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OmniVis could save lives by detecting cholera-infected water in minutes rather than days – TechCrunch

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Clean drinking water is one of the most urgent needs in developing countries and disaster-stricken areas, but safety tests can take days — during which tainted water can infect thousands. OmniVis aims to make detection of cholera and other pathogens as quick, simple, and cheap as a pregnancy test. Its smartphone-powered detection platform could save thousands of lives.

OmniVis, which presented on stage at Disrupt SF’s Startup Battlefield today, emerged from research conducted at Purdue University, where CEO and co-founder Katherine Clayton completed her doctorate. She and her advisors were working on the question of using microfluidics, basically very close inspection of the behavior of fluids, to detect cholera bacteria in water.

In case you forgot your Infectious Diseases 101, cholera is a bacterium that thrives in water polluted by fecal matter. When ingested it multiplies and causes severe diarrhea and dehydration — which as you might imagine can become a life-threatening problem if a community is short on clean water.

While normally uncommon, there was a huge cholera outbreak in Haiti in 2010 following a major earthquake there; 665,000 people were infected and more than 8,000 people died. It was this humanitarian disaster that prompted Clayton to look into how such an event might have been prevented. She’s been working on what would become the OmniVis platform since 2013.

“It’s been a long time coming,” she told me.

That’s not uncommon for academic spin-offs with valuable IP but zero product experience. Moving from lab bench to field-ready hardware has taken years of hard work. But the resulting device could upend a costly and slow water testing process that leaves communities at risk in crucial moments.

Existing water testing is generally done at a central location, a lab run by a university, utility, or the local government. It depends on the region — and of course if there has been a disaster, it may not even be functional. Going from sample collection to results may take several days, and it isn’t cheap, either. Clayton estimated it at $100 per sample.

“But that’s just supplies and labor,” she said. “Not the cost of the lab, the PCR machines — which are tens of thousands of dollars — the pipettes, the dyes, the disposables and consumables, the training… not to mention in a lot of areas you’re not just going to walk by a nice central laboratory. Some countries may only have one or two testing facilities.”

Another option is disposable rapid diagnostic tests, more like pregnancy tests than anything, meant for use with stool samples — but their accuracy is low even then, and with cholera diluted in a water source you may as well be flipping a coin.

Such was the state of testing when Haiti had its outbreak and Clayton began looking into it. In 2013 they began investigating microfluidics as a method for detection. It works by exposing a set of chemical reagents, or “primers,” to a water sample. These primers are engineered to bind to bits of cholera’s DNA and then when heated, replicate it — a process called DNA amplification.

The more cholera is present, the more DNA will be available to amplify, and it multiplies to the point where it affects the viscosity of the water — a factor that can be tested by the device. Interestingly, the device in no way “analyzes” the DNA or identifies it; all it does is measure how viscous the water is, which is a highly reliable proxy for how much cholera was present in it to begin with.

It turns out this method is both quick and accurate: In 30 minutes it gives as good or better results as central testing.

“The worst thing we could ever do is say there’s no cholera in the water when there is,” Clayton said. So they’re focused on robust test results over all else. But ultimately the device still had to go from the lab to the real world. To that end the team conducted pilot tests in Haiti, where they worked with local NGOs and communities to get some direct feedback.

What they found was promising — but also resulted in major changes to the product. For one thing, they had to switch from iPhone to Android.

“People feel safer with Android than iPhone, which is considered a luxury item,” Clayton said. They also found that men and women operated the system equally well — the team is 84 percent women, she noted, and their design choices may have crept into the product the same as can happen on what is much more common, a male-dominated team. English and Svengali users likewise did fine. Interestingly, locals were baffled by roman numerals. “That was surprising,” she said, but illustrative of how even the smallest assumptions need to be questioned.

“I love user-centered design,” Clayton said. “I think it’s the only way to get engineering to work. UX and graphic design is not my or my colleagues’ specialty, so we had to get some outside contractors for that.”

The production device, which OmniVis hopes to ship in about six months, should cost around a thousand dollars — but at about $10 per test it will pay for itself quickly, especially considering how much easily it can be deployed and used. A half-hour turnaround on a test that can be performed by an aid worker with an hour’s training is an invaluable tool in a disaster-stricken area where infrastructure like mail and roads may be in disorder.

These devices, by the way, are not bought and paid for by the people who drink the water. Like the water-testing labs, they’ll be owned and operated by NGOs, governments and others with budgets for this kind of thing.

Cholera is the first pathogen the company is aiming to detect, but the system can just as easily detect several others simply by using different disposable tests equipped with different primers. E. Coli could be next — with the proper testing, Clayton said. And others would follow. It’s not hard to imagine an OmniVis device being a must-have for any relief work where water needs to be tested.



