decrease in hemoglobin, oxygen carrying capacity reduce. In this disease, the red blood cell the amount and volume decrease. In this research, healthy and live food powder were synthesized by a green route. This organic biomaterial was named NBS. The NBS healthy and live food powder has various vitamins, macro and micro molecules, and ingredients. Twenty Wistar rats were randomly divided into 4 equal groups, including control and treatment groups 1, 2 and 3. Nutritional supplements for healthy living were administered orally via gavage to rats in groups 1, 2, and 3 at 12.5, 25, and 50 mg/ kg, respectively, and within a period of 20 days, one day in between. There was no intervention in the control group in order to reach baseline blood factors. At the end of the study, blood samples were taken from the heart, including blood-red blood cells, hemoglobin, hematocrit and platelets using a fully automated blood cell counting machine. The results showed that the new dietary supplement reduced the level of hematocrit and platelets in the studied rats. The healthy and live food supplement at a concentration of 50 mg / kg increased blood levels compared to the control group. The results of this study showed that the use of healthy and live food supplement increased blood factors compared to the control group.
Addressing missing context in regulatory variation across primate evolution
Authors: Genevieve Housman, Audrey Arner, Amy Longtin, Christian Gagnon, Arun Durvasula, Amanda Lea
Date: 2025-04-02 00:00:00

In primates, loci associated with adaptive trait variation often fall in non-coding regions. Understanding the mechanisms linking these regulatory variants to fitness-relevant phenotypes remains challenging, but can be addressed using functional genomic data. However, such data are rarely generated at scale in non-human primates. When they are, only select tissues, cell types, developmental stages, and cellular environments are typically considered, despite appreciation that adaptive variants often exhibit context-dependent effects. In this review, we 1) discuss why context-dependent regulatory loci might be especially evolutionarily relevant in primates, 2) explore challenges and emerging solutions for mapping such context-dependent variation, and 3) discuss the scientific questions these data could address. We argue that filling this gap will provide critical insights into evolutionary processes, human disease, and regulatory adaptation.
Modelling \textit{Aedes albopictus} management, incorporating immigration and bi-directional \textit{Wolbachia} interactions
Authors: Matthew Ryan, Manuela Mendiolar, Dan Pagendam, Roslyn Hickson, Brendan Trewin
Date: 2025-04-03 00:00:00
Tasks: Management

\textit{Aedes albopictus} mosquitoes are competent vectors for the spread of at least 24 different arboviruses, including dengue, Ross River, and Japanese encephalitis viruses. However, they remain less studied than their more urban cousins, \textit{Aedes aegypti}. We model an Incompatible Insect Technique (IIT) strategy for mosquito control, with bi-directional incompatibility between two strains of \textit{Wolbachia} (\walba/\walbb\, $\times$ \arwp) and age-based cytoplasmic incompatibility decay in a well-mixed population. An important consideration in bi-directional IIT control programs is reversibility when immigration is included. We explore the establishment probability after female contamination of an artificially-infected \textit{Wolbachia} mosquito strain, finding a conservative threshold of 40\% likely driven by mating inefficiencies -- this threshold needs validation in future field and lab experiments. We consider the suppression dynamics and probability of mosquito management success for different release strategies, showing differences in success between release cessation and six months later for different immigration rates. Importantly, our model suggests bi-directional IIT control programs are reversible with low amounts of immigration. We determine a corresponding cost proxy (numbers of mosquitoes released), showing similar short-term costs with differences in medium- and longer-term costs between release strategies. This work demonstrates opportunities to optimise the suppression of these medically important mosquitoes.
SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions
Authors: Shengrui XU, Tianchi Lu, Zikun Wang, Jixiu Zhai
Date: 2025-04-03 00:00:00
Tasks: Contrastive Learning, Graph Embedding, Prediction

Protein-protein interaction (PPI) prediction plays a pivotal role in deciphering cellular functions and disease mechanisms. To address the limitations of traditional experimental methods and existing computational approaches in cross-modal feature fusion and false-negative suppression, we propose SCMPPI-a novel supervised contrastive multimodal framework. By effectively integrating sequence-based features (AAC, DPC, ESMC-CKSAAP) with network topology (Node2Vec embeddings) and incorporating an enhanced contrastive learning strategy with negative sample filtering, SCMPPI achieves superior prediction performance. Extensive experiments on eight benchmark datasets demonstrate its state-of-the-art accuracy(98.13%) and AUC(99.69%), along with excellent cross-species generalization (AUC>99%). Successful applications in CD9 networks, Wnt pathway analysis, and cancer-specific networks further highlight its potential for disease target discovery, establishing SCMPPI as a powerful tool for multimodal biological data analysis.
Quantitative assessment of biological dynamics with aggregate data
Authors: Stephen McCoy, Daniel McBride, D. Katie McCullough, Benjamin C. Calfee, Erik Zinser, David Talmy, Ioannis Sgouralis
Date: 2025-04-03 00:00:00
Tasks: parameter estimation

