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Ting Wang

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36 papers
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36

YNIMG Journal 2026 Journal Article

ALPS index and choroid plexus volume as indirect MRI biomarkers of glymphatic function for predicting cognitive decline in Parkinson’s disease

  • Xiuhang Ruan
  • Shuwen Bu
  • Yuting Li
  • Riyu Guo
  • Mengfan Wang
  • Xiaofei Huang
  • Ting Wang
  • Mengyan Li

BACKGROUND AND OBJECTIVE: Emerging evidence suggests that dysfunction of the brain's glymphatic system may be relevant to cognitive decline in Parkinson's disease (PD). However, in vivo indirect imaging correlates and predictive value for disease progression remain unclear. We evaluated whether two MRI-derived indices, diffusion tensor image analysis along the perivascular space (ALPS) and normalized choroid plexus volume (NCPV), are associated with cognitive status and subsequent cognitive decline in PD. METHODS: This study analyzed a self-collected cohort and the PPMI cohort. ALPS and NCPV were extracted from diffusion tensor imaging and T1-weighted images, respectively. Cross-sectional group comparisons were performed using analysis of covariance. Associations with clinical and cognitive measures were assessed using Spearman correlation and multivariate linear regression. To address stage heterogeneity, we added an early-duration subgroup analysis (PD < 1 year vs healthy controls) in the self-collected cohort. Longitudinal cognitive trajectories were evaluated using linear mixed-effects models, and cognitive conversion was assessed using Kaplan-Meier survival analysis and Cox proportional hazards regression. RESULTS: In the self-collected cohort, PD patients showed significantly lower ALPS and higher NCPV than healthy controls. NCPV was significantly associated with MMSE and showed independent predictive value in regression analysis. In the PD <1 year subgroup, no significant PD-HCs differences were detected for ALPS or NCPV, similar to the early, drug-naïve PPMI cohort where cross-sectional differences were not observed. In the PPMI cohort, lower ALPS and higher NCPV at baseline predicted steeper decline in global cognition and verbal memory over time. Higher baseline NCPV was also associated with increased risk of cognitive conversion. CONCLUSIONS: MRI-derived ALPS index and NCPV, while indirect and potentially influenced by multiple factors, were associated with cognitive status and progression in PD. These exploratory markers may aid in identifying individuals at higher risk for cognitive deterioration.

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AAAI Conference 2026 Conference Paper

Beyond Plain Demos: A Demo-Centric Anchoring Paradigm for In-Context Learning in Alzheimer’s Disease Detection

  • Puzhen Su
  • haoran yin
  • Miao Yongzhu
  • Jintao Tang
  • Shasha Li
  • Ting Wang

Detecting Alzheimer’s disease (AD) from narrative transcripts challenges large language models (LLMs): pre-training rarely covers this out-of-distribution task, and all transcript demos describe the same scene, producing highly homogeneous contexts. These factors cripple both the model’s built-in task knowledge (task cognition) and its ability to surface subtle, class-discriminative cues (contextual perception). Because cognition is fixed after pre-training, improving in-context learning (ICL) for AD detection hinges on enriching perception through better demonstration (demo) sets. We demonstrate that standard ICL quickly saturates, its demos lack diversity (context width) and fail to convey fine-grained signals (context depth), and that recent task vector (TV) approaches improve broad task adaptation by injecting TV into the LLMs' hidden states (HSs), they are ill-suited for AD detection due to the mismatch of injection granularity, strength and position. To address these bottlenecks, we introduce DA4ICL, a demo-centric anchoring framework that jointly expands context width via Diverse and Contrastive Retrieval (DCR) and deepens each demo's signal via Projected Vector Anchoring (PVA) at every Transformer layer. Across three AD benchmarks, DA4ICL achieves large, stable gains over both ICL and TV baselines, charting a new paradigm for fine-grained, OOD and low-resource LLM adaptation.

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AAAI Conference 2026 Conference Paper

Enhancing Medical Large Vision-Language Models via Alignment Distillation

  • Aofei Chang
  • Ting Wang
  • Fenglong Ma

Medical Large Vision-Language Models (Med-LVLMs) have shown promising results in clinical applications, but often suffer from hallucinated outputs due to misaligned visual understanding. In this work, we identify two fundamental limitations contributing to this issue: insufficient visual representation learning and poor visual attention alignment. To address these problems, we propose MedAlign, a simple, lightweight alignment distillation framework that transfers visual alignment knowledge from a domain-specific Contrastive Language-Image Pre-training (CLIP) model to Med-LVLMs. MedAlign introduces two distillation losses: a spatial-aware visual alignment loss based on visual token-level similarity structures, and an attention-aware distillation loss that guides attention toward diagnostically relevant regions. Extensive experiments on medical report generation and medical visual question answering (VQA) benchmarks show that MedAlign consistently improves both performance and interpretability, yielding more visually grounded outputs.

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AAAI Conference 2026 Conference Paper

InteChar: A Unified Oracle Bone Character List for Ancient Chinese Language Modeling

  • Xiaolei Diao
  • Zhihan Zhou
  • Lida Shi
  • Ting Wang
  • Ruihua Qi
  • Daqian Shi
  • Hao Xu

Constructing historical language models (LMs) plays a crucial role in aiding archaeological provenance studies and understanding ancient cultures. However, existing resources present major challenges for training effective LMs on historical texts. First, the scarcity of historical language samples renders unsupervised learning approaches based on large text corpora highly inefficient, hindering effective pre-training. Moreover, due to the considerable temporal gap and complex evolution of ancient scripts, the absence of comprehensive character encoding schemes limits the digitization and computational processing of ancient texts, particularly in early Chinese writing. To address these challenges, we introduce InteChar, a unified and extensible character list that integrates unencoded oracle bone characters with traditional and modern Chinese. InteChar enables consistent digitization and representation of historical texts, providing a foundation for robust modeling of ancient scripts. To evaluate the effectiveness of InteChar, we construct the Oracle Corpus Set (OracleCS), an ancient Chinese corpus that combines expert-annotated samples with LLM-assisted data augmentation, centered on Chinese oracle bone inscriptions. Extensive experiments show that models trained with InteChar on OracleCS achieve substantial improvements across various historical language understanding tasks, confirming the effectiveness of our approach and establishing a solid foundation for future research in ancient Chinese NLP.

