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Xiaotong Zhang

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24 papers

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NeurIPS Conference 2025 Conference Paper

HQA-VLAttack: Towards High Quality Adversarial Attack on Vision-Language Pre-Trained Models

Han Liu
Jiaqi Li
Zhi Xu
Xiaotong Zhang
Xiaoming Xu
Fenglong Ma
Yuanman Li
Hong Yu

Black-box adversarial attack on vision-language pre-trained models is a practical and challenging task, as text and image perturbations need to be considered simultaneously, and only the predicted results are accessible. Research on this problem is in its infancy, and only a handful of methods are available. Nevertheless, existing methods either rely on a complex iterative cross-search strategy, which inevitably consumes numerous queries, or only consider reducing the similarity of positive image-text pairs but ignore that of negative ones, which will also be implicitly diminished, thus inevitably affecting the attack performance. To alleviate the above issues, we propose a simple yet effective framework to generate high-quality adversarial examples on vision-language pre-trained models, named HQA-VLAttack, which consists of text and image attack stages. For text perturbation generation, it leverages the counter-fitting word vector to generate the substitute word set, thus guaranteeing the semantic consistency between the substitute word and the original word. For image perturbation generation, it first initializes the image adversarial example via the layer-importance guided strategy, and then utilizes contrastive learning to optimize the image adversarial perturbation, which ensures that the similarity of positive image-text pairs is decreased while that of negative image-text pairs is increased. In this way, the optimized adversarial images and texts are more likely to retrieve negative examples, thereby enhancing the attack success rate. Experimental results on three benchmark datasets demonstrate that HQA-VLAttack significantly outperforms strong baselines in terms of attack success rate.

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AAAI Conference 2025 Conference Paper

Multi-Label Few-Shot Image Classification via Pairwise Feature Augmentation and Flexible Prompt Learning

Han Liu
Yuanyuan Wang
Xiaotong Zhang
Feng Zhang
Wei Wang
Fenglong Ma
Hong Yu

Multi-label few-shot image classification is a crucial and challenging task due to limited annotated data and elusive category specificity. However, research on this topic is still in the rudimentary stage and few methods are available. Existing methods either leverage data augmentation to alleviate data scarcity or utilize label features as auxiliary knowledge to eliminate the negative effect caused by irrelevant categories, but they ignore the utilization of image region features for data augmentation, and overlook to learn appropriate text feature to better match the image features of specific categories. Moreover, these methods only focus on one side and do not effectively tackle the above two issues simultaneously. In this paper, we introduce a novel prototype-based multi-label few-shot learning framework that seamlessly integrates pairwise feature augmentation and flexible prompt learning. Specifically, by pairwise feature augmentation, we leverage the region features of images in the support set to generate more image features and construct image prototypes, thus alleviating the issue of data scarcity. By flexible prompt learning, we adaptively acquire class-specific prompts to build text prototypes that highly match the image features of specific classes, thereby mitigating the impact of irrelevant classes. Finally, with adaptive learnable parameters, we merge image and text prototypes to obtain the final prototypes, achieving a more powerful classifier for multi-label few-shot image classification. Extensive experimental results demonstrate that our proposed method can push the performance to a higher level.

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EAAI Journal 2025 Journal Article

Sliding window regression method for mechanical meter reading recognition in time-series images

