Author name cluster

Xiaoxiao Wang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

1 author row

JBHI Journal 2026 Journal Article

Multi-Channel Temporal Interference Retinal Stimulation Based on Reinforcement Learning

Xiayu Chen
Wennan Chan
Yingqiang Meng
Runze Liu
Yueyi Yu
Sheng Hu
Jijun Han
Xiaoxiao Wang

Retinal degenerative diseases such as age-related macular degeneration and retinitis pigmentosa cause severe vision impairment, while current electrical stimulation therapies are limited by poor spatial targeting precision. As a promising non-invasive alternative, the efficacy of temporal interference stimulation (TIS) for retinal targeting depends on optimized multi-electrode parameters. This study reconstructed a whole-head finite element model with detailed ocular structures and applied reinforcement learning (RL)-based multi-channel electrode parameter optimization to retinal stimulation. Systematic evaluation demonstrated that the focal precision of TIS improves with increasing channel numbers (consistent across all subject head models), with RL significantly outperforming conventional genetic algorithms (GA) and unsupervised neural networks (USNN) in focusing capability. Furthermore, by implementing the computationally intensive envelope calculation using the JAX framework, we achieved a nearly order-of-magnitude reduction in optimization time (to approx. 2 minutes per run on an RTX 4090D), significantly enhancing the practical feasibility of the proposed RL framework. This work provides a novel and computationally efficient methodology for precise non-invasive neuromodulation parameter optimization, applicable not only to retinal diseases but potentially to broader neurological conditions.

Details DOI

AAAI Conference 2025 Conference Paper

An Efficient Framework for Enhancing Discriminative Models via Diffusion Techniques

Chunxiao Li
Xiaoxiao Wang
Boming Miao
Chuanlong Xie
Zizhe Wang
Yao Zhu

Image classification serves as the cornerstone of computer vision, traditionally achieved through discriminative models based on deep neural networks. Recent advancements have introduced classification methods derived from generative models, which offer the advantage of zero-shot classification. However, these methods suffer from two main drawbacks: high computational overhead and inferior performance compared to discriminative models. Inspired by the coordinated cognitive processes of rapid-slow pathway interactions in the human brain during visual signal recognition, we propose the Diffusion-Based Discriminative Model Enhancement Framework (DBMEF). This framework seamlessly integrates discriminative and generative models in a training-free manner, leveraging discriminative models for initial predictions and endowing deep neural networks with rethinking capabilities via diffusion models. Consequently, DBMEF can effectively enhance the classification accuracy and generalization capability of discriminative models in a plug-and-play manner. We have conducted extensive experiments across 17 prevalent deep model architectures with different training methods, including both CNN-based models such as ResNet and Transformer-based models like ViT, to demonstrate the effectiveness of the proposed DBMEF.Specifically, the framework yields a 1.51% performance improvement for ResNet-50 on the ImageNet dataset and 3.02% on the ImageNet-A dataset. In conclusion, our research introduces a novel paradigm for image classification, demonstrating stable improvements across different datasets and neural networks.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

FLARE: A Framework for Stellar Flare Forecasting Using Stellar Physical Properties and Historical Records

Bingke Zhu
Xiaoxiao Wang
Minghui Jia
Yihan Tao
Xiao Kong
Ali Luo
Yingying Chen
Ming Tang

Stellar flare events are critical observational samples for astronomical research; however, recorded flare events remain limited. Stellar flare forecasting can provide additional flare event samples to support research efforts. Despite this potential, no specialized models for stellar flare forecasting have been proposed to date. In this paper, we present extensive experimental evidence demonstrating that both stellar physical properties and historical flare records are valuable inputs for flare forecasting tasks. We then introduce FLARE (Forecasting Light-curve-based Astronomical Records via features Ensemble), the first-of-its-kind large model specifically designed for stellar flare forecasting. FLARE integrates stellar physical properties and historical flare records through a novel Soft Prompt Module and Residual Record Fusion Module. Experiments on the Kepler light curve dataset demonstrate that FLARE achieves superior performance compared to other methods across all evaluation metrics. Finally, we validate the forecast capability of our model through a comprehensive case study.

PDF Details DOI

YNICL Journal 2024 Journal Article

Abnormalities in subcortical function and their treatment response in Wilson’s disease

Sheng Hu
Taohua Wei
Chuanfu Li
Xiaoxiao Wang
Benedictor Alexander Nguchu
Yanming Wang
Ting Dong
Yulong Yang

Extensive neuroimaging abnormalities in subcortical regions build the pathophysiological basis of Wilson's disease (WD). Yet, subcortical topographic organization fails to articulate, leaving a huge gap in understanding the neural mechanism of WD. Thus, how functional abnormalities of WD subcortical regions influence complex clinical symptoms and response to treatment remain unknown. Using resting-state functional MRI data from 232 participants (including 130 WD patients and 102 healthy controls), we applied a connectivity-based parcellation technique to develop a subcortical atlas for WD. The atlas was further used to investigate abnormalities in subcortical function (ASF) by exploring intrasubcortical functional connectivity (FC) and topographic organization of cortico-subcortical FC. We further used support vector machine (SVM) to integrate these functional abnormalities into the ASF score, which serves as a biomarker for characterizing individual subcortical dysfunction for WD. Finally, the baseline ASF score and one-year treatment data of the follow-up WD patients were used to assess treatment response. A group set of subcortical parcellations was evaluated, in which 26 bilateral regions well recapitulated the anatomical nuclei of the subcortical areas of WD. The results of cortico-subcortical FC and intrasubcortical FC reveal that dysfunction of the somatomotor networks-lenticular nucleus-thalamic pathways is involved in complex symptoms of WD. The ASF score was able to characterize disease progression and was significantly associated with treatment response of WD. Our findings provide a comprehensive elaboration of functional abnormalities of WD subcortical regions and reveal their association with clinical presentations, improving our understanding of the functional neural underpinnings in WD. Furthermore, abnormalities in subcortical function could serve as a potential biomarker for understanding the disease progression and evaluating treatment response of WD.

