pwc-archive/papers-with-abstracts huggingface dataset:
Split-Merge: A Difference-based Approach for Dominant Eigenvalue Problem
Authors: Xiaozhi Liu, Yong Xia
Date: 2025-01-25 00:00:00
Tasks: Computational Efficiency

The computation of the dominant eigenvector of symmetric positive semidefinite matrices is a cornerstone operation in numerous optimization-driven applications. Traditional methods, typically based on the \textit{Quotient} formulation, often suffer from challenges related to computational efficiency and reliance on prior spectral knowledge. In this work, we leverage the alternative \textit{Difference} formulation to reinterpret the classical power method as a first-order optimization algorithm. This perspective allows for a novel convergence analysis and facilitates the development of accelerated variants with larger step-sizes, achieving faster convergence without additional computational cost. Building on this insight, we introduce a generalized family of Difference-based methods, with the power method as a special case. Within this family, we propose Split-Merge, an algorithm that attains accelerated convergence without requiring spectral knowledge and operates solely via matrix-vector products. Extensive experiments on both synthetic and real-world datasets demonstrate that Split-Merge consistently outperforms state-of-the-art methods in both efficiency and scalability. In particular, it achieves more than a $\boldsymbol{10\times}$ speedup over the classical power method, underscoring its practical effectiveness for large-scale problems.
A groundwater market model
Authors: Igor Cialenco, Michael Ludkovski
Date: 2025-01-23 00:00:00
Tasks: model

We introduce the problem of groundwater trading, capturing the emergent groundwater market setups among stakeholders in a given groundwater basin. The agents optimize their production, taking into account their available water rights, the requisite water consumption, and the opportunity to trade water among themselves. We study the resulting Nash equilibrium, providing a full characterization of the 1-period setting and initial results about the features of the multi-period game driven by the ability of agents to bank their water rights in order to smooth out the intertemporal shocks.
How fast extinction occurs in bisexual populations with size-depending mating dynamics?
Authors: Ehyter M. Martín-González, Carlos Galván-Galván, Eduardo Calvo-Martínez
Date: 2025-01-24 00:00:00

Given that extinction in a bisexual population is certain, we study a way to approximate the time when this extinction occurs. Our study is based on standard tools from Extreme Value Theory, which in practice are very easy to implement. We present the theoretical results derived from our study and provide a few numerical examples of such results.
Translating and evaluating single-cell Boolean network interventions in the multiscale setting
Authors: John Metzcar, Katie Pletz, Heber L. Rocha, Jordan C Rozum
Date: 2025-01-27 00:00:00

Intracellular networks process cellular-level information and control cell fate. They can be computationally modeled using Boolean networks, which are implicit-time causal models of discrete binary events. These networks can be embedded in computational agents to drive cellular behavior. To explore this integration, we construct a set of candidate interventions that induce apoptosis in a cell-survival network of a rare leukemia using exhaustive search simulation, stable motif control, and an individual-based mean field approach (IBMFA). Due to inherent algorithmic limitations, these interventions are most suitable for cell-level determinations, not the more realistic multicellular setting. To address these limitations, we treat the target control solutions as putative targets for therapeutic interventions and develop a pipeline to translate them to continuous-time multicellular, agent-based models. We set the discrete-to-continuous transitions between the Boolean network and multicellular model via thresholding and produce simple computational simulations designed to emulate situations in experimental and translational biology. These include a series of simulations: constant substrate gradients, global substrate pulses, and time-varying boundary conditions. We find that interventions that perform equally well in the implicit-time single-cell setting are separable in the multiscale setting in ability to impact population growth and spatial distribution. Further analysis shows that the population and spatial distribution differences arise from differences in internal dynamics (stable motif controls versus target controls) and network distance between the intervention and output nodes. This proof of concept work demonstrates the importance of accounting for internal dynamics in multicellular simulations as well as impacts on understanding of Boolean network control.
GiantHunter: Accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree search
Authors: Fuchuan Qu, Cheng Peng, Jiaojiao Guan, Donglin Wang, Yanni Sun, Jiayu Shang
Date: 2025-01-26 00:00:00
Tasks: Benchmarking, Diversity, Sensitivity

