利用预训练计算机视觉模型处理反常扩散轨迹的格拉姆角场方法
Gramian Angular Fields for leveraging pretrained computer vision models with anomalous diffusion trajectories
AI总结：
本研究提出利用格拉姆角场将一维反常扩散轨迹编码为保留时空结构的图像，结合ResNet、MobileNet预训练模型表征扩散机制、推断扩散指数α，在短轨迹任务上超越现有水平并提升方法易用性。
摘要：
反常扩散存在于从原子尺度到大尺度的所有尺度中，典型系统包括超冷原子、细胞核内的端粒、水泥基材料中的水分传输、节肢动物的自由运动以及鸟类的迁徙模式。扩散的表征能为这些系统的动力学提供关键信息，并为研究扩散输运提供跨学科框架。因此，识别潜在的扩散机制并高置信度地推断反常扩散指数α，对物理学、化学、生物学和生态学至关重要。在Anomalous Diffusion Challenge（反常扩散挑战赛，Munoz-Gil等人，2021）中，结合机器学习技术与从原始轨迹提取的统计量来对轨迹进行分类和分析已得到广泛研究。本文提出了一种新的数据驱动方法用于处理扩散轨迹。该方法利用Gramian Angular Fields（格拉姆角场，GAF）将一维轨迹编码为图像（格拉姆矩阵），同时保留其时空结构，作为计算机视觉模型的输入。这使我们能够利用两个成熟的预训练计算机视觉模型ResNet和MobileNet，来表征潜在的扩散机制并推断反常扩散指数α。长度在10到50之间的短原始轨迹在单粒子跟踪实验中很常见，也是最难表征的。我们证明，通过使用GAF图像，我们的方法性能优于当前最先进水平，同时提升了机器学习方法在应用场景中的易用性。
英文摘要：
Anomalous diffusion is present at all scales, from atomic to large scales. Some exemplary systems are; ultra-cold atoms, telomeres in the nucleus of cells, moisture transport in cement-based materials, the free movement of arthropods, and the migration patterns of birds. The characterization of the diffusion gives critical information about the dynamics of these systems and provides an interdisciplinary framework with which to study diffusive transport. Thus, the problem of identifying underlying diffusive regimes and inferring the anomalous diffusion exponent {$α$} with high confidence is critical to physics, chemistry, biology, and ecology. Classification and analysis of raw trajectories combining machine learning techniques with statistics extracted from them have widely been studied in the Anomalous Diffusion Challenge ge (Munoz-Gil et al., 2021). Here we present a new data-driven method for working with diffusive trajectories. This method utilizes Gramian Angular Fields (GAF) to encode one-dimensional trajectories as images (Gramian Matrices), while preserving their spatiotemporal structure for input to computer-vision models. This allows us to leverage two well-established pre-trained computer-vision models, ResNet and MobileNet, to characterize the underlying diffusive regime, and infer the anomalous diffusion exponent {$α$}. Short raw trajectories, of lengths between 10 and 50, are commonly encountered in single-particle tracking experiments and are the most difficult to characterize. We show that by using GAF images, we can outperform the current state-of-the-art while increasing accessibility to machine learning methods in an applied setting.
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