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Future autonomous machines may build trust through emotion

Army research has extended the state-of-the-art in autonomy by providing a more complete picture of how actions and nonverbal signals contribute to promoting cooperation. Researchers suggested guidelines for designing autonomous machines such as robots, self-driving cars, drones and personal assistants that will effectively collaborate with Soldiers.

Dr. Celso de Melo, computer scientist with the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory at CCDC ARL West in Playa Vista, California, in collaboration with Dr. Kazunori Teradafrom Gifu University in Japan, recently published a paper in Scientific Reports where they show that emotion expressions can shape cooperation.

Autonomous machines that act on people’s behalf are poised to become pervasive in society, de Melo said; however, for these machines to succeed and be adopted, it is essential that people are able to trust and cooperate with them.

“Human cooperation is paradoxical,” de Melo said. “An individual is better off being a free rider, while everyone else cooperates; however, if everyone thought like that, cooperation would never happen. Yet, humans often cooperate. This research aims to understand the mechanisms that promote cooperation with a particular focus on the influence of strategy and signaling.”

Strategy defines how individuals act in one-shot or repeated interaction. For instance, tit-for-tat is a simple strategy that specifies that the individual should act as his/her counterpart acted in the previous interaction.

Signaling refers to communication that may occur between individuals, which could be verbal (e.g., natural language conversation) and nonverbal (e.g., emotion expressions).

This research effort, which supports the Next Generation Combat Vehicle Army Modernization Priority and the Army Priority Research Area for Autonomy, aims to apply this insight in the development of intelligent autonomous systems that promote cooperation with Soldiers and successfully operate in hybrid teams to accomplish a mission.

“We show that emotion expressions can shape cooperation,” de Melo said. “For instance, smiling after mutual cooperation encourages more cooperation; however, smiling after exploiting others — which is the most profitable outcome for the self — hinders cooperation.”

The effect of emotion expressions is moderated by strategy, he said. People will only process and be influenced by emotion expressions if the counterpart’s actions are insufficient to reveal the counterpart’s intentions.

For example, when the counterpart acts very competitively, people simply ignore-and even mistrust-the counterpart’s emotion displays.

“Our research provides novel insight into the combined effects of strategy and emotion expressions on cooperation,” de Melo said. “It has important practical application for the design of autonomous systems, suggesting that a proper combination of action and emotion displays can maximize cooperation from Soldiers. Emotion expression in these systems could be implemented in a variety of ways, including via text, voice, and nonverbally through (virtual or robotic) bodies.”

According to de Melo, the team is very optimistic that future Soldiers will benefit from research such as this as it sheds light on the mechanisms of cooperation.

“This insight will be critical for the development of socially intelligent autonomous machines, capable of acting and communicating nonverbally with the Soldier,” he said. “As an Army researcher, I am excited to contribute to this research as I believe it has the potential to greatly enhance human-agent teaming in the Army of the future.”

The next steps for this research include pursuing further understanding of the role of nonverbal signaling and strategy in promoting cooperation and identifying creative ways to apply this insight on a variety of autonomous systems that have different affordances for acting and communicating with the Soldier.

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Cool new worlds found in our cosmic backyard

How complete is our census of the Sun’s closest neighbors? Astronomers using NSF’s NOIRLab facilities and a team of data-sleuthing volunteers participating in Backyard Worlds: Planet 9, a citizen science project, have discovered roughly 100 cool worlds near the Sun — objects more massive than planets but lighter than stars, known as brown dwarfs. Several of these newly discovered worlds are among the very coolest known, with a few approaching the temperature of Earth — cool enough to harbor water clouds.

Discovering and characterizing astronomical objects near the Sun is fundamental to our understanding of our place in, and the history of, the Universe. Yet astronomers are still unearthing new residents of the Solar neighborhood. A remarkable breakthrough was announced today, with the discovery of roughly 100 cool brown dwarfs near the Sun [1].The new Backyard Worlds discoveries bridge a previously empty gap in the range of low-temperature brown dwarfs, identifying a long-sought missing link within the brown dwarf population.

