Here's a new local event featuring local artists. Come to Cloverdale Bottom Park, 3100-3124 Cloverdale Road, Montgomery on Saturday, Nov. 4, 10 a.m.-4 p.m. for jamming in the park to tunes from Village Green Records and enjoying all the talent.

Go Play Calendar for Nov. 2-8

Operator: Good day, and welcome to the Third Quarter 2023 Zebra Technologies Earnings Conference Call. All participants will be in listen-only mode. [Operator Instructions].

Zebra Technologies Corporation (NASDAQ:ZBRA) Q3 2023 Earnings Call Transcript

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Climate Tech, Emerging Tech - October 9, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “Startups of The Year” award, featuring the most promising and innovative companies in the Climate Tech startup ecosystem. The list, compiled by the HackerNoon team, showcases 30 startups making significant contributions and driving innovation in the Climate Tech industry. https://youtu.be/XOpG6B4AKPU?embedable=true Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Climate Tech industry here. The Startups in the Climate Tech industry nominated for Startups of The Year 2024 include: Matteco


IDEALERS B2B PVT LTD


Terrenity


HIJAU (c/o. PT Investasi Hijau Selaras)


Undesert Corporation


Waterly


Felicidad


Tracco


seeng LTD


Tree++ (Tree Plus Plus)


LEAP for Climate


Aatral ESP


ÁCARA CLIMATE


RenTech Global Solutions


VALKYRIE SOLAR SOLUTIONS


Alder Green Solutions


H2next


Eurasia Natural History Association


Climate Change Writers


Global Cerah


Tasteme


Virya Energy


Zetin Solar Technology Kenya


R.Flo


Ocergy


CENmat


Achelous Pure Metal Co. Ltd.


GoNetZero


Silogic Technology


Gaia First “We are thrilled to recognize the top startups in the Climate Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO. “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” Why It Pays Off to Be Nominated Startups of The Year 2024 is a 100% community-driven voting event recognizing startups transforming technology and the world for the better. Starting October 1st, 2024, the internet can nominate and vote for the best ventures in their city and industry. People can nominate their favorite startups until October 31st, and vote once per day per award for who they think should be Startup of The Year up to March 31st, 2025. Winners will be announced in April 2025, after a comprehensive evaluation by the HackerNoon team. Apart from validation from the world's greenest tech publication, all nominees get free interviews relevant to their region and industry. They will also receive curated startup-friendly packages and a free version of their very own Evergreen Tech Company News Page on HackerNoon. In sum, being nominated gives you major credibility as one of the top startups globally and an expert in your industry, meaning even without winning, you still gain tons of exposure. Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Climate Tech industry here. About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries will participate in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many stories have been written about these daring and rising startups. The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. Visit our FAQ page to learn more. Download our design assets here. Check out the Startups of The Year Merch Shop here. HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated startup-friendly packages to solve your marketing challenges. Meet our sponsors: Wellfound: Join the #1 global, startup-focused community. At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months, to build and scale your company with one powerful tool. Get your offer now! Hubspot: If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams and customers in one easy-to-use scalable platform that grows with your business. Get started for free. Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data’s scalable web data collection, businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Algolia: Algolia NeuralSearch is the world's only AI end-to-end Search and Discovery Platform combining powerful keyword and natural language processing in a single API. Climate Tech, Emerging Tech - October 9, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “Startups of The Year” award, featuring the most promising and innovative companies in the Climate Tech startup ecosystem. Climate Tech, Emerging Tech - October 9, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “Startups of The Year” award, featuring the most promising and innovative companies in the Climate Tech startup ecosystem. Climate Tech , Emerging Tech - October 9, 2024 Tech - October 9, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “ Startups of The Year ” award, featuring the most promising and innovative companies in the Startups of The Year startup ecosystem. The list, compiled by the HackerNoon team, showcases 30 startups making significant contributions and driving innovation in the Climate Tech industry. https://youtu.be/XOpG6B4AKPU?embedable=true https://youtu.be/XOpG6B4AKPU?embedable=true Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Climate Tech industry here. Nominations are open - learn more here . You can nominate your company as the Startup of The Year in the Climate Tech industry here . here Startup of The Year in the Climate Tech industry here Startup of The Year in the Climate Tech industry here The Startups in the Climate Tech industry nominated for Startups of The Year 2024 include: Startups in the Climate Tech industry nominated Startups in the Climate Tech industry Startups in the Climate Tech industry Matteco IDEALERS B2B PVT LTD Terrenity HIJAU (c/o. PT Investasi Hijau Selaras) Undesert Corporation Waterly Felicidad Tracco seeng LTD Tree++ (Tree Plus Plus) LEAP for Climate Aatral ESP ÁCARA CLIMATE RenTech Global Solutions VALKYRIE SOLAR SOLUTIONS Alder Green Solutions H2next Eurasia Natural History Association Climate Change Writers Global Cerah Tasteme Virya Energy Zetin Solar Technology Kenya R.Flo Ocergy CENmat Achelous Pure Metal Co. Ltd. GoNetZero Silogic Technology Gaia First Matteco Matteco IDEALERS B2B PVT LTD IDEALERS B2B PVT LTD Terrenity Terrenity HIJAU (c/o. PT Investasi Hijau Selaras) HIJAU (c/o. PT Investasi Hijau Selaras) Undesert Corporation Undesert Corporation Waterly Waterly Felicidad Felicidad Tracco Tracco seeng LTD seeng LTD Tree++ (Tree Plus Plus) Tree++ (Tree Plus Plus) LEAP for Climate LEAP for Climate Aatral ESP Aatral ESP ÁCARA CLIMATE ÁCARA CLIMATE RenTech Global Solutions RenTech Global Solutions VALKYRIE SOLAR SOLUTIONS VALKYRIE SOLAR SOLUTIONS Alder Green Solutions Alder Green Solutions H2next H2next Eurasia Natural History Association Eurasia Natural History Association Climate Change Writers Climate Change Writers Global Cerah Global Cerah Tasteme Tasteme Virya Energy Virya Energy Zetin Solar Technology Kenya Zetin Solar Technology Kenya R.Flo R.Flo Ocergy Ocergy CENmat CENmat Achelous Pure Metal Co. Ltd. Achelous Pure Metal Co. Ltd. GoNetZero GoNetZero Silogic Technology Silogic Technology Gaia First Gaia First “We are thrilled to recognize the top startups in the Climate Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO. “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” “We are thrilled to recognize the top startups in the Climate Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO. “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” “We are thrilled to recognize the top startups in the Climate Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO . “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” David Smooke, HackerNoon Founder & CEO Why It Pays Off to Be Nominated Startups of The Year 2024 is a 100% community-driven voting event recognizing startups transforming technology and the world for the better. Starting October 1st, 2024, the internet can nominate and vote for the best ventures in their city and industry. People can nominate their favorite startups until October 31st , and vote once per day per award for who they think should be Startup of The Year up to March 31st, 2025. Winners will be announced in April 2025, after a comprehensive evaluation by the HackerNoon team. Startups of The Year 2024 nominate their favorite startups until October 31st nominate their favorite startups Apart from validation from the world's greenest tech publication, all nominees get free interviews relevant to their region and industry. They will also receive curated startup-friendly packages and a free version of their very own Evergreen Tech Company News Page on HackerNoon. get free interviews startup-friendly packages startup-friendly packages Evergreen Tech Company News Evergreen Tech Company News In sum, being nominated gives you major credibility as one of the top startups globally and an expert in your industry, meaning even without winning, you still gain tons of exposure. Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Climate Tech industry here. Nominations are open - learn more here . You can nominate your company as the Startup of The Year in the Climate Tech industry here . here Startup of The Year in the Climate Tech industry here Startup of The Year in the Climate Tech industry here About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries will participate in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many stories have been written about these daring and rising startups. Startups of The Year 2024 Startups of The Year 2024 many stories many stories The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. free interview on HackerNoon Evergreen Tech Company News Evergreen Tech Company News Visit our FAQ page to learn more. FAQ FAQ Download our design assets here . here here Check out the Startups of The Year Merch Shop here . here here HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated startup-friendly packages to solve your marketing challenges. startup-friendly packages startup-friendly packages Meet our sponsors: Meet our sponsors: Meet our sponsors: Wellfound: Join the #1 global, startup-focused community . At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Wellfound: Join the #1 global, startup-focused community Join the #1 global, startup-focused community Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months , to build and scale your company with one powerful tool. Get your offer now ! Notion: Try Notion with unlimited AI, FREE for up to 6 months Get your offer now ! Hubspot: If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams and customers in one easy-to-use scalable platform that grows with your business. Get started for free . Hubspot: Get started for free Get started for free Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data’s scalable web data collection , businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Bright Data: Bright Data’s scalable web data collection Bright Data’s scalable web data collection Algolia: Algolia NeuralSearch is the world's only AI end-to-end Search and Discovery Platform combining powerful keyword and natural language processing in a single API. Algolia: AI end-to-end Search and Discovery Platform AI end-to-end Search and Discovery Platform