Hao Xiu
Siran Hu
Yuanxin Cui
Jie He
Xiaotong Zhang
Yue Qi

Remote meter reading is an important part of the intelligent transformation of old meters. In addition to smart meters, installing cameras outside traditional meters and transmitting images to cloud servers via Narrowband Internet of Things technology can make the intelligent transformation process more convenient. Against this backdrop, this paper proposes a sliding window-based joint recognition network to address the significant numerical errors and misrecognition issues in current meter reading methods. It combines maximum probability decoding and maximum average probability decoding to classify and recognize complete instrument images, effectively reducing numerical errors and improving accuracy. After the instrument data acquisition is systematized, combining access to historical data and current images can effectively improve the recognition accuracy rate. The network collaborates with a regression prediction model, performing regression analysis on historical data to establish equations for small-range visual-assisted regression recognition. Experiments on instrument datasets show that the algorithm achieves a 99. 57% recognition accuracy, which is 4% higher than holistic methods and nearly 1% higher than single-digit methods. Moreover, it reduces the maximum error from an average of 68. 5 to 1. 06, and the mean absolute error and mean square error by nearly 20 times. The model is now ready for production use. This method is highly significant for industrial users, especially in old-style electricity meter reading scenarios in factories, such as in power monitoring systems of large-scale manufacturing enterprises, strongly supporting industrial automation.

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AAAI Conference 2024 Conference Paper

A Goal Interaction Graph Planning Framework for Conversational Recommendation

Xiaotong Zhang
Xuefang Jia
Han Liu
Xinyue Liu
Xianchao Zhang

Multi-goal conversational recommender system (MG-CRS) which is more in line with realistic scenarios has attracted a lot of attention. MG-CRS can dynamically capture the demands of users in conversation, continuously engage their interests, and make recommendations. The key of accomplishing these tasks is to plan a reasonable goal sequence which can naturally guide the user to accept the recommended goal. Previous works have demonstrated that mining the correlations of goals from the goal sequences in the dialogue corpus is helpful for recommending the goal that the user is interested in. However, they independently model correlations for each level of goal (i.e., goal type or entity) and neglect the order of goals appear in the dialogue. In this paper, we propose a goal interaction graph planning framework which constructs a directed heterogeneous graph to flexibly model the correlations between any level of goals and retain the order of goals. We design a goal interaction graph learning module to model the goal correlations and propagate goal representations via directed edges, then use an encoder and a dual-way fusion decoder to extract the most relevant information with the current goal from the conversation and domain knowledge, making the next-goal prediction fully exploit the prior goal correlations and user feedback. Finally we generate engaging responses based on the predicted goal sequence to complete the recommendation task. Experiments on two benchmark datasets show that our method achieves significant improvements in both the goal planning and response generation tasks.

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YNIMG Journal 2024 Journal Article

Cerebellar representation during phonetic processing in tonal and non-tonal language speakers: An ALE meta-analysis

Xiaotong Zhang
Zhaowen Zhou
Ying Wang
Jinyi Long
Zhuoming Chen

The role of the cerebellum in phonetic processing has been discovered and widely discussed for decades. However, with the idea that the cerebral representation of phonetic processing is different in tonal language and non-tonal language speakers, whether the cerebellar representation of phonetic processing differs based on language background remains unknown. In the present study, we conducted an activation likelihood estimation (ALE) analysis among 33 functional neuroimaging studies involving 541 healthy adults (213 tonal language speakers and 328 non-tonal language speakers). The aim was to explore the cerebellar representation of phonetic perception and phonetic production in these two language backgrounds. Our results demonstrated the involvement of cerebellum left Crus I, right Crus II, lobules VI, and VIIb in phonetic perception among tonal language speakers, whereas only one focal cluster (right Crus I and Crus II) was demonstrated in non-tonal language speakers. Conjunction analysis revealed overlapping regions located in the right Crus II both in tonal and non-tonal language speakers during phonetic perception. During phonetic production, no significant cluster was detected among tonal language speakers, whereas one focal cluster (within right lobule VI) was detected in non-tonal language speakers. These results highlight the specific cerebellar representation of phonetic processing in tonal and non-tonal languages. Overall, this ALE analysis provides a profound view of the neural mechanism of phonetic processing.

