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  1. Abstract

    Invasive fungal pathogen Candida auris has become a public health threat causing outbreaks of high mortality infections. Drug resistance often limits treatment options. For Candida albicans, subinhibitory concentrations of echinocandins unmask immunostimulatory β-glucan, augmenting immunity. Here we analyze the impact of echinocandin treatment of C. auris on β-glucan exposure and human neutrophil interactions. We show subinhibitory concentrations lead to minimal glucan unmasking and only subtle influences on neutrophil functions for the isolates belonging to circulating clades. The data suggest that echinocandin treatment will not largely alter phagocytic responses. Glucan masking pathways appear to differ between C. auris and C. albicans.

     
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  2. Deformable Convolutional Networks (DCN) have been proposed as a powerful tool to boost the representation power of Convolutional Neural Networks (CNN) in computer vision tasks via adaptive sampling of the input feature map. Much like vision transformers, DCNs utilize a more flexible inductive bias than standard CNNs and have also been shown to improve performance of particular models. For example, drop-in DCN layers were shown to increase the AP score of Mask RCNN by 10.6 points while introducing only 1% additional parameters and FLOPs, improving the state-of-the art model at the time of publication. However, despite evidence that more DCN layers placed earlier in the network can further improve performance, we have not seen this trend continue with further scaling of deformations in CNNs, unlike for vision transformers. Benchmarking experiments show that a realistically sized DCN layer (64H×64W, 64 in-out channel) incurs a 4× slowdown on a GPU platform, discouraging the more ubiquitous use of deformations in CNNs. These slowdowns are caused by the irregular input-dependent access patterns of the bilinear interpolation operator, which has a disproportionately low arithmetic intensity (AI) compared to the rest of the DCN. To address the disproportionate slowdown of DCNs and enable their expanded use in CNNs, we propose DefT, a series of workload-aware optimizations for DCN kernels. DefT identifies performance bottlenecks in DCNs and fuses specific operators that are observed to limit DCN AI. Our approach also uses statistical information of DCN workloads to adapt the workload tiling to the DCN layer dimensions, minimizing costly out-of-boundary input accesses. Experimental results show that DefT mitigates up to half of DCN slowdown over the current-art PyTorch implementation. This translates to a layerwise speedup of up to 134% and a reduction of normalized training time of 46% on a fully DCN-enabled ResNet model. 
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    Free, publicly-accessible full text available March 25, 2024
  3. Spanning c. 1050–1500 CE, a burgeoning Swahili community called Chwaka built a sequence of four mortared coral mosques in their town of wattle-and-daub houses on Pemba Island, Tanzania. The mosques’ placement, construction, and use played an active role in creating and strengthening an Islamic community and help us define changes in social practice within the town and the larger polity in which it existed. It is argued that the construction of each mosque was an act of assembling, drawing people, other-than-human things and affective social practices together in ways that help tell an urban story. This research provides insights into the residents’ socioeconomic and cultural priorities and the town’s changing relationship with villagers from the surrounding region, contributing to understandings of Swahili urbanism and urban practice.

     
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