arXiv subjects

Tianxing Hu

Publications and source records attributed to Tianxing Hu.

4 recordsLinked to original sources

arXiv2026

Acceleration of Ultrahigh Energy Particles from Fast Radio Bursts

Two extreme events in the universe, fast radio bursts (FRBs) and cosmic rays (CRs), could be correlated, where FRBs with extreme field strength near their sources may contribute to CRs. This study investigates localized particle acceleration driven by FRB-like ultra-relativistic electromagnetic pulses in an electron--positron--ion plasma system. It is found ultra-high energy neutral plasma sheets form constantly via the front erosion of an FRB pulse. There are two regimes of ion acceleration depending upon the field strength and the plasma density: the piston regime driven by the Lorentz force of the pulse, and the wakefield regime dominated by charge separation field. The predicted energy scalings align well with particle-in-cell simulations. A power-law energy spectrum with an index close to the CRs naturally emerges during FRBs expansion outward. Detecting high-energy particles possibly produced by FRBs enables deeper insights into their origins and promotes the development of multi-messenger astronomy.

astro-ph.HE↗

arXiv2026

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data

Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but its complexity, which is marked by high dimensionality, sparsity, and batch effects, which poses major computational challenges. Transformer-based models have made significant advances in this domain but are often limited by their quadratic complexity and suboptimal handling of long-range dependencies. In this work, we introduce GeneMamba, a scalable and efficient foundation model for single-cell transcriptomics built on state space modeling. Leveraging the Bi-Mamba architecture, GeneMamba captures bidirectional gene context with linear-time complexity, offering substantial computational gains over transformer baselines. The model is pretrained on nearly 30 million cells and incorporates biologically informed objectives, including pathway-aware contrastive loss and rank-based gene encoding. We evaluate GeneMamba across diverse tasks, including multi-batch integration, cell type annotation, and gene-gene correlation, demonstrating strong performance, interpretability, and robustness. These results position GeneMamba as a practical and powerful alternative to transformer-based methods, advancing the development of biologically grounded, scalable tools for large-scale single-cell data analysis.

cs.CL↗

arXiv2026

Energization of Proton via Beam-Driven Ion Bernstein Waves in p11B Plasmas

Energizing background ions plays a pivotal role in all forms of thermal nuclear fusion, as it can increase the fusion reaction rate without affecting the overall mechanical equilibrium. This is particularly critical for p11B fusion due to its exceptionally high operating temperature and substantial energy losses from bremsstrahlung radiation. Here, we report a nonlinear mechanism that efficiently transfers the energy of injected heating beams to background protons in p11B mixed plasmas, via fully kinetic Particle-In-Cell (PIC) simulations. When a proton neutral beam is injected into p11B plasmas, it triggers the excitation of ion Bernstein waves (IBWs) at harmonics of the proton cyclotron frequency. In the initial linear stage, the energy channels to background electrons and protons might be comparable, consistent with theoretical model for the energy transfer. However, in the latter nonlinear stage, the dominant channel transfers to background protons, generating a non-Maxwellian population of energetic protons. This transition is driven by a nonlinear spectral cascade of IBWs toward lower frequencies and longer wavelengths, which strengthens wave proton coupling while suppressing wave electron coupling.

physics.plasm-ph↗

arXiv2025

Enhancing TCR-Peptide Interaction Prediction with Pretrained Language Models and Molecular Representations

Understanding the binding specificity between T-cell receptors (TCRs) and peptide-major histocompatibility complexes (pMHCs) is central to immunotherapy and vaccine development. However, current predictive models struggle with generalization, especially in data-scarce settings and when faced with novel epitopes. We present LANTERN (Large lAnguage model-powered TCR-Enhanced Recognition Network), a deep learning framework that combines large-scale protein language models with chemical representations of peptides. By encoding TCR \b{eta}-chain sequences using ESM-1b and transforming peptide sequences into SMILES strings processed by MolFormer, LANTERN captures rich biological and chemical features critical for TCR-peptide recognition. Through extensive benchmarking against existing models such as ChemBERTa, TITAN, and NetTCR, LANTERN demonstrates superior performance, particularly in zero-shot and few-shot learning scenarios. Our model also benefits from a robust negative sampling strategy and shows significant clustering improvements via embedding analysis. These results highlight the potential of LANTERN to advance TCR-pMHC binding prediction and support the development of personalized immunotherapies.

q-bio.QM↗