Why the Decision Against Qualcomm Should Stand

Steven RodgersBy Steven Rodgers

Today, Intel filed a brief supporting the Federal Trade Commission (FTC) and opposing Qualcomm’s appeal of the judgment rendered in May against Qualcomm by the United States District Court, Northern District of California. The District Court found that “Qualcomm’s licensing practices have strangled competition in the CDMA and premium LTE modem chip markets for years, and harmed rivals, OEMs and end consumers.” The District Court also found that Qualcomm’s conduct “unfairly tends to destroy competition itself.”

Intel agrees with the District Court’s findings. Intel suffered the brunt of Qualcomm’s anticompetitive behavior, was denied opportunities in the modem market, was prevented from making sales to customers and was forced to sell at prices artificially skewed by Qualcomm. We filed the brief because we believe it is important for the Court of Appeals to hear our perspective.

Qualcomm would have you believe that its position in the market today — as the last surviving U.S. supplier of premium modem chips — is due to its “ingenuity and business acumen,” and that its rivals in the market failed simply because “they did not offer good enough chips at low enough prices.” This is simply not true.

Instead, as detailed in the District Court’s opinion and in our brief, Qualcomm maintained its monopoly through a brazen scheme carefully crafted and implemented over many years. This scheme consists of a web of anticompetitive conduct designed to allow Qualcomm to coerce customers, tilt the competitive playing field and exclude competitors, all the while shielding itself from legal scrutiny and capturing billions in unlawful gains.

The victims were Qualcomm’s own customers (original equipment manufacturers or OEMs), the long list of competitors it forced out of the modem chip market, including Intel, and ultimately consumers. Intel fought for nearly a decade to build a profitable modem chip business. We invested billions, hired thousands, acquired two companies and built innovative world-class products that eventually made their way into Apple’s industry-leading iPhones, including the most recently released iPhone 11. But when all was said and done, Intel could not overcome the artificial and insurmountable barriers to fair competition created by Qualcomm’s scheme and was forced to exit the market this year.

As I have pointed out before, the District Court’s decision finding Qualcomm violated the antitrust laws comes on the heels of governmental entities around the globe reaching the same conclusion. As a result of its anticompetitive practices, Qualcomm has been fined nearly $1 billion in China, $850 million in Korea, $1.2 billion by the European Commission and $773 million in Taiwan (later reduced in settlement). The FTC, however, did not seek monetary relief. Instead, it sought injunctive relief to prevent Qualcomm from continuing to engage in its unlawful conduct.

Among other things, the District Court prohibited Qualcomm from continuing to implement the central component of its scheme, its coercive “no license, no chips” (NLNC) policy. Under the policy, Qualcomm cuts off handset OEMs’ purchases of modem chips unless they enter into a patent license agreement on Qualcomm’s terms. These onerous, one-sided terms enable Qualcomm to artificially lower the price of its modems while simultaneously inflating customers’ costs of using modem chips manufactured by competitors, like Intel, by charging royalties as large as the price of the modems themselves. The District Court concluded that the NLNC policy, together with other anticompetitive behavior on Qualcomm’s part, unlawfully distorted and, in fact, destroyed the competitive playing field.

The world benefits from fair competition in the wireless technology market. Given the importance of wireless technology to the future of connected computing, including the revolutionary promise of 5G, we strongly support the efforts of the FTC and other law enforcement agencies to require Qualcomm to obey the laws and compete on a level playing field. We hope our amicus brief will help in clarifying the full extent of the harm that Qualcomm’s unlawful behavior has caused and will continue to cause if left unchecked. If you are interested in learning more, read a copy of the District Court’s decision and a copy of Intel’s amicus brief.

Steven R. Rodgers is executive vice president and general counsel at Intel Corporation.

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Buzzworthy AI: Startup’s Robo-Hives Counter Bee Population Declines

Honeybee colonies worldwide are under siege by parasites, but they now have a white knight: a band of Israeli entrepreneurs bearing AI. Beewise, an Israel-based startup, is using AI in its small northern community on the border of Lebanon to monitor honeybee colonies. It’s secured seed funding of more than $3 million and launched its Read article >

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Buzzworthy AI: Startup’s Robo-Hives Counter Bee Population Declines

Honeybee colonies worldwide are under siege by parasites, but they now have a white knight: a band of Israeli entrepreneurs bearing AI.

