publications

2025

1. Aphasia severity prediction using a multi-modal machine learning approach

   Xinyi Hu, Maria Varkanitsa, Emerson Kropp, and 3 more authors

   NeuroImage, 2025

   Abs DOI

   The present study examined an integrated multiple neuroimaging modality (T1 structural, Diffusion Tensor Imaging (DTI), and resting-state FMRI (rsFMRI)) to predict aphasia severity using Western Aphasia Battery-Revised Aphasia Quotient (WAB-R AQ) in 76 individuals with post-stroke aphasia. We employed Support Vector Regression (SVR) and Random Forest (RF) models with supervised feature selection and a stacked feature prediction approach. The SVR model outperformed RF, achieving an average root mean square error (RMSE) of 16.38±5.57, Pearson’s correlation coefficient (r) of 0.70±0.13, and mean absolute error (MAE) of 12.67±3.27, compared to RF’s RMSE of 18.41±4.34, r of 0.66±0.15, and MAE of 14.64±3.04. Resting-state neural activity and structural integrity emerged as crucial predictors of aphasia severity, appearing in the top 20% of predictor combinations for both SVR and RF. Finally, the feature selection method revealed that functional connectivity in both hemispheres and between homologous language areas is critical for predicting language outcomes in patients with aphasia. The statistically significant difference in performance between the model using only single modality and the optimal multi-modal SVR/RF model (which included both resting-state connectivity and structural information) underscores that aphasia severity is influenced by factors beyond lesion location and volume. These findings suggest that integrating multiple neuroimaging modalities enhances the prediction of language outcomes in aphasia beyond lesion characteristics alone, offering insights that could inform personalized rehabilitation strategies.

2. Meet Me at the Arm: The Cooperative Multi-Armed Bandits Problem with Shareable Arms

   Xinyi Hu, and Aldo Pacchiano

   2025

   Abs DOI

   We study the decentralized multi-player multi-armed bandits (MMAB) problem under a no-sensing setting, where each player receives only their own reward and obtains no information about collisions. Each arm has an unknown capacity, and if the number of players pulling an arm exceeds its capacity, all players involved receive zero reward. This setting generalizes the classical unit-capacity model and introduces new challenges in coordination and capacity discovery under severe feedback limitations. We propose A-CAPELLA (Algorithm for Capacity-Aware Parallel Elimination for Learning and Allocation), a decentralized algorithm that achieves logarithmic regret in this generalized regime. Our main contribution is a collaborative hypothesis testing protocol that enables synchronized successive elimination and capacity estimation through carefully structured collision patterns. This represents a provably efficient learning result in decentralized no-sensing MMAB with unknown arm capacities.

2024

1. A Mapping of Current Emotion Classification Categories Used in Multimodal Models for Emotion Recognition

   Ziwei Gong, Muyin Yao, Xinyi Hu, and 2 more authors

   In Proceedings of The 18th Linguistic Annotation Workshop (LAW-XVIII), Mar 2024

   Abs

   In Emotion Detection within Natural Language Processing and related multimodal research, the growth of datasets and models has led to a challenge: disparities in emotion classification methods. The lack of commonly agreed-upon conventions on the classification of emotions creates boundaries for model comparisons and dataset adaptation. In this paper, we compare current classification methods in recent models and datasets and propose a valid method to combine different emotion categories. Our proposal arises from experiments across models, psychological theories, and human evaluations, and we examine the effect of the proposed mapping on models.