pwc-archive/papers-with-abstracts huggingface dataset:
Talking to the brain: Using Large Language Models as Proxies to Model Brain Semantic Representation
Authors: Xin Liu, Ziyue Zhang, Jingxin Nie
Date: 2025-02-26 00:00:00
Tasks: Question Answering, valid, Visual Question Answering, Visual Question Answering (VQA)

Traditional psychological experiments utilizing naturalistic stimuli face challenges in manual annotation and ecological validity. To address this, we introduce a novel paradigm leveraging multimodal large language models (LLMs) as proxies to extract rich semantic information from naturalistic images through a Visual Question Answering (VQA) strategy for analyzing human visual semantic representation. LLM-derived representations successfully predict established neural activity patterns measured by fMRI (e.g., faces, buildings), validating its feasibility and revealing hierarchical semantic organization across cortical regions. A brain semantic network constructed from LLM-derived representations identifies meaningful clusters reflecting functional and contextual associations. This innovative methodology offers a powerful solution for investigating brain semantic organization with naturalistic stimuli, overcoming limitations of traditional annotation methods and paving the way for more ecologically valid explorations of human cognition.
Bridging Critical Gaps in Convergent Learning: How Representational Alignment Evolves Across Layers, Training, and Distribution Shifts
Authors: Chaitanya Kapoor, Sudhanshu Srivastava, Meenakshi Khosla
Date: 2025-02-26 00:00:00

Understanding convergent learning -- the degree to which independently trained neural systems -- whether multiple artificial networks or brains and models -- arrive at similar internal representations -- is crucial for both neuroscience and AI. Yet, the literature remains narrow in scope -- typically examining just a handful of models with one dataset, relying on one alignment metric, and evaluating networks at a single post-training checkpoint. We present a large-scale audit of convergent learning, spanning dozens of vision models and thousands of layer-pair comparisons, to close these long-standing gaps. First, we pit three alignment families against one another -- linear regression (affine-invariant), orthogonal Procrustes (rotation-/reflection-invariant), and permutation/soft-matching (unit-order-invariant). We find that orthogonal transformations align representations nearly as effectively as more flexible linear ones, and although permutation scores are lower, they significantly exceed chance, indicating a privileged representational basis. Tracking convergence throughout training further shows that nearly all eventual alignment crystallizes within the first epoch -- well before accuracy plateaus -- indicating it is largely driven by shared input statistics and architectural biases, not by the final task solution. Finally, when models are challenged with a battery of out-of-distribution images, early layers remain tightly aligned, whereas deeper layers diverge in proportion to the distribution shift. These findings fill critical gaps in our understanding of representational convergence, with implications for neuroscience and AI.
Deciphering Functions of Neurons in Vision-Language Models
Authors: Jiaqi Xu, Cuiling Lan, Xuejin Chen, Yan Lu
Date: 2025-02-10 00:00:00

The burgeoning growth of open-sourced vision-language models (VLMs) has catalyzed a plethora of applications across diverse domains. Ensuring the transparency and interpretability of these models is critical for fostering trustworthy and responsible AI systems. In this study, our objective is to delve into the internals of VLMs to interpret the functions of individual neurons. We observe the activations of neurons with respects to the input visual tokens and text tokens, and reveal some interesting findings. Particularly, we found that there are neurons responsible for only visual or text information, or both, respectively, which we refer to them as visual neurons, text neurons, and multi-modal neurons, respectively. We build a framework that automates the explanation of neurons with the assistant of GPT-4o. Meanwhile, for visual neurons, we propose an activation simulator to assess the reliability of the explanations for visual neurons. System statistical analyses on top of one representative VLM of LLaVA, uncover the behaviors/characteristics of different categories of neurons.
Co-Translational mRNA Decay in Plants: Recent advances and future directions
Authors: Jean-Marc Deragon, Rémy Merret
Date: 2025-02-25 00:00:00
Tasks: Translation