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Apple and Google’s AI wizardry promises privacy—at a cost

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Since the dawn of the iPhone, many of the smarts in smartphones have come from elsewhere: the corporate computers known as the cloud. Mobile apps sent user data cloudward for useful tasks like transcribing speech or suggesting message replies. Now Apple and Google say smartphones are smart enough to do some crucial and sensitive machine learning tasks like those on their own.

At Apple’s WWDC event this month, the company said its virtual assistant Siri will transcribe speech without tapping the cloud in some languages on recent and future iPhones and iPads. During its own I/O developer event last month, Google said the latest version of its Android operating system has a feature dedicated to secure, on-device processing of sensitive data, called the Private Compute Core. Its initial uses include powering the version of the company’s Smart Reply feature built into its mobile keyboard that can suggest responses to incoming messages.

Apple and Google both say on-device machine learning offers more privacy and snappier apps. Not transmitting personal data cuts the risk of exposure and saves time spent waiting for data to traverse the internet. At the same time, keeping data on devices aligns with the tech giants’ long-term interest in keeping consumers bound into their ecosystems. People that hear their data can be processed more privately might become more willing to agree to share more data.

The companies’ recent promotion of on-device machine learning comes after years of work on technology to constrain the data their clouds can “see.”

In 2014, Google started gathering some data on Chrome browser usage through a technique called differential privacy, which adds noise to harvested data in ways that restrict what those samples reveal about individuals. Apple has used the technique on data gathered from phones to inform emoji and typing predictions and for web browsing data.

More recently, both companies have adopted a technology called federated learning. It allows a cloud-based machine learning system to be updated without scooping in raw data; instead, individual devices process data locally and share only digested updates. As with differential privacy, the companies have discussed using federated learning only in limited cases. Google has used the technique to keep its mobile typing predictions up to date with language trends; Apple has published research on using it to update speech recognition models.

Rachel Cummings, an assistant professor at Columbia who has previously consulted on privacy for Apple, says the rapid shift to do some machine learning on phones has been striking. “It’s incredibly rare to see something going from the first conception to being deployed at scale in so few years,” she says.

That progress has required not just advances in computer science but for companies to take on the practical challenges of processing data on devices owned by consumers. Google has said that its federated learning system only taps users’ devices when they are plugged in, idle, and on a free internet connection. The technique was enabled in part by improvements in the power of mobile processors.

Beefier mobile hardware also contributed to Google’s 2019 announcement that voice recognition for its virtual assistant on Pixel devices would be wholly on-device, free from the crutch of the cloud. Apple’s new on-device voice recognition for Siri, announced at WWDC this month, will use the “neural engine” the company added to its mobile processorsto power up machine learning algorithms.

The technical feats are impressive. It’s debatable how much they will meaningfully change users’ relationship with tech giants.

Presenters at Apple’s WWDC said Siri’s new design was a “major update to privacy” that addressed the risk associated with accidentally transmitting audio to the cloud, saying that was users’ largest privacy concern about voice assistants. Some Siri commands—such as setting timers—can be recognized wholly locally, making for a speedy response. Yet in many cases transcribed commands to Siri—presumably including from accidental recordings—will be sent to Apple servers for software to decode and respond. Siri voice transcription will still be cloud-based for HomePod smart speakers commonly installed in bedrooms and kitchens, where accidental recording can be more concerning.

Google also promotes on-device data processing as a privacy win and has signaled it will expand the practice. The company expects partners such as Samsung that use its Android operating system to adopt the new Privacy Compute Core and use it for features that rely on sensitive data.

Google has also made local analysis of browsing data a feature of its proposal for reinventing online ad targeting, dubbed FLoC and claimed to be more private. Academics and some rival tech companies have said the design is likely to help Google consolidate its dominance of online ads by making targeting more difficult for other companies.

Michael Veale, a lecturer in digital rights at University College London, says on-device data processing can be a good thing but adds that the way tech companies promote it shows they are primarily motivated by a desire to keep people tied into lucrative digital ecosystems.

“Privacy gets confused with keeping data confidential, but it’s also about limiting power,” says Veale. “If you’re a big tech company and manage to reframe privacy as only confidentiality of data, that allows you to continue business as normal and gives you license to operate.”

A Google spokesperson said the company “builds for privacy everywhere computing happens” and that data sent to the Private Compute Core for processing “needs to be tied to user value.” Apple did not respond to a request for comment.

