Author name cluster

Xinyu Li

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

22 papers

2 author rows

JBHI Journal 2026 Journal Article

Customized SAM-Med3D With Multi-View Representation Fusion and Age-Grade Stratified Loss for Glioma Survival Risk Prediction

Xinyu Li
Hulin Kuang
Jin Liu
Lanlan Wang
Pengcheng Shu
Mengshen He
Jianxin Wang

Survival risk prediction is crucial for personalized treatment of gliomas. Medical image foundational models can explore complex medical features, which are critical for prognosis in gliomas. We propose SAM-Risk, which uses a customized SAM-Med3D with multi-view representation fusion and clinical knowledge-based age-grade stratified loss for glioma survival risk prediction. First, to utilize potential interactions between multiple views at an early stage, we design a 3D representation generation module that transforms 1D handcrafted radiomics and clinical features into 3D representations, which are fused with multimodal MRIs through a multi-view representation fusion module. The fused representation is fed into the customized SAM-Med3D, fine-tuned using LoRA and a disparity function to extract survival risk-related features. We design a feature refinement module to explore the inter-channel relationships among the outputs of the fine-tuned SAM-Med3D. Additionally, we propose an age-grade stratified loss based on glioma prognosis standards to make the predicted risk more consistent with clinical prior knowledge. Validated on two publicly available UCSF-PDGM and BraTS2020 datasets, SAM-Risk achieves a C-index of 75. 08% and 73. 67%, respectively, outperforming several survival risk prediction methods.

Details DOI

EAAI Journal 2026 Journal Article

Edge-Aware Transformer with Shared Axis Feature Alignment and Adaptive Self-Attention for Glioma Grading

Adeel Ahmed Abbasi
Hulin Kuang
Xinyu Li
Jianxin Wang

Accurate glioma grading from Magnetic Resonance Imaging (MRI) is critical for early diagnosis and effective treatment planning. Existing Artificial Intelligence (AI) methods, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), struggle to capture edge details, overlook volumetric spatial context, and lack sensitivity to irregular shapes. Furthermore, inadequate self-attention mechanisms cause ViTs to fail to prioritize semantically meaningful tokens. These limitations can hinder both diagnostic accuracy and generalization. To address these challenges, we propose the Edge-Aware Transformer (EA-Trans) for glioma grading. Our approach integrates four key modules. First, the Edge Sensitive Tokenization (EST) module employs edge enhancement filters using depthwise CNNs to capture edge information. Second, the Shared Axis Spatial Features Alignment (SASFA) module processes MRI volumes along the depth, height, and width axes, preserving spatial consistency and context. Third, the Volumetric Multi-Scale Wavelet Transform Convolution (VMWTC) module employs Wavelet Transform Convolutions (WT-CNNs) to extract shape-sensitive multi-scale features. Fourth, the Adaptive Self-Attention (ASA) module integrates an Interquartile Range Token Selection (IQR-TS) strategy to focus on semantically relevant tokens. Experiments on the publicly available Brain Tumor Segmentation (BraTS2020) and University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) datasets demonstrate that the proposed model achieves accuracies of 95. 7% and 95. 2%, respectively, outperforming 12 baseline methods. Notably, the model, with only 3. 176 million parameters and 1. 970 billion floating-point operations, enhances glioma grading accuracy, supporting AI-driven neuroimaging diagnostics in medical engineering. Interpretability analysis and application to Alzheimer’s disease classification further suggest its potential applicability to other neuroimaging tasks.

Details DOI

AAAI Conference 2026 Conference Paper

NeuroBridge: Bio-Inspired Self-Supervised EEG-to-Image Decoding via Cognitive Priors and Bidirectional Semantic Alignment

