<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>NTIA |</title><link>https://doost.rice.edu/tags/ntia/</link><atom:link href="https://doost.rice.edu/tags/ntia/index.xml" rel="self" type="application/rss+xml"/><description>NTIA</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 01 Jan 2024 00:00:00 +0000</lastBuildDate><image><url>https://doost.rice.edu/media/icon_hu_702a800cd775dbac.png</url><title>NTIA</title><link>https://doost.rice.edu/tags/ntia/</link></image><item><title>ETHOS</title><link>https://doost.rice.edu/projects/ethos/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://doost.rice.edu/projects/ethos/</guid><description>&lt;p&gt;&lt;strong&gt;ETHOS&lt;/strong&gt; develops a comprehensive, multi-dimensional testing framework
for machine-learning components in Open RAN software, with the goal of
accelerating the deployment of trustworthy, interoperable 5G/6G
networks. Funded by the NTIA Public Wireless Supply Chain Innovation
Fund, January 2024 – December 2028.&lt;/p&gt;</description></item></channel></rss>