Cummings of Columbia says new privacy techniques and the way companies market them add complexity to the trade-offs of digital life. Over recent years, as machine learning has become more widely deployed, tech companies have steadily expanded the range of data they collect and analyze. There is evidence some consumers misunderstand the privacy protections trumpeted by tech giants.

A forthcoming survey study from Cummings and collaborators at Boston University and the Max Planck Institute showed descriptions of differential privacy drawn from tech companies, media, and academics to 675 Americans. Hearing about the technique made people about twice as likely to report they would be willing to share data. But there was evidence that descriptions of differential privacy’s benefits also encouraged unrealistic expectations. One-fifth of respondents expected their data to be protected against law enforcement searches, something differential privacy does not do. Apple’s and Google’s latest proclamations about on-device data processing may bring new opportunities for misunderstandings.

This story originally appeared on wired.com.

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Amazon joins Apple, Google by reducing its app store cut

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Enlarge / The Amazon Fire HD 8 tablet, which runs Amazon’s Fire OS.

Apparently following the lead of Apple and Google, Amazon has announced that it will take a smaller revenue cut from apps developed by teams earning less than $1 million annually from their apps on the Amazon Appstore. The same applies to developers who are brand-new to the marketplace.

The new program from Amazon, called the Amazon Appstore Small Business Accelerator Program, launches in Q4 of this year, and it will reduce the cut Amazon takes from app revenue, which was previously 30 percent. (Developers making over $1 million annually will continue to pay the original rate.) For some, it’s a slightly worse deal than Apple’s or Google’s, and for others, it’s better.

Amazon’s new indie-friendly rate is 20 percent, in contrast to Apple’s and Google’s 15 percent. Amazon seeks to offset this difference by granting developers 10 percent of their Appstore revenue in the form of a credit for AWS. For certain developers who use AWS, it could mean that Amazon’s effective cut is actually 10 percent, not 15 or 20 percent.

But for some, it amounts to something more like giving the developer a coupon on a purchase of services from Amazon than actually putting more cash in their pockets. It leaves small developers who aren’t spending a bunch of money on Amazon’s services with a worse deal than they’d get on Apple’s or Google’s marketplaces.

As with Apple’s program—but not Google’s—the lower rate applies to developers only if they made $1 million or less in total (in this case, the numbers assessed are those from the previous year). Crossing that threshold will lead developers to pay the older, higher rate on all of their earnings. In contrast, Google always takes a smaller cut of the first million in a given year and then applies the bigger cut to revenues after $1 million without changing the amount it took from the first million.

The Amazon Appstore primarily exists as the app store for Amazon’s Android-based Fire OS software that runs on tablets. It’s also offered as an alternative App Store for users of other Android-based operating systems.

All three companies are facing various forms of regulatory scrutiny, and that scrutiny was likely a factor in Apple’s decision to cut the fees it applies to apps released by small developers on the Apple App Store. Google followed shortly afterward for its Google Play marketplace.

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Microsoft’s Linux repositories were down for 18+ hours

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Enlarge / In 2017, Tux was sad that he had a Microsoft logo on his chest. In 2021, he’s mostly sad that Microsoft’s repositories were down for most of a day.

Jim Salter

Yesterday, packages.microsoft.com—the repository from which Microsoft serves software installers for Linux distributions including CentOS, Debian, Fedora, OpenSUSE, and more—went down hard, and it stayed down for around 18 hours. The outage impacted users trying to install .NET Core, Microsoft Teams, Microsoft SQL Server for Linux (yes, that’s a thing) and more—as well as Azure’s own devops pipelines.

We first became aware of the problem Wednesday evening when we saw 404 errors in the output of apt update on an Ubuntu workstation with Microsoft Teams installed. The outage is somewhat better documented at this .NET Core-issue report on Github, with many users from all around the world sharing their experiences and theories.

The short version is, the entire repository cluster which serves all Linux packages for Microsoft was completely down—issuing a range of HTTP 404 (content not found) and 500 (Internal Server Error) messages for any URL—for roughly 18 hours. Microsoft engineer Rahul Bhandari confirmed the outage roughly five hours after it was initially reported, with a cryptic comment about the infrastructure team “running into some space issues.”

Eighteen hours after the issue was reported, Bhandari reported that the mirrors were once again available—although with temporarily degraded performance, likely due to cold caches. In this update, Bhandari said that the original cause of the outage was “a regression in [apt repositories] during some feature migration work that resulted in those packages becoming unavailable on the mirrors.”

We’re still waiting for a comprehensive incident report, since Bhandari’s status updates provide clues but no real explanations. The good news is, we can confirm that packages.microsoft.com is indeed up once again, and it is serving packages as it should.

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