Joint Freshness and Age-Dispersion Control over Finite-State Markov Wireless Channels
研究通过有限状态马尔可夫无线信道控制信息新鲜度和分散度，以最小化年龄或分散度超过阈值的概率，使用随机传输策略并将其建模为约束马尔可夫决策过程。
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Selected work
Representative Papers
A Pathway to General-Purpose Scientific AI: Multimodal Comprehension of Scientific Images
This study addresses the challenges of scientific figure retrieval and in-depth interpretation by proposing an "Image Scientific Concept Understanding" objective alongside a Bloom’s Taxonomy-oriented cognitive framework. Through the construction of the ALD/E-ImageMiner benchmark, comprising 1,951 expert-annotated images, this work systematically evaluates multimodal understanding, table extraction, and evidential reasoning capabilities. Furthermore, the project establishes the task framework for the ICDAR 2026 competition, thereby bridging the gap in verifiable multimodal scientific AI evaluation. Ultimately, this research provides standardized benchmarks and methodological foundations to advance intelligent scientific figure analysis from mere perception to higher-order cognition.
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Consensus Clustering of Free-Viewing Gaze Data: New Insights into Human-Information Interaction
This study addresses the lack of systematic modeling of human–machine information interaction patterns in free-viewing eye-tracking data, which hinders the uncovering of deep associations between user behavior and stimulus characteristics. To this end, the paper proposes EnsembleGaze, an end-to-end unsupervised ensemble learning framework that introduces consensus subspace clustering and spectral biclustering to this domain for the first time. By leveraging statistical feature engineering of fixation distributions, EnsembleGaze performs consensus clustering on viewers, images, and their joint structures. The approach overcomes the limitations of traditional unidimensional analyses, demonstrating on public datasets that image groupings exhibit high consistency—reflecting stable ambient–focal viewing modes—whereas viewer groupings are context-dependent and can only be effectively recovered through joint modeling strategies such as biclustering.
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Joint Freshness and Age-Dispersion Control over Finite-State Markov Wireless Channels
研究通过有限状态马尔可夫无线信道控制信息新鲜度和分散度，以最小化年龄或分散度超过阈值的概率，使用随机传输策略并将其建模为约束马尔可夫决策过程。
0 citationsRead paper
A Pathway to General-Purpose Scientific AI: Multimodal Comprehension of Scientific Images
This study addresses the challenges of scientific figure retrieval and in-depth interpretation by proposing an "Image Scientific Concept Understanding" objective alongside a Bloom’s Taxonomy-oriented cognitive framework. Through the construction of the ALD/E-ImageMiner benchmark, comprising 1,951 expert-annotated images, this work systematically evaluates multimodal understanding, table extraction, and evidential reasoning capabilities. Furthermore, the project establishes the task framework for the ICDAR 2026 competition, thereby bridging the gap in verifiable multimodal scientific AI evaluation. Ultimately, this research provides standardized benchmarks and methodological foundations to advance intelligent scientific figure analysis from mere perception to higher-order cognition.
0 citationsRead paper
Consensus Clustering of Free-Viewing Gaze Data: New Insights into Human-Information Interaction
This study addresses the lack of systematic modeling of human–machine information interaction patterns in free-viewing eye-tracking data, which hinders the uncovering of deep associations between user behavior and stimulus characteristics. To this end, the paper proposes EnsembleGaze, an end-to-end unsupervised ensemble learning framework that introduces consensus subspace clustering and spectral biclustering to this domain for the first time. By leveraging statistical feature engineering of fixation distributions, EnsembleGaze performs consensus clustering on viewers, images, and their joint structures. The approach overcomes the limitations of traditional unidimensional analyses, demonstrating on public datasets that image groupings exhibit high consistency—reflecting stable ambient–focal viewing modes—whereas viewer groupings are context-dependent and can only be effectively recovered through joint modeling strategies such as biclustering.
0 citationsRead paper