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Jia Xu

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14 papers

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AAAI Conference 2026 Conference Paper

BrainLMM: A Label-Free Framework for Mapping Multi-Semantic Representation in the Human Visual Cortex

Tan Gao
Mufan Xue
Haofang Zheng
Shuo Lv
Jia Xu
Dabin Sheng
Ziming Mao
Xinyu Wu

Previous studies leveraging artificial neural networks have been used to investigate the semantic coding within human visual cortex. However, building an interpretable label-free framework that can effectively map brain responses to multiple coexisting semantic concepts remains largely unexplored. Here, we propose BrainLMM, a label-free framework for multi-semantic mapping of voxel responses by combining diverse vision encoders with the Describe-and-Dissect strategy, enabling a hypothesis-free analysis of the human high-level visual cortex. First, we construct voxel-wise encoding models leveraging diverse vision encoders to predict visual cortical responses to natural scene images. Then, we use BrainLMM to map individual brain voxels to multiple semantics without requiring any predefined labels. To evaluate the effectiveness of our method, we compute Pearson correlation coefficients to compare the multi-semantic mappings produced by BrainLMM and CLIP-MSM with ground-truth voxel responses within selective cortical areas. Our findings indicate that BrainLMM achieves more accurate predictions of visual responses compared to CLIP-MSM. Finally, to demonstrate the multi-semantic mapping capability of our method, we project multiple representative semantic concepts onto the cortical surface for visualization. Our method enables the discovery of voxels that exhibit strong activation in response to previously undefined semantic concepts across two independent datasets: the Natural Scenes Dataset (NSD) and the Natural Object Dataset (NOD).

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FLAP Journal 2026 Journal Article

On the Structure of Dual-line Standard Contradictions and their General Forms in First-order Logic

Xingxing He
Jia Xu
Yingfang Li
Jun Liu

Contradiction separation (CS) and its first-order version S-CS are multi- clause inference schemes for clausal refutation. They isolate a standard con- tradiction core within a clause set and derive a propagated clause from the remaining literals. This paper develops structural characterizations of non-unit standard contradictions that make such cores explicit and easier to identify. In propositional logic, we introduce a canonical dual-line family and prove that every instance is a standard contradiction. We study admissible literal exten- sions, define ladder structures as maximal dual-line extensions, and present a regular triple-line family with constructive generation schemes. We also analyze how dual-line cores compose via clause connections and give sufficient condi- tions under which the composed clause set remains a standard contradiction. In first-order logic, we exhibit clause families that are not standard contradictions syntactically but become standard contradictions after suitable instantiation and controlled clause reuse. ∗ The corresponding author.

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AAAI Conference 2026 Conference Paper

The Avengers: A Routing Recipe for Collective Intelligence in Language Models

Yiqun Zhang
Hao Li
Chenxu Wang
Linyao Chen
Qiaosheng Zhang
Peng Ye
Shi Feng
Xinrun Wang

Proprietary models are increasingly dominating the race for ever-larger language models. Can open-source, smaller models remain competitive across a broad range of tasks? In this paper, we present the Avengers---a lightweight framework that leverages the collective intelligence of these smaller models. The Avengers builds upon four lightweight operations: (i) embedding: encode queries using a text embedding model; (ii) clustering: group queries based on their semantic similarity; (iii) scoring: scores each model's performance within each cluster; and (iv) voting: improve outputs via repeated sampling and voting. At inference time, each query is embedded and assigned to its nearest cluster. The top-performing model(s) within that cluster are selected to generate the response with repeated sampling. Remarkably, with 10 open-source models (~7B parameters each), the Avengers surpasses GPT-4o, 4.1, and 4.5 in average performance across 15 diverse datasets spanning mathematics, coding, logical reasoning, general knowledge, and affective tasks. In particular, it surpasses GPT-4.1 on mathematics tasks by 18.21% and on code tasks by 7.46%. Furthermore, the Avengers delivers superior out-of-distribution generalization, and remains robust across various embedding models, clustering algorithms, ensemble strategies, data efficiency, and values of its sole parameter---the number of clusters.

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AAAI Conference 2025 Conference Paper

CLIP-MSM: A Multi-Semantic Mapping Brain Representation for Human High-Level Visual Cortex

Guoyuan Yang
Mufan Xue
Ziming Mao
Haofang Zheng
Jia Xu
Dabin Sheng
Ruotian Sun
Ruoqi Yang