Motivation: Nucleocytoplasmic large DNA viruses (NCLDVs) are notable for their large genomes and extensive gene repertoires, which contribute to their widespread environmental presence and critical roles in processes such as host metabolic reprogramming and nutrient cycling. Metagenomic sequencing has emerged as a powerful tool for uncovering novel NCLDVs in environmental samples. However, identifying NCLDV sequences in metagenomic data remains challenging due to their high genomic diversity, limited reference genomes, and shared regions with other microbes. Existing alignment-based and machine learning methods struggle with achieving optimal trade-offs between sensitivity and precision. Results: In this work, we present GiantHunter, a reinforcement learning-based tool for identifying NCLDVs from metagenomic data. By employing a Monte Carlo tree search strategy, GiantHunter dynamically selects representative non-NCLDV sequences as the negative training data, enabling the model to establish a robust decision boundary. Benchmarking on rigorously designed experiments shows that GiantHunter achieves high precision while maintaining competitive sensitivity, improving the F1-score by 10% and reducing computational cost by 90% compared to the second-best method. To demonstrate its real-world utility, we applied GiantHunter to 60 metagenomic datasets collected from six cities along the Yangtze River, located both upstream and downstream of the Three Gorges Dam. The results reveal significant differences in NCLDV diversity correlated with proximity to the dam, likely influenced by reduced flow velocity caused by the dam. These findings highlight the potential of GiantSeeker to advance our understanding of NCLDVs and their ecological roles in diverse environments.
Normalization and selecting non-differentially expressed genes improve machine learning modelling of cross-platform transcriptomic data
Authors: Fei Deng, Catherine H Feng, Nan Gao, Lanjing Zhang
Date: 2025-01-24 00:00:00

Normalization is a critical step in quantitative analyses of biological processes. Recent works show that cross-platform integration and normalization enable machine learning (ML) training on RNA microarray and RNA-seq data, but no independent datasets were used in their studies. Therefore, it is unclear how to improve ML modelling performance on independent RNA array and RNA-seq based datasets. Inspired by the house-keeping genes that are commonly used in experimental biology, this study tests the hypothesis that non-differentially expressed genes (NDEG) may improve normalization of transcriptomic data and subsequently cross-platform modelling performance of ML models. Microarray and RNA-seq datasets of the TCGA breast cancer were used as independent training and test datasets, respectively, to classify the molecular subtypes of breast cancer. NDEG (p>0.85) and differentially expressed genes (DEG, p<0.05) were selected based on the p values of ANOVA analysis and used for subsequent data normalization and classification, respectively. Models trained based on data from one platform were used for testing on the other platform. Our data show that NDEG and DEG gene selection could effectively improve the model classification performance. Normalization methods based on parametric statistical analysis were inferior to those based on nonparametric statistics. In this study, the LOG_QN and LOG_QNZ normalization methods combined with the neural network classification model seem to achieve better performance. Therefore, NDEG-based normalization appears useful for cross-platform testing on completely independent datasets. However, more studies are required to examine whether NDEG-based normalization can improve ML classification performance in other datasets and other omic data types.
Advancing Understanding of Long COVID Pathophysiology Through Quantum Walk-Based Network Analysis
Authors: Jaesub Park, Woochang Hwang, SeokJun Lee, Hyun Chang Lee, Méabh MacMahon, Matthias Zilbauer, Namshik Han
Date: 2025-01-25 00:00:00

Long COVID is a multisystem condition characterized by persistent symptoms such as fatigue, cognitive impairment, and systemic inflammation, following COVID-19 infection, yet its mechanisms remain poorly understood. In this study, we applied quantum walk (QW), a computational approach leveraging quantum interference, to explore large-scale SARS-CoV-2-induced protein (SIP) networks. Compared to the conventional random walk with restart (RWR) method, QW demonstrated superior capacity to traverse deeper regions of the network, uncovering proteins and pathways implicated in Long COVID. Key findings include mitochondrial dysfunction, thromboinflammatory responses, and neuronal inflammation as central mechanisms. QW uniquely identified the CDGSH iron-sulfur domain-containing protein family and VDAC1, a mitochondrial calcium transporter, as critical regulators of these processes. VDAC1 emerged as a potential biomarker and therapeutic target, supported by FDA-approved compounds such as cannabidiol. These findings highlight QW as a powerful tool for elucidating complex biological systems and identifying novel therapeutic targets for conditions like Long COVID.
BOLDreams: Dreaming with pruned in-silico fMRI Encoding Models of the Visual Cortex
Authors: Uzair Hussain, Kamil Uludag
Date: 2025-01-24 00:00:00

In this article we use the Natural Scenes Dataset (NSD) to train a family of feature-weighted receptive field neural encoding models. These models use a pre-trained vision or text backbone and map extracted features to the voxel space via receptive field readouts. We comprehensively assess such models, quantifying performance changes based on using different modalities like text or images, toggling finetuning, using different pre-trained backbones, and changing the width of the readout. We also dissect each model using explainable AI (XAI) techniques, such as feature visualization via input optimization, also referred to as ``dreaming'' in the AI literature, and the integrated gradients approach to calculate implicit attention maps to illustrate which features drive the predicted signal in different brain areas. These XAI tools illustrate biologically plausible features that drive the predicted signal. Traversing the model hyperparameter space reveals the existence of a maximally minimal model, balancing simplicity while maintaining performance.
Towards Dynamic Neural Communication and Speech Neuroprosthesis Based on Viseme Decoding
Authors: Ji-Ha Park, Seo-Hyun Lee, Soowon Kim, Seong-Whan Lee
Date: 2025-01-09 00:00:00
Tasks: Face Reconstruction

