pwc-archive/papers-with-abstracts huggingface dataset:
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift
Authors: Baozhen Wang, Xingye Qiao
Date: 2025-02-25 00:00:00
Tasks: Conformal Prediction, Transfer Learning

In many real applications of statistical learning, collecting sufficiently many training data is often expensive, time-consuming, or even unrealistic. In this case, a transfer learning approach, which aims to leverage knowledge from a related source domain to improve the learning performance in the target domain, is more beneficial. There have been many transfer learning methods developed under various distributional assumptions. In this article, we study a particular type of classification problem, called conformal prediction, under a new distributional assumption for transfer learning. Classifiers under the conformal prediction framework predict a set of plausible labels instead of one single label for each data instance, affording a more cautious and safer decision. We consider a generalization of the \textit{covariate shift with posterior drift} setting for transfer learning. Under this setting, we propose a weighted conformal classifier that leverages both the source and target samples, with a coverage guarantee in the target domain. Theoretical studies demonstrate favorable asymptotic properties. Numerical studies further illustrate the usefulness of the proposed method.
Characterizing the Conformational States of G Protein Coupled Receptors Generated with AlphaFold
Authors: Garima Chib, Parisa Mollaei, Amir Barati Farimani
Date: 2025-02-24 00:00:00
Tasks: Drug Discovery

G-Protein Coupled Receptors (GPCRs) are integral to numerous physiological processes and are the target of approximately one-third of FDA-approved therapeutics. Despite their significance, only a limited subset of GPCRs has been successfully targeted, primarily due to challenges in accurately modeling their structures. AlphaFold, a state-of-the-art deep learning model, has demonstrated remarkable capability in predicting protein structures with high accuracy. This study conducts an evaluation of AlphaFold performance in predicting GPCR structures and their conformational states by comparing its predictions to experimentally determined structures using metrics such as average deformation between alpha carbon atoms and the Helix 3 - Helix 6 (H3-H6) distance. Our analysis reveals that both AlphaFold 2 (AF2) and AlphaFold 3 (AF3) produce more accurate predictions for GPCRs in inactive conformations, with lower activity levels correlating with smaller deformations. Conversely, higher activity levels are associated with increased variability in AlphaFold performance due to difficulties with accurately predicting conformational changes upon GPCR activation and ligand binding. Additionally, AlphaFold performance varies across different GPCR classes, influenced by the availability and quality of training data as well as the structural complexity and diversity of the receptors. These findings demonstrate the potential of AlphaFold in advancing drug discovery efforts, while also highlighting the necessity for continued refinement to enhance predictive accuracy for active conformations.
Non-Markovain Quantum State Diffusion for the Tunneling in SARS-COVID-19 virus
Authors: Muhammad Waqas Haseeb, Mohamad Toutounji
Date: 2025-02-07 00:00:00

In the context of biology, unlike the comprehensively established Standard Model in physics, many biological processes lack a complete theoretical framework and are often described phenomenologically. A pertinent example is olfaction -- the process through which humans and animals distinguish various odors. The conventional biological explanation for olfaction relies on the lock and key model, which, while useful, does not fully account for all observed phenomena. As an alternative or complement to this model, vibration-assisted electron tunneling has been proposed. Drawing inspiration from the vibration-assisted electron tunneling model for olfaction, we have developed a theoretical model for electron tunneling in SARS-CoV-2 virus infection within a non-Markovian framework. We approach this by solving the non-Markovian quantum stochastic Schrodinger equation. In our model, the spike protein and the GPCR receptor are conceptualized as a dimer, utilizing the spin-Boson model to facilitate the description of electron tunneling. Our analysis demonstrates that electron tunneling in this context exhibits inherently non-Markovian characteristics, extending into the intermediate and strong coupling regimes between the dimer components. This behavior stands in stark contrast to predictions from Markovian models, which fail to accurately describe electron tunneling in the strong coupling limit. Notably, Markovian approximations often lead to unphysical negative probabilities in this regime, underscoring their limitations and highlighting the necessity of incorporating non-Markovian dynamics for a more realistic description of biological quantum processes. This approach not only broadens our understanding of viral infection mechanisms but also enhances the biological accuracy and relevance of our theoretical framework in describing complex biological interactions.
Multilevel classification framework for breast cancer cell selection and its integration with advanced disease models
Authors: Catarina Franco Jones, Diogo Dias, Ana C. Moreira, Gil Gonçalves, Stefano Cinti, Mustafa B. A. Djamgoz, Frederico Castelo Ferreira, Paola Sanjuan-Alberte, Rosalia Moreddu
Date: 2025-02-21 00:00:00
Tasks: Drug Discovery, Experimental Design, Model Selection

