arXiv subjects

Xiaoli Wu

Publications and source records attributed to Xiaoli Wu.

3 recordsLinked to original sources

arXiv2025

Investigating AI in Peer Support via Multi-Module System-Driven Embodied Conversational Agents

Young people's mental well-being is a global concern, with peer support playing a key role in daily emotional regulation. Conversational agents are increasingly viewed as promising tools for delivering accessible, personalised peer support, particularly where professional counselling is limited. However, existing systems often suffer from rigid input formats, scripted responses, and limited emotional sensitivity. The emergence of large language models introduces new possibilities for generating flexible, context-aware, and empathetic responses. To explore how individuals with psychological training perceive such systems in peer support contexts, we developed an LLM-based multi-module system to drive embodied conversational agents informed by Cognitive Behavioral Therapy (CBT). In a user study (N=10), we qualitatively examined participants' perceptions, focusing on trust, response quality, workflow integration, and design opportunities for future mental well-being support systems.

cs.HC↗

arXiv2021

An Efficient and Flexible Spike Train Model via Empirical Bayes

Accurate statistical models of neural spike responses can characterize the information carried by neural populations. But the limited samples of spike counts during recording usually result in model overfitting. Besides, current models assume spike counts to be Poisson-distributed, which ignores the fact that many neurons demonstrate over-dispersed spiking behaviour. Although the Negative Binomial Generalized Linear Model (NB-GLM) provides a powerful tool for modeling over-dispersed spike counts, the maximum likelihood-based standard NB-GLM leads to highly variable and inaccurate parameter estimates. Thus, we propose a hierarchical parametric empirical Bayes method to estimate the neural spike responses among neuronal population. Our method integrates both Generalized Linear Models (GLMs) and empirical Bayes theory, which aims to (1) improve the accuracy and reliability of parameter estimation, compared to the maximum likelihood-based method for NB-GLM and Poisson-GLM; (2) effectively capture the over-dispersion nature of spike counts from both simulated data and experimental data; and (3) provide insight into both neural interactions and spiking behaviours of the neuronal populations. We apply our approach to study both simulated data and experimental neural data. The estimation of simulation data indicates that the new framework can accurately predict mean spike counts simulated from different models and recover the connectivity weights among neural populations. The estimation based on retinal neurons demonstrate the proposed method outperforms both NB-GLM and Poisson-GLM in terms of the predictive log-likelihood of held-out data. Codes are available in https://doi.org/10.5281/zenodo.4704423

q-bio.QM↗

arXiv2020

A novel discrete grey seasonal model and its applications

In order to accurately describe real systems with seasonal disturbances, which normally appear monthly or quarterly cycles, a novel discrete grey seasonal model, abbreviated as , is put forward by incorporating the seasonal dummy variables into the conventional model. Moreover, the mechanism and properties of this proposed model are discussed in depth, revealing the inherent differences from the existing seasonal grey models. For validation and explanation purposes, the proposed model is implemented to describe three actual cases with monthly and quarterly seasonal fluctuations (quarterly wind power production, quarterly PM10, and monthly natural gas consumption), in comparison with five competing models involving grey prediction models , conventional econometric technology , and artificial intelligences . Experimental results from the cases consistently demonstrated that the proposed model significantly outperforms the other benchmark models in terms of several error criteria. Moreover, further discussions about the influences of different sequence lengths on the forecasting performance reveal that the proposed model still performs the best with strong robustness and high reliability in addressing seasonal sequences. In general, the new model is validated to be a powerful and promising methodology for handling sequences with seasonal fluctuations.

stat.AP↗