An Analysis on Syntactic and Semantic Factors Found in Newspaper Headlines

As a type of media text, newspaper has an important role in human\u27s life because it presents various local, national and International information and events. In order to attract readers\u27 attention, journalists make the headlines as ambiguous and confusing as possible so that readers are curious to know the content of the whole story and they would read it. Moreover, in presenting the information or events, different reporters will have different linguistic choices which include the choice of words and expressions and different linguistic structures. Thus, this paper analyzes how the different linguistic choices and structures used in the headlines of The Jakarta Post and Indonesian Daily News would construct different linguistic representations of events in the world

Proteomic and metabolomic analyses reveal metabolic responses to 3-hydroxypropionic acid synthesized internally in cyanobacterium Synechocystis sp. PCC 6803

Additional file 1: Figure S1. Comparison of cell growth of the WT and the engineered Synechocystis strains in this study

CLIP Guided Image-perceptive Prompt Learning for Image Enhancement

Image enhancement is a significant research area in the fields of computer vision and image processing. In recent years, many learning-based methods for image enhancement have been developed, where the Look-up-table (LUT) has proven to be an effective tool. In this paper, we delve into the potential of Contrastive Language-Image Pre-Training (CLIP) Guided Prompt Learning, proposing a simple structure called CLIP-LUT for image enhancement. We found that the prior knowledge of CLIP can effectively discern the quality of degraded images, which can provide reliable guidance. To be specific, We initially learn image-perceptive prompts to distinguish between original and target images using CLIP model, in the meanwhile, we introduce a very simple network by incorporating a simple baseline to predict the weights of three different LUT as enhancement network. The obtained prompts are used to steer the enhancement network like a loss function and improve the performance of model. We demonstrate that by simply combining a straightforward method with CLIP, we can obtain satisfactory results.Comment: A trial work to the image enhancemen