Breast cancer cell lines are indispensable tools for unraveling disease mechanisms, enabling drug discovery, and developing personalized treatments, yet their heterogeneity and inconsistent classification pose significant challenges in model selection and data reproducibility. This review aims at providing a comprehensive and user-friendly framework for broadly mapping the features of breast cancer types and commercially available human breast cancer cell lines, defining absolute criteria, i.e. objective features such as origin (e.g., MDA-MB, MCF), histological subtype (ductal, lobular), hormone receptor status (ER/PR/HER2), and genetic mutations (BRCA1, TP53), and relative criteria, which contextualize functional behaviors like metastatic potential, drug sensitivity, and genomic instability. It then examines how the proposed framework could be applied to cell line screening in advanced and emerging disease models. By supporting better informed choices, this work aims to improve experimental design and strengthen the connection between in vitro breast cancer studies and their clinical translation.
Weakly reversible deficiency zero realizations of reaction networks
Authors: Neal Buxton, Gheorghe Craciun, Abhishek Deshpande, Casian Pantea
Date: 2025-02-10 00:00:00

We prove that if a given reaction network $\mathcal{N}$ has a weakly reversible deficiency zero realization for all choice of rate constants, then there exists a $\textit{unique}$ weakly reversible deficiency zero network $\mathcal{N}'$ such that $\mathcal{N}$ is realizable by $\mathcal{N}'$. Additionally, we propose an algorithm to find this weakly reversible deficiency zero network $\mathcal{N}'$ when it exists.
Modeling Neural Activity with Conditionally Linear Dynamical Systems
Authors: Victor Geadah, Amin Nejatbakhsh, David Lipshutz, Jonathan W. Pillow, Alex H. Williams
Date: 2025-02-25 00:00:00
Tasks: Bayesian Inference

Neural population activity exhibits complex, nonlinear dynamics, varying in time, over trials, and across experimental conditions. Here, we develop Conditionally Linear Dynamical System (CLDS) models as a general-purpose method to characterize these dynamics. These models use Gaussian Process (GP) priors to capture the nonlinear dependence of circuit dynamics on task and behavioral variables. Conditioned on these covariates, the data is modeled with linear dynamics. This allows for transparent interpretation and tractable Bayesian inference. We find that CLDS models can perform well even in severely data-limited regimes (e.g. one trial per condition) due to their Bayesian formulation and ability to share statistical power across nearby task conditions. In example applications, we apply CLDS to model thalamic neurons that nonlinearly encode heading direction and to model motor cortical neurons during a cued reaching task
Mapping of Subjective Accounts into Interpreted Clusters (MOSAIC): Topic Modelling and LLM applied to Stroboscopic Phenomenology
Authors: Romy Beauté, David J. Schwartzman, Guillaume Dumas, Jennifer Crook, Fiona Macpherson, Adam B. Barrett, Anil K. Seth
Date: 2025-02-25 00:00:00