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AAAI Conference 2026 Short Paper

Misclassification-Aware Robust Learning from Multiple Human Labelers (Student Abstract)

  • Zuoyuehe Wang
  • Chicheng Ma
  • Pengpeng Chen
  • Lei Chai
  • Yongqiang Yang
  • Zhijun Chen
  • Jingzheng Li
  • Bing Li

Adversarial training is an effective technique for enhancing the robustness of deep neural networks (DNNs). Prior research shows that misclassified examples influence final adversarial robustness much more than correctly classified examples. Ignoring this difference during training can hurt model performance. In crowdsourcing, varying annotator expertise causes noisy, inconsistent labels. As a result, it is hard to distinguish misclassified and correctly classified examples using only provided annotations. Thus, how to use the reliability and discrepancy between these example types to improve robustness within adversarial learning remains a critical but underexplored issue. In this work, we first explore how misclassified and correctly classified examples affect learning from crowds (LFC) in adversarial environments. Then, we formulate the problem of misclassification-aware robust learning from multiple human labelers as a bilevel min-max problem. After that, we introduce MALC, a new approach to make classifiers more robust to adversarial examples via iterative adversarial example generation and parameter estimation. We conduct an extensive evaluation of the proposed MALC, showing that MALC can outperform the state-of-the-art LFC methods in both white-box and black-box settings.

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AAAI Conference 2026 Conference Paper

RSVG-ZeroOV: Exploring a Training-Free Framework for Zero-Shot Open-Vocabulary Visual Grounding in Remote Sensing Images

  • Ke Li
  • Di Wang
  • Ting Wang
  • Fuyu Dong
  • Yiming Zhang
  • Luyao Zhang
  • Xiangyu Wang
  • Shaofeng Li

Remote sensing visual grounding (RSVG) aims to localize objects in remote sensing images based on free-form natural language expressions. Existing approaches are typically constrained to closed-set vocabularies, limiting their applicability in open-world scenarios. While recent attempts to leverage generic foundation models for open-vocabulary RSVG, they overly rely on expensive high-quality datasets and time-consuming fine-tuning. To address these limitations, we propose RSVG-ZeroOV, a training-free framework that aims to explore the potential of frozen generic foundation models for zero-shot open-vocabulary RSVG. Specifically, RSVG-ZeroOV comprises three key stages: (i) Overview: We utilize a vision-language model (VLM) to obtain cross-attention maps that capture semantic correlations between text queries and visual regions. (ii) Focus: By leveraging the fine-grained modeling priors of a diffusion model (DM), we fill in gaps in structural and shape information of objects, which are often overlooked by VLM. (iii) Evolve: A simple yet effective attention evolution module is introduced to suppress irrelevant activations, yielding purified segmentation masks over the referred objects. Without cumbersome task-specific training, RSVG-ZeroOV offers an efficient and scalable solution. Extensive experiments demonstrate that the proposed framework consistently outperforms existing weakly-supervised and zero-shot methods.

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YNIMG Journal 2026 Journal Article

The merit of group-level analyses in TMS targeting: group-based task-driven TMS coil positioning can outperform individual approaches - evidence from resting-state-, diffusion-, and functional-MR

  • Ting Wang
  • Michael Capalbo
  • Felix Duecker
  • Alexander T Sack

Individualized TMS coil positioning enhances the precision of targeting a specific brain region. The gold standard method for this is individual task based fMRI guided TMS. Alternatively, individual resting state fMRI (rs-fMRI) or the combination of rs-fMRI and Diffusion Weighted Imaging (DWI) can be used to guide TMS coil positioning. Another option is to use group MRI maps instead of individual maps. A last possibility is the atlas-based approach, which utilizes group data from previous studies, transferred to atlas space, e.g. Talairach coordinates (TAL). We examined all these positioning approaches while localizing the core nodes of the Dorsal Attention Network (DAN) as well as Ventral Attention Network (VAN). We collected task-based fMRI data from a simple oculomotor task, rs-fMRI data with eyes open, and structural data with DWI (n = 20). The task fMRI data were analyzed with a GLM, the rs-fMRI with ICA, and the DWI data were processed with the TractSeg algorithm. Data from rs-fMRI and DWI were combined to constrain end-point localization. Each approach yielded individual region of interest (ROIs) and group ROIs. First, individual TMS target points were computed by determining the center of gravity within each resulting ROI. Success rates for all TMS target localization approaches and regions were obtained. There was variability per approach and area, with task-based fMRI showing a slight advantage for most regions. Using individual task fMRI coordinates as the reference, localization accuracy was measured as the Euclidean distances from this gold standard. Group-level TMS target points were projected into individual space, after which the localization accuracy was similarly ascertained. Task-based group data (leave-one-out) yielded the closest approximation to individual task-based targets (mean difference = 7.57 mm), while individual resting-state data deviated by >10 mm. Atlas-based approaches performed worst, with deviations >15 mm. These findings highlight spatial discrepancies between TMS target localization approaches and suggest that individual data do not consistently outperform group data.

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AAAI Conference 2025 Conference Paper

Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training

  • Ting Wang
  • Zhixin Zhou
  • Rui Luo

Graph Neural Networks (GNNs) has been widely used in a variety of fields because of their great potential in representing graph-structured data. However, lacking of rigorous uncertainty estimations limits their application in high-stakes. Conformal Prediction (CP) can produce statistically guaranteed uncertainty estimates by using the classifier's probability estimates to obtain prediction sets, which contains the true class with a user-specified probability. In this paper, we propose a Rank-based CP during training framework to GNNs (RCP-GNN) for reliable uncertainty estimates to enhance the trustworthiness of GNNs in the node classification scenario. By exploiting rank information of the classifier's outcome, prediction sets with desired coverage rate can be efficiently constructed. The strategy of CP during training with differentiable rank-based conformity loss function is further explored to adapt prediction sets according to network topology information. In this way, the composition of prediction sets can be guided by the goal of jointly reducing inefficiency and probability estimation errors. Extensive experiments on several real-world datasets show that our model achieves any pre-defined target marginal coverage while significantly reducing the inefficiency compared with state-of-the-art methods.

