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Ling Luo

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12 papers

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JBHI Journal 2025 Journal Article

Breathing Cycle-Aware Segmentation for Patient-Ventilator Asynchrony Detection

Jinxi Wang
Ling Luo
Uwe Aickelin
David J. Berlowitz
Mark E. Howard

Patient–ventilator asynchrony (PVA) is a significant challenge in mechanical ventilation, affecting approximately 25% of intensive care unit patients and increasing the risk of lung and diaphragm injury. Segmenting breathing cycles from long ventilation waveforms is essential for the reliable detection of PVA events. However, existing segmentation methods present several limitations: manual annotation is time-consuming; fixed-length window and rule-based segmentation methods lack adaptability to varying respiratory patterns; and supervised deep learning (DL) segmentation methods require large amounts of labelled data for training. To address these issues, we propose an unsupervised breathing cycle-aware segmentation method tailored for PVA detection. Leveraging the quasi-periodic nature of ventilation waveforms, the proposed segmentation method integrates frequency-adaptive clustering, periodicity hints validation, and dynamic segmentation to identify breathing cycle boundaries. We evaluate the proposed breathing cycle-aware segmentation method on a real-world dataset from Austin Health, Melbourne, Australia, where it outperforms baseline approaches on five out of six evaluation metrics. Furthermore, classification experiments using two state-of-the-art DL-based classification models confirm that accurate segmentation of breathing cycles enhances PVA detection performance. In the future, the proposed breathing cycle-aware segmentation method could be integrated into ventilation systems to support clinical decision-making and improve patient care.

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AIIM Journal 2025 Journal Article

LCDL: Classification of ICD codes based on disease label co-occurrence dependency and LongFormer with medical knowledge

Yumeng Yang
Hongfei Lin
Zhihao Yang
Yijia Zhang
Di Zhao
Ling Luo

Medical coding involves assigning codes to clinical free-text documents, specifically medical records that average over 3, 000 markers, in order to track patient diagnoses and treatments. This is typically accomplished through manual assignments by healthcare professionals. To improve efficiency and accuracy while reducing the workload on these professionals, researchers have employed a multi-label classification approach. Since the long-tail phenomenon impacts tens of thousands of ICD codes, whereby only a few codes (representative of common diseases) are frequently assigned, while the majority of codes (representative of rare diseases) are infrequently assigned, this paper presents an LCDL model that addresses the challenge at hand by examining the LongFormer pre-trained language model and the disease label co-occurrence map. To enhance the performance of automated medical coding in the biomedical domain, hierarchies with medical knowledge, synonyms and abbreviations are introduced, improving the medical knowledge representation. Test evaluations are extensively conducted on the benchmark dataset MIMIC-III, and obtained the competitive performance compared to the previous state-of-the-art methods.

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EAAI Journal 2025 Journal Article

Prediction of high-performance concrete compressive strength using Decision Tree-Guided Artificial Neural Network Pretraining approach

Yang Zhang
Xinghai Yuan
Xuanpeng Zhang
Heng Wang
Pan He
Ling Luo
Chuanyun Xu

Deep learning models exhibit substantial nonlinear learning capabilities, enabling them to predict the compressive strength of high-performance concrete based on its mix proportions and to discern the intricate nonlinear relationships between these proportions and compressive strength. However, the data-hungry nature of deep learning necessitates extensive training datasets on mix proportions, and generating such large-scale, high-quality concrete mix datasets incurs significant costs. To address this issue, this study proposes a Decision Tree-guided Artificial Neural Network Pretraining (TANNP) approach, aimed at training high-accuracy concrete compressive strength prediction models using small-scale datasets. This approach synergizes the advantages of decision trees in managing small datasets and the powerful learning capabilities of neural networks. The TANNP method was experimentally validated on a concrete compressive strength dataset. The results demonstrate that compared to directly employing neural networks, the TANNP approach significantly improves performance metrics: the coefficient of determination increased from 0. 91 to 0. 97, the root mean square error decreased from 5. 10 MPa to 3. 26 MPa (a reduction of approximately 36. 1%), the mean absolute error was reduced from 3. 00 MPa to 2. 10 MPa, the mean absolute percentage error dropped from 10. 72% to 7. 24%, and the A20-Index improved from 0. 88 to 0. 92, achieving state-of-the-art results on this dataset. These findings indicate that the TANNP approach effectively mitigates the data-hunger issue inherent in deep learning models and significantly enhances the accuracy of concrete compressive strength predictions.

