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How anti-vaxxers avoid being detected by Facebook

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Covering COVID-19 is a daily Poynter briefing of story ideas about the coronavirus and other timely topics for journalists, written by senior faculty Al Tompkins. Sign up here to have it delivered to your inbox every weekday morning.

NBC News produced an interesting piece about how anti-vaccine groups manage to stay under Facebook’s radar by changing their names to “dance parties” or “dinner parties.” Then the groups use code words to describe people who have been vaccinated.

I pulled these words out of their story to explain what they mean:

(Al Tompkins) 

A number of news sources including Axios reported over the weekend that the Biden administration is leaning toward pushing for a COVID-19 vaccine booster shot for some but not all fully vaccinated people beginning late this fall. Caitlin Owens writes for Axios:

Some Biden officials are increasingly convinced that high levels of neutralizing antibodies correlate with a higher degree of protection against illness. They worry that means that as more time passes, vaccinated people may be increasingly vulnerable to mild, moderate or even severe disease, a Biden official told Axios.

The New York Times adds:

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Senior officials now say they expect that people who are 65 and older or who have compromised immune systems will most likely need a third shot from Pfizer-BioNTech or Moderna, two vaccines based on the same technology that have been used to inoculate the vast majority of Americans thus far. That is a sharp shift from just a few weeks ago, when the administration said it thought there was not enough evidence to back boosters yet.

On Thursday, a key official at the Centers for Disease Control and Prevention said the agency is exploring options to give patients with compromised immune systems third doses even before regulators broaden the emergency use authorization for coronavirus vaccines, a step that could come soon for the Pfizer vaccine.

You may have heard about an Israeli study that shows the Pfizer vaccine loses some effectiveness after six or seven months while still protecting against severe illness. But U.S. officials have lots of questions about the Israeli study because it does not line up with data that American experts have compiled.

Here is a key point: There is no evidence that if you are vaccinated that you are at risk of becoming severely ill from the coronavirus. And the Biden administration did purchase enough vaccine supplies to give a third shot to anybody who wants extra protection.

Boxes of the Johnson & Johnson vaccine are loaded into a truck for shipment from the McKesson facility in Shepherdsville, Ky., Monday, March 1, 2021. (AP Photo/Timothy D. Easley, Pool)

While much of the world cannot get the vaccines they need to save lives, states report they have batches of vaccines that are about to expire and will be trashed.

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Public health officials in Arkansas, Colorado, Delaware and North Carolina told Stat they have thousands of unused vaccine vials.

The Daily Mail summarizes with these bullet points:

  • Millions of unused COVID-19 vaccines doses are set to expire in the next two months as demand drops off
  • In North Carolina, 119,000 doses will expire in July and 854,000 that will expire in August, even after giving 1.2 million doses back to the federal government. North Carolina, which also has stopped ordering doses, they have 119,000 doses of Pfizer-BioNTech, Moderna and Johnson & Johnson shots that will expire in July and 854,000 that will expire in August, reported STAT News.
  • Arkansas stopped accepting orders in April when it had 500,000 unused doses, but has only cut this down to 380,000, of which 100,000 will expire in July
  • Delaware officials say they are sitting on a stockpile of doss with 25,000 that will expire next month.
  • Several states have asked if their vaccine doses can be sent abroad, but the Biden administration it is too logistically difficult
  • According to data from the Centers for Disease Control and Prevention (CDC), 390.7 million vaccine doses have been distributed across the U.S. However, 338.4 million have been administered, a difference of 52.3 million doses.

My wife and I are teaching in South Dakota this week where temperatures will top 100 degrees. It is cooler at my home in Florida!

I want to point you to some mind-boggling data that shows you how a heat wave in just one part of the country affects people’s health. The data comes in a just-released CDC study.

If you just look at the northwest Health and Human Services region — which includes Alaska, Idaho, Oregon and Washington — from May through June of this year, there were 3,504 heat-related emergency room visits.

79% of those occurred over six days, June 25-30, when most of Oregon and Washington were under an excessive heat warning. A clear peak was detected on June 28, with 1,090 heat-related illness emergency room visits.

