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Ran Zhang

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9 papers

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AAAI Conference 2026 Conference Paper

Invariant Conditional Molecular Generation Under Distribution Shift

Chunyu Hu
Tianyin Liao
Yicheng Sui
Ran Zhang
Xiao Wang
Ziwei Zhang

Conditional molecular generation, aiming to generate 2D and 3D molecules that satisfy given properties, has achieved remarkable progress, thanks to the advances in deep generative models such as graph diffusion. However, existing methods generally assume that the given conditions for training and testing are consistent, failing to handle the realistic challenge when there exist distribution shifts between training and testing conditions. Invariant learning is a mainstream paradigm for addressing distribution shifts, but fusing invariant learning principles with conditional molecular generation faces three core challenges: (1) existing invariant learning methods focus on discriminative tasks and cannot be directly adapted to molecule generative tasks; (2) how to distinguish between invariant subgraph and variant subgraph of a molecule graph, which is treated as an integrated input; (3) how to fuse invariant subgraphs, variant subgraphs, and property conditions for effective generation. To tackle these challenges, we propose Invariant Conditional MOLecular generation (IC-MOL), a framework that combines invariant learning with graph diffusion to improve the generalization ability of conditional molecular generation under distribution shifts. Specifically, we first disentangle molecular graphs into invariant and variant subgraphs while maintaining SE(3) equivariance, an important inductive bias for molecular generation. On this basis, we further design a two-phase graph diffusion generation model. In the first phase, we generate an invariant molecular consistent with the target property. In the second phase, we propose a cross-attention mechanism to fuse variant subgraph representations and property conditions to guide the generation of complete molecules while maintaining property alignment. Extensive experiments on the benchmark dataset show that IC-MOL consistently outperforms state-of-the-art baselines across six property conditions under distribution shifts.

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AAAI Conference 2026 Conference Paper

scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing

Ping Xu
Zaitian Wang
Zhirui Wang
Pengjiang Li
Jiajia Wang
Ran Zhang
Pengfei Wang
Yuanchun Zhou

Cell clustering is crucial for uncovering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data by identifying cell types and marker genes. Despite its importance, existing benchmarks for scRNA-seq clustering remain fragmented, lacking standardized protocols and often omitting recent advances in artificial intelligence.To fill these gaps, we present scCluBench, a comprehensive benchmark of clustering algorithms for scRNA-seq data. scCluBench provides 36 scRNA-seq datasets collected from diverse public sources, covering multiple tissues, which are uniformly processed to ensure consistency for systematic evaluation and downstream analyses. To assess performance, we collect and reproduce a range of scRNA-seq clustering methods, including traditional, deep learning-based, graph-based, and biological foundation models. We comprehensively evaluate each method both quantitatively and qualitatively, using core performance metrics and visualization analyses. Furthermore, we construct representative downstream biological tasks, such as marker gene identification and cell type annotation, to further assess the practical utility. scCluBench then investigates the performance differences and applicability boundaries of various clustering models across diverse analytical tasks, systematically assessing their robustness and scalability in real-world scenarios. Overall, scCluBench offers a standardized and user-friendly benchmark for scRNA-seq clustering, with standardized datasets, unified evaluation protocols, and transparent analyses, facilitating informed method selection and providing valuable insights into model generalizability and application scope.

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EAAI Journal 2025 Journal Article

Comprehensive fault diagnosis of lithium-ion batteries: An innovative approach based on hybrid coding and genetic search

Chunhui Ji
Guang Jin
Ran Zhang

The performance and lifespan of lithium-ion batteries are significantly impacted by various faults. In particular, concurrent faults result in complex crossover and coupling issues, which present considerable challenges to fault diagnosis. To address these issues, this paper proposes a comprehensive fault diagnosis method utilizing hybrid coding and genetic search. The Lyapunov index between predicted and faulty battery states is applied to calculate trajectory divergence rates, facilitating the detection of abnormal battery conditions. Fault modes are uniformly characterized using a hybrid code, and a population is generated for genetic search optimization, from which the most suitable solution is selected. Experimental results indicate that, compared to the original approach, the improved Lyapunov index enhances detection accuracy and robustness in handling abnormal data. Furthermore, the hybrid coding and genetic search-based algorithm effectively isolates and classifies faults in multi-fault scenarios.

