Study of mercury transport and transformation in mangrove forests using stable mercury isotopes.

Mangrove forests are important wetland ecosystems that are a sink for mercury from tides, rivers and precipitation, and can also be sources of mercury production and export. Natural abundance mercury stable isotope ratios have been proven to be a useful tool to investigate mercury behavior in various ecosystems. In this study, mercury isotopic data were collected from seawater, sediments, air, and plant tissues in two mangrove forests in Guangxi and Fujian provinces, China, to study the transport and transformation of mercury in mangrove sediments. The mangroves were primarily subject to mercury inputs from external sources, such as anthropogenic activities, atmospheric deposition, and the surrounding seawater. An isotope mixing model based on mass independent fractionation (MIF) estimated that the mangrove wetland ecosystems accounted for <40% of the mercury in the surrounding seawater. The mercury in plant root tissues was derived mainly from sediments and enriched with light mercury isotopes. The exogenous mercury inputs from the fallen leaves were diluted by seawater, leading to a positive Δ199Hg offset between the fallen leaves and sediments. Unlike river and lake ecosystems, mangrove ecosystems are affected by tidal action, and the δ202Hg and Δ199Hg values of sediments were more negative than that of the surrounding seawater. The isotopic signature differences between these environmental samples were partially due to isotope fractionation driven by various physical and chemical processes (e.g., sorption, photoreduction, deposition, and absorption). These results contribute to a better understanding of the biogeochemical cycling of mercury in mangrove wetland ecosystems

Degradation of Complexity for Join Enumeration via Weight Measure on CMP

Most contemporary database systems query optimizers exploit System-R’s Bottom-Up dynamic programming method (DP) to find the optimal query execution plan (QEP). The dynamic programming algorithm has a worst case running time, thus for queries with more than 10 joins, it becomes infeasible. To resolve this problem, random strategies are used. In this paper we propose a parallel top-down join enumeration algorithm that is optimal with respect to the partial order graph based on Chip Multi-Processor (CMP). This paper firstly transforms the undirected query graph to Weighted Edge Join Graph (WEJG) according to the edge weight and constructs all partial order join and partial order graph within WEJG. Then the global optimal query plan is achieved according to the parallelize top-down enumeration. Our theoretical results and empirical evaluation show that our algorithm could gracefully degrade the complexity degree for top-down join enumeration with large number of tables and gains impressive in the performance in terms of both output quality and running time

Combined Accelerator for Attribute Reduction: A Sample Perspective

In the field of neighborhood rough set, attribute reduction is considered as a key topic. Neighborhood relation and rough approximation play crucial roles in the process of obtaining the reduct. Presently, many strategies have been proposed to accelerate such process from the viewpoint of samples. However, these methods speed up the process of obtaining the reduct only from binary relation or rough approximation, and then the obtained results in time consumption may not be fully improved. To fill such a gap, a combined acceleration strategy based on compressing the scanning space of both neighborhood and lower approximation is proposed, which aims to further reduce the time consumption of obtaining the reduct. In addition, 15 UCI data sets have been selected, and the experimental results show us the following: (1) our proposed approach significantly reduces the elapsed time of obtaining the reduct; (2) compared with previous approaches, our combined acceleration strategy will not change the result of the reduct. This research suggests a new trend of attribute reduction using the multiple views

Improving Multi-Label Learning by Correlation Embedding

In multi-label learning, each object is represented by a single instance and is associated with more than one class labels, where the labels might be correlated with each other. As we all know, exploiting label correlations can definitely improve the performance of a multi-label classification model. Existing methods mainly model label correlations in an indirect way, i.e., adding extra constraints on the coefficients or outputs of a model based on a pre-learned label correlation graph. Meanwhile, the high dimension of the feature space also poses great challenges to multi-label learning, such as high time and memory costs. To solve the above mentioned issues, in this paper, we propose a new approach for Multi-Label Learning by Correlation Embedding, namely MLLCE, where the feature space dimension reduction and the multi-label classification are integrated into a unified framework. Specifically, we project the original high-dimensional feature space to a low-dimensional latent space by a mapping matrix. To model label correlation, we learn an embedding matrix from the pre-defined label correlation graph by graph embedding. Then, we construct a multi-label classifier from the low-dimensional latent feature space to the label space, where the embedding matrix is utilized as the model coefficients. Finally, we extend the proposed method MLLCE to the nonlinear version, i.e., NL-MLLCE. The comparison experiment with the state-of-the-art approaches shows that the proposed method MLLCE has a competitive performance in multi-label learning

