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Petrogenesis of late Mesozoic high-Ba-Sr granites in the Qiushuwan Cu-Mo orefield: Implications for the distribution of porphyry Mo mineralization in the East Qinling area of Central China

The Qinling-Dabie orogenic belt of China is well known for its large Mo resources. There have been many studies of the genesis of the Mo deposits, but the reasons for the intensive mineralization during the late Mesozoic and the spatially uneven distribution of the resulting ore deposits are not fully understood. Deposits with reserves of >6 Mt. of Mo occur predominantly in the southern margin of the North China Craton (NCC) and are mostly late Mesozoic porphyry deposits. In comparison, a few relatively insignificant and Cu-dominated porphyry ore deposits have been identified in the North and South Qinling terranes. This study presents new whole-rock geochemical, isotopic, and zircon U-Pb age data for ore-bearing granites associated with the Qiushuwan Cu-Mo deposit, the largest porphyry deposit in the North Qinling (NQ) terrane, which provide insights into the asymmetric distribution of Mo mineralization in this region. The rocks display high-K calc-alkaline to shoshonitic affinities, and are metaluminous to weakly peraluminous with high Ba and Sr contents, low Rb, Nb, and Ta contents, and depletion in heavy rare earth elements and Y, which are features typical of high-Ba-Sr granites. Zircon U-Pb dating indicates that the granites crystallized at ca 145 Ma. Their narrow ranges of whole-rock initial Sr-87/Sr-86 ratios (0.7056-0.7069) and epsilon(Nd)(t) (-7.4 to -9.7), zircon epsilon(Hf)(t) (-2.49 to -5.13), and delta O-18 (+5.58 parts per thousand to +6.49 parts per thousand) values, together with the presence of significant Nd-Hf isotopic decoupling (Delta(Hf) = +7.61 to +11.3), indicate that the parent magma was derived from partial melting of enriched subcontinental lithospheric mantle with minor assimilation of the lower crust. In contrast, late Mesozoic granitic rocks in the southern margin of the NCC (including both mineralized and barren intrusions) are geochemically associated with the crust of the northern margin of the Yangtze Block (YB). The YB contains Mo-rich shales that are thought to have been subducted beneath the southern margin of the NCC during the Triassic. Melting of these Mo-rich black shales would have mobilized Mo, a process that enabled the formation of the extensive Mo mineralization associated with the late Mesozoic granites of the southern margin of the NCC. This indicates that the extensive Mo mineralization within the southern margin of the NCC is genetically related to a Mo-rich source that was generated by the stagnation of subducted YB continental crust beneath the southern margin of the NCC. In comparison, the less significant Mo mineralization within the East Qinling orogenic belt might reflect the variable removal of the subducted YB continental slab during post-collisional intracontinental orogenesis. (C) 2019 Elsevier B.V. All rights reserved

Mo-rich source and protracted crystallization of Late Mesozoic granites in the East Qinling porphyry Mo belt (central China): Constraints from zircon U/Pb ages and Hf-O isotopes

