Microwave and hard X-ray emissions during the impulsive phase of solar flares: Nonthermal electron spectrum and time delay

On the basis of the summing-up and analysis of the observations and theories about the impulsive microwave and hard X-ray bursts, the correlations between these two kinds of emissions were investigated. It is shown that it is only possible to explain the optically-thin microwave spectrum and its relations with the hard X-ray spectrum by means of the nonthermal source model. A simple nonthermal trap model in the mildly-relativistic case can consistently explain the main characteristics of the spectrum and the relative time delays

Optimal Controller and Filter Realisations using Finite-precision, Floating- point Arithmetic.

The problem of reducing the fragility of digital controllers and filters implemented using finite-precision, floating-point arithmetic is considered. Floating-point arithmetic parameter uncertainty is multiplicative, unlike parameter uncertainty resulting from fixed-point arithmetic. Based on first- order eigenvalue sensitivity analysis, an upper bound on the eigenvalue perturbations is derived. Consequently, open-loop and closed-loop eigenvalue sensitivity measures are proposed. These measures are dependent upon the filter/ controller realization. Problems of obtaining the optimal realization with respect to both the open-loop and the closed-loop eigenvalue sensitivity measures are posed. The problem for the open-loop case is completely solved. Solutions for the closed-loop case are obtained using non-linear programming. The problems are illustrated with a numerical example

A method for modal loss factor estimation based on Gauss-Newton iteration

A new approach based on Gauss-Newton iteration is proposed to estimate modal damping. Noise resistance of the proposed method and half-power bandwidth method are analyzed and compared by plenty of simulations with different signal-to-noise ratios (SNR). The proposed method is more accurate and stable than half-power bandwidth method in all SNRs, especially when the noise level is high. If SNR ≤ 30 dB, the proposed method should be used for damping estimation instead of half-power bandwidth method. A damping estimation experiment is carried out with both methods, and the results indicate and verify that there is smaller variability for the proposed method

On Ranking Consistency of Pre-ranking Stage

Industrial ranking systems, such as advertising systems, rank items by aggregating multiple objectives into one final objective to satisfy user demand and commercial intent. Cascade architecture, composed of retrieval, pre-ranking, and ranking stages, is usually adopted to reduce the computational cost. Each stage may employ various models for different objectives and calculate the final objective by aggregating these models' outputs. The multi-stage ranking strategy causes a new problem - the ranked lists of the ranking stage and previous stages may be inconsistent. For example, items that should be ranked at the top of the ranking stage may be ranked at the bottom of previous stages. In this paper, we focus on the \textbf{ranking consistency} between the pre-ranking and ranking stages. Specifically, we formally define the problem of ranking consistency and propose the Ranking Consistency Score (RCS) metric for evaluation. We demonstrate that ranking consistency has a direct impact on online performance. Compared with the traditional evaluation manner that mainly focuses on the individual ranking quality of every objective, RCS considers the ranking consistency of the fused final objective, which is more proper for evaluation. Finally, to improve the ranking consistency, we propose several methods from the perspective of sample selection and learning algorithms. Experimental results on one of the biggest industrial E-commerce platforms in China validate the efficacy of the proposed metrics and methods.Comment: 9 pagees, 5 figure

Scene Graph Generation with External Knowledge and Image Reconstruction

Scene graph generation has received growing attention with the advancements in image understanding tasks such as object detection, attributes and relationship prediction,~\etc. However, existing datasets are biased in terms of object and relationship labels, or often come with noisy and missing annotations, which makes the development of a reliable scene graph prediction model very challenging. In this paper, we propose a novel scene graph generation algorithm with external knowledge and image reconstruction loss to overcome these dataset issues. In particular, we extract commonsense knowledge from the external knowledge base to refine object and phrase features for improving generalizability in scene graph generation. To address the bias of noisy object annotations, we introduce an auxiliary image reconstruction path to regularize the scene graph generation network. Extensive experiments show that our framework can generate better scene graphs, achieving the state-of-the-art performance on two benchmark datasets: Visual Relationship Detection and Visual Genome datasets.Comment: 10 pages, 5 figures, Accepted in CVPR 201