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Humanity has “eyes” that can detect all different types of light through telescopes around the globe and a fleet of observatories in space. From radio waves to gamma rays, this “multiwavelength” approach to astronomy is crucial to getting a complete understanding of objects in space.

This compilation gives examples of images from different missions and telescopes being combined to better understand the science of the universe. Each of these images contains data from NASA’s Chandra X-ray Observatory as well as other telescopes. Various types of objects are shown (galaxies, supernova remnants, stars, planetary nebulas), but together they demonstrate the possibilities when data from across the electromagnetic spectrum are assembled.

M82
Messier 82, or M82, is a galaxy that is oriented edge-on to Earth. This gives astronomers and their telescopes an interesting view of what happens as this galaxy undergoes bursts of star formation. X-rays from Chandra (appearing as blue and pink) show gas in outflows about 20,000 light years long that has been heated to temperatures above ten million degrees by repeated supernova explosions. Optical light data from NASA’s Hubble Space Telescope (red and orange) shows the galaxy.

NASAs Chandra Opens Treasure Trove of Cosmic Delights

Credits: X-ray: NASA/CXC; Optical: NASA/STScI

Abell 2744
Galaxy clusters are the largest objects in the universe held together by gravity. They contain enormous amounts of superheated gas, with temperatures of tens of millions of degrees, which glows brightly in X-rays, and can be observed across millions of light years between the galaxies. This image of the Abell 2744 galaxy cluster combines X-rays from Chandra (diffuse blue emission) with optical light data from Hubble (red, green, and blue).

1599192510 47 NASAs Chandra Opens Treasure Trove of Cosmic Delights

Credits: NASA/CXC; Optical: NASA/STScI

Supernova 1987A (SN 1987A)
On February 24, 1987, observers in the southern hemisphere saw a new object in a nearby galaxy called the Large Magellanic Cloud. This was one of the brightest supernova explosions in centuries and soon became known as Supernova 1987A (SN 87A). The Chandra data (blue) show the location of the supernova’s shock wave — similar to the sonic boom from a supersonic plane — interacting with the surrounding material about four light years from the original explosion point. Optical data from Hubble (orange and red) also shows evidence for this interaction in the ring.

1599192510 218 NASAs Chandra Opens Treasure Trove of Cosmic Delights

Credits: Radio: ALMA (ESO/NAOJ/NRAO), P. Cigan and R. Indebetouw; NRAO/AUI/NSF, B. Saxton; X-ray: NASA/CXC/SAO/PSU/K. Frank et al.; Optical: NASA/STScI)

Eta Carinae
What will be the next star in our Milky Way galaxy to explode as a supernova? Astronomers aren’t certain, but one candidate is in Eta Carinae, a volatile system containing two massive stars that closely orbit each other. This image has three types of light: optical data from Hubble (appearing as white), ultraviolet (cyan) from Hubble, and X-rays from Chandra (appearing as purple emission). The previous eruptions of this star have resulted in a ring of hot, X-ray emitting gas about 2.3 light years in diameter surrounding these two stars.

Cartwheel Galaxy
This galaxy resembles a bull’s eye, which is appropriate because its appearance is partly due to a smaller galaxy that passed through the middle of this object.

1599192510 618 NASAs Chandra Opens Treasure Trove of Cosmic Delights

Credits: NASA/CXC; Ultraviolet/Optical: NASA/STScI; Combined Image: NASA/ESA/N. Smith (University of Arizona), J. Morese (BoldlyGo Instituts) and A. Pagan)

The violent collision produced shock waves that swept through the galaxy and triggered large amounts of star formation. X-rays from Chandra (purple) show disturbed hot gas initially hosted by the Cartwheel galaxy being dragged over more than 150,000 light years by the collision. Optical data from Hubble (red, green, and blue) show where this collision may have triggered the star formation.

1599192510 632 NASAs Chandra Opens Treasure Trove of Cosmic Delights

Credits: X-ray: NASA/CXC; Optical: NASA/STScI)

Helix Nebula
When a star like the Sun runs out of fuel, it expands and its outer layers puff off, and then the core of the star shrinks. This phase is known as a “planetary nebula,” and astronomers expect our Sun will experience this in about 5 billion years. This Helix Nebula images contains infrared data from NASA’s Spitzer Space Telescope (green and red), optical light from Hubble (orange and blue), ultraviolet from NASA’s Galaxy Evolution Explorer (cyan), and Chandra’s X-rays (appearing as white) showing the white dwarf star that formed in the center of the nebula. The image is about four light years across.