Wenjiang Zhang
Sifeng Wang
Yuwei Su
Xinyu Li
Chen Zhang
Suyu Zhong

Visual neural decoding seeks to reconstruct or infer perceived visual stimuli from brain activity patterns, providing critical insights into human cognition and enabling transformative applications in brain-computer interfaces and artificial intelligence. Current approaches, however, remain constrained by the scarcity of high-quality stimulus-brain response pairs and the inherent semantic mismatch between neural representations and visual content. Inspired by perceptual variability and co-adaptive strategy of the biological systems, we propose a novel self-supervised architecture, named NeuroBridge, which integrates Cognitive Prior Augmentation (CPA) with Shared Semantic Projector (SSP) to promote effective cross-modality alignment. Specifically, CPA simulates perceptual variability by applying asymmetric, modality-specific transformations to both EEG signals and images, enhancing semantic diversity. Unlike previous approaches, SSP establishes a bidirectional alignment process through a co-adaptive strategy, which mutually aligns features from two modalities into a shared semantic space for effective cross-modal learning. NeuroBridge surpasses previous state-of-the-art methods under both intra-subject and inter-subject settings. In the intra-subject scenario, it achieves the improvements of 12.3% in top-1 accuracy and 10.2% in top-5 accuracy, reaching 63.2% and 89.9% respectively on a 200-way zero-shot retrieval task. Extensive experiments demonstrate the effectiveness, robustness, and scalability of the proposed framework for neural visual decoding.

PDF Details DOI

AAAI Conference 2026 Short Paper

When AI Meets AI: A Game-Theoretic Defense Framework Against AI Empowered Cyber Threats

Xinyu Li

The widespread adoption of artificial intelligence (AI) in cybersecurity has led to the emerging threat of AI-driven cyberattacks, such as LLM-empowered Advanced Persistent Threats (APTs), challenging the effect of conventional deception defense mechanisms. To fill this critical gap, my work aims to develop a game-theoretic defense AI agent capable of providing the optimal deception resource deployment strategy, to establish AI-driven defenses against AI-empowered cyberattacks. In this proposal, I model the attacker and defender interaction as a dynamic game with incomplete information between AI agents, and then derive the equilibrium defense strategies. Synthetic data based experiments and real-world implementations would be conducted to validate the proposed framework. This study has the potential to improve the effectiveness of deception defense in three dimensions: scalability, real-time capability, and strategic intelligence.

PDF Details DOI

EAAI Journal 2025 Journal Article

A heterogeneous graph attention-enhanced deep reinforcement learning framework for flexible job shop scheduling problem with variable sublots

Zipeng Yang
Xinyu Li
Liang Gao
Qihao Liu

Variable lot-sizing is an effective approach to improve production efficiency by splitting an operation into several sublots, which has been widely applied in flexible manufacturing systems. However, the flexibility of lot-sizing will dramatically expand the solution space, leading to excessive computation time in converging to the relative optimum. To address this challenge, this paper introduces an end-to-end deep reinforcement learning framework based on heterogeneous graph attention mechanisms (HGADRL) for flexible job shop scheduling problem with variable sublots. Unlike traditional heuristic and rule-based methods, HGADRL dynamically learns the high-dimensional nature, providing a more generalizable solution in a very short time. In HGADRL, a modified heterogeneous disjunctive graph is designed to represent the dynamic scheduling status, including operation selection and sublot division. A dual-scale graph attention network combined with two interconnected attention modules is developed, enabling the precise capture of complex interdependencies between heterogeneous vertices. This approach can significantly enhance the agent's ability to self-learn and evolve optimal policies. By leveraging local and global features extracted through the graph attention network, an actor-critic network is employed for high-quality scheduling in different states. Experimental results demonstrate that the proposed method outperforms the 12 mixed priority dispatching rules, two meta-heuristic methods and two deep reinforcement learning methods in all 500 synthetic instances. Additionally, the proposed method outperforms all compared methods across 16 unseen scales of instances and four real-world instances, demonstrating its strong generalization capabilities.