Startups of The Year 2024: 3,246 Startups Nominated in the Climate Tech Industry

የ2024 የአመቱ ጅምር፡ 3,246 ጅምር በአየር ንብረት ቴክ ኢንዱስትሪ ውስጥ ተመርጠዋል።

Стартапы 2024 года: 3246 стартапаў, намінаваных у індустрыі кліматычных тэхналогій

Startupy roku 2024: 3 246 nominovaných startupů v odvětví Climate Tech

Startups del año 2024: 3246 startups nominadas en la industria de la tecnología climática

Startups do ano 2024: 3.246 startups nomeadas na industria da tecnoloxía climática

Startupovi godine 2024.: 3246 startupova nominiranih u industriji klimatskih tehnologija

Startup dell'anno 2024: 3.246 startup nominate nel settore Climate Tech

2024年のスタートアップ・オブ・ザ・イヤー：気候技術業界で3,246社のスタートアップがノミネート

წლის სტარტაპები 2024: 3246 სტარტაპი ნომინირებულია კლიმატის ტექნოლოგიების ინდუსტრიაში

2024 жылдың стартаптары: климаттық технологиялар индустриясында 3 246 стартап ұсынылды

Startups of the Year 2024: 3 246 startupov nominovaných v odvetví klimatických technológií

Các công ty khởi nghiệp của năm 2024: 3.246 công ty khởi nghiệp được đề cử trong ngành công nghệ khí hậu