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AAAI Conference 2024 Conference Paper

Depression Detection via Capsule Networks with Contrastive Learning

Han Liu
Changya Li
Xiaotong Zhang
Feng Zhang
Wei Wang
Fenglong Ma
Hongyang Chen
Hong Yu

Depression detection is a challenging and crucial task in psychological illness diagnosis. Utilizing online user posts to predict whether a user suffers from depression seems an effective and promising direction. However, existing methods suffer from either poor interpretability brought by the black-box models or underwhelming performance caused by the completely separate two-stage model structure. To alleviate these limitations, we propose a novel capsule network integrated with contrastive learning for depression detection (DeCapsNet). The highlights of DeCapsNet can be summarized as follows. First, it extracts symptom capsules from user posts by leveraging meticulously designed symptom descriptions, and then distills them into class-indicative depression capsules. The overall workflow is in an explicit hierarchical reasoning manner and can be well interpreted by the Patient Health Questionnaire-9 (PHQ9), which is one of the most widely adopted questionnaires for depression diagnosis. Second, it integrates with contrastive learning, which can facilitate the embeddings from the same class to be pulled closer, while simultaneously pushing the embeddings from different classes apart. In addition, by adopting the end-to-end training strategy, it does not necessitate additional data annotation, and mitigates the potential adverse effects from the upstream task to the downstream task. Extensive experiments on three widely-used datasets show that in both within-dataset and cross-dataset scenarios our proposed method outperforms other strong baselines significantly.

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AAAI Conference 2024 Conference Paper

Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframing

Han Liu
Siyang Zhao
Xiaotong Zhang
Feng Zhang
Wei Wang
Fenglong Ma
Hongyang Chen
Hong Yu

Few-shot and zero-shot text classification aim to recognize samples from novel classes with limited labeled samples or no labeled samples at all. While prevailing methods have shown promising performance via transferring knowledge from seen classes to unseen classes, they are still limited by (1) Inherent dissimilarities among classes make the transformation of features learned from seen classes to unseen classes both difficult and inefficient. (2) Rare labeled novel samples usually cannot provide enough supervision signals to enable the model to adjust from the source distribution to the target distribution, especially for complicated scenarios. To alleviate the above issues, we propose a simple and effective strategy for few-shot and zero-shot text classification. We aim to liberate the model from the confines of seen classes, thereby enabling it to predict unseen categories without the necessity of training on seen classes. Specifically, for mining more related unseen category knowledge, we utilize a large pre-trained language model to generate pseudo novel samples, and select the most representative ones as category anchors. After that, we convert the multi-class classification task into a binary classification task and use the similarities of query-anchor pairs for prediction to fully leverage the limited supervision signals. Extensive experiments on six widely used public datasets show that our proposed method can outperform other strong baselines significantly in few-shot and zero-shot tasks, even without using any seen class samples.

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IJCAI Conference 2023 Conference Paper

Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors

Han Liu
Xingshuo Huang
Xiaotong Zhang
Qimai Li
Fenglong Ma
Wei Wang
Hongyang Chen
Hong Yu

Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction. Gradient estimation is a critical step in black-box adversarial attacks, as it will directly affect the query efficiency. Recent works have attempted to utilize gradient priors to facilitate score-based methods to obtain better results. However, these gradient priors still suffer from the edge gradient discrepancy issue and the successive iteration gradient direction issue, thus are difficult to simply extend to decision-based methods. In this paper, we propose a novel Decision-based Black-box Attack framework with Gradient Priors (DBA-GP), which seamlessly integrates the data-dependent gradient prior and time-dependent prior into the gradient estimation procedure. First, by leveraging the joint bilateral filter to deal with each random perturbation, DBA-GP can guarantee that the generated perturbations in edge locations are hardly smoothed, i. e. , alleviating the edge gradient discrepancy, thus remaining the characteristics of the original image as much as possible. Second, by utilizing a new gradient updating strategy to automatically adjust the successive iteration gradient direction, DBA-GP can accelerate the convergence speed, thus improving the query efficiency. Extensive experiments have demonstrated that the proposed method outperforms other strong baselines significantly.

