Characterizing anomalous diffusion is crucial in order to understand the evolution of complex stochastic systems, from molecular interactions to cellular dynamics. In this work, we characterize the performances regarding such a task of Bi-Mamba, a novel state-space deep-learning architecture articulated with a bidirectional scan mechanism. Our implementation is tested on the AnDi-2 challenge datasets among others. Designed for regression tasks, the Bi-Mamba architecture infers efficiently the effective diffusion coefficient and anomalous exponent from single, short trajectories. As such, our results indicate the potential practical use of the Bi-Mamba architecture for anomalousdiffusion characterization.

翻译：异常扩散的表征对于理解复杂随机系统（从分子相互作用到细胞动力学）的演化至关重要。本研究评估了Bi-Mamba——一种采用双向扫描机制的新型状态空间深度学习架构——在此类任务中的性能表现。我们的实现已在AnDi-2挑战数据集等多个数据集上进行测试。该Bi-Mamba架构专为回归任务设计，能够从单个短轨迹中高效推断有效扩散系数与异常指数。因此，我们的研究结果表明Bi-Mamba架构在异常扩散表征方面具有实际应用潜力。