The PandaX-4T experiment, a four-ton scale dark matter direct detection experiment, is being planned at the China Jinping Underground Laboratory. In this paper we present a simulation study of the expected background in this experiment. In a 2.8-ton fiducial mass and the signal region between 1 to 10 keV electron equivalent energy, the total electron recoil background is found to be 4.9 · 10 −5 (kg · day · keV) −1 . The nuclear recoil background in the same region is 2.8 · 10 −7 (kg · day · keV) −1 . With an exposure of 5.6 ton-years, the sensitivity of PandaX-4T could reach a minimum spin-independent dark matter-nucleon cross section of 6 · 10 −48 cm 2 at a dark matter mass of 40 GeV/c 2 .
We present PandaX-II constraints on candidate WIMP-nucleon effective interactions involving the nucleon or WIMP spin, including, in addition to standard axial spin-dependent (SD) scattering, various couplings among vector and axial currents, magnetic and electric dipole moments, and tensor interactions. The data set corresponding to a total exposure of 54-ton-days is reanalyzed to determine constraints as a function of the WIMP mass and isospin coupling. We obtain WIMP-nucleon cross section bounds of 1.6 × 10 −41 cm 2 and 9.0 × 10 −42 cm 2 (90% c.l.) for neutron-only SD and tensor coupling, respectively, for a mass M WIMP ∼ 40 GeV/c 2 . The SD limits are the best currently available for M WIMP > 40 GeV/c 2 . We show that PandaX-II has reached a sensitivity sufficient to probe a variety of other candidate spin-dependent interactions at the weak scale.
Brain-machine interface (BMI) can be used to control the robotic arm to assist paralysis people for performing activities of daily living. However, it is still a complex task for the BMI users to control the process of objects grasping and lifting with the robotic arm. It is hard to achieve high efficiency and accuracy even after extensive trainings. One important reason is lacking of sufficient feedback information for the user to perform the closed-loop control. In this study, we proposed a method of augmented reality (AR) guiding assistance to provide the enhanced visual feedback to the user for a closed-loop control with a hybrid Gaze-BMI, which combines the electroencephalography (EEG) signals based BMI and the eye tracking for an intuitive and effective control of the robotic arm. Experiments for the objects manipulation tasks while avoiding the obstacle in the workspace are designed to evaluate the performance of our method for controlling the robotic arm. According to the experimental results obtained from eight subjects, the advantages of the proposed closed-loop system (with AR feedback) over the open-loop system (with visual inspection only) have been verified. The number of trigger commands used for controlling the robotic arm to grasp and lift the objects with AR feedback has reduced significantly and the height gaps of the gripper in the lifting process have decreased more than 50% compared to those trials with normal visual inspection only. The results reveal that the hybrid Gaze-BMI user can benefit from the information provided by the AR interface, improving the efficiency and reducing the cognitive load during the grasping and lifting processes.
Low-dose CT (LDCT) images have been widely applied in the medical imaging field due to the potential risk of exposing patients to X-ray radiations. Given the fact that reducing the radiation dose may result in increased noise and artifacts, methods that can eliminate the noise and artifacts in the LDCT image have drawn increasing attentions and produced impressive results over the past decades. However, recent proposed methods mostly suffer from noise remaining, over-smoothing structures or false lesions derived from noise. In this paper, we propose a generative adversarial network (GAN) with novel architecture and loss function for restoring the LDCT image. Firstly, the inception-residual block and residual mapping are incoporated in the U-Net structure. The modified U-Net is applied as the generator of the GAN network so that the noise feature can be eliminated during the forward propagation. Secondly, a novel multi-level joint discriminator is designed by concatenating multiple convolutional neural networks (CNNs) where the output of each deconvolutional layer in the generator is compared with the corresponding down-sampled ground truth image. The adversarial training can be sensitive to noise and artifacts in different scales with this discriminator. Thirdly, we novely define a loss function consisting of the least square adversarial loss, VGG based perceptual loss, MSE based pixel loss and the noise loss, so that the differences in pixel, visual perception and noise distribution are comprehensively considered to optimize the network. Experimental results on both simulated and official simulated clinical images have demonstrated that the proposed method can provide superior performance to the state-of-the-art methods in noise removal, structure preservation and false lesions elimination. INDEX TERMS Low-dose CT image denoising, deep learning, generative adversarial network, inception block, residual mapping, joint loss.
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