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How a synthetic data approach is helping COVID-19 research

As medical researchers around the world race to find answers to the COVID-19 pandemic, they need to gather as much clinical data as possible for analysis.

A key challenge many researchers face with clinical data is privacy and the mandate to protect confidential patient information. One way to overcome that privacy challenge is by using synthetic data, an approach that creates data that is not linked to personally identifiable information. Rather than encrypting or attempting to anonymize data to protect privacy, synthetic data represents a different approach that can be useful for medical researchers.

With synthetic data there are no real people, rather the data is a synthetic copy that is statistically comparable, but entirely composed of fictional patients, explained Ziv Ofek, founder and CEO of health IT vendor MDClone, based in Beer Sheba, Israel.

Other popular methods of protecting patient privacy, such as anonymization and encryption, aim to balance patient privacy and data utility. However, a privacy risk still remains because embedded within the data, even after diligent attempts to protect privacy, are real people, Ofek argued.

“There are no real people embedded within the synthetic data,” Ofek said. “Instead, the data is a statistical representation of the original and the risk of reidentification is no longer relevant, even though it may appear as real people and can be analyzed as if it were and yielding the same conclusions.”

Synthetic Data Engine from MDClone
MDClone Synthetic Data Engine creates anonymous data statistically identical to the original.

Synthetic data in the real world

MDClone’s synthetic data technology is being used by Sheba Medical Center in Tel Aviv as part of its COVID-19 research.

Synthetic data provides an opportunity to get quick answers to data-related questions … [and] allows users to work on the data in their own environment, something we do not allow with real data.
Eyal Zimlichman, M.D.Deputy director general, Sheba Medical Center

The MDClone system is critical to his organization’s data efforts to gain more insights into COVID-19, the disease caused by the novel coronavirus, said Eyal Zimlichman, M.D., deputy director general, chief medical officer and chief innovation officer at Sheba Medical.

By regulation, synthetic data is not considered patient data and therefore is not subject to the IRB process. As opposed to real patient data, Ofek noted that synthetic data can be accessed freely by researchers, so long as the institution agrees to provide access.

“Synthetic data provides an opportunity to get quick answers to data-related questions without the need for an IRB approval,”Zimlichman said. “It also allows users to work on the data in their own environment, something we do not allow with real data.”

Zimlichman added that data science groups both within and outside the hospital are using the MDClone system to help predict COVID-19 patient outcomes, as well as to aid in determining a course of action for therapy.

Synthetic data accelerates time to insight

The MDClone platform includes a data engine for collecting and organizing patient data, the discovery studio for analysis and the Synthetic Data Engine for creating data. The vendor on April 14 released the MDClone Pandemic Response Package, which includes a predefined set of visualizations and analyses that are COVID-19-specific. The engine enables clients and networks to ask questions of COVID-19-related data and generate meaningful analysis, including cohort and population-level insights.

In the event a client wants to use their data to share, compare and collaborate with others, they can convert their original data into a synthetic copy for shared review and insight development.

“A synthetic collaboration model allows for that conversation to take place with data flows and analysis performed across both systems without patient privacy and security risks,” Ofek said.

Ofek added that the synthetic model and platform access capability enables clients to invite research and collaboration partners into their data environment rather than simply sharing files on demand. With MDClone, the client’s research and collaboration partners are able to log in to the MDClone data lake and then get access to the data and exploration tools with synthetic output.

“In the context of the pandemic, organizations leveraging the platform can offer partners unfettered synthetic access to accelerate exploration into new avenues for treatment,” Ofek said. “Idea generation and data reviews that enable real-world analysis is our pathway to finding and broadcasting the best healthcare professionals can offer as we combat the disease.”

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Honeywell quantum computing system passes IBM out of the gate

Honeywell International Inc. jumped into the quantum computing race this week with a system that uses trapped ion technology.

The new Honeywell quantum computing system, which has a Quantum Volume of 64 , is double that of existing quantum systems from companies such as IBM and D-Wave Systems. The company expects to deliver the system in 90 days.

The company attributes the system’s 64 rating to its new quantum charge coupled device (QCCD) architecture, which will allow Honeywell to increase its Quantum Volume by an order of magnitude each year for the next five years.

