Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
Clustering Semantically Related Questions
There has been a vast increase of users that use the internet in order to communicate and interact, and as a result, the amount of data created follows the same upward trend making data handling overwhelming. Users are often asked to submit their questions on various topics of their interest, and usually, that itself creates an information overload ...
openaire +1 more source
The Neural Processes Underpinning Flexible Semantic Retrieval in Visual and Auditory Modalities. [PDF]
Shao X +7 more
europepmc +1 more source
The Interoperability Challenge in DFT Workflows Across Implementations
Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen +13 more
wiley +1 more source
Consistency-accuracy correlation in hard-prompted LLMs for entity and relation extraction: empirical findings from plant-health data. [PDF]
Yao X, Nédellec C, Xia J, Bossy R.
europepmc +1 more source
An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto +5 more
wiley +1 more source
Grammar error diagnosis using graph convolutional networks with knowledge graph integration. [PDF]
Zhang J, Ma Y.
europepmc +1 more source
When Biology Meets Medicine: A Perspective on Foundation Models
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu +3 more
wiley +1 more source
Morpho―Semantic Relations in Lexical―Semantic Network
null Joon-Kyung Cha, null 임해창
openaire +1 more source
Adaptive knowledge distillation based structure-text embedding integrating for knowledge graph completion. [PDF]
Li Q +6 more
europepmc +1 more source