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AAAI Conference 2023 Conference Paper

Boosting Few-Shot Text Classification via Distribution Estimation

Han Liu
Feng Zhang
Xiaotong Zhang
Siyang Zhao
Fenglong Ma
Xiao-ming Wu
Hongyang Chen
Hong Yu

Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain. However, directly applying this approach to few-shot text classification is challenging, since leveraging the statistics of known classes with sufficient samples to calibrate the distributions of novel classes may cause negative effects due to serious category difference in text domain. To alleviate this issue, we propose two simple yet effective strategies to estimate the distributions of the novel classes by utilizing unlabeled query samples, thus avoiding the potential negative transfer issue. Specifically, we first assume a class or sample follows the Gaussian distribution, and use the original support set and the nearest few query samples to estimate the corresponding mean and covariance. Then, we augment the labeled samples by sampling from the estimated distribution, which can provide sufficient supervision for training the classification model. Extensive experiments on eight few-shot text classification datasets show that the proposed method outperforms state-of-the-art baselines significantly.

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YNIMG Journal 2023 Journal Article

Design and application of a multimodality-compatible 1Tx/6Rx RF coil for monkey brain MRI at 7T

Shuxian Qu
Sunhang Shi
Zhiyan Quan
Yang Gao
Minmin Wang
Yueming Wang
Gang Pan
Hsin-Yi Lai

OBJECTIVE: Blood-oxygen-level-dependent functional MRI allows to investigte neural activities and connectivity. While the non-human primate plays an essential role in neuroscience research, multimodal methods combining functional MRI with other neuroimaging and neuromodulation enable us to understand the brain network at multiple scales. APPROACH: In this study, a tight-fitting helmet-shape receive array with a single transmit loop for anesthetized macaque brain MRI at 7T was fabricated with four openings constructed in the coil housing to accommodate multimodal devices, and the coil performance was quantitatively evaluated and compared to a commercial knee coil. In addition, experiments over three macaques with infrared neural stimulation (INS), focused ultrasound stimulation (FUS), and transcranial direct current stimulation (tDCS) were conducted. MAIN RESULTS: The RF coil showed higher transmit efficiency, comparable homogeneity, improved SNR and enlarged signal coverage over the macaque brain. Infrared neural stimulation was applied to the amygdala in deep brain region, and activations in stimulation sites and connected sites were detected, with the connectivity consistent with anatomical information. Focused ultrasound stimulation was applied to the left visual cortex, and activations were acquired along the ultrasound traveling path, with all time course curves consistent with pre-designed paradigms. The existence of transcranial direct current stimulation electrodes brought no interference to the RF system, as evidenced through high-resolution MPRAGE structure images. SIGNIFICANCE: This pilot study reveals the feasibility for brain investigation at multiple spatiotemporal scales, which may advance our understanding in dynamic brain networks.

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NeurIPS Conference 2023 Conference Paper

HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text

Han Liu
Zhi Xu
Xiaotong Zhang
Feng Zhang
Fenglong Ma
Hongyang Chen
Hong Yu
Xianchao Zhang

Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted label is accessible. Research on this problem is still in the embryonic stage and only a few methods are available. Nevertheless, existing methods rely on the complex heuristic algorithm or unreliable gradient estimation strategy, which probably fall into the local optimum and inevitably consume numerous queries, thus are difficult to craft satisfactory adversarial examples with high semantic similarity and low perturbation rate in a limited query budget. To alleviate above issues, we propose a simple yet effective framework to generate high quality textual adversarial examples under the black-box hard-label attack scenarios, named HQA-Attack. Specifically, after initializing an adversarial example randomly, HQA-attack first constantly substitutes original words back as many as possible, thus shrinking the perturbation rate. Then it leverages the synonym set of the remaining changed words to further optimize the adversarial example with the direction which can improve the semantic similarity and satisfy the adversarial condition simultaneously. In addition, during the optimizing procedure, it searches a transition synonym word for each changed word, thus avoiding traversing the whole synonym set and reducing the query number to some extent. Extensive experimental results on five text classification datasets, three natural language inference datasets and two real-world APIs have shown that the proposed HQA-Attack method outperforms other strong baselines significantly.

