skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Channel Impulse Analysis of Light Propagation for Point-to-Point Nano Communications Through Cortical Neurons
Award ID(s):
1706050
PAR ID:
10280065
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
IEEE Transactions on Communications
Volume:
68
Issue:
11
ISSN:
0090-6778
Page Range / eLocation ID:
7111 to 7122
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. The interaction and dimension of points are two important axes in designing point operators to serve hierarchical 3D models. Yet, these two axes are heterogeneous and challenging to fully explore. Existing works craft point operator under a single axis and reuse the crafted operator in all parts of 3D models. This overlooks the opportunity to better combine point interactions and dimensions by exploiting varying geometry/density of 3D point clouds. In this work, we establish PIDS, a novel paradigm to jointly explore point interactions and point dimensions to serve semantic segmentation on point cloud data. We establish a large search space to jointly consider versatile point interactions and point dimensions. This supports point operators with various geometry/density considerations. The enlarged search space with heterogeneous search components calls for a better ranking of candidate models. To achieve this, we improve the search space exploration by leveraging predictor-based Neural Architecture Search (NAS), and enhance the quality of prediction by assigning unique encoding to heterogeneous search components based on their priors. We thoroughly evaluate the networks crafted by PIDS on two semantic segmentation benchmarks, showing ∼ 1% mIOU improvement on SemanticKITTI and S3DIS over state-of-the-art 3D models. 
    more » « less