The short-time linear canonical transform (STLCT) is a novel time-frequency analysis tool, which has attracted some attention recently. However, its applications in signal processing are limited because the time-frequency properties of the STLCT are still little known. Most existing studies focus on mathematical properties rather than time-frequency properties in signal processing. To handle this problem, first, we investigate some basic time-frequency properties such as 2-D resolution of the time-frequency plane, the STLCT domain support, computation of the STLCT, etc by generalizing the short-time Fourier transform to the STLCT. Then, based on these derived properties, we find that the Gaussian window is optimal window of the STLCT. Signal separation verified the results.

Current stance detection models predominantly rely on text analysis, often overlooking the wealth of information embedded in user profiles. This oversight is largely attributed to the scarcity of stance detection datasets that encompass detailed user profiles. Bridging this gap, we have created the first dataset for Chinese stance detection that includes user profile information (PC-STANCE). The dataset contains 31,033 Chinese microblogs annotated with stances toward 8 targets, as well as user gender, age, and location information. We have conducted a detailed analysis of the dataset and performed extensive empirical experiments using classic neural network models. Our experiments achieve state-of-the-art results, surpassing several large language models like Llama and ChatGPT. This confirms the effectiveness of integrating user profile information in stance detection and underscores the challenging nature of the dataset. To facilitate further research in stance detection, we have made the dataset publicly available. ¹