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Everything you wanted to understand about potential August 1 fork but were afraid to ask. T about what will happen on August 1. Will the cryptocurrency split into two new ones? Will it not? What is BIP 91? What is BIP 148? What is SegWit? here’s a lot of fuss in the bitcoin community The incredibly significant date is just around the corner but there are still so many unanswered questions! By the way, I am curator of a weekly newsletter, , which delivers one idea from the future to your inboxes. Unmade I have put together this guide to walk you through the whole situation to keep you informed about the developments. As of this writing, it seems like, bitcoin will avoid the chain split — . However, even though it seems like the opposing parties have reached a joint conclusion, there’s still a lot that needs to happen to avert the split. for now To understand what’s actually happening and why the bitcoin community has split into two, it’s best to take a look at one of the fundamental issues of cryptocurrency — or to be more exact — how people disagree about how to fix that issue. On one side, there are the people who manage the open sourced software for bitcoin. They are like the organisation/community that keeps the development on the bitcoin protocol running. On the other side are the miners, who deploy computers to run the bitcoin network (blockchain). Both sides are crucial to keep the bitcoin running — one side can’t do it without the other. And to fully understand the issue, you need to understand the mechanics of bitcoin, and that’s why I’ve tried to explain the complete deal with bitcoin chain split in this guide. What is the problem with bitcoin? “Death is the solution to all problems. No man — no problem.” — Joseph Stalin that you can use to buy and sell things online. That’s technically correct, but that’s not bitcoin’s real value. Most people think of bitcoin as a digital currency Bitcoins aren’t really feasible for everyday transactions, and we’re nowhere near being able to walk into a Starbucks and pay for our coffee in bitcoins. But why is that? Wouldn’t make sense for the makers of bitcoin to implement it as an real alternative to other currency, and thereby increase the value of bitcoin? Well, the reason that hasn’t happened is that the bitcoin blockchain is slow and expensive. It’s nowhere close to meeting the standards for payment technology as the bitcoin network can process up to six transactions per second, while the VISA network can process over 1600 transactions per second. The only way the general public will adopt to the decentralized network is if it will be as fast and convenient as existing payment networks. Unless that happens, bitcoin will be used mostly as an instrument to store value. Why is the network so slow? “The horse I bet on was so slow, the jockey kept a diary of the trip.” — Henny Youngman of a blockchain. If you haven’t yet read the , think of blockchain as a register containing several pages (blocks) where each page contains several transactions. As soon as a page has been filled with transactions, it needs to be added to the register before starting to record transactions on the next page. Bitcoin’s network is built on top ultimate guide to understanding blockchain Before a page (block) can be added to the register (chain), there’s some processing that needs to be done to ensure that everyone agrees with the contents it contains. The process approximately takes 10 minutes for each block. Imagine, you are sending 1 BTC to your friend, Joe. The transaction will look something like this. information about the sender, the recipient, the amount and the transaction fee. Among other things, a transaction contains Wait a minute, transaction fee? Yes, there’s an additional fee that you can pay to incentivize miners to include your transaction in a block as soon as possible. There’s no set price and it’s entirely up to you how much you are willing to pay to speed up the process. At any given moment, there are several transactions available to be recorded on the current page. If you look for number of unconfirmed (pending) transactions any time through the day, the number will always be . That’s when the 99 percent of the world is yet to come to cryptocurrencies. The number then can easily be north of several millions. close to ten thousand The miners (computers working in the blockchain network) have to decide which of the available transactions to include in the current block. To help them decide, they look at which transactions yield the most rewards — meaning that the transactions with the highest transaction fee will be included first. If there are enough transactions with higher transaction fee than yours to fill up the block, your transaction will have to wait in queue. The wait can last from a few minutes to a few hours. And sometimes, even days. The more you pay in transaction fee, the quicker your transaction is processed. slow and expensive. Not exactly what the ‘money of the internet’ should be like. For the users, using Bitcoin has become However, this has prompted smart people to ask an important question: “How can we scale the capacity of the bitcoin blockchain?” The funny thing is that there were too many smart people asking that question. The people who are running Bitcoin (the people who are maintaining the open sourced code that runs on people’s computers) believe in one solution, while the miners (the people who run the code on their own computers) believe in another. The solution proposed by the people running Bitcoin favors the users, while the solution proposed by the miners favors themselves (what a surprise!). It’s this disagreement that’s led to the confusion around August 1 and the potential split of the Bitcoin blockchain. We’ll come back on this topic later in the post; let’s understand the solutions first. The solution that favors the users “When money is seen as a solution for every problem, money itself becomes the problem.” — Richard J. Needham with originated in how we store a transaction in a block. The solution was originally to remove the malleability of blocks, but it had a positive side effect: it increased the capacity of the blockchain too. To understand it better, we’ll need to understand how blocks look like. What the people running Bitcoin came up The solution is known by several names on the internet, such as SegWit, BIP 148 and UASF (User Activated Soft Fork). What does a block look like? In the most simple view, a block in the chain has two kinds of data in it — header and transactions. Header contains meta data of the block, but that’s not relevant to the August 1 fork. The header is tiny and only takes up about a percent of the block’s data. The other 99 percent of the block’s data is made up of transactions that are included in the block. A block on bitcoin blockchain If we dig deeper to see what makes up a single transaction, we’ll find that besides the obvious things — like sender, recipients, amount and transaction fee — it also contains something called a ‘Signature’ or ‘Witness’. The Signature verifies that the sender has enough balance to send the said amount of bitcoins in the transactions. This particular data is only used once at the beginning, but it makes up almost 65 percent of the data in a transaction. The total allowed size of a block is 1MB, and most of it is filled with data that isn’t critical to the block. The idea is to move the Signature/Witness data towards the end of the block in a separate structure. This separate structure isn’t required to be included in the blockchain. If a node (individual participating computer in the