Improving Japanese Zero Pronoun Resolution by Global Word Sense Disambiguation

This paper proposes unsupervised word sense disambiguation based on automatically constructed case frames and its incorporation into our zero pronoun resolution system. The word sense disambiguation is applied to verbs and nouns. We consider that case frames define verb senses and semantic features in a thesaurus define noun senses, respectively, and perform sense disambiguation by selecting them based on case analysis. In addition, according to the one sense per discourse heuristic, the word sense disambiguation results are cached and applied globally to the subsequent words. We integrated this global word sense disambiguation into our zero pronoun resolution system, and conducted experiments of zero pronoun resolution on two different domain corpora. Both of the experimental results indicated the effectiveness of our approach.

Prediction of Dew Condensation of Windows with Airflow

AbstractThermal insulating performance of windows is often poor. Thus, effective windows are important for sustainable buildings. In this study, we propose using a dynamic insulation (DI) window that uses indispensable ventilation effectively. The principle of the DI system is that airflow opposite to the direction of heat loss recovers part of the heat that would be lost. Another merit of DI windows is that they decrease the risk of dew condensation. In this paper, we report evaluation of humidity using a non-dimensional index: humidity index (HI), and dew condensation frequency for DI window frames, at several locations in Japan

T1-weighted and T2-weighted MRI image synthesis with convolutional generative adversarial networks

Background: The objective of this study was to propose an optimal input image quality for a conditional generative adversarial network (GAN) in T1-weighted and T2-weighted magnetic resonance imaging (MRI) images. Materials and methods: A total of 2,024 images scanned from 2017 to 2018 in 104 patients were used. The prediction framework of T1-weighted to T2-weighted MRI images and T2-weighted to T1-weighted MRI images were created with GAN. Two image sizes (512 × 512 and 256 × 256) and two grayscale level conversion method (simple and adaptive) were used for the input images. The images were converted from 16-bit to 8-bit by dividing with 256 levels in a simple conversion method. For the adaptive conversion method, the unused levels were eliminated in 16-bit images, which were converted to 8-bit images by dividing with the value obtained after dividing the maximum pixel value with 256. Results: The relative mean absolute error (rMAE ) was 0.15 for T1-weighted to T2-weighted MRI images and 0.17 for T2-weighted to T1-weighted MRI images with an adaptive conversion method, which was the smallest. Moreover, the adaptive conversion method has a smallest mean square error (rMSE) and root mean square error (rRMSE), and the largest peak signal-to-noise ratio (PSNR) and mutual information (MI). The computation time depended on the image size. Conclusions: Input resolution and image size affect the accuracy of prediction. The proposed model and approach of prediction framework can help improve the versatility and quality of multi-contrast MRI tests without the need for prolonged examinations

Learning Head-modifier Pairs to Improve Lexicalized Dependency Parsing on a Chinese Treebank

Proceedings of the Sixth International Workshop on Treebanks and Linguistic Theories. Editors: Koenraad De Smedt, Jan Hajič and Sandra Kübler. NEALT Proceedings Series, Vol. 1 (2007), 201-212. © 2007 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/4476

A Reranking Approach for Dependency Parsing with Variable-sized Subtree Features

Employing higher-order subtree structures in graph-based dependency parsing has shown substantial improvement over the accuracy, however suffers from the inefficiency increasing with the order of subtrees. We present a new reranking approach for dependency parsing that can utilize complex subtree representation by applying efficient subtree selection heuristics. We demonstrate the effective-ness of the approach in experiments conducted on the Penn Treebank and the Chinese Treebank. Our system improves the baseline accuracy from 91.88 % to 93.37 % for English, and in the case of Chinese from 87.39 % to 89.16%. 1

Optimization of irradiation interval for fractionated stereotactic radiosurgery by a cellular automata model with reoxygenation effects

The current study aims to determine the optimal irradiation interval of fractionated stereotactic radiosurgery (SRS) by using an improved cellular automata (CA) model. The tumor growth process was simulated by considering the amount of oxygen and the density of blood vessels, which supplied oxygen and nutrient required for cell growth. Cancer cells died by the mitotic death process due to radiation, which was quantified by the LQ-model, or the apoptosis due to the lack of nutrients. The radiation caused increased permeation of plasma protein through the blood vessel or the breakdown of the vasculature. Consequently, these changes lead to a change in radiation sensitivity of cancer cells and tumor growth rate after irradiation. The optimal model parameters were determined with experimental data of the rat tumor volume. The tumor control probability (TCP) was defined as the ratio of the number of histories in which all cancer cells died after the irradiation to the total number of the histories per simulation. The optimal irradiation interval was defined as the irradiation interval that TCP was the maximum. For one fractionation treatment, the ratio of hypoxic cells to the total number of cancer cells kept decreasing until day 16th after irradiation; whereas the number of surviving cancer cells begun increasing immediately after irradiation. This intricate relationship between the hypoxia (or reoxygenation) and the number of cancer cells lead to an optimal irradiation interval for the second irradiation. The optimal irradiation interval for two-fraction SRS was six days. The optimum intervals for the first-second irradiations and the second-third irradiations were five and two days, respectively, for three fraction SRS. For 4 and 5-fraction treatments, the optimum first-interval was five days, which was similar to three fraction treatment. The remaining intervals should be one day. We showed that the improved CA model could be used to optimize the irradiation interval by explicitly including the reoxygenation after irradiation in the model.The part of the work was previously published as an electronic poster at the annual meeting of the American Association of Physicists in Medicine, San Antonio, Texas, USA, July 13-18, 2019

Image synthesis of monoenergetic CT image in dual-energy CT using kilovoltage CT with deep convolutional generative adversarial networks

Purpose: To synthesize a dual-energy computed tomography (DECT) image from an equivalent kilovoltage computed tomography (kV-CT) image using a deep convolutional adversarial network. Methods: A total of 18,084 images of 28 patients are categorized into training and test datasets. Monoenergetic CT images at 40, 70, and 140 keV and equivalent kVCT images at 120 kVp are reconstructed via DECT and are defined as the reference images. An image prediction framework is created to generate monoenergetic computed tomography (CT) images from kV-CT images. The accuracy of the images generated by the CNN model is determined by evaluating the mean absolute error (MAE), mean square error (MSE), relative root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information between the synthesized and reference monochromatic CT images. Moreover, the pixel values between the synthetic and reference images are measured and compared using a manually drawn region of interest (ROI). Results: The difference in the monoenergetic CT numbers of the ROIs between the synthetic and reference monoenergetic CT images is within the standard deviation values. The MAE, MSE, RMSE, and SSIM are the smallest for the image conversion of 120 kVp to 140 keV. The PSNR is the smallest and the MI is the largest for the synthetic 70 keV image. Conclusions: The proposed model can act as a suitable alternative to the existing methods for the reconstruction of monoenergetic CT images in DECT from single-energy CT images