Stroboscopic light stimulation (SLS) on closed eyes typically induces simple visual hallucinations (VHs), characterised by vivid, geometric and colourful patterns. A dataset of 862 sentences, extracted from 422 open subjective reports, was recently compiled as part of the Dreamachine programme (Collective Act, 2022), an immersive multisensory experience that combines SLS and spatial sound in a collective setting. Although open reports extend the range of reportable phenomenology, their analysis presents significant challenges, particularly in systematically identifying patterns. To address this challenge, we implemented a data-driven approach leveraging Large Language Models and Topic Modelling to uncover and interpret latent experiential topics directly from the Dreamachine's text-based reports. Our analysis confirmed the presence of simple VHs typically documented in scientific studies of SLS, while also revealing experiences of altered states of consciousness and complex hallucinations. Building on these findings, our computational approach expands the systematic study of subjective experience by enabling data-driven analyses of open-ended phenomenological reports, capturing experiences not readily identified through standard questionnaires. By revealing rich and multifaceted aspects of experiences, our study broadens our understanding of stroboscopically-induced phenomena while highlighting the potential of Natural Language Processing and Large Language Models in the emerging field of computational (neuro)phenomenology. More generally, this approach provides a practically applicable methodology for uncovering subtle hidden patterns of subjective experience across diverse research domains.
Dynamical phases of short-term memory mechanisms in RNNs
Authors: Bariscan Kurtkaya, Fatih Dinc, Mert Yuksekgonul, Marta Blanco-Pozo, Ege Cirakman, Mark Schnitzer, Yucel Yemez, Hidenori Tanaka, Peng Yuan, Nina Miolane
Date: 2025-02-24 00:00:00

Short-term memory is essential for cognitive processing, yet our understanding of its neural mechanisms remains unclear. Neuroscience has long focused on how sequential activity patterns, where neurons fire one after another within large networks, can explain how information is maintained. While recurrent connections were shown to drive sequential dynamics, a mechanistic understanding of this process still remains unknown. In this work, we introduce two unique mechanisms that can support this form of short-term memory: slow-point manifolds generating direct sequences or limit cycles providing temporally localized approximations. Using analytical models, we identify fundamental properties that govern the selection of each mechanism. Precisely, on short-term memory tasks (delayed cue-discrimination tasks), we derive theoretical scaling laws for critical learning rates as a function of the delay period length, beyond which no learning is possible. We empirically verify these results by training and evaluating approximately 80,000 recurrent neural networks (RNNs), which are publicly available for further analysis. Overall, our work provides new insights into short-term memory mechanisms and proposes experimentally testable predictions for systems neuroscience.
Automated generation of epilepsy surgery resection masks; The RAMPS pipeline
Authors: Callum Simpson, Gerard Hall, John S. Duncan, Yujiang Wang, Peter N. Taylor
Date: 2025-02-24 00:00:00

MRI-based delineation of brain tissue removed by epilepsy surgery can be challenging due to post-operative brain shift. In consequence, most studies use manual approaches which are prohibitively time-consuming for large sample sizes, require expertise, and can be prone to errors. We propose RAMPS (Resections And Masks in Preoperative Space), an automated pipeline to generate a 3D resection mask of pre-operative tissue. Our pipeline leverages existing software including FreeSurfer, SynthStrip, Sythnseg and ANTS to generate a mask in the same space as the patient's pre-operative T1 weighted MRI. We compare our automated masks against manually drawn masks and two other existing pipelines (Epic-CHOP and ResectVol). Comparing to manual masks (N=87), RAMPS achieved a median(IQR) dice similarity of 0.86(0.078) in temporal lobe resections, and 0.72(0.32) in extratemporal resections. In comparison to other pipelines, RAMPS had higher dice similarities (N=62) (RAMPS:0.86, Epic-CHOP: 0.72, ResectVol: 0.72). We release a user-friendly, easy to use pipeline, RAMPS, open source for accurate delineation of resected tissue.
How constraints on editing affects cultural evolution
Authors: Ofer Tchernichovski, Eitan Globerson, Peter Harrison, Nori Jacoby
Date: 2025-02-23 00:00:00

