A Riemannian Primal-dual Algorithm Based on Proximal Operator and its Application in Metric Learning

In this paper, we consider optimizing a smooth, convex, lower semicontinuous function in Riemannian space with constraints. To solve the problem, we first convert it to a dual problem and then propose a general primal-dual algorithm to optimize the primal and dual variables iteratively. In each optimization iteration, we employ a proximal operator to search optimal solution in the primal space. We prove convergence of the proposed algorithm and show its non-asymptotic convergence rate. By utilizing the proposed primal-dual optimization technique, we propose a novel metric learning algorithm which learns an optimal feature transformation matrix in the Riemannian space of positive definite matrices. Preliminary experimental results on an optimal fund selection problem in fund of funds (FOF) management for quantitative investment showed its efficacy.Comment: 8 pages, 2 figures, published as a conference paper in 2019 International Joint Conference on Neural Networks (IJCNN

Flow field calculation and dynamic characteristic analysis of spherical hybrid gas bearings based on passive grid

In order to research the spherical spiral groove hybrid gas bearings, the Realizable k − ε turbulence model of gas film was established based on FLUENT. The simulation calculation method of 6-degrees of freedom passive grid was used, which can simulate the lubrication characteristics of the gas film transient flow field accurately. And the gas film pressure distribution and dynamic characteristic coefficients are numerically calculated. The dynamic and static pressure coupling effects of the gas flow field were analyzed, and the axis motion trajectory was simulated. The effect of rotation speed, gas supply pressure and tangential angle on the dynamic characteristic coefficients during bearing operation was analyzed. And the stability of the gas bearing was studied. The conclusion from the analysis shows that different rotation speed and gas supply pressure will change the pressure distribution of the gas bearing during the operation. The dynamic characteristics of the gas film can be changed by reasonably optimizing the operation parameters, which can change the whirl characteristics of the gas film and improve the stability. Through calculation and analysis, the tangential angle is selected between 55° and 60°, to ensure that the gas film has a high stiffness, while it also can obtain the larger damping. The simulation results and the experimental results are compared and analyzed to verify the correctness and effectiveness of the simulation method. At the same time, the research of this paper provided a theoretical basis for optimizing the bearing structure and operating parameters, improving the dynamic characteristics of gas bearings and improving the operation stability

The contributions of key countries, enterprises and refineries to greenhouse gas emissions in global oil refining 2000-2021

The refining industry is the third-largest source of global greenhouse gas (GHG) emissions from stationary sources, so it is at the forefront of the energy transition and net zero pathways. The dynamics of contributors in this sector such as crucial countries, leading enterprises, and key emission processes are vital to identifying key GHG emitters and supporting targeted emission reduction, yet they are still poorly understood. Here, we established a global sub-refinery GHG emission dataset in a long time series based on life cycle method. Globally, cumulative GHG emissions from refineries reached approximately 34.1 gigatons (Gt) in the period 2000–2021 with an average annual increasing rate of 0.7%, dominated by the United States, EU27&UK, and China. In 2021, the top 20 countries with the largest GHG emissions of oil refining accounted for 83.9% of global emissions from refineries, compared with 79.5% in 2000. Moreover, over the past two decades, 53.9–57.0% of total GHG emissions came from the top 20 oil refining enterprises with the largest GHG emissions in 12 of these 20 countries. Retiring or installing mitigation technologies in the top 20% of refineries with the largest GHG emissions and refineries with GHG emissions of more than 0.1 Gt will reduce the level of GHG emissions by 38.0%–100.0% in these enterprises. Specifically, low-carbon technologies installed on furnaces and boilers as well as steam methane reforming will enable substantial GHG mitigation of more than 54.0% at the refining unit level. Therefore, our results suggest that policies targeting a relatively small number of super-emission contributors could significantly reduce GHG emissions from global oil refining

Study on dynamic characteristics of gas films of spherical spiral groove hybrid gas bearings

According to the gas film force variation law, when the bearing axis is slightly displaced from the static equilibrium position, displacement and velocity disturbance relation expressions for the gas film force increment are constructed. Moreover, combined with the bearing rotor system motion equation, calculation model equations for the gas film stiffness and damping coefficients are established. The axial and radial vibration and velocity of the gas bearings during operation are collected. The instantaneous stiffness and damping coefficients of the gas film are calculated by the rolling iteration algorithm using MATLAB. The dynamic changes in the gas film stiffness and damping under different motion states are analyzed, and the mechanism of the gas film vortex and oscillation is studied. The results demonstrate the following: (1) When the gas bearing is running in the linear steady state in cycle 1, the dynamic pressure effect is enhanced and the stability is improved by increasing the eccentricity; when the gas supply pressure is increased, the static pressure effect is enhanced and the gas film vortex is reduced, but the oscillation is strengthened. (2) With the increase in rotational speed, the gas film vortex force gradually exceeds the gas film damping force, and the stability gradually worsens, causing a fluctuation in the gas film stiffness and damping, following which singularity occurs and a half-speed vortex is formed. Meanwhile, the gas film oscillation is intensified, and the rotor enters the nonlinear stable cycle 2 state operation. (3) As the fluctuation of the film force increases, the instantaneous stiffness and damping oscillation of the film intensifies, most of the stiffness and damping coefficients exhibit distortion, and the rotor operation will enter a chaotic or unstable state. Therefore, the gas bearing stiffness and damping variation characteristics can be used to study and predict the gas bearing operating state. Finally, measures for reducing the vortex and oscillation of the gas film and improving the stability of the gas bearing operation are proposed

