Computer-Aided Design (CAD) sketches, composed of geometric primitives and constraints, are fundamental to CAD models and play a critical role in industrial design and manufacturing. Leveraging artificial intelligence to convert hand-drawn sketches and rendered images into CAD sketches has the potential to streamline and accelerate the design process. Existing approaches predominantly focus on separately learning primitives and constraints, often employing two-stage methods or learnable tokens to model these elements independently. However, such methods fail to fully exploit the intrinsic relationships between primitives and constraints. In this paper, we propose LuBan, a lightweight, end-to-end model for CAD sketch generation that eliminates the need for separate constraint models or tokens. LuBan leverages the DEtection TRansformer (DETR) architecture for primitive modeling and distinguishes between parametric and non-parametric features. By deriving sub-primitive features from the intermediate outputs of the primitive model, LuBan facilitates constraint prediction, effectively capturing the inherent relationships between primitives and constraints. This enables the generation of CAD sketches as hierarchical graphs. Qualitative and quantitative experiments on both precise and hand-drawn renderings demonstrate that LuBan achieves state-of-the-art performance. Ablation studies further confirm its superiority over independently trained primitive models, validating its effectiveness. Additionally, LuBan embodies the principle of "what you draw is what you get," offering significant enhancements to the design process for designers.

In this paper, we proposed a novel scheme to infer user's location using microblog text and friendships, without known geo information. The major part of our research is identifying local words, words that associated with some particular location. With local words we identified, we use conditional random fields (CRF), to detect location specific microblog. Then we can estimate the most possible location of a user. And we take advantage of users' friendships to improve the result. Another key feature of our approach is that we consider timeliness of local words, as some local words are descriptions of local events and they are only associated with location during a certain period of time. Experimental evidence suggests that our algorithm works well in practice and outperforms the existing algorithms for estimating the location of microblog users.