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EAAI Journal 2025 Journal Article

Food multi-factor decoupling recognition based on progressive spatial-frequency attention distillation learning

Minkang Chai
Lu Wei
Zheng Qian
Ran Zhang
Ye Zhu
Baoqing Zhou

With the widespread application of image recognition technology in daily life, food image recognition faces challenges such as diverse categories and complex forms. Particularly when dealing with subtle differences between similar food items, imbalanced categories, feature ambiguities, and classification confusion caused by the coupling of multiple factors in food representation, existing models still have room for improvement in their recognition accuracy and generalization ability. Therefore, constructing a recognition model that can precisely differentiate food categories while effectively addressing the complexities of coupled factors has become a key issue in this field. In response to these challenges, we propose the innovative Progressive Spatial-Frequency Distillation Network (PSFDNet). By utilizing a unique multidimensional progressive learning strategy combined with an adaptive spatial-frequency attention mechanism, the model significantly enhances its feature extraction and discrimination capabilities within complex food structures. Additionally, we introduce the food correlation evaluation loss to decouple the mutual interference among food features effectively, thereby improving the accuracy and robustness of food image recognition. Extensive experiments verified the outstanding performance of PSFDNet across datasets, demonstrating a notable increase of 0. 87% in the Top-1 recognition accuracy and a 50% increase in inference speed. Particularly in recognizing food images characterized by highly coupled features and extremely imbalanced categories, PSFDNet exhibited significant performance advantages over other methods.

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AAAI Conference 2025 Conference Paper

Motif-Oriented Representation Learning with Topology Refinement for Drug-Drug Interaction Prediction

Ran Zhang
Xuezhi Wang
Guannan Liu
Pengyang Wang
Yuanchun Zhou
Pengfei Wang

Drug-Drug Interaction (DDI) prediction has attracted considerable attention in designing multi-drug combination strategies and avoiding adverse reactions. Notably, Artificial Intelligence (AI)-driven DDI prediction methods have emerged as a pivotal research paradigm. However, most AI-driven DDI prediction methods fall short in exploring intra-molecular motifs, and heavily rely on the overly idealized assumption of the complete inter-molecular topology, limiting their expressive capacities. To this end, we propose a Motif-Oriented representation learning with TOpology Refinement for DDI prediction, namely MOTOR, to exploit both the multi-granularity motif information and the topological structure of DDI networks. Specifically, MOTOR effectively captures motif internal structures, motif local contexts, and motif global semantics. Furthermore, MOTOR employs an iterative learning strategy to continuously refine the DDI topology and optimize the corresponding drug representations. Extensive experimental results demonstrate that MOTOR exhibits superior performance with interpretable insights in DDI prediction tasks across three real-world datasets, thereby opening up new avenues in AI-driven DDI prediction.

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EAAI Journal 2023 Journal Article