Prior work employing deep neural networks (DNNs) with explainable techniques has identified human visual cortical selective representation to specific categories. However, constructing high-performing encoding models that accurately capture brain responses to coexisting multi-semantics remains elusive. Here, we used CLIP models combined with CLIP Dissection to establish a multi-semantic mapping framework (CLIP-MSM) for hypothesis-free analysis in human high-level visual cortex. First, we utilize CLIP models to construct voxel-wise encoding models for predicting visual cortical responses to natural scene images. Then, we apply CLIP Dissection and normalize the semantic mapping score to achieve the mapping of single brain voxels to multiple semantics. Our findings indicate that CLIP Dissection applied to DNNs modeling the human high-level visual cortex demonstrates better interpretability accuracy compared to Network Dissection. In addition, to demonstrate how our method enables fine-grained discovery in hypothesis-free analysis, we quantify the accuracy between CLIP-MSM’s reconstructed brain activation in response to categories of faces, bodies, places, words and food, and the ground truth of brain activation. We demonstrate that CLIP-MSM provides more accurate predictions of visual responses compared to CLIP Dissection. Our results have been validated using two large natural image datasets: the Natural Scenes Dataset (NSD) and the Natural Object Dataset (NOD).

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JBHI Journal 2024 Journal Article

Application of Zero-Watermarking Scheme Based on Swin Transformer for Securing the Metaverse Healthcare Data

Baoru Han
Han Wang
Dawei Qiao
Jia Xu
Tianyu Yan

The existing medical image privacy solutions cannot completely solve the security problems created by applying the metaverse healthcare system. A robust zero-watermarking scheme based on the Swin Transformer is proposed in this article to improve the security of medical images in the metaverse healthcare system. This scheme uses a pretrained Swin Transformer to extract deep features from the original medical images with a good generalization performance and multiscale, and binary feature vectors are generated by using the mean hashing algorithm. Then, the logistic chaotic encryption algorithm boosts the security of the watermarking image by encrypting it. Finally, an encrypted watermarking image is XORed with the binary feature vector to create a zero-watermarking, and the validity of the proposed scheme is verified through experimentation. According to the results of the experiments, the proposed scheme has excellent robustness to common attacks and geometric attacks, and implements privacy protections for medical image security transmissions in the metaverse. The research results provide a reference for the data security and privacy protection of the metaverse healthcare system.

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AAAI Conference 2023 System Paper

ConceptX: A Framework for Latent Concept Analysis

Firoj Alam
Fahim Dalvi
Nadir Durrani
Hassan Sajjad
Abdul Rafae Khan
Jia Xu

The opacity of deep neural networks remains a challenge in deploying solutions where explanation is as important as precision. We present ConceptX, a human-in-the-loop framework for interpreting and annotating latent representational space in pre-trained Language Models (pLMs). We use an unsupervised method to discover concepts learned in these models and enable a graphical interface for humans to generate explanations for the concepts. To facilitate the process, we provide auto-annotations of the concepts (based on traditional linguistic ontologies). Such annotations enable development of a linguistic resource that directly represents latent concepts learned within deep NLP models. These include not just traditional linguistic concepts, but also task-specific or sensitive concepts (words grouped based on gender or religious connotation) that helps the annotators to mark bias in the model. The framework consists of two parts (i) concept discovery and (ii) annotation platform.

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NeurIPS Conference 2023 Conference Paper

Fair Canonical Correlation Analysis

Zhuoping Zhou
Davoud Ataee Tarzanagh
Bojian Hou
Boning Tong
Jia Xu
Yanbo Feng
Qi Long
Li Shen

This paper investigates fairness and bias in Canonical Correlation Analysis (CCA), a widely used statistical technique for examining the relationship between two sets of variables. We present a framework that alleviates unfairness by minimizing the correlation disparity error associated with protected attributes. Our approach enables CCA to learn global projection matrices from all data points while ensuring that these matrices yield comparable correlation levels to group-specific projection matrices. Experimental evaluation on both synthetic and real-world datasets demonstrates the efficacy of our method in reducing correlation disparity error without compromising CCA accuracy.

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IJCAI Conference 2022 Conference Paper

Learning by Interpreting

Xuting Tang
Abdul Rafae Khan
Shusen Wang
Jia Xu

This paper introduces a novel way of enhancing NLP prediction accuracy by incorporating model interpretation insights. Conventional efforts often focus on balancing the trade-offs between accuracy and interpretability, for instance, sacrificing model performance to increase the explainability. Here, we take a unique approach and show that model interpretation can ultimately help improve NLP quality. Specifically, we employ our learned interpretability results using attention mechanisms, LIME, and SHAP to train our model. We demonstrate a significant increase in accuracy of up to +3. 4 BLEU points on NMT and up to +4. 8 points on GLUE tasks, verifying our hypothesis that it is possible to achieve better model learning by incorporating model interpretation knowledge.

