Information network contains abundant knowledge about relationships among people or entities. Unfortunately, such kind of knowledge is often hidden in a network where different kinds of relationships are not explicitly categorized. For example, in a research publication network, the advisor-advisee relationships among researchers are hidden in the coauthor network. Discovery of those relationships can benefit many interesting applications such as expert finding and research community analysis. In this paper, we take a computer science bibliographic network as an example, to analyze the roles of authors and to discover the likely advisoradvisee relationships. In particular, we propose a time-constrained probabilistic factor graph model (TPFG), which takes a research publication network as input and models the advisor-advisee relationship mining problem using a jointly likelihood objective function. We further design an efficient learning algorithm to optimize the objective function. Based on that our model suggests and ranks probable advisors for every author. Experimental results show that the proposed approach infer advisor-advisee relationships efficiently and achieves a state-of-the-art accuracy (80-90%). We also apply the discovered advisor-advisee relationships to a specific expert finding task and empirical study shows that the search performance can be effectively improved (+4.09% by NDCG@5).

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Video completion is the problem of automatically filling space-time holes in video sequences left by the removal of unwanted objects in a scene. We solve it using texture synthesis, filling a hole inwards using three steps iteratively: we select the most promising target pixel at the edge of the hole, we find the source fragment most similar to the known part of the target's neighborhood, and we merge source and target fragments to complete the target neighborhood, reducing the size of the hole. Earlier methods were slow, due to searching the whole video data for source fragments or completing holes pixel by pixel; they also produced blurred results due to sampling and smoothing. For speed, we track moving objects, allowing us to use a much smaller search space when seeking source fragments; we also complete holes fragment by fragment instead of pixelwise. Fine details are maintained by use of a graph cut algorithm when merging source and target fragments. Further techniques ensure temporal consistency of hole filling over successive frames. Examples demonstrate the effectiveness of our method.

Bunny: 3 simple shaders, sorted shading 5% slower.Car: 6 simple shaders, sorted shading 38% faster. Lucy: 2 simple and 2 procedural shaders, sorted shading 2.7× faster.Stairway: 3 simple and 2 procedural shaders, sorted shading 3.5× faster.Lab: 11 moderate shaders, sorted shading 51% faster. Figure 1: Efficient execution of multiple shaders poses a challenge for data parallel ray tracing and other deferred shading algorithms. We demonstrate that stream compaction can significantly increase SIMD shading efficiency by collecting and scheduling like shaders. AbstractThe GPU leverages SIMD efficiency when shading because it rasterizes a triangle at a time, running the same shader on all of its fragments. Ray tracing sacrifices this shader coherence, and the result is that SIMD units often must run different shaders simultaneously resulting in serialization. We study this problem and define a new measure called heterogeneous efficiency to measure SIMD divergence among multiple shaders of different complexities in a ray tracing application. We devise seven different algorithms for scheduling shaders onto SIMD processors to avoid divergence. In all but simply shaded scenes, we show the expense of sorting shaders pays off with better overall shading performance.

Generating soft shadows in real time is difficult. Exact methods (such as ray tracing, and multiple light source simulation) are too slow, while approximate methods often overestimate the umbra regions. In this paper, we introduce a new algorithm based on the shadow map method to quickly and highly accurately render soft shadows produced by a light source. Our method builds inner and outer translucent fins on objects to represent the penumbra area inside and outside hard shadows, respectively. The fins are traced into multilayered light space maps to store illuminance adjustment to shadows. The viewing space illuminance buffer is then calculated using those maps. Finally, by blending illuminance and shading, a scene with highly accurate soft shadow effects is produced. Our method does not suffer from umbra overestimation. Physical relations between light, objects and shadows demonstrate the soundness of our approach.