Details DOI

AAAI Conference 2025 Conference Paper

DZAD: Diffusion-based Zero-shot Anomaly Detection

Tianrui Zhang
Liang Gao
Xinyu Li
Yiping Gao

Zero-shot anomaly detection (ZSAD) aims to identify anomalies in new classes of images, and it’s vital in industry and other fields. Most current methods are based on the multimodal models CLIP and SAM, which have prior knowledge to assist model training, but they are highly dependent on the input of the prompts and their accuracy. We found that some diffusion model-based anomaly detection methods generate a large amount of semantic information and are very valuable for the ZSAD task. Therefore, we propose a diffusion model based zero-shot anomaly detection method, DZAD, and no additional prompt input is required. First, we propose the first diffusion-based zero-shot anomaly detection framework, which uses the proposed multi-timestep noise features extraction method to achieve anomaly detection in the denoising process of a latent space diffusion model with a semantic-guided (SG) network. Second, based on the detection results, we proposed a two-branch feature extractor for anomaly maps at different scales. Third, based on the difference between the anomaly detection task and other general image detection tasks, we propose a noise feature weight function for the diffusion model in the zero-shot anomaly detection task. Comparing with 7 recently state-of-the-art (SOTA) methods on MVTec AD and VisA datasets and analysis of the role of each component in ablation studies. The experiments demonstrate the validity of the method beyond the existing methods.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

GarmentDiffusion: 3D Garment Sewing Pattern Generation with Multimodal Diffusion Transformers

Xinyu Li
QI YAO
Yuanda Wang

Garment sewing patterns are fundamental design elements that bridge the gap between design concepts and practical manufacturing. The generative modeling of sewing patterns is crucial for creating diversified garments. However, existing approaches are limited either by reliance on a single input modality or by suboptimal generation efficiency. In this work, we present GarmentDiffusion, a new generative model capable of producing centimeter-precise, vectorized 3D sewing patterns from multimodal inputs (text, image, and incomplete sewing pattern). Our method efficiently encodes 3D sewing pattern parameters into compact edge token representations, achieving a sequence length that is 10 times shorter than that of the autoregressive SewingGPT in DressCode. By employing a diffusion transformer, we simultaneously denoise all edge tokens along the temporal axis, while maintaining a constant number of denoising steps regardless of dataset-specific edge and panel statistics. With all combination of designs of our model, the sewing pattern generation speed is accelerated by 100 times compared to SewingGPT. We achieve new state-of-the-art results on DressCodeData, as well as on the largest sewing pattern dataset, namely GarmentCodeData. The project website is available at https: //shenfu-research. github. io/Garment-Diffusion.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

PAC-Bayes Bounds for Multivariate Linear Regression and Linear Autoencoders

Ruixin Guo
Ruoming Jin
Xinyu Li
Yang Zhou

Linear Autoencoders (LAEs) have shown strong performance in state-of-the-art recommender systems. However, this success remains largely empirical, with limited theoretical understanding. In this paper, we investigate the generalizability -- a theoretical measure of model performance in statistical learning -- of multivariate linear regression and LAEs. We first propose a PAC-Bayes bound for multivariate linear regression, extending the earlier bound for single-output linear regression by Shalaeva et al. , and establish sufficient conditions for its convergence. We then show that LAEs, when evaluated under a relaxed mean squared error, can be interpreted as constrained multivariate linear regression models on bounded data, to which our bound adapts. Furthermore, we develop theoretical methods to improve the computational efficiency of optimizing the LAE bound, enabling its practical evaluation on large models and real-world datasets. Experimental results demonstrate that our bound is tight and correlates well with practical ranking metrics such as Recall@K and NDCG@K.

PDF Details

ECAI Conference 2025 Conference Paper

Refining Dataset Distillation via Critical Region Selection and Multiview Teacher Guidance

Wenqing Ye
Xinyu Li
Hui Liu
Di Wu
Xiaoyan Sun 0001
Ronald X. Xu
Mingzhai Sun

Dataset distillation (DD) aims to improve training efficiency by condensing large datasets into compact yet informative subsets. Existing DD methods primarily use optimization-based approaches for image synthesis, which are computationally intensive. While some recent studies have explored optimization-free alternatives, their simplistic region selection strategies result in poor representations of the original dataset and inefficient utilization of synthetic data during downstream training. To address these limitations, we propose a method called Selection-and-Guidance for Dataset Distillation (SGDD). This approach refines the distillation process through two key stages: region selection and guidance enhancement. Specifically, we first obtain a candidate set of regions from various locations within the images. Then, we utilize the Representative Region Selector and Diverse Region Selector to identify the critical regions for image classification. Furthermore, we generate multiview guidance information for the synthetic data to enhance the distillation process further. By selecting representative and diverse regions while incorporating multiview guidance, our method unleashes the potential of optimization-free DD. Experimental results substantiate the superiority of our approach across various datasets and network architectures.

