Seek for Incantations: Towards Accurate Text-to-Image Diffusion Synthesis through Prompt Engineering

The text-to-image synthesis by diffusion models has recently shown remarkable performance in generating high-quality images. Although performs well for simple texts, the models may get confused when faced with complex texts that contain multiple objects or spatial relationships. To get the desired images, a feasible way is to manually adjust the textual descriptions, i.e., narrating the texts or adding some words, which is labor-consuming. In this paper, we propose a framework to learn the proper textual descriptions for diffusion models through prompt learning. By utilizing the quality guidance and the semantic guidance derived from the pre-trained diffusion model, our method can effectively learn the prompts to improve the matches between the input text and the generated images. Extensive experiments and analyses have validated the effectiveness of the proposed method

BACH1 is critical for homologous recombination and appears to be the Fanconi anemia gene product FANCJ

SummaryWe showed in this study that cells deficient of the BRCA1-associated BACH1 helicase, also known as BRIP1, failed to elicit homologous recombination (HR) after DNA double-stranded breaks (DSBs). BACH1-deficient cells were also sensitive to mitomycin C (MMC) and underwent MMC-induced chromosome instability. Moreover, we identified a homozygous nonsense mutation in BACH1 in a FA-J patient-derived cell line and could not detect BACH1 protein in this cell line. Expression of wild-type BACH1 in this cell line reduced the accumulation of cells at G2/M phases following exposure to DNA crosslinkers, a characteristic of Fanconi anemia (FA) cells. These results support the conclusion that BACH1 is FANCJ

Volatile Substances of Different Hosts of Cistanche deserticola in Xinjiang Based on GC-IMS

GC-IMS (gas chromatography-ion mobility spectroscopy) was used to analyze the differences between the volatile substances of two hosts of Cistanches deserticola in the Hotan area, Xinjiang. The results showed that 35 volatile substances were detected by GC-IMS, among which 27 volatile substances were identified qualitatively, including 8 aldehydes, 5 alcohols, 4 esters, and 1 ketone, predominantly aldehydes, and alcohols. There were obvious differences in the volatile substances of Cistanche deserticola between 'Red Willow' and 'Hoxylon'. The main substances that differed between the two were 2-phenylacetaldehyde, benzaldehyde, (E)-2-heptenal, 3-(Methylmercapto) propionaldehyde, 1-hexanal, heptanal, 3-methyl-2-butenal, acetic acid methyl ester, acetic acid hexyl ester, 1-hexanol, 3-hydroxy-2-butanone, 2-methyl-1-propanol, acetic acid ethyl ester. The two different hosts of Cistanche deserticola could be effectively distinguished by principal component analysis, and the cumulative variance contribution of PC1 and PC2 reached 91%. Meanwhile, by constructing the heat map of volatile substances clustering and fingerprinting, they can provide theoretical references for the identification of different hosts of Cistanche deserticola and the study of volatile substances

OWL: A Large Language Model for IT Operations

With the rapid development of IT operations, it has become increasingly crucial to efficiently manage and analyze large volumes of data for practical applications. The techniques of Natural Language Processing (NLP) have shown remarkable capabilities for various tasks, including named entity recognition, machine translation and dialogue systems. Recently, Large Language Models (LLMs) have achieved significant improvements across various NLP downstream tasks. However, there is a lack of specialized LLMs for IT operations. In this paper, we introduce the OWL, a large language model trained on our collected OWL-Instruct dataset with a wide range of IT-related information, where the mixture-of-adapter strategy is proposed to improve the parameter-efficient tuning across different domains or tasks. Furthermore, we evaluate the performance of our OWL on the OWL-Bench established by us and open IT-related benchmarks. OWL demonstrates superior performance results on IT tasks, which outperforms existing models by significant margins. Moreover, we hope that the findings of our work will provide more insights to revolutionize the techniques of IT operations with specialized LLMs.Comment: 31 page