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JBHI Journal 2025 Journal Article

Localized Intra- and Inter-Tumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer

  • Yinhao Liang
  • Wenjie Tang
  • Qingcong Kong
  • Ting Wang
  • Jianjun Zhang
  • Wing W. Y. Ng
  • Siyi Chen
  • Ying Li

This study proposes a novel method for extracting breast cancer tumor heterogeneity descriptors to non-invasively predict whether pathological complete response (pCR) can be achieved after neoadjuvant chemotherapy (NAC). These localized descriptors extract corresponding heterogeneity features for different radiomic features and are able to capture tumor characteristics at various localization levels. These descriptors also capture tumor heterogeneity both at the individual tumor level and across the whole dataset, providing decision-making models with features that are both more effective and interpretable. We validated the effectiveness of the proposed features with the Kolmogorov-Arnold network (KAN) across multiple centers, yielding an AUC of 0. 92 when combined with pathological features and demonstrating good performance in external datasets (AUCs of 0. 84 and 0. 81). Additionally, we transform the best model into a symbolic formula to intuitively explain the machine learning model's prediction process, showing how factors such as age, HER2, Ki-67 and heterogeneity influence the prediction. The symbolized model is consistent with the experience of clinical experts, which enhances users' confidence in deep models. The experimental results show that our proposed features and method outperform classical heterogeneity features and end-to-end neural networks with a small additional computational cost.

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EAAI Journal 2025 Journal Article

Longest & uniform subsequence representation: Time interval dependent data augmentation for sequential recommendation

  • Xuegang Wu
  • Ting Wang

Sequential Recommendation (SR) aims to effectively infer users’ interests or behaviors in future, widely using in e-commerce and social media fields. Existing studies show different time intervals in a sequence may lead to preference drift, and especially that uniform time intervals in a sequence can improve the prediction performance compared with non-uniform has been proved. However, this kind of methods only focus on global time interval changes usually, but often neglect the role of local time intervals, which may lead to not capturing short-term user interest fluctuations. Thus, we propose the longest uniform subsequence insertion (LUI) in single-user (intra-sequence), and the longest uniform subsequence substitution (LUS) of multi-user (inter-sequence) methods, which consider not only local but global sequence-related view information. Additionally, considering the sensitivity of sequence length, we use different combinations of augmentations to address the problem of skewed sequence length distribution in the dataset. Next, Fourier transform is employed to integrate low-frequency with high-frequency information by injecting fine-grained sequential patterns to inductive biases. Self-supervised learning method is also used to alleviate the problems of data sparsity and noise. At last, the experimental results on three benchmark datasets show that the proposed LURec model outperforms several typical recommendation models.

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JBHI Journal 2025 Journal Article

XRadNet: A Radiomics-Guided Breast Cancer Molecular Subtype Prediction Network With a Radiomics Explanation

  • Yinhao Liang
  • Wenjie Tang
  • Jianjun Zhang
  • Ting Wang
  • Wing W. Y. Ng
  • Siyi Chen
  • Kuiming Jiang
  • Xinhua Wei

In this work, we propose a radiomics-guided neural network, XRadNet, for breast cancer molecular subtype prediction. XRadNet is a two-head neural network, with one for predicting molecular subtypes and the other for approximating radiomic features. In addition, a training scheme with radiomics guidance is proposed to improve performance. First, we conduct a series of experiments to test the radiomic feature learning capacity of different neural networks, which determines the backbone of XRadNet. Moreover, significant radiomic features are also determined according to radiomics and prior knowledge. XRadNet is subsequently pretrained in a self-supervised manner. The pretraining uses synthetic samples to train the backbone and radiomic feature regression head. This mitigates the impact of an insufficient number of samples. Finally, XRadNet is fine-tuned with a downstream real-world dataset by enabling all heads. Furthermore, a logistic regression is built with radiomic features and learned features, which provides a new way to interpreting the trained model with concepts familiar to radiologists. The experimental results show that XRadNet effectively predicts the four molecular subtypes of breast cancer. These results also demonstrate that the proposed training scheme yields better or competitive performance than those models pretrained on ImageNet or medical datasets.

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JBHI Journal 2024 Journal Article

EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT

  • Shiliang Shao
  • Guangjie Han
  • Ting Wang
  • Chuan Lin
  • Chunhe Song
  • Chen Yao

Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of Things technology will also enable the identification of mental workload remotely based on human physiological signals. In this article, a method based on the spatial and time–frequency domains of electroencephalography (EEG) signals is proposed to improve the classification accuracy of mental workload. Moreover, a hybrid deep learning model is presented. First, the spatial domain features of different brain regions are proposed. Simultaneously, EEG time–frequency domain information is obtained based on wavelet transform. The spatial and time–frequency domain features are input into two types of deep learning models for mental workload classification. To validate the performance of the proposed method, the Simultaneous Task EEG Workload public database is used. Compared with the existing methods, the proposed approach shows higher classification accuracy. It provides a novel means of assessing mental workload.

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AAAI Conference 2024 Conference Paper

Inspecting Prediction Confidence for Detecting Black-Box Backdoor Attacks

  • Tong Wang
  • Yuan Yao
  • Feng Xu
  • Miao Xu
  • Shengwei An
  • Ting Wang

Backdoor attacks have been shown to be a serious security threat against deep learning models, and various defenses have been proposed to detect whether a model is backdoored or not. However, as indicated by a recent black-box attack, existing defenses can be easily bypassed by implanting the backdoor in the frequency domain. To this end, we propose a new defense DTInspector against black-box backdoor attacks, based on a new observation related to the prediction confidence of learning models. That is, to achieve a high attack success rate with a small amount of poisoned data, backdoor attacks usually render a model exhibiting statistically higher prediction confidences on the poisoned samples. We provide both theoretical and empirical evidence for the generality of this observation. DTInspector then carefully examines the prediction confidences of data samples, and decides the existence of backdoor using the shortcut nature of backdoor triggers. Extensive evaluations on six backdoor attacks, four datasets, and three advanced attacking types demonstrate the effectiveness of the proposed defense.