Tight regulation of messenger RNA (mRNA) stability is essential to ensure accurate gene expression in response to developmental and environmental cues. mRNA stability is controlled by mRNA decay pathways, which have traditionally been proposed to occur independently of translation. However, the recent discovery of a co-translational mRNA decay pathway (also known as CTRD) reveals that mRNA translation and decay can be coupled. While being translated, a mRNA can be targeted for degradation. This pathway was first described in yeast and rapidly identified in several plant species. This review explores recent advances in our understanding of CTRD in plants, emphasizing its regulation and its importance for development and stress response. The different metrics used to assess CTRD activity are also presented. Furthermore, this review outlines future directions to explore the importance of mRNA decay in maintaining mRNA homeostasis in plants.
Eigenvector-Based Sensitivity Analysis of Contact Patterns in Epidemic Modeling
Authors: Evans Kiptoo Korir, Zsolt Vizi
Date: 2025-02-27 00:00:00
Tasks: Sensitivity

Understanding how age-specific social contact patterns and susceptibility influence infectious disease transmission is crucial for accurate epidemic modeling. This study presents an eigenvector-based sensitivity analysis framework to quantify the impact of age-structured interactions on disease spread. By applying perturbation analysis to the Next Generation Matrix, we reformulate the basic reproduction number, $\mathcal{R}_0$, as a generalized eigenproblem, enabling the identification of key age group interactions that drive transmission. Using real-world COVID-19 contact data from Hungary, we demonstrate the framework's ability to highlight critical transmission pathways. We compare these findings with results obtained earlier using Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficients (PRCC), validating the effectiveness of our approach. Additionally, we extend the analysis to contact structures in the UK and British Columbia, Canada, providing broader epidemiological insights. This work enhances our understanding of demographic interactions in epidemic propagation and offers a robust methodological foundation for improving infectious disease modeling and informing public health interventions.
The effect of environmental factors in biofilm and phage interactions in Agent Based Model
Authors: Blessing Emerenini, Edward Beck, Kathryn Cantrel, Vedat Kurtay
Date: 2025-02-22 00:00:00

As antibiotic resistance continues to pose a significant threat to public health, alternative treatments are urgently needed. Phage therapy, which utilizes bacteriophages to specifically target bacterial pathogens, has emerged as a promising solution. Given that bacteria often exist in biofilms -complex micro-communities that complicate treatment strategies, there is a clear need for models that account for spatial dynamics. This study aims to employ mathematical and statistical methodologies to identify optimal treatment strategies involving phage-antibiotic combinations. We developed an agent-based model to analyze how environmental factors (e.g. temperature, pH, and resource availability) influence bacteria-phage interactions during therapy, focusing on both healthy and immunocompromised patients. Utilizing \textit{Escherichia coli} as a case study, we observed that bacterial cells exhibit mutations that enhance their adaptability to varying environmental conditions and treatment approaches. Our findings suggest that the effectiveness of therapies targeting pathogenic and mutated bacterial cells can be significantly improved through strategic control of application timing and dosing. Additionally, we investigated the impact of biofilm structure on the efficacy of phage therapy, underscoring its importance in developing targeted treatment strategies.
Lagrangians and Newtonian analogues for Biological Systems
Authors: Andronikos Paliathanasis, Kevin Duffy
Date: 2025-02-18 00:00:00

This study investigates the potential for biological systems to be governed by a variational principle, suggesting that such systems may evolve to minimize or optimize specific quantities. To explore this idea, we focus on identifying Lagrange functions that can effectively model the dynamics of selected population systems. These functions provide a deeper understanding of population evolution by framing their behavior in terms of energy-like variables. We present an algorithm for generating Lagrangian functions applicable to a family of population dynamics models and demonstrate the equivalence between two-dimensional population models and a one-dimensional Newtonian mechanical analog. Furthermore, we explore the existence of conservation laws for these models, utilizing Noether's theorems to investigate their implications.
How cancer emerges: Data-driven universal insights into tumorigenesis via hallmark networks
Authors: Jiahe Wang, Yan Wu, Yuke Hou, Yang Li, Dachuan Xu, Changjing Zhuge, Yue Han
Date: 2025-02-27 00:00:00

