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Category: information science

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ARLINGTON, Va., 27 June 2016. U.S. military researchers are asking industry for new algorithms and protocols for large, mission-aware, computer, communications, and battlefield network systems that physically are dispersed over large forward-deployed areas.

Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., issued a broad agency announcement on Friday (DARPA-BAA-16–41) for the Dispersed Computing project, which seeks to boost application and network performance of dispersed computing architectures by orders of magnitude with new algorithms and protocol stacks.

Examples of such architectures include network elements, radios, smart phones, or sensors with programmable execution environments; and portable micro-clouds of different form factors.

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(Phys.org)—Inspired by natural selection and the concept of “survival of the fittest,” genetic algorithms are flexible optimization techniques that can find the best solution to a problem by repeatedly selecting for and breeding ever “fitter” generations of solutions.

Now for the first time, researchers Urtzi Las Heras et al. at the University of the Basque Country in Bilbao, Spain, have applied genetic algorithms to digital and shown that genetic algorithms can reduce quantum errors, and may even outperform existing optimization techniques. The research, which is published in a recent issue of Physical Review Letters, was led by Ikerbasque Prof. Enrique Solano and Dr. Mikel Sanz in the QUTIS group.

In general, quantum simulations can provide a clearer picture of the dynamics of systems that are impossible to understand using conventional computers due to their high degree of complexity. Whereas computers calculate the behavior of these systems, quantum simulations approximate or “simulate” the behavior.

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A high-tech version of an old-fashioned balance scale at the National Institute of Standards and Technology (NIST) has just brought scientists a critical step closer toward a new and improved definition of the kilogram. The scale, called the NIST-4 watt balance, has conducted its first measurement of a fundamental physical quantity called Planck’s constant to within 34 parts per billion — demonstrating the scale is accurate enough to assist the international community with the redefinition of the kilogram, an event slated for 2018.

The redefinition-which is not intended to alter the value of the kilogram’s mass, but rather to define it in terms of unchanging fundamental constants of nature-will have little noticeable effect on everyday life. But it will remove a nagging uncertainty in the official kilogram’s mass, owing to its potential to change slightly in value over time, such as when someone touches the metal artifact that currently defines it.

Planck’s constant lies at the heart of quantum mechanics, the theory that is used to describe physics at the scale of the atom and smaller. Quantum mechanics began in 1900 when Max Planck described how objects radiate energy in tiny packets known as “quanta.” The amount of energy is proportional to a very small quantity called h, known as Planck’s constant, which subsequently shows up in almost all equations in quantum mechanics. The value of h — according to NIST’s new measurement — is 6.62606983×10−34 kg?m2/s, with an uncertainty of plus or minus 22 in the last two digits.

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Because of a plethora of data from sensor networks, Internet of Things devices and big data resources combined with a dearth of data scientists to effectively mold that data, we are leaving many important applications – from intelligence to science and workforce management – on the table.

It is a situation the researchers at DARPA want to remedy with a new program called Data-Driven Discovery of Models (D3M). The goal of D3M is to develop algorithms and software to help overcome the data-science expertise gap by facilitating non-experts to construct complex empirical models through automation of large parts of the model-creation process. If successful, researchers using D3M tools will effectively have access to an army of “virtual data scientists,” DARPA stated.

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This army of virtual data scientists is needed because some experts project deficits of 140,000 to 190,000 data scientists worldwide in 2016 alone, and increasing shortfalls in coming years. Also, because the process to build empirical models is so manual, their relative sophistication and value is often limited, DARPA stated.

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When an astronomical observatory detected two black holes colliding in deep space, scientists celebrated confirmation of Einstein’s prediction of gravitational waves. A team of astrophysicists wondered something else: Had the experiment found the “dark matter” that makes up most of the mass of the universe?