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Then if you just take one of those dates — for this example, say, June 28, 2021 — and compare it to a more normal weather day — June 28, 2019 — you would find:

  • There were 1,090 heat-related visits in 2021.
  • There were nine heat-related visits in 2019.
  • Although the region includes approximately 4% of the U.S. population, it accounted for approximately 15% of the total heat-related illness emergency room visits nationwide during June.
  • The mean daily number of heat-related illness emergency room visits in HHS Region 10 for June 2021 (102) was more than seven times higher than that in June 2019 (14).
  • During June 25-30, 2021, that number (424) was 69 times higher than during the same days in 2019 (six), when no heat advisory was in effect.

Not long ago my boss escaped Florida to go to Maine for a cool stay in the woods. It was hotter there than it was here in Florida … and we have air conditioning. The Washington Post reports that we may soon be a nation of air-conditioned buildings as heat waves, like the new one moving into the Northwest, make people rethink their decisions to take a pass on AC units:

Although Seattle remains the nation’s least air-conditioned major metropolitan area — Houston is at the other extreme, with 99.4 percent of housing units cooled — what the Seattle Times called “the unimaginable” is coming soon: an air-conditioned majority among Seattle homes. (Nationwide, about 87 percent of homes have AC, according to the U.S. Energy Information Administration.)

Portland is already there. Though the city is about an hour inland from the ocean, it still ranks third nationally among least air-conditioned metro areas, but census data shows that longer heat waves have pushed the percentage of people with AC at home from 44 percent in 2002 to 78 percent in 2019.

In this April 14, 1947 file photo, a long line winds toward the entrance to Morrisania Hospital in the Bronx borough of New York, where doctors are inoculating against smallpox. In an attempt to halt the spread of the disease, officials said city residents were being vaccinated at the rate of eight a minute. (AP Photo/File)

The opposition to vaccine passports is a curious one. There was a time a generation or so ago when they were considered to be part of a person’s patriotic duty. People used them to show they had been vaccinated against typhus or polio. If your grandparents ever traveled internationally, they may well have had a yellow booklet called the World Health Organization proof of vaccination form.

In fact, vaccine passports have been a part of U.S. history stretching back to the 1800s. And, it should be noted, even then there was an underground market for fake vaccine passports.

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History.com reminds us that there is nothing new about people resisting others who try to tell them what to do. Go back more than 100 years when America was facing a smallpox outbreak:

The mandatory vaccination orders angered many Americans who formed anti-vaccination leagues to defend their personal liberties. In an attempt to dodge public health officials, who went door-to-door (often with a police escort) to enforce vaccination laws, some anti-vaccination activists would forge certificates of vaccination. Unable to tell if certificates were legitimate, health officials fell back on physical evidence: they demanded to see a vaccination scar.

Partly because the vaccination process was so brutal, and partly because anti-vaccination crusaders exaggerated the risk of contracting tetanus or syphilis through the vaccine, there were plenty of people who tried to avoid vaccination by any means necessary. The most common tactic was to buy a fake vaccination certificate.

Even as late as 1904, an article in The New York Times headlined “Vaccination Certificate Frauds” reports on “an extensive traffic in worthless certificates of sufficient vaccination by east side physicians” perpetrating a “petty swindle on the poor, ignorant and credulous.”

With all public schools requiring proof of vaccination, anti-vaccination leagues circulated names of doctors who would sign a piece of paper saying that a child was medically “unfit” for vaccination. If parents didn’t want to pay the doctor, they forged the medical certificate themselves.

Enforcement was so strong that at schools, factories and on immigrant ships heading into U.S. ports, if a person claimed to be vaccinated but could not show a scar where the vaccine was given, officials would vaccinate the person right then and there. Remember, smallpox vaccines were not the gentle little poke that the COVID-19 vaccination is. It involved scratching the skin with a knife then spreading a live virus onto the open wound.

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This is one of those things that journalists should know about but not overreact to at the moment.

