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To further improve the accuracy of the homogenization, we extract the image patch set that is invariant to degradation changes as Robust Neighbor Resources (RNR), with which these two homogenization projections re-express the input LR images and the initial inferred HR images successively. Both quantitative and qualitative results on the public datasets demonstrate the effectiveness of the proposed algorithm against the state-of-the-art methods.The amount of videos over the Internet and electronic surveillant cameras is growing dramatically, meanwhile paired sentence descriptions are significant clues to select attentional contents from videos. The task of natural language moment retrieval (NLMR) has drawn great interests from both academia and industry, which aims to associate specific video moments with the text descriptions figuring complex scenarios and multiple activities. In general, NLMR requires temporal context to be properly comprehended, and the existing studies suffer from two problems (1) limited moment selection and (2) insufficient comprehension of structural context. To address these issues, a multi-agent boundary-aware network (MABAN) is proposed in this work. To guarantee flexible and goal-oriented moment selection, MABAN utilizes multi-agent reinforcement learning to decompose NLMR into localizing the two temporal boundary points for each moment. Specially, MABAN employs a two-phase cross-modal interaction to exploit the rich contextual semantic information. Moreover, temporal distance regression is considered to deduce the temporal boundaries, with which the agents can enhance the comprehension of structural context. Extensive experiments are carried out on two challenging benchmark datasets of ActivityNet Captions and Charades-STA, which demonstrate the effectiveness of the proposed approach as compared to state-of-the-art methods. The project page can be found in https//mic.tongji.edu.cn/e5/23/c9778a189731/page.htm.Existing vision-based action recognition is susceptible to occlusion and appearance variations, while wearable sensors can alleviate these challenges by capturing human motion with one-dimensional time-series signals (e.g. acceleration, gyroscope, and orientation). For the same action, the knowledge learned from vision sensors (videos or images) and wearable sensors, may be related and complementary. However, there exists a significantly large modality difference between action data captured by wearable-sensor and vision-sensor in data dimension, data distribution, and inherent information content. In this paper, we propose a novel framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) by adaptively transferring and distilling the knowledge from multiple wearable sensors. The SAKDN uses multiple wearable-sensors as teacher modalities and uses RGB videos as student modalities. SW033291 molecular weight To preserve the local temporal relationshities. The code is publicly available at https//github.com/YangLiu9208/SAKDN.Low-dose computed tomography (LDCT) scans, which can effectively alleviate the radiation problem, will degrade the imaging quality. In this paper, we propose a novel LDCT reconstruction network that unrolls the iterative scheme and performs in both image and manifold spaces. Because patch manifolds of medical images have low-dimensional structures, we can build graphs from the manifolds. Then, we simultaneously leverage the spatial convolution to extract the local pixel-level features from the images and incorporate the graph convolution to analyze the nonlocal topological features in manifold space. The experiments show that our proposed method outperforms both the quantitative and qualitative aspects of state-of-the-art methods. In addition, aided by a projection loss component, our proposed method also demonstrates superior performance for semi-supervised learning. The network can remove most noise while maintaining the details of only 10% (40 slices) of the training data labeled.Correction of B1 field non-uniformity is critical for many quantitative MRI methods including variable flip angle (VFA) T1 mapping and single-point macromolecular proton fraction (MPF) mapping. The latter method showed promising results as a fast and robust quantitative myelin imaging approach and involves VFA-based R1=1/T1 map reconstruction as an intermediate processing step. The need for B1 correction restricts applications of the above methods, since B1 mapping sequences increase the examination time and are not commonly available in clinics. A new algorithm was developed to enable retrospective data-driven simultaneous B1 correction in VFA R1 and single-point MPF mapping. The principle of the algorithm is based on different mathematical dependences of B1-related errors in R1 and MPF allowing extraction of a surrogate B1 field map from uncorrected R1 and MPF maps. To validate the method, whole-brain R1 and MPF maps with isotropic 1.25 mm3 resolution were obtained on a 3 T MRI scanner from 11 volunteers. Mean parameter values in segmented brain tissues were compared between three reconstruction options including the absence of correction, actual B1 correction, and surrogate B1 correction. Surrogate B1 maps closely reproduced actual patterns of B1 inhomogeneity. Without correction, B1 non-uniformity caused highly significant biases in R1 and MPF (P less then 0.001). Surrogate B1 field correction reduced the biases in both R1 and MPF to a non-significant level (0.1≤P≤0.8). The described algorithm obviates the use of dedicated B1 mapping sequences in fast single-point MPF mapping and provides an alternative solution for correction of B1 non-uniformities in VFA R1 mapping.In this article, assistance to bone cement in- jection is studied, with a focus on vertebroplasty, a procedure dedicated to the treatment of painful vertebral compression fractures. A robotic system that can remotely be operated at pressures up to 140 bar is presented. It is specifically designed to improve cement polymerization control, combining a cold passive exchanger that slows down the cement curing in the syringe, and an active exchanger that controls the injected cement temperature. The cement remote injection uses a rate control teleoperation strategy, particularly well suited for very slow injection speeds. During the injection, force feedback is rendered to the radiologist to help monitor the cement viscosity increase. A first assessment in laboratory conditions has been achieved to quantify the performance of the thermal exchanger. Then, cadaver experiments have been performed to illustrate the satisfactory operation of the whole system.
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