pwc-archive/papers-with-abstracts huggingface dataset:
A Neuro-Dynamic Mathematical Model of Dream Formation and Spontaneous Cognitive Activity
Authors: Shirmohammad Tavangari, Sajjad Janfaza, Zahra Shakarami, Aref Yelghi
Date: 2025-04-25 00:00:00
Tasks: EEG

This paper introduces a biomathematical model designed to describe the internal dynamics of dream formation and spontaneous cognitive processes. The model incorporates neurocognitive factors such as dissatisfaction, acceptance, forgetting, and mental activity, each of which is linked to established neural systems. We formulate a system of differential equations to simulate interactions among these variables and validate the model using simulated neural data. Our results demonstrate biologically plausible cognitive patterns consistent with findings from EEG and fMRI studies, particularly related to the default mode network (DMN), anterior cingulate cortex (ACC), and hippocampal memory mechanisms.
Structure & Quality: Conceptual and Formal Foundations for the Mind-Body Problem
Authors: Ryan Williams
Date: 2025-04-23 00:00:00

This paper explores the hard problem of consciousness from a different perspective. Instead of drawing distinctions between the physical and the mental, an exploration of a more foundational relationship is examined: the relationship between structure and quality. Information-theoretic measures are developed to quantify the mutual determinability between structure and quality, including a novel Q-S space for analyzing fidelity between the two domains. This novel space naturally points toward a five-fold categorization of possible relationships between structural and qualitative properties, illustrating each through conceptual and formal models. The ontological implications of each category are examined, shedding light on debates around functionalism, emergentism, idealism, panpsychism, and neutral monism. This new line of inquiry has established a framework for deriving theoretical constraints on qualitative systems undergoing evolution that is explored in my companion paper, Qualia & Natural Selection.
Qualia & Natural Selection: Formal Constraints on the Evolution of Consciousness
Authors: Ryan Williams
Date: 2025-04-23 00:00:00

This paper explores foundational questions about the relationship of qualia to natural selection. The primary result is a derivation of specific formal conditions under which structural systems subject to natural selection can convey consistent effects in an associated qualitative domain, placing theoretical and empirical constraints on theories of consciousness. In order to achieve this result, information-theoretic measures are developed to quantify the mutual determinability between structure and quality, quantifying fidelity between the two domains. The fidelities represented by that space are then incorporated into the Price Equation to yield key bounds on the transmission of selective effects between domains. Finally, transmission of higher-order structures between domains is explored. Placement within a broader philosophical context can be found in the companion paper Structure & Quality.
Attachment: a predictive coding approach
Authors: Anthony Lin
Date: 2025-04-10 00:00:00

We introduce a novel predictive coding framework for studying attachment theory. Building off an established model of attachment, the dynamic-maturational model (DMM), as well as the neuroanatomical Embodied Predictive Interoception Coding (EPIC) model of interoception and emotion, we not only elucidate how neural processes can shape attachment strategies, but also explore how early attachment experiences can shape those processes in the first place.
Understanding nature's choice of genetic languages
Authors: Apoorva D. Patel
Date: 2025-05-10 00:00:00

All living organisms use the same genetic languages in their molecular biology machinery. They can be understood as the optimal solutions to the replication tasks involving DNA and proteins. These solutions perfectly fit the pattern predicted by Grover's algorithm, which is more efficient than the best Boolean search algorithm (i.e. binary tree search) that predicts a different pattern. The challenge is to demonstrate how Grover's algorithm would be executed in vivo by the living organisms.
Contributions of the Petabyte Scale Sequence Search Codeathon toward efforts to scale sequence-based searches on SRA
Authors: Priyanka Ghosh, Kjiersten Fagnan, Ryan Connor, Ravinder Pannu, Travis J. Wheeler, Mihai Pop, C. Titus Brown, Tessa Pierce-Ward, Rob Patro, Jacquelyn S. Michaelis, Thomas L. Madden, Christiam Camacho, Olaitan I. Awe, Arianna I. Krinos, René KM Xavier, Rodrigo Ortega Polo, Jack W. Roddy, Adelaide Rhodes, Alexander Sweeten, Adrian Viehweger, Bariş Ekim, Harihara Subrahmaniam Muralidharan, Amatur Rahman, Vinícius W. Salazar, Andrew Tritt, Thomas Colligan, Katrina Kalantar, Genevieve R. Krause, Taylor Reiter, George Lesica, Artem Babaian, Victor Lin, Sergey Madaminov, Vadim Zalunin, David M. Kristensen, Alexa Salsbury, Daniel P. Rice, J. Rodney Brister
Date: 2025-05-09 00:00:00
Tasks: Benchmarking, scientific discovery