Beewise, an Israel-based startup, is using AI in its small northern community on the border of Lebanon to monitor honeybee colonies. It’s secured seed funding of more than $3 million and launched its robo-hive sporting image recognition to support bee populations.

In the U.S., honeybee colonies have collapsed by 40 percent in the past year, according to a recent report. The culprit is widely viewed to be varroa mites, which feed off the liver-like organs of honeybees and larvae, causing weakness as well as greater susceptibility to diseases and viruses.

Farmers everywhere count on honeybees for pollination of fruits and vegetables, and many now have to rent colonies from beekeepers to support their crops. Without bees to pollinate them, plants would have a difficult time reproducing and bearing fruit for people to eat.

A cottage industry of small private companies and researchers alike is developing image recognition for early detection of the varroa mite so that beekeepers can act before it’s too late for colonies.

“We’re trying to work on the colony loss — I call it ‘eyes on the hives, 24/7,’” said Saar Safra, CEO and co-founder of Beewise.

Traditional Colony Work

Managing commercial hives is labor-intensive for beekeepers, who manually pull frames (see image below), or sections of the honeycombs, from beehives and visually inspect them.

This time-consuming work can span as many as 1,000 beehives under management by a single professional beekeeper. That means a beehive might not get inspected for several weeks as it waits in line for the busy beekeeper to come along.

A few weeks of an undetected varroa mite infestation can have disastrous results for bee colonies. Computer vision with AI provides a faster way to keep on top of problems.

By replacing that traditional manual process with image recognition and robotics, keepers can recognize and treat the problem in real time, said Safra.

Beewise has developed a proprietary robotics system that can remotely treat infestations.

“When you take AI and apply it to traditional industries, the level of social impact is so much bigger than when you keep it enclosed in high tech — NVIDIA GPUs are basically doing a lot of that work,” he said.

Robo Beehive AI 

Beewise trained its neural networks on thousands of images of bees. Its convolutional neural networks are doing unsupervised learning capable of image classification to identify bees with mites in its autonomous hives now in deployment.

Once image classification has identified bees that have been infested with mites, a recurrent neural network makes a decision on the best course of action. That could include automatically administering pesticides by the robot or to quarantine the beehive frame from others.

Beewise has made this possible with its autonomous beehives that rely on multiple cameras. Images from these prototype hives are fed into the compact supercomputing of NVIDIA Jetson for real-time processing on its deep learning models.

“It’s a whole AI-based control system — our AI detects and identifies the varroa mite in real time and sterilizes it. Clean healthy colonies operate completely different than infested ones,” said Safra.


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Life Observed: Nobel Winner Sees Biology’s Future with GPUs

Five years ago, when Eric Betzig got the call he won a Nobel Prize for inventing a microscope that could see features as small as 20 nanometers, he was already working on a new one. The new device captures the equivalent of 3D video of living cells — and now it’s using NVIDIA GPUs and Read article >

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Life Observed: Nobel Winner Sees Biology’s Future with GPUs

Five years ago, when Eric Betzig got the call he won a Nobel Prize for inventing a microscope that could see features as small as 20 nanometers, he was already working on a new one.

The new device captures the equivalent of 3D video of living cells — and now it’s using NVIDIA GPUs and software to see the results.

Betzig’s collaborator at the University of California at Berkeley, Srigokul Upadhyayula (aka Gokul), helped refine the so-called Lattice Light Sheet Microscopy (LLSM) system. It generated 600 terabytes of data while exploring part of the visual cortex of a mouse in work published earlier this year in Science magazine. A 1.3TB slice of that effort was on display at NVIDIA’s booth at last week’s SC19 supercomputing show.

Attendees got a glimpse of how tomorrow’s scientists may unravel medical mysteries. Researchers, for example, can use LLSM to watch how protein coverings on nerve axons degrade as diseases such as muscular sclerosis take hold.

Future of Biology: Direct Visualization

“It’s our belief we will never understand complex living systems by breaking them into parts,” Betzig said of methods such as biochemistry and genomics. “Only optical microscopes can look at living systems and gather information we need to truly understand the dynamics of life, the mobility of cells and tissues, how cancer cells migrate. These are things we can now directly observe.

