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Abstract High entropy oxides are a class of materials distinguished by the use of configurational entropy to drive material synthesis. These materials are being examined for their exciting physiochemical properties and hold promise in numerous fields, such as chemical sensing, electronics, and catalysis. Patterning and integration of high entropy materials into devices and platforms can be difficult due to their thermal sensitivity and incompatibility with many conventional thermally-based processing techniques. In this work, we present a laser-based technique, laser-induced thermal voxels, that combines the synthesis and patterning of high entropy oxides into a single process step, thereby allowing patterning of high entropy materials directly onto substrates. As a proof-of-concept, we target the synthesis and patterning of a well-characterized rock salt-phase high entropy oxide, (Mg_0.2Co_0.2Ni_0.2Cu_0.2Zn_0.2)O, as well as a spinel-phase high entropy oxide, (Mg_0.2Ni_0.2Co_0.2Cu_0.2Zn_0.2)Cr₂O₄. We show through electron microscopy and x-ray analysis that the materials created are atomically homogenous and are primarily of the rock salt or spinel phase. These findings show the efficacy of laser induced thermal voxel processing for the synthesis and patterning of high entropy materials and enable new routes for integration of high entropy materials within microscale platform and devices. 

Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc. Conventionally, scene ?ow is estimated from dense/regular RGB video frames. With the development of depth-sensing technologies, precise 3D measurements are available via point clouds which have sparked new research in 3D scene flow. Nevertheless, it remains challenging to extract scene flow from point clouds due to the sparsity and irregularity in typical point cloud sampling patterns. One major issue related to irregular sampling is identified as the randomness during point set abstraction/feature extraction an elementary process in many flow estimation scenarios. A novel Spatial Abstraction with Attention (SA2) layer is accordingly proposed to alleviate the unstable abstraction problem. Moreover, a Temporal Abstraction with Attention (TA2) layer is proposed to rectify attention in temporal domain, leading to benefits with motions scaled in a larger range. Extensive analysis and experiments verified the motivation and significant performance gains of our method, dubbed as Flow Estimation via Spatial-Temporal Attention (FESTA), when compared to several state-of-the-art benchmarks of scene flow estimation.