“The performance metric the [quantum computing] community is agreeing on now is Quantum Volume,” said Tony Uttley, president of Honeywell’s quantum solutions group. “We have seen over time that it matters more how low an error rate your system has, not just how many physical qubits you have. The right question to ask is how many effective qubits do you have.”

Honeywell chose the trapped ion approach, which is similar to the approach startup IonQ employs in its quantum system, because it allows you to start with “a perfect qubit,” Uttley said.

“When you start with a perfect qubit in your system, any errors that then occur can be more easily traced back to things you put into the surrounding infrastructure,” Uttley said. “What Honeywell is good at is taking a systems engineering approach to complex system design, so we are aware of all the potential entry points of error.”

Some analysts believe the new QCCD architecture the system uses could give Honeywell at least a temporary lead in the game of performance leapfrog many quantum system makers find themselves in. But Honeywell will have to keep leaping if they hope to maintain that lead over time.

“They have to do some things beyond the [QCCD architecture] in order to achieve these huge Quantum Volume numbers they are talking about, like and integrating more capabilities at the chip level,” said Paul Smith-Goodson, analyst-in-residence for quantum computing at Moor Insights & Strategy. “But what they have right now looks pretty solid going forward.”

They have to do some things beyond the [QCCD architecture] in order to achieve these huge Quantum Volume numbers … but what they have right now looks pretty solid going forward.
Paul Smith-GoodsonAnalyst-in-residence for quantum computing, Moor Insights & Strategy

The technologies in the new Honeywell quantum computing system began development 10 years ago, according to Uttley. Some of those technologies are borrowed from its various control systems, a market the company has built a reputation in decades ago.

“As [quantum systems] get bigger and start to resemble process control plants, that plays to our core strength,” Uttley said. “Being able to control massively complex systems in a way that simplifies an operation you need to do is something we have a long history with.”

Another analyst agreed that Honeywell’s expertise in developing and manufacturing control systems gives them a technology advantage over quantum computing competitors that have never ventured into that business.

“Their experience in precision manufacturing and environmental controls should allow them to create a quantum system that blocks out more environmental noise which, in part, helps them achieve higher Quantum Volume,” said James Sanders, a cloud transformation analyst with 451 Research.

Along with the new system, Honeywell’s venture capital group, Honeywell Ventures, has made an investment in Cambridge Quantum Computing and Zapata Computing Inc., both producers of quantum software and quantum algorithms that will work jointly with Honeywell. Cambridge Quantum Computing focuses on a number of markets including chemistry, machine learning and augmented cybersecurity. Zapata’s algorithms focus on areas such as simulation of chemical reactions, machine learning and a range of optimization problems.

“We already work in vertical markets we believe will be profoundly impacted by quantum computing, like the aerospace, chemicals, and oil and gas industries,” Uttley said. “We already have domain experts in areas now that will focus on use cases applicable to quantum computing.”

The company is also partnering with JPMorgan Chase to develop quantum algorithms using Honeywell quantum computing. Last fall, Honeywell announced a partnership with Microsoft that will see the software giant provide cloud access to Honeywell’s quantum system through Microsoft Azure Quantum services.

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A brother and sister team are rowing 3,000 miles across the Atlantic Ocean – their equipment includes Microsoft Teams

A brother and sister team taking part in a 3,000-mile race across the Atlantic Ocean have stayed in touch with family and friends by using Microsoft Teams, despite being hundreds of miles from land.

Anna and Cameron McLean have used the Microsoft tool to contact loved ones, and receive weather and race updates from a crew on shore during the Talisker Whisky Atlantic Challenge.

Known as “the world’s toughest row”, participants spend 60 days at sea in a small boat, braving 40-foot waves, sharks, illness and a schedule that sees them sleep and row in two-hour shifts as they make their way from La Gomera in The Canary Islands to Antigua. To put the gruelling race in context, fewer people have rowed the Atlantic than reached the summit of Everest.

While many mixed-sex teams have completed the challenge, Anna and Cameron believe they are the first brother and sister to take part.

Speaking via Teams on the 35th day of their journey, Anna said the Microsoft tool had been crucial for receiving messages of support that have kept the siblings going.

  • Part of the Microsoft Teams call between Anna McLean, in the Atlantic, and Andy Trotman, in the UK

“We can use Teams to communicate with anyone in the world from the middle of the Atlantic Ocean. That’s been essential,” she said. “Teams has been such a dream because we’ve been able to maintain a two-way dialogue with our family and friends back home, as well as our sponsors. We have been able to share real-time updates and pictures, and get information such as the weather forecast. That’s been a big contributing factor to the success and speed of our crossing. Teams has helped us navigate the best and most direct course.