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EAAI Journal 2024 Journal Article

Multi-task label noise learning for classification

  • Zongmin Liu
  • Ziyi Wang
  • Ting Wang
  • Yitian Xu

Multi-task classification improves generalization performance via exploiting the correlations between tasks. However, most multi-task learning methods fail to recognize and filter noisy labels for the classification problems with label noises. To address this issue, this paper proposes a novel multi-task label noise learning method based on loss correction, called MTLNL. MTLNL introduces the class-wise denoising (CWD) method for loss decomposition and centroid estimation of the loss function in multi-task learning, and eliminates the impact of label noise by using label flipping rate. It also extends to the multi-task positive-unlabeled (PU) learning domain, which offers better flexibility and generalization performance. Moreover, Nesterov’s method is applied to accelerate the solution of the model. MTLNL is compared with other algorithms on five benchmark datasets, five image datasets, and a multi-task PU dataset to demonstrate its effectiveness.

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AAAI Conference 2024 Conference Paper

Situation-Dependent Causal Influence-Based Cooperative Multi-Agent Reinforcement Learning

  • Xiao Du
  • Yutong Ye
  • Pengyu Zhang
  • Yaning Yang
  • Mingsong Chen
  • Ting Wang

Learning to collaborate has witnessed significant progress in multi-agent reinforcement learning (MARL). However, promoting coordination among agents and enhancing exploration capabilities remain challenges. In multi-agent environments, interactions between agents are limited in specific situations. Effective collaboration between agents thus requires a nuanced understanding of when and how agents' actions influence others.To this end, in this paper, we propose a novel MARL algorithm named Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning (SCIC), which incorporates a novel Intrinsic reward mechanism based on a new cooperation criterion measured by situation-dependent causal influence among agents.Our approach aims to detect inter-agent causal influences in specific situations based on the criterion using causal intervention and conditional mutual information. This effectively assists agents in exploring states that can positively impact other agents, thus promoting cooperation between agents.The resulting update links coordinated exploration and intrinsic reward distribution, which enhance overall collaboration and performance.Experimental results on various MARL benchmarks demonstrate the superiority of our method compared to state-of-the-art approaches.

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AAAI Conference 2024 Conference Paper

VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models

  • Ziyi Yin
  • Muchao Ye
  • Tianrong Zhang
  • Jiaqi Wang
  • Han Liu
  • Jinghui Chen
  • Ting Wang
  • Fenglong Ma

Visual Question Answering (VQA) is a fundamental task in computer vision and natural language process fields. Although the “pre-training & finetuning” learning paradigm significantly improves the VQA performance, the adversarial robustness of such a learning paradigm has not been explored. In this paper, we delve into a new problem: using a pre-trained multimodal source model to create adversarial image-text pairs and then transferring them to attack the target VQA models. Correspondingly, we propose a novel VQATTACK model, which can iteratively generate both im- age and text perturbations with the designed modules: the large language model (LLM)-enhanced image attack and the cross-modal joint attack module. At each iteration, the LLM-enhanced image attack module first optimizes the latent representation-based loss to generate feature-level image perturbations. Then it incorporates an LLM to further enhance the image perturbations by optimizing the designed masked answer anti-recovery loss. The cross-modal joint attack module will be triggered at a specific iteration, which updates the image and text perturbations sequentially. Notably, the text perturbation updates are based on both the learned gradients in the word embedding space and word synonym-based substitution. Experimental results on two VQA datasets with five validated models demonstrate the effectiveness of the proposed VQATTACK in the transferable attack setting, compared with state-of-the-art baselines. This work reveals a significant blind spot in the “pre-training & fine-tuning” paradigm on VQA tasks. The source code can be found in the link https://github.com/ericyinyzy/VQAttack.

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NeurIPS Conference 2023 Conference Paper

Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks

  • Zhaohan Xi
  • Tianyu Du
  • Changjiang Li
  • Ren Pang
  • Shouling Ji
  • Jinghui Chen
  • Fenglong Ma
  • Ting Wang

Pre-trained language models (PLMs) have demonstrated remarkable performance as few-shot learners. However, their security risks under such settings are largely unexplored. In this work, we conduct a pilot study showing that PLMs as few-shot learners are highly vulnerable to backdoor attacks while existing defenses are inadequate due to the unique challenges of few-shot scenarios. To address such challenges, we advocate MDP, a novel lightweight, pluggable, and effective defense for PLMs as few-shot learners. Specifically, MDP leverages the gap between the masking-sensitivity of poisoned and clean samples: with reference to the limited few-shot data as distributional anchors, it compares the representations of given samples under varying masking and identifies poisoned samples as ones with significant variations. We show analytically that MDP creates an interesting dilemma for the attacker to choose between attack effectiveness and detection evasiveness. The empirical evaluation using benchmark datasets and representative attacks validates the efficacy of MDP. The code of MDP is publicly available.

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NeurIPS Conference 2023 Conference Paper

IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI

  • Bochuan Cao
  • Changjiang Li
  • Ting Wang
  • Jinyuan Jia
  • Bo Li
  • Jinghui Chen

Diffusion-based image generation models, such as Stable Diffusion or DALL·E 2, are able to learn from given images and generate high-quality samples following the guidance from prompts. For instance, they can be used to create artistic images that mimic the style of an artist based on his/her original artworks or to maliciously edit the original images for fake content. However, such ability also brings serious ethical issues without proper authorization from the owner of the original images. In response, several attempts have been made to protect the original images from such unauthorized data usage by adding imperceptible perturbations, which are designed to mislead the diffusion model and make it unable to properly generate new samples. In this work, we introduce a perturbation purification platform, named IMPRESS, to evaluate the effectiveness of imperceptible perturbations as a protective measure. IMPRESS is based on the key observation that imperceptible perturbations could lead to a perceptible inconsistency between the original image and the diffusion-reconstructed image, which can be used to devise a new optimization strategy for purifying the image, which may weaken the protection of the original image from unauthorized data usage (e. g. , style mimicking, malicious editing). The proposed IMPRESS platform offers a comprehensive evaluation of several contemporary protection methods, and can be used as an evaluation platform for future protection methods.

