79 papers
The paper introduces CITS, a nonparametric statistical framework that overcomes the limitations of existing causal inference tools to accurately identify interpretable causal networks in high-resolution neural time series, as validated by both theoretical proofs and applications to large-scale mouse brain recordings.
This paper presents an agent-based model within the EpiCast framework that simulates the coupled dynamics of disease and fear transmission, demonstrating how non-local fear spread via media combined with strong behavioral responses can generate multiple epidemic waves and highlighting the critical importance of accounting for these feedback loops in outbreak management.
This paper develops a method to derive simple, analytically explicit upper or lower bounds for the survival probability of beneficial mutations in supercritical Galton-Watson processes by approximating their generating functions with fractional linear ones, and applies these results to model the evolution of quantitative traits under directional selection in finite populations.
This study proposes a label-free deep learning methodology using autofluorescence imaging to rapidly and accurately differentiate non-small cell lung cancer subtypes and generate clinical-grade virtual immunohistochemical stains, thereby streamlining diagnostic workflows without the need for traditional tissue processing.
This paper introduces CAN-STRESS, a publicly available multimodal dataset comprising physiological data from Empatica E4 wristbands and self-reported surveys from 82 participants, designed to investigate the distinct physiological stress responses of cannabis users compared to non-users in real-world settings.
The paper introduces UNISEP, a unified framework that standardizes sensor placement protocols across diverse sensing modalities and applications by defining anatomical landmark-based coordinate systems to enhance reproducibility, consistency, and interoperability in human motion capture and wearable health monitoring.
This paper proposes a nonlinear local mean-field approximation method that utilizes a first-order Taylor expansion of hazard rates to enable efficient and robust parameter estimation for quasi-reaction systems, particularly outperforming existing SDE and ODE-based approaches when dealing with large time gaps between observations and stiff biological dynamics.
This paper presents a hybrid discrete-continuum modeling framework that couples a stochastic agent-based model with partial differential equations to investigate the spatial dynamics of oncolytic virotherapy and immune system interactions, revealing that while both models generally agree, stochastic effects and the timing of immune responses are critical factors that can significantly influence therapeutic efficacy.
This paper presents an overview of GenomeBits, a physics-inspired signal analysis tool that utilizes Discrete Fourier Transform and quantum-based extensions to map nucleotide sequences into numerical signals, successfully revealing intrinsic genomic features, mutation patterns, and unique "order-disorder" transitions in viral genomes such as SARS-CoV-2 variants and Monkeypox.
This paper proposes an approximate expectation-maximization algorithm that directly reconstructs 3-D molecular structures from low signal-to-noise ratio cryo-EM micrographs, bypassing the traditional and often failing step of locating individual projection images.
This paper presents a multiscale mathematical model based on the kinetic theory of active particles to simulate and compare the efficacy of combined radiotherapy, chemotherapy, and anti-angiogenic therapy on glioma invasion within realistic brain geometries derived from DTI data.
SeekRBP is a novel sequence-structure framework that leverages reinforcement learning and a multi-armed bandit strategy to dynamically optimize negative sampling, thereby overcoming the limitations of traditional methods in accurately identifying receptor-binding proteins despite extreme sequence divergence and class imbalance.
This study reveals that the human hippocampus encodes positions of self, others, and gaze within geometrically related, mostly orthogonal neural subspaces that can be flexibly aligned via linear transformations, thereby enabling reliable individuation and generalization across different agents and viewpoints.
INTENSE is an open-source, information-theoretic framework that robustly detects and disentangles neuronal selectivity from calcium imaging data by accounting for temporal autocorrelation, indicator kinetics, and behavioral covariance to link large-scale neural activity with complex behaviors.
This paper demonstrates that a parameter-free biophysical model, derived from Shannon's information theory and the principle of minimal energy, accurately predicts how synaptic information efficiency (bits per Joule) decreases as conductance deviates from its natural value, confirming that synapses operate at signal-to-noise ratios that maximize energy efficiency.
This paper develops a mean-field game framework for SEIR epidemics on heterogeneous networks to characterize Nash equilibrium contact policies, revealing how the incubation period delays precautionary behavior and influences outbreak severity through a coupled Hamilton-Jacobi-Bellman/Kolmogorov system.
This review synthesizes two decades of theoretical and experimental research to argue that grid cells function as a biologically plausible, high-fidelity, non-linearly decodable code for path integration, offering valuable lessons for developing normative theories of neural computation.
This paper introduces a tractable and flexible normative framework for neural coding by optimizing representational similarity rather than direct neural activity, demonstrating that a broad class of such problems is convex and using this property to derive new results on model identifiability, the uniqueness of neural tunings, and the optimal structure of ON/OFF channels in retinal versus cortical codes.
This study demonstrates that enhancing morpho-kinematic analysis of leaf movements through non-linear descriptors, irrigation-context variables, and adaptive ensemble methods significantly improves the robustness and accuracy of low-cost, RGB-based plant water stress classification.