No abstract

Childhood Maltreatment (CM) has been associated with significant impairment of social, emotional and behavioural functioning in later life. Nevertheless, some individuals who have experienced CM seem to function better than expected. Here, we provide an integrated understanding of the complex interrelated mechanisms that facilitate such resilient functioning after CM. We show that resilient functioning after CM may be characterized by improved reward, emotion, and stress processing in MPFC and Limbic regions, lower cortisol and inflammatory responses, poly-genetic influences, and supportive environments. As these factors are inextricably intertwined, future resilience studies should investigate multiple levels of biological organization and their temporal dynamics. We further provide a method for such studies to appropriately quantify resilient functioning as functioning across multiple domains conditional on the severity of CM experiences. We hope that our perspective fuels further research in the complex neurobiological mechanisms that facilitate resilient functioning after CM.

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The inevitable environmental and technical limitations of image capturing has as a consequence that many images are frequently taken in inadequate and unbalanced lighting conditions. Low-light image enhancement has been very popular for improving the visual quality of image representations, while low-light images often require advanced techniques to improve the perception of information for a human viewer. One of the main objectives in increasing the lighting conditions is to retain patterns, texture, and style with minimal deviations from the considered image. To this direction, we propose a low-light image enhancement method with Haar wavelet-based pooling to preserve texture regions and increase their quality. The presented framework is based on the U-Net architecture to retain spatial information, with a multi-layer feature aggregation (MFA) method. The method obtains the details from the low-level layers in the stylization processing. The encoder is based on dense blocks, while the decoder is the reverse of the encoder, and extracts features that reconstruct the image. Experimental results show that the combination of the U-Net architecture with dense blocks and the wavelet-based pooling mechanism comprises an efficient approach in low-light image enhancement applications. Qualitative and quantitative evaluation demonstrates that the proposed framework reaches state-ofthe-art accuracy but with less resources than LeGAN.