Oleg Zabluda's blog
Thursday, December 29, 2016
 
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JPMorgan Chase & Co. and Deutsche Bank AG both turned down the opportunity to offer their wealthiest clients the option to invest in Uber, [...] because the ride-share company wasn’t willing to provide financial details about its business
"""
https://www.bloomberg.com/news/articles/2016-11-07/banks-said-to-have-passed-up-uber-share-sale-on-lack-of-data
https://www.bloomberg.com/news/articles/2016-11-07/banks-said-to-have-passed-up-uber-share-sale-on-lack-of-data

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"""
the truck ground to a halt less than 300 feet after its autonomous brakes sensed a collision.
"""
http://gizmodo.com/autonomous-brakes-may-have-stopped-berlin-truck-attacke-1790571821

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Allan Mishra, MD | APEX Platelet Rich Plasma
Allan Mishra, MD | APEX Platelet Rich Plasma
http://www.apexprp.com/

Platelet Rich Plasma Dr. Allan Mishra
https://www.youtube.com/watch?v=5MyW2kTeZrE

KRON-4 Talks to Allan Mishra, MD, About Platelet-Rich Plasma Treatment
https://www.youtube.com/watch?v=2yyrzuUQjzU

http://www.allanmishra.com/
http://www.bloodcure.com/

http://plateletrichplasma.blogspot.com/2016/09/the-power-to-heal-comes-from-within.html
http://plateletrichplasma.blogspot.com/2016/01/platelet-rich-plasma-beats-surgery-for.html
http://plateletrichplasma.blogspot.com/2014/10/platelet-rich-plasma-prp-helps-patients.html
https://www.youtube.com/watch?v=5MyW2kTeZrE

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DeNovo® NT Natural Tissue Graft
DeNovo® NT Natural Tissue Graft
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DeNovo® NT Graft is a particulated juvenile cartilage implant used for the repair of articular cartilage damage. It is intended to provide surgeons with an early-intervention option for articular cartilage repair and cartilage restoration.

Advantages of using DeNovo NT Graft include:

Juvenile tissue has 10x greater chondrocyte density than adult tissue and does not elicit an allogeneic immune response
[...]
DeNovo NT Graft is an FDA-listed tissue product used for knee, hip, ankle and shoulder cartilage restoration since 2007 with over 6,000 surgeries completed. There have been no reports of graft rejection or disease transmission.
"""
http://www.zimmer.com/medical-professionals/products/biologics-sports-medicine/denovo-nt-natural-tissue.html

"Allograft Cartilage DeNovo Transplantation"
http://www.zimmer.com/medical-professionals/products/biologics-sports-medicine/denovo-nt-natural-tissue.html

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Navy repeatedly dismissed evidence that ‘Fat Leonard’ was cheating the 7th Fleet
Navy repeatedly dismissed evidence that ‘Fat Leonard’ was cheating the 7th Fleet
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The Navy allowed the worst corruption scandal in its history to fester for several years by dismissing a flood of evidence that the rotund Asian defense contractor was cheating the service out of millions of dollars and bribing officers with booze, sex and lavish dinners [...] The Singapore-based contractor, Leonard Glenn Francis [...] Known as Fat Leonard for his 350-pound physique
[...]
the Navy moved more aggressively to investigate Glenn Defense starting in 2010 but that it took three more years to arrest Francis because he had bought off so many Navy officials.

“Francis had established a pretty good network, including a lot of Department of Navy personnel, who were able to explain away these allegations. There were people inside the Navy who were trying to shut this down, who were coming up with reasons not to pursue it.”

Federal court records show that Francis methodically cultivated a network of paid Navy informants over many years and planted them throughout the bureaucracy. He had spies inside the Navy’s regional contracting office in Singapore, the U.S. Embassy in Manila and the wardroom of the USS Blue Ridge, a command ship that serves as the flagship for the Navy’s 7th Fleet and the hub for maritime operations in Asia.

In exchange for paid sex with prostitutes, cash and luxury vacations, Francis’s informants fed him a near-daily diet of classified material and inside information that enabled him to keep gouging the Navy and outfox his pursuers for years, according to court records.

Francis’s most valuable mole was John Beliveau II, a turncoat NCIS supervisor who received a 12-year prison sentence in October.
[...]
A supervisor, Paul Simpkins, pleaded guilty and was sentenced this month to six years in federal prison for taking $350,000 in bribes, as well as the services of prostitutes, from Francis.
[...]
A subordinate, Sharon Gursharan Kaur, was charged this year by Singapore authorities with accepting $100,000 in cash and luxury vacations in Bali and Dubai.
[...]
Stories were filtering back to headquarters about Francis’s extravagant banquets in ports such as Singapore; Bali; Tokyo; Phuket, Thailand; and Kuala Lumpur, Malaysia.