The Centers for Disease Control and Prevention says a new “superbug” fungus has shown up in a nursing home in Washington, D.C., and two hospitals in Dallas.

The fungus is called Candida auris and “is an emerging, often multidrug-resistant yeast that is highly transmissible, resulting in health care-associated outbreaks, especially in long-term care facilities.”

The fungus is not unheard of in the United States but it is rare. Other reported cases are thought to have been instances where a fungus resisted treatment and evolved. But now, researchers are exploring whether the fungus spreads from person to person.

Not only is the fungus resistant to all of the usual medicines, but it is also difficult to spot unless a lab has specialized equipment.

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The fungus can cause “severe illness” in hospitalized patients, and the patients at highest risk are people who have IVs or other tubes entering the body. Here is more information than you probably need right now.

I thought this map from Axios was cool.

(Axios)

Axios reports:

126 athletes are from California, which is more than twice as many as second-place Florida (51). Colorado (34), Texas (31) and New York (28) round out the top five. Colorado counts 34 athletes in the Olympic Games in more than 14 sports and 23 disciplines, according to Team USA.

Insider explains why some states have so many athletes:

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First, based solely on population, California has roughly 10 million more residents than any other state. Second, California dominates some sports that are specific to the Summer Olympics. Of the 26 water polo players America is sending to Tokyo, 22 of them call California home. The same goes for 10 of the 15 softball players and seven of the 15 cyclists Team USA has on its roster.

As the second and third most populous states, Florida and Texas coming in behind California is also not that surprising. Colorado is punching above its weight a bit, sending the fourth most athletes to Tokyo despite having just the 21st highest population in the country.

In reality however, Axios points out that, “Dozens more Olympians live in Colorado because they train here — and the same goes for athletes from other countries, too.

We’ll be back tomorrow with a new edition of Covering COVID-19. Are you subscribed? Sign up here to get it delivered right to your inbox.

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Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

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Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

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Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

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Updated July 18: Developers and advertising partners may be required to share information on their app’s privacy practices in third party app stores, such as Google Play and the Apple App Store, including the functionality of SDKs provided by Meta. To help make it easier for you to complete these requirements, we have consolidated information that explains our data collection practices for the Facebook and Audience Network SDKs.

Facebook SDK

To provide functionality within the Facebook SDK, we may receive and process certain contact, location, identifier, and device information associated with Facebook users and their use of your application. The information we receive depends on what SDK features 3rd party applications use and we have structured the document below according to these features.

App Ads, Facebook Analytics, & App Events

Facebook App Events allow you to measure the performance of your app using Facebook Analytics, measure conversions associated with Facebook ads, and build audiences to acquire new users as well as re-engage existing users. There are a number of different ways your app can use app events to keep track of when people take specific actions such as installing your app or completing a purchase.

With Facebook SDK, there are app events that are automatically logged (app installs, app launches, and in-app purchases) and collected for Facebook Analytics unless you disable automatic event logging. Developers determine what events to send to Facebook from a list of standard events, or via a custom event.

When developers send Facebook custom events, these events could include data types outside of standard events. Developers control sending these events to Facebook either directly via application code or in Events Manager for codeless app events. Developers can review their code and Events Manager to determine which data types they are sending to Facebook. It’s the developer’s responsibility to ensure this is reflected in their application’s privacy policy.

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Advanced Matching

Developers may also send us additional user contact information in code, or via the Events Manager. Advanced matching functionality may use the following data, if sent:

  • email address, name, phone number, physical address (city, state or province, zip or postal code and country), gender, and date of birth.
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Facebook Login

There are two scenarios for applications that use Facebook Login via the Facebook SDK: Authenticated Sign Up or Sign In, and User Data Access via Permissions. For authentication, a unique, app-specific identifier tied to a user’s Facebook Account enables the user to sign in to your app. For Data Access, a user must explicitly grant your app permission to access data.

Note: Since Facebook Login is part of the Facebook SDK, we may collect other information referenced here when you use Facebook Login, depending on your settings.

