Searcharxiv⌕ Search

arXiv subjects

Oliver Li

Publications and source records attributed to Oliver Li.

8 recordsLinked to original sources
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↗
arXiv2024

Duality of differential operators and algebraic de Rham cohomology

Given a smooth proper morphism $f\colon X\rightarrow S$, we introduce a certain derived category where morphisms are permitted to be $\mathcal{O}_S$-linear differential operators. We then prove a generalisation of Serre duality that applies to two-term complexes of this type. We apply this to give a new proof of Poincar\'e duality for relative algebraic de Rham cohomology.

math.AG↗
arXiv2024

K-stability and space sextic curves of genus three

We study Fano threefolds that can be obtained by blowing up the three-dimensional projective space along a smooth curve of degree six and genus three. We produce many new K-stable examples of such threefolds, and we describe all finite groups that can act faithfully on them.

math.AG↗
arXiv2023

NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation

Social norms fundamentally shape interpersonal communication. We present NormDial, a high-quality dyadic dialogue dataset with turn-by-turn annotations of social norm adherences and violations for Chinese and American cultures. Introducing the task of social norm observance detection, our dataset is synthetically generated in both Chinese and English using a human-in-the-loop pipeline by prompting large language models with a small collection of expert-annotated social norms. We show that our generated dialogues are of high quality through human evaluation and further evaluate the performance of existing large language models on this task. Our findings point towards new directions for understanding the nuances of social norms as they manifest in conversational contexts that span across languages and cultures.

cs.CL↗
arXiv2023

Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment

Designing systems that can reason across cultures requires that they are grounded in the norms of the contexts in which they operate. However, current research on developing computational models of social norms has primarily focused on American society. Here, we propose a novel approach to discover and compare descriptive social norms across Chinese and American cultures. We demonstrate our approach by leveraging discussions on a Chinese Q&A platform (Zhihu) and the existing SocialChemistry dataset as proxies for contrasting cultural axes, align social situations cross-culturally, and extract social norms from texts using in-context learning. Embedding Chain-of-Thought prompting in a human-AI collaborative framework, we build a high-quality dataset of 3,069 social norms aligned with social situations across Chinese and American cultures alongside corresponding free-text explanations. To test the ability of models to reason about social norms across cultures, we introduce the task of explainable social norm entailment, showing that existing models under 3B parameters have significant room for improvement in both automatic and human evaluation. Further analysis of cross-cultural norm differences based on our dataset shows empirical alignment with the social orientations framework, revealing several situational and descriptive nuances in norms across these cultures.

cs.CL↗
arXiv2022

Affective Idiosyncratic Responses to Music

Affective responses to music are highly personal. Despite consensus that idiosyncratic factors play a key role in regulating how listeners emotionally respond to music, precisely measuring the marginal effects of these variables has proved challenging. To address this gap, we develop computational methods to measure affective responses to music from over 403M listener comments on a Chinese social music platform. Building on studies from music psychology in systematic and quasi-causal analyses, we test for musical, lyrical, contextual, demographic, and mental health effects that drive listener affective responses. Finally, motivated by the social phenomenon known as w\v{a}ng-y\`i-y\'un, we identify influencing factors of platform user self-disclosures, the social support they receive, and notable differences in discloser user activity.

cs.CL↗