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

Bifurcation analysis of a population model and the resulting SIS epidemic model with delay

AbstractThis paper deals with the model for matured population growth proposed in Cooke et al. [Interaction of matiration delay and nonlinear birth in population and epidemic models, J. Math. Biol. 39 (1999) 332–352] and the resulting SIS epidemic model. The dynamics of these two models are still largely undetermined, and in this paper, we perform some bifurcation analysis to the models. By applying the global bifurcation theory for functional differential equations, we are able to show that the population model allows multiple periodic solutions. For the SIS model, we obtain some local bifurcation results and derive formulas for determining the bifurcation direction and the stability of the bifurcated periodic solution

Wide-field fast-scanning photoacoustic microscopy of brain functions in action

We have developed fast functional photoacoustic microscopy for 3D high-resolution high-speed imaging of the mouse brain. In particular, a novel single-wavelength pulse-width-based method can image blood oxygenation with capillary-level resolution at 100 kHz frame rate

M2^2DQN: A Robust Method for Accelerating Deep Q-learning Network

Deep Q-learning Network (DQN) is a successful way which combines reinforcement learning with deep neural networks and leads to a widespread application of reinforcement learning. One challenging problem when applying DQN or other reinforcement learning algorithms to real world problem is data collection. Therefore, how to improve data efficiency is one of the most important problems in the research of reinforcement learning. In this paper, we propose a framework which uses the Max-Mean loss in Deep Q-Network (M$^2$DQN). Instead of sampling one batch of experiences in the training step, we sample several batches from the experience replay and update the parameters such that the maximum TD-error of these batches is minimized. The proposed method can be combined with most of existing techniques of DQN algorithm by replacing the loss function. We verify the effectiveness of this framework with one of the most widely used techniques, Double DQN (DDQN), in several gym games. The results show that our method leads to a substantial improvement in both the learning speed and performance.Comment: 5 page

Rapid changes in heatwaves pose dual challenge in Eastern China and its adjacent seas

This paper performs a comparative analysis of the spatiotemporal variations of the statistical characteristics of both atmospheric heatwaves over the land (AHWs) in eastern China and marine heatwaves (MHWs) in adjacent seas using a unified heatwave definition. The multi-year average total days and frequency of MHWs during 1982-2019 were 5 and 2 times higher than those of AHWs, respectively, while the mean intensities of AHWs and MHWs were unchanged. The future frequency and duration of AHWs will continue to increase, leading to a superimposed increase in AHW total days. The decreasing frequency and increasing duration of MHWs will result in nearly year-round MHWs from 2060. Under the control of high-pressure systems, clear skies dominate the summer weather conditions in eastern China and its adjacent seas, which will trigger heatwaves. Heatwaves in turn can release substantial ocean latent heat. Enhanced convection and heating will further drive a stronger anticyclone over the western North Pacific, leading to a stronger and more westwardextending western North Pacific subtropical high (WNPSH). Moreover, super El Niño can promote an anomalous WNPSH in decaying summer, which may cause more serious heatwaves. The multi-year average persons affected by AHWs (PAHWs) during 1982-2019 were larger in the North China Plain, Yangtze River Delta, and Sichuan Basin with the regional sum exceeding 3 million. The future maximum PAHWs under SSP2-4.5 and SSP5-8.5 scenarios will be 3.9 billion in 2076 and 4.7 billion in 2085, respectively. Marine ecosystems like artificial ranches and coral reefs will be more threatened by longerlasting MHWs