A Latent Oscillator Measurement Model to Simulate Emotional-Expression Score Dynamics in Video
该研究通过引入潜振荡测量模型(LOMM)来模拟视频中的情感表达分数动态,以解决分类器、视频和记录条件带来的测量误差问题。
该研究通过引入潜振荡测量模型(LOMM)来模拟视频中的情感表达分数动态,以解决分类器、视频和记录条件带来的测量误差问题。
本文通过应用超参数优化(特别是多保真贪婪坐标搜索方法)来改进多目标跟踪中的手动调参问题,从而提高跟踪性能。
本文针对金融市场的不稳定性,提出基于修正Hankel变换和Laplace变换的两类新的非参数变点检测方法,并通过模拟和实际数据验证了其有效性。
This work proposes a systematic methodology for constructing high-quality training corpora for South Slavic language models from raw Wikimedia data. Starting with multilingual Wiki project texts, the approach first parses Wiki markup to extract natural language content and then employs an n-gram–based redundancy detection mechanism to effectively filter out highly repetitive, low-information articles. This pipeline substantially enhances the linguistic richness and authenticity of the resulting corpus while maintaining cross-lingual applicability. The final resource encompasses seven South Slavic languages, offering a reliable foundation for large language model training and cross-linguistic comparative studies.
This study addresses the challenge of testing variable independence in high-dimensional data with missing values by extending the nonparametric Kendall’s rank correlation framework to settings involving incomplete observations. The authors propose two novel corrected test statistics specifically designed to accommodate missingness, effectively integrating high-dimensional inference with explicit modeling of the missing data mechanism. Theoretical analysis establishes the statistical validity of the proposed methods, while extensive simulations demonstrate their robustness and high power across various missingness mechanisms—including both missing at random and not missing at random scenarios. This work substantially enhances the reliability and applicability of independence testing in high-dimensional settings where data incompleteness is prevalent.
该研究通过引入潜振荡测量模型(LOMM)来模拟视频中的情感表达分数动态,以解决分类器、视频和记录条件带来的测量误差问题。
本文通过应用超参数优化(特别是多保真贪婪坐标搜索方法)来改进多目标跟踪中的手动调参问题,从而提高跟踪性能。
本文针对金融市场的不稳定性,提出基于修正Hankel变换和Laplace变换的两类新的非参数变点检测方法,并通过模拟和实际数据验证了其有效性。
This work proposes a systematic methodology for constructing high-quality training corpora for South Slavic language models from raw Wikimedia data. Starting with multilingual Wiki project texts, the approach first parses Wiki markup to extract natural language content and then employs an n-gram–based redundancy detection mechanism to effectively filter out highly repetitive, low-information articles. This pipeline substantially enhances the linguistic richness and authenticity of the resulting corpus while maintaining cross-lingual applicability. The final resource encompasses seven South Slavic languages, offering a reliable foundation for large language model training and cross-linguistic comparative studies.
This study addresses the challenge of testing variable independence in high-dimensional data with missing values by extending the nonparametric Kendall’s rank correlation framework to settings involving incomplete observations. The authors propose two novel corrected test statistics specifically designed to accommodate missingness, effectively integrating high-dimensional inference with explicit modeling of the missing data mechanism. Theoretical analysis establishes the statistical validity of the proposed methods, while extensive simulations demonstrate their robustness and high power across various missingness mechanisms—including both missing at random and not missing at random scenarios. This work substantially enhances the reliability and applicability of independence testing in high-dimensional settings where data incompleteness is prevalent.