DGFaceNet: Lightweight and efficient face recognition

Feng Zhao
Peng Zhang
Ran Zhang
Mengwei Li

Face recognition has achieved great success due to the development of deep convolutional neural networks (DCNNs). However, complex DCNNs bring a large number of parameters as well as computational effort, which poses a significant challenge to resource-constrained embedded devices. Meanwhile, the commonly popular loss functions and lightweight networks are not so effective for face recognition. In this paper, we first investigate the impact of the number of similar features generated by inexpensive operations on model performance. It is shown that DCNNs can tolerate more similar features generated by cheap operations in the early stage of the network. We construct Dynamic Ghost Bottleneck based on this idea, and DGFaceNet is composed of stacking Dynamic Ghost Bottleneck. In addition, we propose a new class-margin-linear softmax loss function (CML-softmax) for lightweight networks. CML-softmax designs a quadratic function to replace the cosine function as the target logit, which allows better performance and convergence in low-dimensional output for face recognition. Meanwhile, CML-softmax introduces two margin functions to alleviate class imbalance and softmax early saturation problems, respectively. Our method demonstrates competitive results in many validation datasets and large-scale popular benchmark tests. Speed tests on embedded devices show that the actual inference time of DGFaceNet is 11. 08 times, 8. 57 times, 2. 75 times, and 2. 82 times faster than ResNet-50, EfficientNet, MobileNetV2, and MobileFaceNet, respectively. DGFaceNet can significantly improve the running efficiency of the model in resource-constrained embedded devices while ensuring the model’s performance.

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NeurIPS Conference 2023 Conference Paper

Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts

Ruth Dannenfelser
Jeffrey Zhong
Ran Zhang
Vicky Yao

Many of the most commonly explored natural language processing (NLP) information extraction tasks can be thought of as evaluations of declarative knowledge, or fact-based information extraction. Procedural knowledge extraction, i. e. , breaking down a described process into a series of steps, has received much less attention, perhaps in part due to the lack of structured datasets that capture the knowledge extraction process from end-to-end. To address this unmet need, we present FlaMBé (Flow annotations for Multiverse Biological entities), a collection of expert-curated datasets across a series of complementary tasks that capture procedural knowledge in biomedical texts. This dataset is inspired by the observation that one ubiquitous source of procedural knowledge that is described as unstructured text is within academic papers describing their methodology. The workflows annotated in FlaMBé are from texts in the burgeoning field of single cell research, a research area that has become notorious for the number of software tools and complexity of workflows used. Additionally, FlaMBé provides, to our knowledge, the largest manually curated named entity recognition (NER) and disambiguation (NED) datasets for tissue/cell type, a fundamental biological entity that is critical for knowledge extraction in the biomedical research domain. Beyond providing a valuable dataset to enable further development of NLP models for procedural knowledge extraction, automating the process of workflow mining also has important implications for advancing reproducibility in biomedical research.

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NeurIPS Conference 2022 Conference Paper

IKEA-Manual: Seeing Shape Assembly Step by Step

Ruocheng Wang
Yunzhi Zhang
Jiayuan Mao
Ran Zhang
Chin-Yi Cheng
Jiajun Wu

Human-designed visual manuals are crucial components in shape assembly activities. They provide step-by-step guidance on how we should move and connect different parts in a convenient and physically-realizable way. While there has been an ongoing effort in building agents that perform assembly tasks, the information in human-design manuals has been largely overlooked. We identify that this is due to 1) a lack of realistic 3D assembly objects that have paired manuals and 2) the difficulty of extracting structured information from purely image-based manuals. Motivated by this observation, we present IKEA-Manual, a dataset consisting of 102 IKEA objects paired with assembly manuals. We provide fine-grained annotations on the IKEA objects and assembly manuals, including decomposed assembly parts, assembly plans, manual segmentation, and 2D-3D correspondence between 3D parts and visual manuals. We illustrate the broad application of our dataset on four tasks related to shape assembly: assembly plan generation, part segmentation, pose estimationand 3D part assembly.

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EAAI Journal 2017 Journal Article

An improved TLBO based memetic algorithm for aerodynamic shape optimization

Xinghua Qu
Ran Zhang
Bo Liu
Huifeng Li

Aerodynamic shape optimization (ASO) for aircraft is the focus of concern as well as the subject of substantial research issue in aerospace engineering. This paper proposes a novel TLBO (teaching-learning based optimization based) memetic algorithm (TLBO-MA) for optimizing the aerodynamic shape. In the proposed TLBO-MA, an adaptive teaching factor, conservation of information inspired operator and multi-meme learning are incorporated to enhance the searching behavior of standard TLBO. Simulation based on well-known benchmarks and ASO for HTV-2 prototype demonstrates the efficiency of the proposed TLBO-MA.

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