485 research outputs found
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
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