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NeurIPS Conference 2025 Conference Paper

ViewCraft3D: High-fidelity and View-Consistent 3D Vector Graphics Synthesis

Chuang Wang
Haitao Zhou
Ling Luo
Qian Yu

3D vector graphics play a crucial role in various applications including 3D shape retrieval, conceptual design, and virtual reality interactions due to their ability to capture essential structural information with minimal representation. While recent approaches have shown promise in generating 3D vector graphics, they often suffer from lengthy processing times and struggle to maintain view consistency. To address these limitations, we propose VC3D ( V iew C raft 3D ), an efficient method that leverages 3D priors to generate 3D vector graphics. Specifically, our approach begins with 3D object analysis, employs a geometric extraction algorithm to fit 3D vector graphics to the underlying structure, and applies view-consistent refinement process to enhance visual quality. Our comprehensive experiments demonstrate that VC3D outperforms previous methods in both qualitative and quantitative evaluations, while significantly reducing computational overhead. The resulting 3D sketches maintain view consistency and effectively capture the essential characteristics of the original objects.

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AAAI Conference 2024 Conference Paper

From Retrieval to Generation: A Simple and Unified Generative Model for End-to-End Task-Oriented Dialogue

Zeyuan Ding
Zhihao Yang
Ling Luo
Yuanyuan Sun
Hongfei Lin

Retrieving appropriate records from the external knowledge base to generate informative responses is the core capability of end-to-end task-oriented dialogue systems (EToDs). Most of the existing methods additionally train the retrieval model or use the memory network to retrieve the knowledge base, which decouples the knowledge retrieval task from the response generation task, making it difficult to jointly optimize and failing to capture the internal relationship between the two tasks. In this paper, we propose a simple and unified generative model for task-oriented dialogue systems, which recasts the EToDs task as a single sequence generation task and uses maximum likelihood training to train the two tasks in a unified manner. To prevent the generation of non-existent records, we design the prefix trie to constrain the model generation, which ensures consistency between the generated records and the existing records in the knowledge base. Experimental results on three public benchmark datasets demonstrate that our method achieves robust performance on generating system responses and outperforms the baseline systems. To facilitate future research in this area, the code is available at https://github.com/dzy1011/Uni-ToD.

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IJCAI Conference 2022 Conference Paper

Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution

Feiyang Pan
Tongzhe Zhang
Ling Luo
Jia He
Shuoling Liu

Optimal execution is a sequential decision-making problem for cost-saving in algorithmic trading. Studies have found that reinforcement learning (RL) can help decide the order-splitting sizes. However, a problem remains unsolved: how to place limit orders at appropriate limit prices? The key challenge lies in the ``continuous-discrete duality'' of the action space. On the one hand, the continuous action space using percentage changes in prices is preferred for generalization. On the other hand, the trader eventually needs to choose limit prices discretely due to the existence of the tick size, which requires specialization for every single stock with different characteristics (e. g. , the liquidity and the price range). So we need continuous control for generalization and discrete control for specialization. To this end, we propose a hybrid RL method to combine the advantages of both of them. We first use a continuous control agent to scope an action subset, then deploy a fine-grained agent to choose a specific limit price. Extensive experiments show that our method has higher sample efficiency and better training stability than existing RL algorithms and significantly outperforms previous learning-based methods for order execution.

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AAAI Conference 2022 Conference Paper

Noise-Robust Learning from Multiple Unsupervised Sources of Inferred Labels

Amila Silva
Ling Luo
Shanika Karunasekera
Christopher Leckie

Deep Neural Networks (DNNs) generally require large-scale datasets for training. Since manually obtaining clean labels for large datasets is extremely expensive, unsupervised models based on domain-specific heuristics can be used to efficiently infer the labels for such datasets. However, the labels from such inferred sources are typically noisy, which could easily mislead and lessen the generalizability of DNNs. Most approaches proposed in the literature to address this problem assume the label noise depends only on the true class of an instance (i. e. , class-conditional noise). However, this assumption is not realistic for the inferred labels as they are typically inferred based on the features of the instances. The few recent attempts to model such instance-dependent (i. e. , feature-dependent) noise require auxiliary information about the label noise (e. g. , noise rates or clean samples). This work proposes a theoretically motivated framework to correct label noise in the presence of multiple labels inferred from unsupervised models. The framework consists of two modules: (1) MULTI-IDNC, a novel approach to correct label noise that is instance-dependent yet not class-conditional; (2) MULTI- CCNC, which extends an existing class-conditional noiserobust approach to yield improved class-conditional noise correction using multiple noisy label sources. We conduct experiments using nine real-world datasets for three different classification tasks (images, text and graph nodes). Our results show that our approach achieves notable improvements (e. g. , 6. 4% in accuracy) against state-of-the-art baselines while dealing with both instance-dependent and classconditional noise in inferred label sources.