Improving Multi-Label Learning by Correlation Embedding

In multi-label learning, each object is represented by a single instance and is associated with more than one class labels, where the labels might be correlated with each other. As we all know, exploiting label correlations can definitely improve the performance of a multi-label classification model. Existing methods mainly model label correlations in an indirect way, i.e., adding extra constraints on the coefficients or outputs of a model based on a pre-learned label correlation graph. Meanwhile, the high dimension of the feature space also poses great challenges to multi-label learning, such as high time and memory costs. To solve the above mentioned issues, in this paper, we propose a new approach for Multi-Label Learning by Correlation Embedding, namely MLLCE, where the feature space dimension reduction and the multi-label classification are integrated into a unified framework. Specifically, we project the original high-dimensional feature space to a low-dimensional latent space by a mapping matrix. To model label correlation, we learn an embedding matrix from the pre-defined label correlation graph by graph embedding. Then, we construct a multi-label classifier from the low-dimensional latent feature space to the label space, where the embedding matrix is utilized as the model coefficients. Finally, we extend the proposed method MLLCE to the nonlinear version, i.e., NL-MLLCE. The comparison experiment with the state-of-the-art approaches shows that the proposed method MLLCE has a competitive performance in multi-label learning

Spectrophotometric flow injection determination of dissolved titanium in seawater exploiting in-line nitrilotriacetic acid resin preconcentration and a long path length liquid waveguide capillary cell

A sensitive spectrophotometric method for the determination of dissolved titanium (Ti) in seawater is developed. It involves in-line preconcentration and a long path length liquid waveguide capillary cell (LWCC). Nitrilotriacetic acid (NTA) resin is used to preconcentrate Ti from ∼25 mL seawater sample at pH 1.7, and elution is accomplished with 0.8 mol L−1 hydrochloride acid. The eluted Ti solution is buffered to pH 6.0 with 1.0 mol L−1 ammonium acetate and mixed with 1.5 mmol L−1 Tiron solution. The mixture is then injected into LWCC and measured by spectrophotometry at 420 nm. Before the preconcentration step, the sample is treated with 7 mmol L−1 ascorbic acid to reduce Fe(III) to Fe(II), in order to eliminate the Fe interference. The method is not interfered by Fe(III) and Cu(II) present in seawater samples at concentrations 50-fold higher in relation to Ti, and by Cd(II), Pb(II), Cr(VI), Mn(II), Al(III), Zn(II), and Ni(II) at concentrations 100-fold higher in relation to Ti. It is time efficient (7.5 minutes per sample), sensitive (0.10 nmol L−1 detection limit), precise (1.40% measurement RSD at 1.00 nmol L−1 Ti) and is characterized by a linear range of 0.50–5.00 nmol L−1 Ti. The method was applied to analysis of natural water samples collected from the Jiulongjiang Estuary, Fujian, China. [Display omitted] •Sensitive spectrophotometric detection of dissolved Ti in seawater is developed.•NTA resin is used to preconcentrate dissolved Ti in seawater at pH 1.7.•Fe(III) is reduced to Fe(II) before resin to avoid Fe retention and interference.•In-line preconcentration and detection is realized with flow analysis and LWCC.•Estuarine dissolved Ti is analyzed and reported

Cross-Modality Earth Mover’s Distance for Visible Thermal Person Re-identification

Visible thermal person re-identification (VT-ReID) suffers from inter-modality discrepancy and intra-identity variations. Distribution alignment is a popular solution for VT-ReID, however, it is usually restricted to the influence of the intra-identity variations. In this paper, we propose the Cross-Modality Earth Mover's Distance (CM-EMD) that can alleviate the impact of the intra-identity variations during modality alignment. CM-EMD selects an optimal transport strategy and assigns high weights to pairs that have a smaller intra-identity variation. In this manner, the model will focus on reducing the inter-modality discrepancy while paying less attention to intra-identity variations, leading to a more effective modality alignment. Moreover, we introduce two techniques to improve the advantage of CM-EMD. First, Cross-Modality Discrimination Learning (CM-DL) is designed to overcome the discrimination degradation problem caused by modality alignment. By reducing the ratio between intra-identity and inter-identity variances, CM-DL leads the model to learn more discriminative representations. Second, we construct the Multi-Granularity Structure (MGS), enabling us to align modalities from both coarse- and fine-grained levels with the proposed CM-EMD. Extensive experiments show the benefits of the proposed CM-EMD and its auxiliary techniques (CM-DL and MGS). Our method achieves state-of-the-art performance on two VT-ReID benchmarks