Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension

This study considers the task of machine reading at scale (MRS) wherein, given a question, a system first performs the information retrieval (IR) task of finding relevant passages in a knowledge source and then carries out the reading comprehension (RC) task of extracting an answer span from the passages. Previous MRS studies, in which the IR component was trained without considering answer spans, struggled to accurately find a small number of relevant passages from a large set of passages. In this paper, we propose a simple and effective approach that incorporates the IR and RC tasks by using supervised multi-task learning in order that the IR component can be trained by considering answer spans. Experimental results on the standard benchmark, answering SQuAD questions using the full Wikipedia as the knowledge source, showed that our model achieved state-of-the-art performance. Moreover, we thoroughly evaluated the individual contributions of our model components with our new Japanese dataset and SQuAD. The results showed significant improvements in the IR task and provided a new perspective on IR for RC: it is effective to teach which part of the passage answers the question rather than to give only a relevance score to the whole passage.Comment: 10 pages, 6 figure. Accepted as a full paper at CIKM 201

Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments

Image recognition models that work in challenging environments (e.g., extremely dark, blurry, or high dynamic range conditions) must be useful. However, creating training datasets for such environments is expensive and hard due to the difficulties of data collection and annotation. It is desirable if we could get a robust model without the need for hard-to-obtain datasets. One simple approach is to apply data augmentation such as color jitter and blur to standard RGB (sRGB) images in simple scenes. Unfortunately, this approach struggles to yield realistic images in terms of pixel intensity and noise distribution due to not considering the non-linearity of Image Signal Processors (ISPs) and noise characteristics of image sensors. Instead, we propose a noise-accounted RAW image augmentation method. In essence, color jitter and blur augmentation are applied to a RAW image before applying non-linear ISP, resulting in realistic intensity. Furthermore, we introduce a noise amount alignment method that calibrates the domain gap in the noise property caused by the augmentation. We show that our proposed noise-accounted RAW augmentation method doubles the image recognition accuracy in challenging environments only with simple training data.Comment: Accepted to CVPR202

DynamicISP: Dynamically Controlled Image Signal Processor for Image Recognition

Image signal processor (ISP) plays an important role not only for human perceptual quality but also for computer vision. In most cases, experts resort to manual tuning of many parameters in the ISPs for perceptual quality. It failed in sub-optimal, especially for computer vision. Aiming to improve ISPs, two approaches have been actively proposed; tuning the parameters with machine learning, or constructing an ISP with DNN. The former is lightweight but lacks expressive powers. The latter has expressive powers but it was too heavy to calculate on edge devices. To this end, we propose DynamicISP, which consists of traditional simple ISP functions but their parameters are controlled dynamically per image according to what the downstream image recognition model felt to the previous frame. Our proposed method successfully controlled parameters of multiple ISP functions and got state-of-the-art accuracy with a small computational cost

3-years Occurrence Variability of Concentric Gravity Waves in the Mesopause Observed by IMAP/VISI

第6回極域科学シンポジウム分野横断型セッション：[IM] 横断　中層大気・熱圏11月17日（火）　統計数理研究所 セミナー室2（D304

Comparison in gene expression of secretory human endometrium using laser microdissection

BACKGROUND: The endometrium prepares for implantation under the control of steroid hormones. It has been suggested that there are complicated interactions between the epithelium and stroma in the endometrium during menstrual cycle. In this study, we demonstrate a difference in gene expression between the epithelial and stromal areas of the secretory human endometrium using microdissection and macroarray technique. METHODS: The epithelial and stromal areas were microdissected from the human endometrium during the secretory phase. RNA was extracted and amplified by PCR. Macroarray analysis of nearly 1000 human genes was carried out in this study. Some genes identified by macroarray analysis were verified using real-time PCR. RESULTS: In this study, changes in expression <2.5-fold in three samples were excluded. A total of 28 genes displayed changes in expression from array data. Fifteen genes were strongly expressed in the epithelial areas, while 13 genes were strongly expressed in the stromal areas. The strongly expressed genes in the epithelial areas with a changes >5-fold were WAP four-disulfide core domain 2 (44.1 fold), matrix metalloproteinase 7 (40.1 fold), homeo box B5 (19.8 fold), msh homeo box homolog (18.8 fold), homeo box B7 (12.7 fold) and protein kinase C, theta (6.4 fold). On the other hand, decorin (55.6 fold), discoidin domain receptor member 2 (17.3 fold), tissue inhibitor of metalloproteinase 1 (9 fold), ribosomal protein S3A (6.3 fold), and tyrosine kinase with immunoglobulin and epidermal growth factor homology domains (5.2 fold) were strongly expressed in the stromal areas. WAP four-disulfide core domain 2 (19.4 fold), matrix metalloproteinase 7 (9.7-fold), decorin (16.3-fold) and tissue inhibitor of metalloproteinase 1 (7.2-fold) were verified by real-time PCR. CONCLUSIONS: Some of the genes we identified with differential expression are related to the immune system. These results are telling us the new information for understanding the secretory human endometrium

Cloning, Sequencing, and Functional Analysis of the Biosynthetic Gene Cluster of Macrolactam Antibiotic Vicenistatin in Streptomyces halstedii

AbstractVicenistatin, an antitumor antibiotic isolated from Streptomyces halstedii, is a unique 20-membered macrocyclic lactam with a novel aminosugar vicenisamine. The vicenistatin biosynthetic gene cluster (vin) spanning ∼64 kbp was cloned and sequenced. The cluster contains putative genes for the aglycon biosynthesis including four modular polyketide synthases (PKSs), glutamate mutase, acyl CoA-ligase, and AMP-ligase. Also found in the cluster are genes of NDP-hexose 4,6-dehydratase and aminotransferase for vicenisamine biosynthesis. For the functional confirmation of the cluster, a putative glycosyltransferase gene product, VinC, was heterologously expressed, and the vicenisamine transfer reaction to the aglycon was chemically proved. A unique feature of the vicenistatin PKS is that the loading module contains only an acyl carrier protein domain, in contrast to other known PKS-loading modules containing certain activation domains. Activation of the starter acyl group by separate polypeptides is postulated as well