Publications (184)
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Chen Ling, Xujiang Zhao, Jiaying Lu +21
Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of app…
Neighborhood-Regularized Self-Training for Learning with Few Labels
Ran Xu, Yue Yu, Hejie Cui +5
Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been succ…
Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM
Hejie Cui, Xinyu Fang, Ran Xu +3
Electronic Health Records (EHRs) have become increasingly popular to support clinical decision-making and healthcare in recent decades. EHRs usually contain heterogeneous informati…
ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
Yuhao Xu, Xiaoda Wang, Yi Wu +3
Electrocardiography (ECG) analysis is crucial for cardiac diagnosis, yet existing foundation models often fail to capture the periodicity and diverse features required for varied c…
Federated Graph Classification over Non-IID Graphs
Han Xie, Jing Ma, Li Xiong +1
Federated learning has emerged as an important paradigm for training machine learning models in different domains. For graph-level tasks such as graph classification, graphs can al…
Scaling Agentic Reinforcement Learning for Tool-Integrated Reasoning in VLMs
Meng Lu, Ran Xu, Yi Fang +14
While recent vision-language models (VLMs) demonstrate strong image understanding, their ability to "think with images", i.e., to reason through multi-step visual interactions, rem…
PromptLink: Leveraging Large Language Models for Cross-Source Biomedical Concept Linking
Yuzhang Xie, Jiaying Lu, Joyce Ho +3
Linking (aligning) biomedical concepts across diverse data sources enables various integrative analyses, but it is challenging due to the discrepancies in concept naming convention…
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
Hejie Cui, Wei Dai, Yanqiao Zhu +7
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neur…
Reinforcement Learning with LLM-Guided Action Spaces for Synthesizable Lead Optimization
Tao Li, Kaiyuan Hou, Tuan Vinh +3
Lead optimization in drug discovery requires improving therapeutic properties while ensuring that molecular modifications correspond to feasible synthetic routes. Existing approach…
Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptive Residual Module
Jingbo Zhou, Yixuan Du, Ruqiong Zhang +7
Graph Neural Networks (GNNs), a type of neural network that can learn from graph-structured data through neighborhood information aggregation, have shown superior performance in va…
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation
Kai Zhang, Yu Wang, Hongyi Wang +4
Federated learning (FL) can be essential in knowledge representation, reasoning, and data mining applications over multi-source knowledge graphs (KGs). A recent study FedE first pr…
Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting
Hejie Cui, Xinyu Fang, Zihan Zhang +7
Images contain rich relational knowledge that can help machines understand the world. Existing methods on visual knowledge extraction often rely on the pre-defined format (e.g., su…
CONE: Community Oriented Network Embedding
Carl Yang, Hanqing Lu, Kevin Chen-Chuan Chang
Detecting communities has long been popular in the research on networks. It is usually modeled as an unsupervised clustering problem on graphs, based on heuristic assumptions about…
Towards Synthesizing Normative Data for Cognitive Assessments Using Generative Multimodal Large Language Models
Victoria Yan, Honor Chotkowski, Fengran Wang +6
Cognitive assessments require normative data as essential benchmarks for evaluating individual performance. Hence, developing new cognitive tests based on novel image stimuli is ch…
HypKG: Hypergraph-based Knowledge Graph Contextualization for Precision Healthcare
Yuzhang Xie, Xu Han, Ran Xu +3
Knowledge graphs (KGs) are important products of the semantic web, which are widely used in various application domains. Healthcare is one of such domains where KGs are intensively…
A Pure Transformer Pretraining Framework on Text-attributed Graphs
Yu Song, Haitao Mao, Jiachen Xiao +6
Pretraining plays a pivotal role in acquiring generalized knowledge from large-scale data, achieving remarkable successes as evidenced by large models in CV and NLP. However, progr…
LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction
Hejie Cui, Zhuocheng Shen, Jieyu Zhang +4
Electronic health records (EHRs) contain valuable patient data for health-related prediction tasks, such as disease prediction. Traditional approaches rely on supervised learning m…
Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration
Keqi Han, Songlin Zhao, Yao Su +4
Transforming neuroimaging data into clinically actionable biomarkers is a knowledge-intensive and labor-intensive process. Standardized workflows such as fMRIPrep have improved rob…
