A Novel Convolutional Neural Network Architecture with a Continuous Symmetry

This paper introduces a new Convolutional Neural Network (ConvNet) architecture inspired by a class of partial differential equations (PDEs) called quasi-linear hyperbolic systems. With comparable performance on the image classification task, it allows for the modification of the weights via a continuous group of symmetry. This is a significant shift from traditional models where the architecture and weights are essentially fixed. We wish to promote the (internal) symmetry as a new desirable property for a neural network, and to draw attention to the PDE perspective in analyzing and interpreting ConvNets in the broader Deep Learning community.Comment: Accepted by the 3rd CAAI International Conference on Artificial Intelligence (CICAI), 2023; with Addendum + minor edit

Privileged Knowledge Distillation for Sim-to-Real Policy Generalization

Reinforcement Learning (RL) has recently achieved remarkable success in robotic control. However, most RL methods operate in simulated environments where privileged knowledge (e.g., dynamics, surroundings, terrains) is readily available. Conversely, in real-world scenarios, robot agents usually rely solely on local states (e.g., proprioceptive feedback of robot joints) to select actions, leading to a significant sim-to-real gap. Existing methods address this gap by either gradually reducing the reliance on privileged knowledge or performing a two-stage policy imitation. However, we argue that these methods are limited in their ability to fully leverage the privileged knowledge, resulting in suboptimal performance. In this paper, we propose a novel single-stage privileged knowledge distillation method called the Historical Information Bottleneck (HIB) to narrow the sim-to-real gap. In particular, HIB learns a privileged knowledge representation from historical trajectories by capturing the underlying changeable dynamic information. Theoretical analysis shows that the learned privileged knowledge representation helps reduce the value discrepancy between the oracle and learned policies. Empirical experiments on both simulated and real-world tasks demonstrate that HIB yields improved generalizability compared to previous methods.Comment: 22 page

Accessing and Manipulating Dispersive Shock Waves in a Nonlinear and Nonlocal Rydberg Medium

Dispersive shock waves (DSWs) are fascinating wave phenomena occurring in media when nonlinearity overwhelms dispersion (or diffraction). Creating DSWs with low generation power and realizing their active controls is desirable but remains a longstanding challenge. Here, we propose a scheme to generate weak-light DSWs and realize their manipulations in an atomic gas involving strongly interacting Rydberg states under the condition of electromagnetically induced transparency (EIT). We show that for a two-dimensional (2D) Rydberg gas a weak nonlocality of optical Kerr nonlinearity can significantly change the edge speed of DSWs and induces a singular behavior of the edge speed and hence an instability of the DSWs. However, by increasing the degree of the Kerr nonlocality, the singular behavior of the edge speed and the instability of the DSWs can be suppressed. We also show that in a 3D Rydberg gas, DSWs can be created and propagate stably when the system works in the intermediate nonlocality regime. Due to the EIT effect and the giant nonlocal Kerr nonlinearity contributed by the Rydberg-Rydberg interaction, DSWs found here have extremely low generation power. In addition, an active control of DSWs can be realized; in particular, they can be stored and retrieved with high efficiency and fidelity through switching off and on a control laser field. The results reported here are useful not only for unveiling intriguing physics of DSWs but also for finding promising applications of nonlinear and nonlocal Rydberg media.Comment: 20 pages, 13 figure

An Invulnerability Algorithm for Wireless Sensor Network\u27s Topology Based on Distance and Energy

To improve the topological stability of wireless sensor networks, an anti-destructive algorithm based on energy-aware weighting is proposed. The algorithm takes the Weighted Dynamic Topology Control (WDTC) algorithm as a reference, and calculates the weight of nodes by using the distance between nodes and the residual energy of nodes. Then chooses optimal weights and constructs a stable balanced topological network with multiple-connectivity paths using the K-connection idea. The simulation results show that the proposed algorithm improves the average connectivity of the topological network, enhances the robustness of the network, ensures the stable transmission of network information, and optimizes the betweenness centrality of the network nodes, making the network has a good invulnerability