Xing Liu

AAAI Conference 2026 Conference Paper

3One2: One-Step Regression plus One-Step Diffusion for One-Hot Modulation in Dual-Path Video Snapshot Compressive Imaging

• Ge Wang
• Xing Liu
• Xin Yuan

Video snapshot compressive imaging (SCI) captures dynamic scene sequences through a two-dimensional (2D) snapshot, fundamentally relying on optical modulation for hardware compression and the corresponding software reconstruction. While mainstream video SCI using random binary modulation has demonstrated success, it inevitably results in temporal aliasing during compression. One-hot modulation, activating only one sub-frame per pixel, provides a promising solution for achieving perfect temporal decoupling, thereby alleviating issues associated with aliasing. However, no algorithms currently exist to fully exploit this potential. To bridge this gap, we propose an algorithm specifically designed for one-hot masks. First, leveraging the decoupling properties of one-hot modulation, we transform the reconstruction task into a generative video inpainting problem and introduce a stochastic differential equation (SDE) of the forward process that aligns with the hardware compression process. Next, we identify limitations of the pure diffusion method for video SCI and propose a novel framework that combines one-step regression initialization with one-step diffusion refinement. Furthermore, to mitigate the spatial degradation caused by one-hot modulation, we implement a dual optical path at the hardware level, utilizing complementary information from another path to enhance the inpainted video. To our knowledge, this is the first work integrating diffusion into video SCI reconstruction. Experiments conducted on synthetic datasets and real scenes demonstrate the effectiveness of our method.

JBHI Journal 2025 Journal Article

BiSAUM: Bi-Directional Sparse Attention Transformer for Cancer Cell Prediction in Multi-Domain Sustainable Healthcare Systems

• Xing Liu
• Byung-Gyu Kim
• Jianhui Lv
• Zhuo Wu

This paper presents BiSAUM, a bi-directional sparse attention transformer framework that simultaneously analyzes sustainable healthcare patterns and cancer cell characteristics while maintaining high computational efficiency. The model introduces a novel bi-directional sparse selection mechanism that reduces computational complexity while preserving crucial relationships between healthcare delivery systems and cancer cell behaviors. Through experiments, BiSAUM demonstrates superior performance in both sustainable healthcare prediction and cancer cell pattern classification, particularly in understanding how healthcare interventions affect cancer progression. The model achieves 12. 1% lower MSE in healthcare system prediction and 4. 4% higher accuracy in cancer pattern classification compared to state-of-the-art baselines while reducing computational time by 35. 8% and memory usage by 32. 6%. These results demonstrate BiSAUM's potential for advancing our understanding of how multi-domain sustainable healthcare systems impact cancer progression, enabling more effective, evidence-based medical decisions in cancer prevention and treatment

JBHI Journal 2025 Journal Article

Graph Attention Fusion With Kolmogorov-Arnold Network for Drug-Gene Interaction Prediction

• Xinguo Lu
• Zihao Li
• Ping Liu
• Anqi Tang
• Xing Liu
• Hongrui Liu

Deep learning-based computational methods have emerged as powerful tools for predicting novel drug-gene interactions. It is essential to parse the joint influence of diverse attention focuses in large complex datasets in the model's decision-making process. Here, we propose graph attention fusion with Kolmogorov-Arnold network (KAN) for drug-gene interaction prediction (dgKAN). This approach parses the mutual influence of heterogeneous attention in drug-gene relationships by constructing an interpretable KAN network. Specifically, we use dynamic neighbor selection module by dynamic attention sampling to construct subgraphs and generate embedding representations for drugs and genes within these subgraphs. Then, we utilize a module consisted of Transformer and GNN architectures (TransGNN) to fuse the mechanism of global attention and local attention. Finally, we develop an interpretable KAN network with spline functions to model and analyze the cross-domain information flow between drugs and genes, enabling the prediction of drug-gene interactions. We conducted comprehensive experiments on various datasets, and the results demonstrate that dgKAN outperforms other baseline methods. Meanwhile, results illustrate that dgKAN captures the implicit characteristics by parsing heterogeneous attention in drug-gene relationships. The predicted drug-gene interactions have the potential to significantly aid in drug development for disease treatment.

