MotionAug: Augmentation with Physical Correction for Human Motion Prediction

This paper presents a motion data augmentation scheme incorporating motion synthesis encouraging diversity and motion correction imposing physical plausibility. This motion synthesis consists of our modified Variational AutoEncoder (VAE) and Inverse Kinematics (IK). In this VAE, our proposed sampling-near-samples method generates various valid motions even with insufficient training motion data. Our IK-based motion synthesis method allows us to generate a variety of motions semi-automatically. Since these two schemes generate unrealistic artifacts in the synthesized motions, our motion correction rectifies them. This motion correction scheme consists of imitation learning with physics simulation and subsequent motion debiasing. For this imitation learning, we propose the PD-residual force that significantly accelerates the training process. Furthermore, our motion debiasing successfully offsets the motion bias induced by imitation learning to maximize the effect of augmentation. As a result, our method outperforms previous noise-based motion augmentation methods by a large margin on both Recurrent Neural Network-based and Graph Convolutional Network-based human motion prediction models. The code is available at https://github.com/meaten/MotionAug.Comment: Accepted at CVPR202

Fast Inference and Update of Probabilistic Density Estimation on Trajectory Prediction

Safety-critical applications such as autonomous vehicles and social robots require fast computation and accurate probability density estimation on trajectory prediction. To address both requirements, this paper presents a new normalizing flow-based trajectory prediction model named FlowChain. FlowChain is a stack of conditional continuously-indexed flows (CIFs) that are expressive and allow analytical probability density computation. This analytical computation is faster than the generative models that need additional approximations such as kernel density estimation. Moreover, FlowChain is more accurate than the Gaussian mixture-based models due to fewer assumptions on the estimated density. FlowChain also allows a rapid update of estimated probability densities. This update is achieved by adopting the \textit{newest observed position} and reusing the flow transformations and its log-det-jacobians that represent the \textit{motion trend}. This update is completed in less than one millisecond because this reuse greatly omits the computational cost. Experimental results showed our FlowChain achieved state-of-the-art trajectory prediction accuracy compared to previous methods. Furthermore, our FlowChain demonstrated superiority in the accuracy and speed of density estimation. Our code is available at \url{https://github.com/meaten/FlowChain-ICCV2023}Comment: Accepted at ICCV202

Immunostimulation-Mediated Anti-tumor Activity of Bamboo (Sasa senanensis) Leaf Extracts Obtained Under ‘Vigorous’ Condition

Traditional Japanese medicine uses the leaves of Kumaizasa bamboo extracted in hot water at 100°C. For this study, we developed a new, ‘vigorous’ extraction method involving steps at 100, 121 and 196°C. This procedure not only yielded greater amounts of extract but also with significant increase in immunostimulating activity, which induces activation of human natural killer (NK) cells, macrophages and potent induction of IL-2, IL-12 and IFN-γ in tumor bearing mice. The efficacy of the extract to facilitate phagocytosis and nitric oxide production by mouse peritoneal macrophages was determined and compared with that of 1,3-β-glucan. Anti-tumor activity was evaluated in vivo in several mouse tumor models (S-180, C38 and Meth-A). Oral administration of the extracts was carried out when tumor reached size of approximately 6 mm at concentrations of 0.05% or higher. The extracts significantly suppressed tumor growth in S-180 and C38 tumor models. Overall survival was significantly prolonged in the treatment group than that of control. Activation of macrophages and NK cells by the extracts suggests that the anti-tumor efficacy of the extract is mediated by immunopotentiation. The extracts resolved into three major fractions (F-I, F-II and F-III) in Sephadex gel chromatography. Fraction F-I consists of 1,3-β-glucan and stimulated both macrophages and NK cells suggesting that it may be the primary immunopotentiating factor in suppressing cancer. Fraction F-III has potent free radical scavenging effects and may play an important role in cancer prevention. These results warrant further translation and clinical investigations

Remote defect imaging for plate-like structures based on the scanning laser source technique

In defect imaging with a scanning laser source technique, the use of a fixed receiver realizes stable measurements of flexural waves generated by laser at multiple rastering points. This study discussed the defect imaging by remote measurements using a laser Doppler vibrometer as a receiver. Narrow-band burst waves were generated by modulating laser pulse trains of a fiber laser to enhance signal to noise ratio in frequency domain. Averaging three images obtained at three different frequencies suppressed spurious distributions due to resonance. The experimental system equipped with these newly-devised means enabled us to visualize defects and adhesive objects in plate-like structures such as a plate with complex geometries and a branch pipe.44TH ANNUAL REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION, VOLUME 37, 16–21 July 2017, Provo, Utah, USATakahiro Hayashi, Atsuya Maeda, and Shogo Nakao, "Remote defect imaging for plate-like structures based on the scanning laser source technique", AIP Conference Proceedings 1949, 090006 (2018) https://doi.org/10.1063/1.503156