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IJCAI Conference 2021 Conference Paper

Bayesian Nonparametric Space Partitions: A Survey

Xuhui Fan
Bin Li
Ling Luo
Scott A. Sisson

Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a D-dimensional space into a set of blocks, such that the data within the same block share certain kinds of homogeneity. BNSP models are applicable to many areas, including regression/classification trees, random feature construction, and relational modelling. This survey provides the first comprehensive review of this subject. We explore the current progress of BNSP research through three perspectives: (1) Partition strategies, where we review the various techniques for generating partitions and discuss their theoretical foundation, `self-consistency'; (2) Applications, where we detail the current mainstream usages of BNSP models and identify some potential future applications; and (3) Challenges, where we discuss current unsolved problems and possible avenues for future research.

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AAAI Conference 2021 Conference Paper

Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data

Amila Silva
Ling Luo
Shanika Karunasekera
Christopher Leckie

With the rapid evolution of social media, fake news has become a significant social problem, which cannot be addressed in a timely manner using manual investigation. This has motivated numerous studies on automating fake news detection. Most studies explore supervised training models with different modalities (e. g. , text, images, and propagation networks) of news records to identify fake news. However, the performance of such techniques generally drops if news records are coming from different domains (e. g. , politics, entertainment), especially for domains that are unseen or rarely-seen during training. As motivation, we empirically show that news records from different domains have significantly different word usage and propagation patterns. Furthermore, due to the sheer volume of unlabelled news records, it is challenging to select news records for manual labelling so that the domain-coverage of the labelled dataset is maximized. Hence, this work: (1) proposes a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains; and (2) introduces an unsupervised technique to select a set of unlabelled informative news records for manual labelling, which can be ultimately used to train a fake news detection model that performs well for many domains while minimizing the labelling cost. Our experiments show that the integration of the proposed fake news model and the selective annotation approach achieves state-of-the-art performance for cross-domain news datasets, while yielding notable improvements for rarely-appearing domains in news datasets.

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IJCAI Conference 2019 Conference Paper

Unsupervised Neural Aspect Extraction with Sememes

Ling Luo
Xiang Ao
Yan Song
Jinyao Li
Xiaopeng Yang
Qing He
Dong Yu

Aspect extraction relies on identifying aspects by discovering coherence among words, which is challenging when word meanings are diversified and processing on short texts. To enhance the performance on aspect extraction, leveraging lexical semantic resources is a possible solution to such challenge. In this paper, we present an unsupervised neural framework that leverages sememes to enhance lexical semantics. The overall framework is analogous to an autoenoder which reconstructs sentence representations and learns aspects by latent variables. Two models that form sentence representations are proposed by exploiting sememes via (1) a hierarchical attention; (2) a context-enhanced attention. Experiments on two real-world datasets demonstrate the validity and the effectiveness of our models, which significantly outperforms existing baselines.

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IJCAI Conference 2018 Conference Paper

Beyond Polarity: Interpretable Financial Sentiment Analysis with Hierarchical Query-driven Attention

Ling Luo
Xiang Ao
Feiyang Pan
Jin Wang
Tong Zhao
Ningzi Yu
Qing He

Sentiment analysis has played a significant role in financial applications in recent years. The informational and emotive aspects of news texts may affect the prices, volatilities, volume of trades, and even potential risks of financial subjects. Previous studies in this field mainly focused on identifying polarity~(e. g. positive or negative). However, as financial decisions broadly require justifications, only plausible polarity cannot provide enough evidence during the decision making processes of humanity. Hence an explainable solution is in urgent demand. In this paper, we present an interpretable neural net framework for financial sentiment analysis. First, we design a hierarchical model to learn the representation of a document from multiple granularities. In addition, we propose a query-driven attention mechanism to satisfy the unique characteristics of financial documents. With the domain specified questions provided by the financial analysts, we can discover different spotlights for queries from different aspects. We conduct extensive experiments on a real-world dataset. The results demonstrate that our framework can learn better representation of the document and unearth meaningful clues on replying different users? preferences. It also outperforms the state-of-the-art methods on sentiment prediction of financial documents.

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IJCAI Conference 2017 Conference Paper

Tracking the Evolution of Customer Purchase Behavior Segmentation via a Fragmentation-Coagulation Process

Ling Luo
Bin Li
Irena Koprinska
Shlomo Berkovsky
Fang Chen

Customer behavior modeling is important for businesses in order to understand, attract and retain customers. It is critical that the models are able to track the dynamics of customer behavior over time. We propose FC-CSM, a Customer Segmentation Model based on a Fragmentation-Coagulation process, which can track the evolution of customer segmentation, including the splitting and merging of customer groups. We conduct a case study using transaction data from a major Australian supermarket chain, where we: 1) show that our model achieves high fitness of purchase rate, outperforming models using mixture of Poisson processes; 2) compare the impact of promotions on customers for different products; and 3) track how customer groups evolve over time and how individual customers shift across groups. Our model provides valuable information to stakeholders about the different types of customers, how they change purchase behavior, and which customers are more receptive to promotion campaigns.

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