Cancer is a complex disease driven by dynamic regulatory shifts that cannot be fully captured by individual molecular profiling. We employ a data-driven approach to construct a coarse-grained dynamic network model based on hallmark interactions, integrating stochastic differential equations with gene regulatory network data to explore key macroscopic dynamic changes in tumorigenesis. Our analysis reveals that network topology undergoes significant reconfiguration before hallmark expression shifts, serving as an early indicator of malignancy. A pan-cancer examination across $15$ cancer types uncovers universal patterns, where Tissue Invasion and Metastasis exhibits the most significant difference between normal and cancer states, while the differences in Reprogramming Energy Metabolism are the least pronounced, consistent with the characteristic features of tumor biology. These findings reinforce the systemic nature of cancer evolution, highlighting the potential of network-based systems biology methods for understanding critical transitions in tumorigenesis.
Purported quantitative support for multiple introductions of SARS-CoV-2 into humans is an artefact of an imbalanced hypothesis testing framework
Authors: Angus McCowan
Date: 2025-02-27 00:00:00

A prominent report claimed substantial support for two introductions of SARS-CoV-2 into humans using a calculation that combined phylodynamic inferences and epidemic models. Inspection of the calculation identifies an imbalance in the hypothesis testing framework that confounds this result; the single-introduction model was tested against more stringent conditions than the two-introduction model. Here, I show that when the two-introduction model is tested against the same conditions, the support disappears.
Homeostatic Kinematic Growth Model for Arteries -- Residual Stresses and Active Response
Authors: Klemens Uhlmann, Daniel Balzani
Date: 2025-02-27 00:00:00

A simple kinematic growth model for muscular arteries is presented which allows the incorporation of residual stresses such that a homeostatic in-vivo stress state under physiological loading is obtained. To this end, new evolution equations for growth are proposed, which avoid issues with instability of final growth states known from other kinematric growth models. These evolution equations are connected to a new set of growth driving forces. By introducing a formulation using the principle Cauchy stresses, reasonable in vivo stress distributions can be obtained while ensuring realistic geometries of arteries after growth. To incorporate realistic Cauchy stresses for muscular arteries under varying loading conditions, which appear in vivo, e.g., due to physical activity, the growth model is combined with a newly proposed stretch-dependent model for smooth muscle activation. To account for the changes of geometry and mechanical behavior during the growth process, an optimization procedure is described which leads to a more accurate representation of an arterial ring and its mechanical behavior after the growth-related homogenization of the stresses is reached. Based thereon, parameters are adjusted to experimental data of a healthy middle cerebral artery of a rat showing that the proposed model accurately describes real material behavior. The successful combination of growth and active response indicates that the new growth model can be used without problems for modeling active tissues under various conditions.
Association of normalization, non-differentially expressed genes and data source with machine learning performance in intra-dataset or cross-dataset modelling of transcriptomic and clinical data
Authors: Fei Deng, Lanjing Zhang
Date: 2025-02-26 00:00:00