“The future of biology is direct visualization of living things rather than piecing together information gleaned by very indirect means,” he added.

It Takes a Cluster — and More

Such work comes with heavy computing demands. Generating the 600TB dataset for the Science paper “monopolized our institution’s computing cluster for days and weeks,” said Betzig.

“These microscopes produce beautifully rich data we often cannot visualize because the vast majority of it sits in hard drives, completely useless,” he said. “With NVIDIA, we are finding ways to start looking at it.”

The SC19 demo — a multi-channel visualization of a preserved slice of mouse cortex — ran remotely on six NVIDIA DGX-1 servers, each packing eight NVIDIA V100 Tensor Core GPUs. The systems are part of an NVIDIA SATURNV cluster located near its headquarters in Santa Clara, Calif.

Berkeley researchers gave SC19 attendees a look inside the visual cortex of a mouse — visualized using NVIDIA IndeX.

The key ingredient for the demo and future visualizations is NVIDIA IndeX software, an SDK that allows scientists and researchers to see and interact in real time with massive 3D datasets.

Version 2.1 of IndeX debuted at SC19, sporting a host of new features, including GPUDirect Storage, as well as support for Arm and IBM POWER9 processors.

After seeing their first demos of what IndeX can do, the research team installed it on a cluster at UC Berkeley that uses a dozen NVIDIA TITAN RTX and four V100 Tensor Core GPUs. “We could see this had incredible potential,” Gokul said.

Closing a Big-Data Gap

The horizon holds plenty of mountains to climb. The Lattice scope generates as much as 3TB of data an hour, so visualizations are still often done on data that must be laboriously pre-processed and saved offline.

“In a perfect world, we’d have all the information for analysis as we get the data from the scope, not a month or six months later,” said Gokul. The time between collecting and visualizing data can stretch from weeks to months, but “we need to tune parameters to react to data as we’re collecting it” to make the scope truly useful for biologists, he added.

NVIDIA IndeX software, running on its increasingly powerful GPUs, helps narrow that gap.

In the future, the team aims to apply the latest deep learning techniques, but this too presents heady challenges. “There are no robust AI models to deploy for this work today,” Gokul said.

Making the data available to AI specialists who could craft AI models would require shipping crates of hard drives on an airplane, a slow and expensive proposition. That’s because the most recent work produced over half a petabyte of data, but cloud services often limit uploads and downloads to a terabyte or so per day.

Betzig and Gokul are talking with researchers at cloud giants about new options, and they’re exploring new ways to leverage the power of GPUs because the potential of their work is so great.

Coping with Ups and Downs

“Humans are visual animals,” said Betzig. “When most people I know think about a hypothesis, they create mental visual models.

“The beautiful thing about microscopy is you can take a model in your head with all its biases and immediately compare it to the reality of living biological images. This capability already has and will continue to reveal surprises,” he said.

The work brings big ups and downs. Winning a Nobel Prize “was a shock,” Betzig said. “It kind of felt like getting hit by a bus. You feel like your life is settled and then something happens to change you in ways you wouldn’t expect — it has good and bad sides to it.”

Likewise, “in the last several years working with Gokul, every microscope had its limits that led us to the next one. You take five or six steps up to a plateau of success and then there is a disappointment,” he said.

In the partnership with NVIDIA, “we get to learn what we may have missed,” he added. “It’s a chance for us to reassess things, to understand the GPU from folks who designed the architecture, to see how we can merge our problem sets with new solutions,” he said.

Note: The picture at top shows Berkeley researchers Eric Betzig, Ruixian Gao and Srigokul Upadhyayula with the Lattice Light Sheet microscope.

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Tiny Intel EMIB Helps Chips ‘Talk’ with Each Other

Most chips in today’s smartphones, computers and servers are comprised of multiple smaller chips invisibly sealed inside one rectangular package.

How do these multiple chips — often including CPU, graphics, memory, IO and more — communicate? An Intel innovation called EMIB (embedded multi-die interconnect bridge) is a complex multi-layered sliver of silicon no bigger than a grain of rice. It lets chips fling enormous quantities of data back and forth among adjoining chips at blinding speeds: several gigabytes per second.

