Commuting foliations

The aim of this paper is to extend the notion of commutativity of vector fields to the category of singular foliations, using Nambu structures, i.e. integrable multi-vector fields. We will classify the relationship between singular foliations and Nambu structures, and show some basic results about commuting Nambu structures.Comment: New version, with a completely new section which clarifies the relationship between singular foliations and Nambu structures. The size of the paper has doubled from 10 to 20 page

Exotic States Emerged By Spin-Orbit Coupling, Lattice Modulation and Magnetic Field in Lieb Nano-ribbons

The Lieb nano-ribons with the spin-orbit coupling, the lattice modulation and the magnetic field are exactly studied. They are constructed from the Lieb lattice with two open boundaries in a direction. The interplay between the spin-orbit coupling, the lattice modulation and the magnetic field emerges various exotic ground states. With certain conditions of the spin-orbit coupling, the lattice modulation, the magnetic field and filling the ground state becomes half metallic or half topological. In the half metallic ground state, one spin component is metallic, while the other spin component is insulating. In the half topological ground state, one spin component is topological, while the other spin component is topological trivial. The model exhibits very rich phase diagram

Enhance Incomplete Utterance Restoration by Joint Learning Token Extraction and Text Generation

This paper introduces a model for incomplete utterance restoration (IUR). Different from prior studies that only work on extraction or abstraction datasets, we design a simple but effective model, working for both scenarios of IUR. Our design simulates the nature of IUR, where omitted tokens from the context contribute to restoration. From this, we construct a Picker that identifies the omitted tokens. To support the picker, we design two label creation methods (soft and hard labels), which can work in cases of no annotation of the omitted tokens. The restoration is done by using a Generator with the help of the Picker on joint learning. Promising results on four benchmark datasets in extraction and abstraction scenarios show that our model is better than the pretrained T5 and non-generative language model methods in both rich and limited training data settings. The code will be also available.Comment: This is the early version of the paper accepted by NAACL 2022. It includes 10 pages, 2 figure

PSO based Hybrid PID-FLC Sugeno Control for Excitation System of Large Synchronous Motor

This paper proposes a hybrid control system integrating a PID controller and a fuzzy logic controller, using the particle swarm optimization (PSO) algorithm to optimize control parameters. The control object is an excitation system for a large synchronous motor, which is widely used in large power transmission systems. In practice, the change in load and excitation source can affect the operating mode of the motor. Therefore, a hybrid controller is designed to stabilize the power factor, resulting in better working performance. In the control algorithm, a PID controller is initially designed using PSO to optimize the control coefficients. The FLC-Sugeno control is then integrated with the PID, in which PSO is utilized to optimize membership functions. Numerical simulation results demonstrate the advantages of the proposed approach. Doi: 10.28991/ESJ-2022-06-02-01 Full Text: PD

Analysis of the effect of spray mode on coating porosity and hardness when spraying press screws by the high velocity oxy fuel method

Porosity and coating hardness are two very important properties of the coating. In order to achieve low coating porosity and high hardness, a suitable spray mode is desired. In the particular application for press screws with the complex surface, a suitable spray mode plays a significant role in the formation of the coating properties. This paper employs the Taguchi experimental design method combined with ANOVA analysis to evaluate the impact of the spray mode on the porosity and hardness of the coating while spraying the screw surface using the High Velocity Oxy Fuel (HVOF) method. The injection material used is WC HMSP1060-00 +60 % 4070, with its main components being Nickel and Carbide Wolfram. And the press screw material is 1045 steel. The impactful parameters of the spray mode investigated and tested are the flow rate of spray (F) with a range varying from 25 g/min to 35 g/min, spray distance (D) with a range of values varying from 0.25 m to 0.35 m, and an oxygen/propane ratio (R) from 4 to 6. The analysis shows that the spray mode significantly affects the coating properties, and a suitable set of spray parameters is found to achieve low coating porosity and high coating hardness. The spray mode with the lowest porosity is achieved at a spray rate (F) of 35 g/min, a spray distance (D) of 0.3 m, and an oxygen/propane ratio (R) of 6. The interactions between D and R, as well as between F and D, are statistically significant, influencing each other's effects on porosity. However, the interaction between F and R is relatively low, indicating that changes in one parameter have less impact on porosity when the other parameter is varied. Similarly, for the highest coating hardness, the optimal spray mode includes an F of 35 g/min, D of 0.25 m, and R of 6. There is a significant interaction between F and D, while the interaction between F and R is relatively low. Notably, there is no interaction between F and

Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games

Training agents in multi-agent competitive games presents significant challenges due to their intricate nature. These challenges are exacerbated by dynamics influenced not only by the environment but also by opponents' strategies. Existing methods often struggle with slow convergence and instability. To address this, we harness the potential of imitation learning to comprehend and anticipate opponents' behavior, aiming to mitigate uncertainties with respect to the game dynamics. Our key contributions include: (i) a new multi-agent imitation learning model for predicting next moves of the opponents -- our model works with hidden opponents' actions and local observations; (ii) a new multi-agent reinforcement learning algorithm that combines our imitation learning model and policy training into one single training process; and (iii) extensive experiments in three challenging game environments, including an advanced version of the Star-Craft multi-agent challenge (i.e., SMACv2). Experimental results show that our approach achieves superior performance compared to existing state-of-the-art multi-agent RL algorithms

Activists’ Strategic Communication in an Authoritarian Setting: Integrating Social Movement Framing into Issues Management

Triangulating 18 in-depth interviews with activists and campaign participants, news coverage, and social media content related to the campaign “6,700 people for 6,700 trees,” this study integrates social movement framing theory and issues management framework to examine activists’ strategic communications in an authoritarian setting. Results indicate activists’ sophisticated use of framing strategies following different stages of the issue life cycle to legitimately form an issue and successfully manage the issue in order to achieve their goals. This study offers meaningful theoretical implications for examining strategic communication in social movement campaigns. It also discusses practical lessons for applying these strategies to foster social change in similar contexts