PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal Processing

Reconstructing RGB image from RAW data obtained with a mobile device is related to a number of image signal processing (ISP) tasks, such as demosaicing, denoising, etc. Deep neural networks have shown promising results over hand-crafted ISP algorithms on solving these tasks separately, or even replacing the whole reconstruction process with one model. Here, we propose PyNET-CA, an end-to-end mobile ISP deep learning algorithm for RAW to RGB reconstruction. The model enhances PyNET, a recently proposed state-of-the-art model for mobile ISP, and improve its performance with channel attention and subpixel reconstruction module. We demonstrate the performance of the proposed method with comparative experiments and results from the AIM 2020 learned smartphone ISP challenge. The source code of our implementation is available at https://github.com/egyptdj/skyb-aim2020-publicComment: ECCV 2020 AIM workshop accepted versio

Kidney and Kidney Tumor Segmentation Using Two- stage Convolutional Neural Network

Kidney tumor is typically diagnosed using computed tomography (CT) imaging by investigating geometric features of kidney tumor. For a reliable diagnosis and treatment planning, kidney tumor quantification is necessary. However, manual segmentation by human requires time and expertise. In addition, inter/intra variability of segmentation results can lead to suboptimal decision. In this study, we propose the two-stage segmentation method using 2.5D and 3D convolutional neural network for kidney and kidney tumor delineation. The two stage model was trained with multi-task loss for pixel-wise cross-entropy loss function for segmentation task and mean square error function for regression task. Experimental results confirm that the proposed method effectively segments kidney and kidney tumor

Evaluation of Functional Decline in Alzheimer’s Dementia Using 3D Deep Learning and Group ICA for rs-fMRI Measurements

Purpose: To perform automatic assessment of dementia severity using a deep learning framework applied to resting-state functional magnetic resonance imaging (rs-fMRI) data.Method: We divided 133 Alzheimer’s disease (AD) patients with clinical dementia rating (CDR) scores from 0.5 to 3 into two groups based on dementia severity; the groups with very mild/mild (CDR: 0.5–1) and moderate to severe (CDR: 2–3) dementia consisted of 77 and 56 subjects, respectively. We used rs-fMRI to extract functional connectivity features, calculated using independent component analysis (ICA), and performed automated severity classification with three-dimensional convolutional neural networks (3D-CNNs) based on deep learning.Results: The mean balanced classification accuracy was 0.923 ± 0.042 (p < 0.001) with a specificity of 0.946 ± 0.019 and sensitivity of 0.896 ± 0.077. The rs-fMRI data indicated that the medial frontal, sensorimotor, executive control, dorsal attention, and visual related networks mainly correlated with dementia severity.Conclusions: Our CDR-based novel classification using rs-fMRI is an acceptable objective severity indicator. In the absence of trained neuropsychologists, dementia severity can be objectively and accurately classified using a 3D-deep learning framework with rs-fMRI independent components

Anthropomorphic Grasping of Complex-Shaped Objects Using Imitation Learning

This paper presents an autonomous grasping approach for complex-shaped objects using an anthropomorphic robotic hand. Although human-like robotic hands have a number of distinctive advantages, most of the current autonomous robotic pickup systems still use relatively simple gripper setups such as a two-finger gripper or even a suction gripper. The main difficulty of utilizing human-like robotic hands lies in the sheer complexity of the system; it is inherently tough to plan and control the motions of the high degree of freedom (DOF) system. Although data-driven approaches have been successfully used for motion planning of various robotic systems recently, it is hard to directly apply them to high-DOF systems due to the difficulty of acquiring training data. In this paper, we propose a novel approach for grasping complex-shaped objects using a high-DOF robotic manipulation system consisting of a seven-DOF manipulator and a four-fingered robotic hand with 16 DOFs. Human demonstration data are first acquired using a virtual reality controller with 6D pose tracking and individual capacitive finger sensors. Then, the 3D shape of the manipulation target object is reconstructed from multiple depth images recorded using the wrist-mounted RGBD camera. The grasping pose for the object is estimated using a residual neural network (ResNet), K-means clustering (KNN), and a point-set registration algorithm. Then, the manipulator moves to the grasping pose following the trajectory created by dynamic movement primitives (DMPs). Finally, the robot performs one of the object-specific grasping motions learned from human demonstration. The suggested system is evaluated by an official tester using five objects with promising results