A Benchmark Analysis of Graph and Non-Graph Methods for Caenorhabditis Elegans Neuron Classification
Jingqi Lu, Keqi Han, Yun Wang +2
This study establishes a benchmark for Caenorhabditis elegans neuron classification, comparing four graph methods (GCN, GraphSAGE, GAT, GraphTransformer) against four non-graph met…
Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training
Ran Xu, Yue Yu, Joyce C. Ho +1
Scientific document classification is a critical task for a wide range of applications, but the cost of obtaining massive amounts of human-labeled data can be prohibitive. To addre…
Transferable Graph Condensation from the Causal Perspective
Huaming Du, Yijie Huang, Su Yao +10
The increasing scale of graph datasets has significantly improved the performance of graph representation learning methods, but it has also introduced substantial training challeng…
Mining Social Determinants of Health for Heart Failure Patient 30-Day Readmission via Large Language Model
Mingchen Shao, Youjeong Kang, Xiao Hu +3
Heart Failure (HF) affects millions of Americans and leads to high readmission rates, posing significant healthcare challenges. While Social Determinants of Health (SDOH) such as s…
GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU
Carl Yang, Aydin Buluc, John D. Owens
High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs because of three challenges: (1) the difficulty of coming up…
MANI-Pure: Magnitude-Adaptive Noise Injection for Adversarial Purification
Xiaoyi Huang, Junwei Wu, Kejia Zhang +2
Adversarial purification with diffusion models has emerged as a promising defense strategy, but existing methods typically rely on uniform noise injection, which indiscriminately p…
HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting
Jiaying Lu, Jiaming Shen, Bo Xiong +3
Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a unifor…
Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration
Ran Xu, Wenqi Shi, Yuchen Zhuang +4
Retrieval-Augmented Generation (RAG) systems often struggle to handle multi-hop question-answering tasks accurately due to irrelevant context retrieval and limited complex reasonin…
TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data
Ziyang Zhang, Hejie Cui, Ran Xu +3
The growing availability of well-organized Electronic Health Records (EHR) data has enabled the development of various machine learning models towards disease risk prediction. Howe…
Enhanced Atrial Fibrillation Prediction in ESUS Patients with Hypergraph-based Pre-training
Yuzhang Xie, Yuhua Wu, Ruiyu Wang +3
Atrial fibrillation (AF) is a major complication following embolic stroke of undetermined source (ESUS), elevating the risk of recurrent stroke and mortality. Early identification…
Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks
Jiarong Xu, Renhong Huang, Xin Jiang +4
Pre-training on graph neural networks (GNNs) aims to learn transferable knowledge for downstream tasks with unlabeled data, and it has recently become an active research area. The…
Peeling Context from Cause for Molecular Property Prediction
Tao Li, Kaiyuan Hou, Tuan Vinh +2
Deep models are used for molecular property prediction, yet they are often difficult to interpret and may rely on spurious context rather than causal structure, which reduces relia…
A Survey on Graph Structure Learning: Progress and Opportunities
Yanqiao Zhu, Weizhi Xu, Jinghao Zhang +5
Graphs are widely used to describe real-world objects and their interactions. Graph Neural Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly sensit…
From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR
Ran Xu, Yiwen Lu, Chang Liu +5
Electronic Health Records (EHRs) contain rich patient information and are crucial for clinical research and practice. In recent years, deep learning models have been applied to EHR…
Transformer-Based Hierarchical Clustering for Brain Network Analysis
Wei Dai, Hejie Cui, Xuan Kan +3
Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions. Within the complex brain syst…
Unsupervised Differentiable Multi-aspect Network Embedding
Chanyoung Park, Carl Yang, Qi Zhu +3
Network embedding is an influential graph mining technique for representing nodes in a graph as distributed vectors. However, the majority of network embedding methods focus on lea…
Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking
Shengbo Gong, Xianfeng Tang, Qi He +2
Retrieval-augmented generation (RAG) is critical for reducing hallucinations and incorporating external knowledge into Large Language Models (LLMs). However, advanced RAG systems f…
When to Pre-Train Graph Neural Networks? From Data Generation Perspective!