The volume of biological data being generated by the scientific community is growing exponentially, reflecting technological advances and research activities. The National Institutes of Health's (NIH) Sequence Read Archive (SRA), which is maintained by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM), is a rapidly growing public database that researchers use to drive scientific discovery across all domains of life. This increase in available data has great promise for pushing scientific discovery but also introduces new challenges that scientific communities need to address. As genomic datasets have grown in scale and diversity, a parade of new methods and associated software have been developed to address the challenges posed by this growth. These methodological advances are vital for maximally leveraging the power of next-generation sequencing (NGS) technologies. With the goal of laying a foundation for evaluation of methods for petabyte-scale sequence search, the Department of Energy (DOE) Office of Biological and Environmental Research (BER), the NIH Office of Data Science Strategy (ODSS), and NCBI held a virtual codeathon 'Petabyte Scale Sequence Search: Metagenomics Benchmarking Codeathon' on September 27 - Oct 1 2021, to evaluate emerging solutions in petabyte scale sequence search. The codeathon attracted experts from national laboratories, research institutions, and universities across the world to (a) develop benchmarking approaches to address challenges in conducting large-scale analyses of metagenomic data (which comprises approximately 20% of SRA), (b) identify potential applications that benefit from SRA-wide searches and the tools required to execute the search, and (c) produce community resources i.e. a public facing repository with information to rebuild and reproduce the problems addressed by each team challenge.
GeoThinneR: An R Package for Efficient Spatial Thinning of Species Occurrences and Point Data
Authors: J. Mestre-Tomás
Date: 2025-05-09 00:00:00

In this paper we present GeoThinneR, an R package for efficient and flexible spatial thinning of species occurrence data. Spatial thinning is a widely used preprocessing step in species distribution modeling (SDM) that can help reduce sampling bias, but existing R implementations rely on brute-force algorithms that scale poorly with large datasets. GeoThinneR implements multiple thinning approaches, including ensuring a minimum distance between points, subsampling points on a grid, and filtering based on decimal precision. To handle large datasets, it introduces two optimized algorithms based on local kd-trees and adaptive neighbor estimation, which greatly reduce memory usage and execution time. Additional functionalities such as group-wise thinning and point prioritization are included to facilitate its use in SDM workflows. We here provide performance benchmarks using both simulated and real-world data to demonstrate substantial performance improvements over existing tools.
Preventing SARS-CoV-2 superspreading events with antiviral intranasal sprays
Authors: George Booth, Christoforos Hadjichrysanthou, Keira L Rice, Jacopo Frallicciardi, Zoltán Magyarics, Frank de Wolf, Jaap Goudsmit, Anna L Beukenhorst, Roy Anderson
Date: 2025-05-12 00:00:00

Superspreading events are known to disproportionally contribute to onwards transmission of epidemic and pandemic viruses. Preventing infections at a small number of high-transmission settings is therefore an attractive public health goal. Here, we use deterministic and stochastic mathematical modelling to quantify the impact of intranasal sprays in containing outbreaks at a known superspreading event (the 2020 SARS-CoV-2 outbreak at the Diamond Princess cruise ship) and a conference event that led to extensive transmission. We find that in the Diamond Princess cruise ship case study, there exists a 7-14-day window of opportunity for widespread prophylactic spray usage to significantly impact the number of infections averted. Given an immediate response to a known SARS-CoV-2 outbreak, alongside testing and social distancing measures, prophylactic efficacy and coverage greater than 65% could reduce the average number of infections by over 90%. In the conference case study, in the absence of additional public health interventions, analyses suggest much higher prophylactic efficacies and coverages are required to achieve a similar outcome. However, prophylactic use can half an individual's probability of being infected, and significantly reduce the probability of developing a severe infection. These results suggest that at a known potential superspreading event, early use of intranasal sprays can complement quarantining measures and significantly suppress a SARS-CoV-2 outbreak, even at suboptimal coverage. At a potential superspreading event of short duration, intranasal sprays can reduce individuals' risk of infection, but cannot prevent all infections or onwards community transmission.
A Systems Biology view of Breast cancer via Fractal Geometry and Fractional Calculus
Authors: Abhijeet Das, Ramray Bhat, Mohit Kumar Jolly
Date: 2025-05-12 00:00:00
Tasks: Diagnostic