These cool worlds offer the opportunity for new insights into the formation and atmospheres of planets beyond the Solar System,” said Aaron Meisner from the National Science Foundation’s NOIRLab and the lead author of the research paper. “This collection of cool brown dwarfs also allows us to accurately estimate the number of free-floating worlds roaming interstellar space near the Sun.”

This major advancement was made possible with archival data from the Nicholas U. Mayall 4-meter Telescope at Kitt Peak National Observatory (KPNO) and the Víctor M. Blanco 4-meter Telescope at Cerro Tololo Inter-American Observatory (CTIO), which were made available through the Community Science and Data Center (CSDC), all programs of NSF’s NOIRLab. Large survey data sets were then made available to the Backyard Worlds volunteers using NOIRLab’s Astro Data Lab science platform. The results, to be published in TheAstrophysical Journal, demonstrate the rapidly growing role of survey and archival data research in astronomy today.

Brown dwarfs lie somewhere between the most massive planets and the smallest stars. Lacking the mass needed to sustain nuclear reactions in their core, brown dwarfs resemble cooling embers. Their low mass, low temperature and lack of internal nuclear reactions make them extremely faint — and therefore extremely difficult to detect. Because of this, when searching for the very coolest brown dwarfs, astronomers can only hope to detect such objects relatively close to the Sun.

To help find our Sun’s coldest and nearest neighbors, the astronomers of the Backyard Worlds project turned to a worldwide network of more than 100,000 citizen scientists [2]. These volunteers diligently inspect trillions of pixels of telescope images to identify the subtle movements of brown dwarfs and planets. Despite the abilities of machine learning and supercomputers, there’s no substitute for the human eye when it comes to scouring telescope images for moving objects.

The keen eyes of the Backyard Worlds volunteers have already discovered more than 1,500 cold worlds near to the Sun, and today’s paper presents roughly 100 of the coldest in that sample. According to Meisner, this is a record for any citizen science program by a factor of about 20, and 20 citizen scientists are listed as co-authors of the study. A handful of these cool worlds — which are among the very coldest brown dwarfs known — approach the temperature of Earth. NASA’s Spitzer Space Telescope provided the brown dwarf temperature estimates [3].

Brown dwarfs are expected to cool as they age, passing from near-stellar temperatures down to planetary temperatures and below, fading all the while and eventually winking out. The new discoveries attest to this picture by uncovering elusive examples of brown dwarfs approaching Earth-temperature.

“This paper is evidence that the solar neighborhood is still uncharted territory and citizen scientists are excellent astronomical cartographers,” said co-author Jackie Faherty of the American Museum of Natural History. “Mapping the coldest brown dwarfs down to the lowest masses gives us key insights into the low-mass star formation process while providing a target list for detailed studies of the atmospheres of Jupiter analogs.”

Citizen scientist, Astro Data Lab user, and paper co-author Jim Walla added, “It’s awesome to know that our discoveries are now counted among the Sun’s neighbors and will be targets of further research.”

Alongside the dedicated efforts of the Backyard Worlds volunteers, NOIRLab’s Astro Data Lab was instrumental in this research. The technical burden of downloading billion-object astronomical catalogs is typically insurmountable for individual investigators — including most professional astronomers. “AstroData Lab’s open and accessible web portal allowed Backyard Worlds citizen scientists to easily query massive catalogs for brown dwarf candidates,” explained NOIRLab astronomer Stephanie Juneau, who helped introduce the citizen scientists to Astro Data Lab. Astro Data Lab also enables convenient matching between data sets from NOIRLab telescopes and external facilities, such as NASA’s WISE satellite, that jointly contributed to these brown dwarf discoveries.

In addition to Astro Data Lab’s making data accessible to the Backyard Worlds collaboration, archival observations by telescopes at two other NOIRLab Programs — CTIO and KPNO — were also key to this discovery. “Wide-area imaging from NOIRLab’s Mayall and Blanco telescopes was also critical,” explained Aaron Meisner. “To select only the very coldest brown dwarfs, we inspected deep images from a variety of sensitive astronomical surveys.”

