A venous occlusion plethysmography using a load cell as the sensing element

An application of the load cell as a sensor in venous occlusion plethysmography is presented. In this method the limb volume changes that follow venous occlusion are converted into water volume changes using a water tank for volume change detection. The hydrostatic pressure, as well as the water surface level, is measured and used for the calculation of the volume change. By using this method the influence of water pressure on limb blood flow, as well as drift and leakage of the sensing element, is avoided. The load cell has the advantage of measuring the weight of the displaced water volume, which simplifies the design principles of the plethysmography. The plethysmography is found to be sensitive, highly linear, and easy to handle. It has been evaluated in several subjects, and the results of these studies are in agreement with earlier results </p

Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture

Learning to represent and generate videos from unlabeled data is a very challenging problem. To generate realistic videos, it is important not only to ensure that the appearance of each frame is real, but also to ensure the plausibility of a video motion and consistency of a video appearance in the time direction. The process of video generation should be divided according to these intrinsic difficulties. In this study, we focus on the motion and appearance information as two important orthogonal components of a video, and propose Flow-and-Texture-Generative Adversarial Networks (FTGAN) consisting of FlowGAN and TextureGAN. In order to avoid a huge annotation cost, we have to explore a way to learn from unlabeled data. Thus, we employ optical flow as motion information to generate videos. FlowGAN generates optical flow, which contains only the edge and motion of the videos to be begerated. On the other hand, TextureGAN specializes in giving a texture to optical flow generated by FlowGAN. This hierarchical approach brings more realistic videos with plausible motion and appearance consistency. Our experiments show that our model generates more plausible motion videos and also achieves significantly improved performance for unsupervised action classification in comparison to previous GAN works. In addition, because our model generates videos from two independent information, our model can generate new combinations of motion and attribute that are not seen in training data, such as a video in which a person is doing sit-up in a baseball ground.Comment: Our supplemental material is available on http://www.mi.t.u-tokyo.ac.jp/assets/publication/hierarchical_video_generation_sup/ Accepted to AAAI201

Character expression for spoken dialogue systems with semi-supervised learning using Variational Auto-Encoder

Character of spoken dialogue systems is important not only for giving a positive impression of the system but also for gaining rapport from users. We have proposed a character expression model for spoken dialogue systems. The model expresses three character traits (extroversion, emotional instability, and politeness) of spoken dialogue systems by controlling spoken dialogue behaviors: utterance amount, backchannel, filler, and switching pause length. One major problem in training this model is that it is costly and time-consuming to collect many pair data of character traits and behaviors. To address this problem, semi-supervised learning is proposed based on a variational auto-encoder that exploits both the limited amount of labeled pair data and unlabeled corpus data. It was confirmed that the proposed model can express given characters more accurately than a baseline model with only supervised learning. We also implemented the character expression model in a spoken dialogue system for an autonomous android robot, and then conducted a subjective experiment with 75 university students to confirm the effectiveness of the character expression for specific dialogue scenarios. The results showed that expressing a character in accordance with the dialogue task by the proposed model improves the user’s impression of the appropriateness in formal dialogue such as job interview

Mass spectrometry of hydrogen/deuterium exchange in 70S ribosomal proteins from E. coli

AbstractThe 70S ribosome from Escherichia coli is a supermacro complex (MW: 2.7MDa) comprising three RNA molecules and more than 50 proteins. We have for the first time successfully analyzed the flexibility of 70S ribosomal proteins in solution by detecting the hydrogen/deuterium exchange with mass spectrometry. Based on the deuterium incorporation map of the X-ray structure obtained at the time of each exchange, we demonstrate the structure–flexibility–function relationship of ribosome focusing on the deuterium incorporation of the proteins binding ligands (tRNA, mRNA, and elongation factor) and the relation with structural assembly processes