TMEDRP is a novel two-stage learning framework that integrates tumor-intrinsic signatures and TME-specific components for enhanced clinical drug response prediction and exhibits promising zero-shot generalization across unseen cancer lineages and novel compounds, suggesting its robustness across diverse scenarios.
Ya-Bin Kuang, Hao-Chen Zhao, Yi Luo et al.· Bioinformatics· 0 citations
This work identifies the high-sparsity trap, a unified acceleration framework for visual generation that combines a short sparse warm-up, few-step trajectory-mixed distillation, and FP8 quantization with fused kernels, and sustains attention sparsity with strong visual quality on long-sequence 720P generation across Wa...
Yu-Xi Liu, Hao-Yu Li, Ze-Kun Zhang et al.· 0 citations
Diffusion Transformers (DiTs) achieve strong video generation quality, but their dense spatiotemporal self-attention scales quadratically with sequence length and quickly becomes the dominant inference bottleneck. Sparse low-rank hybrids alleviate this cost by combining a local sparse branch with a global compressed br...
Ze-Kun Zhang, Yi-Xiang Cai, Yu-Xi Liu et al.· 1 citation
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