Decoding text, speech, or images from human neural signals holds promising potential both as neuroprosthesis for patients and as innovative communication tools for general users. Although neural signals contain various information on speech intentions, movements, and phonetic details, generating informative outputs from them remains challenging, with mostly focusing on decoding short intentions or producing fragmented outputs. In this study, we developed a diffusion model-based framework to decode visual speech intentions from speech-related non-invasive brain signals, to facilitate face-to-face neural communication. We designed an experiment to consolidate various phonemes to train visemes of each phoneme, aiming to learn the representation of corresponding lip formations from neural signals. By decoding visemes from both isolated trials and continuous sentences, we successfully reconstructed coherent lip movements, effectively bridging the gap between brain signals and dynamic visual interfaces. The results highlight the potential of viseme decoding and talking face reconstruction from human neural signals, marking a significant step toward dynamic neural communication systems and speech neuroprosthesis for patients.
Localization of Seizure Onset Zone based on Spatio-Temporal Independent Component Analysis on fMRI
Authors: Seyyed Mostafa Sadjadi, Elias Ebrahimzadeh, Alireza Fallahi, Jafar Mehvari Habibabadi, Mohammad-Reza Nazem-Zadeh, Hamid Soltanian-Zadeh
Date: 2025-01-03 00:00:00
Tasks: Component Classification

Localizing the seizure onset zone (SOZ) as a step of presurgical planning leads to higher efficiency in surgical and stimulation treatments. However, the clinical localization including structural, ictal, and invasive data acquisition and assessment is a difficult and long procedure with increasing challenges in patients with complex epileptic foci. The interictal methods are proposed to assist in presurgical planning with simpler data acquisition and higher speed. This study presents a spatiotemporal component classification for the localization of epileptic foci using resting-state functional magnetic resonance imaging data. This method is based on spatiotemporal independent component analysis on rsfMRI with a component-sorting procedure upon dominant power frequency, biophysical constraints, spatial lateralization, local connectivity, temporal energy, and functional non-Gaussianity. This method utilized the rs-fMRI potential to reach a high spatial accuracy in localizing epileptic foci from interictal data while retaining the reliability of results for clinical usage. Thirteen patients with temporal lobe epilepsy who underwent surgical resection and had seizure-free surgical outcomes after a 12-month follow-up were included in this study. All patients had presurgical structural MRI and rsfMRI while postsurgical MRI images were available for ten. Based on the relationship between the localized foci and resection, the results were classified into three groups fully concordant, partially concordant, and discordant. These groups had the resulting cluster aligned with, in the same lobe with, and outside the lobe of the resection area, respectively.
Leveraging 13C NMR spectroscopic data derived from SMILES to predict the functionality of small biomolecules by machine learning: a case study on human Dopamine D1 receptor antagonists
Authors: Mariya L Ivanova, Nicola Russo, Konstantin Nikolic
Date: 2025-01-23 00:00:00

This study contributes to ongoing research which aims to predict small biomolecule functionality using Carbon-13 Nuclear Magnetic Resonance ($^{13}$C NMR) spectrum data and machine learning (ML). The approach was demonstrated using a bioassay on human dopamine D1 receptor antagonists. The Simplified Molecular Input Line Entry System (SMILES) notations of compounds in this bioassay were extracted and converted into spectroscopic data by software designed for this purpose. The resulting data was then used for ML with scikit-learn algorithms. The ML models were trained by 27,756 samples and tested by 5,466. From the estimators K-Nearest neighbor, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, XGBoost Classifier, and Support Vector Classifier, the last performed the best, achieving 71.5 % accuracy, 77.4 % precision, 60.6% recall, 68 % F1, 71.5 % ROC, and 0.749 cross-validation score with 0.005 standard deviation. The methodology can be applied to predict any functionality of any compound when relevant data are available. It was hypothesized also that increasing the number of samples would increase accuracy. In addition to the SMILES $^{13}$C NMR spectrum ML model, the time- , and cost-efficient CID_SID ML model was developed. This model allows researchers who have developed a compound and obtained its PubChem CID and SID to check whether their compound is also a human dopamine D1 receptor antagonist based solely on the PubChem identifiers. The metrics of the CID_SID ML model were 80.2% accuracy, 86.3% precision, 70.4% recall, 77.6% F1, 79.9% ROC, five-fold cross-validation score of 0.8071 with 0.0047 Standard deviation.
Active Intracellular Mechanics: A Key to Cellular Function and Organization
Authors: Mohammad Amin Eskandari, Jannis Fischer, Noémie Veyret, Dorian Marx, Timo Betz
Date: 2025-01-24 00:00:00

