Metascreen-Based Acoustic Passive Phased Array

Conventional phased arrays require a large number of sources in forming a complex wave front, resulting in complexity and a high cost to operate the individual sources. We present a passive phased array using an acoustic metascreen that transmits sound energy from a single source and steers the transmitted wave front to form the desired fields. The metascreen is composed of elements that have a discrete resolution along the screen at an order smaller than the wavelength, allowing for fine wave-front shaping beyond the paraxial approximation. The performance is verified in experiment by forming a self-bending beam. Our metascreen-based passive array with its simplicity and capability has applications in places where conventional active arrays are complex and have limitations.Acoustical Society of AmericaNational Basic Research Program of China (973 Program) 2010CB327803 2012CB921504National Natural Science Foundation of China 11174138 11174139 11222442 81127901 11274168Physic

Compositional Law Parsing with Latent Random Functions

Human cognition has compositionality. We understand a scene by decomposing the scene into different concepts (e.g. shape and position of an object) and learning the respective laws of these concepts which may be either natural (e.g. laws of motion) or man-made (e.g. laws of a game). The automatic parsing of these laws indicates the model's ability to understand the scene, which makes law parsing play a central role in many visual tasks. In this paper, we propose a deep latent variable model for Compositional LAw Parsing (CLAP). CLAP achieves the human-like compositionality ability through an encoding-decoding architecture to represent concepts in the scene as latent variables, and further employ concept-specific random functions, instantiated with Neural Processes, in the latent space to capture the law on each concept. Our experimental results demonstrate that CLAP outperforms the compared baseline methods in multiple visual tasks including intuitive physics, abstract visual reasoning, and scene representation. In addition, CLAP can learn concept-specific laws in a scene without supervision and one can edit laws through modifying the corresponding latent random functions, validating its interpretability and manipulability