“It’s been easy to set up, too. We connect to the internet via a satellite, and then open up the Teams app on my phone. That’s it.”

Anna, 25, and Cameron, 32, are currently third in the pairs race, in a field of 34 vessels. They are each burning 10,000 calories a day and fighting against sleep deprivation, exhaustion, blisters and bruises. Meals consist of “space food” that has to be mixed with water and left on deck so the sun can warm it up. Sea water is filtered for drinking, and they aim to drink at least 10 litres a day.

Even though they are experienced rowers, having competed at university, nothing could prepare them for a race of this magnitude.

Anna McLean rowing across the Atlantic
Anna and Cameron are spending 60 days at sea in a small boat, braving 40-foot waves, sharks, illness and a schedule that sees them sleep and row in two-hour shifts

“The nights are brutal,” said Anna, who works for Microsoft partner AlfaPeople. “With a lack of moonlight, the nights are so dark that you can’t see your hand in front of your face or the waves that crash over the side of the boat and threaten to capsize you. The sea was so rough one night that we broke an oar.

“Then, each new day brings new challenges. Our water maker and autohelm broke, and we have been followed by what I estimate to be a 14-foot shark. But we have no choice but to overcome those challenges through strength and perseverance.”

Anna and Cameron are rowing to raise money for UN Women, an organisation dedicated to gender equality and the empowerment of women. “The impact they have for women and girls everywhere is just phenomenal,” Anna added.

The pair have around 300 miles to go before they reach the finish line, and Anna is already looking forward to some simple luxuries.

“I can’t wait to see my mum and dad, and give them a big hug,” she said. “I’m also looking forward to a hot shower and eating fresh fruit and vegetables.”

  • Subscribe to the UK News Centre to learn more about Anna and Cameron’s challenge in an upcoming feature

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BI for mobile remains a challenge for vendors

While the demand for analytics software grows and vendors old and new race to come up with the next innovations, the majority of vendors’ research and development resources are being dedicated to desktop applications as opposed to BI for mobile devices.

A handful of vendors stand out as exceptions, but mobile apps remain largely underdeveloped by many others.

BI for mobile, simply, presents a conundrum for developers. Some have chosen to invest in mobile, attacked the challenge and made headway, while others have elected to just focus their attention on their desktop applications.

The problem is the screen.

Digestible data is largely visual — it’s charts and graphs, and often more than just one on a single dashboard. Once, it was numbers on a page, but that time is now in the distant past. 

Mobile screens, however, are tiny compared to computer screens. Recreating desktop dashboards doesn’t work particularly well on a mobile device. Recreating the analytic capabilities of desktop device, therefore, doesn’t work either.

Instead, the vendors who have developed successful BI for mobile apps have viewed phones and tablets as different entities than desktop computers, and they’ve created a different experience on their mobile apps.

“[The phone] is not an effective tool for doing data analysis,” said Donald Farmer, principal at TreeHive Strategy in Woodinville, Wash. “It’s an effective tool for conveying short, well-formatted, concise insights. The people who have done a good job … have focused on that. They pick out significant things to tell you on the phone in a format that works for a mobile device, but they’re not trying to give you an analytic tool on a mobile device – that wouldn’t be practical or helpful.”

[The phone] is not an effective tool for doing data analysis. It’s an effective tool for conveying short, well-formatted, concise insights. The people who have done a good job … have focused on that.
Donald FarmerPrincipal, TreeHive Strategy

Similarly, the vendors that have developed good mobile apps have developed their apps specifically for mobile devices, noted Mike Leone, a senior analyst with Enterprise Strategy Group in Milford, Mass.

“First and foremost, [a good mobile BI app is] one that is designed from the ground up for a mobile device,” he said. “I’ve seen all too often organizations try and port their applications and [user interfaces] to a mobile device and the results are underwhelming and in some cases unusable.”

The good and the bad

More than a decade ago, in 2008, Yellowfin introduced its first mobile app. The vendor updated it once, and it didn’t attract many users.

But, recently the vendor completely overhauled the app, instead of attempting to recreate the desktop experience, transforming it into a timeline that looks and acts much like social media feeds. The mobile interface now highlights what it deems to be the most pertinent information and presents it in a way mobile users can easily view it.