The eight scientists from the Johns Hopkins Henry A. Rowland Department of Physics and Astronomy had already started making calculations when the discovery by the Laser Interferometer Gravitational-Wave Observatory (LIGO) was announced in February. Their results, published recently in Physical Review Letters, unfold as a hypothesis suggesting a solution for an abiding mystery in astrophysics.

“We consider the possibility that the black hole binary detected by LIGO may be a signature of dark matter,” wrote the scientists in their summary, referring to the black hole pair as a “binary.” What follows are five pages of annotated mathematical equations showing how the researchers considered the mass of the two objects LIGO detected as a point of departure, suggesting that these objects could be part of the mysterious substance known to make up about 85 percent of the mass of the universe.

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Provided the variants for intelligence can be figured out and the genetic editing tools sufficiently refined (no small matter, in either case), Hsu thinks there’s an incredible potential for improvement. He’s written for the science magazine Nautilus that his calculations from the work at BGI indicate a potential for “very roughly, about 100 standard deviations of improvement, corresponding to an IQ of over 1,000.”

That’s a level of intelligence beyond what we can comprehend right now.

At the same time, we’re not just working on improving biological intelligence. We’re also working on artificial intelligence and machine learning. Smarter humans might be better able to solve those problems, helping us create smarter machines. Smart machines capable of processing big data are already essential for efforts to understand millions of human genomes. These things work together.

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All is promising for Biometrics and biometric informatics; however, the technologies to date leveraged in IoT and other environments for parsing, analysis (especially predictive analysis), as well as better presented needs to be improved to be of value. We have seen great progress in the collection of the information and for some basic identification capabilities it looks good; however, to truly be effective and of value we need a lot more work done in this space especially when you look at today’s landscape of collecting information in areas of IoT and processing/ analysis with big data.

The global biometrics market is projected to cross US $ 24.8 billion by 2021. Fingerprint recognition biometric systems are the most preferred type of biometric systems used across the globe, owing to their ease of use, low cost, high speed and accurate results.

Biometric systems are used across various public as well as private offices for enhancing the security of data and information, as these systems provide an accurate validation as compared to traditional methods such as ID cards, PINs, passwords, etc. Increasing use of biometrics in e-commerce and cloud computing solutions, coupled with initiatives taken by the government of various countries across the world to adopt biometrics systems for identification and verification purposes are some of the major factors driving demand for biometric solutions, globally.

Moreover, introduction of e-passports and e-visas, use of biometrics in criminal identification, increasing demand for smartphones integrated with biometric technologies and implementation of biometric technology in election administration are anticipated to drive the global biometrics market over the next five years.

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Agree. So as a tech engineer, futurist, innovator, leader you have 3 key tracks to remain relevant in the future: bio/ living technology, quantum, and a hybrid of living/ bio meets quantum computing.

Editor €™s Note: Richard van Hooijdonk is a futurist and international keynote speaker on future technologies and disruption and how these technologies change our everyday lives. Van Hooijdonk and his international team research €˜mega trends €™ on digital health, robotic surgery, drones, the internet-of-things, 3D/4D printing, Big Data and other how new technologies affects many industries.

With people living increasingly longer lives, medical care from surgeons, physicians, pharmacists and dentists will increase as well. And since the future of healthcare will look very different from what it is today, the medical field may just be the right industry for you, even if being a doctor or nurse is not your calling. Many new technologies will be incorporated into the healthcare industry and we will see things like robotic surgeries and 3D-printed organ implants, to name a few. This means we will be seeing a whole new host of career opportunities, even for jobs that don €™t actually exist yet.

1. Healthcare Navigator €“ Guides patients through the complex medical system of the future

Being sick can be extremely stressful €“ to yourself, the doctors and nursing staff. But your family and loved ones also have a lot to deal with when you are ill. Technology will make healthcare more and more complex to navigate in the future. We €™ll be introduced to bio-printers, electronic pills, 3D-printed medication, surgical robots and DNA manipulation. To make sense of all these new technologies and treatments, and guide the patient as well as family members, healthcare navigators will become indispensible.

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