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IJCAI Conference 2023 Conference Paper

InitLight: Initial Model Generation for Traffic Signal Control Using Adversarial Inverse Reinforcement Learning

  • Yutong Ye
  • Yingbo Zhou
  • Jiepin Ding
  • Ting Wang
  • Mingsong Chen
  • Xiang Lian

Due to repetitive trial-and-error style interactions between agents and a fixed traffic environment during the policy learning, existing Reinforcement Learning (RL)-based Traffic Signal Control (TSC) methods greatly suffer from long RL training time and poor adaptability of RL agents to other complex traffic environments. To address these problems, we propose a novel Adversarial Inverse Reinforcement Learning (AIRL)-based pre-training method named InitLight, which enables effective initial model generation for TSC agents. Unlike traditional RL-based TSC approaches that train a large number of agents simultaneously for a specific multi-intersection environment, InitLight pre-trains only one single initial model based on multiple single-intersection environments together with their expert trajectories. Since the reward function learned by InitLight can recover ground-truth TSC rewards for different intersections at optimality, the pre-trained agent can be deployed at intersections of any traffic environments as initial models to accelerate subsequent overall global RL training. Comprehensive experimental results show that, the initial model generated by InitLight can not only significantly accelerate the convergence with much fewer episodes, but also own superior generalization ability to accommodate various kinds of complex traffic environments.

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IROS Conference 2023 Conference Paper

Monolithic Microchannels in Miniature Pneumatic Soft Robots for Sequential Motions

  • Dongliang Fan
  • Hao Liu 0094
  • Ting Wang
  • Renjie Zhu
  • Hongqiang Wang 0003

Miniature soft robots present great potential in delicate manipulations due to their gentle force, complaint structures, and flexible motions. Easy control and fast response make pneumatic actuation a prevalent method for driving soft robotics. In addition, sequential motions are also crucial for enhancing the grasping and moving abilities of soft robots. How-ever, existing miniature pneumatic soft robots are limited to one-dimensional geometries and simple motions due to the difficulties in designing and fabricating intricate small airways in miniature pneumatic soft robots, which restricts them from more versatile deformations. Here, we employ intricate monolithic microchannels embedded into miniature soft robots' mon-olithic bodies for sequential motions. After verifying the effects of the channel diameter, strain-limiting layer, and elastic modulus of the robot's body on the bending behaviors of the ID soft robots, we fabricated a soft flower robot capable of sequential and simultaneous 3D-to-3D shape morphing through five individual microchannels and a soft carnivorous plant robot containing 2D interconnected microchannels capable of sequential enclosed grasping through a single inlet.

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EAAI Journal 2023 Journal Article

Mul-DesLSTM: An integrative multi-time granularity deep learning prediction method for urban rail transit short-term passenger flow

  • Wenbo Lu
  • Yong Zhang
  • Peikun Li
  • Ting Wang

It is critical for the management and control of urban rail transit (URT) to be able to predict passenger flow accurately and in real time. Considering that the high-resolution data aggregated by the automatic fare collection (AFC) system is wasted, this paper analyzes the problem of applying a multi-time granularity passenger flow data fusion forecasting process. First, we examine the challenge of constructing a dataset of passenger flow data with different time granularities. Thus, an algorithm is proposed for selecting passenger flow datasets with multi-time granularity. Furthermore, a multi-time granularity dense residual network (Mul-DesLSTM) with a dense residual structure and LSTM (long short-term memory) as the predictor is constructed, inspired by a residual network. Using Mul-DesLSTM, finer-grained passenger flow features can be fused layer by layer while maintaining the accuracy of traditional single-granularity passenger flow predictions. Lastly, Mul-DesLSTM is applied to the URT system of Shanghai, China, and compared with baselines. As a result, the proposed Mul-DesLSTM outperforms the baselines with LSTM as a predictor and state-of-the-art model. When the predicted time granularity is 30 min, compared to the single-time granularity LSTM network, the mean absolute error, root mean square error, and symmetric mean absolute percentage error can be reduced by 51%, 63%, and 15%, respectively. The results can serve as a reference and basis for the operation and management of URT systems.

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NeurIPS Conference 2023 Conference Paper

UniT: A Unified Look at Certified Robust Training against Text Adversarial Perturbation

  • Muchao Ye
  • Ziyi Yin
  • Tianrong Zhang
  • Tianyu Du
  • Jinghui Chen
  • Ting Wang
  • Fenglong Ma

Recent years have witnessed a surge of certified robust training pipelines against text adversarial perturbation constructed by synonym substitutions. Given a base model, existing pipelines provide prediction certificates either in the discrete word space or the continuous latent space. However, they are isolated from each other with a structural gap. We observe that existing training frameworks need unification to provide stronger certified robustness. Additionally, they mainly focus on building the certification process but neglect to improve the robustness of the base model. To mitigate the aforementioned limitations, we propose a unified framework named UniT that enables us to train flexibly in either fashion by working in the word embedding space. It can provide a stronger robustness guarantee obtained directly from the word embedding space without extra modules. In addition, we introduce the decoupled regularization (DR) loss to improve the robustness of the base model, which includes two separate robustness regularization terms for the feature extraction and classifier modules. Experimental results on widely used text classification datasets further demonstrate the effectiveness of the designed unified framework and the proposed DR loss for improving the certified robust accuracy.

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NeurIPS Conference 2023 Conference Paper

VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models

  • Ziyi Yin
  • Muchao Ye
  • Tianrong Zhang
  • Tianyu Du
  • Jinguo Zhu
  • Han Liu
  • Jinghui Chen
  • Ting Wang

Vision-Language (VL) pre-trained models have shown their superiority on many multimodal tasks. However, the adversarial robustness of such models has not been fully explored. Existing approaches mainly focus on exploring the adversarial robustness under the white-box setting, which is unrealistic. In this paper, we aim to investigate a new yet practical task to craft image and text perturbations using pre-trained VL models to attack black-box fine-tuned models on different downstream tasks. Towards this end, we propose VLATTACK to generate adversarial samples by fusing perturbations of images and texts from both single-modal and multi-modal levels. At the single-modal level, we propose a new block-wise similarity attack (BSA) strategy to learn image perturbations for disrupting universal representations. Besides, we adopt an existing text attack strategy to generate text perturbations independent of the image-modal attack. At the multi-modal level, we design a novel iterative cross-search attack (ICSA) method to update adversarial image-text pairs periodically, starting with the outputs from the single-modal level. We conduct extensive experiments to attack three widely-used VL pretrained models for six tasks on eight datasets. Experimental results show that the proposed VLATTACK framework achieves the highest attack success rates on all tasks compared with state-of-the-art baselines, which reveals a significant blind spot in the deployment of pre-trained VL models.