Device Information

We may also receive and process the following information if your app is integrated with the Facebook SDK:

  • Device identifiers;
  • Device attributes, such as device model and screen dimensions, CPU core, storage size, SDK version, OS and app versions, and app package name; and
  • Networking information, such as the name of the mobile operator or ISP, language, time zone, and IP address.

Audience Network SDK

We may receive and process the following information when you use the Audience Network SDK to integrate Audience Network ads in your app:

  • Device identifiers;
  • Device attributes, such as device model and screen dimensions, operating system, mediation platform and SDK versions; and
  • Ad performance information, such as impressions, clicks, placement, and viewability.

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Enabling Faster Python Authoring With Wasabi

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This article was written by Omer Dunay, Kun Jiang, Nachi Nagappan, Matt Bridges and Karim Nakad.


Motivation

At Meta, Python is one of the most used programming languages in terms of both lines of code and number of users. Everyday, we have thousands of developers working with Python to launch new features, fix bugs and develop the most sophisticated machine learning models. As such, it is important to ensure that our Python developers are productive and efficient by giving them state-of-the-art tools.

Introducing Wasabi

Today we introduce Wasabi, a Python language service that implements the language server protocol (LSP) and is designed to help our developers use Python easier and faster. Wasabi assists our developers to write Python code with a series of advanced features, including:

  • Lints and diagnostics: These are available as the user types.
  • Auto import quick fix: This is available for undefined-variable lint.
  • Global symbols autocomplete: When a user types a prefix, all symbols (e.g. function names, class names) that are defined in other files and start with that prefix will appear in the autocomplete suggestion automatically.
  • Organize Imports + Remove unused: A quick fix that removes all unused imports and reformats the import section according to pep8 rules. This feature is powered by other tools that are built inside Meta such as libCST that helps with safe code refactoring.
  • Python snippets: Snippet suggestions are available as the user types for common code patterns.

Additionally, Wasabi is a surface-agnostic service that can be deployed into multiple code repositories and various development environments (e.g., VSCode, Bento Notebook). Since its debut, Wasabi has been adopted by tens of thousands of Python users at Meta across Facebook, Instagram, Infrastructure teams and many more.

Figure 1: Example for global symbols autocomplete, one of Wasabi’s features

Language Services at Meta Scale

A major design requirement for language services is low latency / user responsiveness. Autocomplete suggestions, lints and quickFixes should appear to the developer immediately as they type.

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At Meta, code is organized in a monorepo, meaning that developers have access to all python files as they develop. This approach has major advantages for the developer workflow including better discoverability, transparency, easier to share libraries and increased collaboration between teams. It also introduces unique challenges for building developer tools such as language services that need to handle hundreds of thousands of files.

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The scaling problem is one of the reasons that we tried to avoid using off-the-shelf language services available in the industry (e.g., pyright, jedi) to perform those operations. Most of those tools were built in the mindset of a relatively small to medium workspace of projects, maybe with the assumptions of thousands of files for large projects for operations that require o(repo) information.

For example, consider the “auto import” quick fix for undefined variables. In order to suggest all available symbols the language server needs to read all source files, the quick fix parses them and keeps an in-memory cache of all parsed symbols in order to respond to requests.

While this may scale to be performed in a single process on the development machine for small-medium repositories, this approach doesn’t scale in the monorepo use case. Reading and parsing hundreds of thousands of files can take many minutes, which means slow startup times and frustrated developers. Moving to an in-memory cache might help latency, but also may not fit in a single machine’s memory.

For example, assume an average python file takes roughly 10ms to be parsed and to extract symbols in a standard error recoverable parser. This means that on 1000 files it can take 10 seconds to initialize which is a fairly reasonable startup time. Running it on 1M files would take 166 minutes which is obviously a too lengthy startup time.

