Deep Attentive Time Warping

Similarity measures for time series are important problems for time series classification. To handle the nonlinear time distortions, Dynamic Time Warping (DTW) has been widely used. However, DTW is not learnable and suffers from a trade-off between robustness against time distortion and discriminative power. In this paper, we propose a neural network model for task-adaptive time warping. Specifically, we use the attention model, called the bipartite attention model, to develop an explicit time warping mechanism with greater distortion invariance. Unlike other learnable models using DTW for warping, our model predicts all local correspondences between two time series and is trained based on metric learning, which enables it to learn the optimal data-dependent warping for the target task. We also propose to induce pre-training of our model by DTW to improve the discriminative power. Extensive experiments demonstrate the superior effectiveness of our model over DTW and its state-of-the-art performance in online signature verification.Comment: Accepted at Pattern Recognitio

Magnetotransport of carbon nanotubes: magnetic-field-induced metal-insulator transition

We have measured magnetotransport of an individual multi-walled carbon nanotube. Though the resistance without magnetic field increases with decreasing temperature (non-metallic) from room temperature down to 2 K, it becomes metallic below 30 K by applying a magnetic field (H ≒ 4 T) perpendicular to the nanotube axis. The transverse magnetoresistance (TMR) below 30 K decreases with increasing magnetic field, and after reaching minimum value around 4 T, it increases. On the other hand, under the intense magnetic field above 6 T the TMR does not show continuous increase but tends to be saturated. The results can be explained by the theoretical prediction of the magnetic-field-induced metal-insulator transition of semiconducting carbon nanotubes