Today, Intel EMIBs speed the flow of data inside nearly 1 million laptops and field programmable gate array devices worldwide. That number will soon soar and include more products as EMIB technology enters the mainstream. For example, Intel’s Ponte Vecchio processor, a general-purpose GPU the company unveiled Nov. 17, contains EMIB silicon.

To meet customers’ unique needs, this innovative technology allows chip architects to cobble together specialized chips faster than ever. And compared with an older, competing design called an interposer — in which chips inside a package sit atop what is essentially a single electronic baseboard, with each chip plugged into it — tiny, flexible, cost-effective EMIB silicon offers an 85% increase in bandwidth. That can make your tech — laptop, server, 5G processor, graphics card— run dramatically faster. And next-generation EMIB could double or even triple that bandwidth.

More: All Intel Images | Intel EMIB Packaging Technology (Intel.com)

Intel EMIB 1
Intel’s embedded multi-die interconnect bridge (EMIB) technology helps multiple chips – CPU, graphics, memory, IO and more — communicate. EMIB is a complex multi-layered sliver of silicon no bigger than a grain of Basmati rice that moves large quantities of data among adjoining chips. (Credit: Walden Kirsch/Intel Corporation)
» Click for full image

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p2k19 Hackathon Report: Stefan Sperling on iwm(4) wifi progress, more

Next up in our hackathon series from p2k19 is one from Stefan Sperling (stsp@), who writes:

My main goal for the p2k19 hackathon was 9260 device support in iwm(4). Firmware updates for previous device generation were an important prerequisite step. One day before p2k19, the oldest generation of hardware supported by the iwm(4) driver was switched to latest available firmware images.

Read more…

Read ‘em and Reap: 6 Success Factors for AI Startups

Now that data is the new oil, AI software startups are sprouting across the tech terrain like pumpjacks in Texas. A whopping $80 billion in venture capital is fueling as many as 12,000 new companies. Only a few will tap a gusher. Those who do, experts say, will practice six key success factors. Master your Read article >

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Read ‘em and Reap: 6 Success Factors for AI Startups

Now that data is the new oil, AI software startups are sprouting across the tech terrain like pumpjacks in Texas. A whopping $80 billion in venture capital is fueling as many as 12,000 new companies.

Only a few will tap a gusher. Those who do, experts say, will practice six key success factors.

  1. Master your domain
  2. Gather big data fast
  3. See (a little) ahead of the market
  4. Make a better screwdriver
  5. Scale across the clouds
  6. Stay flexible

Some of the biggest wins will come from startups with AI apps that “turn an existing provider on its head by figuring out a new approach for call centers, healthcare or whatever it is,” said Rajeev Madhavan who manages a $300 million fund at Clear Ventures, nurturing nine AI startups.

1. Master Your Domain

Madhavan sold his electronic design automation startup Magma Design in 2012 to Synopsys for $523 million. His first stop on the way to becoming a VC was to take Andrew Ng’s Stanford course in AI.

“For a brief period in Silicon Valley every startup’s pitch would just throw in jargon on AI, but most of them were just doing collaborative filtering,” he said. “The app companies we look for have to be heavy on AI, but success comes down to how good a startup is in its domain space,” he added.

Chris Rowen agrees. The veteran entrepreneur who in 2013 sold his startup Tensilica to Cadence Design for $380 million considers domain expertise the top criteria for an AI software startup’s success.

Rowen’s latest startup, BabbleLabs, uses AI to filter noise from speech in real time. “At the root of it, I’m doing something analogous to what I’ve done in much of my career — work on really hard real-time computing problems that apply to mass markets,” Rowen said.

Overall, “deep learning is still at the stage where people are having challenges understanding which problems can be handled with this technique. The companies that recognize a vertical-market need and deliver a solution for it have a bigger chance of getting early traction. Over time, there will be more broad, horizontal opportunities,” he added.

Jeff Herbst nurtures more than 5,000 AI startups under the NVIDIA Inception program that fuels entrepreneurs with access to its technology and market connections. But the AI tag is just shorthand.