network) can, it’s great. If it cannot, no big deal. Because of this property, it is called a Soft Fork. Some nodes will update their software and begin working on the new block structure, while the nodes that decide to stay on the legacy software will simply ignore the Witness portion. This optimization in the structure allows more transactions to be included in a block. This solution is technically called SegWit because of Segregating (separating) Witness from the block structure. And it favors the users. How does SegWit favor the users? The new structure can hold a lot more transactions than the current structure can. It causes the competition among unconfirmed transactions to be included in the block goes down. And with it, the transaction fee goes down. Transaction fees are a part of the reward that the miners get. With lower fee, the reward for mining the block will go down too. Not just that, this new structure also something that is called Lightning Network that allows micro payments (you paying for coffee at Starbucks) to be processed instantaneously without any fee. The leading argument against this solution is that it’s a temporary fix and that a ‘Hard Fork’ will eventually be implemented down the road. And the Hard Fork is the miners’ proposition. The solution that favors the miners “The government solution to a problem is usually as bad as the problem.” — Milton Friedman will scale the network for a longer term. Their proposition is to implement SegWit (Soft Fork) like the users propose, and in addition to that, they also want to increase the size of the block from current 1MB to 2MB. Thus, the name — SegWit 2x. The miners propose a solution that Besides being known as SegWit 2x, the miners’ solution is also called MASF (Miner Activated Soft Fork) and BIP 91. At first glance, this solution looks way better. I can almost hear you shouting, “The network will be even faster. Yay!” However, the devil lies in the details. If this solution is implemented, every miner in the network will have to upgrade their hardware and software. There is no concept of backward compatibility included in this solution. Those who won’t upgrade will be orphaned by the network. That’s why it is called a Hard Fork. Let’s take a closer look. Imagine there are ten miners in the network, and that six of them decide to upgrade to larger sized blocks. These six will continue working on making the longest chain even longer, but their mined blocks will be discarded by the rest four who still consider old block size to be true. How does SegWit 2x favor miners? What do miners like? Higher rewards. What do miners like even more? Less competition. Due to the fact that not everyone will have enough money to upgrade their computation power to mine larger blocks profitably, smaller miners will vanish away from the network. This will leave larger and fewer miners in the network — which dramatically reduces the competition. When Satoshi Nakamoto the glorious idea of building a decentralized future, he didn’t mean it just in theory, but he meant it in practice. Putting power in the hands of few large miners goes against the foundational idea of bitcoin’s existence. presented If you’d ask miners they’d probably say something like “We are running the network. We must have a say how we want to run it. If we shut our computers and go away, there’ll be no bitcoin.” And if they did, they’d be right. But they’re also wrong. there’ll be no bitcoin if they go away, yet they’re wrong because when they say only they are running the network, they’re saying it with the same intentions as that of people running regular banks and governments. They are right when they say Blockchain is not just a software protocol that allows financial transactions. It is the protocol that changes the fundamental beliefs of our species. With this protocol, there is no place for the sense of ownership. Everybody owns the network and nobody owns the network. The sooner we accept it, the sooner we’ll be able to make real progress. What exactly is the split that everybody is talking about? “It is double pleasure to deceive the deceiver.” — Niccolo Machiavelli our miners were discarded by the rest four? Let’s see what the blockchain looks like for them. The four miners who are working on the smaller block size, will continue mining their own set of blocks and will be making their own version of the longest chain. Remember when the blocks mined by six of When that happens, the blockchain will split in two blockchains. Some nodes will be working on one version and some on the other. Let’s label the resulting blockchain like so: The ‘Bitcoin Common’ part of the chain is the part before the split. Miners with the upgraded computers will work on the ‘Bitcoin Current’ branch of the chain while the miners who are still on the old block size will form the ‘Bitcoin Classic’ branch of the chain. Bitcoin, the currency, will therefore effectively be split into two. Coins that were collected by users during the Bitcoin Common can be spent on both Bitcoin Classic and Bitcoin Current. However, the coins that will be traded on Bitcoin Classic will be different from those traded on Bitcoin Current. They will be different in quantity, different in value and different in adoption. They will be as different as any two pre-existing cryptocurrencies can be. What will happen to the bitcoins you own? It depends where you’ve kept your bitcoins. If they are on your own computer, you can spend them twice — once on each blockchain. But if they are stored in an online wallet, the fate of your coins is in their hands. Every online wallet will have their own take how they want to proceed with this blockchain split. They might either migrate your coins to one version of the blockchain or allow you to spend on both. It depends. What I’d advise you to do is not make any transactions a few days before and after August 1, 2017. Because of the fork, you might lose your bitcoins into the thin air. After all, bitcoins are nothing but records of transactions. If you transaction doesn’t get recorded by either of the chains, they will be gone forever. Poof. “All a poet can do today is warn.” — Wilfred Owen There’s a lot of turmoil around what might happen on August 1. Over last couple of weeks, markets have been more volatile than ever. As of this writing on July 24, 2017, almost all the miners are signaling BIP 91 (the miner’s proposition). With this, it seems like bitcoin will avoid the split on August 1, but it is not a guarantee still. Miners saying that they will implement BIP 91 doesn’t mean necessarily mean that they will. This is just a statement of intent from the miners. To really implement BIP 91, majority of the miners will have to upgrade the software on all of their computer before August 1. Miners running the updated software is still under 50 percent. If the miners stick to their intent over next week too, the SegWit part of SegWit 2x will be activated, and the 2x part (increasing the block size from 1MB to 2MB) will be on its way later this year, which will be a hard fork. What might happen during the hard fork? I’ll bring another guide then to keep you informed on the matter. If there’s still something bugging your head, let’s talk in the comments below. — About the author Mohit Mamoria is the curator of a weekly newsletter, Unmade , which delivers one idea from the future to your inboxes. The story first appeared on The Next Web . Have feedback? I am available on Twitter . Also read WTF is Blockchain? The ultimate 3500-word guide in plain English to understand Blockchain. If you enjoyed this story, please 💚 recommend and share to help others find it! Feel free to leave a comment below!