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YNICL Journal 2023 Journal Article

Predicting treatment response in adolescents and young adults with major depressive episodes from fMRI using graph isomorphism network

Jia Duan
Yueying Li
Xiaotong Zhang
Shuai Dong
Pengfei Zhao
Jie Liu
Junjie Zheng
Rongxin Zhu

BACKGROUND: Major depressive episode (MDE) is the main clinical feature of mood disorders (major depressive disorder and bipolar disorder) in adolescents and young adults and accounts for most of the disease course. However, 30%-40% of MDE patients not responding to clinical first-line interventions. It is crucial to predict treatment response in the early stages and identify biomarkers associated with treatment response. Graph Isomorphism Network (GIN), a deep learning method, is promising for predicting treatment response for individual MDE patients with more powerful representation ability to capture the features of brain functional connectivity. METHODS: In this study, GIN was used to predict individual treatment response in 198 adolescents and young adults with MDE. The most discriminating regions were also identified for the treatment response prediction. RESULTS: Using GIN approach, the baseline functional connectivity could predict 79.8% responders and 67.4% non-responders to treatment (accuracy 74.24%). Furthermore, the most discriminating brain regions were mainly involved in paralimbic and subcortical areas. CONCLUSIONS: GIN has shown potential in predicting treatment response for individual patients, which may enable personalized treatment decisions. Furthermore, targeted interventions focused on modulating the activity and connectivity within paralimbic and subcortical regions could potentially improve treatment outcomes and enable personalized interventions for adolescents and young adults with MDE.

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AAAI Conference 2023 Conference Paper

SSPAttack: A Simple and Sweet Paradigm for Black-Box Hard-Label Textual Adversarial Attack

Han Liu
Zhi Xu
Xiaotong Zhang
Xiaoming Xu
Feng Zhang
Fenglong Ma
Hongyang Chen
Hong Yu

Hard-label textual adversarial attack is a challenging task, as only the predicted label information is available, and the text space is discrete and non-differentiable. Relevant research work is still in fancy and just a handful of methods are proposed. However, existing methods suffer from either the high complexity of genetic algorithms or inaccurate gradient estimation, thus are arduous to obtain adversarial examples with high semantic similarity and low perturbation rate under the tight-budget scenario. In this paper, we propose a simple and sweet paradigm for hard-label textual adversarial attack, named SSPAttack. Specifically, SSPAttack first utilizes initialization to generate an adversarial example, and removes unnecessary replacement words to reduce the number of changed words. Then it determines the replacement order and searches for an anchor synonym, thus avoiding going through all the synonyms. Finally, it pushes substitution words towards original words until an appropriate adversarial example is obtained. The core idea of SSPAttack is just swapping words whose mechanism is simple. Experimental results on eight benchmark datasets and two real-world APIs have shown that the performance of SSPAttack is sweet in terms of similarity, perturbation rate and query efficiency.

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YNIMG Journal 2021 Journal Article

Infrared neural stimulation with 7T fMRI: A rapid in vivo method for mapping cortical connections of primate amygdala

Sunhang Shi
Augix Guohua Xu
Yun-Yun Rui
Xiaotong Zhang
Lizabeth M. Romanski
Katalin M. Gothard
Anna Wang Roe

We have previously shown that INS-fMRI is a rapid method for mapping mesoscale brain networks in the macaque monkey brain. Focal stimulation of single cortical sites led to the activation of connected cortical locations, resulting in a global connectivity map. Here, we have extended this method for mapping brainwide networks following stimulation of single subcortical sites. As a testbed, we focused on the basal nucleus of the amygdala in the macaque monkey. We describe methods to target basal nucleus locations with submillimeter precision, pulse train stimulation methods, and statistical tests for assessing non-random nature of activations. Using these methods, we report that stimulation of precisely targeted loci in the basal nucleus produced sparse and specific activations in the brain. Activations were observed in the insular and sensory association cortices as well as activations in the cingulate cortex, consistent with known anatomical connections. What is new here is that the activations were focal and, in some cases, exhibited shifting topography with millimeter shifts in stimulation site. The precision of the method enables networks mapped from different nearby sites in the basal nucleus to be distinguished. While further investigation is needed to improve the sensitivity of this method, our analyses do support the reproducibility and non-random nature of some of the activations. We suggest that INS-fMRI is a promising method for mapping large-scale cortical and subcortical networks at high spatial resolution.

