Detailed behavioral modeling of bang-bang phase detectors

In this paper, the metastability of current-mode logic (CML) latches and flip-flops is studied in detail. Based on the results of this analysis, a behavioral model of bang-bang phase detectors (BBPDs) is proposed, which is able to reliably capture the critical deadzone effect. The impact of jitter and of process, voltage and temperature variations on the BBPD behavior is also investigated. The proposed model can be used with advantage in the high-level design and verification of e.g. clock and data recovery (CDR) circuit

AACP: Aesthetics assessment of children's paintings based on self-supervised learning

The Aesthetics Assessment of Children's Paintings (AACP) is an important branch of the image aesthetics assessment (IAA), playing a significant role in children's education. This task presents unique challenges, such as limited available data and the requirement for evaluation metrics from multiple perspectives. However, previous approaches have relied on training large datasets and subsequently providing an aesthetics score to the image, which is not applicable to AACP. To solve this problem, we construct an aesthetics assessment dataset of children's paintings and a model based on self-supervised learning. 1) We build a novel dataset composed of two parts: the first part contains more than 20k unlabeled images of children's paintings; the second part contains 1.2k images of children's paintings, and each image contains eight attributes labeled by multiple design experts. 2) We design a pipeline that includes a feature extraction module, perception modules and a disentangled evaluation module. 3) We conduct both qualitative and quantitative experiments to compare our model's performance with five other methods using the AACP dataset. Our experiments reveal that our method can accurately capture aesthetic features and achieve state-of-the-art performance.Comment: AAAI 202