NeurIPS Conference 2025 Conference Paper

HAODiff: Human-Aware One-Step Diffusion via Dual-Prompt Guidance

• JUE GONG
• Tingyu Yang
• Jingkai Wang
• Zheng Chen
• Xing Liu
• Hong Gu
• Yulun Zhang
• Xiaokang Yang

Human-centered images often suffer from severe generic degradation during transmission and are prone to human motion blur (HMB), making restoration challenging. Existing research lacks sufficient focus on these issues, as both problems often coexist in practice. To address this, we design a degradation pipeline that simulates the coexistence of HMB and generic noise, generating synthetic degraded data to train our proposed HAODiff, a human-aware one-step diffusion. Specifically, we propose a triple-branch dual-prompt guidance (DPG), which leverages high-quality images, residual noise (LQ minus HQ), and HMB segmentation masks as training targets. It produces a positive–negative prompt pair for classifier‑free guidance (CFG) in a single diffusion step. The resulting adaptive dual prompts let HAODiff exploit CFG more effectively, boosting robustness against diverse degradations. For fair evaluation, we introduce MPII‑Test, a benchmark rich in combined noise and HMB cases. Extensive experiments show that our HAODiff surpasses existing state-of-the-art (SOTA) methods in terms of both quantitative metrics and visual quality on synthetic and real-world datasets, including our introduced MPII-Test. Code is available at: https: //github. com/gobunu/HAODiff.

JMLR Journal 2025 Journal Article

On the Robustness of Kernel Goodness-of-Fit Tests

• Xing Liu
• François-Xavier Briol

Goodness-of-fit testing is often criticized for its lack of practical relevance: since "all models are wrong", the null hypothesis that the data conform to our model is ultimately always rejected as the sample size grows. Despite this, probabilistic models are still used extensively, raising the more pertinent question of whether the model is good enough for the task at hand. This question can be formalized as a robust goodness-of-fit testing problem by asking whether the data were generated from a distribution that is a mild perturbation of the model. In this paper, we show that existing kernel goodness-of-fit tests are not robust under common notions of robustness including both qualitative and quantitative robustness. We further show that robustification techniques using tilted kernels, while effective in the parameter estimation literature, are not sufficient to ensure both types of robustness in the testing setting. To address this, we propose the first robust kernel goodness-of-fit test, which resolves this open problem by using kernel Stein discrepancy (KSD) balls. This framework encompasses many well-known perturbation models, such as Huber's contamination and density-band models. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2025. ( edit, beta )

ICML Conference 2024 Conference Paper

Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations

• Jiaqi Zhai
• Lucy Liao
• Xing Liu
• Yueming Wang
• Rui Li
• Xuan Cao
• Leon Gao
• Zhaojie Gong

Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a daily basis. Despite being trained on huge volume of data with thousands of features, most Deep Learning Recommendation Models (DLRMs) in industry fail to scale with compute. Inspired by success achieved by Transformers in language and vision domains, we revisit fundamental design choices in recommendation systems. We reformulate recommendation problems as sequential transduction tasks within a generative modeling framework (“Generative Recommenders”), and propose a new architecture, HSTU, designed for high cardinality, non-stationary streaming recommendation data. HSTU outperforms baselines over synthetic and public datasets by up to 65. 8% in NDCG, and is 5. 3x to 15. 2x faster than FlashAttention2-based Transformers on 8192 length sequences. HSTU-based Generative Recommenders, with 1. 5 trillion parameters, improve metrics in online A/B tests by 12. 4% and have been deployed on multiple surfaces of a large internet platform with billions of users. More importantly, the model quality of Generative Recommenders empirically scales as a power-law of training compute across three orders of magnitude, up to GPT-3/LLaMa-2 scale, which reduces carbon footprint needed for future model developments, and further paves the way for the first foundation models in recommendations.

