Object-Oriented Dynamics Learning through Multi-Level Abstraction

Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-supervised learning framework, called Multi-level Abstraction Object-oriented Predictor (MAOP), which employs a three-level learning architecture that enables efficient object-based dynamics learning from raw visual observations. We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability. Our results show that MAOP significantly outperforms previous methods in terms of sample efficiency and generalization over novel environments for learning environment models. We also demonstrate that learned dynamics models enable efficient planning in unseen environments, comparable to true environment models. In addition, MAOP learns semantically and visually interpretable disentangled representations.Comment: Accepted to the Thirthy-Fourth AAAI Conference On Artificial Intelligence (AAAI), 202

Self-Organized Polynomial-Time Coordination Graphs

Coordination graph is a promising approach to model agent collaboration in multi-agent reinforcement learning. It conducts a graph-based value factorization and induces explicit coordination among agents to complete complicated tasks. However, one critical challenge in this paradigm is the complexity of greedy action selection with respect to the factorized values. It refers to the decentralized constraint optimization problem (DCOP), which and whose constant-ratio approximation are NP-hard problems. To bypass this systematic hardness, this paper proposes a novel method, named Self-Organized Polynomial-time Coordination Graphs (SOP-CG), which uses structured graph classes to guarantee the accuracy and the computational efficiency of collaborated action selection. SOP-CG employs dynamic graph topology to ensure sufficient value function expressiveness. The graph selection is unified into an end-to-end learning paradigm. In experiments, we show that our approach learns succinct and well-adapted graph topologies, induces effective coordination, and improves performance across a variety of cooperative multi-agent tasks

Unified framework of the microscopic Landau-Lifshitz-Gilbert equation and its application to Skyrmion dynamics

The Landau-Lifshitz-Gilbert (LLG) equation is widely used to describe magnetization dynamics. We develop a unified framework of the microscopic LLG equation based on the nonequilibrium Green's function formalism. We present a unified treatment for expressing the microscopic LLG equation in several limiting cases, including the adiabatic, inertial, and nonadiabatic limits with respect to the precession frequency for a magnetization with fixed magnitude, as well as the spatial adiabatic limit for the magnetization with slow variation in both its magnitude and direction. The coefficients of those terms in the microscopic LLG equation are explicitly expressed in terms of nonequilibrium Green's functions. As a concrete example, this microscopic theory is applied to simulate the dynamics of a magnetic Skyrmion driven by quantum parametric pumping. Our work provides a practical formalism of the microscopic LLG equation for exploring magnetization dynamics

Expression and Clinical Relevance of uPA and ET-1 in Non-small Cell Lung Cancer

Background and objective uPA and ET-1 proteins have been reported to be up-regulated in some of human cancers. The aim of this study is to investigate the alteration and clinical relevance of uPA and ET-1 protein levels in non-small cell lung cancer (NSCLC). Methods Expressions of uPA and ET-1 protein were detected in 155 cases of NSCLC with tissue microarrays and immunohistochemistry (TMA-IHC) technique. The correlations between the alteration of the two proteins and clinicopathological parameters were analyzed. Results Negative/weak, moderate and high expression of uPA were observed in 12.3%, 64.4% and 23.3% of squamous cell carcinomas, in 12.2%, 53.7% and 34.1% of adenocarcinomas, and in 12.3%, 58.7% and 29.0% of all cases. ET-1 presented negative/weak, moderate and high expression in 2.7%, 42.5% and 54.8% of squamous cell carcinomas, in 11.0%, 30.5% and 58.5% of adenocarcinomas, and in 7.1%, 36.1% and 56.8% of all cases. Simultaneously high expression of uPA and ET-1 were found in adenocarcinomas without lymph node metastasis (P=0.017). Adenocarcinoma patients with high expression of uPA or with high expression of both ET-1 and uPA had the longer survival time (P=0.007 and 0.016). Conclusion Detection of uPA and ET-1 protein levels might contribute to the prognosis evaluation of NSCLC

Improved active disturbance rejection controller for rotor system of magnetic levitation turbomachinery