Three of these images — SN 1987A, Eta Carinae, and the Helix Nebula — were developed as part of NASA’s Universe of Learning (UoL), an integrated astrophysics learning and literacy program, and specifically UoL’s ViewSpace project. The UoL brings together experts who work on Chandra, the Hubble Space Telescope, Spitzer Space Telescope, and other NASA astrophysics missions.

1599192510 48 NASAs Chandra Opens Treasure Trove of Cosmic Delights

Credits: X-ray: NASA/CXC; Ultraviolet: NASA/JPL-Caltech/SSC; Optical: NASA/STScI(M. Meixner)/ESA/NRAO(T.A. Rector); Infrared: NASA/JPL-Caltech/K. Su)

NASA’s Marshall Space Flight Center manages the Chandra program. The Smithsonian Astrophysical Observatory’s Chandra X-ray Center controls science from Cambridge Massachusetts and flight operations from Burlington, Massachusetts.

Read more from NASA’s Chandra X-ray Observatory.

Source: NASA




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VDI vs. DaaS: What is the difference, and which is best for your virtualization needs?

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Desktop virtualization is nothing new, but now you have two popular forms to choose from: VDI and DaaS. Learn how VDI and DaaS differ so you can make the best investment for your business.

Image: Denis Isakov, Getty Images/iStockphoto

Anyone who has spent much time in an enterprise computing environment has played with a virtual machine (VM) at some point. Local virtual desktop infrastructures (VDIs) were the standard, but today’s bandwidth availability and cloud options make Desktop as a Service (DaaS) much more practical, and COVID-19 is making DaaS more attractive than ever.

What is VDI?

Virtual desktop infrastructure (VDI) has been around for a long time, and traditionally was the only way to run a virtual desktop. Slap a server in the data center, load it up with virtualization software, turn on some machines, and you’re good to go.

Since VDIs are centrally located, the IT team is responsible for managing them. That means the hardware, software, licensing, and deployment are all handled in-house. Latency is minimal, the IT department has complete control, and even if access to the internet goes out, work can still get done.

SEE: Virtualization policy (TechRepublic Premium)

But that’s not to say VDI doesn’t have its drawbacks. While it’s convenient to manage hardware and software internally, VDI systems require dedicated IT staff to handle all possible contingencies. Hardware failure, software issues, and anything else that could go wrong has to be handled in-house, and that can get expensive.

What is DaaS?

DaaS, as Citrix’s Kenneth Oestreich said, is “VDI that’s someone else’s problem.” At its most basic level, that’s true: DaaS is a VDI that is hosted in the cloud by a company like Citrix, Amazon, VMware, Microsoft, or Google. With DaaS, all of the hardware is managed by the provider, so you won’t have to worry about rackspace, hardware breakdown, or maintenance.

DaaS systems are subscription based and are generally charged by seat. It can be tempting to rush into a DaaS system to clear the clutter of a data center and the calendars of IT staff, but there are quite a few reasons why that may not be the best idea.

“There are two types of DaaS providers: Those that give bare bones systems and those that are business ready,” Oestreich said. Most DaaS providers offer the most basic of systems that only come with standard Windows software–anything users need to do their jobs still has to be supplied and configured by the IT department.

Is the DaaS revolution upon us?

Infrastructure, software, and computing platforms are all increasingly being hosted in the cloud, so surely desktop computers are only a step away, right? Not necessarily, at least if you ask Gartner’s Nathan Hill.

Long-term pricing, Hill says, is a major impediment to greater DaaS implementation. “The pricing of DaaS can often be misleading, as the entry point price … often covers a very light resource profile for not much more than OS or workspace hosting.”

Hill said that DaaS is great for agile computing needs, but as a replacement for the average employee’s desktop it isn’t going to always fit the bill. “To replace a permanent desk-based employee today is invariably going to result in a higher total cost of ownership, so the question becomes can organizations justify increased investment for the agility the platform can bring,” he said.

TechRepublic’s Bill Detwiler, writing about the top DaaS providers in 2020, notes that predictions for DaaS market growth forecasted by Gartner in 2016 haven’t kept up with more recent findings: In 2016 Gartner predicted 50% of new business VDI deployments transitioning to DaaS by 2019, which hasn’t happened. 

Gartner’s Market Guide for Desktop as a Service, released in November 2019, indicates that DaaS adoption has been slower than expected, with the company forecasting only a 20% move from VDI to DaaS by 2023. 

