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Microtubule-kinesin active fluids are distinguished from conventional passive fluids by their unique ability to consume local fuel, ATP, to generate internal active stress. This stress drives internal flow autonomously and promotes micromixing, without the need for external pumps. When confined within a looped boundary, these active fluids can spontaneously self-organize into river-like flows. However, the influence of a moving boundary on these flow behaviors has remained elusive. Here, we investigate the role of a moving boundary on the flow kinematics of active fluids. We confined the active fluid within a thin cuboidal boundary with one side serving as a mobile boundary. Our data reveals that when the boundary's moving speed does not exceed the intrinsic flow speed of the active fluid, the fluid is dominated by chaotic, turbulence-like flows. The velocity correlation length of the flow is close to the intrinsic vortex size induced by the internal active stress. Conversely, as the boundary's moving speed greatly exceeds that of the active fluid, the flow gradually transitions to a conventional cavity flow pattern. In this regime, the velocity correlation length increases and saturates to those of water. Our work elucidates the intricate interplay between a moving boundary and active fluid behavior. *We acknowledge support from the National Science Foundation (NSF-CBET-2045621). 

Recent research shows that the dynamics of an infinitely wide neural network (NN) trained by gradient descent can be characterized by Neural Tangent Kernel (NTK) [27]. Under the squared loss, the infinite-width NN trained by gradient descent with an infinitely small learning rate is equivalent to kernel regression with NTK [4]. However, the equivalence is only known for ridge regression currently [6], while the equivalence between NN and other kernel machines (KMs), e.g. support vector machine (SVM), remains unknown. Therefore, in this work, we propose to establish the equivalence between NN and SVM, and specifically, the infinitely wide NN trained by soft margin loss and the standard soft margin SVM with NTK trained by subgradient descent. Our main theoretical results include establishing the equivalence between NN and a broad family of L2 regularized KMs with finite width bounds, which cannot be handled by prior work, and showing that every finite-width NN trained by such regularized loss functions is approximately a KM. Furthermore, we demonstrate our theory can enable three practical applications, including (i) non-vacuous generalization bound of NN via the corresponding KM; (ii) nontrivial robustness certificate for the infinite-width NN (while existing robustness verification methods would provide vacuous bounds); (iii) intrinsically more robust infinite-width NNs than those from previous kernel regression. 

Recent studies have shown that deep reinforcement learning agents are vulnerable to small adversarial perturbations on the agent’s inputs, which raises concerns about deploying such agents in the real world. To address this issue, we propose RADIAL-RL, a principled framework to train reinforcement learning agents with improved robustness against lp-norm bounded adversarial attacks. Our framework is compatible with popular deep reinforcement learning algorithms and we demonstrate its performance with deep Q-learning, A3C and PPO. We experiment on three deep RL benchmarks (Atari, MuJoCo and ProcGen) to show the effectiveness of our robust training algorithm. Our RADIAL-RL agents consistently outperform prior methods when tested against attacks of varying strength and are more computationally efficient to train. In addition, we propose a new evaluation method called Greedy Worst-Case Reward (GWC) to measure attack agnostic robustness of deep RL agents. We show that GWC can be evaluated efficiently and is a good estimate of the reward under the worst possible sequence of adversarial attacks. All code used for our experiments is available at https://github.com/tuomaso/radial_rl_v2.