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JBHI Journal 2022 Journal Article

Analysis of Functional Corticomuscular Coupling Based on Multiscale Transfer Spectral Entropy

  • Xugang Xi
  • Jinsuo Ding
  • Junhong Wang
  • Yun-Bo Zhao
  • Ting Wang
  • Wanzeng Kong
  • Jingqi Li

Functional corticomuscular coupling (FCMC) between the cerebral motor cortex and muscle activity reflects multi-layer and nonlinear interactions in the sensorimotor system. Considering the inherent multiscale characteristics of physiological signals, we proposed multiscale transfer spectral entropy (MSTSE) and introduced the unidirectionally coupled Hénon maps model to verify the effectiveness of MSTSE. We recorded electroencephalogram (EEG) and surface electromyography (sEMG) in steady-state grip tasks of 29 healthy participants and 27 patients. Then, we used MSTSE to analyze the FCMC base on EEG of the bilateral motor areas and the sEMG of the flexor digitorum superficialis (FDS). The results show that MSTSE is superior to transfer spectral entropy (TSE) method in restraining the spurious coupling and detecting the coupling more accurately. The coupling strength was higher in the ${\boldsymbol{\beta}}$ 1, ${\boldsymbol{\beta}}$ 2, and $\ {\boldsymbol{\gamma}}$ 2 bands, among which, it was highest in the ${\boldsymbol{\beta}}$ 1 band, and reached its maximum at the 22–30 scale. On the directional characteristics of FCMC, the coupling strength of EEG→sEMG is superior to the opposite direction in most cases. In addition, the coupling strength of the stroke-affected side was lower than that of healthy controls’ right hand in the ${\boldsymbol{\beta}}$ 1 and ${\boldsymbol{\beta}}$ 2 bands and the stroke-unaffected side in the ${\boldsymbol{\beta}}$ 1 band. The coupling strength of the stroke-affected side was higher than that of the stroke-unaffected side and the right hand of healthy controls in the sEMG→EEG direction of ${\boldsymbol{\gamma}}$ 2 band. This study provides a new perspective and lays a foundation for analyzing FCMC and motor dysfunction.

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IJCAI Conference 2022 Conference Paper

Eliminating Backdoor Triggers for Deep Neural Networks Using Attention Relation Graph Distillation

  • Jun Xia
  • Ting Wang
  • Jiepin Ding
  • Xian Wei
  • Mingsong Chen

Due to the prosperity of Artificial Intelligence (AI) techniques, more and more backdoors are designed by adversaries to attack Deep Neural Networks (DNNs). Although the state-of-the-art method Neural Attention Distillation (NAD) can effectively erase backdoor triggers from DNNs, it still suffers from non-negligible Attack Success Rate (ASR) together with lowered classification ACCuracy (ACC), since NAD focuses on backdoor defense using attention features (i. e. , attention maps) of the same order. In this paper, we introduce a novel backdoor defense framework named Attention Relation Graph Distillation (ARGD), which fully explores the correlation among attention features with different orders using our proposed Attention Relation Graphs (ARGs). Based on the alignment of ARGs between teacher and student models during knowledge distillation, ARGD can more effectively eradicate backdoors than NAD. Comprehensive experimental results show that, against six latest backdoor attacks, ARGD outperforms NAD by up to 94. 85% reduction in ASR, while ACC can be improved by up to 3. 23%.

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JBHI Journal 2022 Journal Article

Metabolic and Transcriptional Analysis of Recombinant Saccharomyces Cerevisiae for Xylose Fermentation: A Feasible and Efficient Approach

  • Xin-Chi Shi
  • Yan Zhang
  • Ting Wang
  • Xiang-Chen Wang
  • Hai-Bin Lv
  • Pedro Laborda
  • Ting-Ting Duan

Lignocellulose is an abundant xylose-containing biomass found in agricultural wastes, and has arisen as a suitable alternative to fossil fuels for the production of bioethanol. Although Saccharomyces cerevisiae has been thoroughly used for the production of bioethanol, its potential to utilize lignocellulose remains poorly understood. In this work, xylose-metabolic genes of Pichia stipitis and Candida tropicalis, under the control of different promoters, were introduced into S. cerevisiae. RNA-seq analysis was use to examine the response of S. cerevisiae metabolism to the introduction of xylose-metabolic genes. The use of the PGK1 promoter to drive xylitol dehydrogenase (XDH) expression, instead of the TEF1 promoter, improved xylose utilization in “XR-pXDH” strain by overexpressing xylose reductase (XR) and XDH form C. tropicalis, enhancing the production of xylitol (13. 66 $\pm$ 0. 54 g/L after 6 days fermentation). Overexpression of xylulokinase and XR/XDH from P. stipitis remarkably decreased xylitol accumulation (1. 13 $\pm$ 0. 06 g/L and 0. 89 $\pm$ 0. 04 g/L xylitol, respectively) and increased ethanol production (196. 14 $\%$ and 148. 50 $\%$ increases during the xylose utilization stage, respectively), in comparison with the results of XR-pXDH. This result may be produced due to the enhanced xylose transport, Embden-Meyerhof and pentose phosphate pathways, as well as alleviated oxidative stress. The low xylose consumption rate in these recombinant as well as alleviated strains comparing with P. stipitis and C. tropicalis may be explained by the insufficient supplementation of NADPH and NAD $^+$. The results obtained in this work provide new insights on the potential utilization of xylose using bioengineered S. cerevisiae strains.

