The Hong Kong University of Science and Technology (Guangzhou)

Using satellite observations of solar-induced fluorescence together with model estimates of water table depth and aridity, and applying causality-guided explainable machine learning, this study quantified the relative roles of groundwater and climatic aridity in shaping the spatial pattern of photosynthesis across the contiguous United States, finding that groundwater's relative importance equals 48% to 101% of aridity's effect on forest photosynthesis, 30% to 58% in savannahs and shrublands, 22% to 42% in grasslands, and 15% to 32% in croplands.

Using satellite observations of solar-induced fluorescence together with model estimates of water table depth and aridity, and applying causality-guided explainable machine learning, this study quantified the relative roles of groundwater and climatic aridity in shaping the spatial pattern of photosynthesis across the contiguous United States, finding that groundwater's relative importance equals 48% to 101% of aridity's effect on forest photosynthesis, 30% to 58% in savannahs and shrublands, 22% to 42% in grasslands, and 15% to 32% in croplands.

The work proposes PrefixAgent, a two-phase large-language-model-driven framework for prefix adder optimization: in Phase I a fine-tuned large reasoning model iteratively optimizes the backbone via regroup tool calls, and in Phase II it performs local timing repair with level-opt, fanout-opt, and node clone tools; the authors use e-graph equality saturation and explanation to generate interpretable rewrite trajectories as supervision data, and under the NanGate45 and OpenROAD flow PrefixAgent produces smaller-area adders than DP, MCTS, PrefixRL, CircuitVAE, and PrefixGPT in nearly all configurations, achieving up to 11.3% area reduction over the best baseline at 64 bits and also improving area in a commercial flow and a 256-PE systolic array.

The work proposes PrefixAgent, a two-phase large-language-model-driven framework for prefix adder optimization: in Phase I a fine-tuned large reasoning model iteratively optimizes the backbone via regroup tool calls, and in Phase II it performs local timing repair with level-opt, fanout-opt, and node clone tools; the authors use e-graph equality saturation and explanation to generate interpretable rewrite trajectories as supervision data, and under the NanGate45 and OpenROAD flow PrefixAgent produces smaller-area adders than DP, MCTS, PrefixRL, CircuitVAE, and PrefixGPT in nearly all configurations, achieving up to 11.3% area reduction over the best baseline at 64 bits and also improving area in a commercial flow and a 256-PE systolic array.

The work presents SegDINO, which uses a frozen DINOv3-S encoder to collect intermediate features from layers 3, 6, 9, and 12, reorganizes same-resolution tokens into a pseudo multi-scale pyramid via Token Pyramid Adaptation (TPA), and applies Scale-Aware Decoding (SAD) for intra-scale refinement and top-down inter-scale propagation, alongside a new PanCT dataset of 284 pancreatic cancer patient CT scans; on PanCT and the public TN3K, Kvasir-SEG, and ISIC benchmarks, SegDINO outperforms U-Net, SegNet, R2U-Net, Attention U-Net, TransUNet, U-NeXt, and U-KAN in both DSC and HD95, with 27.68M total parameters and 51 FPS inference.

The work presents SegDINO, which uses a frozen DINOv3-S encoder to collect intermediate features from layers 3, 6, 9, and 12, reorganizes same-resolution tokens into a pseudo multi-scale pyramid via Token Pyramid Adaptation (TPA), and applies Scale-Aware Decoding (SAD) for intra-scale refinement and top-down inter-scale propagation, alongside a new PanCT dataset of 284 pancreatic cancer patient CT scans; on PanCT and the public TN3K, Kvasir-SEG, and ISIC benchmarks, SegDINO outperforms U-Net, SegNet, R2U-Net, Attention U-Net, TransUNet, U-NeXt, and U-KAN in both DSC and HD95, with 27.68M total parameters and 51 FPS inference.