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Logged in Referee: Reviewer User (University of Oxford, Mathematical Institute)
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Adaptive Graph Attention Networks for Single-Cell Spatial Transcriptomics
Resolving Microenvironment Cell-Cell Interactions with Positional Embeddings
Blinded Manuscript Abstract
Spatial transcriptomics technology measures gene expression while preserving tissue coordinates, but noise and sparse gene capture hinder cell-type deconvolution. We design SpatialGAT, an attention-weighted graph neural architecture with harmonic positional encodings that dynamically models ligand-receptor interaction weights across cellular neighborhoods. Experiments on 10x Visium mouse coronal brain slices show a 19.3% boost in cell type identification accuracy over non-spatial graph convolutions. SpatialGAT establishes an end-to-end topological framework for characterizing cellular microenvironments and tumor-stroma boundaries.
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Scalable Distributed Tensor Decomposition for Multi-Omics Clinical Phenotyping
High-Performance Asynchronous Factorization on Heterogeneous GPU Clusters
✓ Official Referee Evaluation Record
Exceptional contribution to high-performance tensor computing. Algorithm convergence proofs are rigorous and GPU kernels are well structured.
The authors provide an outstanding algorithmic solution for multi-omics decomposition. The speedup curves across heterogeneous clusters are convincing and code reproducibility is excellent.
Quantum-Resilient Lattice Cryptography for Decentralized Clinical Trial Records
Zero-Knowledge Verification Over Distributed Ledger Topologies
✓ Official Referee Evaluation Record
Outstanding manuscript. All requested revision clarifications on zero-knowledge circuit constraints were fully addressed. Ready for instant publication.
Thank you for thoroughly addressing the verification benchmarks and circuit size comparisons. The paper is ready for publication.
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