“It’s great to see such thrilling results from NOIRLab’s efforts to broaden participation in astronomy research,” said Chris Davis of the National Science Foundation, the US agency that supports operations at the Kitt Peak and Cerro Tololo observatories and at CSDC. “By making archival data from NSF’s Mayall and Blanco telescopes publicly available and easily accessible through CSDC, folks with a fascination for astronomy can make a real contribution to science and to our understanding of the Universe.”

The approach of the Backyard Worlds project — searching for rare objects in large data sets — is also one of the goals for the upcoming Vera C. Rubin Observatory [4]. Currently under construction on Cerro Pachón in the Chilean Andes, Rubin Observatory will image the visible sky from the southern hemisphere every three nights over ten years, providing a vast amount of data that will enable new ways of doing astrophysical research.

“Vast modern data sets can unlock landmark discoveries, and it’s exciting that these could be spotted first by a citizen scientist,” concludes Aaron Meisner. “These Backyard Worlds discoveries show that members of the public can play an important role in reshaping our scientific understanding of our solar neighborhood.”

Notes

[1] The closest of these new discoveries is roughly 23 light-years away from the Sun. Many more of these brown dwarfs are in the 30-60 light-year distance range.

[2] Backyard Worlds: Planet 9 is hosted by Zooniverse.

[3] Complementary follow-up observations were also supplied by Keck Observatory, Mont Mégantic Observatory, and Carnegie Institution for Science’s Las Campanas Observatory.

[4] Rubin Observatory and Department of Energy (DOE) Legacy Survey of Space and Time Camera are operated by NSF’s NOIRLab and SLAC National Accelerator Laboratory (SLAC).

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ProgrammableWeb

​Fitango Health Releases Catalogue of APIs for Health Analytics

Fitango Health, an Israeli-American digital health company, has launched a complete library of customizable APIs enabling providers and payers to add innovative patient management capabilities that integrate active patient engagement, telehealth and secured messaging, health analytics and Durable Medical Equipment management. Fitango’s API-based solution allows partners to easily incorporate a variety of features and functionalities into existing platforms and workflows to support a robust patient management offering.

Fitango’s technology supports providers and payers that are transitioning to value-based care with a product suite that actively engages patients and offers them insight about the patient at home. Fitango’s API solution allows organizations to expand their remote monitoring capabilities through improved patient communication while empowering patients with interactive engagement resources. These APIs can be implemented across the healthcare continuum—from prevention through post-acute and post-operative to chronic and complex care management.

The active patient engagement offering enables organizations to improve the efficacy of their virtual health solutions. Fitango’s active patient engagement includes highly customizable and personalized features that go beyond traditional patient management. Fitango delivers to users the next generation of virtual care, fostering bilateral communication and information exchange. It also provides patients with the resources they need to stay healthy at home.

Fitango uses an active approach that promotes interactive learning throughout the individual’s healthcare journey, rather than traditional passive forms of patient management. Providers can prescribe action plans, education plans, assessments, and Social Determinants of Health referrals. “We are excited to enable payers and providers to embed our innovative patient management layer within days” said Fitango Heath’s Founder and CEO, Dr. Dov Biran.

Fitango Health’s robust GraphQL or Rest API simplifies the process for all stakeholders to manage the full patient journey. The API functionality promotes growth and scalability for all partners while maintaining patient security and remaining HIPAA compliant. This new fully customizable experience allows healthcare providers and payers to satisfy their patients’ health and wellness needs for improved integrated care.

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Author: <a href="https://www.programmableweb.com/user/%5Buid%5D">ProgrammableWeb PR</a>

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ProgrammableWeb

Google Improves Paging API with Kotlin

Google has released a new version of the Paging API. The company says Paging API 3 is a complete overhaul of the library based on Kotlin. The idea was to incorporate the changes asked for by developers, which include simpler handling of errors, wider list transformations, and support for list, footer, and header separators. The alpha API still supports Java users. Here’s what you need to know. 