The rotor of the magnetic suspension turbomachinery is supported by the magnetic suspension bearing without contact and mechanical friction, which directly drives the high-efficiency fluid impeller. It has the advantages of high efficiency, low noise, less fault and no lubrication. However, the system often has some unknown mutation, time variation, load perturbation and other un-certainties when working, and the traditional Proportion Integration Differentiation (PID) control strategy has great limitations to overcome the above disturbances. Therefore, this paper firstly establishes a mathematical model of the rotor of magnetic levitation turbomachinery. Then, a linear active disturbance rejection controller (LADRC) is presented, which can not only improve the above problems of PID control, but also avoid the complex parameter tuning process of traditional nonlinear active disturbance rejection control (ADRC). However, LADRC is easy to induce the overshoot of the system and cannot filter the given signal. On this basis, an improved LADRC with a fast-tracking differentiator (FTD) is proposed to arrange the transition process of input signals. The simulation results show that compared with the traditional PID controller and single LADRC, the improved linear active disturbance rejection control method with fast tracking differentiator (FTD-LADRC) can better suppress some unknown abrupt changes, time variation and other uncertainties of the electromagnetic bearing-rotor system. At the same time, the overshoot of the system is smaller, and the parameters are easy to be set, which is convenient for engineering application

Associations of long-term exposure to air pollution, physical activity with blood pressure and prevalence of hypertension: the China Health and Retirement Longitudinal Study

BackgroundLong-term exposure to air pollution and physical activity (PA) are linked to blood pressure and hypertension. However, the joint effect of air pollution and PA on blood pressure and hypertension are still unknown in Chinese middle-aged and older adults.MethodsA total of 14,622 middle-aged and older adults from the China Health and Retirement Longitudinal Study wave 3 were included in this study. Ambient air pollution [particulate matter with diameter ≤ 2.5 μm (PM2.5), or ≤10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbonic oxide (CO)] were estimated using satellite-based spatiotemporal models. PA was investigated using International Physical Activity Questionnaire. Generalized linear models were used to examine the associations of air pollution, PA score with blood pressure [systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP)], and the prevalence of hypertension. Subgroup analysis was conducted to investigate the effects of air pollution on blood pressure and the prevalence of hypertension in different PA groups.ResultsThe results showed that for each inter-quartile range (IQR) increase in PM2.5 (25.45 μg/m3), PM10 (40.56 μg/m3), SO2 (18.61 μg/m3), NO2 (11.16 μg/m3), CO (0.42 mg/m3) and PA score (161.3 MET/h-week), the adjusted odd ratio (OR) of hypertension was 1.207 (95% confidence interval (CI): 1.137, 1.281), 1.189 (95%CI: 1.122, 1.260), 1.186 (95%CI: 1.112, 1.266), 1.186 (95%CI: 1.116, 1.260), 1.288 (95%CI: 1.223, 1.357), 0.948 (95%CI: 0.899, 0.999), respectively. Long-term exposure to PM2.5, PM10, SO2, NO2, and CO was associated with increased SBP, DBP, and MAP levels. For example, each IQR increase in PM2.5 was associated with 1.20 mmHg (95%CI: 0.69, 1.72) change in SBP, 0.66 mmHg (95%CI: 0.36, 0.97) change in DBP, and 0.84 mmHg (95%CI: 0.49, 1.19) change in MAP levels, respectively. Each IQR increase in PA score was associated with −0.56 mmHg (95%CI: −1.03, −0.09) change in SBP, −0.32 mmHg (95%CI: −0.59, −0.05) change in DBP, and −0.33 mmHg (95%CI: −0.64, −0.02) change in MAP levels, respectively. Subgroup analysis found that the estimated effects in the sufficient PA group were lower than that in the insufficient PA group.ConclusionLong-term exposure to air pollutants is associated with increased blood pressure and hypertension risk, while high-level PA is associated with decreased blood pressure and hypertension risk. Strengthening PA might attenuate the adverse effects of air pollution on blood pressure and hypertension risk