In a way, it’s like a rerun of The Invasion of the DotComs. “We call them AI companies today, but they are all in specialized markets — in the not-so-distant future, every company will be an AI company,” said Herbst, vice president of business development at NVIDIA.

Today’s AI software landscape looks like a barbell to Herbst. Lots of activity by a handful of cloud-computing giants at one end and a bazillion startups at the other.

2. Get Big Data Fast

Collecting enough bits to fill a data lake is perhaps the hardest challenge for an AI startup.

Among NVIDIA’s Inception startups, Zebra Medical Vision uses AI on medical images to make faster, smarter diagnoses. To get the data it needed, it partnered both with Israel’s largest healthcare provider as well as Intermountain Healthcare, which manages 215 clinics and 24 hospitals in the U.S.

“We understood data was the most important asset we needed to secure, so we invested a lot in the first two years of the startup not only in data but also in developing all kinds of algorithms in parallel,” said Eyal Toledano, co-founder and CTO of Zebra. “To find one good clinical solution, you have to go through many candidates.”

Getting access to 20 years of digital data from top drawer healthcare organizations “took a lot of convincing” both from Zebra’s chief executive and Toledano.

“My contribution was showing how security, compliance and anonymity could be done. There was a lot of education and co-development so they would release the data and we could do research that could contribute back to their patient population in return,” he added.

It’s working. To date Zebra has raised $50 million, received FDA approvals on three products with two more pending “and a few other submissions are on the way,” he said.

Toledano also gave kudos to NVIDIA’s Inception program.

“We had many opportunities to examine new technologies before they became widely used. We saw the difference in applying new GPUs to current processes, and looked at inference in the hospital with GPUs to improve the user experience, especially in time-critical applications,” he said.

“We also got some good know-how and ideas to improve our own infrastructure with training and infrastructure libraries to build projects. We tried quite a lot of the NVIDIA technologies and some were really amazing and fruitful, and we adopted a DGX server and decreased our development and training time substantially in many evaluations,” he added.

Six Steps to AI Startup Gold

Success FactorCall to ActionStartups Using It
Master your domainHave deep expertise in your target applicationBabbleLabs
Gather big data fastTap partners, customers to gather data and refine modelsZebra Medical Vision, Scale
See (a little) ahead of the marketFind solutions to customer pain points before rivals see themFASTDATA.io, Netflix
Make a better screwdriverCreate tools that simplify the work of data scientistsScale, Dataiku
Scale across the cloudsSupport private and multiple public cloud servicesRobin.io
Stay flexibleFollow changing customer pain points to novel solutionsKeyhole Corp.

Another Inception startup, Scale, which provides training and validation data for self-driving cars and other platforms, got on board with Toyota and Lyft. “Working with more people makes your algorithms smarter, and then more people want to work with you — you get into a cycle of success,” said Herbst.

Reflektion, one of Madhavan’s startups, now has a database of 200 million unique shoppers, the third largest retail database after Amazon and Walmart. It started with zero. Getting big took three years and a few great partners.

Rowen’s BabbleLabs applied a little creativity and elbow grease to get a lot of data cheaply and fast. It siphoned speech data from free sources as diverse as YouTube and the Library of Congress. When it needed specialized data, it activated a network of global contractors “quite economically,” he said.

“You can find low-cost, low-quality data sources, then use algorithms to filter and curate the data. Controlling the amount of noise associated with the speech helped simplify training.” he added.

“In AI, access to data no one else has is the big win,” said Herbst. “The world has a lot of open source frameworks and tools, but a lot of the differentiation comes from proprietary access to the data that does the programming,” he added.

When seeking data-rich customers and partners “the fastest way to get in the door is knowing what their pain points are,” said Alen Capalik, founder of FASTDATA.io.

Work in high-frequency trading on Wall Street taught Capalik the value of GPUs. When he came up with an idea for using them to ingest real-time data fast for any application, he sought out Herbst at NVIDIA in 2017.

“He almost immediately wrote me a check for $1.5 million,” Capalik said.

3. See (a Little) Ahead of the Market

Today, FastData.io is poised for a Series A financing round to fuel its recently released PlasmaENGINE, which already has two customers and over 20 more in the pipeline. “I think we are 12-18 months ahead of the market, which is a great spot to be in,” said Capalik, whose product can process as much data as 100 Spark instances.