We develop and apply a learning framework for parameter estimation in initial value problems that are assessed only indirectly via aggregate data such as sample means and/or standard deviations. Our comprehensive framework follows Bayesian principles and consists of specialized Markov chain Monte Carlo computational schemes that rely on modified Hamiltonian Monte Carlo to align with constraints induced by summary statistics and a novel elliptical slice sampler adapted to the parameters of biological models. We benchmark our methods with synthetic data on microbial growth in batch culture and test them with real growth curve data from laboratory replication experiments on $\textit{Prochlorococcus}$ microbes. The results indicate that our learning framework can utilize experimental or historical data and lead to robust parameter estimation and data assimilation in ODE models that outperform least-squares fitting.
Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates
Authors: Kecheng Chen, Xinyu Luo, Tiexin Qin, Jie Liu, Hui Liu, Victor Ho Fun Lee, Hong Yan, Haoliang Li
Date: 2025-04-02 00:00:00
Tasks: Computational Efficiency, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Test-time Adaptation

Foundation medical segmentation models, with MedSAM being the most popular, have achieved promising performance across organs and lesions. However, MedSAM still suffers from compromised performance on specific lesions with intricate structures and appearance, as well as bounding box prompt-induced perturbations. Although current test-time adaptation (TTA) methods for medical image segmentation may tackle this issue, partial (e.g., batch normalization) or whole parametric updates restrict their effectiveness due to limited update signals or catastrophic forgetting in large models. Meanwhile, these approaches ignore the computational complexity during adaptation, which is particularly significant for modern foundation models. To this end, our theoretical analyses reveal that directly refining image embeddings is feasible to approach the same goal as parametric updates under the MedSAM architecture, which enables us to realize high computational efficiency and segmentation performance without the risk of catastrophic forgetting. Under this framework, we propose to encourage maximizing factorized conditional probabilities of the posterior prediction probability using a proposed distribution-approximated latent conditional random field loss combined with an entropy minimization loss. Experiments show that we achieve about 3\% Dice score improvements across three datasets while reducing computational complexity by over 7 times.
A 3D-1D-0D Multiscale Model of the Neuro-Glial-Vascular Unit for Synaptic and Vascular Dynamics in the Dorsal Vagal Complex
Authors: Alexander Hermann, Tobias Köppl, Andreas Wagner, Arman Shojaei, Barbara Wohlmuth, Roland Aydin, Christian J. Cyron, Roustem Miftahof
Date: 2025-04-03 00:00:00

Cerebral blood flow regulation is critical for brain function, and its disruption is implicated in various neurological disorders. Many existing models do not fully capture the complex, multiscale interactions among neuronal activity, astrocytic signaling, and vascular dynamics--especially in key brainstem regions. In this work, we present a 3D-1D-0D multiscale computational framework for modeling the neuro-glial-vascular unit (NGVU) in the dorsal vagal complex (DVC). Our approach integrates a quadripartite synapse model--which represents the interplay among excitatory and inhibitory neurons, astrocytes, and vascular smooth muscle cells--with a hierarchical description of vascular dynamics that couples a three-dimensional microcirculatory network with a one-dimensional macrocirculatory representation and a zero-dimensional synaptic component. By linking neuronal spiking, astrocytic calcium and gliotransmitter signaling, and vascular tone regulation, our model reproduces key features of functional hyperemia and elucidates the feedback loops that help maintain cerebral blood flow. Simulation results demonstrate that neurotransmitter release triggers astrocytic responses that modulate vessel radius to optimize oxygen and nutrient delivery. This integrated framework, to our knowledge the first model to combine these elements for the NGVU in the DVC, provides a robust and modular platform for future investigations into the pathophysiology of cerebral blood flow regulation and its role in autonomic control, including the regulation of stomach function.
Systemic Risk and Default Cascades in Global Equity Markets: Extending the Gai-Kapadia Framework with Stochastic Simulations and Network Analysis
Authors: Ana I. C. Pereda
Date: 2025-03-20 00:00:00
Tasks: Clustering

