Recognizable Picture Languages: Separating UREC from coUREC via Communication Complexity
通过引入通信复杂性提升技术，解决了Anselmo等人提出的关于可识别图像语言的UREC和coUREC分离问题。
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Selected work
Representative Papers
Ev-YOLO: Uncertainty-Aware Object Detection via a Unified Evidential Formulation
本文提出了一种基于证据理论的YOLOv8版本（Ev-YOLO），通过统一的证据框架处理分类和边界框回归任务，以提高在不确定环境下的物体检测可靠性。
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CFB-GBM v2.0: An Augmented Longitudinal Dataset for Multi-Modal Glioblastoma Segmentation, Radiomics, and RANO Progression Tracking
该研究通过使用预训练的nnU-Net模型完成GTV标注，提高了CFB-GBM数据集在多模态胶质母细胞瘤分割、放射组学及RANO进展跟踪中的完整性与实用性。
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Generalized random forest for extreme quantile regression
Traditional quantile regression methods fail in extreme quantile regression due to sparse tail data and the highly nonlinear, complex structure of conditional quantile functions. To address this, we propose a novel method integrating extreme value theory with statistical learning: it models the covariate-dependent generalized extreme value (GEV) distribution using generalized random forests (GRF) and estimates the conditional tail distribution via the block maxima approach. Our key contribution is the first incorporation of GRF into the extreme quantile regression framework, enabling adaptive, nonparametric modeling of tail characteristics under high-dimensional and heterogeneous covariates. Extensive simulations and empirical analysis on U.S. Fort Collins meteorological data demonstrate that our method substantially outperforms existing quantile regression and extreme-value learning approaches—particularly for quantiles above the 99th percentile, where prediction accuracy improves markedly.
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Task Allocation of UAVs for Monitoring Missions via Hardware-in-the-Loop Simulation and Experimental Validation
To address low task allocation efficiency and the disconnect between theoretical models and actual energy consumption in industrial multi-UAV monitoring, this paper proposes a hybrid optimization framework integrating genetic algorithms with 2-Opt local search, coupled with a hardware-in-the-loop (HIL) simulation platform supporting multi-UAV coordination. By incorporating realistic flight dynamics and high-fidelity battery models, the framework significantly enhances the physical realizability of path planning. Empirical validation demonstrates strong correlation (>0.96) between the optimized theoretical cost function and measured battery depletion and flight time. In real-world industrial deployments, the approach improves task allocation efficiency by 23.5% while maintaining operational feasibility. This work is the first to systematically reveal the strong coupling between abstract task allocation objectives and hardware-level energy metrics, establishing a verifiable modeling and optimization paradigm for physics-aware intelligent UAV scheduling.
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Latest Papers
Recognizable Picture Languages: Separating UREC from coUREC via Communication Complexity
通过引入通信复杂性提升技术，解决了Anselmo等人提出的关于可识别图像语言的UREC和coUREC分离问题。
0 citationsRead paper
Ev-YOLO: Uncertainty-Aware Object Detection via a Unified Evidential Formulation
本文提出了一种基于证据理论的YOLOv8版本（Ev-YOLO），通过统一的证据框架处理分类和边界框回归任务，以提高在不确定环境下的物体检测可靠性。
0 citationsRead paper
CFB-GBM v2.0: An Augmented Longitudinal Dataset for Multi-Modal Glioblastoma Segmentation, Radiomics, and RANO Progression Tracking
该研究通过使用预训练的nnU-Net模型完成GTV标注，提高了CFB-GBM数据集在多模态胶质母细胞瘤分割、放射组学及RANO进展跟踪中的完整性与实用性。
0 citationsRead paper
Generalized random forest for extreme quantile regression
Traditional quantile regression methods fail in extreme quantile regression due to sparse tail data and the highly nonlinear, complex structure of conditional quantile functions. To address this, we propose a novel method integrating extreme value theory with statistical learning: it models the covariate-dependent generalized extreme value (GEV) distribution using generalized random forests (GRF) and estimates the conditional tail distribution via the block maxima approach. Our key contribution is the first incorporation of GRF into the extreme quantile regression framework, enabling adaptive, nonparametric modeling of tail characteristics under high-dimensional and heterogeneous covariates. Extensive simulations and empirical analysis on U.S. Fort Collins meteorological data demonstrate that our method substantially outperforms existing quantile regression and extreme-value learning approaches—particularly for quantiles above the 99th percentile, where prediction accuracy improves markedly.
0 citationsRead paper
Task Allocation of UAVs for Monitoring Missions via Hardware-in-the-Loop Simulation and Experimental Validation
To address low task allocation efficiency and the disconnect between theoretical models and actual energy consumption in industrial multi-UAV monitoring, this paper proposes a hybrid optimization framework integrating genetic algorithms with 2-Opt local search, coupled with a hardware-in-the-loop (HIL) simulation platform supporting multi-UAV coordination. By incorporating realistic flight dynamics and high-fidelity battery models, the framework significantly enhances the physical realizability of path planning. Empirical validation demonstrates strong correlation (>0.96) between the optimized theoretical cost function and measured battery depletion and flight time. In real-world industrial deployments, the approach improves task allocation efficiency by 23.5% while maintaining operational feasibility. This work is the first to systematically reveal the strong coupling between abstract task allocation objectives and hardware-level energy metrics, establishing a verifiable modeling and optimization paradigm for physics-aware intelligent UAV scheduling.
0 citationsRead paper