Breast cancer (BC) is the most widespread cancer globally, yet current diagnostic and prognostic methods inadequately capture its biological complexity, despite the benefits of early detection. Cancer systems biology (SB) has advanced over the past two decades as a multiscale approach, but often neglects morphological complexity observable at cellular, tissue, and tumor levels. Structural organization significantly influences system behavior, necessitating the identification of scales where morphological features emerge. Fractal geometry (FG) provides quantitative tools to assess the irregular, scale-invariant structures typical of cancer. Alongside FG, fractional calculus (FC) offers a framework to model memory-dependent and non-local dynamics. However, existing research remains constrained to mono-fractal analyses or limited imaging scopes, lacking integration into broader SB models. This review aims to bridge that gap by presenting core FG concepts, both mono- and multi-fractal measures, and their application to BC across histological, cytological, and radiological data. It further explores how FC enhances dynamic morphological modeling. Finally, it considers how number-theoretic tools like p-adic analysis may represent hierarchical genomic and morphological patterns, unifying insights from tumor morphology to genome organization.
Rigorous Evaluation of Predictive Toxicity Models by Multi-Objective Optimization of Reference Compound Lists Using Genetic Algorithms
Authors: Yohei Ohto, Tadahaya Mizuno, Yasuhiro Yoshikai, Hiromi Fujimoto, Hiroyuki Kusuhara
Date: 2025-05-11 00:00:00
Tasks: Diversity

In pharmaceutical safety assessments, validation studies are essential for evaluating the predictive performance and reliability of alternative methods prior to regulatory acceptance. Typically, these studies utilize reference compound lists selected to balance multiple critical factors, including chemical structure, physicochemical properties, and toxicity profiles. However, the inherent trade-offs among these criteria complicate the independent optimization of each factor, necessitating a comprehensive multi-objective optimization approach. To address this challenge, we propose a novel multi-objective optimization framework employing a Genetic Algorithm (GA) to simultaneously maximize structural, physicochemical, and toxicity diversity of reference compound lists. Applying this methodology to existing validation study datasets, we demonstrated that GA-optimized compound lists achieved significantly higher overall diversity compared to randomly generated lists. Additionally, toxicity prediction models tested on GA-optimized compound lists exhibited notably lower predictive performance compared to random selections, confirming that these lists provide a rigorous and unbiased assessment environment. These findings emphasize the potential of our GA-based method to enhance the robustness and generalizability of toxicity prediction models. Overall, our approach provides valuable support for developing balanced and rigorous reference compound lists, potentially accelerating the adoption of alternative safety assessment methods by facilitating smoother regulatory validation processes.
Impact of SMILES Notational Inconsistencies on Chemical Language Model Performance
Authors: Yosuke Kikuchi, Tadahaya Mizuno, Yasuhiro Yoshikai, Shumpei Nemoto, Hiroyuki Kusuhara
Date: 2025-05-11 00:00:00
Tasks: feature selection, Language Modeling, Language Modelling, Molecular Property Prediction, Property Prediction