Cross-dataset testing is critical for examining machine learning (ML) model's performance. However, most studies on modelling transcriptomic and clinical data only conducted intra-dataset testing. It is also unclear whether normalization and non-differentially expressed genes (NDEG) can improve cross-dataset modeling performance of ML. We thus aim to understand whether normalization, NDEG and data source are associated with performance of ML in cross-dataset testing. The transcriptomic and clinical data shared by the lung adenocarcinoma cases in TCGA and ONCOSG were used. The best cross-dataset ML performance was reached using transcriptomic data alone and statistically better than those using transcriptomic and clinical data. The best balance accuracy, area under curve and accuracy were significantly better in ML algorithms training on TCGA and tested on ONCOSG than those trained on ONCOSG and tested on TCGA (p<0.05 for all). Normalization and NDEG greatly improved intra-dataset ML performances in both datasets, but not in cross-dataset testing. Strikingly, modelling transcriptomic data of ONCOSG alone outperformed modelling transcriptomic and clinical data whereas including clinical data in TCGA did not significantly impact ML performance, suggesting limited clinical data value or an overwhelming influence of transcriptomic data in TCGA. Performance gains in intra-dataset testing were more pronounced for ML models trained on ONCOSG than TCGA. Among the six ML models compared, Support vector machine was the most frequent best-performer in both intra-dataset and cross-dataset testing. Therefore, our data show data source, normalization and NDEG are associated with intra-dataset and cross-dataset ML performance in modelling transcriptomic and clinical data.
Leveraging Retrieval-Augmented Generation and Large Language Models to Predict SERCA-Binding Protein Fragments from Cardiac Proteomics Data
Authors: Taylor A Phillips, Alejandro W. Huskey, Patrick T. Huskey, Seth L. Robia, Peter M. Kekenes-Huskey
Date: 2025-02-26 00:00:00
Tasks: Large Language Model, RAG, Retrieval-augmented Generation

Large language models (LLMs) have shown promise in various natural language processing tasks, including their application to proteomics data to classify protein fragments. In this study, we curated a limited mass spectrometry dataset with 1000s of protein fragments, consisting of proteins that appear to be attached to the endoplasmic reticulum in cardiac cells, of which a fraction was cloned and characterized for their impact on SERCA, an ER calcium pump. With this limited dataset, we sought to determine whether LLMs could correctly predict whether a new protein fragment could bind SERCA, based only on its sequence and a few biophysical characteristics, such as hydrophobicity, determined from that sequence. To do so, we generated random sequences based on cloned fragments, embedded the fragments into a retrieval augmented generation (RAG) database to group them by similarity, then fine-tuned large language model (LLM) prompts to predict whether a novel sequence could bind SERCA. We benchmarked this approach using multiple open-source LLMs, namely the Meta/llama series, and embedding functions commonly available on the Huggingface repository. We then assessed the generalizability of this approach in classifying novel protein fragments from mass spectrometry that were not initially cloned for functional characterization. By further tuning the prompt to account for motifs, such as ER retention sequences, we improved the classification accuracy by and identified several proteins predicted to localize to the endoplasmic reticulum and bind SERCA, including Ribosomal Protein L2 and selenoprotein S. Although our results were based on proteomics data from cardiac cells, our approach demonstrates the potential of LLMs in identifying novel protein interactions and functions with very limited proteomic data.
Spatial Analysis of Neuromuscular Junctions Activation in Three-Dimensional Histology-based Muscle Reconstructions
Authors: Alessandro Ascani Orsini, Manan Bhatt, Pierce L. Perkins, Siyu Wang, Kiara N. Quinn, Kenzi Griffith, Fausto Kang, Nitish V. Thakor
Date: 2025-02-25 00:00:00
Tasks: 3D Reconstruction

Histology has long been a foundational technique for studying anatomical structures through tissue slicing. Advances in computational methods now enable three dimensional (3D) reconstruction of organs from histology images, enhancing the analysis of structural and functional features. Here, we present a novel multimodal computational method to reconstruct rodent muscles in 3D using classical image processing and data analysis techniques, analyze their structural features and correlate them to previously recorded electrophysiological data. The algorithm analyzes spatial distribution patterns of features identified through histological staining, normalizing them across multiple samples. Further, the algorithm successfully correlates spatial patterns with high density epimysial ElectroMyoGraphy (hdEMG) recordings, providing a multimodal perspective on neuromuscular dynamics, linking spatial and electrophysiological information. The code was validated by looking at the distribution of NeuroMuscular Junctions (NMJs) in naive soleus muscles and compared the distributions and patterns observed with ones observed in previous literature. Our results showed consistency with the expected results, validating our method for features and pattern recognition. The multimodal aspect was shown in a naive soleus muscle, where a strong correlation was found between motor unit locations derived via hdEMG, and NMJ locations obtained from histology, highlighting their spatial relationship. This multimodal analysis tool integrates 3D structural data with electrophysiological activity, opening new avenues in muscle diagnostics, regenerative medicine, and personalized therapies where spatial insights could one day predict electrophysiological behavior or vice versa.
A Method for Evaluating the Interpretability of Machine Learning Models in Predicting Bond Default Risk Based on LIME and SHAP
Authors: Yan Zhang, Lin Chen, Yixiang Tian
Date: 2025-02-26 00:00:00
Tasks: Prediction