Anthropomorphic Grasping of Complex-Shaped Objects Using Imitation Learning

This paper presents an autonomous grasping approach for complex-shaped objects using an anthropomorphic robotic hand. Although human-like robotic hands have a number of distinctive advantages, most of the current autonomous robotic pickup systems still use relatively simple gripper setups such as a two-finger gripper or even a suction gripper. The main difficulty of utilizing human-like robotic hands lies in the sheer complexity of the system; it is inherently tough to plan and control the motions of the high degree of freedom (DOF) system. Although data-driven approaches have been successfully used for motion planning of various robotic systems recently, it is hard to directly apply them to high-DOF systems due to the difficulty of acquiring training data. In this paper, we propose a novel approach for grasping complex-shaped objects using a high-DOF robotic manipulation system consisting of a seven-DOF manipulator and a four-fingered robotic hand with 16 DOFs. Human demonstration data are first acquired using a virtual reality controller with 6D pose tracking and individual capacitive finger sensors. Then, the 3D shape of the manipulation target object is reconstructed from multiple depth images recorded using the wrist-mounted RGBD camera. The grasping pose for the object is estimated using a residual neural network (ResNet), K-means clustering (KNN), and a point-set registration algorithm. Then, the manipulator moves to the grasping pose following the trajectory created by dynamic movement primitives (DMPs). Finally, the robot performs one of the object-specific grasping motions learned from human demonstration. The suggested system is evaluated by an official tester using five objects with promising results

Thickness-Dependent Superconductor-Insulator Transition of TaN Thin Film Grown with Atomic Layer Deposition

Atomic layer deposition (ALD) is a well-known method to grow a thin film which can ensure the uniformity and conformality of the grown film. In this work, TaN thin films with thicknesses ranging 8.9 nm to 32.6 nm were grown by using plasma enhanced ALD with Tris(diethylamido)(tert-butylimido)tantalum(TBTDET) precursor and H2 reactant. The electrical properties of grown films including carrier density, mobility, and Hall coefficient obtained from Hall effect measurements are presented. From the temperature-dependency of sheet resistance and Hall coefficient above superconducting critical temperature ~4.3 K, the thickest TaN film appeared strongly disordered with kFl ~ 0.4 and showed the unusual metallic behavior (d??/dT<0). The slope of Hall coefficient vs. sheet resistance plot was found to be more toward the strong localization limit, which would be a valid interpretation under the weak scattering assumption. Most relevantly, the critical temperature was extracted to keep decreasing as the film became thinner and thinner. From the thickness dependence of critical temperature, superconductor-insulator transition is expected to occur as the film thickness goes below ~18.5 nm

RoboCup@Home 2021 Domestic Standard Platform League Winner

Adoption of the World Robot Summit (WRS) rules and simulated environments for the RoboCup@Home Leagues in 2021 pose significant challenges for perception, manipulation and autonomy of the robot. Especially, the randomized item placement and longer task time highlight the need for a robust long-term autonomy that can recover from various failure cases. In this paper, we present how we have prepared our software for such challenges, which helped us to get the highest score among all the teams participated in RoboCup@Home 2021.N

Intracapillary LPL levels in brown adipose tissue, visualized with an antibody-based approach, are regulated by ANGPTL4 at thermoneutral temperatures.

Lipoprotein lipase (LPL) is secreted into the interstitial spaces by parenchymal cells and then transported into capillaries by GPIHBP1. LPL carries out the lipolytic processing of triglyceride (TG)-rich lipoproteins (TRLs), but the tissue-specific regulation of LPL is incompletely understood. Plasma levels of TG hydrolase activity after heparin injection are often used to draw inferences about intravascular LPL levels, but the validity of these inferences is unclear. Moreover, plasma TG hydrolase activity levels are not helpful for understanding LPL regulation in specific tissues. Here, we sought to elucidate LPL regulation under thermoneutral conditions (30 °C). To pursue this objective, we developed an antibody-based method to quantify (in a direct fashion) LPL levels inside capillaries. At 30 °C, intracapillary LPL levels fell sharply in brown adipose tissue (BAT) but not heart. The reduced intracapillary LPL levels were accompanied by reduced margination of TRLs along capillaries. ANGPTL4 expression in BAT increased fourfold at 30 °C, suggesting a potential explanation for the lower intracapillary LPL levels. Consistent with that idea, Angptl4 deficiency normalized both LPL levels and TRL margination in BAT at 30 °C. In Gpihbp1-/- mice housed at 30 °C, we observed an ANGPTL4-dependent decrease in LPL levels within the interstitial spaces of BAT, providing in vivo proof that ANGPTL4 regulates LPL levels before LPL transport into capillaries. In conclusion, our studies have illuminated intracapillary LPL regulation under thermoneutral conditions. Our approaches will be useful for defining the impact of genetic variation and metabolic disease on intracapillary LPL levels and TRL processing