Yuxuan Cao, Jiarong Xu, Carl Yang +5
In recent years, graph pre-training has gained significant attention, focusing on acquiring transferable knowledge from unlabeled graph data to improve downstream performance. Desp…
Identifying Evidence Subgraphs for Financial Risk Detection via Graph Counterfactual and Factual Reasoning
Huaming Du, Lei Yuan, Qing Yang +6
Company financial risks pose a significant threat to personal wealth and national economic stability, stimulating increasing attention towards the development of efficient andtimel…
MedAgentGym: A Scalable Agentic Training Environment for Code-Centric Reasoning in Biomedical Data Science
Ran Xu, Yuchen Zhuang, Yishan Zhong +13
We introduce MedAgentGym, a scalable and interactive training environment designed to enhance coding-based biomedical reasoning capabilities in large language model (LLM) agents. M…
Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining
Yanchao Tan, Hang Lv, Pengxiang Zhan +2
Graphs with abundant attributes are essential in modeling interconnected entities and enhancing predictions across various real-world applications. Traditional Graph Neural Network…
MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fields
Jiaying Lu, Yongchen Qian, Shifan Zhao +2
Previous research has demonstrated the advantages of integrating data from multiple sources over traditional unimodal data, leading to the emergence of numerous novel multimodal ap…
Structure-Preserving Graph Kernel for Brain Network Classification
Jun Yu, Zhaoming Kong, Aditya Kendre +5
This paper presents a novel graph-based kernel learning approach for connectome analysis. Specifically, we demonstrate how to leverage the naturally available structure within the…
CubeNet: Multi-Facet Hierarchical Heterogeneous Network Construction, Analysis, and Mining
Carl Yang, Dai Teng, Siyang Liu +8
Due to the ever-increasing size of data, construction, analysis and mining of universal massive networks are becoming forbidden and meaningless. In this work, we outline a novel fr…
Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks
Zewen Liu, Xiaoda Wang, Bohan Wang +3
Graph Neural Networks (GNNs) and differential equations (DEs) are two rapidly advancing areas of research that have shown remarkable synergy in recent years. GNNs have emerged as p…
Privacy-Enhancing Paradigms within Federated Multi-Agent Systems
Zitong Shi, Guancheng Wan, Wenke Huang +4
LLM-based Multi-Agent Systems (MAS) have proven highly effective in solving complex problems by integrating multiple agents, each performing different roles. However, in sensitive…
Piecing It All Together: Verifying Multi-Hop Multimodal Claims
Haoran Wang, Aman Rangapur, Xiongxiao Xu +4
Existing claim verification datasets often do not require systems to perform complex reasoning or effectively interpret multimodal evidence. To address this, we introduce a new tas…
Mathematical Foundations of the GraphBLAS
Jeremy Kepner, Peter Aaltonen, David Bader +13
The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. Mathematically the Graph- BLAS…
Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation
Xiaoda Wang, Kaiqiao Han, Yuhao Xu +4
Cardiovascular disease (CVD) is a leading cause of mortality worldwide. Electrocardiograms (ECGs) are the most widely used non-invasive tool for cardiac assessment, yet large, well…
Large Language Model Empowered Privacy-Protected Framework for PHI Annotation in Clinical Notes
Guanchen Wu, Linzhi Zheng, Han Xie +7
The de-identification of private information in medical data is a crucial process to mitigate the risk of confidentiality breaches, particularly when patient personal details are n…
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Han Xie, Da Zheng, Jun Ma +9
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be…
Controllable Gradient Item Retrieval
Haonan Wang, Chang Zhou, Carl Yang +2
In this paper, we identify and study an important problem of gradient item retrieval. We define the problem as retrieving a sequence of items with a gradual change on a certain att…
MIRAGE: Knowledge Graph-Guided Cross-Cohort MRI Synthesis for Alzheimer's Disease Prediction
Guanchen Wu, Zhe Huang, Yuzhang Xie +6
Reliable Alzheimer's disease (AD) diagnosis increasingly relies on multimodal assessments combining structural Magnetic Resonance Imaging (MRI) and Electronic Health Records (EHR).…
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks
Gongxu Luo, Jianxin Li, Jianlin Su +5
Graph representation learning has achieved great success in many areas, including e-commerce, chemistry, biology, etc. However, the fundamental problem of choosing the appropriate…
PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model Distillation
Juntong Ni, Saurabh Kataria, Shengpu Tang +3
Photoplethysmography (PPG) is widely used in wearable health monitoring, yet large PPG foundation models remain difficult to deploy on resource-limited devices. We present PPG-Dist…
A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data and LLMs Perspective
Huaming Du, Cancan Feng, Yuqian Lei +5
Enterprise financial risk analysis aims at predicting the future financial risk of enterprises. Due to its wide and significant application, enterprise financial risk analysis has…
cube2net: Efficient Query-Specific Network Construction with Data Cube Organization
Carl Yang, Mengxiong Liu, Frank He +2
Networks are widely used to model objects with interactions and have enabled various downstream applications. However, in the real world, network mining is often done on particular…
Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders
Carl Yang, Jieyu Zhang, Haonan Wang +5
While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i.e., social relations. Ideal edge semant…
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Chaoyang He, Keshav Balasubramanian, Emir Ceyani +11
Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in learning distributed representations from graph-structured data. However, centralizing a ma…
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction
Yuxin Xiao, Zecheng Zhang, Yuning Mao +2
Stepping from sentence-level to document-level, the research on relation extraction (RE) confronts increasing text length and more complicated entity interactions. Consequently, it…
Knowledge Graph Augmented Large Language Models for Disease Prediction
Ruiyu Wang, Tuan Vinh, Ran Xu +5
Electronic health records (EHRs) enable strong clinical prediction, but explanations are often coarse and hard to use for patient-level decisions. We propose a knowledge graph (KG)…
RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation
Ran Xu, Yuchen Zhuang, Yue Yu +3
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieved at inference time. While RAG demonstrates strong performance…
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
Hejie Cui, Zijie Lu, Pan Li +1
Graph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where superior performance is mainly esta…
Gunrock: GPU Graph Analytics
Yangzihao Wang, Yuechao Pan, Andrew Davidson +8
For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to d…
BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning
Junru Chen, Yang Yang, Tao Yu +3
Epilepsy is one of the most serious neurological diseases, affecting 1-2% of the world's population. The diagnosis of epilepsy depends heavily on the recognition of epileptic waves…
I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application
Carl Yang, Xiaolin Shi, Jie Luo +1
As online platforms are striving to get more users, a critical challenge is user churn, which is especially concerning for new users. In this paper, by taking the anonymous large-s…
Boosting Reward Model with Preference-Conditional Multi-Aspect Synthetic Data Generation
Jiaming Shen, Ran Xu, Yennie Jun +6
Reward models (RMs) are crucial for aligning large language models (LLMs) with human preferences. They are trained using preference datasets where each example consists of one inpu…
Design Principles for Sparse Matrix Multiplication on the GPU
Carl Yang, Aydin Buluc, John D. Owens
We implement two novel algorithms for sparse-matrix dense-matrix multiplication (SpMM) on the GPU. Our algorithms expect the sparse input in the popular compressed-sparse-row (CSR)…
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains
Ran Xu, Hui Liu, Sreyashi Nag +8
Retrieval-augmented generation (RAG) enhances the question-answering (QA) abilities of large language models (LLMs) by integrating external knowledge. However, adapting general-pur…
Contrastive Unlearning: A Contrastive Approach to Machine Unlearning
Hong kyu Lee, Qiuchen Zhang, Carl Yang +2
Machine unlearning aims to eliminate the influence of a subset of training samples (i.e., unlearning samples) from a trained model. Effectively and efficiently removing the unlearn…