Details

NeurIPS Conference 2025 Conference Paper

Sample-Efficient Tabular Self-Play for Offline Robust Reinforcement Learning

Na Li
Zewu Zheng
Wei Ni
Hangguan Shan
Wenjie Zhang
Xinyu Li

Multi-agent reinforcement learning (MARL), as a thriving field, explores how multiple agents independently make decisions in a shared dynamic environment. Due to environmental uncertainties, policies in MARL must remain robust to tackle the sim-to-real gap. We focus on robust two-player zero-sum Markov games (TZMGs) in offline settings, specifically on tabular robust TZMGs (RTZMGs). We propose a model-based algorithm ( RTZ-VI-LCB ) for offline RTZMGs, which is optimistic robust value iteration combined with a data-driven Bernstein-style penalty term for robust value estimation. By accounting for distribution shifts in the historical dataset, the proposed algorithm establishes near-optimal sample complexity guarantees under partial coverage and environmental uncertainty. An information-theoretic lower bound is developed to confirm the tightness of our algorithm's sample complexity, which is optimal regarding both state and action spaces. To the best of our knowledge, RTZ-VI-LCB is the first to attain this optimality, sets a new benchmark for offline RTZMGs, and is validated experimentally.

PDF Details

NeurIPS Conference 2025 Conference Paper

Towards Accurate Time Series Forecasting via Implicit Decoding

Xinyu Li
Yuchen Luo
Hao Wang
Haoxuan Li
Liuhua Peng
Feng Liu
Yandong Guo
Kun Zhang

Recent booming time series models have demonstrated remarkable forecasting performance. However, these methods often place greater focus on more effectively modelling the historical series, largely neglecting the forecasting phase, which generates long-term forecasts by separately predicting multiple time points. Given that real-world time series typically consist of various long short-term dynamics, independent predictions over individual time points may fail to express complex underlying patterns and can lead to a lack of global views. To address these issues, this work explores new perspectives from the forecasting phase and proposes a novel Implicit Forecaster (IF) as an additional decoding module. Inspired by decomposition forecasting, IF adopts a more nuanced approach by implicitly predicting constituent waves represented by their frequency, amplitude, and phase, thereby accurately forming the time series. Extensive experimental results from multiple real-world datasets show that IF can consistently boost mainstream time series models, achieving state-of-the-art forecasting performance. Code is available at this repository: https: //github. com/rakuyorain/Implicit-Forecaster.

PDF Details

AAAI Conference 2024 Short Paper

Adapting Animal Models to Assess Sufficiency of Fluid Resuscitation in Humans (Student Abstract)

Ryan Schuerkamp
Xinyu Li
Brian Kunzer
Leonard S. Weiss
Hernando Gómez
Francis X. Guyette
Michael R. Pinsky
Artur Dubrawski

Fluid resuscitation is an initial treatment frequently employed to treat shock, restore lost blood, protect tissues from injury, and prevent organ dysfunction in critically ill patients. However, it is not without risk (e.g., overly aggressive resuscitation may cause organ damage and even death). We leverage machine learning models trained to assess sufficiency of resuscitation in laboratory animals subjected to induced hemorrhage and transfer them to use with human trauma patients. Our key takeaway is that animal experiments and models can inform human healthcare, especially when human data is limited or when collecting relevant human data via potentially harmful protocols is unfeasible.

