Marine Autonomous Vehicle Fleet Scheduling to Maximise Scientific Impact
为解决海洋自主车辆（MAV）大规模部署时的手动规划难题，提出一种混合整数线性规划模型，旨在优化调度、最大化数据收集并减少能耗。
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Warping Earth Observations for better ice labeling in the Marginal Marginal Ice Zone
This study addresses the challenge of pixel-level misalignment between multi-source satellite images in dynamic marginal ice zones, which hinders effective multimodal fusion and accurate sea ice segmentation. To overcome this, the authors propose a novel mutual information-based warping framework—the first to apply this approach for spatial alignment between Sentinel-1 synthetic aperture radar and MODIS visible/thermal infrared imagery. Integrated with sparse point annotations, the method enables high-precision dense semantic segmentation, departing from conventional training paradigms that rely on coarse-grained ice charts. Evaluated on a dataset comprising 43 scenes with 2,088 pixel-level labels derived from 7,046 expert-annotated points, experiments demonstrate significantly improved segmentation accuracy after alignment, effectively achieving robust generalization from sparse annotations to dense predictions.
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ConSens: Assessing context grounding in open-book question answering
In open-book question answering, existing evaluation methods suffer from bias, poor scalability, and reliance on external systems, hindering accurate measurement of a model’s contextual dependency. To address this, we propose ConSens—a novel metric that quantifies a language model’s “context anchoring ability” by measuring the relative perplexity difference between context-aware and context-agnostic generations—using the model itself as both evaluator and generator. ConSens requires no fine-tuning, eliminates dependence on external judge models, and supports zero-shot, cross-model evaluation with high interpretability. Extensive experiments across multiple datasets demonstrate that ConSens achieves strong correlation with human judgments (Spearman’s ρ > 0.85), significantly outperforms baselines such as LLM-as-a-judge in discriminative power, and reduces computational overhead by over 90%.
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Marine Autonomous Vehicle Fleet Scheduling to Maximise Scientific Impact
为解决海洋自主车辆（MAV）大规模部署时的手动规划难题，提出一种混合整数线性规划模型，旨在优化调度、最大化数据收集并减少能耗。
0 citationsRead paper
Warping Earth Observations for better ice labeling in the Marginal Marginal Ice Zone
This study addresses the challenge of pixel-level misalignment between multi-source satellite images in dynamic marginal ice zones, which hinders effective multimodal fusion and accurate sea ice segmentation. To overcome this, the authors propose a novel mutual information-based warping framework—the first to apply this approach for spatial alignment between Sentinel-1 synthetic aperture radar and MODIS visible/thermal infrared imagery. Integrated with sparse point annotations, the method enables high-precision dense semantic segmentation, departing from conventional training paradigms that rely on coarse-grained ice charts. Evaluated on a dataset comprising 43 scenes with 2,088 pixel-level labels derived from 7,046 expert-annotated points, experiments demonstrate significantly improved segmentation accuracy after alignment, effectively achieving robust generalization from sparse annotations to dense predictions.
0 citationsRead paper
ConSens: Assessing context grounding in open-book question answering
In open-book question answering, existing evaluation methods suffer from bias, poor scalability, and reliance on external systems, hindering accurate measurement of a model’s contextual dependency. To address this, we propose ConSens—a novel metric that quantifies a language model’s “context anchoring ability” by measuring the relative perplexity difference between context-aware and context-agnostic generations—using the model itself as both evaluator and generator. ConSens requires no fine-tuning, eliminates dependence on external judge models, and supports zero-shot, cross-model evaluation with high interpretability. Extensive experiments across multiple datasets demonstrate that ConSens achieves strong correlation with human judgments (Spearman’s ρ > 0.85), significantly outperforms baselines such as LLM-as-a-judge in discriminative power, and reduces computational overhead by over 90%.
0 citationsRead paper