End-to-end Flow Correlation Tracking with Spatial-temporal Attention

Discriminative correlation filters (DCF) with deep convolutional features have achieved favorable performance in recent tracking benchmarks. However, most of existing DCF trackers only consider appearance features of current frame, and hardly benefit from motion and inter-frame information. The lack of temporal information degrades the tracking performance during challenges such as partial occlusion and deformation. In this work, we focus on making use of the rich flow information in consecutive frames to improve the feature representation and the tracking accuracy. Firstly, individual components, including optical flow estimation, feature extraction, aggregation and correlation filter tracking are formulated as special layers in network. To the best of our knowledge, this is the first work to jointly train flow and tracking task in a deep learning framework. Then the historical feature maps at predefined intervals are warped and aggregated with current ones by the guiding of flow. For adaptive aggregation, we propose a novel spatial-temporal attention mechanism. Extensive experiments are performed on four challenging tracking datasets: OTB2013, OTB2015, VOT2015 and VOT2016, and the proposed method achieves superior results on these benchmarks.Comment: Accepted in CVPR 201

Structural Embedding of Syntactic Trees for Machine Comprehension

Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods

TRPA1 mediates sensation of the rate of temperature change in Drosophila larvae.

Avoidance of noxious ambient heat is crucial for survival. A well-known phenomenon is that animals are sensitive to the rate of temperature change. However, the cellular and molecular underpinnings through which animals sense and respond much more vigorously to fast temperature changes are unknown. Using Drosophila larvae, we found that nociceptive rolling behavior was triggered at lower temperatures and at higher frequencies when the temperature increased rapidly. We identified neurons in the brain that were sensitive to the speed of the temperature increase rather than just to the absolute temperature. These cellular and behavioral responses depended on the TRPA1 channel, whose activity responded to the rate of temperature increase. We propose that larvae use low-threshold sensors in the brain to monitor rapid temperature increases as a protective alert signal to trigger rolling behaviors, allowing fast escape before the temperature of the brain rises to dangerous levels