No abstract

We introduce the first method for automatic image generation from scene-level freehand sketches. Our model allows for controllable image generation by specifying the synthesis goal via freehand sketches. The key contribution is an attribute vector bridged Generative Adversarial Network called EdgeGAN, which supports high visual-quality object-level image content generation without using freehand sketches as training data. We have built a largescale composite dataset called SketchyCOCO to support and evaluate the solution. We validate our approach on the tasks of both object-level and scene-level image generation on SketchyCOCO. Through quantitative, qualitative results, human evaluation and ablation studies, we demonstrate the method's capacity to generate realistic complex scene-level images from various freehand sketches.

No abstract

In this paper, we provide a detailed analysis on the venue popularity in Foursquare, a leading location-based social network. By collecting 2.4 million venues from 14 geographic regions all over the world, we study the common characteristics of popular venues, and make the following observations. First, venues with more complete profile information are more likely to be popular. Second, venues in the Food category attract the most (43%) public tips (comments) by users, and the Travel & Transport category is the most popular category with the highest per venue check-ins, i.e., each venue in this category attracts on average 376 check-ins. Moreover, the stickiness of users checking in venues in the residence, office, and school categories is higher than in other categories. Last but not least, in general, old venues created at the early stage of Foursquare are more popular than new venues. Our results help to understand the factors that cause venues to become popular, and have applications in venue recommendations and advertisement in location based social networks.

No abstract

No abstract

This paper develops the optimal fault-tolerant guaranteed cost control scheme for a batch process with actuator failures. Based on an equivalent two-dimensional Fornasini-Marchsini (2D-FM) model description of a batch process, the relevant concepts of the fault-tolerant guaranteed cost control are introduced. The robust iterative learning reliable guaranteed cost controller (ILRGCC), which includes a robust extended feedback control for ensuring the performances over time and an iterative learning control (ILC) for improving the tracking performance from cycle to cycle, is formulated such that it cannot only guarantee the closed-loop convergency along both the time and the cycle directions but also satisfy both theH∞performance level and a cost function having upper bounds for all admissible uncertainties and any actuator failures. Conditions for the existence of the controller are derived in terms of linear matrix inequalities (LMIs), and a design procedure of the controller is presented. Furthermore, a convex optimization problem with LMI constraints is formulated to design the optimal guaranteed cost controller which minimizes the upper bound of the closed-loop system cost. Finally, an illustrative example of injection molding is given to demonstrate the effectiveness and advantages of the proposed 2D design approach.

Abstract. In this paper, we introduce and discuss the notion of D Cprojective modules over commutative rings, where C is a semidualizing module. This extends Gillespie and Ding, Mao's notion of Ding projective modules. The properties of D C -projective dimensions are also given.