2019 was a big year for DaaS industry announcements, with Microsoft’s DaaS offering, Windows Virtual Desktop, going into general release, and Citrix’s DaaS product, Citrix Managed Desktops, doing the same. These moves came prior to Gartner’s market guide, meaning it was still predicting slower growth as of late 2019. 

SEE: Microsoft Windows Virtual Desktop: A cheat sheet (TechRepublic)

That may have changed with the spread of COVID-19, however: An article on digital workplace trends from Gartner published in August 2020 lists DaaS as one of six trends to keep an eye on, largely because of how quickly the pandemic accelerated shifts to remote work. 

“COVID-19 highlighted the value and business continuity strength of DaaS in its ability to rapidly enable remote work where on-premises options have stalled. The pandemic is likely to accelerate adoption of DaaS, and it may even perpetuate as a delivery architecture when employees return to the office,” Gartner said.

What are good use cases for DaaS?

Both Oestreich and Hill agree that business-ready DaaS systems can benefit certain kinds of organizations. Schools can use DaaS for student computing in labs and as follow-along training tools; temp workers can be given a workstation without hardware setup; and anyone needing to test hardware and software profiles can benefit from a completely cloud-based system.

Oestreich said that Citrix partners offering vertical integration have had success, which he sees as the eventual path of DaaS offerings. Partner companies like Approach Technology and TekLinks offer industry-specialized DaaS platforms, and they continue to grow rapidly. These specialized providers can offer industry-specific software bundled right into DaaS machines, taking all the licensing and implementation out of a company’s hands. Amazon WorkSpaces, Evolve IP, and MTM Technologies AnywhereApp also offer compliance options for various industries.

SEE: Business continuity policy (TechRepublic Premium)

Should I wait to invest in DaaS?

“When you add all the service components for a fully managed service,” Hill said, “DaaS can often climb to $100 per user per month or more.” The cost, he says, is one of the biggest reasons why VDI isn’t going to vanish overnight.

The initial investment in DaaS can seem cheap, but that’s for the most bare-bones system; in those cases, IT staff are going to still need to manage software installation and VM deployment. Couple that with costs in the hundreds per seat, and you might not save any money.

Think of local VDI systems as a car you’ve already paid off: All you need to worry about are maintenance costs. A new car might seem appealing, but the monthly fees can quickly outpace the annual cost of repairs on the older model. Likewise, a large chunk of cash spent on VDI servers might seem like a lot, but spaced out over a year, it’s much more affordable.

“DaaS is a commodity,” Oestreich said. “A lot of businesses spend time chasing prices for no more advantage over a locally installed VDI.” To get the maximum benefit out of DaaS, a business needs to invest a lot of money monthly, and then there are still problems of latency, data hosting, and regulations.

Fields like government, healthcare, and financial services haven’t always been able to adopt DaaS because vendors weren’t compliant with industry standards; that has changed, with several vendors mentioned in Detwiler’s DaaS providers article offering multiple compliance options for different industries. Hill suggests that IT staff monitor the changing face of the DaaS market, and when considering a move think about the benefit to specific employees, not the whole organization. IT professionals should also check with potential vendors to verify they offer connections that meet industry regulations.

How does COVID-19 affect VDI vs. DaaS decision-making?

DaaS is still a newer technology, and it’s growing along with the rest of the Everything as a Service (XaaS) world. In the future, it’s entirely possible that work desktops will all be thin clients connected to the cloud, but we’re not quite there yet, especially in light of Gartner’s data that DaaS adoption has been slower than expected.

“The reality is that VDI and SBC as technologies are more mature than DaaS,” Hill said. Maturity may not matter if COVID-19 keeps businesses distributed to home offices. Gartner made another excellent point in favor of DaaS adoption in its digital workplace trends article: It’s costly to invest in VDI. DaaS prices vary widely from vendor to vendor, but with remote work likely to be permanent even after the pandemic ends, those costs may be preferable to in-house deployments. 

If pandemic-triggered remote work trends continue, many physical offices may never reopen, which is another nail in the coffin for the data center in favor of a complete transition to the cloud. 

Also see

Editor’s note: This article was updated to reflect the latest information about VDI and DaaS.

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Fast and Robust Bio-inspired Teach and Repeat Navigation

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The navigation of robots is a demanding task. Luckily, we can rely on biological systems, such as ants, which can navigate with limited vision and computing power. A recent study suggests a teach and repeat navigation system for repeated route following.

It is based on wheel odometry, with vision providing a periodic correction signal. This technique can be applied to small low-cost robots, which usually have wheel odometry sensors and a monocular camera but do not have stereo vision or LiDAR sensors. The rate of visual correction can be changed accordingly to available computation resources.

