arXiv subjects

Fei Zou

Publications and source records attributed to Fei Zou.

5 recordsLinked to original sources

arXiv2026

Anomalous Transverse Response and Multi-Field Ferrialtermagnetic-Ferroelectric Valve with CrSb Flakes

Altermagnets combine the zero-stray-field of antiferromagnets with the spin polarization of ferromagnets, showing great potential for spintronic applications. Here, we propose ferrialtermagnetism as a distinct subclass of altermagnetic family, where symmetry-inequivalent altermagnetic sublattices possess nonidentical Neel vectors, preventing mutual cancellation of alternating spin splitting and conferring intrinsic robustness against perturbations. This concept is realized in the three-atomic-layer CrSb (110) flakes, which exhibits spin splitting of 344 meV, moderate uniaxial magnetic anisotropy, and high Neel temperature of 657 K. The magneto-optical Kerr and the anomalous Hall effects are observed. Integrating this ferrialtermagnetic CrSb with ferroelectric Sc2CO2 and Cu spacer, we design an ferrialtermagnetic-ferroelectric valve. This device displays equilibrium tunneling magnetoresistance and electroresistance of ~10^3%, and non-equilibrium magnitudes under bias, thermal, or light field reaches ~10^4% with high spin filtering of 90%. The negative differential resistance and photogalvanic effects, and photocurrent extinction ratio of 283.8 are achieved. These findings establish ferrialtermagnetism as a fertile platform for multi-field-controlled, ultracompact, and self-powered spintronics and electronics.

cond-mat.mtrl-sci↗

arXiv2026

LoopPerm-CPD: A Robust Loop Permutation Framework for Automatic Multiple Change-Point Detection in Longitudinal Data

Human viral challenge studies, in which participants are deliberately inoculated with influenza strains such as H1N1 or H3N2 and monitored through longitudinal transcriptomic profiling before and after inoculation, are critical for characterizing dynamic biological immune responses to viral infection. A key analytical goal in such settings is to detect critical transition times, or change points, at which an underlying trajectory shifts direction or rate, indicating events such as the onset of an immune response or recovery. However, change-point detection in these longitudinal data is fundamentally challenging because observations are often sparse and irregularly spaced, sample sizes are small, outliers are common, and the number of change points is unknown in advance. To address these challenges, we propose LoopPerm-CPD, a robust change-point detection approach with a built-in loop permutation procedure for automatic multiple change-point detection. The method evaluates candidate slope change points and assesses their significance using within-subject circular permutation combined with binary segmentation, jointly estimating both the number and locations of change points. The accompanying R package, LoopPerm-CPD, implements this framework and flexibly accommodates generalized least squares, quantile regression, and quantile rank-score statistics for different types of longitudinal outcomes. The proposed approach is evaluated through simulations, demonstrating Type I error control and improved power compared with competing methods. Applied to real data, the framework identifies interpretable transition points in multiple human respiratory viral inoculation studies. Together, these results establish LoopPerm-CPD and its companion software as a robust and user-friendly tool for change-point detection in complex human longitudinal cohort data.

stat.ME↗

arXiv2025

HR-VILAGE-3K3M: A Human Respiratory Viral Immunization Longitudinal Gene Expression Dataset for Systems Immunity

Respiratory viral infections pose a global health burden, yet the cellular immune mechanisms underlying protection and pathology remain unclear. Natural infection cohorts often lack pre-exposure baselines and time-controlled sampling, whereas inoculation and vaccination trials generate well-structured longitudinal transcriptomic data. However, these datasets are scattered across repositories and processed inconsistently, hindering integrative and AI-driven analyses. To address these challenges, we developed the Human Respiratory Viral Immunization LongitudinAl Gene Expression (HR-VILAGE-3K3M) repository: an AI-ready resource integrating bulk and single-cell transcriptomic profiles from 3,178 subjects across 66 studies. The dataset spans vaccination, inoculation, and mixed exposures, with samples from blood and nasal swabs collected from public repositories including GEO, ImmPort, and ArrayExpress. We curated and harmonized subject-level metadata, standardized outcome measures, and applied unified preprocessing with rigorous quality control. We further provide benchmark analyses illustrating its utility. This resource supports discovery of biomarkers, immune mechanisms, and methodological development. As one of the largest longitudinal transcriptomic resources for human respiratory viral immunization, HR-VILAGE-3K3M enables reproducible and scalable analyses to accelerate vaccine and antiviral research.

q-bio.GN↗

arXiv2014

IsoDOT Detects Differential RNA-isoform Expression/Usage with respect to a Categorical or Continuous Covariate with High Sensitivity and Specificity

We have developed a statistical method named IsoDOT to assess differential isoform expression (DIE) and differential isoform usage (DIU) using RNA-seq data. Here isoform usage refers to relative isoform expression given the total expression of the corresponding gene. IsoDOT performs two tasks that cannot be accomplished by existing methods: to test DIE/DIU with respect to a continuous covariate, and to test DIE/DIU for one case versus one control. The latter task is not an uncommon situation in practice, e.g., comparing paternal and maternal allele of one individual or comparing tumor and normal sample of one cancer patient. Simulation studies demonstrate the high sensitivity and specificity of IsoDOT. We apply IsoDOT to study the effects of haloperidol treatment on mouse transcriptome and identify a group of genes whose isoform usages respond to haloperidol treatment.

stat.AP↗

arXiv2012

Convergence and prediction of principal component scores in high-dimensional settings

A number of settings arise in which it is of interest to predict Principal Component (PC) scores for new observations using data from an initial sample. In this paper, we demonstrate that naive approaches to PC score prediction can be substantially biased toward 0 in the analysis of large matrices. This phenomenon is largely related to known inconsistency results for sample eigenvalues and eigenvectors as both dimensions of the matrix increase. For the spiked eigenvalue model for random matrices, we expand the generality of these results, and propose bias-adjusted PC score prediction. In addition, we compute the asymptotic correlation coefficient between PC scores from sample and population eigenvectors. Simulation and real data examples from the genetics literature show the improved bias and numerical properties of our estimators.

math.ST↗