When is it beneficial to constrain creativity? Creativity thrives with freedom, but when people collaborate to create artifacts, there is tension between giving individuals freedom to revise, and protecting prior achievements. To test how imposing constraints may affect collective creativity, we performed cultural evolution experiments where participants collaborated to create melodies and images in chains. With melodies, we found that limiting step size (number of musical notes that can be changed) improved pleasantness ratings for created tunes. Similar results were observed in cohorts of musicians, and with different selection regimes. In contrast, limiting step size in creating images consistently reduced pleasantness. These conflicting findings suggest that in domains such as music, where artifacts can be easily damaged, and where evolutionary outcomes are hard to foresee, collective creativity may benefit from imposing small step sizes. We discuss parallels with search algorithms and the evolution of conservative birdsong cultures.
Reasoning within and between collective action problems
Authors: Ofer Tchernichovski, Seth Frey, Dalton C. Conley, Nori Jacoby
Date: 2025-02-23 00:00:00
Tasks: Decision Making

Understanding cooperation in social systems is challenging because the ever-changing rules that govern societies interact with individual actions, resulting in intricate collective outcomes. In virtual-world experiments, we allowed people to make changes in the systems that they are making decisions within and investigated how they weigh the influence of different rules in decision-making. When choosing between worlds differing in more than one rule, a naive heuristics model predicted participants decisions as well, and in some cases better, than game earnings (utility) or by the subjective quality of single rules. In contrast, when a subset of engaged participants made instantaneous (within-world) decisions, their behavior aligned very closely with objective utility and not with the heuristics model. Findings suggest that, whereas choices between rules may deviate from rational benchmarks, the frequency of real time cooperation decisions to provide feedback can be a reliable indicator of the objective utility of these rules.
Predictability of temporal network dynamics in normal ageing and brain pathology
Authors: Annalisa Caligiuri, David Papo, Görsev Yener, Bahar Güntekin, Tobias Galla, Lucas Lacasa, Massimiliano Zanin
Date: 2025-02-23 00:00:00

Spontaneous brain activity generically displays transient spatiotemporal coherent structures, which can selectively be affected in various neurological and psychiatric pathologies. Here we model the full brain's electroencephalographic activity as a high-dimensional functional network performing a trajectory in a latent graph phase space. This approach allows us to investigate the orbital stability of brain's activity and in particular its short-term predictability. We do this by constructing a non-parametric statistic quantifying the expansion of initially close functional network trajectories. We apply the method to cohorts of healthy ageing individuals, and patients previously diagnosed with Parkinson's or Alzheimer's disease. Results not only characterise brain dynamics from a new angle, but further show that functional network predictability varies in a marked scale-dependent way across healthy controls and patient groups. The path towards both pathologies is markedly different. Furthermore, healthy ageing's predictability appears to strongly differ from that of Parkinson's disease, but much less from that of patients with Alzheimer's disease.
Lattice-Based Pruning in Recurrent Neural Networks via Poset Modeling
Authors: Rakesh Sengupta
Date: 2025-02-23 00:00:00
Tasks: Functional Connectivity

Recurrent neural networks (RNNs) are central to sequence modeling tasks, yet their high computational complexity poses challenges for scalability and real-time deployment. Traditional pruning techniques, predominantly based on weight magnitudes, often overlook the intrinsic structural properties of these networks. We introduce a novel framework that models RNNs as partially ordered sets (posets) and constructs corresponding dependency lattices. By identifying meet irreducible neurons, our lattice-based pruning algorithm selectively retains critical connections while eliminating redundant ones. The method is implemented using both binary and continuous-valued adjacency matrices to capture different aspects of network connectivity. Evaluated on the MNIST dataset, our approach exhibits a clear trade-off between sparsity and classification accuracy. Moderate pruning maintains accuracy above 98%, while aggressive pruning achieves higher sparsity with only a modest performance decline. Unlike conventional magnitude-based pruning, our method leverages the structural organization of RNNs, resulting in more effective preservation of functional connectivity and improved efficiency in multilayer networks with top-down feedback. The proposed lattice-based pruning framework offers a rigorous and scalable approach for reducing RNN complexity while sustaining robust performance, paving the way for more efficient hierarchical models in both machine learning and computational neuroscience.
Epilepsy and its driving forces: understanding the forces behind epileptical pathogenisis
Authors: Shreya Shah, Manan Shah, Bhavin Parekh
Date: 2025-02-22 00:00:00

