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Xue-Qun Shang

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Book Open access Aug 2026

FLASH: Fast Generative Retrieval via Autoregressive Semantic Hashing with Provably Distance Bounds

Retrieval-Augmented Generation (RAG) relies critically on the effectiveness of its retrieval component. Dense retrieval with Approximate Nearest Neighbor (ANN) search is efficient but relies on symmetric similarity metrics that can limit the modeling of directional relevance. Generative retrieval addresses this limitat...

Yifei Zhang, Hao Zhu, Haoran Shi et al. · 0 citations
Open access Aug 2026

Structure-aware deep learning predicts influenza antigenicity and guides vaccine strain recommendation

The continuous accumulation of genetic mutations in influenza A viruses (IAVs) drives antigenic drift, necessitating precise antigenic prediction for optimal vaccine strain selection. While sequence-based methods have advanced antigenic surveillance, they neglect the three-dimensional structural context that fundamenta...

Xing-Yi Li, Chun-Yan Zhou, Ke-Xin Xiao et al. · 0 citations
Open access Sep 2026

VirPLM: Antigenic prediction of influenza A/H3N2 viruses with a fine-tuned protein language model.

MOTIVATION Human influenza A/H3N2 viruses undergo rapid antigenic evolution primarily driven by the hemagglutinin subunit 1 (HA1). Within HA1, amino acid substitutions under immune pressure cause antigenic drift, necessitating frequent updates to vaccine strains. While hemagglutination inhibition (HI) assays remain the...

Xing-Yi Li, Ke-Xin Xiao, Chun-Yan Zhou et al. · 0 citations
Book Open access Aug 2026

When Gradient Boosting Meets Adapter: Exploring Weak Learners for Parameter-Efficient Fine-tuning of LLMs

This work proposes eXtreme Gradient Boosting LoRA (XGBLoRA), a novel framework grounded in gradient boosting theory that provides theoretical analysis establishing convergence guarantees and expressiveness bounds, which formally justify why weaker (lower-rank) adapters, when properly combined, can match or exceed the p...

Yifei Zhang, Hao Zhu, Haoran Shi et al. · 0 citations
Open access Jul 2026

Deciphering spatial heterogeneity by multimodal spatial transcriptomics modelling with SpatialModal

Abstract Motivation Advances in spatial transcriptomics (ST) technologies have made it possible to jointly acquire gene expression and histological image information while preserving spatial coordinates. This breakthrough presents unprecedented opportunities for the precise dissection of spatial heterogeneity in comple...

Xingyi Li, Dongmin Zhao, Xiangting Jia et al. · 0 citations
Open access Jul 2026

Geometric-aware deep learning for deciphering tissue structure from spatially resolved transcriptomics.

SpatialGEO, a geometric-aware deep learning framework that integrates gene expression profiles with spatial coordinates to generate biologically meaningful low-dimensional embeddings, enabling the dissection of complex tissue architectures, achieves superior performance in tissue structure dissection and data denoising...

Xing-Yi Li, Xiang-Ting Jia, Dong-Min Zhao et al. · 1 citation
Book Open access Aug 2026

When Gradient Boosting Meets Adapter: Exploring Weak Learners for Parameter-Efficient Fine-tuning of LLMs

Fine-tuning Large Language Models (LLMs) has become a crucial technique for adapting pre-trained models to downstream tasks. However, the enormous size of LLMs poses significant challenges in terms of computational complexity and resource requirements. Low-Rank Adaptation (LoRA) has emerged as a promising solution, yet...

Yifei Zhang, Hao Zhu, Haoran Shi et al. · 0 citations

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