User-Guided Clustering in Heterogeneous Information Networks via Motif-Based Comprehensive Transcription
Yu Shi, Xinwei He, Naijing Zhang +2
Heterogeneous information networks (HINs) with rich semantics are ubiquitous in real-world applications. For a given HIN, many reasonable clustering results with distinct semantic…
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks
Yuanxin Zhuang, Lingjuan Lyu, Chuan Shi +2
Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications. Recently, knowl…
Effective and Interpretable fMRI Analysis via Functional Brain Network Generation
Xuan Kan, Hejie Cui, Ying Guo +1
Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions. However, existing func…
Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning
Yi Yang, Yanqiao Zhu, Hejie Cui +4
Brain networks characterize complex connectivities among brain regions as graph structures, which provide a powerful means to study brain connectomes. In recent years, graph neural…
Shift-Robust Node Classification via Graph Adversarial Clustering
Qi Zhu, Chao Zhang, Chanyoung Park +2
Graph Neural Networks (GNNs) are de facto node classification models in graph structured data. However, during testing-time, these algorithms assume no data shift, i.e., $\Pr_\text…
Generalist vs Specialist Time Series Foundation Models: Investigating Potential Emergent Behaviors in Assessing Human Health Using PPG Signals
Saurabh Kataria, Yi Wu, Zhaoliang Chen +21
Foundation models are large-scale machine learning models that are pre-trained on massive amounts of data and can be adapted for various downstream tasks. They have been extensivel…
Scalable Graph Condensation with Evolving Capabilities
Shengbo Gong, Mohammad Hashemi, Juntong Ni +2
The rapid growth of graph data creates significant scalability challenges as most graph algorithms scale quadratically with size. To mitigate these issues, Graph Condensation (GC)…
Lab-AI: Using Retrieval Augmentation to Enhance Language Models for Personalized Lab Test Interpretation in Clinical Medicine
Xiaoyu Wang, Haoyong Ouyang, Balu Bhasuran +5
Accurate interpretation of lab results is crucial in clinical medicine, yet most patient portals use universal normal ranges, ignoring conditional factors like age and gender. This…
Integrating Planning into Single-Turn Long-Form Text Generation
Yi Liang, You Wu, Honglei Zhuang +8
Generating high-quality, in-depth textual documents, such as academic papers, news articles, Wikipedia entries, and books, remains a significant challenge for Large Language Models…
NeuroClaw Technical Report
Cheng Wang, Zhibin He, Zhihao Peng +7
Agentic artificial intelligence systems promise to accelerate scientific workflows, but neuroimaging poses unique challenges: heterogeneous modalities (sMRI, fMRI, dMRI, EEG), long…
Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation
Jamie Cui, Chaochao Chen, Lingjuan Lyu +2
Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing work assumes that a…
Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery
Carl Yang, Mengxiong Liu, Frank He +3
Heterogeneous networks are widely used to model real-world semi-structured data. The key challenge of learning over such networks is the modeling of node similarity under both netw…
PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis
Yi Yang, Hejie Cui, Carl Yang
The human brain is the central hub of the neurobiological system, controlling behavior and cognition in complex ways. Recent advances in neuroscience and neuroimaging analysis have…
Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting
Xiaoda Wang, Yuji Zhao, Kaiqiao Han +8
Parkinson's disease (PD) shows heterogeneous, evolving brain-morphometry patterns. Modeling these longitudinal trajectories enables mechanistic insight, treatment development, and…
Deep Efficient Private Neighbor Generation for Subgraph Federated Learning
Ke Zhang, Lichao Sun, Bolin Ding +2
Behemoth graphs are often fragmented and separately stored by multiple data owners as distributed subgraphs in many realistic applications. Without harming data privacy, it is natu…
BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations
Kaiqiao Han, Yi Yang, Zijie Huang +8
Brain network analysis is vital for understanding the neural interactions regarding brain structures and functions, and identifying potential biomarkers for clinical phenotypes. Ho…
End-to-End Deep Learning for Structural Brain Imaging: A Unified Framework
Yao Su, Keqi Han, Mingjie Zeng +5
