Creating controllable, responsive avatars is an important problem in computer games and virtual environments. Recently, large collections of motion capture data have been exploited for increased realism in avatar animation and control. Large motion sets have the advantage of accommodating a broad variety of natural human motion. However, when a motion set is large, the time required to identify an appropriate sequence of motions is the bottleneck for achieving interactive avatar control. In this paper, we present a novel method of precomputing avatar behavior from unlabelled motion data in order to animate and control avatars at minimal runtime cost. Based on dynamic programming, our method finds a control policy that indicates how the avatar should act in any given situation. We demonstrate the effectiveness of our approach through examples that include avatars interacting with each other and with the user.

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Given a pair of keyframe formations for a group consisting of multiple individuals, we present a spectral-based approach to smoothly transforming a source group formation into a target formation while respecting the clusters of the involved individuals. The proposed method provides an effective means for controlling the macroscopic spatiotemporal arrangement of individuals for applications such as expressive formations in mass performances and tactical formations in team sports. Our main idea is to formulate this problem as rotation interpolation of the eigenbases for the Laplacian matrices, each of which represents how the individuals are clustered in a given keyframe formation. A stream of time-varying formations is controlled by editing the underlying adjacencyrelationships among individuals as well as their spatial positions at each keyframe, and interpolating the keyframe formations while producing plausible collective behaviors over a period of time. An interactive system of editing existing group behaviors in a hierarchical fashion has been implemented to provide flexible formation control of large crowds.

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Animating a crowd of characters is an important problem in computer graphics. The latest techniques enable highly realistic group motions to be produced in feature animation films and video games. However, interactive methods have not emerged yet for editing the existing group motion of multiple characters. We present an approach to editing group motion as a whole while maintaining its neighborhood formation and individual moving trajectories in the original animation as much as possible. The user can deform a group motion by pinning or dragging individuals. Multiple group motions can be stitched or merged to form a longer or larger group motion while avoiding collisions. These editing operations rely on a novel graph structure, in which vertices represent positions of individuals at specific frames and edges encode neighborhood formations and moving trajectories. We employ a shape-manipulation technique to minimize the distortion of relative arrangements among adjacent vertices while editing the graph structure. The usefulness and flexibility of our approach is demonstrated through examples in which the user creates and edits complex crowd animations interactively using a collection of group motion clips.

Skeletal muscle metastases show good enhancement of contrast medium and frequent edema and necrosis. The possibility of skeletal muscle metastases should be borne in mind for patients with painful and multiple muscle masses.

BackgroundThe eradication rate of Helicobacter pylori (H. pylori) with triple therapy which was considered as standard first-line treatment has decreased to 70–85%. The aim of this study is to compare 7-day triple therapy versus 10-day sequential therapy as the first line treatment.MethodsData of 1240 H. pylori positive patients treated with triple therapy or sequential therapy from January 2013 to December 2015 were analyzed retrospectively. The patients who had undertaken previous H. pylori eradication therapy or gastric surgery were excluded.ResultsThere were 872 (74.3%) patients in the triple therapy group, and 302 (25.7%) patients in the sequential therapy group. There was no significant difference between the two groups regarding age, residence, comorbidities or drug compliance, but several differences were noted in endoscopic characteristics and indication for the treatment. The eradication rate of H. pylori by intention to treat analysis was 64.3% in the triple therapy group, and 81.9% in the sequential therapy group (P = 0.001). In per protocol analysis, H. pylori eradication rate in the triple therapy and sequential therapy group was 81.9 and 90.3%, respectively (P = 0.002). There was no significant difference in overall adverse events between the two groups (P = 0.706). For the rescue therapy, bismuth-containing quadruple therapy showed comparable treatment efficacy after sequential therapy, as following triple therapy.ConclusionsThe eradication rate of triple therapy was below the recommended threshold. Sequential therapy could be effective and tolerable candidate for the first-line H. pylori eradication therapy.

Figure 1: Morphable crowd models synthesize virtual crowds of any size and any length from input crowd data. The synthesized crowds can be interpolated to produce a continuous span of intervening crowd styles. AbstractCrowd simulation has been an important research field due to its diverse range of applications that include film production, military simulation, and urban planning. A challenging problem is to provide simple yet effective control over captured and simulated crowds to synthesize intended group motions. We present a new method that blends existing crowd data to generate a new crowd animation. The new animation can include an arbitrary number of agents, extends for an arbitrary duration, and yields a naturallooking mixture of the input crowd data. The main benefit of this approach is to create new spatio-temporal crowd behavior in an intuitive and predictable manner. It is accomplished by introducing a morphable crowd model that allows us to encode the formations and individual trajectories in crowd data. Then, its original spatiotemporal behavior can be reconstructed and interpolated at an arbitrary scale using our morphable model.