Why the heck Bitcoin ‘might’ split in two? (explained in plain English)

T in the tech sector are not only wrong — they are irrelevant. he recent spate of scare stories about declining prices If you’re a corporate investor looking to acquire a startup, this latest round of media noise, gossip, and chitchat is distracting. Rather than obsessing over the market and whether there’s a newly pumped-up bubble or not, you may well be a lot better off taking a step back; giving yourself the chance to ask not whether the price of Snap, Cloudera, or Alfa Financial Software is sensible, but whether — as an investor — it’s sensible for . you If you look at five poorly performing tech stocks, their declining values probably have much more to do with their individual business strategies than market conditions generally . Prices are indication of what the market is prepared to pay for shares in a business at any one particular point in time. More importantly yet, prices signal the value an acquirer feels it can extract from an acquisition target. So, rather than trying to look outside of your company to find the value of a startup target, look inside first. That is because prices don’t exist in isolation Tech is a fast-moving world, and so are the stories This latest round of hysteria started with about a string of startups who are, potentially, floating on the stock market at a price below their pre-IPO valuations. Cloudera was cited as the first tech unicorn to go public at a value “significantly” below its private valuation. HortonWorks and Apptio were also referenced. an article in Bloomberg The original graph from the Bloomberg article But, what’s the real news here? . Some go up, some go down. That is usually because a company’s individual strategy has succeeded or failed, its revenues are growing or shrinking against expectations, or its outlook is positive or negative. Companies fluctuate in price — and value — all the time For every company you can find whose valuation declined after going public, you can probably find another one that rose , and draw much bigger, wider conclusions — especially about an industry as diverse and broad as the technology sector. If you look at five poorly performing tech stocks, their declining values probably have much more to do with their individual business strategies than market conditions generally. It’s very difficult, if not impossible, to extricate the individual circumstances of company valuations For every company you can find whose valuation declined after going public, you can probably find another one that rose. Facebook is a case in point. Since its float in 2012 at a peak market capitalisation of about $100bn, it has increased in value more than fourfold. Currently its market capitalisation is over $430 billion. At the time it floated, you could find a lot of scare stories about the tech bubble and how Facebook was yet another example of an overvalued tech company that would eventually collapse. Five-year view of Facebook’s price on the NASDAQ (source: Google Finance) The same reaction took place when it announced it was acquiring Instagram for $1 billion. How very wrong they were. It’s natural to see price jumps when going from private to public You also hear a lot about private tech companies’ valuations jumping about all over the place. This volatility, we may think, is a signal that something is not quite right in the market. So, perhaps, it’s not that Cloudera’s price has declined, but that it has declined by so much so quickly. But that’s not clear either. . This is not volatility. It’s not even really correct to call it a “price jump” at all. It’s natural to see price jumps when a company is private, or goes from private to public , such as capital raises, acquisitions, or IPOs. And a very long time — usually years — can pass between these landmark events, meaning most of the time the true value of the company is hidden; whereas public companies are valued day-in, day-out on the public markets, meaning that price declines usually follow smooth downward curves. That is usually enough to explain what seems, on the surface, to be a rapid, immediate change in value. These fluctuations in price simply reflect the fact that private companies are typically valued only at landmark events The risk of scare stories, and why they truly matter The risk of all . You can see this happening in two ways. On one hand, the CFO of a major corporate might be scared off by the stories from acquiring a startup, thinking that a market correction is imminent and they could end up destroying value. On the other hand, they may sweep into the market, acquiring a startup at what they think is a bargain price without doing enough due diligence. this media noise around the price of tech startups is that it distracts strategic investors from making the right decision When a corporate is looking to invest or acquire a startup, they shouldn’t be thinking primarily about the wider market but, instead, about what value the target company will provide to them These are both wrong. When a corporate is looking to invest or acquire a startup, they shouldn’t be thinking primarily about the wider market but, instead, about what value the target company will provide to them: the synergies the investment or acquisition will create, and how the target can help the corporate transform itself to keep pace with digital change. . This is why a target company, which may seem small or insignificant, can actually command a large valuation, and price, to the right company In retrospect Google’s acquisition of Android seems like a fantastic strategic move For example, in 2005, Google spent $50 million on a tiny company, which very few people knew about at the time and which was developing software for mobile phones well before such a thing was fashionable. It had only been in existence for 22 months. Many commentators couldn’t understand the acquisition, and accused Google of making a strategic error. What was the company? Android. It has since gone on to become the leading mobile phone operating system in the world, used on more than 1.6 billion devices around the world. While the price might have seemed high at the time, it wasn’t. . Would Android have been worth $50 million to another company? Perhaps not, but it was worth it to Google because it was a great strategic fit, and enabled Google to unleash enormous latent value. Google had a vision to bring its services to mobile — as consumers increasingly adopted handheld devices — and Android was a key rung in its strategy Almost any price can be the right price The lesson from this is that almost any price can be the right price from the right company for the right asset. Corporate investors, who are increasingly struggling to keep pace with the latest technological developments, need to look inside their own companies, develop a strategy to transform themselves for the future and then — and only then — go out to the market to find a good strategic fit, ignoring in the first instance prices, and even in some cases numbers. Prices can distract from finding the right company, which is what truly matters. The lesson from this is that almost any price can be the right price from the right company for the right asset After this company has been uncovered, then a corporate can start asking the difficult questions about value: what historic value information do we have? Can we benchmark the company against other similar competitors? And, most importantly of all, what strategic value will the acquisition unleash in my company that it wouldn’t be possible to uncap otherwise? , and agree to a price and terms that make sense with all the other value elements taken into account. If corporates can do that, they will have the best chance of finding a transformative acquisition target that works for them. Then they have to execute, which is a topic for another article… Corporates need to find a startup that fits their company like a glove Paul Cuatrecasas is CEO and Founder of Aquaa Partners , a London-based investment bank that works with growth-focused corporates to help them find and acquire the right technology company. Find out more about how Aquaa Partners can help you at: http://aquaapartners.com

Ignore Cloudera: tech startup acquisitions aren’t about price, they’re about company fit

The Noonification: Gone Are Those Days of AI (7/13/2024)