PDF Details DOI

NeurIPS Conference 2024 Conference Paper

Video Token Merging for Long Video Understanding

Seon-Ho Lee
Jue Wang
Zhikang Zhang
David Fan
Xinyu Li

As the scale of data and models for video understanding rapidly expand, handling long-form video input in transformer-based models presents a practical challenge. Rather than resorting to input sampling or token dropping, which may result in information loss, token merging shows promising results when used in collaboration with transformers. However, the application of token merging for long-form video processing is not trivial. We begin with the premise that token merging should not rely solely on the similarity of video tokens; the saliency of tokens should also be considered. To address this, we explore various video token merging strategies for long-form video classification, starting with a simple extension of image token merging, moving to region-concentrated merging, and finally proposing a learnable video token merging (VTM) algorithm that dynamically merges tokens based on their saliency. Extensive experimental results show that we achieve better or comparable performances on the LVU, COIN, and Breakfast datasets. Moreover, our approach significantly reduces memory costs by 84% and boosts throughput by approximately 6. 89 times compared to baseline algorithms.

PDF Details DOI

EAAI Journal 2023 Journal Article

A multi-population co-evolutionary algorithm for green integrated process planning and scheduling considering logistics system

Qihao Liu
Cuiyu Wang
Xinyu Li
Liang Gao

Nowadays, green manufacturing is an increasingly significant theme in the worldwide industry fields. System integration can fully exploit the potential of subsystems to achieve satisfying green and low-carbon manufacturing. In particular, integrated process planning and scheduling (IPPS) can obtain better process routes and scheduling schemes to realize more efficient and less energy-consuming production by utilizing the complementary attributes of the process and scheduling subsystems. Additional consideration of the shop logistics system including task assignment of automated guided vehicles (AGVs) can improve shop productivity while ensuring the smooth running of the whole manufacturing system. Taking into account AGV transportation, this paper studies a green multi-objective IPPS problem considering logistics system (MO_IPPS_LS) to minimize the maximum completion time and energy consumption simultaneously. A multi-population co-evolutionary algorithm (MPCEA) is proposed with a novel integrated encoding method. The co-evolutionary framework can ensure the diversity of the populations through a backtracking mechanism and different evolutionary operators including modified critical-path based local searching. The proposed MPCEA is tested on the open benchmarks with different proportions of transport resources. The comparative experimental results show the effectiveness and superiority of MPCEA for solving MO_IPPS_LS problem.

Details DOI

EAAI Journal 2023 Journal Article

Ada-CCFNet: Classification of multimodal direct immunofluorescence images for membranous nephropathy via adaptive weighted confidence calibration fusion network

Ruili Wang
Xueyu Liu
Fang Hao
Xing Chen
Xinyu Li
Chen Wang
Dan Niu
Ming Li

In the pathological diagnosis of early, late and non-membranous nephropathy, direct immunofluorescence is highly likely to present potentially specific lesions, while it is often overlooked due to the difficulty of screening with naked eyes. With the advanced progress of deep learning, they have shown powerful abilities in detecting potential lesions. In this paper, we propose an adaptive weighted confidence calibration fusion framework (Ada-CCFNet) consisting of a preprocessing module, an adaptive weighted confidence calibration fusion (Ada-CCF) module and a classification module for diagnosis of membranous nephropathy by classifying the multimodal direct immunofluorescence images. In the preprocessing module, we use the well-known U-Net to segment individual glomeruli and standardize their luminance appearance by the average luminance difference method, allowing the subsequent modules to focus more on the diseased glomerular region. Subsequently, in the Ada-CCF module, six confidence calibration methods are utilized for two main direct immunofluorescence images, IgG and C3, and the comprehensive calibration scores are obtained based on the adaptive weighted fusion of six confidence calibration methods to obtain more reliable confidence level, in which the adaptive weights are related with expected calibration error reductions. For the classification module, the weighted probability scores of IgG and C3 are jointly fed into the module to achieve the classification by random forest. Experimental results showed that Ada-CCFNet achieves the classification accuracy of 73. 52%, surpassing the methods of using single IgG or C3 images and positive grade indicator with 8. 24%, 8. 94% and 22. 76%, and outperforming the compared methods in the classification of membranous nephropathy.