Brain imaging analysis is fundamental in neuroscience, providing valuable insights into brain structure and function. Traditional workflows follow a sequential pipeline-brain extra…
Query-Specific Knowledge Summarization with Entity Evolutionary Networks
Carl Yang, Lingrui Gan, Zongyi Wang +3
Given a query, unlike traditional IR that finds relevant documents or entities, in this work, we focus on retrieving both entities and their connections for insightful knowledge su…
Weakly Supervised Concept Map Generation through Task-Guided Graph Translation
Jiaying Lu, Xiangjue Dong, Carl Yang
Recent years have witnessed the rapid development of concept map generation techniques due to their advantages in providing well-structured summarization of knowledge from free tex…
Beyond MedQA: Towards Real-world Clinical Decision Making in the Era of LLMs
Yunpeng Xiao, Carl Yang, Mark Mai +2
Large language models (LLMs) show promise for clinical use. They are often evaluated using datasets such as MedQA. However, Many medical datasets, such as MedQA, rely on simplified…
Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration
Xuan Kan, Hejie Cui, Carl Yang
Relation prediction among entities in images is an important step in scene graph generation (SGG), which further impacts various visual understanding and reasoning tasks. Existing…
Secure Deep Graph Generation with Link Differential Privacy
Carl Yang, Haonan Wang, Ke Zhang +2
Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes). Since relational data are often sen…
An Electrocardiogram Multi-task Benchmark with Comprehensive Evaluations and Insightful Findings
Yuhao Xu, Jiaying Lu, Sirui Ding +3
In the process of patient diagnosis, non-invasive measurements are widely used due to their low risks and quick results. Electrocardiogram (ECG), as a non-invasive method to collec…
Seeing Through the Brain: New Insights from Decoding Visual Stimuli with fMRI
Zheng Huang, Enpei Zhang, Weikang Qiu +7
Understanding how the brain encodes visual information is a central challenge in neuroscience and machine learning. A promising approach is to reconstruct visual stimuli, essential…
Measuring Spiritual Values and Bias of Large Language Models
Songyuan Liu, Ziyang Zhang, Runze Yan +3
Large language models (LLMs) have become integral tool for users from various backgrounds. LLMs, trained on vast corpora, reflect the linguistic and cultural nuances embedded in th…
MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning
Wenqi Shi, Ran Xu, Yuchen Zhuang +5
Despite their improved capabilities in generation and reasoning, adapting large language models (LLMs) to the biomedical domain remains challenging due to their immense size and co…
Biomedical Visual Instruction Tuning with Clinician Preference Alignment
Hejie Cui, Lingjun Mao, Xin Liang +5
Recent advancements in multimodal foundation models have showcased impressive capabilities in understanding and reasoning with visual and textual information. Adapting these founda…
Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis
Xuan Kan, Antonio Aodong Chen Gu, Hejie Cui +2
Recent neuroimaging studies have highlighted the importance of network-centric brain analysis, particularly with functional magnetic resonance imaging. The emergence of Deep Neural…
A Comparative Study on Exact Triangle Counting Algorithms on the GPU
Leyuan Wang, Yangzihao Wang, Carl Yang +1
We implement exact triangle counting in graphs on the GPU using three different methodologies: subgraph matching to a triangle pattern; programmable graph analytics, with a set-int…
KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs
Yuzhang Xie, Hejie Cui, Ziyang Zhang +5
Medical diagnosis prediction plays a critical role in disease detection and personalized healthcare. While machine learning (ML) models have been widely adopted for this task, thei…
FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling
Emir Ceyani, Han Xie, Baturalp Buyukates +2
Graphs are crucial for modeling relational and biological data. As datasets grow larger in real-world scenarios, the risk of exposing sensitive information increases, making privac…
Attack as Defense: Run-time Backdoor Implantation for Image Content Protection
Haichuan Zhang, Meiyu Lin, Zhaoyi Liu +4
As generative models achieve great success, tampering and modifying the sensitive image contents (i.e., human faces, artist signatures, commercial logos, etc.) have induced a signi…