arXiv:2511.02565v1 Announce Type: cross Abstract: Subject-agnostic brain decoding, which aims to reconstruct continuous visual experiences from fMRI without subject-specific training, holds great potential for clinical applications. However, this direction remains underexplored due to challenges in cross-subject generalization and the complex nature of brain signals. In this work, we propose Visual Cortex Flow Architecture (VCFlow), a […]
Epidemic Momentum
arXiv:2511.01939v1 Announce Type: new Abstract: Infectious disease outbreaks have precipitated a profusion of mathematical models. We introduce a unifying concept of “epidemic momentum” — prevalence weighted by the capacity to infect in the future — and use it to reveal a common underlying geometry that corresponds to contours of a generic first integral. Exploiting this […]
Vibe Learning: Education in the age of AI
arXiv:2511.01956v1 Announce Type: cross Abstract: The debate over whether “thinking machines” could replace human intellectual labor has existed in both public and expert discussions since the mid-twentieth century, when the concept and terminology of Artificial Intelligence (AI) first emerged. For decades, this idea remained largely theoretical. However, with the recent advent of Generative AI – […]
From the RNA world to land plants: Evolutionary insights from tRNA genes
arXiv:2511.01943v1 Announce Type: new Abstract: Transfer RNAs (tRNAs) are universal adaptors of the genetic code, yet their evolutionary dynamics across photosynthetic eukaryotes remain underexplored. Here, we present the largest comparative re-analysis integrating the PlantRNA database with published data to explore tRNA gene evolution. We find that tRNA gene repertoires have been deeply shaped by ecological […]
Quantum-Enhanced Generative Models for Rare Event Prediction
arXiv:2511.02042v1 Announce Type: cross Abstract: Rare events such as financial crashes, climate extremes, and biological anomalies are notoriously difficult to model due to their scarcity and heavy-tailed distributions. Classical deep generative models often struggle to capture these rare occurrences, either collapsing low-probability modes or producing poorly calibrated uncertainty estimates. In this work, we propose the […]
Stochastic Models and Estimation of Undetected Infections in the Transmission of Zika Virus
arXiv:2511.01920v1 Announce Type: new Abstract: Zika fever, a mosquito-borne viral disease with potential severe neurological complications and birth defects, remains a significant public health concern. The epidemiological models often oversimplify the dynamics of Zika transmission by assuming immediate detection of all infected cases. This study provides an enhanced SEIR (Susceptible-Exposed-Infectious-Recovered) model to incorporate partial information […]
Geometric Data Valuation via Leverage Scores
arXiv:2511.02100v1 Announce Type: cross Abstract: Shapley data valuation provides a principled, axiomatic framework for assigning importance to individual datapoints, and has gained traction in dataset curation, pruning, and pricing. However, it is a combinatorial measure that requires evaluating marginal utility across all subsets of the data, making it computationally infeasible at scale. We propose a […]
The impact of nonheritable variation in division rates on population growth across environments
arXiv:2511.01905v1 Announce Type: new Abstract: We analyse a series of bacterial growth models with in-built inter-individual variation in rates of cell division. We show that this variation leads to reduced population growth in favorable regimes and reduced population killing in detrimental environments. By treating environmental stress as a model parameter, we then show that the […]
MM-UNet: Morph Mamba U-shaped Convolutional Networks for Retinal Vessel Segmentation
arXiv:2511.02193v1 Announce Type: cross Abstract: Accurate detection of retinal vessels plays a critical role in reflecting a wide range of health status indicators in the clinical diagnosis of ocular diseases. Recently, advances in deep learning have led to a surge in retinal vessel segmentation methods, which have significantly contributed to the quantitative analysis of vascular […]
Mirror-Neuron Patterns in AI Alignment
arXiv:2511.01885v1 Announce Type: new Abstract: As artificial intelligence (AI) advances toward superhuman capabilities, aligning these systems with human values becomes increasingly critical. Current alignment strategies rely largely on externally specified constraints that may prove insufficient against future super-intelligent AI capable of circumventing top-down controls. This research investigates whether artificial neural networks (ANNs) can develop patterns […]