Retail space planning, arranging store sections and product placements to optimize customer flow and stimulate purchases helps retailers to increase sales and enhances the customer shopping experience. It can be challenging for retailers to arrange numerous products within limited shelf space. This paper introduces StoreSketcher, an interactive tool that assists retailers in planning retail layouts efficiently at macro and micro levels by providing intelligent suggestions. We have extracted commercial relationships between products and categories, built spatial rules for commercial objects, and developed an interactive framework for synthesizing retail layouts. When the user points to shelf space in the layout, StoreSketcher evaluates the spatial significance of the location and its commercial relation to the surrounding context to present appropriate suggestions. Quantitative experiments demonstrate that StoreSketcher significantly assists in planning well-organized retail layouts. The suggestions provided by StoreSketcher not only boost cross-selling and impulse purchasing for retailers, but also enhance product findability for customers.

Scene synthesis is crucial to computer graphics. However, the current interactive scene synthesis methods usually cost the user too much time and interactions to edit objects. This paper presents a new interactive scene synthesis method that alleviates the designer from interacting with the 3D scene and its objects. Designers only need to select an object group in an independent panel through coarse clustering and fine clustering. Then, the furniture objects will be automatically added to the scene. This paper proposes a two-level clustering that applies the Affinity Propagation Algorithm (APA) to groups of furniture objects such that the object groups can even be clustered without linear representations, latent encoding, etc. To fully apply the APA, we also propose quantitatively measuring how different the two layouts are, i.e., how quantitatively the arrangements of two object groups differ. Experiments first show that our method is more user-friendly and interactively efficient than other interactive synthesis methods. By comparing our method with recent automatic scene synthesis methods, we demonstrate that our methods still have competitive plausibility. We also verify that our method does not harm the diversity and generalization of 3D scenes.

We discuss a general method to forecast movements of the chaos. The method is based on functions of multi layer neural networks and a recurrent representation of functions. We are sure that forecasting of the chaos is an important problem. Movements of the chaos are extremely complex, and similar to natural phenomena. The forecasting for the chaos has been studied based on the embedding theory. They are called "one-step prediction". The embedding theory is effective and useful, and the results are accurate. But we wish to know long-term futures in practical objects. We need to take one step forward from the embedding theory. Our target is the long-term forecasting.