No abstract

In this paper, the gold grade series along drifts have been analyzed using two methods, rescaled range analysis and lacunarity analysis which are commonly used in nonlinear systems analysis. The aim of this study is to better understand the ore-forming processes and identify the local mineralized intensity and interactions that influence the spatial structure of gold element grade distribution, in the Dayingezhuang fault-controlled, disseminated-veinlet gold deposit in the Jiaodong gold province, eastern China. The result shows that the efficiency of two methods, in distinguishing between weakly mineralized, moderately mineralized and intensely mineralized of ore-forming area. It is obvious that the two parameters of both Hurst and lacunarity index in the weakly mineralized drifts are distinguished from those in the mineralized drifts, and the lower the index is, the more homogeneously distributed of the elements and the mineral intensity is relatively smaller. The methods used in this paper provide a relatively comprehensive description for local mineral intensity, offering an evidence for the identification of mineralization intensity and providing a guidance for further determination to the extent of deposit concentration and delineation of target mineralization zone.

Recently, the problem of inaccurate learning targets in crowd counting draws increasing attention. Inspired by a few pioneering work, we solve this problem by trying to predict the indices of pre-defined interval bins of counts instead of the count values themselves. However, an inappropriate interval setting might make the count error contributions from different intervals extremely imbalanced, leading to inferior counting performance. Therefore, we propose a novel count interval partition criterion called Uniform Error Partition (UEP), which always keeps the expected counting error contributions equal for all intervals to minimize the prediction risk. Then to mitigate the inevitably introduced discretization errors in the count quantization process, we propose another criterion called Mean Count Proxies (MCP). The MCP criterion selects the best count proxy for each interval to represent its count value during inference, making the overall expected discretization error of an image nearly negligible. As far as we are aware, this work is the first to delve into such a classification task and ends up with a promising solution for count interval partition. Following the above two theoretically demonstrated criterions, we propose a simple yet effective model termed Uniform Error Partition Network (UEP-Net), which achieves state-of-the-art performance on several challenging datasets. The codes will be available at: TencentYoutuResearch/CrowdCounting-UEPNet.