Along with pricey champagne, Cuban cigars and platters of Kobe beef, Francis would pay for strippers that he called his Elite Thai SEAL Team, court records show. One party in Manila — for officers from the USS Blue Ridge — lasted for days in the presidential suite of the Makati Shangri-La Hotel and featured a steady flow of alcohol and prostitutes.
[...]
several reasons Glenn Defense was able to get away with its crimes for so long. The biggest factor, was that NCIS sharply ­­de-emphasized fraud and corruption investigations after the Sept. 11, 2001, attacks, when counterterrorism became the agency’s primary mission. Before then, NCIS had 140 special agents around the world assigned to economic crimes. By 2002, there were just eight. The numbers have gradually rebounded in recent years, to 83 agents today. Until 2008, however, NCIS did not have a single agent stationed in Asia who focused on fraud or financial crimes. [...] Even then, NCIS agents who specialized in fraud were more focused on whether contractors were supplying counterfeit or substandard equipment or parts — a big problem for the military. Glenn Defense had a reputation within the Navy for providing reliable, high-quality service. If there were disputes over how much it charged, many NCIS agents considered that a problem for Navy contracting personnel to sort out.
"""
https://www.washingtonpost.com/investigations/navy-repeatedly-dismissed-evidence-that-fat-leonard-was-cheating-the-7th-fleet/2016/12/27/0afb2738-c5ab-11e6-85b5-76616a33048d_story.html
https://www.washingtonpost.com/investigations/navy-repeatedly-dismissed-evidence-that-fat-leonard-was-cheating-the-7th-fleet/2016/12/27/0afb2738-c5ab-11e6-85b5-76616a33048d_story.html

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Deep Learning AI Listens to Machines For Signs of Trouble
Deep Learning AI Listens to Machines For Signs of Trouble
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The service of 3DSignals, a startup based in Kefar Sava, Israel, relies on [...] deep learning to understand the noise patterns of troubled machines and predict problems in advance. 3DSignals has already begun talking with leading European automakers about possibly using the deep learning service to detect possible trouble both in auto factory machinery and in the cars themselves. The startup has even chatted with companies about using their service to automatically detect problems in future taxi fleets of driverless cars.
[...]
For each client, 3DSignals installs ultrasonic microphones that can detect sounds ranging up to 100 kilohertz (human hearing range is between 20 hertz and 20 kilohertz). The startup’s “Internet of Things” service connects the microphones to a computing device that can process some of the data and then upload the information to an online network where the deep learning algorithms do their work. Clients can always check the status of their machines by using any Web-connected device such as a smartphone or tablet.

The first clients for 3DSignals include heavy industry companies operating machinery such as circular cutting blades in mills or hydroelectric turbines in power plants. These companies started out by purchasing the first tier of the 3DSignals service that does not use deep learning. Instead, this first tier of service uses software that relies on basic physics modeling of certain machine parts—such as circular cutting saws—to predict when some parts may start to wear out. That allows the clients to begin getting value from day one.

The second tier of the service uses a deep learning algorithm and the sounds coming from the microphones to help detect strange or unusual noises from the machines. The deep learning algorithms train on sound patterns that can signal general problems with the machines. But only the third tier of the service, also using deep learning, can classify the sounds as indicating specific types of problems. Before this can happen, though, the clients need to help train the deep learning algorithm by first labeling certain sound patterns as belonging to specific types of problems.

“After a while, we can not only say when problem type A happens, but we can say before it happens, you’re going to have problem type A in five hours,” Lavi says. “Some problems don’t happen instantly; there’s a deterioration.”

When trained, the 3DSignals deep learning algorithms are able to identify predict specific problems in advance with 98 percent accuracy. But the current clients using the 3DSignals system have not yet begun taking advantage of this classification capability; they are still building their training datasets by having people manually label specific sound signatures as belonging to specific problems.

The one-year-old startup has just 15 employees, but it has grown fairly fast and raised $3.3 million so far from investors such as Dov Moran, the Israeli entrepreneur credited with being one of the first to invent USB flash drives. Lavi and his fellow co-founders are already eying several big markets that include automobiles and the energy sector beyond hydroelectric power plants. A series A funding round to attract venture capital is planned for sometime in 2017.

If all goes well, 3DSignals could expand its lead in the growing market for providing “predictive maintenance” to factories, power plants, and car owners. The impending arrival of driverless cars may put even more responsibility on the metaphorical shoulders of a deep learning AI that could listen for problems while the human passengers tune out from the driving experience. On top of all this, 3DSignals has the chance to pioneer the advancement of deep learning in listening to general sounds. Not bad for a small startup.

“It’s important for us to be specialists in general acoustic deep learning, because the research literature does not cover it,” Lavi says.
"""
http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/deep-learning-ai-listens-to-machines-for-signs-of-trouble
http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/deep-learning-ai-listens-to-machines-for-signs-of-trouble

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Alexey Shor (*1970) Childhood Memories
Alexey Shor (*1970) Childhood Memories
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Pianistic retrospection

“Childhood Memories” is a suite of 14 short pieces for solo piano written by Maltese-American composer Alexey Shor. Born in 1970 in Kiev Shore now lives in Malta and New York. The Suite’s movements loosely follows a person’s path from early childhood, with a kid Chasing Fireflies and playing in a Sandbox, to his/her Coming of Age and First Love. The 14 pieces are of medium difficulty being (not only thematic) a wonderful enrichment for both advanced piano students and adult restarters.
"""
https://www.breitkopf.com/work/9506/childhood-memories
https://www.breitkopf.com/work/9506/childhood-memories

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