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Molecular along with Supramolecular Constructions regarding Triiodides as well as Polyiodobismuthates involving Phenylenediammonium and its particular In,N-dimethyl By-product.
The proposed SG-Net is applied to typical Transformer encoders. Extensive experiments on popular benchmark tasks, including machine reading comprehension, natural language inference, and neural machine translation show the effectiveness of the proposed SG-Net design.Weakly supervised object detection has attracted great attention in the computer vision community. Pitstop 2 order Although numerous deep learning-based approaches have been proposed in the past years, such an ill-posed problem is still challenging and the learning performance is behind the expectation. Most of the existing approaches only consider the visual appearance of each proposal region but ignore to adopt the helpful context information. To this end, this paper introduces two levels of context into the weakly supervised learning framework. The first one is the proposal-level context, i.e., the relationship of the spatially adjacent proposals. The second one is the semantic-level context, i.e., the relationship of the co-occurring object categories. Therefore, the proposed weakly supervised learning framework contains not only the cognition process on the visual appearance but also the reasoning process on the proposal- and semantic-level relationships, which leads to the novel deep multiple instance reasoning framework. Specifically, built upon a conventional CNN-based network architecture, the proposed framework is equipped with two additional graph convolutional network-based reasoning models to implement object location reasoning and multi-label reasoning within an end-to-end network training procedure. Experiments on the PASCAL VOC benchmarks have been implemented, which demonstrate the superior capacity of the proposed approach.The advances made in predicting visual saliency using deep neural networks come at the expense of collecting large-scale annotated data. However, pixel-wise annotation is labor-intensive and overwhelming. In this paper, we propose to learn saliency prediction from a single noisy labelling, which is easy to obtain (e.g., from imperfect human annotation or from unsupervised saliency prediction methods). With this goal, we address a natural question can we learn saliency prediction while identifying clean labels in a unified framework? To answer this question, we call on the theory of robust model fitting and formulate deep saliency prediction from a single noisy labelling as robust network learning and exploit model consistency across iterations to identify inliers and outliers (i.e., noisy labels). Extensive experiments on different benchmark datasets demonstrate the superiority of our proposed framework, which can learn comparable saliency prediction with state-of-the-art fully supervised saliency methods. Furthermore, we show that simply by treating ground truth annotations as noisy labelling, our framework achieves tangible improvements over state-of-the-art methods.The principal rank-one (RO) components of an image represent the self-similarity of the image, which is an important property for image restoration. However, the RO components of a corrupted image could be decimated by the procedure of image denoising. We suggest that the RO property should be utilized and the decimation should be avoided in image restoration. To achieve this, we propose a new framework comprised of two modules, i.e., the RO decomposition and RO reconstruction. The RO decomposition is developed to decompose a corrupted image into the RO components and residual. This is achieved by successively applying RO projections to the image or its residuals to extract the RO components. The RO projections, based on neural networks, extract the closest RO component of an image. The RO reconstruction is aimed to reconstruct the important information, respectively from the RO components and residual, as well as to restore the image from this reconstructed information. Experimental results on four tasks, i.e., noise-free image super-resolution (SR), realistic image SR, gray-scale image denoising, and color image denoising, show that the method is effective and efficient for image restoration, and it delivers superior performance for realistic image SR and color image denoising.Camera calibration is among the most challenging aspects of the investigation of fluid flows around complex transparent geometries, due to the optical distortions caused by the refraction of the lines-of-sight at the solid/fluid interfaces. This work presents a camera model which exploits the pinhole-camera approximation and represents the refraction of the lines-of-sight directly via Snell's law. The model is based on the computation of the optical ray distortion in the 3D scene and dewarping of the object points to be projected. The present procedure is shown to offer a faster convergence rate and greater robustness than other similar methods available in the literature. Issues inherent to estimation of the refractive extrinsic and intrinsic parameters are discussed and feasible calibration approaches are proposed. The effects of image noise, volume size of the control point grid and number of cameras on the calibration procedure are analyzed. Finally, an application of the camera model to the 3D optical velocimetry measurements of thermal convection inside a polymethylmethacrylate (PMMA) cylinder immersed in water is presented. A specific calibration procedure is designed for such a challenging experiment where the cylinder interior is not physically accessible and its effectiveness is demonstrated by providing velocity field reconstructions.This paper aims to build a supervised classifier in presence of imbalanced datasets, uncertain class proportions, dependencies between features, presence of both numeric and categorical features, and arbitrary loss functions. The Bayesian classifier suffers when prior probability shifts occur between the training and the testing sets. A solution is to look for an equalizer classifier whose class-conditional risks are equal. Such a classifier always corresponds to a minimax classifier which maximizes the Bayes risk. We develop a novel box-constrained minimax classifier which takes into account some constraints on the class proportions to control the risk maximization. We analyze the empirical Bayes risk with respect to the box-constrained class proportions for discrete inputs. We show that the risk is a concave non-differentiable multivariate piecewise affine function. A projected subgradient algorithm is derived to find the maximum of the risk. Its convergence is established and its speed is bounded. The optimization algorithm is scalable when the number of classes is large.
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