Photoacoustic computed tomography guided microrobots for targeted navigation in intestines in vivo

Tremendous progress in synthetic micro/nanomotors has been made for potential biomedical applications. However, existing micro/nanomotor platforms are inefficient for deep tissue imaging and motion control in vivo. Here, we present a photoacoustic computed tomography (PACT) guided investigation of micromotors in intestines in vivo. The micromotors enveloped in microcapsules exhibit efficient propulsion in various biofluids once released. PACT has visualized the migration of micromotor capsules toward the targeted regions in real time in vivo. The integration of the developed microrobotic system and PACT enables deep imaging and precise control of the micromotors in vivo

Bubbles and Black Branes in Grand Canonical Ensemble

When the phase structure of the black brane in grand canonical ensemble is discussed, a new bubble phase with the same boundary data is found to exist in this structure. As such, the phase transitions among bubble, black brane and "hot flat space" are possible, therefore giving a much enriched phase structure. We argue that under some conditions, either the grand canonical ensemble itself is unstable or there are some unknown new phases.Comment: v2. 12 pages. Expanded discussion, references added, typos corrected, published versio

Towards Omni-generalizable Neural Methods for Vehicle Routing Problems

Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This paper studies a challenging yet realistic setting, which considers generalization across both size and distribution in VRPs. We propose a generic meta-learning framework, which enables effective training of an initialized model with the capability of fast adaptation to new tasks during inference. We further develop a simple yet efficient approximation method to reduce the training overhead. Extensive experiments on both synthetic and benchmark instances of the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP) demonstrate the effectiveness of our method. The code is available at: https://github.com/RoyalSkye/Omni-VRP.Comment: Accepted at ICML 202