EAAI Journal 2024 Journal Article

Residual squeeze-and-excitation convolutional auto-encoder for fault detection and diagnosis in complex industrial processes

• Jianbo Yu
• Shijin Li
• Xing Liu
• Hao Li
• Mingyan Ma
• Peilun Liu
• Lichun You

ICML Conference 2023 Conference Paper

Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy

• Xing Liu
• Andrew B. Duncan
• Axel Gandy

Kernelized Stein discrepancy (KSD) is a score-based discrepancy widely used in goodness-of-fit tests. It can be applied even when the target distribution has an unknown normalising factor, such as in Bayesian analysis. We show theoretically and empirically that the KSD test can suffer from low power when the target and the alternative distributions have the same well-separated modes but differ in mixing proportions. We propose to perturb the observed sample via Markov transition kernels, with respect to which the target distribution is invariant. This allows us to then employ the KSD test on the perturbed sample. We provide numerical evidence that with suitably chosen transition kernels the proposed approach can lead to substantially higher power than the KSD test.

YNICL Journal 2022 Journal Article

Fornix alterations induce the disruption of default mode network in patients with adamantinomatous craniopharyngiomas

• Jie Kang
• Lei Cao
• Taoyang Yuan
• Lu Jin
• Yanjiao He
• Xing Liu
• Cuiping Zhang
• Nan Chen

Adamantinomatous craniopharyngioma (ACPs) are rare embryonic tumors and often involve the hypothalamus. The underlying neural substrate of the hypothalamic involvement (HI)-related cognitive decline in patients with ACP is still unclear. We aimed to combine the multi-modal neuroimaging and histological characteristics of the ACP to explore the potential neural substrate of the HI-related cognitive decline. 45 patients with primary ACPs (invasive, 23; noninvasive, 22) and 52 healthy control subjects (HCs) were admitted to the cross-sectional study. No significant difference in cognitive domains was observed between HCs and patients with noninvasive ACPs (NACP). Patients with invasive ACPs (IACP) showed significantly lower working memory performance (WM, p = 0.002) than patients with NACP. The WM decline was correlated with the disruption of the medial temporal lobe (MTL) subsystem in the default mode network (DMN) (r = 0.45, p = 0.004). The increased radial diffusivity of the fornix, indicating demyelinating process, was correlated with the disruption of the MTL subsystem (r = -0.48, p = 0.002). Our study demonstrated that the fornix alterations link DMN disruption to HI-related cognitive decline in patients with ACPs. ACPs that invade the hypothalamus can provide a natural disease model to investigate the potential neural substrate of HI-related cognitive decline.

NeurIPS Conference 2021 Conference Paper

A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems

• Yi Ma
• Xiaotian Hao
• Jianye Hao
• Jiawen Lu
• Xing Liu
• Tong Xialiang
• Mingxuan Yuan
• Zhigang Li

The Dynamic Pickup and Delivery Problem (DPDP) is an essential problem in the logistics domain, which is NP-hard. The objective is to dynamically schedule vehicles among multiple sites to serve the online generated orders such that the overall transportation cost could be minimized. The critical challenge of DPDP is the orders are not known a priori, i. e. , the orders are dynamically generated in real-time. To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further. However, the solution quality and efficiency of these methods are unsatisfactory, especially when the problem scale is very large. In this paper, we propose a novel hierarchical optimization framework to better solve large-scale DPDPs. Specifically, we design an upper-level agent to dynamically partition the DPDP into a series of sub-problems with different scales to optimize vehicles routes towards globally better solutions. Besides, a lower-level agent is designed to efficiently solve each sub-problem by incorporating the strengths of classical operational research-based methods with reinforcement learning-based policies. To verify the effectiveness of the proposed framework, real historical data is collected from the order dispatching system of Huawei Supply Chain Business Unit and used to build a functional simulator. Extensive offline simulation and online testing conducted on the industrial order dispatching system justify the superior performance of our framework over existing baselines.

