Color Complexity of Recolorable Graph Exploration: Upper and Lower Bounds via Block Structure
研究无内存代理通过可重着色图探索问题，利用块结构确定最优颜色数，并给出相应算法及上下界。
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The Maximum Mutual Visibility Set on a Cactus Graph and the Self-stabilizing Constructions
本文解决了仙人掌图中最大互视集问题，提出两种自稳定算法构建该集合，分别基于单个BFS树和平行BFS树。
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Shared-Memory Range-Tiled CDF Sort for Small-Range Integer Keys on GPUs
研究了GPU上小范围整数键的不稳定排序问题，提出并评估了Range-Tiled CDF排序方法，通过将值范围划分为适合共享内存的小区间来提高性能。
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Basins of Attraction to Multiple Fixed Points in Discrete-time Hysteresis Neural Networks
本文研究了离散时间滞后神经网络中的多稳态问题，通过调整阈值参数控制吸引域大小分布，并以二进制数据分类为例进行评估。
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GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers
Traditional approaches to graphical abstract generation produce raster images that are difficult to edit and fail to support the iterative demands of academic writing. This work proposes GenGA, a novel framework that formulates graphical abstract generation as an editable vector graphic synthesis task, directly producing a hierarchically structured set of vector elements from paper content, which can be edited at the element level in mainstream illustration software. We introduce the Structure Independence Coefficient (SIC) to quantitatively assess editability and develop an end-to-end system integrating vision–language models with vector graphic generation. Experiments demonstrate that GenGA surpasses existing methods in editability and outperforms human-created abstracts in conciseness and semantic alignment; furthermore, SIC strongly correlates with human editing effort.
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Color Complexity of Recolorable Graph Exploration: Upper and Lower Bounds via Block Structure
研究无内存代理通过可重着色图探索问题，利用块结构确定最优颜色数，并给出相应算法及上下界。
0 citationsRead paper
The Maximum Mutual Visibility Set on a Cactus Graph and the Self-stabilizing Constructions
本文解决了仙人掌图中最大互视集问题，提出两种自稳定算法构建该集合，分别基于单个BFS树和平行BFS树。
0 citationsRead paper
Shared-Memory Range-Tiled CDF Sort for Small-Range Integer Keys on GPUs
研究了GPU上小范围整数键的不稳定排序问题，提出并评估了Range-Tiled CDF排序方法，通过将值范围划分为适合共享内存的小区间来提高性能。
0 citationsRead paper
Basins of Attraction to Multiple Fixed Points in Discrete-time Hysteresis Neural Networks
本文研究了离散时间滞后神经网络中的多稳态问题，通过调整阈值参数控制吸引域大小分布，并以二进制数据分类为例进行评估。
0 citationsRead paper
GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers
Traditional approaches to graphical abstract generation produce raster images that are difficult to edit and fail to support the iterative demands of academic writing. This work proposes GenGA, a novel framework that formulates graphical abstract generation as an editable vector graphic synthesis task, directly producing a hierarchically structured set of vector elements from paper content, which can be edited at the element level in mainstream illustration software. We introduce the Structure Independence Coefficient (SIC) to quantitatively assess editability and develop an end-to-end system integrating vision–language models with vector graphic generation. Experiments demonstrate that GenGA surpasses existing methods in editability and outperforms human-created abstracts in conciseness and semantic alignment; furthermore, SIC strongly correlates with human editing effort.
0 citationsRead paper