Low-light image enhancement (LLIE) aims to improve visibility and signal-to-noise ratio in images captured under poor lighting conditions. Despite impressive improvement, deep learning-based LLIE approaches require extensive training data, which is often difficult and costly to obtain. In this paper, we propose a zero-shot LLIE framework leveraging pre-trained latent diffusion models for the first time, which act as powerful priors to recover latent images from low-light inputs. Our approach introduces several components to alleviate the inherent challenges in utilizing pre-trained latent diffusion models, modeling the degradation process in an image-adaptive manner, penalizing the latent outside the manifold of natural images, and balancing the strengths of the guidance from the given low-light image during the denoising process. Experimental results demonstrate that our framework outperforms existing methods, achieving superior performance across various datasets.
The reform and opening-up of China have greatly improved the scale and quality of doctoral education for women. However, female doctors still face the “leaky pipeline” and the “unbreakable glass ceiling” in their development of academic careers. In this study, gender differences are investigated in doctoral graduates’ career choices, the level of educational institutions they attend, and their scientific research productivity after joining the institution. We analyzed the administrative data and scientific research publication information from ten years of doctoral graduates at a top research university in China. Results suggest that compared to their male counterparts, female doctors are more likely to pursue an academic career upon graduation, but they are also more likely to be employed in lower-level institutions as well as to publish Chinese scientific studies with lower influence and poorer quality. Moreover, gender differences in academic disciplines are heterogeneous. While academic career development for doctors in natural sciences is not gender-biased, female doctors in social sciences face the most significant challenges, and these results persist even after controlling for their scientific publications during graduate school. In other words, gender differences in academic career development are likely to result from gender symbols rather than differences in academic ability.
A fundamental difference between “core-fed” and “clump-fed” star-formation theories lies in the existence or absence of high-mass cores at the prestellar stage. However, only a handful of such cores have been observed. Here, different than previous search in distributed star-formation regions in the Galactic plane, we search for high-mass prestellar cores in the Orion GMC, by observing the seven most massive starless cores selected from previous deep continuum surveys. We present ALMA Atacama Compact Array Band 6 and Band 7 continuum and line observations toward the seven cores, in which we identify nine dense cores at both bands. The derived maximum core mass is less than 11 M ⊙, based on different dust temperatures. We find no high-mass prestellar cores in this sample, aligning with the results of previous surveys, thereby challenging the existence of such cores in Orion. Outside Orion, further detailed studies are needed for remaining high-mass prestellar core candidates to confirm their status as massive, starless cores.