That wasn’t the position Capalik found himself in his last time out. His cybersecurity startup — GoSecure, formerly CounterTack — pioneered the idea of end-point threat detection as much as six years before it caught on.

“People told me I was crazy. Palo Alto Networks and FireEye were doing perimeter security, and users thought they’d never install agents again because they slowed systems down. So, we struggled for a while and had to educate the market a lot,” he said.

Education and awareness are the kinds of jobs established corporations tackle. For startups, being visionary is like Steve Jobs unveiling an iPhone — “show them what they didn’t know they wanted,” he said.

“Netflix went after video streaming before there was enough bandwidth or end points — they skated to where the puck was going,” said Herbst.

4. Make a Better Screwdriver

AI holds opportunities for arms dealers, too — the kind who sell the software tools data scientists use to tighten down the screws on their neural networks.

The current Swiss Army knife of AI is the workbench. It’s a software platform for developing and deploying machine-learning models in today’s DevOps IT environment.

Jupyter notebooks could be seen as a sort of two-blade model you get for free as open source. Giants such as AWS, IBM and Microsoft and dozens of startups such as H20.ai and Dataiku are rolling out versions with more forks, corkscrews and toothpicks.

Despite all the players and a fast-moving market, there are still opportunities here, said James Kobielus, a lead analyst for AI and data science at Wikibon. Start as a plug-in for a popular workbench, he suggested.

Startups can write modules to support emerging frameworks and languages, or a mod to help a workbench tap into the AI goodness embedded in the latest smartphones. Alternatively, you can automate streaming operations or render logic automatically into code, the former IBM data-science evangelist advised.

If workbenches aren’t for you, try robotic process automation, another emerging category trying to make AI easier for more people to use. “You can clean up if you can democratize RPA for makers and kids — that’s exciting,” Kobielus said.

There’s a wide-open opportunity for tools that cram neural nets into the kilobytes of memory on devices such as smart speakers, appliances and even thermostats, BabbleLabs’ Rowen said. His company aims to run its speech models on some of the world’s smallest microcontrollers.

“We need compilers that take trained models and do quantization, model compression and optimized model generation to fit into the skinny memory of embedded systems — nothing solves this problem yet,” he said.

5. Expand Across the Clouds

The playing field is very competitive with more startups than ever because it’s easier than ever to start a company, said Herbst, who worked closely with entrepreneurs as a corporate and IP attorney even before he joined NVIDIA 18 years ago.

All you need to get started today is an idea, a laptop, a cup of coffee and a cloud-computing account. “All the infrastructure is a service now,” he said.

But if you get lucky and scale, that one cloud-computing account can become a bottleneck and your biggest cost after payroll.

“That’s a good problem to have, but to hit breakeven and make it easier for customers, you need your software running on any cloud,” said Madhavan.

The need is so striking, he wound up funding a startup to address it. Robin.io is an expert in stateful and stateless workloads, helping companies become cloud-agnostic. “We have been extremely successful with 5G telcos going cloud native and embracing containers,” he said.

6. Stay Flexible as a Yogi

Few startups wind up where they thought they were going. Apple planned to make desktop computers, Amazon aimed to sell books online.

Over time “they pivot one way or another. They go in with a problem to solve, but as they talk to customers the smart ones learn from those interactions how to re-target or tailor themselves,” said Herbst, who gives an example from his pre-AI days

Keyhole Corp. wanted to provide 3D mapping services initially for real estate agents and other professionals. Its first product was distributed on CDs

As a veteran of early search startup AltaVista, “I thought this startup belonged more to a Yahoo! or some other internet company. I realized it was not a professional but a major consumer app,” said Herbst, who was happy to fund them as one of NVIDIA’s first investments outside gaming.

In time, Google agreed with Herbst and acquired the company. Keyhole’s technology became part of the underpinnings of Google Maps and Google Earth.

“They had a nice exit, their people went on to have rock-star careers at Google, and I believe were among the original creators of Pokemon Go,” he said.

The lesson is simple: Follow good directions — like the six success factors for AI software startups — and there’s no telling where you may end up.

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