TET-GAN: Text Effects Transfer via Stylization and Destylization

Text effects transfer technology automatically makes the text dramatically more impressive. However, previous style transfer methods either study the model for general style, which cannot handle the highly-structured text effects along the glyph, or require manual design of subtle matching criteria for text effects. In this paper, we focus on the use of the powerful representation abilities of deep neural features for text effects transfer. For this purpose, we propose a novel Texture Effects Transfer GAN (TET-GAN), which consists of a stylization subnetwork and a destylization subnetwork. The key idea is to train our network to accomplish both the objective of style transfer and style removal, so that it can learn to disentangle and recombine the content and style features of text effects images. To support the training of our network, we propose a new text effects dataset with as much as 64 professionally designed styles on 837 characters. We show that the disentangled feature representations enable us to transfer or remove all these styles on arbitrary glyphs using one network. Furthermore, the flexible network design empowers TET-GAN to efficiently extend to a new text style via one-shot learning where only one example is required. We demonstrate the superiority of the proposed method in generating high-quality stylized text over the state-of-the-art methods.Comment: Accepted by AAAI 2019. Code and dataset will be available at http://www.icst.pku.edu.cn/struct/Projects/TETGAN.htm

Functional linear regression via canonical analysis

We study regression models for the situation where both dependent and independent variables are square-integrable stochastic processes. Questions concerning the definition and existence of the corresponding functional linear regression models and some basic properties are explored for this situation. We derive a representation of the regression parameter function in terms of the canonical components of the processes involved. This representation establishes a connection between functional regression and functional canonical analysis and suggests alternative approaches for the implementation of functional linear regression analysis. A specific procedure for the estimation of the regression parameter function using canonical expansions is proposed and compared with an established functional principal component regression approach. As an example of an application, we present an analysis of mortality data for cohorts of medflies, obtained in experimental studies of aging and longevity.Comment: Published in at http://dx.doi.org/10.3150/09-BEJ228 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Utilizing Import Vector Machines to Identify Dangerous Pro-active Traffic Conditions

Traffic accidents have been a severe issue in metropolises with the development of traffic flow. This paper explores the theory and application of a recently developed machine learning technique, namely Import Vector Machines (IVMs), in real-time crash risk analysis, which is a hot topic to reduce traffic accidents. Historical crash data and corresponding traffic data from Shanghai Urban Expressway System were employed and matched. Traffic conditions are labelled as dangerous (i.e. probably leading to a crash) and safe (i.e. a normal traffic condition) based on 5-minute measurements of average speed, volume and occupancy. The IVM algorithm is trained to build the classifier and its performance is compared to the popular and successfully applied technique of Support Vector Machines (SVMs). The main findings indicate that IVMs could successfully be employed in real-time identification of dangerous pro-active traffic conditions. Furthermore, similar to the "support points" of the SVM, the IVM model uses only a fraction of the training data to index kernel basis functions, typically a much smaller fraction than the SVM, and its classification rates are similar to those of SVMs. This gives the IVM a computational advantage over the SVM, especially when the size of the training data set is large.Comment: 6 pages, 3 figures, 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC

Discrete Simulation of Gas-solid Flow and Softening-melting Behaviour in a Blast Furnace

The blast furnace is a complicated multiphase flow reactor with hazardous working conditions, and its understanding is still a challenge in research community. In the recent decades, the discrete element modelling is becoming a popular tool to study this process, especially for the particle related phenomena, such as gas-solid flow, particle softening-melting behaviour and gas-solid heat transfer. This work aims to develop some new and better methods to describe this process based on the discrete model. The discrete model shows some unique advantages in describing particle motion; however the high computing cost limits its application in the study of blast furnace. A sector model is successfully developed to represent the full 3D cylinder vessel, which can effectively reduce the number of particles and hence the computational cost. Its validity is first examined through two common industrial processes; hopper flow and pile formation. The results generated by the sector model are exactly the same as the full 3D model, but saved 90% computing time. Then, the sector model is applied to study the gas-solid flow in a blast furnace, and the comparison between the sector model and the slot model are given in detail. Understanding the particle softening and melting behavior in the cohesive zone is the basis to describe the gas/liquid distribution and thermal-chemical behavior in this zone, which is critical to understanding the complex physical and chemical phenomena in a blast furnace. The CFD-DEM method accompanying with the gas-particle heat transfer is one powerful tool to carry out this study. The softening and melting behaviour of wax particles is successfully captured, by implementing the correlation between Young’s modulus and temperature of wax. And the multi-layer behaviour is also studied and then a parametric study. Further, in order to study the heat transfer in the raceway of blast furnace, the gas-solid heat transfer based on the discrete model is first used in a moving bed. The simulation is quantitatively consistent with the previous experimental data, that demonstrating the capability to accurately describe the thermal phenomenon in the raceway