Pictorial visualizations portray data with figurative messages and approximate the audience to the visualization. Previous research on pictorial visualizations has developed authoring tools or generation systems, but their methods are restricted to specific visualization types and templates. Instead, we propose to augment pictorial visualization authoring with visual style transfer, enabling a more extensible approach to visualization design. To explore this, our work presents Vistylist, a design support tool that disentangles the visual style of a source pictorial visualization from its content and transfers the visual style to one or more intended pictorial visualizations. We evaluated Vistylist through a survey of example pictorial visualizations, a controlled user study, and a series of expert interviews. The results of our evaluation indicated that Vistylist is useful for creating expressive and faithful pictorial visualizations.

As an effective form of narrative visualization, visual data stories are widely used in data-driven storytelling to communicate complex insights and support data understanding. Although important, they are difficult to create, as a variety of interdisciplinary skills, such as data analysis and design, are required. In this work, we introduce Erato, a human-machine cooperative data story editing system, which allows users to generate insightful and fluent data stories together with the computer. Specifically, Erato only requires a number of keyframes provided by the user to briefly describe the topic and structure of a data story. Meanwhile, our system leverages a novel interpolation algorithm to help users insert intermediate frames between the keyframes to smooth the transition. We evaluated the effectiveness and usefulness of the Erato system via a series of evaluations including a Turing test, a controlled user study, a performance validation, and interviews with three expert users. The evaluation results showed that the proposed interpolation technique was able to generate coherent story content and help users create data stories more efficiently.

Summary The fast Fourier transform (FFT) based on modular arithmetic can compute convolution without round‐off errors, which is desirable in many applications such as computational algebra and combinatory pattern matching. One of the critical challenges of the FFT is to enhance the performance. An effective approach is to optimize the high‐cost operations. Modular reduction is one of the most frequently used high‐cost operations that is a bottleneck of the FFT using modular arithmetic. In this article, we present three modular reduction methods and apply them to the implementation of the FFT. We use the strategy of delaying the modular reduction in the first method. We apply the Montgomery reduction to the FFT in the second method. The two methods both first transform the input, and then replace the modular reductions with lightweight replacements, and apply the reverse transform in the output stage to compute the right results. In the third method, we design an efficient modular reduction for the specific form of modular used in FFT. Experiments show that the incorporation of the new modular reductions speedups the FFT based on modular arithmetic.

Abstract In this article, we present an improved hybrid algorithm based on ant colony optimization and the polycephalum algorithm. First, we use improved the probability selection mechanism in the ant colony algorithm in order to improve the efficiency of next point searching. Second, in each iteration we update the pheromone concentration of the optimal route by using the polycephalum algorithm. We regard the starting point of the optimal route as the water injection point and the end point as the water outlet point. The hybrid algorithm is compared on multiple TSPLIB problems and flexible job shop scheduling problems. And experiments show that the improved algorithm has good application results and resultful in accuracy and optimal solutions.