In the East Qinling orogenic belt, central China, there are numerous Mo deposits with over 6 Mt Mo metal. The giant Nannihu, Shangfanggou and Yuchiling porphyry Mo deposits are hosted in porphyry stocks associated with the Heyu batholith, whereas the giant Donggou porphyry Mo deposit is hosted in a stock associated with the Taishanmiao batholith. Zircon grains from the Heyu batholith have concordant U-238/Pb-206 ages scattered from 150 +/- 3 to 130 +/- 2 Ma with an age interval of nearly 20 my., whereas ore-hosting granitic porphyry stocks from the Nannihu, Shangfanggou and Yuchiling deposits have Pb-206/U-238 ages ranging from 151 +/- 1 to 135 +/- 1 Ma, 143 +/- 1 to 132 +/- 1 Ma, and 143 +/- 5 to 131 +/- 5 Ma, respectively. It is likely that the Heyu batholith has a prolonged history of incremental assembly and the ore-bearing granitic porphyry stocks may have originated from the same magma reservoir and emplaced in different stages. Likewise, zircon grains from the Taishanmiao batholith have concordant U-238/Pb-206 ages spanning in a period from 130 +/- 2 to 111 +/- 3 Ma, whereas those from the Donggou granitic porphyry stock have U-238/Pb-206 ages ranging from 125 +/- 2 to 110 +/- 3 Ma, nearly coeval with the Taishanmiao batholith. The molybdenite Re-Os model ages for each deposit are coincident with relatively young zircon U-238/Pb-206 ages of the host stock, indicating that Mo mineralization is likely related to late-stage magmatism. Zircon grains from the batholiths and granitic porphyry stocks have epsilon(H)(f)(t) ranging from -10 to - 30, T-DM(2) ages from 1.6 to 2.5 Ga and delta O-18 from +5.0 to +8.7%s, which are taken to suggest the involvement of the subducted continental crust of the Yangtze Block in the source. We thus propose that the extensive Mo mineralization in the East Qinling porphyry Mo belt is genetically related to the prolonged granitic magmatism in the Late Mesozoic and a Mo-rich source that was related to the subducted continental crust of the Yangtze Block stagnating beneath the southern margin of the North China Craton

Domain generalized person reidentification based on skewness regularity of higher-order statistics

Publisher Copyright: © 2024 Elsevier B.V.The goal of domain-generalized person reidentification (DG-ReID) is to train a model in the source domain and apply it directly to unknown target domains for specific pedestrian retrieval. Existing methods rely primarily on low-order statistics (such as the mean, standard deviation, or variance), thereby ensuring the stability of the source domain data distribution for model training. However, such methods underperform when the data follow a non-Gaussian distribution, thereby reducing the generalization ability of the model on unseen target domains. To address this issue, this study proposes an instance normalization-based skewness regularity (INSR) framework that uses high-order statistics (skewness and high-order moments) to measure the skewness and regularity of the data distribution. Such measures allow further learning of the morphological features (skewness degree, trait of data near the mean, etc.) of complex data distributions for DG-ReID. Specifically, the proposed framework first extracts the skewness and third-order moments from the source domains, which provide more features (high-order moments, variance, etc.) to characterize the data distribution. Subsequently, a batch normalization-like operation was implemented to project the data into a new feature space with zero mean and unit variance, enhancing model adaption and accuracy. Extensive experiments were conducted on small-scale (VIPeR, PRID, GRID, and i-LIDS) and large-scale (Market-1501, DukeMTMC-reID, CUHK03, MSMT17) public datasets using two different protocols, demonstrating that the proposed INSR framework significantly outperforms other state-of-the-art counterparts for DG-ReID.Peer reviewe

An Efficient Method of Approximate Particular Solutions Using Polynomial Basis Functions

© 2019 Elsevier Ltd The most challenging task of the method of approximate particular solutions (MAPS) is the generation of the closed-form particular solutions with respect to the given differential operator using various basis functions. These particular solutions have to be generated prior to the solution process of the partial differential equations. In this paper, we propose a different approach without the tedious and inefficient solution procedure using symbolic computation to produce the closed-form particular solutions. The proposed approach is introduced and extended to solve a large class of elliptic partial differential equations (PDEs) based on the method of approximate particular solutions (MAPS). Numerical results show the proposed approach is simple, efficient, accurate, and stable. Five different numerical examples are presented to demonstrate the effectiveness of the proposed method

A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation

Person re-identification (re-ID) technology has attracted extensive interests in critical applications of daily lives, such as autonomous surveillance systems and intelligent control. However, light-weight and efficient person re-ID solutions are rare because the limited computing resources cannot guarantee accuracy and efficiency in detecting person features, which inevitably results in performance bottleneck in real-time applications. Aiming at this research challenge, this study developed a lightweight framework for generation of the person multi-attribute feature. The framework mainly consists of three sub-networks each conforming to a convolutional neural network architecture: (1) the accessory attribute network (a-ANet) grasps the person ornament information for an accessory descriptor; (2) the body attribute network (b-ANet) captures the person region structure for a body descriptor; and (3) the color attribute network (c-ANet) forms the color descriptor to maintain the consistency of the color of the person(s). Inspired by the human visual processing mechanism, these descriptors (each “descriptor” corresponds to the attribute of an individual person) are integrated via a tree-based feature-selection method to construct a global “feature”, i.e., a multi-attribute descriptor of the person serving as the key to identify the person. Distance learning is then exploited to measure the person similarity for the final person re-identification. Experiments have been performed on four public datasets to evaluate the proposed framework: CUHK-01, CUHK-03, Market-1501, and VIPeR. The results indicate that (1) the multi-attribute feature outperforms most of the existing feature-representation methods by 5–10% at rank@1 in terms of the cumulative matching curve criterion; and (2) the time required for recognition is as low as O(n) for real-time person re-ID applications

