A Stochastic Variance Reduced Nesterov's Accelerated Quasi-Newton Method

Recently algorithms incorporating second order curvature information have become popular in training neural networks. The Nesterov's Accelerated Quasi-Newton (NAQ) method has shown to effectively accelerate the BFGS quasi-Newton method by incorporating the momentum term and Nesterov's accelerated gradient vector. A stochastic version of NAQ method was proposed for training of large-scale problems. However, this method incurs high stochastic variance noise. This paper proposes a stochastic variance reduced Nesterov's Accelerated Quasi-Newton method in full (SVR-NAQ) and limited (SVRLNAQ) memory forms. The performance of the proposed method is evaluated in Tensorflow on four benchmark problems - two regression and two classification problems respectively. The results show improved performance compared to conventional methods.Comment: Accepted in ICMLA 201

Evaluation of future storm surge risk in East Asia based on state-of-the-art climate change projection

The present study evaluates future storm surge risk due to tropical cyclones (typhoons) in East Asia. A state-of-the-art atmospheric general circulation model (GCM) outputs are employed as the driving force for simulating storm surges associated with the projected changes in climate. The reproducibility of tropical cyclone (TC) characteristics from the GCM in the Northwest Pacific (NWP) is confirmed by comparing with the observed best track data, and future typhoon changes were presented. Storm surge simulation is carried out for East Asia, with the finest nested domain on the Japanese coast. The probability of maximum storm surge heights with specified return periods is determined using extreme value statistics. We show a strong regional dependency on future changes of severe storm surges

Intraocular pressure-lowering effects of ripasudil on open-angle glaucoma in eyes with high myopia and pathological myopia

Abstract The aim is to study the intraocular pressure (IOP)-lowering effects of additional administration of ripasudil in open-angle glaucoma (OAG) patients including high myopia (HM) and pathological myopia (PM). Study design is retrospective cohort study. We assessed the changes in the mean IOP between the HM eyes (axial length ≧ 26.5 mm 33 eyes) and the non-HM eyes (axial length < 26.5 mm 29 eyes) at 4 and 12 weeks from baseline. We also assessed the IOP changes between the PM eyes (21 eyes) and the non-PM eyes (41 eyes). The significant IOP reduction by the ripasudil administration was observed at 4 weeks in the non-HM eyes and at 12 weeks in HM and non-HM eyes. And the IOP reduction in the HM eyes was significantly less than the non-HM eyes at 4 and 12 weeks. IOP reduction by ripasudil had statistically significant association with the baseline IOP and presence of PM. Furthermore, significant IOP reduction by the ripasudil administration was observed at 4 and 12 weeks in the non-PM eyes, but not in the PM eyes. The additional administration of ripasudil was effective in the HM eyes, but less than non-HM eyes. And the PM may negatively contribute to reducing the IOP by ripasudil

Better diagnostic performance using computer-assisted diagnostic support systems in internal medicine

The recent application of artificial intelligence（AI）to clinical medicine has confirmed the usefulness of AI for diagnostic imaging, histopathological examinations, and dermatologic screening. Clinical decision support systems are another promising area to which AI could contribute toward better clinical decisions. We have developed computer-assisted diagnostic support systems to reduce human diagnostic errors such as delayed diagnoses, misdiagnoses, and overdiagnoses. Our three Diagnosis Reminder（DR）systems include two AI systems that use machine learning in their diagnosis algorithms. Here, we compared the diagnostic accuracy of a DR-supported group with that of an unassisted physicians group, using three difficult patient cases provided by experts in general medicine. 　Our analyses revealed that the three AI diagnostic systems could not provide accurate differential diagnoses up to top 10 in all three patient cases because of incomplete data inputs for machine learning. However, the first DR system, which was developed by an experienced diagnostician over the last 35 years, showed very useful performance in reducing human diagnostic errors when it was used by an expert physician. The use of AI diagnostic systems by knowledgeable physicians will lead to better diagnostic performance. We also discuss the current scenario, future challenges, and prospects for AI diagnostic systems herein

A complex dominance hierarchy is controlled by polymorphism of small RNAs and their targets

In diploid organisms, phenotypic traits are often biased by effects known as Mendelian dominant–recessive interactions between inherited alleles. Phenotypic expression of SP11 alleles, which encodes the male determinants of self-incompatibility in Brassica rapa, is governed by a complex dominance hierarchy1–3. Here, we show that a single polymorphic 24 nucleotide small RNA, named SP11 methylation inducer 2 (Smi2), controls the linear dominance hierarchy of the four SP11 alleles (S44 > S60 > S40 > S29). In all dominant–recessive interactions, small RNA variants derived from the linked region of dominant SP11 alleles exhibited high sequence similarity to the promoter regions of recessive SP11 alleles and acted in trans to epigenetically silence their expression. Together with our previous study4, we propose a new model: sequence similarity between polymorphic small RNAs and their target regulates mono-allelic gene expression, which explains the entire five-phased linear dominance hierarchy of the SP11 phenotypic expression in Brassica