This week, I am taking a slight detour from my usual articles that explain AI Concepts or coding tutorials. Yes, it's a philosophical, nostalgic tour down the road I was fortunate enough to tread all along. One of the rare privileges of starting in a field quite early is that you see the field grow like your baby. When I started my Masters in Computer Vision a decade ago, a CS graduate friend of mine asked, “What does computer vision even mean?” It's a trivial question today with enough videos of computer vision tasks like segmentation (see below) floating around the internet. A result of image segmentation — a Computer Vision task that was all over the internet when deep learning took off a decade ago. So, what has changed in the last decade? There are always two sides to a coin. Also, growth can be both good and bad. Closed Community — Small Was Beautiful They say that people in Iceland are somehow related to each other and if someone is beyond your second cousin, you are good to marry them. Likewise, the AI community was close-knit. Gone are those feelings of a tight-knit community. When interacting with anyone from research you generally knew which group they belonged to. You somehow would relate to and respect their work as you read and appreciated their impact on the field. As the field is blowing up endlessly, it's becoming increasingly difficult to even say that you know someone’s work, forget about them. There are too many new names and novel branches that it's even challenging to keep track of. As another example, PyTorch was in its infancy. The Slack community was so small and helpful that the developers of PyTorch directly responded to questions we had about using the library. This encouraged learning more and more about it. Today, the framework is so mature, and new frameworks like LangChain and Llamaindex have propped up. The focus is all on LLMs over any other branch of AI. Hardware Luxury Those days, we used to train a deep neural network on a single GPU to create something impactful. Most work published in premier conferences like CVPR, NeurIPS, and ICML could be trained and replicated on a single 8 GB GPU machine or in the worst case, a single machine with 4 GPUs in rare cases. An image of the latest GPU clusters used by Tesla for AI (source: https://www.hpcwire.com/off-the-wire/tesla-to-roll-out-advanced-supercomputer) I vividly remember how happy I was to buy a single commodity GPU with just 8 GB of RAM to participate in Kaggle competitions. Some of the winning solutions from Kaggle Grandmasters trained deep learning models on a single GPU machine at home. Today’s AI world needs a GPU cluster to train foundational models. Even fine-tuning these models needs 24GB GPUs which are beasts, expensive, and affordable only by businesses with an “AI budget.” Skillset Demand Those were days when the field did not yet get the AI wrapper around it. Recruiters were given this alien task of hunting for “deep learning” engineers. Recruiters and start-up founders were hunting for deep learning experts through all channels. It was a norm to receive regular messages on LinkedIn asking me to join their team as a deep learning engineer. A screenshot of a job advert for a Machine Learning Engineer posted only 5 days ago receiving over 100 applicants! The current situation is that openings for “Machine Learning Engineer” get over 100 applications on LinkedIn well within a day of advertising. Take a look at the screenshot above if you find it hard to believe. The question remains as to how relevant the applicant's skills are to the job spec. But the market is saturated with skills — quite quickly! Sub-skills — Operations and Architecture Growth means more diversity and opportunity. There are new roles like ML-ops, LLM-ops, and ML-architects cropping up. Gone are those days of tiny, single-model files (< 1 GB). The growth of models both in size and capabilities has given rise to new skills in deploying and maintaining them. Moreover, training and deployment of models are being automated with tools such as MLFLow. The cloud infrastructure for training needs to be sophisticated enough. All this has given rise to full-time roles with dedicated responsibilities. Bye Bye ML Engineer, Hello AI Engineer The most fun in working in AI is writing the model architecture, and training the model from scratch using our in-house data. Though this involves pre-processing the data a lot, training models, and visualizing the training results used to be a lot of fun. There was/still is a specialized role for this called Machine Learning (ML) Engineer. The development of foundation models from tech giants is redefining this role. As model sizes grow, training budgets are enormous. As a matter of fact, the cost of training the LLama 2 model was $20m for Meta. Clearly start-ups or organizations trying to adopt AI do not wish to throw away this sum of money. It is now established that foundation models are for the tech giants to develop, with the exception of some companies like Mistral and Anthropic. Sadly, this means the ML Engineer role is getting cast into an AI Engineer role. The ML Engineer role was all about developing model architectures, training, and evaluating them. The new AI Engineer role mostly involves developing APIs or invoking APIs provided by Tech giants (OpenAI, Meta, and Google) to prompt the foundation models. In rare cases, it involves fine-tuning these foundation models. But companies have the option to build RAG pipelines or use the foundation models “as-is” over fine-tuning them. Conclusion To conclude, I see this as a slow amalgamation of the Software engineering and Machine Learning roles. The line between software engineers and deep learning experts is fading. So, in the years to come, software engineers will be AI engineers who work alongside foundation models, both to write code and to solve customer needs. Furthermore, in the coming years, companies will be grouped into two categories — the AI product and AI services companies. AI product companies would again be OpenAI, Meta, and Google to develop foundational models. AI services companies would provide API services by fine-tuning or developing RAG-style pipelines around AI foundational models to serve their customers. Lastly, does the spike in job applications indicate an impending bursting of the bubble like the dot com bubble? I feel so, YES. But let’s wait and watch it… Shout Out Hope that was useful. If you liked this article, why not follow me on Twitter for research updates, video tutorial links, and new AI tools from top AI labs? Also, please subscribe to my YouTube channel where I explain AI concepts and papers visually. This week, I am taking a slight detour from my usual articles that explain AI Concepts or coding tutorials. Yes, it's a philosophical, nostalgic tour down the road I was fortunate enough to tread all along. One of the rare privileges of starting in a