5 citations · 10 across the 11 of their papers we have counts for
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Learning and Predicting Patent Technology Reuse Trajectories from Emergence-Time Signals
Ayham Yousef, Qiang Ye, Qiang Cheng
Forecasting how a newly emerged patent technology will be reused is central to technology intelligence, but reuse-pattern labels do not exist in advance: they must be constructed f…
OmicSync: Reliability-Aware Spatial Multi-Omics Clustering with Evidence-Constrained LLM Reasoning
Rabeya Tus Sadia, Qiang Ye, Qiang Cheng
Spatial multi-omics technologies jointly profile gene expression, surface proteins, and histology at each tissue spot, yet most spatial domain discovery methods provide only cluste…
STRIDE: Reasoning-Aware Training for Time Series Forecasting
Md Atik Ahamed, Mihir Parmar, Palash Goyal +4
Time Series Foundation Models (TSFMs) excel at numerical forecasting but operate as black boxes lacking qualitative reasoning. Conversely, applying LLMs directly to temporal data i…
TFRBench: A Reasoning Benchmark for Evaluating Forecasting Systems
Md Atik Ahamed, Mihir Parmar, Palash Goyal +7
We introduce TFRBench, the first benchmark designed to evaluate the reasoning capabilities of forecasting systems. Traditionally, time-series forecasting has been evaluated solely…
CrossLLM-Mamba: Multimodal State Space Fusion of LLMs for RNA Interaction Prediction
Rabeya Tus Sadia, Qiang Ye, Qiang Cheng
Accurate prediction of RNA-associated interactions is essential for understanding cellular regulation and advancing drug discovery. While Biological Large Language Models (BioLLMs)…