8 citations · 14 across the 15 of their papers we have counts for
16 papers
Sensor-Language-Action Models
Yuekai Xu, Zitao Shuai, Yuzhe Yang
Sensors are useful not only for understanding the world but also for deciding what to do next. Existing sensor models however largely stop at perception: they recognize states or p…
Inertia-1: An Open Exploration of Wearable Motion Foundation Models
Zongzhe Xu, Aakarsh Anand, Sarah Jiang +4
Wearable motion sensing provides a continuous and scalable window into human behavior and health, making it a natural fit for foundation models, yet its pretraining and scaling pri…
Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series
Zitao Shuai, Zongzhe Xu, Yuntian Wu +3
Generative models have changed how machine learning represents complex data distributions, especially in language and vision, yet many real-world systems are observed instead as co…
OSF: On Pre-training and Scaling of Sleep Foundation Models
Zitao Shuai, Zongzhe Xu, David Yang +2
Polysomnography (PSG) provides the gold standard for sleep assessment but suffers from substantial heterogeneity across recording devices and cohorts. There have been growing effor…
REMIND: Rethinking Medical High-Modality Learning under Missingness--A Long-Tailed Distribution Perspective
Chenwei Wu, Zitao Shuai, Liyue Shen
Medical multi-modal learning is critical for integrating information from a large set of diverse modalities. However, when leveraging a high number of modalities in real clinical a…
SleepLM: Natural-Language Intelligence for Human Sleep
Zongzhe Xu, Zitao Shuai, Eideen Mozaffari +3
We present SleepLM, a family of sleep-language foundation models that enable human sleep alignment, interpretation, and interaction with natural language. Despite the critical role…
SeqBench: Benchmarking Sequential Narrative Generation in Text-to-Video Models
Zhengxu Tang, Zizheng Wang, Luning Wang +8
Text-to-video (T2V) generation models have made significant progress in creating visually appealing videos. However, they struggle with generating coherent sequential narratives th…
Dynamical Multimodal Fusion with Mixture-of-Experts for Localizations
Bohao Wang, Zitao Shuai, Fenghao Zhu +6
Multimodal fingerprinting is a crucial technique to sub-meter 6G integrated sensing and communications (ISAC) localization, but two hurdles block deployment: (i) the contribution e…
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
Adnan Shahid, Adrian Kliks, Ahmed Al-Tahmeesschi +132
This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions…
MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation
Hsin-Ling Hsu, Cong-Tinh Dao, Luning Wang +12
Despite recent success in applying large language models (LLMs) to electronic health records (EHR), most systems focus primarily on assessment rather than treatment planning. We id…
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs
David Restrepo, Chenwei Wu, Zhengxu Tang +14
Current ophthalmology clinical workflows are plagued by over-referrals, long waits, and complex and heterogeneous medical records. Large language models (LLMs) present a promising…
Latent Space Disentanglement in Diffusion Transformers Enables Precise Zero-shot Semantic Editing
Zitao Shuai, Chenwei Wu, Zhengxu Tang +2
Diffusion Transformers (DiTs) have recently achieved remarkable success in text-guided image generation. In image editing, DiTs project text and image inputs to a joint latent spac…
Multi-Sources Fusion Learning for Multi-Points NLOS Localization in OFDM System
Bohao Wang, Zitao Shuai, Chongwen Huang +7
Accurate localization of mobile terminals is a pivotal aspect of integrated sensing and communication systems. Traditional fingerprint-based localization methods, which infer coord…
Latent Space Disentanglement in Diffusion Transformers Enables Zero-shot Fine-grained Semantic Editing
Zitao Shuai, Chenwei Wu, Zhengxu Tang +2
Diffusion Transformers (DiTs) have achieved remarkable success in diverse and high-quality text-to-image(T2I) generation. However, how text and image latents individually and joint…
Efficient In-Context Medical Segmentation with Meta-driven Visual Prompt Selection
Chenwei Wu, David Restrepo, Zitao Shuai +2
In-context learning (ICL) with Large Vision Models (LVMs) presents a promising avenue in medical image segmentation by reducing the reliance on extensive labeling. However, the ICL…
Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity
Zitao Shuai, Chenwei Wu, Zhengxu Tang +1
Vision-language pre-training (VLP) has emerged as an effective scheme for multimodal representation learning, but its reliance on large-scale multimodal data poses significant chal…