Chemical language models (CLMs), inspired by natural language processing (NLP), have recently emerged as powerful tools for various cheminformatics tasks such as molecular property prediction and molecule generation. The simplified molecular input line entry system (SMILES) is commonly employed in CLMs for representing molecular structures. However, despite attempts at standardization through Canonical SMILES, significant representational inconsistencies remain due to variations in canonicalization methods (grammatical inconsistencies) and incomplete stereochemical annotations (stereochemical inconsistencies). This study systematically investigates the prevalence and impact of these inconsistencies. Our literature review reveals that nearly half (45.4%) of the studies employing CLMs omit explicit mention of SMILES canonicalization, potentially impairing reproducibility. Through quantitative analysis of publicly available datasets, we observed substantial variability in SMILES representations, particularly highlighting significant gaps in stereochemical information approximately 50% of enantiomers and 30% of geometric isomers lacked complete annotations. Empirical evaluations using an encoder-decoder CLM demonstrated that representational variations significantly affect latent molecular representations, notably reducing translation accuracy. Interestingly, these variations minimally impacted downstream property prediction tasks, likely due to robust feature selection driven by label information. Furthermore, explicit manipulation of stereochemical annotations confirmed their crucial role in accurate cyclic structure reconstruction.
Hillclimb-Causal Inference: A Data-Driven Approach to Identify Causal Pathways Among Parental Behaviors, Genetic Risk, and Externalizing Behaviors in Children
Authors: Mengman Wei, Qian Peng
Date: 2025-05-10 00:00:00
Tasks: Causal Discovery, Causal Inference, Dimensionality Reduction

Motivation: Externalizing behaviors in children, such as aggression, hyperactivity, and defiance, are influenced by complex interplays between genetic predispositions and environmental factors, particularly parental behaviors. Unraveling these intricate causal relationships can benefit from the use of robust data-driven methods. Methods: We developed a method called Hillclimb-Causal Inference, a causal discovery approach that integrates the Hill Climb Search algorithm with a customized Linear Gaussian Bayesian Information Criterion (BIC). This method was applied to data from the Adolescent Brain Cognitive Development (ABCD) Study, which included parental behavior assessments, children's genotypes, and externalizing behavior measures. We performed dimensionality reduction to address multicollinearity among parental behaviors and assessed children's genetic risk for externalizing disorders using polygenic risk scores (PRS), which were computed based on GWAS summary statistics from independent cohorts. Once the causal pathways were identified, we employed structural equation modeling (SEM) to quantify the relationships within the model. Results: We identified prominent causal pathways linking parental behaviors to children's externalizing outcomes. Parental alcohol misuse and broader behavioral issues exhibited notably stronger direct effects (0.33 and 0.20, respectively) compared to children's polygenic risk scores (0.07). Moreover, when considering both direct and indirect paths, parental substance misuse (alcohol, drug, and tobacco) collectively resulted in a total effect exceeding 1.1 on externalizing behaviors. Bootstrap and sensitivity analyses further validated the robustness of these findings.
CGM Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications
Authors: David C. Klonoff, Richard M. Bergenstal, Eda Cengiz, Mark A. Clements, Daniel Espes, Juan Espinoza, David Kerr, Boris Kovatchev, David M. Maahs, Julia K. Mader, Nestoras Mathioudakis, Ahmed A. Metwally, Shahid N. Shah, Bin Sheng, Michael P. Snyder, Guillermo Umpierrez, Alessandra T. Ayers, Cindy N. Ho, Elizabeth Healey
Date: 2025-05-10 00:00:00
Tasks: Management

New methods of CGM data analysis are emerging that are valuable for interpreting CGM patterns and underlying metabolic physiology. These new methods use functional data analysis and artificial intelligence (AI), including machine learning (ML). Compared to traditional metrics for evaluating CGM tracing results (CGM Data Analysis 1.0), these new methods, which we refer to as CGM Data Analysis 2.0, can provide a more detailed understanding of glucose fluctuations and trends and enable more personalized and effective diabetes management strategies once translated into practical clinical solutions.
Voice biomarkers of perinatal depression: cross-sectional nationwide pilot study report
Authors: Rachel L. Wiley, Jim Schwoebel, Joel Shor, Bindu Chanagala, Matias Caccia, Adolfo M. García, Sheehan D. Fisher, Martin G. Frasch
Date: 2025-05-09 00:00:00
Tasks: Binary Classification, feature selection

