Computer Science and Information TechnologyUSRA has continued to advance computer science and information technology in support of a broad range of scientific and engineering application domains of relevance to NASA. Accomplishments this past year have contributed to the revolutionization of exploration and science through innovation in information technology. Included below are a few highlights of accomplishments from this past year that show examples from multiple USRA programs.##### Current HighlightsQuantum Computer Advances with Each Generation ProcessorUSRA operates a Quantum Artificial Intelligence Lab with NASA and Google resulting in research papers by USRA, NASA, Google and the University community.Use of the lab continues to grow with thirteen proposals received to date for the third Request for Proposal Cycle that was announced after the lab’s quantum annealing computer upgrade to a D-Wave 2000 Q processor with 2048 qubits and other system enhancements. Improvements include reduction of the minimum annealing time from 5 to 1 microseconds, and ability to offset the annealing cycle with a global pause and quench cycle on a per-qubit basis. The 2000Q represents the third processor that will have been installed in the lab, which started with a 512-qubit processor in 2013 and was upgraded to an 1152-qubit processor in 2015.Research and development on autonomous systems greatly expanded this past year at USRA through the RIACS and NAMS programs, including achievement of a major milestone in our collaborative research agreement with Nissan Research Center – Silicon Valley.USRA’s continued collaboration with Nissan resulted in a live demonstration at the Consumer Electronics Show (CES). In January 2017, thousands of visitors to the Nissan CES exhibit watched live televised demonstrations of Nissan Leaf cars being driven autonomously at NASA Ames Research Center. A Nissan “mobility manager” at CES demonstrated capability for humans to remotely assist autonomous vehicles in unpredictable and dif cult situations where the vehicle cannot solve the problem itself.The software that enables this remote assistance was developed with significant support from USRA/RIACS technical staff working onsite at Nissan, and was built on NASA’s open-source VERVE software that was developed for human-robot teaming in space exploration. In the live demonstration, the mobility managers were able to remotely help the self-driving cars navigate around construction machinery and other dif cult road obstacles. Current research is focused on enhanced support for use of Artificial Intelligence and Human-Autonomy teaming, as well as eld operation trials for driverless vehicles in the U.S. and other countries related to operating fleets of autonomous taxis and shuttle buses.Planned exploration missions will take human beings to distances further from Earth and for longer durations than any previous mission, during which, returning to Earth or communicating with Earth may not be an available option for crew members.In some deep-space situations, the exploration crew may have to manage their medical care without assistance from group support. To enable crew autonomy when far from Earth, the seamless management of medical data provided by a self-contained medical system will be necessary to support these missions.
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Code · Fields · Cost ·
Run it keyless, no account
The same call, in code.
The capture above came back as markdown. These examples add a key, so you get browser rendering, proxies, and concurrency on usra.edu.
usra-edu-scraper.ts
import { SpiderBrowser } from "spider-browser";const spider = new SpiderBrowser({ apiKey: process.env.SPIDER_API_KEY!,});await spider.connect();const page = spider.page!;await page.goto("https://usra.edu");// No selectors, no schema. Spider reads the page and names the fields.const data = await page.scrape();console.log(data);await spider.close();
import { Spider } from "@spider-cloud/spider-client";const spider = new Spider({ apiKey: process.env.SPIDER_API_KEY! });const result = await spider.scrapeUrl("https://www.usra.edu", { return_format: "markdown",});console.log(result);
Spider names these from the page. The capture above came back as markdown; the same
call with return_format: "json" returns them as keys.
What usra.edu costs to scrape.
The capture above cost $0.000085 to fetch. Pricing is $1 per GB of pre-transformation bandwidth plus $0.001 per CPU minute, so a page like this one lands at a fraction of a cent. Failed requests are billed at $0.
You already have the call. A key raises the rate limit and turns on browser rendering,
proxies, and concurrency. Balance never expires, and top-ups go through secure checkout.