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NeurIPS Conference 2021 Conference Paper

Action-guided 3D Human Motion Prediction

Jiangxin Sun
Zihang Lin
Xintong Han
Jian-Fang Hu
Jia Xu
Wei-Shi Zheng

The ability of forecasting future human motion is important for human-machine interaction systems to understand human behaviors and make interaction. In this work, we focus on developing models to predict future human motion from past observed video frames. Motivated by the observation that human motion is closely related to the action being performed, we propose to explore action context to guide motion prediction. Specifically, we construct an action-specific memory bank to store representative motion dynamics for each action category, and design a query-read process to retrieve some motion dynamics from the memory bank. The retrieved dynamics are consistent with the action depicted in the observed video frames and serve as a strong prior knowledge to guide motion prediction. We further formulate an action constraint loss to ensure the global semantic consistency of the predicted motion. Extensive experiments demonstrate the effectiveness of the proposed approach, and we achieve state-of-the-art performance on 3D human motion prediction.

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AAAI Conference 2019 Conference Paper

DDFlow: Learning Optical Flow with Unlabeled Data Distillation

Pengpeng Liu
Irwin King
Michael R. Lyu
Jia Xu

We present DDFlow, a data distillation approach to learning optical flow estimation from unlabeled data. The approach distills reliable predictions from a teacher network, and uses these predictions as annotations to guide a student network to learn optical flow. Unlike existing work relying on handcrafted energy terms to handle occlusion, our approach is data-driven, and learns optical flow for occluded pixels. This enables us to train our model with a much simpler loss function, and achieve a much higher accuracy. We conduct a rigorous evaluation on the challenging Flying Chairs, MPI Sintel, KITTI 2012 and 2015 benchmarks, and show that our approach significantly outperforms all existing unsupervised learning methods, while running at real time.

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AAAI Conference 2017 Conference Paper

Efficient Online Model Adaptation by Incremental Simplex Tableau

Zhixian Lei
Xuehan Ye
Yongcai Wang
Deying Li
Jia Xu

Online multi-kernel learning is promising in the era of mobile computing, in which a combined classiﬁer with multiple kernels are ofﬂine trained, and online adapts to personalized features for serving the end user precisely and smartly. The online adaptation is mainly carried out at the end-devices, which requires the adaptation algorithms to be light, efﬁcient and accurate. Previous results focused mainly on efﬁciency. This paper proposes an novel online model adaptation framework for not only efﬁciency but also optimal online adaptation. At ﬁrst, an online optimal incremental simplex tableau (IST) algorithm is proposed, which approaches the model adaption by linear programming and produces the optimized model update in each step when a personalized training data is collected. But keeping online optimal in each step is expensive and may cause over-ﬁtting especially when the online data is noisy. A Fast-IST approach is therefore proposed, which measures the deviation between the training data and the current model. It schedules updating only when enough deviation is detected. The efﬁciency of each update is further enhanced by running IST only limited iterations, which bounds the computation complexity. Theoretical analysis and extensive evaluations show that Fast-IST saves computation cost greatly, while achieving speedy and accurate model adaptation. It provides better model adaptation speed and accuracy while using even lower computing cost than the state-of-theart.

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TCS Journal 2015 Journal Article

Online scheduling with equal processing times and machine eligibility constraints

Jia Xu
Zhaohui Liu

We consider the online scheduling problem on m parallel machines with eligibility constraints. The jobs arrive over time and have equal processing times. The objective is to minimize the makespan. We develop optimal deterministic online algorithms for the nested processing set case and the inclusive processing set case with an arbitrary number of machines, as well as the tree-like processing set case with three machines.

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AAAI Conference 2014 Conference Paper

Bagging by Design (on the Suboptimality of Bagging)

Periklis Papakonstantinou
Jia Xu
Zhu Cao

Bagging (Breiman 1996) and its variants is one of the most popular methods in aggregating classifiers and regressors. Originally, its analysis assumed that the bootstraps are built from an unlimited, independent source of samples, therefore we call this form of bagging ideal-bagging. However in the real world, base predictors are trained on data subsampled from a limited number of training samples and thus they behave very differently. We analyze the effect of intersections between bootstraps, obtained by subsampling, to train different base predictors. Most importantly, we provide an alternative subsampling method called design-bagging based on a new construction of combinatorial designs, and prove it universally better than bagging. Methodologically, we succeed at this level of generality because we compare the prediction accuracy of bagging and design-bagging relative to the accuracy ideal-bagging. This finds potential applications in more involved bagging-based methods. Our analytical results are backed up by experiments on classification and regression settings.

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AAAI Conference 2014 Conference Paper

Converting Instance Checking to Subsumption: A Rethink for Object Queries over Practical Ontologies

Jia Xu
Ubbo Visser
Mansur Kabuka

Instance checking is considered a central service for data retrieval from description logic (DL) ontologies. In this paper, we propose a revised most specific concept (MSC) method for DL SHI, which converts instance checking into subsumption problems. This revised method can generate small concepts that are specific-enough to answer a given query, and allow reasoning to explore only a subset of the ABox data to achieve efficiency. Experiments show effectiveness of our proposed method in terms of concept size reduction and the improvement in reasoning efficiency.

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