Introducing Nonuniform Strain to Graphene Using Dielectric Nanopillars

A method for inducing nonuniform strain in graphene films is developed. Pillars made of a dielectric material (electron beam resist) are placed between graphene and the substrate, and graphene sections between pillars are attached to the substrate. The strength and spatial pattern of the strain can be controlled by the size and separation of the pillars. Application of strain is confirmed by Raman spectroscopy as well as from scanning electron microscopy (SEM) images. From SEM images, the maximum stretch of the graphene film reaches about 20%. This technique can be applied to the formation of band gaps in graphene.Comment: Appl. Phys. Express, in pres

地震発生帯における深部掘削孔を用いた長期計測

Large earthquakes occur frequently in subduction zones. Most earthquakes are generated in the seismogenic zone, a fairly limited area confined to the shallower regions of the subduction plate boundary. To understand the processes of earthquake generation, it is essential to monitor the physical and mechanical properties of the seismogenic zone over long periods. At present, there are no deep borehole observations of the seismogenic zone more than 3km below seafloor, because it has, until now, been impossible to penetrate to such depths below the sea floor. The Integrated Ocean Drilling Program (IODP), scheduled to begin in 2003, plans to drill boreholes beneath the ocean floor using a multiple-drilling platform operation. The IODP riser-quipped drilling ship (Chikyu) enables the emplacement of boreholes up to 0km beneath the ocean floor, and will provide opportunities to conduct long-term deep borehole observations in the seismogenic zone. Long-term borehole observations in the seismogenic zone are expected to require the development of advanced sampling, monitoring, and recording technology. Here, we discuss the scientific objectives, engineering and technical challenges, and experimental design for a deep borehole, long-term deepborehole monitoring system aimed at understanding the processes of earthquake generation in the seismogenic zone of subduction plate boundaries. We focus specifically on the relationships between environmental conditions in the deep subsurface, details of monitoring and recording, and design and implementation of scientific tools and programs

Distortion Model Based on Word Sequence Labeling for Statistical Machine Translation

This article proposes a new distortion model for phrase-based statistical machine translation. In decoding, a distortion model estimates the source word position to be translated next (subsequent position; SP) given the last translated source word position (current position; CP). We propose a distortion model that can simultaneously consider the word at the CP, the word at an SP candidate, the context of the CP and an SP candidate, relative word order among the SP candidates, and the words between the CP and an SP candidate. These considered elements are called rich context. Our model considers rich context by discriminating label sequences that specify spans from the CP to each SP candidate. It enables our model to learn the effect of relative word order among SP candidates as well as to learn the effect of distances from the training data. In contrast to the learning strategy of existing methods, our learning strategy is that the model learns preference relations among SP candidates in each sentence of the training data. This leaning strategy enables consideration of all of the rich context simultaneously. In our experiments, our model had higher BLUE and RIBES scores for Japanese-English, Chinese-English, and German-English translation compared to the lexical reordering models