SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

Vision-based vehicle detection approaches achieve incredible success in recent years with the development of deep convolutional neural network (CNN). However, existing CNN based algorithms suffer from the problem that the convolutional features are scale-sensitive in object detection task but it is common that traffic images and videos contain vehicles with a large variance of scales. In this paper, we delve into the source of scale sensitivity, and reveal two key issues: 1) existing RoI pooling destroys the structure of small scale objects, 2) the large intra-class distance for a large variance of scales exceeds the representation capability of a single network. Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales. First, we present a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects. Second, we present a multi-branch decision network to minimize the intra-class distance of features. These lightweight techniques bring zero extra time complexity but prominent detection accuracy improvement. The proposed techniques can be equipped with any deep network architectures and keep them trained end-to-end. Our SINet achieves state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on the KITTI benchmark and a new highway dataset, which contains a large variance of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS

Predictability of the corneal flap creation with the VisuMax femtosecond laser in LASIK

AIM: To observe the predictability of corneal flap creation with the VisuMax femtosecond laser and preliminarily analyze the factors correlated to the thickness and diameter of the flap. <p>METHODS: This retrospective case series study included 300 eyes of 150 consecutive patients. The eyes were assigned to two groups according to intended flap thickness, 100μm(204 eyes)and 110μm(96 eyes), which created with the VisuMax femtosecond laser. Intended flap diameters were 7.9mm and 8.3mm. Difference analysis of flap diameter and intended diameter as well as flap thickness and intended thickness were made. The data were analyzed with SPSS to sum up a multiple stepwise regression formula that could express their quantitative relationship. <p>RESULTS: The 100μm flap group had an average flap thickness of 103.11±4.07μm, while for the 110μm group the average flap thickness was 113.35±5.71μm. The difference between right and left eyes was not statistically significant(<i>t</i>_100μm =-0.901, <i>t</i> _110μm=-0.490; <i>P</i>>0.05). Corneal flap thickness was not related to flap diameter(<i>r</i>=0.003, 0.018; <i>P</i>>0.05), preoperative patient age(<i>r</i>=0.022, 0.050; <i>P</i>>0.05), corneal thickness(<i>r</i>=0.051, 0.101; <i>P</i>>0.05), keratometric value K(<i>r</i>=-0.048, -0.136; <i>P</i>>0.05)or intraocular pressure(<i>r</i>=-0.113, 0.047; <i>P</i>>0.05). Preoperative corneal keratometric value K was positively correlated with corneal flap diameter(<i>r</i>=0.359, 0.532; <i>P</i>=0.01, 0.007<0.05). <p>CONCLUSION:The LASIK flap creation with the VisuMax femtosecond laser has relatively good predictability. There is no influencing factor for flap thickness

Zebrafish foxo3b Negatively Regulates Canonical Wnt Signaling to Affect Early Embryogenesis

FOXO genes are involved in many aspects of development and vascular homeostasis by regulating cell apoptosis, proliferation, and the control of oxidative stress. In addition, FOXO genes have been showed to inhibit Wnt/β-catenin signaling by competing with T cell factor to bind to β-catenin. However, how important of this inhibition in vivo, particularly in embryogenesis is still unknown. To demonstrate the roles of FOXO genes in embryogenesis will help us to further understand their relevant physiological functions. Zebrafish foxo3b gene, an orthologue of mammalian FOXO3, was expressed maternally and distributed ubiquitously during early embryogenesis and later restricted to brain. After morpholino-mediated knockdown of foxo3b, the zebrafish embryos exhibited defects in axis and neuroectoderm formation, suggesting its critical role in early embryogenesis. The embryo-developmental marker gene staining at different stages, phenotype analysis and rescue assays revealed that foxo3b acted its role through negatively regulating both maternal and zygotic Wnt/β-catenin signaling. Moreover, we found that foxo3b could interact with zebrafish β-catenin1 and β-catenin2 to suppress their transactivation in vitro and in vivo, further confirming its role relevant to the inhibition of Wnt/β-catenin signaling. Taken together, we revealed that foxo3b played a very important role in embryogenesis and negatively regulated maternal and zygotic Wnt/β-catenin signaling by directly interacting with both β-catenin1 and β-catenin2. Our studies provide an in vivo model for illustrating function of FOXO transcription factors in embryogenesis

Enabling controlling complex networks with local topological information

Complex networks characterize the nature of internal/external interactions in real-world systems including social, economic, biological, ecological, and technological networks. Two issues keep as obstacles to fulflling control of large-scale networks: structural controllability which describes the ability to guide a dynamical system from any initial state to any desired fnal state in fnite time, with a suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost for driving the network to a predefned state with a given number of control inputs. For large complex networks without global information of network topology, both problems remain essentially open. Here we combine graph theory and control theory for tackling the two problems in one go, using only local network topology information. For the structural controllability problem, a distributed local-game matching method is proposed, where every node plays a simple Bayesian game with local information and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity. Starring from any structural controllability solution, a minimizing longest control path method can efciently reach a good solution for the optimal control in large networks. Our results provide solutions for distributed complex network control and demonstrate a way to link the structural controllability and optimal control together.The work was partially supported by National Science Foundation of China (61603209), and Beijing Natural Science Foundation (4164086), and the Study of Brain-Inspired Computing System of Tsinghua University program (20151080467), and Ministry of Education, Singapore, under contracts RG28/14, MOE2014-T2-1-028 and MOE2016-T2-1-119. Part of this work is an outcome of the Future Resilient Systems project at the Singapore-ETH Centre (SEC), which is funded by the National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. (61603209 - National Science Foundation of China; 4164086 - Beijing Natural Science Foundation; 20151080467 - Study of Brain-Inspired Computing System of Tsinghua University program; RG28/14 - Ministry of Education, Singapore; MOE2014-T2-1-028 - Ministry of Education, Singapore; MOE2016-T2-1-119 - Ministry of Education, Singapore; National Research Foundation of Singapore (NRF) under Campus for Research Excellence and Technological Enterprise (CREATE) programme)Published versio