Perinatal depression (PND) affects 1 in 5 mothers, with 85% lacking support. Digital health tools offer early identification and prevention, potentially reducing PND risk by over 50% and improving engagement. Despite high interest, user retention needs improvement for maximum benefit. Voice biomarkers have emerged as a possible digital alternative to collecting mood questionnaires to inform individualized user adherence and retention. However, no voice biomarkers have been developed specifically for pregnant mothers. We aimed to test an unstructured speech sample for the feasibility of yielding a machine learning (ML) model to predict PND. We surveyed 446 women nationwide at 22 weeks of gestation. We measured PHQ-8 and modified EPDS and four voice samples. Participants were classified as depressed if their PHQ-8 or EPDS were equal or higher than 10. In the present report, we focus on a 2-minute unstructured speech prompt. A standardized cleaning pipeline was applied to the audio files, including a uniform format conversion, segment filtering for background noise, extraneous acoustic events, and file size. This resulted in 112 high-quality voice recordings, i.e., a rate of 25%. The ML modeling strategy utilized the Allie ML framework on 80% of the data for training on the binary classification task (depressed or not). The analysis included steps for feature selection using referenced speech audio embeddings, followed by a TPOT classifier for ML. We report the validation results for the 20% held-out data. For PHQ-8, we achieved a balanced accuracy of ~71%, precision of 73%, recall of 68%, and AUC of 0.71. For EPDS, we achieved a balanced accuracy of 80%, precision of 92%, recall of 80%, and AUC of 0.80. In conclusion, we demonstrate the feasibility at scale and a promising accuracy for detecting PND using 120 seconds of unstructured speech audio data in expectant mothers.
Forecasting Intraday Volume in Equity Markets with Machine Learning
Authors: Mihai Cucuringu, Kang Li, Chao Zhang
Date: 2025-05-13 00:00:00

This study focuses on forecasting intraday trading volumes, a crucial component for portfolio implementation, especially in high-frequency (HF) trading environments. Given the current scarcity of flexible methods in this area, we employ a suite of machine learning (ML) models enriched with numerous HF predictors to enhance the predictability of intraday trading volumes. Our findings reveal that intraday stock trading volume is highly predictable, especially with ML and considering commonality. Additionally, we assess the economic benefits of accurate volume forecasting through Volume Weighted Average Price (VWAP) strategies. The results demonstrate that precise intraday forecasting offers substantial advantages, providing valuable insights for traders to optimize their strategies.
DeFi Liquidation Risk Modeling Using the Reflection Principle for Zero-Drift Brownian Motion
Authors: Timofei Belenko, Georgii Vosorov
Date: 2025-05-12 00:00:00

In this paper, we propose an analytical method to compute the collateral liquidation probability in decentralized finance (DeFi) stablecoin single-collateral lending. Our approach models the collateral exchange rate as a zero-drift geometric Brownian motion, converts it into a regular zero-drift Brownian motion, and employs the reflection principle to derive the liquidation probability. Unlike most existing methods that rely on computationally intensive simulations such as Monte Carlo, our formula provides a lightweight, exact solution. This advancement offers a more efficient alternative for risk assessment in DeFi platforms.
Copula Analysis of Risk: A Multivariate Risk Analysis for VaR and CoVaR using Copulas and DCC-GARCH
Authors: Aryan Singh, Paul O Reilly, Daim Sharif, Patrick Haughey, Eoghan McCarthy, Sathvika Thorali Suresh, Aakhil Anvar, Adarsh Sajeev Kumar
Date: 2025-05-11 00:00:00
Tasks: Time Series

A multivariate risk analysis for VaR and CVaR using different copula families is performed on historical financial time series fitted with DCC-GARCH models. A theoretical background is provided alongside a comparison of goodness-of-fit across different copula families to estimate the validity and effectiveness of approaches discussed.
The Exploratory Multi-Asset Mean-Variance Portfolio Selection using Reinforcement Learning
Authors: Yu Li, Yuhan Wu, Shuhua Zhang
Date: 2025-05-12 00:00:00
Tasks: Reinforcement Learning (RL)

In this paper, we study the continuous-time multi-asset mean-variance (MV) portfolio selection using a reinforcement learning (RL) algorithm, specifically the soft actor-critic (SAC) algorithm, in the time-varying financial market. A family of Gaussian portfolio selections is derived, and a policy iteration process is crafted to learn the optimal exploratory portfolio selection. We prove the convergence of the policy iteration process theoretically, based on which the SAC algorithm is developed. To improve the algorithm's stability and the learning accuracy in the multi-asset scenario, we divide the model parameters that influence the optimal portfolio selection into three parts, and learn each part progressively. Numerical studies in the simulated and real financial markets confirm the superior performance of the proposed SAC algorithm under various criteria.