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JBHI Journal 2022 Journal Article

Obstructive Sleep Apnea Detection Scheme Based on Manually Generated Features and Parallel Heterogeneous Deep Learning Model Under IoMT

  • Shiliang Shao
  • Guangjie Han
  • Ting Wang
  • Chunhe Song
  • Chen Yao
  • Jianxia Hou

Obstructive sleep apnea (OSA) syndrome is a common sleep disorder and a key cause of cardiovascular and cerebrovascular diseases that seriously affect the lives and health of people. The development of Internet of Medical Things (IoMT) has enabled the remote diagnosis of OSA. The physiological signals of human sleep are sent to the cloud or medical facilities through Internet of Things, after which diagnostic models are employed for OSA detection. In order to improve the detection accuracy of OSA, in this study, a novel OSA detection system based on manually generated features and utilizing a parallel heterogeneous deep learning model in the context of IoMT is proposed, and the accuracy of the proposed diagnostic model is investigated. The OSA recognition scheme used in our model is based on short-term heart rate variability (HRV) signals extracted from ECG signals. First, the HRV signals and the linear and nonlinear features of HRV are combined into a one-dimensional (1-D) sequence. Simultaneously, a two-dimensional (2-D) HRV time-frequency spectrum image is obtained. The 1-D data sequences and 2-D images are coded in different branches of the proposed deep learning network for OSA diagnosis. To validate the performance of the proposed scheme, the Physionet Apnea–ECG public database is used. The proposed scheme outperforms the existing methods in terms of accuracy and provides a novel direction for OSA recognition.

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JBHI Journal 2022 Journal Article

Predicting Cardiovascular and Cerebrovascular Events Based on Instantaneous High-Order Singular Entropy and Deep Belief Network

  • Shiliang Shao
  • Ting Wang
  • Asad Mumtaz
  • Chunhe Song
  • Chen Yao

Automatically predicting cardiovascular and cerebrovascular events (CCEs) is a key technology that can prevent deaths and disabilities. Herein, we propose predicting CCE occurrences based on heart rate variability (HRV) analysis and a deep belief network (DBN). The proposed prediction algorithm uses eight novel HRV signal features, which are calculated based on the following steps. First, the instantaneous amplitude (IA), instantaneous frequency (IF), and instantaneous phase (IP) are calculated for the HRV signals. Second, the high-order cumulant is estimated for the HRV and its IA, IF, and IP. Third, a high-order singular entropy is calculated to measure the fluctuation in signals. Fourth, eight novel features are obtained and processed using a DBN classifier designed for CCE prediction. The DBN classification method, with the novel HRV features, outperformed existing methods in terms of accuracy. Thus, the scheme proposed herein provided a novel direction for predicting CCEs.

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AAAI Conference 2022 Conference Paper

TextHoaxer: Budgeted Hard-Label Adversarial Attacks on Text

  • Muchao Ye
  • Chenglin Miao
  • Ting Wang
  • Fenglong Ma

This paper focuses on a newly challenging setting in hardlabel adversarial attacks on text data by taking the budget information into account. Although existing approaches can successfully generate adversarial examples in the hard-label setting, they follow an ideal assumption that the victim model does not restrict the number of queries. However, in realworld applications the query budget is usually tight or limited. Moreover, existing hard-label adversarial attack techniques use the genetic algorithm to optimize discrete text data by maintaining a number of adversarial candidates during optimization, which can lead to the problem of generating low-quality adversarial examples in the tight-budget setting. To solve this problem, in this paper, we propose a new method named TextHoaxer by formulating the budgeted hard-label adversarial attack task on text data as a gradientbased optimization problem of perturbation matrix in the continuous word embedding space. Compared with the genetic algorithm-based optimization, our solution only uses a single initialized adversarial example as the adversarial candidate for optimization, which significantly reduces the number of queries. The optimization is guided by a new objective function consisting of three terms, i. e. , semantic similarity term, pair-wise perturbation constraint, and sparsity constraint. Semantic similarity term and pair-wise perturbation constraint can ensure the high semantic similarity of adversarial examples from both comprehensive text-level and individual word-level, while the sparsity constraint explicitly restricts the number of perturbed words, which is also helpful for enhancing the quality of generated text. We conduct extensive experiments on eight text datasets against three representative natural language models, and experimental results show that TextHoaxer can generate high-quality adversarial examples with higher semantic similarity and lower perturbation rate under the tight-budget setting.

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AAAI Conference 2021 Conference Paper

i-Algebra: Towards Interactive Interpretability of Deep Neural Networks

  • Xinyang Zhang
  • Ren Pang
  • Shouling Ji
  • Fenglong Ma
  • Ting Wang

Providing explanations for deep neural networks (DNNs) is essential for their use in domains wherein the interpretability of decisions is a critical prerequisite. Despite the plethora of work on interpreting DNNs, most existing solutions offer interpretability in an ad hoc, one-shot, and static manner, without accounting for the perception, understanding, or response of end-users, resulting in their poor usability in practice. In this paper, we argue that DNN interpretability should be implemented as the interactions between users and models. We present i-Algebra, a first-of-its-kind interactive framework for interpreting DNNs. At its core is a library of atomic, composable operators, which explain model behaviors at varying input granularity, during different inference stages, and from distinct interpretation perspectives. Leveraging a declarative query language, users are enabled to build various analysis tools (e. g. , “drill-down”, “comparative”, “what-if” analysis) via flexibly composing such operators. We prototype i-Algebra and conduct user studies in a set of representative analysis tasks, including inspecting adversarial inputs, resolving model inconsistency, and cleansing contaminated data, all demonstrating its promising usability.

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AIIM Journal 2019 Journal Article

Motor imagery EEG recognition with KNN-based smooth auto-encoder

  • Xianlun Tang
  • Ting Wang
  • Yiming Du
  • Yuyan Dai

As new human-computer interaction technology, brain-computer interface has been widely used in various fields of life. The study of EEG signals cannot only improve people's awareness of the brain, but also establish new ways for the brain to communicate with the outside world. This paper takes the motion imaging EEG signal as the research object and proposes an innovative semi-supervised model called KNN-based smooth auto-encoder (k-SAE). K-SAE looks for the nearest neighbor values of the samples to construct a new input and learns the robust features representation by reconstructing this new input instead of the original input, which is different from the traditional automatic encoder (AE). The Gaussian filter is selected as the convolution kernel function in k-SAE to smooth the noise in the feature. Besides, the data information and spatial position of the feature map are recorded by max-pooling and unpooling, that help to prevent loss of important information. The method is applied to two data sets for feature extraction and classification experiments of motor imaging EEG signals. The experimental results show that k-SAE achieves good recognition accuracy and outperforms other state-of-the-art recognition algorithms.