Yellowfin CEO Glen Rabie said that he realized BI for mobile could be effective only if “the content and experience being delivered are uniquely designed for mobile versus trying to force fit a dashboard desktop experience onto a phone.”

MicroStrategy, which started developing its app in 2009, is another vendor that’s invested aggressively in BI for mobile.

“For us, we’ve always been focused on intelligence everywhere, how to arm as many people as possible,” said Hugh Owen, senior vice president of product marketing at MicroStrategy. “Mobile opened up another opportunity to arm people who aren’t looking at BI on their desktop with BI.”

While some BI software vendors have invested in creating effective mobile apps, others have all but ignored mobile innovation or ineffectively tried to simply recreate the desktop experience on mobile devices.
While BI vendors have invested heavily in developing their desktop computing software, many have chosen not yet to make the same kind of investment in their mobile apps.

The vendor’s current BI for mobile capabilities enables clients to build custom apps. Retail customers, for example, are able to embed the ability to execute transactions.

Meanwhile, MicroStrategy offers HyperIntelligence for Mobile as part of its HyperIntelligence product line. The app, due to its augmented intelligence and machine learning capabilities, provides a level of contextual understanding and intuitively provides users with information cards.

“If you walk into a store, it gives you a card about the store without you asking,” Owen said. “It can look at your calendar, scan through words and invitations, and match it with cards and give you a push note. It’s proven to be a different approach, and it’s helped us stand out.”

Domo, according to Farmer, is another vendor that has learned how to adapt BI for mobile. So have Qlik and Oracle.

But there are many others that have struggled to develop an effective BI for mobile app and have “an unclear strategy with some mobile being done but nothing very exciting and nothing very compelling,” Farmer said.

Innovation

Despite the limits placed on BI for mobile by a phone’s miniscule screen, there remains room for growth.

Phones and tablets have unique capabilities that desktop computers don’t.

Among the features they possess that desktop devices don’t is GPS. And while desktop devices also have cameras, the cameras on mobile phones and tablets are, well, mobile, while the ones on desktops are rooted in place.

Thanks to GPS, for example, someone who has business in multiple locations can travel to a location and — using a well-designed BI for mobile app — get actionable data about that location delivered directly to their mobile device.

“A good mobile app leverages the physical appendages of the phone or tablet,” Owen said. “It’s aware of your location and takes advantage. It uses the camera to take a picture and scan a QR code or other bar code — you wouldn’t do that with a clamshell laptop.”

The next step in the evolution of BI for mobile, according to Owen, is becoming more proactive rather than reactive by using AI and machine learning and learning behavioral patterns.

“It’s presenting answers back to you before you know you need it,” he said.

Vendors will also need to address security as the development of BI for mobile apps progresses.

“All too often, workers will utilize their personal devices for work,” Leone said. “With security top of mind for virtually every organization, ensuring the right level of controls and governance are in place, not just based on a user, but based on the device, will be important going forward.”

Ultimately, however, mobile devices are tools to connect people so that they can converse. The vendors who view them for what they are and develop BI for mobile apps that take advantage of a mobile device’s unique powers are the ones who will set the pace for innovations.

“It’s not a device for deep contemplation and analysis — it’s a device to look at to glimpse to see what’s important — so a really good mobile app does two things,” Farmer said. “It enables the glimpse of what is important, and it enables the communication of that, because ultimately mobile devices are communication devices.”

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HR execs and politicians eye student debt relief

Student debt relief is not only an election issue in the 2020 race for president, but a problem for HR managers. Some firms, including a hospital in New York, are doing something about it.

Montefiore St. Luke’s Cornwall Hospital began offering a student loan relief program this year for its non-union employees. It employs 1,500 people and provides employees 32 vacation days a year.

Most employees don’t take all that time off, said Dan Bengyak, vice president of administrative services at the not-for-profit medical center with hospitals in Newburgh and Cornwall. He oversees HR, IT and other administrative operations.

In February, the hospital detailed its plan to apply paid time off to student debt relief. Employees in the Parents Plus Loan program had the option as well. The hospital set two sign-up windows, the first in May. Forty employees signed up. The next window is in November.

The program “has been extremely well received and it definitely has offered us a real competitive advantage in the recruiting world,” Bengyak said. He believes it will help with retention as well.

