Publisher Correction: Effects of porosity on dynamic indentation resistance of silica nanofoam.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper

Effects of porosity on dynamic indentation resistance of silica nanofoam.

The dynamic indentation behaviors of monolithic silica nanofoams of various porosities are investigated. When the pore size is on the nm scale, as the porosity increases, despite the decrease in mass density, the resistance offered by silica nanofoam to dynamic indentation is maintained at a high level, higher than the resistance of solid silica or regular porous silica. This phenomenon is related to the fast collapse of nanocells, which produces a locally hardened region and significantly increases the volume of material involved in impact energy dissipation

Identification and analysis of candidate fungal tRNA 3'-end processing endonucleases tRNase Zs, homologs of the putative prostate cancer susceptibility protein ELAC2

<p>Abstract</p> <p>Background</p> <p>tRNase Z is the endonuclease that is responsible for the 3'-end processing of tRNA precursors, a process essential for tRNA 3'-CCA addition and subsequent tRNA aminoacylation. Based on their sizes, tRNase Zs can be divided into the long (tRNase Z^L) and short (tRNase Z^S) forms. tRNase Z^Lis thought to have arisen from a tandem gene duplication of tRNase Z^Swith further sequence divergence. The species distribution of tRNase Z is complex. Fungi represent an evolutionarily diverse group of eukaryotes. The recent proliferation of fungal genome sequences provides an opportunity to explore the structural and functional diversity of eukaryotic tRNase Zs.</p> <p>Results</p> <p>We report a survey and analysis of candidate tRNase Zs in 84 completed fungal genomes, spanning a broad diversity of fungi. We find that tRNase Z^Lis present in all fungi we have examined, whereas tRNase Z^Sexists only in the fungal phyla Basidiomycota, Chytridiomycota and Zygomycota. Furthermore, we find that unlike the Pezizomycotina and Saccharomycotina, which contain a single tRNase Z^L, <it>Schizosaccharomyces </it>fission yeasts (Taphrinomycotina) contain two tRNase Z^Ls encoded by two different tRNase Z^Lgenes. These two tRNase Z^Ls are most likely localized to the nucleus and mitochondria, respectively, suggesting partitioning of tRNase Z function between two different tRNase Z^Ls in fission yeasts. The fungal tRNase Z phylogeny suggests that tRNase Z^Ss are ancestral to tRNase Z^Ls. Additionally, the evolutionary relationship of fungal tRNase Z^Ls is generally consistent with known phylogenetic relationships among the fungal species and supports tRNase Z^Lgene duplication in certain fungal taxa, including <it>Schizosaccharomyces </it>fission yeasts. Analysis of tRNase Z protein sequences reveals putative atypical substrate binding domains in most fungal tRNase Z^Ss and in a subset of fungal tRNase Z^Ls. Finally, we demonstrate the presence of pseudo-substrate recognition and catalytic motifs at the N-terminal halves of tRNase Z^Ls.</p> <p>Conclusions</p> <p>This study describes the first comprehensive identification and sequence analysis of candidate fungal tRNase Zs. Our results support the proposal that tRNase Z^Lhas evolved as a result of duplication and diversification of the tRNase Z^Sgene.</p

Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar Instances

Despite the great progress of Visual Question Answering (VQA), current VQA models heavily rely on the superficial correlation between the question type and its corresponding frequent answers (i.e., language priors) to make predictions, without really understanding the input. In this work, we define the training instances with the same question type but different answers as \textit{superficially similar instances}, and attribute the language priors to the confusion of VQA model on such instances. To solve this problem, we propose a novel training framework that explicitly encourages the VQA model to distinguish between the superficially similar instances. Specifically, for each training instance, we first construct a set that contains its superficially similar counterparts. Then we exploit the proposed distinguishing module to increase the distance between the instance and its counterparts in the answer space. In this way, the VQA model is forced to further focus on the other parts of the input beyond the question type, which helps to overcome the language priors. Experimental results show that our method achieves the state-of-the-art performance on VQA-CP v2. Codes are available at \href{https://github.com/wyk-nku/Distinguishing-VQA.git}{Distinguishing-VQA}.Comment: Published in COLING 202