Fast and Robust Bio inspired Teach and Repeat Navigation

Example of a navigation Doppler lidar instrument. Credits: NASA

The approach was verified in indoor and outdoor trials at different times of the day and in varying weather conditions. It can be used for new robotic systems with minimal tuning. The method is robust to odometry errors and can work with low-resolution images.

Fully autonomous mobile robots have a multitude of potential applications, but guaranteeing robust navigation performance remains an open research problem. For many tasks such as repeated infrastructure inspection, item delivery or inventory transport, a route repeating capability rather than full navigation stack can be sufficient and offers potential practical advantages. Previous teach and repeat research has achieved high performance in difficult conditions generally by using sophisticated, often expensive sensors, and has often had high computational requirements. Biological systems, such as small animals and insects like seeing ants, offer a proof of concept that robust and generalisable navigation can be achieved with extremely limited visual systems and computing power. In this work we create a novel asynchronous formulation for teach and repeat navigation that fully utilises odometry information, paired with a correction signal driven by much more computationally lightweight visual processing than is typically required. This correction signal is also decoupled from the robot’s motor control, allowing its rate to be modulated by the available computing capacity. We evaluate this approach with extensive experimentation on two different robotic platforms, the Consequential Robotics Miro and the Clearpath Jackal robots, across navigation trials totalling more than 6000 metres in a range of challenging indoor and outdoor environments. Our approach is more robust and requires significantly less compute than the state-of-the-art. It is also capable of intervention-free — no parameter changes required — cross-platform generalisation, learning to navigate a route on one robot and repeating that route on a different type of robot with different camera.

Link: https://arxiv.org/abs/2010.11326




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Facebook Demands Academics Disable Ad-Targeting Data Tool

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Academics, journalists, and First Amendment lawyers are rallying behind New York University researchers in a showdown with Facebook over its demand that they halt the collection of data showing who is being micro-targeted by political ads on the world’s dominant social media platform.

The researchers say the disputed tool is vital to understanding how Facebook has been used as a conduit for disinformation and manipulation.

In an October 16 letter to the researchers, a Facebook executive demanded they disable a special plug-in for Chrome and Firefox browsers used by 6,500 volunteers across the United States and delete the data obtained. The plug-in lets researchers see which ads are shown to each volunteer; Facebook lets advertisers tailor ads based on specific demographics that go far beyond race, age, gender and political preference.

The executive, Allison Hendrix, said the tool violates Facebook rules prohibiting automated bulk collection of data from the site. Her letter threatened “additional enforcement action” if the takedown was not effected by Nov. 30.

Company spokesman Joe Osborne said in an emailed statement Saturday that Facebook “informed NYU months ago that moving forward with a project to scrape people’s Facebook information would violate our terms.” The company has long claimed protecting user privacy is its main concern, though NYU researchers say their tool is programmed so the data collected from participating volunteers is anonymous.

The outcry over Facebook’s threat was immediate after The Wall Street Journal first reported the news Friday considering the valuable insights the “Ad Observer” tool provides. It has been used since its September launch by local reporters from Wisconsin to Utah to Florida to write about the November 3 presidential election.

“That Facebook is trying to shut down a tool crucial to exposing disinformation in the run up to one of the most consequential elections in US history is alarming,” said Ramya Krishnan, an attorney with the Knight First Amendment Institute at Columbia University, which is representing the researchers. “The public has a right to know what political ads are being run and how they are being targeted. Facebook shouldn’t be allowed to be the gatekeeper to information necessary to safeguard our democracy. “

“The NYU Ad Observatory is the only window researchers have to see microtargeting information about political ads on Facebook,” Julia Angwin, editor of the data-centric investigative tech news website The Markup, tweeted in disappointment.

The tool lets researchers see how some Facebook advertisers use data gathered by the company to profile citizens “and send them misinformation about candidates and policies that are designed to influence or even suppress their vote,” Damon McCoy, an NYU professor involved in the project, said in a statement.

After an uproar over its lack of transparency on political ads Facebook ran ahead of the 2016 election, a sharp contrast to how ads are regulated on traditional media, the company created an ad archive that includes details such as who paid for an ad and when it ran. But Facebook does not share information about who gets served the ad.

The company has resisted allowing researchers access to the platform, where right-wing content has consistently been trending in recent weeks. Last year, more than 200 researchers signed a letter to Facebook calling on it to lift restrictions on public-interest research and journalism that would permit automated digital collection of data from the platform.



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