The maximum employee contribution for student debt relief is $5,000. The hospital also provides tuition help. This combination “offers significant financial assistance,” to employees seeking advanced degrees, Bengyak said.

A SaaS platform handles payments

The hospital uses Tuition.io, a startup founded in 2013 and based in Santa Monica, Calif. The platform manages all of the payments to the loan services. Its users pay a lump sum to cover the cost of the assistance. The employer doesn’t know the amount of the employee’s debt. The platform notifies the employee when a payment is posted.

It definitely has offered us a real competitive advantage in the recruiting world.
Dan BengyakVP of administrative services, Montefiore St. Luke’s Cornwall Hospital

Payments can be made as a monthly contribution, a lump sum on an employment anniversary or other methods, according to Scott Thompson, CEO at Tuition.io.

Tuition.io also analyzes repayment data, which can show the program’s retention impact, according to Thompson.

“Those individuals who are participating in this benefit stay longer with the employer — they just do,” he said. 

About one in five students has over $100,000 in debt and is, by definition, broke, Thompson said. They can’t afford an employer’s 401K program or buy a house. Employees with a burdensome loan “are always looking for a new job that pays you more money because you simply have to,” he said.

Legislation in pipeline

The amount of student loan debt is in excess of $1.5 trillion and exceeds credit card and auto debt combined, said Robert Keach, a past president at the American Bankruptcy Institute, in testimony at a recent U.S. House Judiciary Committee hearing on bankruptcy. More than a quarter of borrowers are in delinquency or default, he said. Student loan debt is expected to exceed $2 trillion by 2022.

“High levels of post-secondary education debt correlate with lower earnings, lower rates of home ownership, fewer automobile purchases, higher household financial distress, and delayed marriage and family formation, among other ripple effects,” Keach said.

Congress is considering legislation that may make it easier for firms to help employees with debt. One example is the Employer Participation in Repayment Act, a bill that has bipartisan support in both chambers. It would enable employers to give up to $5,250 annually per employee, tax free.

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Artificial Intelligence and Formula One: Bots on pole position in the race for technology – Asia News Center

Not only would faster AI-integrated information boost race performance, it might also mean avoiding a crash or anticipating the failure of a crucial part inside a car’s power unit. And it would free up human crew members to do other tasks. d’Imbleval is planning for trackside AI to be in place next racing season.

“Artificial intelligence would be so important during a race,” he says. “We need help to take the best decisions that have to be taken during each lap time.”

ALSO READ: Artificial Intelligence in Asia: Here, there and everywhere

He sees AI as a logical next step in an ambitious digital transformation plan for Renault Sport Formula One Team, which is aiming to be on top of the F1 world three years from now.

‘’Next year, we will target podiums. And then in 2019 and 2020, we want to fight for the championship,” he said ahead of Singapore’s rain-affected night-time Grand Prix on September 17 when Renault Sport Formula One Team’s Jolyon Palmer finished in sixth place while teammate Nico Hulkenberg retired in the pits not long before the end.

d’Imbleval is unapologetically enthralled by the complexities and excitement of racing. He even describes himself as a “petrolhead” – a word he finds hard to translate into his native French.

Nonetheless, he comes from hard-headed corporate background. The team will only succeed, he says, if it runs as a  business that functions and delivers on time and on budget. To get there, Renault Sport Formula One Team has turned to longtime technology partner, Microsoft.

Next year, we will target podiums. And then in 2019 and 2020, we want to fight for the championship.

An array of Microsoft business solutions has already made its mark. Moving many operations into the cloud and using Microsoft Dynamics 365 solutions have better integrated the administration, coordination, and production of its two main design and manufacturing plants in Viry-Châtillon in France, and Enstone in England.

Renault Sport Formula One Team has four decades of experience in F1. It returned as a full Constructor in 2016 when it took over the Enstone outfit and began the journey of taking it back to the glory of 2005 and 2006 when the team, back then under Renault ownership, secured two consecutive Drivers and Constructors Championships.

Technology has also moved the needle in design and development. Azure Machine Learning helps predict the effects of changing car configurations. The hybrid capabilities of the Microsoft Cloud also come into play when Azure Stream Analytics syncs with Renault’s own supercomputer as it conducts 3D virtual testing of car designs to lessen the need for full-on wind tunnel tests.