The Paging 3 API covers a wide range of functions. At its core, it lets developers load in large sets of data over time in such a way as to reduce network use and cut down on system resource drain. It automates certain tasks that developers would otherwise have to undertake themselves. 

Some of the functionalities include the ability to keep track of the keys used for retrieving the next and previous page, as well as automatically requesting the correct page when users scroll down through loaded data. The API ensures that users can’t trigger multiple requests at the same time and tracks the loading state so it can be displayed in the right spot of the user interface. Easy retry functions are offered, and the API allows for common operations, such as map or filters to be displayed, whether the developer is using Flow, RxJava, or others. It provides for simple list separator implementation, and, last, reduces data caching to a simple task. Google says some of these are backward compatible with Paging API 2.0, though it didn’t specify which ones.  

Google insists that adopting the API within an app is a straight-forward process. For example, the Paging library talks directly to the recommended Android app architecture in every single layer of the app. Depending on where you’re drawing the data from, developers can implement a PagingSource or a PagingSource and RemoteMediator. Single data sources, such as a network or database, can get away with the PagingSource, while data that’s coming from layered sources, such as a network and a local database with also need the RemoteMediator to merge the sources. A PagingSource defines the source of the data and how to pull that data in and display it correctly. This is found in the repository layer

Google created the Paging 3 library to help developers handle simple and complex data pulls thanks to the way it manages information from multiple sources. The Paging 3 API is still in alpha, so as always Google is asking for help in ironing out the bugs. Documentation is available here.

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Author: <a href="https://www.programmableweb.com/user/%5Buid%5D">EricZeman</a>

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ProgrammableWeb

EU Countries Likely to Adopt the Covid19 “Contact Tracing API” (Apple + Google Collab)

A complete adoption of the Apple/Google coronavirus contact tracing API within the EU looks to be moving forward. Reporting for 9to5mac, tech writer Ben Lovejoy writes that the European Commission has endorsed the collective adoption of one app or common standard (e.g. the Pan-European Privacy-Preserving Proximity Tracing (PEPP-PT) platform).

Speaking to Reuters, Chris Boos (a champion of the PEPP-PT and the founder of Arago, a German startup for business process automation) praised the move as a shorter path to deployment, stating “We need to worry less about operating system stability and device calibration.” 

While Boos praises a centralized model as offering “much better pandemic management potential without infringing privacy,” this method is far from fait accompli. Alternatively, Lovejoy compares the Apple/Google API as a “very deliberately a decentralized approach, where data is held only on the phone itself unless the owner (a) tests positive and (b) gives permission for their Bluetooth contact codes to be uploaded.”

Boos’ final thought emphasizes that there is no one-size-fits-all solution: “… it should be a country’s choice. You can gather the same data on top of a decentralized model – it just means more people have to move data on infected people.”

A progress report for PEPP-PT was planned for last Friday, 17 April. 

Where privacy is the top concern, Apple/Google API offers the highest standard. This reassurance of privacy is the likely key to reassuring cautious adopters. In this same vein, Apple is working on “anonymized Apple Maps mobility data available to health authorities to help them track the effectiveness of lockdown measures.” 9to5mac has made an outline (in layman’s terms) of what messages are needed from Apple and Google to reassure users concerned about privacy. 

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Author: <a href="https://www.programmableweb.com/user/%5Buid%5D">Katherine-Harrison-Adcock</a>

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ProgrammableWeb

Twitter’s New Dev Policy Embraces Research and Trustworthy Bots

Twitter has announced a complete rewrite of the company’s developer policies. This iteration is intended to clearly communicate appropriate developer use cases while also promoting quality platform research and development.

The new policies are expanded to include a more direct path for developers that are hoping to engage in non-commercial research. Twitter understands that the breadth of data present on the platform is a treasure trove for researchers. The company hopes that expanded access for researchers will be helpful in analyzing spam, abuse, and public health factors present on the platform. Additionally, the new policies allow non-commercial researchers to share an unlimited number of Tweet IDs and/or User IDs on behalf of an academic institution.