A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation

Person re-identification (re-ID) technology has attracted extensive interests in critical applications of daily lives, such as autonomous surveillance systems and intelligent control. However, light-weight and efficient person re-ID solutions are rare because the limited computing resources cannot guarantee accuracy and efficiency in detecting person features, which inevitably results in performance bottleneck in real-time applications. Aiming at this research challenge, this study developed a lightweight framework for generation of the person multi-attribute feature. The framework mainly consists of three sub-networks each conforming to a convolutional neural network architecture: (1) the accessory attribute network (a-ANet) grasps the person ornament information for an accessory descriptor; (2) the body attribute network (b-ANet) captures the person region structure for a body descriptor; and (3) the color attribute network (c-ANet) forms the color descriptor to maintain the consistency of the color of the person(s). Inspired by the human visual processing mechanism, these descriptors (each “descriptor” corresponds to the attribute of an individual person) are integrated via a tree-based feature-selection method to construct a global “feature”, i.e., a multi-attribute descriptor of the person serving as the key to identify the person. Distance learning is then exploited to measure the person similarity for the final person re-identification. Experiments have been performed on four public datasets to evaluate the proposed framework: CUHK-01, CUHK-03, Market-1501, and VIPeR. The results indicate that (1) the multi-attribute feature outperforms most of the existing feature-representation methods by 5–10% at rank@1 in terms of the cumulative matching curve criterion; and (2) the time required for recognition is as low as O(n) for real-time person re-ID applications

Person Re-identification with Spatial Multi-granularity Feature Exploration for Social Risk Situational Assessment

Publisher Copyright: © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.Recently, the “human-oriented” concept of security development has become a consensus among all countries. This depends mainly on intelligent surveillance systems that can support person re-identification (Re-ID) technology to empower social risk situational assessment applications. However, existing Re-ID methods mainly focus on single and fixed convolutional operations for feature extraction, ignoring the multi-dimensional spatial association of the human body, which limits the performance of Re-ID. Human cognition when identifying people does not solely rely on visual cues of the individual in sight, but also on his/her behavioral and gestural characteristics. To solve this issue and inspired by the aforementioned cognitive mechanism of the human brain, this study developed a spatial multi-granularity feature exploration (SMGFE) model for person Re-ID. The proposed SMGFE model comprises two main steps: (i) a multi-granularity feature exploration strategy and (ii) a human spatial association scheme. The former mainly includes coarse (original person images), medium (multi-regional divided person images), and fine-tuned (keypoints of the human body) level features, which form the multi-granularity feature representation. An undirected graph model was then developed to construct multi-dimensional spatial relations for each person. Finally, the unified optimization strategy was applied to train the framework to achieve promising accuracy. We evaluated the proposed algorithm on frequently used and benchmark person Re-ID datasets (Market-1501 and DukeMTMC-reID). The cumulative match curve (CMC) and mean average precision (mAP), which are the common measuring criteria for most person Re-ID methods reported to date, were used to verify the experimental results. Experiments show that our proposed algorithm achieved unrivaled performance levels. In addition, based on the spatial multi-granularity feature exploration strategy, the time efficiency of the proposed method for detecting specific instances can reach O(n), making it suitable for deployment in low-resource terminals for security risk assessment, including Android/iOS analysis servers, urban safety risk surveillance systems, and warning platforms for situational awareness.Peer reviewe