In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach. Our method, dubbed SiamMask, improves the offline training procedure of popular fully-convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task. Once trained, SiamMask solely relies on a single bounding box initialisation and operates online, producing class-agnostic object segmentation masks and rotated bounding boxes at 55 frames per second. Despite its simplicity, versatility and fast speed, our strategy allows us to establish a new state of the art among real-time trackers on VOT-2018, while at the same time demonstrating competitive performance and the best speed for the semisupervised video object segmentation task on DAVIS-2016 and DAVIS-2017. The project website is

Cerium oxide nanoparticles (CeO2-NPs) are increasingly used in polishing, engine enhancement agents and many other products. Even though the acute toxicity of CeO2-NPs to plants has been investigated, the long-term effects of CeO2-NPs in the environment are still unknown. The main objective of this study was to investigate whether the treatment of tomato plants with relatively low concentrations of CeO2-NPs (10 mg L(-1)) through their lifecycle would affect the seed quality and the development of second generation seedlings. The results indicated that second generation seedlings grown from seeds collected from treated parent plants with CeO2-NPs (treated second generation seedlings) were generally smaller and weaker, as indicated by their smaller biomass, lower water transpiration and slightly higher reactive oxygen species content. An interesting phenomenon noticed in the study was that the second generation seedlings grown from treated seeds developed extensive root hairs compared with the control second generation seedlings (seedlings grown from seeds collected from untreated parent plants) regardless of the treatment. Treated second generation seedlings also accumulate a higher amount of ceria than control second generation seedlings under the same treatment conditions even though such differences are not statistically significant.

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This survey gives a comprehensive overview of tensor techniques and applications in machine learning. Tensor represents higher order statistics. Nowadays, many applications based on machine learning algorithms require a large amount of structured high-dimensional input data. As the set of data increases, the complexity of these algorithms increases exponentially with the increase of vector size. Some scientists found that using tensors instead of the original input vectors can effectively solve these high-dimensional problems. This survey introduces the basic knowledge of tensor, including tensor operations, tensor decomposition, some tensor-based algorithms, and some applications of tensor in machine learning and deep learning for those who are interested in learning tensors. The tensor decomposition is highlighted because it can effectively extract structural features of data and many algorithms and applications are based on tensor decomposition. The organizational framework of this paper is as follows. In part one, we introduce some tensor basic operations, including tensor decomposition. In part two, applications of tensor in machine learning and deep learning, including regression, supervised classification, data preprocessing, and unsupervised classification based on low rank tensor approximation algorithms are introduced detailly. Finally, we briefly discuss urgent challenges, opportunities and prospects for tensor.