Earth and space observations meet complex algebras: from complex to octonions for multivariate autoregressive time series analysis
论文提出了一种基于超复数自回归过程的框架，用于处理不规则时间间隔下的多变量时间序列分析问题，并通过卡尔曼滤波技术估计模型参数。
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Earth and space observations meet complex algebras: from complex to octonions for multivariate autoregressive time series analysis
论文提出了一种基于超复数自回归过程的框架，用于处理不规则时间间隔下的多变量时间序列分析问题，并通过卡尔曼滤波技术估计模型参数。
0 citationsRead paper
On the Limits of Quantum Multiparty Simultaneous Communication
本文研究了多玩家同时通信模型中经典通信与量子通信的界限问题，通过Index Coordination游戏的泛化版本展示了量子通信在无共享纠缠或公共随机性情况下的局限。
0 citationsRead paper
Classifying Directional Trajectories Near Criticality in the Three-State Majority-Vote Model with Deep Belief Networks and Bidirectional GRUs
研究使用DBN和Bi-GRU来区分三态多数投票模型中的四种动态轨迹类型，解决了基于静态样本训练的模型无法完全解析时间方向性结构的问题。
0 citationsRead paper
Turning spectra into images improves plant trait retrieval with 2D-CNNs
This study addresses the limitation of one-dimensional deep learning in capturing long-range dependencies within spectral data by proposing a 2D spectral reshaping strategy. Converting spectra into 2D images, this work integrates 2D-CNNs with masked autoencoders for multi-trait prediction. Results demonstrate that simple grid transformations outperform complex architectures, with model interpretability validated via Integrated Gradients. The direct reshaping method achieved an R² of 0.684, surpassing state-of-the-art performance by 0.097. Furthermore, self-supervised pretraining significantly exceeded 1D baselines, and identified key wavelength importance aligns with radiative transfer models. Collectively, these findings confirm that 2D reshaping effectively overcomes the constraints of traditional sequential processing in spectroscopic analysis.
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This study addresses the quality heterogeneity of LLM-generated samples by proposing a geometric filtering framework. The method leverages Euclidean distance in embedding space to select geometrically consistent samples and incorporates a soft-weighting mechanism for classifier training, demonstrating that simple distance metrics outperform complex multi-criteria strategies. Experiments across 13 text classification datasets show an average improvement of 2.61 percentage points, significantly surpassing SMOTE. Furthermore, the framework generalizes seamlessly to named entity recognition tasks without modification, achieving a 9.26 percentage point gain and exhibiting strong cross-model robustness. These findings establish geometric filtering as an efficient paradigm for low-resource data augmentation, highlighting the efficacy of geometric consistency over elaborate selection heuristics in enhancing synthetic data utility.
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