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YNIMG Journal 2017 Journal Article

Factor analysis linking functions for simultaneously modeling neural and behavioral data

  • Brandon M. Turner
  • Ting Wang
  • Edgar C. Merkle

A growing number of researchers have advocated for the advancement of cognitive neuroscience by blending cognitive models with neurophysiology. The recently proposed joint modeling framework is one way to bridge the gap between the abstractions assumed by cognitive models and the neurophysiology obtained by modern methods in neuroscience. Despite this advancement, the current method for linking the two domains is hindered by the dimensionality of the neural data. In this article, we present a new linking function based on factor analysis that allows joint models to grow linearly in complexity with increases in the number of neural features. The new linking function is then evaluated in two simulation studies. The first simulation study shows how the model parameters can be accurately recovered when there are many neural features, that mimics real-world applications. The second simulation shows how the new linking function can (1) properly recover a representation of the data generating model, even in the case of model misspecification, and (2) outperform the previous linking function in a cross-validation test. We close by applying a model equipped with the new linking function to real-world data from a perceptual decision making task. The model allows us to understand how differences in the model parameters emerge as a function of differences in brain function across speed and accuracy instruction.

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YNIMG Journal 2014 Journal Article

Linking inter-individual differences in the conflict adaptation effect to spontaneous brain activity

  • Ting Wang
  • Zhencai Chen
  • Guang Zhao
  • Glenn Hitchman
  • Congcong Liu
  • Xiaoyue Zhao
  • Yijun Liu
  • Antao Chen

Conflict adaptation has been widely researched in normal and clinical populations. There are large individual differences in conflict adaptation, and it has been linked to the schizotypal trait. However, no study to date has examined how individual differences in spontaneous brain activity are related to behavioral conflict adaptation (performance). Resting-state functional magnetic resonance imaging (RS-fMRI) is a promising tool to investigate this issue. The present study evaluated the regional homogeneity (ReHo) of RS-fMRI signals in order to explore the neural basis of individual differences in conflict adaptation across two independent samples comprising a total of 67 normal subjects. A partial correlation analysis was carried out to examine the relationship between ReHo and behavioral conflict adaptation, while controlling for reaction time, standard deviation and flanker interference effects. This analysis was conducted on 39 subjects' data (sample 1); the results showed significant positive correlations in the left dorsolateral prefrontal cortex (DLPFC) and left ventrolateral prefrontal cortex. We then conducted a test-validation procedure on the remaining 28 subjects' data (sample 2) to examine the reliability of the results. Regions of interest were defined based on the correlation results. Regression analysis showed that variability in ReHo values in the DLPFC accounted for 48% of the individual differences in the conflict adaptation effect in sample 2. The present findings provide further support for the importance of the DLPFC in the conflict adaptation process. More importantly, we demonstrated that ReHo of RS-fMRI signals in the DLPFC can predict behavioral performance in conflict adaptation, which provides potential biomarkers for the early detection of cognitive control deterioration.

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AAAI Conference 2012 Conference Paper

Improving Twitter Retrieval by Exploiting Structural Information

  • Zhunchen Luo
  • Miles Osborne
  • Saša Petrovic ́
  • Ting Wang

Most Twitter search systems generally treat a tweet as a plain text when modeling relevance. However, a series of conventions allows users to tweet in structural ways using combination of different blocks of texts. These blocks include plain texts, hashtags, links, mentions, etc. Each block encodes a variety of communicative intent and sequence of these blocks captures changing discourse. Previous work shows that exploiting the structural information can improve the structured document (e. g. , web pages) retrieval. In this paper we utilize the structure of tweets, induced by these blocks, for Twitter retrieval. A set of features, derived from the blocks of text and their combinations, is used into a learning-to-rank scenario. We show that structuring tweets can achieve state-of-the-art performance. Our approach does not rely upon social media features, but when we do add this additional information, performance improves significantly.

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IJCAI Conference 2011 Conference Paper

A Wikipedia Based Semantic Graph Model for Topic Tracking in Blogsphere

  • Jintao Tang
  • Ting Wang
  • Qin Lu
  • Ji Wang
  • Wenjie Li

There are two key issues for information diffusion in blogosphere: (1) blog posts are usually short, noisy and contain multiple themes, (2) information diffusion through blogosphere is primarily driven by the "word-of-mouth" effect, thus making topics evolve very fast. This paper presents a novel topic tracking approach to deal with these issues by modeling a topic as a semantic graph in which the semantic relatedness between terms are learned from Wikipedia. For a given topic/post, the named entities, Wikipedia concepts, and the semantic relatedness are extracted to generate the graph model. Noises are filtered out through a graph clustering algorithm. To handle topic evolution, the topic model is enriched by using Wikipedia as background knowledge. Furthermore, graph edit distance is used to measure the similarity between a topic and its posts. The proposed method is tested using real-world blog data. Experimental results show the advantage of the proposed method on tracking topics in short, noisy text.

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YNIMG Journal 2010 Journal Article

The impact of social comparison on the neural substrates of reward processing: An event-related potential study

  • Jiang Qiu
  • Caiyun Yu
  • Hong Li
  • Jerwen Jou
  • Shen Tu
  • Ting Wang
  • Dongtao Wei
  • Qinglin Zhang

Event-related potentials (ERPs) were recorded to explore the electrophysiological correlates of reward processing in the social comparison context when subjects performed a simple number estimation task that entailed monetary rewards for correct answers. Three social comparison stimulus categories (three relative reward levels/self reward related to the other subject's) were mainly prepared: Self: Other=1: 2 (Disadvantageous inequity condition); Self: Other=1: 1 (Equity condition); and Self: Other=2: 1 (Advantageous inequity condition). Results showed that: both Disadvantageous and Advantageous inequity elicited a more negative ERP deflection (N350–550) than did Equity between 350 and 550 ms, and the generators of N350–550 were localized near the parahippocampal gyrus and the medial frontal/anterior cingulate cortex, which might be related to monitor and control reward prediction error during reward processing. Then, Disadvantageous and Advantageous inequity both elicited a more late negative complex (LNC1 and LNC2) than did Equity between 550 and 750 ms. The generators of LNC1 and LNC2 were both localized near the caudate nucleus, which might be related to reward processing under social comparison.

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v2026.09.13