Existing part-aware person re-identification methods typically employ two separate steps namely, body part detection and part-level feature extraction. However, part detection introduces an additional computational cost and is inherently challenging for low-quality images. Accordingly, in this work, we propose a simple framework named Batch Coherence-Driven Network (BCD-Net) that bypasses body part detection during both the training and testing phases while still learning semantically aligned part features. Our key observation is that the statistics in a batch of images are stable, and therefore that batch-level constraints are robust. First, we introduce a batch coherence-guided channel attention (BCCA) module that highlights the relevant channels for each respective part from the output of a deep backbone model. We investigate channel-part correspondence using a batch of training images, then impose a novel batch-level supervision signal that helps BCCA to identify part-relevant channels. Second, the mean position of a body part is robust and consequently coherent between batches throughout the training process. Accordingly, we introduce a pair of regularization terms based on the semantic consistency between batches. The first term regularizes the high responses of BCD-Net for each part on one batch in order to constrain it within a predefined area, while the second encourages the aggregate of BCD-Net's responses for all parts covering the entire human body.  https://www.selleckchem.com/products/ly333531.html  The above constraints guide BCD-Net to learn diverse, complementary, and semantically aligned part-level features. Extensive experimental results demonstrate that BCD-Net consistently achieves state-of-the-art performance on four large-scale ReID benchmarks.Haze-free images are the prerequisites of many vision systems and algorithms, and thus single image dehazing is of paramount importance in computer vision. In this field, prior-based methods have achieved initial success. However, they often introduce annoying artifacts to outputs because their priors can hardly fit all situations. By contrast, learning-based methods can generate more natural results. Nonetheless, due to the lack of paired foggy and clear outdoor images of the same scenes as training samples, their haze removal abilities are limited. In this work, we attempt to merge the merits of prior-based and learning-based approaches by dividing the dehazing task into two sub-tasks, i.e., visibility restoration and realness improvement. Specifically, we propose a two-stage weakly supervised dehazing framework, RefineDNet. In the first stage, RefineDNet adopts the dark channel prior to restore visibility. Then, in the second stage, it refines preliminary dehazing results of the first stage to improve realness via adversarial learning with unpaired foggy and clear images. To get more qualified results, we also propose an effective perceptual fusion strategy to blend different dehazing outputs. Extensive experiments corroborate that RefineDNet with the perceptual fusion has an outstanding haze removal capability and can also produce visually pleasing results. Even implemented with basic backbone networks, RefineDNet can outperform supervised dehazing approaches as well as other state-of-the-art methods on indoor and outdoor datasets. To make our results reproducible, relevant code and data are available at https//github.com/xiaofeng94/RefineDNet-for-dehazing.In this study, effects of rare-earth elements such as Nd, Gd, and Ce on the structural and the electrical properties of lead-free bismuth sodium potassium barium titanate Bi0.487Na0.427K0.06Ba0.026TiO3 (0.854BNT-0.12BKT-0.026BT) (BNKBT) ceramics have been investigated in detail. Solid-state reaction method was used to prepare undoped, 1.0 mol% Nd, 1.0 mol% Gd, 1.0 mol%, 2.1 mol%, and 2.7 mol% Ce doped BNKBT ceramic powder compositions. A pure single perovskite structure was observed in the XRD patterns for all the BNKBT ceramic systems, although doping was found to cause changes in the peak splitting and peak positions due to their site preference. The Curie temperatures have not shifted significantly with doping, but the relative permittivity values were found to have increased. The non-ergodic normal ferroelectric character of undoped BNKBT ceramic switched to an ergodic relaxor character at room temperature with Nd, Gd, and Ce doping with pinched polarization vs electric field hysteresis loops. Increased field induced strain levels were observed in the doped BNKBT ceramics with 1 mole% Ce doping yielding a giant field induced strain of 0.38% under an E-field of 65 kV/cm. Nd-doping, on the other hand, resulted in the highest releasable energy density of 0.64 J/cm3 at 65 kV/cm. Consequently, the rare-earth doped BNKBT ceramics were found to be promising for both digital actuator and high energy density capacitor applications due to their favorable electrical properties.Only one High Intensity Focused Ultrasound device has been clinically approved for transcranial brain surgery at the time of writing. The device operates within 650 kHz and 720 kHz and corrects the phase distortions induced by the skull of each patient using a multi-element phased array. Phase correction is estimated adaptively using a proprietary algorithm based on computed-tomography (CT) images of the patient's skull. In this paper, we assess the performance of the phase correction computed by the clinical device and compare it to (i) the correction obtained with a previously validated full-wave simulation algorithm using an open-source pseudo-spectral toolbox and (ii) a hydrophone-based correction performed invasively to measure the aberrations induced by the skull at 650 kHz. For the full-wave simulation, three different mappings between CT Hounsfield units and the longitudinal speed of sound inside the skull were tested. All methods are compared with the exact same setup thanks to transfer matrices acquired with the clinical system for N=5 skulls and T=2 different targets for each skull. We show that the clinical ray-tracing software and the full-wave simulation restore respectively 84 +/- 5% and 86 +/- 5% of the pressure obtained with hydrophone-based correction for targets located in central brain regions. On the second target (off-center), we also report that the performance of both algorithms degrades when the average incident angles of the acoustic beam at the skull surface increases. When incident angles are higher than 200, the restored pressure drops below 75% of the pressure restored with hydrophone-based correction.