Modified memoryless spectral-scaling Broyden family on Riemannian manifolds

This paper presents modified memoryless quasi-Newton methods based on the spectral-scaling Broyden family on Riemannian manifolds. The method involves adding one parameter to the search direction of the memoryless self-scaling Broyden family on the manifold. Moreover, it uses a general map instead of vector transport. This idea has already been proposed within a general framework of Riemannian conjugate gradient methods where one can use vector transport, scaled vector transport, or an inverse retraction. We show that the search direction satisfies the sufficient descent condition under some assumptions on the parameters. In addition, we show global convergence of the proposed method under the Wolfe conditions. We numerically compare it with existing methods, including Riemannian conjugate gradient methods and the memoryless spectral-scaling Broyden family. The numerical results indicate that the proposed method with the BFGS formula is suitable for solving an off-diagonal cost function minimization problem on an oblique manifold.Comment: 20 pages, 8 figure

Feature representations useful for predicting image memorability

Predicting image memorability has attracted interest in various fields. Consequently, prediction accuracy with convolutional neural network (CNN) models has been approaching the empirical upper bound estimated based on human consistency. However, identifying which feature representations embedded in CNN models are responsible for such high prediction accuracy of memorability remains an open question. To tackle this problem, this study sought to identify memorability-related feature representations in CNN models using brain similarity. Specifically, memorability prediction accuracy and brain similarity were examined and assessed by Brain-Score across 16,860 layers in 64 CNN models pretrained for object recognition. A clear tendency was shown in this comprehensive analysis that layers with high memorability prediction accuracy had higher brain similarity with the inferior temporal (IT) cortex, which is the highest stage in the ventral visual pathway. Furthermore, fine-tuning the 64 CNN models revealed that brain similarity with the IT cortex at the penultimate layer was positively correlated with memorability prediction accuracy. This analysis also showed that the best fine-tuned model provided accuracy comparable to the state-of-the-art CNN models developed specifically for memorability prediction. Overall, this study's results indicated that the CNN models' great success in predicting memorability relies on feature representation acquisition similar to the IT cortex. This study advanced our understanding of feature representations and its use for predicting image memorability

Methylovulum miyakonense gen. nov., sp. nov., a type I methanotroph isolated from forest soil.

A novel methanotroph, designated strain HT12(T), was isolated from forest soil in Japan. Cells of strain HT12(T) were Gram-reaction-negative, aerobic, non-motile, coccoid and formed pale-brown colonies. The strain grew only with methane and methanol as sole carbon and energy sources. Cells grew at 5-34 °C (optimum 24-32 °C). The strain possessed both particulate and soluble methane monooxygenases and assimilated formaldehyde using the ribulose monophosphate pathway. The major cellular fatty acids were C(16 : 0) (46.9 %) and C(14 : 0) (34.2 %), whereas unsaturated C(16) fatty acids, typical of type I methanotrophs, were absent. Comparative 16S rRNA gene sequence analysis showed that the most closely related strains were Methylosoma difficile LC 2(T) (93.1 % sequence similarity) and Methylobacter tundripaludum SV96(T) (92.6 % similarity). Phylogenetic analysis based on the pmoA gene indicated that strain HT12(T) formed a distinct lineage within the type I methanotrophs and analysis of the deduced pmoA amino acid sequence of strain HT12(T) showed that it had a 7 % divergence from that of its most closely related species. The DNA G+C content was 49.3 mol%. Based on this evidence, strain HT12(T) represents a novel species and genus of the family Methylococcaceae, for which the name Methylovulum miyakonense gen. nov., sp. nov. is proposed. The type strain of the type species is HT12(T) ( = NBRC 106162(T)  = DSM 23269(T)  = ATCC BAA-2070(T))