The company is modifying the way that they communicate acceptable use cases for developer access. The announcement noted that as the platform evolves it may be necessary for developers to update the specific use case for continued access to Twitter data. 

In addition to more clarity around use cases, Twitter is also tightening its standards for the use of bots on the platform. Developers will now be required to specify the intended purpose of a bot and who is operating it. The company is hoping to remove some of the ambiguity behind account identity.

Make sure to check out the announcement for additional details. Twitter also hinted at the next version of the API that the company has been working on for some time. Make sure to follow the Twitter APITrack this API profile for future updates.

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Author: <a href="https://www.programmableweb.com/user/%5Buid%5D">KevinSundstrom</a>

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IEEE Spectrum

Amazon Reports Collision Risk for Mega-Constellation of Kuiper Internet Satellites

For the first time, we have a complete, representative number for the overall orbital collision risk of a satellite mega-constellation.

Last month, Amazon provided the U.S. Federal Communications Commission (FCC) with data for its planned fleet of 3,236 Kuiper System broadband Internet satellites.

If one in 10 satellites fails while on orbit, and loses its ability to dodge other spacecraft or space junk, Amazon’s figures [PDF] show that there is a 12 percent chance that one of those failed satellites will suffer a collision with a piece of space debris measuring 10 centimeters or larger. If one in 20 satellites fails—the same proportion as failed in rival SpaceX’s first tranche of Starlink satellites—there is a six percent chance of a collision.

More than a third of all the orbital debris being tracked today came from just two collisions that occurred about a decade ago. Researchers are concerned that more explosions or breakups could accelerate the Kessler Syndrome—a runaway chain reaction of orbital collisions that could render low earth orbit (LEO) hostile to almost any spacecraft.

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ScienceDaily

Getting an ‘eel’ for the water: The physics of undulatory human swimming

A research team led by the University of Tsukuba created the most complete recording to date of a human swimming underwater like an eel or lamprey. Using motion capture equipment and particle velocity monitors, the scientists were able to study this “undulatory” underwater propulsion and the nearby water flows it created. They found that jets produced by coalescing vortices help explain the efficiency of this swimming method, which might be applied to novel propulsion systems.

If you watch an Olympic swimming event, you may be surprised to see the athletes wriggling like eels when starting the race or just after turning around. Regardless of the type of swimming stroke used for the rest of the lap, these competitors have discovered that this undulatory motion is the best way to accelerate quickly. However, it was not previously known why this is the case, and a better understanding of underwater propulsion can lead to more efficient submarines and ships. For this research, a national-level swimmer was recorded swimming in a water flume while wearing 18 LED markers. Streams of microbubbles were used as tracers of the 3D water velocity fields. This allowed the scientists to more fully understand the source of the swimmer’s thrust while undulating underwater.

“Propulsion through a fluid, whether air or water, usually relies on the principle of conservation of momentum,” explains author Hirofumi Shimojo. “For example, pushing water backwards with your hands or feet when swimming in the ocean will make you move forward. Similarly, a jet engine can zoom through the sky by pushing a stream of air backwards out of its engines.”

The researchers saw that the swimmer’s downward kick created leading-edge vortices that moved from the front to the back of his feet. After these vortices were shed from the swimmer’s body, they combined into a “vortex wake,” which led to a jet of water flow that propelled him forward.

“Our work shows the importance of visualizing the complex water flows to understanding the origin of propulsion efficiency. In this case, the swimmer gains thrust from his downward kick due to the vortices and jet flow in his wake,” says senior author Hideki Takagi.

These findings could potentially provide insight beyond human motion. Adds Shimojo, “This work may help us to understand the wakes created by other forms of underwater propulsion, including those that power boats and submarines.”

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Materials provided by University of Tsukuba. Note: Content may be edited for style and length.

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