field quite early is that you see the field grow like your baby. When I started my Masters in Computer Vision a decade ago, a CS graduate friend of mine asked, “What does computer vision even mean?” It's a trivial question today with enough videos of computer vision tasks like segmentation (see below) floating around the internet. A result of image segmentation — a Computer Vision task that was all over the internet when deep learning took off a decade ago. So, what has changed in the last decade? There are always two sides to a coin. Also, growth can be both good and bad. Closed Community — Small Was Beautiful Closed Community — Small Was Beautiful They say that people in Iceland are somehow related to each other and if someone is beyond your second cousin, you are good to marry them. Likewise, the AI community was close-knit. Gone are those feelings of a tight-knit community. When interacting with anyone from research you generally knew which group they belonged to. You somehow would relate to and respect their work as you read and appreciated their impact on the field. As the field is blowing up endlessly, it's becoming increasingly difficult to even say that you know someone’s work, forget about them. There are too many new names and novel branches that it's even challenging to keep track of. As another example, PyTorch was in its infancy. The Slack community was so small and helpful that the developers of PyTorch directly responded to questions we had about using the library. This encouraged learning more and more about it. Today, the framework is so mature, and new frameworks like LangChain and Llamaindex have propped up. The focus is all on LLMs over any other branch of AI. Hardware Luxury Hardware Luxury Those days, we used to train a deep neural network on a single GPU to create something impactful. Most work published in premier conferences like CVPR, NeurIPS, and ICML could be trained and replicated on a single 8 GB GPU machine or in the worst case, a single machine with 4 GPUs in rare cases. An image of the latest GPU clusters used by Tesla for AI (source: https://www.hpcwire.com/off-the-wire/tesla-to-roll-out-advanced-supercomputer ) https://www.hpcwire.com/off-the-wire/tesla-to-roll-out-advanced-supercomputer https://www.hpcwire.com/off-the-wire/tesla-to-roll-out-advanced-supercomputer I vividly remember how happy I was to buy a single commodity GPU with just 8 GB of RAM to participate in Kaggle competitions. Some of the winning solutions from Kaggle Grandmasters trained deep learning models on a single GPU machine at home. Today’s AI world needs a GPU cluster to train foundational models. Even fine-tuning these models needs 24GB GPUs which are beasts, expensive, and affordable only by businesses with an “AI budget.” Skillset Demand Skillset Demand Those were days when the field did not yet get the AI wrapper around it. Recruiters were given this alien task of hunting for “deep learning” engineers. Recruiters and start-up founders were hunting for deep learning experts through all channels. It was a norm to receive regular messages on LinkedIn asking me to join their team as a deep learning engineer. A screenshot of a job advert for a Machine Learning Engineer posted only 5 days ago receiving over 100 applicants! The current situation is that openings for “Machine Learning Engineer” get over 100 applications on LinkedIn well within a day of advertising. Take a look at the screenshot above if you find it hard to believe. The question remains as to how relevant the applicant's skills are to the job spec. But the market is saturated with skills — quite quickly! Sub-skills — Operations and Architecture Growth means more diversity and opportunity. There are new roles like ML-ops, LLM-ops, and ML-architects cropping up. Gone are those days of tiny, single-model files (< 1 GB). The growth of models both in size and capabilities has given rise to new skills in deploying and maintaining them. Moreover, training and deployment of models are being automated with tools such as MLFLow. The cloud infrastructure for training needs to be sophisticated enough. All this has given rise to full-time roles with dedicated responsibilities. Bye Bye ML Engineer, Hello AI Engineer The most fun in working in AI is writing the model architecture, and training the model from scratch using our in-house data. Though this involves pre-processing the data a lot, training models, and visualizing the training results used to be a lot of fun. There was/still is a specialized role for this called Machine Learning (ML) Engineer. The development of foundation models from tech giants is redefining this role. As model sizes grow, training budgets are enormous. As a matter of fact, the cost of training the LLama 2 model was $20m for Meta. Clearly start-ups or organizations trying to adopt AI do not wish to throw away this sum of money. It is now established that foundation models are for the tech giants to develop, with the exception of some companies like Mistral and Anthropic. Sadly, this means the ML Engineer role is getting cast into an AI Engineer role. The ML Engineer role was all about developing model architectures, training, and evaluating them. The new AI Engineer role mostly involves developing APIs or invoking APIs provided by Tech giants (OpenAI, Meta, and Google) to prompt the foundation models. In rare cases, it involves fine-tuning these foundation models. But companies have the option to build RAG pipelines or use the foundation models “as-is” over fine-tuning them. Conclusion To conclude, I see this as a slow amalgamation of the Software engineering and Machine Learning roles. The line between software engineers and deep learning experts is fading. So, in the years to come, software engineers will be AI engineers who work alongside foundation models, both to write code and to solve customer needs. Furthermore, in the coming years, companies will be grouped into two categories — the AI product and AI services companies. AI product companies would again be OpenAI, Meta, and Google to develop foundational models. AI services companies would provide API services by fine-tuning or developing RAG-style pipelines around AI foundational models to serve their customers. Lastly, does the spike in job applications indicate an impending bursting of the bubble like the dot com bubble? I feel so, YES. But let’s wait and watch it… Shout Out Hope that was useful. If you liked this article, why not follow me on Twitter for research updates, video tutorial links, and new AI tools from top AI labs? If you liked this article, why not follow me on Twitter for research updates, video tutorial links, and new AI tools from top AI labs? Twitter Twitter Also, please subscribe to my YouTube channel where I explain AI concepts and papers visually. Also, please subscribe to my YouTube channel where I explain AI concepts and papers visually. YouTube channel YouTube channel