Interpretability analysis methods for artificial intelligence models, such as LIME and SHAP, are widely used, though they primarily serve as post-model for analyzing model outputs. While it is commonly believed that the transparency and interpretability of AI models diminish as their complexity increases, currently there is no standardized method for assessing the inherent interpretability of the models themselves. This paper uses bond market default prediction as a case study, applying commonly used machine learning algorithms within AI models. First, the classification performance of these algorithms in default prediction is evaluated. Then, leveraging LIME and SHAP to assess the contribution of sample features to prediction outcomes, the paper proposes a novel method for evaluating the interpretability of the models themselves. The results of this analysis are consistent with the intuitive understanding and logical expectations regarding the interpretability of these models.
Optimal risk-aware interest rates for decentralized lending protocols
Authors: Bastien Baude, Damien Challet, Ioane Muni Toke
Date: 2025-02-27 00:00:00

Decentralized lending protocols within the decentralized finance ecosystem enable the lending and borrowing of crypto-assets without relying on traditional intermediaries. Interest rates in these protocols are set algorithmically and fluctuate according to the supply and demand for liquidity. In this study, we propose an agent-based model tailored to a decentralized lending protocol and determine the optimal interest rate model. When the responses of the agents are linear with respect to the interest rate, the optimal solution is derived from a system of Riccati-type ODEs. For nonlinear behaviors, we propose a Monte-Carlo estimator, coupled with deep learning techniques, to approximate the optimal solution. Finally, after calibrating the model using block-by-block data, we conduct a risk-adjusted profit and loss analysis of the liquidity pool under industry-standard interest rate models and benchmark them against the optimal interest rate model.
Corporate Fraud Detection in Rich-yet-Noisy Financial Graph
Authors: Shiqi Wang, Zhibo Zhang, Libing Fang, Cam-Tu Nguyen, Wenzhong Li
Date: 2025-02-26 00:00:00
Tasks: Fraud Detection, Knowledge Graph Embeddings

Corporate fraud detection aims to automatically recognize companies that conduct wrongful activities such as fraudulent financial statements or illegal insider trading. Previous learning-based methods fail to effectively integrate rich interactions in the company network. To close this gap, we collect 18-year financial records in China to form three graph datasets with fraud labels. We analyze the characteristics of the financial graphs, highlighting two pronounced issues: (1) information overload: the dominance of (noisy) non-company nodes over company nodes hinders the message-passing process in Graph Convolution Networks (GCN); and (2) hidden fraud: there exists a large percentage of possible undetected violations in the collected data. The hidden fraud problem will introduce noisy labels in the training dataset and compromise fraud detection results. To handle such challenges, we propose a novel graph-based method, namely, Knowledge-enhanced GCN with Robust Two-stage Learning (${\rm KeGCN}_{R}$), which leverages Knowledge Graph Embeddings to mitigate the information overload and effectively learns rich representations. The proposed model adopts a two-stage learning method to enhance robustness against hidden frauds. Extensive experimental results not only confirm the importance of interactions but also show the superiority of ${\rm KeGCN}_{R}$ over a number of strong baselines in terms of fraud detection effectiveness and robustness.