Details DOI

ICML Conference 2023 Conference Paper

Difference-in-Differences Meets Tree-based Methods: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding

Caizhi Tang
Huiyuan Wang
Xinyu Li
Qing Cui
Longfei Li
Jun Zhou 0011

This study considers the estimation of conditional causal effects in the presence of unmeasured confounding for a balanced panel with treatment imposed at the last time point. To address this, we combine Difference-in-differences (DiD) and tree-based methods and propose a new identification assumption that allows for the violation of the (conditional) parallel trends assumption adopted by most existing DiD methods. Under this new assumption, we prove partial identifiability of the conditional average treatment effect on the treated group (CATT). Our proposed method estimates CATT through a tree-based causal approach, guided by a novel splitting rule that avoids model misspecification and unnecessary auxiliary parameter estimation. The splitting rule measures both the error of fitting observed data and the violation of conditional parallel trends simultaneously. We also develop an ensemble of multiple trees via gradient boosting to further enhance performance. Experimental results on both synthetic and real-world datasets validate the effectiveness of our proposed method.

Details

EAAI Journal 2023 Journal Article

One-step unsupervised clustering based on information theoretic metric and adaptive neighbor manifold regularization

Xinyu Li
Hui Fan
Jinglei Liu

Graph-based clustering is a basic subject in the field of machine learning, but most of them still have the following deficiencies. First, the extra discretization procedures leads to instability of the algorithm. In addition, the traditional method of constructing similarity graphs is based on the pairwise distance, so it is extremely sensitive to the original data, and also lacks specific physical meaning from the perspective of probabilistic prediction. Final, the traditional metrics based on Euclidean distance is difficult to tackle non-Gaussian noise. In order to eliminate these limitations, a one-step unsupervised clustering based on information theoretic metric and adaptive neighbor manifold regularization method (ITMNMR) is proposed. (1) The clustering results are directly obtained according to the constructed similarity graph, avoiding extra discretization procedures; (2) A maximum entropy regularization term is introduced into the probabilistic model to avoid trivial similarity distributions. Furthermore, we introduce a Laplacian rank constraint and ℓ 0 -norm to construct adaptive neighbors with sparsity and strength segmentation capabilities; (3) To overcome the impression of noise, reconstruction based on correntropy is introduced to solve the non-Gaussian noise, and graph regularization is performed based on clean data. Furthermore, a half-quadratic optimization method is used to transform the problem into a quadratic form to facilitate subsequent solutions. Finally, our empirical study shows encouraging results of ITMNMR in comparison to classical algorithms and the state-of-the-art algorithms on 9 datasets. The robustness of the proposed method is also demonstrated from three experiments of adding Laplacian noise, salt&pepper noise, and block occlusion.

Details DOI

NeurIPS Conference 2022 Conference Paper

Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding

Caizhi Tang
Huiyuan Wang
Xinyu Li
Qing Cui
Ya-Lin Zhang
Feng Zhu
Longfei Li
Jun Zhou

Unmeasured confounding poses a significant threat to the validity of causal inference. Despite that various ad hoc methods are developed to remove confounding effects, they are subject to certain fairly strong assumptions. In this work, we consider the estimation of conditional causal effects in the presence of unmeasured confounding using observational data and historical controls. Under an interpretable transportability condition, we prove the partial identifiability of conditional average treatment effect on the treated group (CATT). For tree-based models, a new notion, \emph{confounding entropy}, is proposed to measure the discrepancy introduced by unobserved confounders between the conditional outcome distribution of the treated and control groups. The confounding entropy generalizes conventional confounding bias, and can be estimated effectively using historical controls. We develop a new method, debiased causal tree, whose splitting rule is to minimize the empirical risk regularized by the confounding entropy. Notably, our method integrates current observational data (for empirical risk) and their historical controls (for confounding entropy) harmoniously. We highlight that, debiased causal tree can not only estimate CATT well in the presence of unmeasured confounding, but also is a robust estimator of conditional average treatment effect (CATE) against the imbalance of the treated and control populations when all confounders are observed. An extension of combining multiple debiased causal trees to further reduce biases by gradient boosting is considered. The computational feasibility and statistical power of our method are evidenced by simulations and a study of a credit card balance dataset.