While mechanobiology has demonstrated that precise control over mechanical properties at the whole-cell level is crucial for many biological functions, comparatively little attention has been paid to the intracellular mechanical properties. Experimental tools have only recently become available to adequately measure the viscoelasticity and activity of the cytosol, revealing, revealing that the active, non-equilibrium nature of the intracellular environment must be carefully considered. To explore the interplay between active forces and viscoelastic properties, it is helpful to consider our current understanding of intracellular active mechanics. In this review, we aim not only to provide an intuitive and quantitative introduction to the relevant physical concepts, but also to offer an overview of the proteins that establish intracellular active mechanics, highlighting their spatial and temporal variation with a particular focus on the role of activity. Although we are only beginning to uncover the importance of intracellular active mechanics for cellular mechanisms, it is increasingly clear that these properties must be precisely regulated to ensure proper cellular function.
Preventing Household Bankruptcy: The One-Third Rule in Financial Planning with Mathematical Validation and Game-Theoretic Insights
Authors: Aditi Godbole, Zubin Shah, Ranjeet S. Mudholkar
Date: 2025-01-26 00:00:00
Tasks: Decision Making

This paper analyzes the 1/3 Financial Rule, a method of allocating income equally among debt repayment, savings, and living expenses. Through mathematical modeling, game theory, behavioral finance, and technological analysis, we examine the rule's potential for supporting household financial stability and reducing bankruptcy risk. The research develops theoretical foundations using utility maximization theory, demonstrating how equal allocation emerges as a solution under standard economic assumptions. The game-theoretic analysis explores the rule's effectiveness across different household structures, revealing potential strategic advantages in financial decision-making. We investigate psychological factors influencing financial choices, including cognitive biases and neurobiological mechanisms that impact economic behavior. Technological approaches, such as AI-driven personalization, blockchain tracking, and smart contract applications, are examined for their potential to support financial planning. Empirical validation using U.S. Census data and longitudinal studies assesses the rule's performance across various household types. Stress testing under different economic conditions provides insights into its adaptability and resilience. The research integrates mathematical analysis with behavioral insights and technological perspectives to develop a comprehensive approach to household financial management.
Quantifying firm-level risks from nature deterioration
Authors: Ricardo Crisostomo
Date: 2025-01-24 00:00:00

We estimate the loss of value that companies might suffer from nature overexploitation. We find that global equities shed 26.8% in a scenario of unabated nature decline, while the worst-performing firms lose ~75% of their value. Our risk framework considers five environmental hazards: biodiversity loss, land degradation, climate change, human population and nature capital. We also introduce two metrics to assess nature-related risks: a Country Degradation Index that tracks the damage caused by environmental hazards in specific territories, including nonlinear dynamics and tipping points; and a Nature Risk Score that summarizes the risk that companies face due to the decline of nature and its services.
A multi-factor model for improved commodity pricing: Calibration and an application to the oil market
Authors: Luca Vincenzo Ballestra, Christian Tezza
Date: 2025-01-26 00:00:00

We present a new model for commodity pricing that enhances accuracy by integrating four distinct risk factors: spot price, stochastic volatility, convenience yield, and stochastic interest rates. While the influence of these four variables on commodity futures prices is well recognized, their combined effect has not been addressed in the existing literature. We fill this gap by proposing a model that effectively captures key stylized facts including a dynamic correlation structure and time-varying risk premiums. Using a Kalman filter-based framework, we achieve simultaneous estimation of parameters while filtering state variables through the joint term structure of futures prices and bond yields. We perform an empirical analysis focusing on crude oil futures, where we benchmark our model against established approaches. The results demonstrate that the proposed four-factor model effectively captures the complexities of futures term structures and outperforms existing models.
Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscaling
Authors: Fabio Merizzi, Davide Evangelista, Harilaos Loukos
Date: 2025-01-21 00:00:00
Tasks: Denoising

In recent years, diffusion models have emerged as powerful tools for generating ensemble members in meteorology. In this work, we demonstrate that a Denoising Diffusion Implicit Model (DDIM) can effectively control ensemble variance by varying the number of diffusion steps. Introducing a theoretical framework, we relate diffusion steps to the variance expressed by the reverse diffusion process. Focusing on reanalysis downscaling, we propose an ensemble diffusion model for the full ERA5-to-CERRA domain, generating variance-calibrated ensemble members for wind speed at full spatial and temporal resolution. Our method aligns global mean variance with a reference ensemble dataset and ensures spatial variance is distributed in accordance with observed meteorological variability. Additionally, we address the lack of ensemble information in the CARRA dataset, showcasing the utility of our approach for efficient, high-resolution ensemble generation.