This study pioneers the application of the Gai-Kapadia framework, originally developed for interbank contagion, to global equity markets. It offers a novel approach to assess systemic risk and default cascades. Using a 20-asset network (13 Brazilian and 7 developed market assets) from 2015 to 2025, we construct exposure-based networks from price co-movements, applying thresholds theta = 0.3 and theta = 0.5 to capture significant interconnections. Cascade dynamics are evaluated through Monte Carlo simulations (n = 1000) with shocks ranging from 10 to 50 percent, complemented by deterministic propagation analysis. Results show that high clustering among Brazilian assets (Ci approx 1.0) leads to localized contagion, with an average of 2.0 failed assets per simulation. In contrast, developed markets with lower connectivity (Ci approx 0.2 to 0.4) show resilience, with zero failures beyond Brazil in all scenarios. Network visualizations highlight structural vulnerabilities: deterministic cascades reach up to 20 assets at theta = 0.3, but only 3 to 4 at theta = 0.5. Risk measures such as VaR and CVaR at 95 percent confidence confirm higher tail risks in emerging markets. This adaptation of the Gai-Kapadia model provides a robust framework for systemic risk assessment. The findings suggest that regulators should target high-clustering nodes in emerging markets, while portfolio managers may benefit from the resilience of developed markets to enhance diversification.
What Can 240,000 New Credit Transactions Tell Us About the Impact of NGEU Funds?
Authors: Alvaro Ortiz, Tomasa Rodrigo, David Sarasa, Sirenia Vazquez
Date: 2025-03-16 00:00:00

Using a panel data local projections model and controlling for firm characteristics, procurement bid attributes, and macroeconomic conditions, the study estimates the dynamic effects of procurement awards on new lending, a more precise measure than the change in the stock of credit. The analysis further examines heterogeneity in credit responses based on firm size, industry, credit maturity, and value chain position of the firms. The empirical evidence confirms that public procurement awards significantly increase new lending, with NGEU-funded contracts generating stronger credit expansion than traditional procurement during the recent period. The results show that the impact of NGEU procurement programs aligns closely with historical procurement impacts, with differences driven mainly by lower utilization rates. Moreover, integrating high-frequency financial data with procurement records highlights the potential of Big Data in refining public policy design.
Semiparametric Counterfactual Regression
Authors: Kwangho Kim
Date: 2025-04-03 00:00:00
Tasks: counterfactual, Decision Making, regression, Stochastic Optimization

We study counterfactual regression, which aims to map input features to outcomes under hypothetical scenarios that differ from those observed in the data. This is particularly useful for decision-making when adapting to sudden shifts in treatment patterns is essential. We propose a doubly robust-style estimator for counterfactual regression within a generalizable framework that accommodates a broad class of risk functions and flexible constraints, drawing on tools from semiparametric theory and stochastic optimization. Our approach uses incremental interventions to enhance adaptability while maintaining consistency with standard methods. We formulate the target estimand as the optimal solution to a stochastic optimization problem and develop an efficient estimation strategy, where we can leverage rapid development of modern optimization algorithms. We go on to analyze the rates of convergence and characterize the asymptotic distributions. Our analysis shows that the proposed estimators can achieve $\sqrt{n}$-consistency and asymptotic normality for a broad class of problems. Numerical illustrations highlight their effectiveness in adapting to unseen counterfactual scenarios while maintaining parametric convergence rates.
A Dynamic, Ordinal Gaussian Process Item Response Theoretic Model
Authors: Yehu Chen, Jacob Montgomery, Roman Garnett
Date: 2025-04-03 00:00:00

Social scientists are often interested in using ordinal indicators to estimate latent traits that change over time. Frequently, this is done with item response theoretic (IRT) models that describe the relationship between those latent traits and observed indicators. We combine recent advances in Bayesian nonparametric IRT, which makes minimal assumptions on shapes of item response functions, and Gaussian process time series methods to capture dynamic structures in latent traits from longitudinal observations. We propose a generalized dynamic Gaussian process item response theory (GD-GPIRT) as well as a Markov chain Monte Carlo sampling algorithm for estimation of both latent traits and response functions. We evaluate GD-GPIRT in simulation studies against baselines in dynamic IRT, and apply it to various substantive studies, including assessing public opinions on economy environment and congressional ideology related to abortion debate.