PDF Details

YNIMG Journal 2022 Journal Article

Long-term memory retrieval bypasses working memory

Baiwei Liu
Xinyu Li
Jan Theeuwes
Benchi Wang

For decades, it has been assumed that when humans retrieve information from long-term memory (LTM), information need first to be brought back into working memory (WM). However, as WM capacity is limited, it is unclear what happens if information from LTM needs to be retrieved while WM is fully engaged? To address this question, observers had to retrieve colors from LTM while WM storage capacity was fully engaged. The behavioral results showed that retrieving information from LTM is possible even when WM capacity is fully occupied. Additional evidence from electroencephalogram (EEG) confirmed that WM was fully engaged as the suppression of alpha oscillation reached its maximum when memorizing the maximum amount of information into WM; yet the suppression in alpha oscillation was even further amplified when items were retrieved simultaneously from LTM, providing a neural signature of additional LTM retrieval capacity above and beyond the maximum WM capacity. Together, our findings indicate that information retrieved from LTM does not always have to be brought back into WM, but instead might be accessed through a different mechanism when WM is fully engaged.

Details DOI

JBHI Journal 2021 Journal Article

Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks

Chirag Nagpal
Xinyu Li
Artur Dubrawski

We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making strong assumptions of constant proportional hazards of the underlying survival distribution, as required by the Cox-proportional hazard model. By jointly learning deep nonlinear representations of the input covariates, we demonstrate the benefits of our approach when used to estimate survival risks through extensive experimentation on multiple real world datasets with different levels of censoring. We further demonstrate advantages of our model in the competing risks scenario. To the best of our knowledge, this is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring.

Details DOI

NeurIPS Conference 2021 Conference Paper

Long Short-Term Transformer for Online Action Detection

Mingze Xu
Yuanjun Xiong
Hao Chen
Xinyu Li
Wei Xia
Zhuowen Tu
Stefano Soatto

We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm for online action detection, which employs a long- and short-term memory mechanism to model prolonged sequence data. It consists of an LSTR encoder that dynamically leverages coarse-scale historical information from an extended temporal window (e. g. , 2048 frames spanning of up to 8 minutes), together with an LSTR decoder that focuses on a short time window (e. g. , 32 frames spanning 8 seconds) to model the fine-scale characteristics of the data. Compared to prior work, LSTR provides an effective and efficient method to model long videos with fewer heuristics, which is validated by extensive empirical analysis. LSTR achieves state-of-the-art performance on three standard online action detection benchmarks, THUMOS'14, TVSeries, and HACS Segment. Code has been made available at: https: //xumingze0308. github. io/projects/lstr.

PDF Details

EAAI Journal 2017 Journal Article

A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry

Chao Lu
Liang Gao
Xinyu Li
Shengqiang Xiao

Welding is one of the most important technologies in manufacturing industries due to its extensive applications. Welding scheduling can affect the efficiency of the welding process greatly. Thus, welding scheduling problem is important in welding production. This paper studies a challenging problem of dynamic scheduling in a real-world welding industry. To satisfy needs of dynamic production, three types of dynamic events, namely, machine breakdown, job with poor quality and job release delay, are considered. Furthermore, controllable processing times (CPT), sequence-dependent setup times (SDST) and job-dependent transportation times (JDTT) are also considered. Firstly, we formulate a model for the multi-objective dynamic welding scheduling problem (MODWSP). The objectives are to minimize the makespan, machine load and instability simultaneously. Secondly, we develop a hybrid multi-objective grey wolf optimizer (HMOGWO) to solve this MODWSP. In the HMOGWO, a modified social hierarchy is designed to improve its exploitation and exploration abilities. To further enhance the exploration, genetic operator is embedded into the HMOGWO. Since one characteristic of this problem is that multiple machines can handle one operation at a time, the solution is encoded as a two-part representation including a permutation vector and a machine assignment matrix. To evaluate the effectiveness of the proposed HMOGWO, we compare it with other well-known multi-objective metaheuristics including NSGA-II, SPEA2, and multi-objective grey wolf optimizer. Experimental studies demonstrate that the proposed HMOGWO outperforms other algorithms in terms of convergence, spread and coverage. In addition, the case study shows that this method can solve the real-world welding scheduling problem well.

Details DOI