Details DOI

YNIMG Journal 2023 Journal Article

White matter BOLD signals at 7 Tesla reveal visual field maps in optic radiation and vertical occipital fasciculus

Huan Wang
Xiaoxiao Wang
Yanming Wang
Du Zhang
Yan Yang
Yifeng Zhou
Bensheng Qiu
Peng Zhang

There is growing evidence that blood-oxygen-level-dependent (BOLD) activity in the white matter (WM) can be detected by functional magnetic resonance imaging (fMRI). However, the functional relevance and significance of WM BOLD signals remain controversial. Here we investigated whether 7T BOLD fMRI can reveal fine-scale functional organizations of a WM bundle. Population receptive field (pRF) analyses of the 7T retinotopy dataset from the Human Connectome Project revealed clear contralateral retinotopic organizations of two visual WM bundles: the optic radiation (OR) and the vertical occipital fasciculus (VOF). The retinotopic maps of OR are highly consistent with post-mortem dissections and diffusion tractographies, while the VOF maps are compatible with the dorsal and ventral visual areas connected by the WM. Similar to the grey matter (GM) visual areas, both WM bundles show over-representations of the central visual field and increasing pRF size with eccentricity. Hemodynamic response functions of visual WM were slower and wider compared with those of GM areas. These findings clearly demonstrate that WM BOLD at 7 Tesla is closely coupled with neural activity related to axons, encoding highly specific information that can be used to characterize fine-scale functional organizations of a WM bundle.

Details DOI

AAAI Conference 2020 Conference Paper

SPSTracker: Sub-Peak Suppression of Response Map for Robust Object Tracking

Qintao Hu
Lijun Zhou
Xiaoxiao Wang
Yao Mao
Jianlin Zhang
Qixiang Ye

Modern visual trackers usually construct online learning models under the assumption that the feature response has a Gaussian distribution with target-centered peak response. Nevertheless, such an assumption is implausible when there is progressive interference from other targets and/or background noise, which produce sub-peaks on the tracking response map and cause model drift. In this paper, we propose a rectiﬁed online learning approach for sub-peak response suppression and peak response enforcement and target at handling progressive interference in a systematic way. Our approach, referred to as SPSTracker, applies simple-yetefﬁcient Peak Response Pooling (PRP) to aggregate and align discriminative features, as well as leveraging a Boundary Response Truncation (BRT) to reduce the variance of feature response. By fusing with multi-scale features, SPSTracker aggregates the response distribution of multiple sub-peaks to a single maximum peak, which enforces the discriminative capability of features for robust object tracking. Experiments on the OTB, NFS and VOT2018 benchmarks demonstrate that SPSTrack outperforms the state-of-the-art real-time trackers with signiﬁcant margins1

PDF Details

IJCAI Conference 2019 Conference Paper

AdaLinUCB: Opportunistic Learning for Contextual Bandits

Xueying Guo
Xiaoxiao Wang
Xin Liu

In this paper, we propose and study opportunistic contextual bandits - a special case of contextual bandits where the exploration cost varies under different environmental conditions, such as network load or return variation in recommendations. When the exploration cost is low, so is the actual regret of pulling a sub-optimal arm (e. g. , trying a suboptimal recommendation). Therefore, intuitively, we could explore more when the exploration cost is relatively low and exploit more when the exploration cost is relatively high. Inspired by this intuition, for opportunistic contextual bandits with Linear payoffs, we propose an Adaptive Upper-Confidence-Bound algorithm (AdaLinUCB) to adaptively balance the exploration-exploitation trade-off for opportunistic learning. We prove that AdaLinUCB achieves O((log T)^2) problem-dependent regret upper bound, which has a smaller coefficient than that of the traditional LinUCB algorithm. Moreover, based on both synthetic and real-world dataset, we show that AdaLinUCB significantly outperforms other contextual bandit algorithms, under large exploration cost fluctuations.

PDF Details

YNIMG Journal 2012 Journal Article

From N400 to N300: Variations in the timing of semantic processing with repetition

Louis Renoult
Xiaoxiao Wang
Vincent Calcagno
Marie Prévost
J. Bruno Debruille

The present study aimed to explore the variations of semantic processing according to the number of target words (i. e. , 4, 12 and 24) and according to the number of repetitions (i. e, 1 to 15). The number of targets had no impact on the N400 brain potential, the index of semantic processing, nor on the late positive component (LPC), an index of episodic encoding and retrieval. Analyses of the effects of the number of repetitions showed that the duration of semantic processes – assessed by measuring N400 latency – was linearly shortened along repetitions while their extent – as indexed by N400 amplitude – remained constant after the second presentation. In contrast, the extent of episodic processes – as indexed by LPC amplitude – was found to increase linearly with repetition. By showing that N400 latency may be much less stable than previously thought, these results bring new constraints on the functional correlates of this key stage in the processing of semantic information. They also suggest that semantic processes can be studied at high repetition rates whatever the number of target stimuli. Finally, our findings show that each episode of prior presentation has an impact on the late processing of a stimulus despite the absence of an explicit memory task.

Details DOI