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How Wasabi Works

Offline + Online Processing:

In order to support low latency in Meta scale repositories, Wasabi is powered by two phases of parsing, background processing (offline) done by an external indexers, and local processing of locally changed “dirty files” (online):

  1. A background process indexes all committed source files and maintains the parsed symbols in a special database (glean) that is designed for storing code symbol information.
  2. Wasabi, which is a local process running on the user machine, calculates the delta between the base revision, stack of diffs and uncommitted changes that the user currently has, and extracts symbols only out of those “dirty” files. Since this set of “dirty” files is relatively small, the operation is performed very fast.
  3. Upon an LSP request such as auto import, Wasabi parses the abstract syntax tree (AST) of the file, then based on the context of the cursor, creates a query for both glean and local changes symbols, merges the results and returns it to the user.
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As a result, all Wasabi features are low latency and available to the user seamlessly as they type.

Note: Wasabi currently doesn’t handle the potential delta between the revision that glean indexed (happens once every few hours) and the locally base revision that the user currently has. We plan on adding that in the future.

Figure 2: Wasabi’s high level architecture

Ranking the Results

In some cases, due to the scale of the repository, there may be many valid suggestions in the set of results. For example, consider “auto import” suggestions for the “utils” symbol. There may be many modules that define a class named “utils” across the repository, therefore we invest in ranking the results to ensure that users see the most relevant suggestions on the top.

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For example, auto import ranking is done by taking into account:

  • Locality:
    • The distance of the suggested module directory path from the directory paths of modules that are already imported in this file.
    • The distance of the suggested module directory path from the current directory path of the local file.
    • Whether the file has been locally changed (“dirty” files are ranked higher).
  • Usage: The number of occurrences the import statement was used by other files in the repository.

To measure our success, we measured the index in the suggestion list of an accepted suggestion and noted that in almost all cases the accepted suggestion was ranked in one of top 3 suggestions.

Positive feedbacks from developers

After launching Wasabi to several pilot runs inside Meta, we have received numerous positive feedbacks from our developers. Here is one example of the quote from a software engineer at Instagram:

“I’ve been using Wasabi for a couple months now, it’s been a boon to my productivity! Working in Instagram Server, especially on larger files, warnings from pyre are fairly slow. With Wasabi, they’re lightning fast 😃!”

“I use features like spelling errors and auto import several times an hour. This probably makes my development workflow 10% faster on average (rough guess, might be more, definitely not less), a pretty huge improvement!”

As noted above, Wasabi has made a meaningful change to keep our developers productive and make them feel delightful.

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The metric to measure authoring velocity

In order to quantitatively understand how much value Wasabi has delivered to our Python developers, we have considered a number of metrics to measure its impact. Ultimately, we landed on a metric that we call ‘Authoring Velocity’ to measure how fast developers write code. In essence, Authoring Velocity is the inverse function of the time taken on a specific diff (a collection of code changes) during the authoring stage. The authoring stage starts from the timestamp when a developer checks out from the source control repo to the timestamp when the diff is created. We have also normalized it against the number of lines of code changed in the diff, as a proxy for diff size, to offset any possible variance. The greater the value for ‘Authoring Velocity,’ the faster we think developers write their code.

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Figure 3: Authoring Velocity Metric Formula

The result

With the metric defined, we ran an experiment to measure the difference that Wasabi brings to our developers. Specifically, we selected ~700 developers who had never used Wasabi before, and then randomly put them into two independent groups at a 50:50 split ratio. For these developers in the test group, they were enabled with Wasabi when they wrote in Python, whereas there was no change for those in the control group. For both groups, we compare the changes in relative metric values before and after the Wasabi enablement. From our results, we find that for developers in the test group, the median value of authoring velocity has increased by 20% after they started using Wasabi. Meanwhile, we don’t see any significant change in the control group before and after, which is expected.

Figure 4: Authoring Velocity measurements for control and test groups, before and after Wasabi was rolled out to the test group.

Summary

With Python’s unprecedented growth, it is an exciting time to be working in the area to make it better and handy to use. Together with its advanced features, Wasabi has successfully improved developers’ productivity at Meta, allowing them to write Python faster and easier with a positive developer experience. We hope that our prototype and findings can benefit more people in the broader Python community.

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To learn more about Meta Open Source, visit our open source site, subscribe to our YouTube channel, or follow us on Twitter, Facebook and LinkedIn.

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