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YNIMG Journal 2020 Journal Article

A 16-channel AC/DC array coil for anesthetized monkey whole-brain imaging at 7T

Yang Gao
Azma Mareyam
Yi Sun
Thomas Witzel
Nicolas Arango
Irene Kuang
Jacob White
Anna Wang Roe

Functional magnetic resonance imaging (fMRI) in monkeys is important for bridging the gap between invasive animal brain studies and non-invasive human brain studies. To resolve the finer functional structure of the monkey brain, ultra-high-field (UHF) MR is essential, and high-performance, close-fitting RF receive coils are typically desired to fully leverage the intrinsic gains provided by UHF MRI. Moreover, static field (B0) inhomogeneity arising from the tissue susceptibility interface is more severe at UHF, presenting an obstacle to achieving high-resolution fMRI. B0 shim of the monkey head is challenging due to its smaller size and more complex sources of B0 offsets in multi-modal imaging tasks. In the present work, we have customized an array coil for lightly-anesthetized monkey fMRI in the 7T human scanner that combines RF and multi-coil (MC) B0 shim functionality (also referred to as AC/DC coils) to provide high imaging SNR and high-spatial-order, rapidly switchable B0-shim capability. Additional space was retained on the coil to render it compatible with monkey multi-modal imaging studies. Both MC global (whole-volume) and dynamic (slice-optimized) shim methods were tested and evaluated, and the benefits of MC shim for fMRI experiments was also studied. A minor reduction in RF coil performance was found after introducing additional B0 shim circuitry. However, the proposed RF coil provided higher image SNR and more uniform contrast compared to a commercially available coil for human knee imaging. Compared with static 2nd-order shim, the B0 inhomogeneity was reduced by 56.8%, and 95-percentile B0 offset was reduced to within 28.2 Hz through MC shim, versus 68.7 Hz with 2nd-order static shim. As a result, functional image quality could be improved, and brain activation can be better detected using the proposed AC/DC monkey coil.

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IJCAI Conference 2019 Conference Paper

Attributed Graph Clustering via Adaptive Graph Convolution

Xiaotong Zhang
Han Liu
Qimai Li
Xiao-ming Wu

Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that graph convolution is effective in combining structural and content information, and several recent methods based on it have achieved promising clustering performance on some real attributed networks. However, there is limited understanding of how graph convolution affects clustering performance and how to properly use it to optimize performance for different graphs. Existing methods essentially use graph convolution of a fixed and low order that only takes into account neighbours within a few hops of each node, which underutilizes node relations and ignores the diversity of graphs. In this paper, we propose an adaptive graph convolution method for attributed graph clustering that exploits high-order graph convolution to capture global cluster structure and adaptively selects the appropriate order for different graphs. We establish the validity of our method by theoretical analysis and extensive experiments on benchmark datasets. Empirical results show that our method compares favourably with state-of-the-art methods.

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YNICL Journal 2019 Journal Article

Structural connectome alterations in patients with disorders of consciousness revealed by 7-tesla magnetic resonance imaging

Xufei Tan
Zhen Zhou
Jian Gao
Fanxia Meng
Yamei Yu
Jie Zhang
Fangping He
Ruili Wei

