Salman Ahmed Shaikh, Akiyoshi Matono, Kyoung-Sook Kim:
A Distance-Window Based Real-Time Processing of Spatial Data Streams, Proc. 5th IEEE International Conference on Multimedia Big Data, BigMM 2019, Singapore, September 11-13, 2019.
Salman Ahmed Shaikh, Jun Lee, Akiyoshi Matono, Kyoung-Sook Kim:
A Robust and Scalable Pipeline for the Real-time Processing and Analysis of Massive 3D Spatial Streams, In The 21st International Conference on Information Integration and Web-based Applications & Services (iiWAS2019), December 2-4, 2019, Munich, Germany. (to appear)
Salman Ahmed Shaikh, Kousuke Nakabasami, Toshiyuki Amagasa, Hiroyuki Kitagawa:
Multidimensional Analysis of Big Data, Emerging Perspectives in Big Data Warehousing, pp. 198-224, IGI Global, 2019.
Actin Cytoskeletal Reorganization Function of JRAB/MICAL-L2 Is Fine-tuned by Intramolecular Interaction between First LIM Zinc Finger and C-terminal Coiled-coil Domains
Kazuhisa Miyake, Ayuko Sakane, Yuko Tsuchiya, Ikuko Sagawa, Yoko Tomida, Jiro Kasahara, Issei Imoto, Shio Watanabe, Daisuke Higo, Kenji Mizuguchi, Takuya Sasaki
Scientific Reports 2019: 9: 12794.
Genome-Wide Analysis of Known and Potential Tetraspanins in Entamoeba histolytica
Kentaro Tomii, Herbert J. Santos, Tomoyoshi Nozaki
Genes. 2019; 10(11); 885.
Recurrent feedback CNN for Water Region Estimation from Multitemporal Satellite Images
Vinayaraj Poliyaprama, Nevrez Imamoglub, and Ryousuke Nakamura
Water region estimation is considered as one of the fundamental classification tasks in remote sensing. Several previous research works focused on traditional practices such as spectral analysis, and statistical approaches for water region estimation. However, producing a consistent global scale water estimation results are stillconsidered as relatively challenging task. On the other hand, in computer vision applications Convolutional Neural Network (CNN) emerged as greater tool for classification tasks. Recently, Recurrent Convolutional Neural
Network(R-CNN) proposed for improved classification results. Therefore, inspired from R-CNN, this research proposes a Recurrent feedback Encoder-Decoder without max-pooling for global scale water region estimation using temporal Landsat-8 images. The proposed R-CNN uses three Landsat-8 images which consist of current observation (t0) to predict water region and two previous observation of the same location (t-1, t -2), and these three temporal observation of the same location were employed for training with the ground truth labelled data (water/non-water) from the current observation. Proposed R-CNN model uses temporal input data and results in multi-temporal output for water region estimation. Experiments show promising results especially while using concatenated recurrent feedback features. The model significantly outperforms baseline model and UNet (without recurrent and feedback structure). Detailed comparison study on temporal Landsat-8 images that highly aected by sunglint, cloud and other atmospheric conditions shows that the proposed model has a potential to produce reliable water region estimation where UNet, baseline model R-CNN single model fail.
Accurate Classification of Biological and non-Biological Interfaces in Protein Crystal Structures using Subtle Covariation Signals
Yoshinori Fukasawa, Kentaro Tomii
Scientific Reports. 2019; 9(1): 12603.
Global structure of thermal tides in the upper cloud layer of Venus revealed by LIR onboard Akatsuki
T.Kouyama, M.Taguchi, T.Fukuhara, T.Imamura, T.Horinouchi, T.M.Sato, S.Murakami, G.L.Hashimoto, Y.J. Lee, M. Futaguchi, T. Yamada, M.Akiba, T.Satoh, M.Nakamura
Longwave Infrared Camera (LIR) on board Akatsukifirst revealed the global structure of thethermal tides in the upper cloud layer of Venus. The data were acquired over three Venusian years, andthe analysis was done over the areas from the equator to the midlatitudes in both hemispheres and over thewhole local time. Thermal tides at two vertical levels were analyzed by comparing data at two differentemission angles. Dynamical wave modes consisting of tides were identified; the diurnal tide consistedmainly of Rossby‐wave and gravity‐wave modes, while the semidiurnal tide predominantly consisted of agravity‐wave mode. The revealed vertical structures were roughly consistent with the above wave modes, butsome discrepancy remained if the waves were supposed to be monochromatic. In turn, the heating profilethat excites the tidal waves can be constrained to match this discrepancy, which would greatly advance theunderstanding of the Venusian atmosphere
Autoencoder-based detection of dynamic allostery triggered by ligand binding based on molecular dynamics
Yuko Tsuchiya, Kei Taneishi, Yasushige Yonezawa
J. Chem. Inf. Model. 2019; 59(9); 4043-4051.