Yuejie Zhang — Page 3

arXiv2021

Large Magneto-Electric Resistance in the Topological Dirac Semimetal alpha Sn

The spin-momentum locking of surface states in topological quantum materials can produce a resistance that scales linearly with magnetic and electric fields. Such a bilinear magneto-electric resistance (BMER) effect offers a completely new approach for magnetic storage and magnetic field sensing applications. The effects demonstrated so far, however, are relatively weak or for low temperatures. Strong room-temperature BMER effects have now been found in topological Dirac semimetal alpha-Sn thin films. The epitaxial alpha-Sn films were grown by sputtering on silicon substrates. They showed BMER responses that are 10^6 times larger than previously reported at room temperature and also larger than that previously reported at low temperatures. These results represent a major advance toward realistic BMER applications. The data also made possible the first characterization of the three-dimensional, Fermi-level spin texture of topological surface states in alpha-Sn.

cond-mat.mes-hall↗

arXiv2020

Look, Listen, and Attend: Co-Attention Network for Self-Supervised Audio-Visual Representation Learning

When watching videos, the occurrence of a visual event is often accompanied by an audio event, e.g., the voice of lip motion, the music of playing instruments. There is an underlying correlation between audio and visual events, which can be utilized as free supervised information to train a neural network by solving the pretext task of audio-visual synchronization. In this paper, we propose a novel self-supervised framework with co-attention mechanism to learn generic cross-modal representations from unlabelled videos in the wild, and further benefit downstream tasks. Specifically, we explore three different co-attention modules to focus on discriminative visual regions correlated to the sounds and introduce the interactions between them. Experiments show that our model achieves state-of-the-art performance on the pretext task while having fewer parameters compared with existing methods. To further evaluate the generalizability and transferability of our approach, we apply the pre-trained model on two downstream tasks, i.e., sound source localization and action recognition. Extensive experiments demonstrate that our model provides competitive results with other self-supervised methods, and also indicate that our approach can tackle the challenging scenes which contain multiple sound sources.

cs.MM↗