A Graph Regularized Point Process Model For Event Propagation Sequence

Point process is the dominant paradigm for modeling event sequences occurring at irregular intervals. In this paper we aim at modeling latent dynamics of event propagation in graph, where the event sequence propagates in a directed weighted graph whose nodes represent event marks (e.g., event types). Most existing works have only considered encoding sequential event history into event representation and ignored the information from the latent graph structure. Besides they also suffer from poor model explainability, i.e., failing to uncover causal influence across a wide variety of nodes. To address these problems, we propose a Graph Regularized Point Process (GRPP) that can be decomposed into: 1) a graph propagation model that characterizes the event interactions across nodes with neighbors and inductively learns node representations; 2) a temporal attentive intensity model, whose excitation and time decay factors of past events on the current event are constructed via the contextualization of the node embedding. Moreover, by applying a graph regularization method, GRPP provides model interpretability by uncovering influence strengths between nodes. Numerical experiments on various datasets show that GRPP outperforms existing models on both the propagation time and node prediction by notable margins.Comment: IJCNN 202

The effect of PtRuIr nanoparticle crystallinity in electrocatalytic methanol oxidation

Two structural forms of a ternary alloy PtRuIr/C catalyst, one amorphous and one highly crystalline, were synthesized and compared to determine the effect of their respective structures on their activity and stability as anodic catalysts in methanol oxidation. Characterization techniques included TEM, XRD, and EDX. Electrochemical analysis using a glassy carbon disk electrode for cyclic voltammogram and chronoamperometry were tested in a solution of 0.5 mol L−1 CH3OH and 0.5 mol L−1 H2SO4. Amorphous PtRuIr/C catalyst was found to have a larger electrochemical surface area, while the crystalline PtRuIr/C catalyst had both a higher activity in methanol oxidation and increased CO poisoning rate. Crystallinity of the active alloy nanoparticles has a big impact on both methanol oxidation activity and in the CO poisoning rate

An interpretable imbalanced semi-supervised deep learning framework for improving differential diagnosis of skin diseases

Dermatological diseases are among the most common disorders worldwide. This paper presents the first study of the interpretability and imbalanced semi-supervised learning of the multiclass intelligent skin diagnosis framework (ISDL) using 58,457 skin images with 10,857 unlabeled samples. Pseudo-labelled samples from minority classes have a higher probability at each iteration of class-rebalancing self-training, thereby promoting the utilization of unlabeled samples to solve the class imbalance problem. Our ISDL achieved a promising performance with an accuracy of 0.979, sensitivity of 0.975, specificity of 0.973, macro-F1 score of 0.974 and area under the receiver operating characteristic curve (AUC) of 0.999 for multi-label skin disease classification. The Shapley Additive explanation (SHAP) method is combined with our ISDL to explain how the deep learning model makes predictions. This finding is consistent with the clinical diagnosis. We also proposed a sampling distribution optimisation strategy to select pseudo-labelled samples in a more effective manner using ISDLplus. Furthermore, it has the potential to relieve the pressure placed on professional doctors, as well as help with practical issues associated with a shortage of such doctors in rural areas

Genome-wide identification of QTL for age at puberty in gilts using a large intercross F2 population between White Duroc and Erhualian

Puberty is a fundamental development process experienced by all reproductively competent adults, yet the specific factors regulating age at puberty remain elusive in pigs. In this study, we performed a genome scan to identify quantitative trait loci (QTL) affecting age at puberty in gilts using a White Duroc × Erhualian intercross. A total of 183 microsatellites covering 19 porcine chromosomes were genotyped in 454 F2 gilts and their parents and grandparents in the White Duroc × Erhualian intercross. A linear regression method was used to map QTL for age at puberty via QTLexpress. One 1% genome-wise significant QTL and one 0.1% genome-wise significant QTL were detected at 114 cM (centimorgan) on SSC1 and at 54 cM on SSC7, respectively. Moreover, two suggestive QTL were found on SSC8 and SSC17, respectively. This study confirmed the QTL for age at puberty previously identified on SSC1, 7 and 8, and reports for the first time a QTL for age at puberty in gilts on SSC17. Interestingly, the Chinese Erhualian alleles were not systematically favourable for younger age at puberty