Although the functional connectivity of patients with disorders of consciousness (DOC) has been widely examined, less is known about brain white matter connectivity. The aim of this study was to explore structural network alterations for the diagnosis and prognosis of patients with chronic DOC. Eleven DOC patients and 11 sex- and age-matched controls were included in the study. Participants underwent diffusion magnetic resonance imaging (MRI) and T1-weighted structural MRI at 7 tesla (7 T). Graph-theoretical analysis and network-based statistics were used to analyze the group differences. Two patients were scanned twice for a longitudinal study to examine the relationship between connectome metrics and the patients' prognoses. Compared with healthy controls, DOC patients showed significantly elevated transitivity (p < .001), local efficiency (p = .009), and clustering coefficient (p = .039). When comparing the connectome metrics within the three groups (healthy controls, minimally conscious state (MCS), and vegetative state/unresponsive wakefulness syndrome (VS/UWS)), significant group differences were observed in transitivity (p < .001) and local efficiency (p = .031). Significantly increased transitivity was observed in vegetative state/unresponsive wakefulness syndrome compared with minimally conscious state (p = .0217, Bonferroni corrected). Transitivity showed significant negative correlations with the Coma Recovery Scale-Revised score (r = -0.6902, p = .023), consistent with the longitudinal study results. A subnetwork with significantly decreased structural connections was identified using network-based statistical analysis comparing DOC patients with healthy controls, which was mainly located in the frontal cortex, limbic system, and occipital and parietal lobes. This preliminary study suggests that graph theoretical approaches for assessing white matter connectivity may enable various states of DOC to be distinguished. Of the metrics analyzed, transitivity had a critical role in distinguishing the diagnostic groups. Larger cohorts will be necessary to confirm the predictive value of 7 T MRI in the prognosis of DOC patients.

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IJCAI Conference 2018 Conference Paper

Multi-Task Clustering with Model Relation Learning

Xiaotong Zhang
Xianchao Zhang
Han Liu
Jiebo Luo

Multi-task clustering improves the clustering performance of each task by transferring knowledge among the related tasks. An important aspect of multi-task clustering is to assess the task relatedness. However, to our knowledge, only two previous works have assessed the task relatedness, but they both have limitations. In this paper, we propose a multi-task clustering with model relation learning (MTCMRL) method, which automatically learns the model parameter relatedness between each pair of tasks. The objective function of MTCMRL consists of two parts: (1) within-task clustering: clustering each task by introducing linear regression model into symmetric nonnegative matrix factorization; (2) cross-task relatedness learning: updating the parameter of the linear regression model in each task by learning the model parameter relatedness between the clusters in each pair of tasks. We present an effective alternating algorithm to solve the non-convex optimization problem. Experimental results show the superiority of the proposed method over traditional single-task clustering methods and existing multi-task clustering methods.

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IJCAI Conference 2016 Conference Paper

Self-Adapted Multi-Task Clustering

Xianchao Zhang
Xiaotong Zhang
Han Liu

Multi-task clustering improves the clustering performance of each task by transferring knowledge across related tasks. Most existing multi-task clustering methods are based on the ideal assumption that the tasks are completely related. However, in many real applications, the tasks are usually partially related, and brute-force transfer may cause negative effect which degrades the clustering performance. In this paper, we propose a self-adapted multi-task clustering (SAMTC) method which can automatically identify and transfer reusable instances among the tasks, thus avoiding negative transfer. SAMTC begins with an initialization by performing single-task clustering on each task, then executes the following three steps: first, it finds the reusable instances by measuring related clusters with Jensen-Shannon divergence between each pair of tasks, and obtains a pair of possibly related subtasks; second, it estimates the relatedness between each pair of subtasks with kernel mean matching; third, it constructs the similarity matrix for each task by exploiting useful information from the other tasks through instance transfer, and adopts spectral clustering to get the final clustering result. Experimental results on several real data sets show the superiority of the proposed algorithm over traditional single-task clustering methods and existing multitask clustering methods.

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IJCAI Conference 2015 Conference Paper

Multi-Task Multi-View Clustering for Non-Negative Data

Xianchao Zhang
Xiaotong Zhang
Han Liu

Multi-task clustering and multi-view clustering have severally found wide applications and received much attention in recent years. Nevertheless, there are many clustering problems that involve both multi-task clustering and multi-view clustering, i. e. , the tasks are closely related and each task can be analyzed from multiple views. In this paper, for non-negative data (e. g. , documents), we introduce a multi-task multi-view clustering (MTMVC) framework which integrates withinview-task clustering, multi-view relationship learning and multi-task relationship learning. We then propose a specific algorithm to optimize the MT- MVC framework. Experimental results show the superiority of the proposed algorithm over either multi-task clustering algorithms or multi-view clustering algorithms for multi-task clustering of multiview data.

