Hydrochemical variations of the springs on Jinfo Mountain, Chongqing, China

There are 18 springs within the Jinfo Mountain area of Chongqing, SW China (of which 10 epikarst springs are within the National Nature Reserve, and 4 epikarst springs and 4 non-epikarst springs are outside the National Nature Reserve). The hydrochemical characteristics of these springs were measured in 1977, 2004-2009, and 2011. The data show that the hydrochemistry type of springs in different areas, and for different years, is Ca-HCO3 and Ca-HCO3-SO4, whereas the concentrations of SO4 and NO3 are very sensitive to changes in human activities. All the springs with the highest SO4 and NO3 concentrations in the study area showed minimum concentrations in 1977 and an upward trend in concentrations from 2004 to 2008, followed by a period of lower concentrations. Springs with low SO4 and NO3 concentrations were distributed solely at the top of Jinfo Mountain in the National Nature Reserve. The hydrochemical variations observed in springs on Jinfo Mountain demonstrate that the implementation of environmental policy measures and industrial restructuring have successfully contributed to environmental protection of the springs

MOEA/D with Adaptive Weight Adjustment

Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem (MOP) into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front (PF; i.e., discontinuous PF or PF with sharp peak or low tail). To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment (MOEA/D-AWA). According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover, an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discontinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, [Formula: see text]-MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.</jats:p

A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks

Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes. To evaluate the influence spread of a seed set efficiently, existing studies have proposed transformations with lower computational costs to replace the expensive Monte Carlo simulation process. These alternate transformations, based on network prior knowledge, induce different search behaviors with similar characteristics to various perspectives. Specifically, it is difficult for users to determine a suitable transformation a priori. This article proposes a multi-transformation evolutionary framework for influence maximization (MTEFIM) with convergence guarantees to exploit the potential similarities and unique advantages of alternate transformations and to avoid users manually determining the most suitable one. In MTEFIM, multiple transformations are optimized simultaneously as multiple tasks. Each transformation is assigned an evolutionary solver. Three major components of MTEFIM are conducted via: 1) estimating the potential relationship across transformations based on the degree of overlap across individuals of different populations, 2) transferring individuals across populations adaptively according to the inter-transformation relationship, and 3) selecting the final output seed set containing all the transformation's knowledge. The effectiveness of MTEFIM is validated on both benchmarks and real-world social networks. The experimental results show that MTEFIM can efficiently utilize the potentially transferable knowledge across multiple transformations to achieve highly competitive performance compared to several popular IM-specific methods. The implementation of MTEFIM can be accessed at https://github.com/xiaofangxd/MTEFIM.Comment: This work has been submitted to the IEEE Computational Intelligence Magazine for publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

POEM: Reconstructing Hand in a Point Embedded Multi-view Stereo

Enable neural networks to capture 3D geometrical-aware features is essential in multi-view based vision tasks. Previous methods usually encode the 3D information of multi-view stereo into the 2D features. In contrast, we present a novel method, named POEM, that directly operates on the 3D POints Embedded in the Multi-view stereo for reconstructing hand mesh in it. Point is a natural form of 3D information and an ideal medium for fusing features across views, as it has different projections on different views. Our method is thus in light of a simple yet effective idea, that a complex 3D hand mesh can be represented by a set of 3D points that 1) are embedded in the multi-view stereo, 2) carry features from the multi-view images, and 3) encircle the hand. To leverage the power of points, we design two operations: point-based feature fusion and cross-set point attention mechanism. Evaluation on three challenging multi-view datasets shows that POEM outperforms the state-of-the-art in hand mesh reconstruction. Code and models are available for research at https://github.com/lixiny/POEM.Comment: Accepted by CVPR 202

An Orthogonal Learning Differential Evolution Algorithm for Remote Sensing Image Registration

We introduce an area-based method for remote sensing image registration. We use orthogonal learning differential evolution algorithm to optimize the similarity metric between the reference image and the target image. Many local and global methods have been used to achieve the optimal similarity metric in the last few years. Because remote sensing images are usually influenced by large distortions and high noise, local methods will fail in some cases. For this reason, global methods are often required. The orthogonal learning (OL) strategy is efficient when searching in complex problem spaces. In addition, it can discover more useful information via orthogonal experimental design (OED). Differential evolution (DE) is a heuristic algorithm. It has shown to be efficient in solving the remote sensing image registration problem. So orthogonal learning differential evolution algorithm (OLDE) is efficient for many optimization problems. The OLDE method uses the OL strategy to guide the DE algorithm to discover more useful information. Experiments show that the OLDE method is more robust and efficient for registering remote sensing images

CHORD: Category-level Hand-held Object Reconstruction via Shape Deformation

In daily life, humans utilize hands to manipulate objects. Modeling the shape of objects that are manipulated by the hand is essential for AI to comprehend daily tasks and to learn manipulation skills. However, previous approaches have encountered difficulties in reconstructing the precise shapes of hand-held objects, primarily owing to a deficiency in prior shape knowledge and inadequate data for training. As illustrated, given a particular type of tool, such as a mug, despite its infinite variations in shape and appearance, humans have a limited number of 'effective' modes and poses for its manipulation. This can be attributed to the fact that humans have mastered the shape prior of the 'mug' category, and can quickly establish the corresponding relations between different mug instances and the prior, such as where the rim and handle are located. In light of this, we propose a new method, CHORD, for Category-level Hand-held Object Reconstruction via shape Deformation. CHORD deforms a categorical shape prior for reconstructing the intra-class objects. To ensure accurate reconstruction, we empower CHORD with three types of awareness: appearance, shape, and interacting pose. In addition, we have constructed a new dataset, COMIC, of category-level hand-object interaction. COMIC contains a rich array of object instances, materials, hand interactions, and viewing directions. Extensive evaluation shows that CHORD outperforms state-of-the-art approaches in both quantitative and qualitative measures. Code, model, and datasets are available at https://kailinli.github.io/CHORD.Comment: To be presented at ICCV 2023, Pari

Excellent performance of Pt-C/TiO2 for methanol oxidation:contribution of mesopores and partially coated carbon

Partial deposition of carbon onto mesoporous TiO2 (C/TiO2) were prepared as supporting substrate for Pt catalyst development. Carbon deposition is achieved by in-situ carbonization of furfuryl alcohol. The hybrid catalysts were characterized by XRD, Raman, SEM and TEM and exhibited outstanding catalytic activity and stability in methanol oxidation reaction. The heterogeneous carbon coated on mesoporous TiO2 fibers provided excellent electrical conductivity and strong interfacial interaction between TiO2 support and Pt metal nanoparticles. Methanol oxidation reaction results showed that the activity of Pt-C/TiO2 is 3.0 and 1.5 times higher than that of Pt-TiO2 and Pt-C, respectively. In addition, the Pt-C/TiO2 exhibited a 6.7 times enhanced stability compared with Pt-C after 2000 cycles. The synergistic effect of C/TiO2 is responsible for the enhanced activity of Pt-C/TiO2, and its excellent durability could be ascribed to the strong interfacial interaction between Pt nanoparticles and C/TiO2 support