AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Model compression strategies are widely employed to reduce memory footprint and network complexity, particularly for devices with constrained computational, memory, and energy resources. Prior works that rely on simultaneous conversion from floating-point high-precision (FP32) to integer low-precision (INT4) representa...
Medical Vision-Language Models (Med-VLMs) have demonstrated strong capabilities in clinical tasks. However, they often struggle to understand anatomical structures and spatial positioning, which are crucial for medical reasoning. To address this, we propose a localization-aware enhancement to the Med-VLM pipeline, intr...
Next-generation sequencing (NGS) has catalyzed the generation of individualized multi-omics datasets, potentiating precise molecular insights into cancer initiation, progression, and metastasis. The subsequent data analytics have necessitated the development of computational pipelines that couple patient-specific omics...
A definitive taxonomy of the medical VLM landscape is provided, tracing the evolution from early Contrastive Alignment and Generative MLLMs to the cutting-edge frontiers of Dense Pixel-Grounding, Sparse Mixture-of-Experts (MoE), and Reasoning-Incentivized (RL) architectures.
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