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AAAI Conference 2014 Conference Paper

Novel Density-Based Clustering Algorithms for Uncertain Data

Xianchao Zhang
Han Liu
Xiaotong Zhang
Xinyue Liu

Density-based techniques seem promising for handling data uncertainty in uncertain data clustering. Nevertheless, some issues have not been addressed well in existing algorithms. In this paper, we firstly propose a novel density-based uncertain data clustering algorithm, which improves upon existing algorithms from the following two aspects: (1) it employs an exact method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in previous work; (2) it introduces new definitions of core object probability and direct reachability probability, thus reducing the complexity and avoiding sampling. We then further improve the algorithm by using a novel assignment strategy to ensure that every object will be assigned to the most appropriate cluster. Experimental results show the superiority of our proposed algorithms over existing ones.

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AAAI Conference 2013 Conference Paper

Smart Multi-Task Bregman Clustering and Multi-Task Kernel Clustering

Xianchao Zhang
Xiaotong Zhang

Multitask Bregman Clustering (MBC) alternatively updates clusters and learns relationship between clusters of different tasks, and the two phases boost each other. However, the boosting does not always have positive effect, it may also cause negative effect. Another issue of MBC is that it cannot deal with nonlinear separable data. In this paper, we show that MBC’s process of using cluster relationship to boost the updating clusters phase may cause negative effect, i. e. , cluster centroid may be skewed under some conditions. We propose a smart multi-task Bregman clustering (S-MBC) algorithm which identifies negative effect of the boosting and avoids the negative effect if it occurs. We then extend the framework of S-MBC to a smart multi-task kernel clustering (S-MKC) framework to deal with nonlinear separable data. We also propose a specific implementation of the framework which could be applied to any Mercer kernel. Experimental results confirm our analysis, and demonstrate the superiority of our proposed methods.

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AIJ Journal 2010 Journal Article

Reasoning about cardinal directions between extended objects

Weiming Liu
Xiaotong Zhang
Sanjiang Li
Mingsheng Ying

Direction relations between extended spatial objects are important commonsense knowledge. Recently, Goyal and Egenhofer proposed a relation model, known as the cardinal direction calculus (CDC), for representing direction relations between connected plane regions. The CDC is perhaps the most expressive qualitative calculus for directional information, and has attracted increasing interest from areas such as artificial intelligence, geographical information science, and image retrieval. Given a network of CDC constraints, the consistency problem is deciding if the network is realizable by connected regions in the real plane. This paper provides a cubic algorithm for checking the consistency of complete networks of basic CDC constraints, and proves that reasoning with the CDC is in general an NP-complete problem. For a consistent complete network of basic CDC constraints, our algorithm returns a ‘canonical’ solution in cubic time. This cubic algorithm is also adapted to check the consistency of complete networks of basic cardinal constraints between possibly disconnected regions.

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AAAI Conference 2008 Conference Paper

Reasoning with Cardinal Directions: An Efficient Algorithm

Xiaotong Zhang
Sanjiang Li

Direction relations between extended spatial objects are important commonsense knowledge. Recently, Goyal and Egenhofer proposed a formal model, called Cardinal Direction Calculus (CDC), for representing direction relations between connected plane regions. CDC is perhaps the most expressive qualitative calculus for directional information, and has attracted increasing interest from areas such as artificial intelligence, geographical information science, and image retrieval. Given a network of CDC constraints, the consistency problem is deciding if the network is realizable by connected regions in the real plane. This paper provides a cubic algorithm for checking consistency of basic CDC constraint networks. As one byproduct, we also show that any consistent network of CDC constraints has a canonical realization in digital plane. The cubic algorithm can also been adapted to cope with disconnected regions, in which case the current best algorithm is of time complexity O(n5 ).

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