Epilepsy is a neurological disorder characterized by seizures and epileptic events intertwined with religious and personal beliefs since prehistoric times. This review paper explores the historical context and challenges in defining epilepsy. The formal definition was established twenty years ago, and the multifaceted causes of this neurological disorder. It aims to pave the way for personalised therapeutic strategies, research advancements, and informed public health planning to enhance the lives of those affected by this complex neurological condition. In addition, this review paper focuses on the mechanisms and etiologies of epileptogenesis, categorizing them by mechanisms and the underlying causes of the disorder. The review paper provides a brief overview of the current state of the art in the diagnosis, diagnosis, treatment, and treatment of epileptiform seizures.
Electrophysiological Investigation of Insect Pain Threshold
Authors: Marc Josep Montagut Marques, Pan Minghao, Ryuichi Okada, Midori Sakura, Kayo Hirose, Shinjiro Umezu
Date: 2025-02-22 00:00:00
Tasks: EEG, EEG Signal Classification

The question of whether insects experience pain has long been debated in neuroscience and animal behavior research. Increasing evidence suggests that insects possess the ability to detect and respond to noxious stimuli, exhibiting behaviors indicative of pain perception. This study investigates the relationship between pain stimuli and physiological responses in crickets (Gryllidae), focusing on heart rate (ECG) and brain wave (EEG) patterns. We applied a range of mechanical, chemical, thermal, and electrical stimuli to crickets, recording ECG and EEG data while employing a deep learning-based model to classify pain levels. Our findings revealed significant heart rate changes and EEG fluctuations in response to various stimuli, with the highest intensity stimuli inducing marked physiological stress. The AI-based analysis, utilizing AlexNet for EEG signal classification, achieved 90% accuracy in distinguishing between resting, low-pain, and high-pain states. While no social sharing of pain was observed through ECG measurements, these results contribute to the growing body of evidence supporting insect nociception and offer new insights into their physiological responses to external stressors. This research advances the understanding of insect pain mechanisms and demonstrates the potential for AI-driven analysis in entomological studies.
Interpretable Dual-Filter Fuzzy Neural Networks for Affective Brain-Computer Interfaces
Authors: Xiaowei Jiang, Yanan Chen, Nikhil Ranjan Pal, Yu-Cheng Chang, Yunkai Yang, Thomas Do, Chin-Teng Lin
Date: 2025-01-29 00:00:00
Tasks: Brain Computer Interface, Decision Making, EEG

Fuzzy logic provides a robust framework for enhancing explainability, particularly in domains requiring the interpretation of complex and ambiguous signals, such as brain-computer interface (BCI) systems. Despite significant advances in deep learning, interpreting human emotions remains a formidable challenge. In this work, we present iFuzzyAffectDuo, a novel computational model that integrates a dual-filter fuzzy neural network architecture for improved detection and interpretation of emotional states from neuroimaging data. The model introduces a new membership function (MF) based on the Laplace distribution, achieving superior accuracy and interpretability compared to traditional approaches. By refining the extraction of neural signals associated with specific emotions, iFuzzyAffectDuo offers a human-understandable framework that unravels the underlying decision-making processes. We validate our approach across three neuroimaging datasets using functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG), demonstrating its potential to advance affective computing. These findings open new pathways for understanding the neural basis of emotions and their application in enhancing human-computer interaction.