TCS Journal 2020 Journal Article

A note of vertex arboricity of planar graphs without 4-cycles intersecting with 6-cycles

• Xuyang Cui
• Wenshun Teng
• Xing Liu
• Huijuan Wang

NeurIPS Conference 2020 Conference Paper

Bayesian Probabilistic Numerical Integration with Tree-Based Models

• Harrison Zhu
• Xing Liu
• Ruya Kang
• Zhichao Shen
• Seth Flaxman
• Francois-Xavier Briol

Bayesian quadrature (BQ) is a method for solving numerical integration problems in a Bayesian manner, which allows users to quantify their uncertainty about the solution. The standard approach to BQ is based on a Gaussian process (GP) approximation of the integrand. As a result, BQ is inherently limited to cases where GP approximations can be done in an efficient manner, thus often prohibiting very high-dimensional or non-smooth target functions. This paper proposes to tackle this issue with a new Bayesian numerical integration algorithm based on Bayesian Additive Regression Trees (BART) priors, which we call BART-Int. BART priors are easy to tune and well-suited for discontinuous functions. We demonstrate that they also lend themselves naturally to a sequential design setting and that explicit convergence rates can be obtained in a variety of settings. The advantages and disadvantages of this new methodology are highlighted on a set of benchmark tests including the Genz functions, on a rare-event simulation problem and on a Bayesian survey design problem.

YNICL Journal 2018 Journal Article

A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas

• Xing Liu
• Yiming Li
• Zenghui Qian
• Zhiyan Sun
• Kaibin Xu
• Kai Wang
• Shuai Liu
• Xing Fan

OBJECTIVE: The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature. METHODS: In this retrospective study, training (n = 216) and validation (n = 84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T2-weighted magnetic resonance images. A radiomics signature was generated in the training cohort, and its prognostic value was evaluated in both the training and validation cohorts. The genetic characteristics of the group with high-risk scores were identified by radiogenomic analysis, and a nomogram was established for prediction of PFS. RESULTS: There was a significant association between the radiomics signature (including 9 screened radiomics features) and PFS, which was independent of other clinicopathologic factors in both the training (P < 0.001, multivariable Cox regression) and validation (P = 0.045, multivariable Cox regression) cohorts. Radiogenomic analysis revealed that the radiomics signature was associated with the immune response, programmed cell death, cell proliferation, and vasculature development. A nomogram established using the radiomics signature and clinicopathologic risk factors demonstrated high accuracy and good calibration for prediction of PFS in both the training (C-index, 0.684) and validation (C-index, 0.823) cohorts. CONCLUSIONS: PFS can be predicted non-invasively in patients with LGGs by a group of radiomics features that could reflect the biological processes of these tumors.

YNICL Journal 2018 Journal Article

MRI features predict p53 status in lower-grade gliomas via a machine-learning approach

• Yiming Li
• Zenghui Qian
• Kaibin Xu
• Kai Wang
• Xing Fan
• Shaowu Li
• Tao Jiang
• Xing Liu

Background: P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images. Methods: = 92) set. A total of 431 radiomic features were extracted from each patient. The lest absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomic signature construction. Subsequently, a machine-learning model to predict p53 status was established using the selected features and a Support Vector Machine classifier. The predictive performance of all individual features and the model was calculated using receiver operating characteristic curves in both the training and validation sets. Results: The p53-related radiomic signature was built using the LASSO algorithm; this procedure consisted of four first-order statistics or related wavelet features (including Maximum, Median, Minimum, and Uniformity), a shape and size-based feature (Spherical Disproportion), and ten textural features or related wavelet features (including Correlation, Run Percentage, and Sum Entropy). The prediction accuracies based on the area under the curve were 89.6% in the training set and 76.3% in the validation set, which were better than individual features. Conclusions: These results demonstrate that MR image texture features are predictive of p53 mutation status in lower-grade gliomas. Thus, our procedure can be conveniently used to facilitate presurgical molecular pathological diagnosis.

YNICL Journal 2018 Journal Article

Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas

• Zhenyu Liu
• Yinyan Wang
• Xing Liu
• Yang Du
• Zhenchao Tang
• Kai Wang
• Jingwei Wei
• Di Dong