Improved quantum entropic uncertainty relations

We study entropic uncertainty relations by using stepwise linear functions and quadratic functions. Two kinds of improved uncertainty lower bounds are constructed: the state-independent one based on the lower bound of Shannon entropy and the tighter state-dependent one based on the majorization techniques. The analytical results for qubit and qutrit systems with two or three measurement settings are explicitly derived, with detailed examples showing that they outperform the existing bounds. The case with the presence of quantum memory is also investigated.Comment: 14 pages,6 figure

Research progress and controversy on T wave formation mechanism

Although ECG has been developed for a hundred years, the mechanism of T wave formation is unknown. The proposal of in vitro wedge-shaped model has greatly promoted the understanding of T-wave formation mechanism. By comparing the action potentials of epicardial cells, medial cells and endocardial cells in wedge-shaped ventricular mass with the T wave of body surface ECG, it was found that the T wave was mainly formed by the dispersion of transmural repolarization of ventricular muscle. However, in the subsequent in vivo experiments, electrophysiologists found that the formation of T wave was related to the dispersion of ventricular global repolarization, and the repolarization order of different parts of the three-dimensional global heart determined the polarity of T wave. In the real heart, the mechanism of T wave formation may be more complex, its repolarization gradient may include repolarization in each axis of the heart, and the polarity of T wave may also be the result of multiple factors

Sequential Action-Induced Invariant Representation for Reinforcement Learning

How to accurately learn task-relevant state representations from high-dimensional observations with visual distractions is a realistic and challenging problem in visual reinforcement learning. Recently, unsupervised representation learning methods based on bisimulation metrics, contrast, prediction, and reconstruction have shown the ability for task-relevant information extraction. However, due to the lack of appropriate mechanisms for the extraction of task information in the prediction, contrast, and reconstruction-related approaches and the limitations of bisimulation-related methods in domains with sparse rewards, it is still difficult for these methods to be effectively extended to environments with distractions. To alleviate these problems, in the paper, the action sequences, which contain task-intensive signals, are incorporated into representation learning. Specifically, we propose a Sequential Action--induced invariant Representation (SAR) method, in which the encoder is optimized by an auxiliary learner to only preserve the components that follow the control signals of sequential actions, so the agent can be induced to learn the robust representation against distractions. We conduct extensive experiments on the DeepMind Control suite tasks with distractions while achieving the best performance over strong baselines. We also demonstrate the effectiveness of our method at disregarding task-irrelevant information by deploying SAR to real-world CARLA-based autonomous driving with natural distractions. Finally, we provide the analysis results of generalization drawn from the generalization decay and t-SNE visualization. Code and demo videos are available at https://github.com/DMU-XMU/SAR.git

Genetic Algorithm for Solving a Nonlinear Combinatorial Optimization Problem

Computer Science