Gone Are Those Days of AI

AI এর সেই দিনগুলি চলে গেছে

Die Zeiten der KI sind vorbei

Atrás quedaron esos días de la IA

Fini l’époque de l’IA

एआई के वे दिन चले गए

AIの時代は終わった

AI의 시대는 지났다

Longe vão aqueles dias de IA

Прошли те времена искусственного интеллекта

Yapay Zekanın O Günleri Geride Kaldı

Đã qua rồi những ngày của AI

人工智能时代已经一去不复返

This paper is available on arxiv under CC 4.0 license. Authors: (1) Vianney Brouard, ENS de Lyon, UMPA, CNRS UMR 5669, 46 All´ee d’Italie, 69364 Lyon Cedex 07, France; E-mail: vianney.brouard@ens-lyon.f. Table of Links Abstract & Introduction and presentation of the model Main results and biological interpretation First-order asymptotics of the mutant sub-populations for an infinite mono-directional grap First-order asymptotics of the mutant sub-populations for a general finite trait space (Theorem 2.1) Convergence for the stochastic exponents (Theorem 2.2) Acknowledgements & References Acknowledgements The author would like to thank H´el`ene Leman for inspiring and helpful discussions and feedback. References [1] Guillaume Achaz. Frequency spectrum neutrality tests: one for all and all for one. Genetics, 183(1):249–258, 2009. [2] Jochen Blath, Tobias Paul, and Andr´as T´obi´as. A stochastic adaptive dynamics model for bacterial populations with mutation, dormancy and transfer. ALEA, Lat. Am. J. Probab. Math. Stat., 20:313–357, 2023. [3] Anton Bovier, Loren Coquille, and Charline Smadi. Crossing a fitness valley as a metastable transition in a stochastic population model. The Annals of Applied Probability, 29(6):3541–3589, 2019. [4] Ivana Bozic and Martin A Nowak. Timing and heterogeneity of mutations associated with drug resistance in metastatic cancers. Proceedings of the National Academy of Sciences, 111(45):15964– 15968, 2014. [5] Nicolas Champagnat, Sylvie M´el´eard, and Viet Chi Tran. Stochastic analysis of emergence of evolutionary cyclic behavior in population dynamics with transfer. The Annals of Applied Probability, 31(4):1820–1867, 2021. [6] David Cheek and Tibor Antal. Genetic composition of an exponentially growing cell population. Stochastic Processes and their Applications, 2020. [7] David Cheek, Tibor Antal, et al. Mutation frequencies in a birth–death branching process. The Annals of Applied Probability, 28(6):3922–3947, 2018. [8] Loren Coquille, Anna Kraut, and Charline Smadi. Stochastic individual-based models with power law mutation rate on a general finite trait space. Electronic Journal of Probability, 26:1–37, 2021. [9] Alexander Davis, Ruli Gao, and Nicholas Navin. Tumor evolution: Linear, branching, neutral or punctuated? Biochimica et Biophysica Acta (BBA)-Reviews on Cancer, 1867(2):151–161, 2017. [10] Claude Dellacherie and Paul andr´e Meyer. Probabilit´es et Potentiel. Collection Enseignement des Sciences. Hermann, Paris, 1975. [11] Richard Durrett. Branching process models of cancer. Springer, 2015. [12] Richard Durrett and Stephen Moseley. Evolution of resistance and progression to disease during clonal expansion of cancer. Theoretical population biology, 77(1):42–48, 2010. [13] Rick Durrett and John Mayberry. Traveling waves of selective sweeps. The Annals of Applied Probability, 21(2):699–744, 2011. [14] Manuel Esser and Anna Kraut. A general multi-scale description of metastable adaptive motion across fitness valleys. arXiv preprint arXiv:2112.12675, 2021. [15] Jasmine Foo and Kevin Leder. Dynamics of cancer recurrence. The Annals of Applied Probability, 23(4):1437–1468, 2013. [16] Jasmine Gamblin, Sylvain Gandon, Fran¸cois Blanquart, and Amaury Lambert. Bottlenecks can constrain and channel evolutionary paths. Genetics, 224(2):iyad001, 2023. [17] Einar Bjarki Gunnarsson, Kevin Leder, and Jasmine Foo. Exact site frequency spectra of neutrally evolving tumors: A transition between power laws reveals a signature of cell viability. Theoretical population biology, 142:67–90, 2021. [18] Einar Bjarki Gunnarsson, Kevin Leder, and Xuanming Zhang. Limit theorems for the site frequency spectrum of neutral mutations in an exponentially growing population. arXiv preprint arXiv:2307.03346, 2023. [19] Agnes Hamon and Bernard Ycart. Statistics for the luria-delbr¨uck distribution. Electronic Journal of Statistics, 2012. [20] Siˆan Jones, Wei-dong Chen, Giovanni Parmigiani, Frank Diehl, Niko Beerenwinkel, Tibor Antal, Arne Traulsen, Martin A Nowak, Christopher Siegel, Victor E Velculescu, et al. Comparative lesion sequencing provides insights into tumor evolution. Proceedings of the National Academy of Sciences, 105(11):4283–4288, 2008. [21] Peter Keller and Tibor Antal. Mutant number distribution in an exponentially growing population. Journal of Statistical Mechanics: Theory and Experiment, 2015(1):P01011, 2015. [22] David G Kendall. Birth-and-death processes, and the theory of carcinogenesis. Biometrika, 47(1/2):13–21, 1960. [23] David A Kessler and Herbert Levine. Scaling solution in the large population limit of the general asymmetric stochastic luria–delbr¨uck evolution process. Journal of statistical physics, 158(4):783– 805, 2015. [24] Jean-Fran¸cois Le Gall et al. Brownian motion, martingales, and stochastic calculus, volume 274. Springer, 2016. [25] Douglas E Lea and Charles Alfred Coulson. The distribution of the numbers of mutants in bacterial populations. Journal of genetics, 49:264–285, 1949. [26] Shaoping Ling, Zheng Hu, Zuyu Yang, Fang Yang, Yawei Li, Pei Lin, Ke Chen, Lili Dong, Lihua Cao, Yong Tao, et al. Extremely high genetic diversity in a single tumor points to prevalence of non-darwinian cell evolution. Proceedings of the National Academy of Sciences, 112(47):E6496– E6505, 2015. [27] Salvador E Luria and Max Delbr¨uck. Mutations of bacteria from virus sensitivity to virus resistance. Genetics, 28(6):491, 1943. [28] Michael D Nicholson and Tibor Antal. Competing evolutionary paths in growing populations with applications to multidrug resistance. PLoS computational biology, 15(4):e1006866, 2019. [29] Michael D Nicholson, David Cheek, and Tibor Antal. Sequential mutations in exponentially growing populations. PLOS Computational Biology, 19(7):e1011289, 2023. [30] Tobias Paul. The canonical equation of adaptive dynamics in individual-based models with power law mutation rates, 2023. [31] Andrea Sottoriva, Haeyoun Kang, Zhicheng Ma, Trevor A Graham, Matthew P Salomon, Junsong Zhao, Paul Marjoram, Kimberly Siegmund, Michael F Press, Darryl Shibata, et al. A big bang model of human colorectal tumor growth. Nature genetics, 47(3):209–216, 2015. [32] Subramanian Venkatesan and Charles Swanton. Tumor evolutionary principles: how intratumor heterogeneity influences cancer treatment and outcome. American Society of Clinical Oncology Educational Book, 36:e141–e149, 2016. [33] Marc J Williams, Benjamin Werner, Chris P Barnes, Trevor A Graham, and Andrea Sottoriva. Identification of neutral tumor evolution across cancer types. Nature genetics, 48(3):238–244, 2016. [34] Kai Zeng, Yun-Xin Fu, Suhua Shi, and Chung-I Wu. Statistical tests for detecting positive selection by utilizing high-frequency variants. Genetics, 174(3):1431–1439, 2006.

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